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--- title: 'Deconstructing Complex Interventions: Piloting a Framework of Delivery Features and Intervention Strategies for the Eating Disorders in Weight-Related Therapy (EDIT) Collaboration' authors: - Natalie B. Lister - Hiba Jebeile - Rabia Khalid - Samantha Pryde - Brittany J. Johnson journal: Nutrients year: 2023 pmcid: PMC10056322 doi: 10.3390/nu15061414 license: CC BY 4.0 --- # Deconstructing Complex Interventions: Piloting a Framework of Delivery Features and Intervention Strategies for the Eating Disorders in Weight-Related Therapy (EDIT) Collaboration ## Abstract [1] Background: weight-management interventions vary in their delivery features and intervention strategies. We aimed to establish a protocol to identify these intervention components. [ 2] Methods: a framework was developed through literature searches and stakeholder consultation. Six studies were independently coded by two reviewers. Consensus included recording conflict resolutions and framework changes. [ 3] Results: more conflicts occurred for intervention strategies compared to delivery features; both required the updating of definitions. The average coding times were 78 min (SD: 48) for delivery features and 54 min (SD: 29) for intervention strategies. [ 4] Conclusions: this study developed a detailed framework and highlights the complexities in objectively mapping weight-management trials. ## 1. Introduction Behavioural interventions including diet, physical activity, and psychological components are first-line treatments for obesity [1,2,3,4]. However, there are ongoing concerns that behavioural weight management, or some included strategies or methods of delivery, may induce or exacerbate eating disorders [5,6,7]. The eating disorders in weight-related therapy (EDIT) Collaboration aims to identify individual changes in eating disorder risk during weight management; further details are available elsewhere [8,9]. We hypothesise that specific components of interventions may influence eating disorder risk. For example, dieting predicts eating-disorder development in community samples [10], and dieting at a “severe” level is considered risky in terms of the likelihood of triggering binge-eating episodes [11]. Hence, there is a need to examine interventional evidence to determine whether specific intervention components may increase or decrease eating-disorder risk. Understanding how intervention components may increase or decrease the risk of eating disorders would enable future interventions to be optimised. To date, no systematic examination of weight-management intervention components relevant to eating disorder risk has been conducted. Evidence shows that multicomponent (dieting, physical activity and behavioural) interventions are, for most people, effective for weight loss in the short term [12,13]. Further, systematic reviews have demonstrated that some components of these complex interventions may be more effective than others. For example, certain behaviour-change techniques, encouragement for positive health-related behaviours and contact with a dietitian are associated with intervention success [14,15,16]. Such research provides a useful insight into the tailoring of weight-management interventions to improve effectiveness. However, no such evidence synthesis has examined the risk of weight-management interventions. Detailed mapping of weight-management intervention components is an important step in understanding how weight-management trials may increase or decrease eating disorder risk. This study aimed to establish a detailed, objective coding framework of intervention components (i.e., delivery features and intervention strategies) used in weight-management interventions for the EDIT Collaboration. ## 2.1. Development of a Coding Framework The coding framework was developed through an iterative consultation process. An initial list of intervention components was drafted (HJ) and refined (NBL and BJJ). The initial codes were then expanded and refined through consultation with experts in the field via the EDIT Collaboration Scientific Advisory Panel and Stakeholder Advisory Panel including clinicians, researchers and people with lived experience of obesity and/or eating disorders [8]. The revised coding framework was further refined via a stakeholder consultation survey, with input from researchers, clinicians and those with lived experience of obesity and/or eating disorders internationally [17]. Stakeholders were asked to rate intervention strategies for likelihood to increase or decrease eating disorder risk within the context of weight management, and to identify any additional strategies which may be relevant to eating disorder risk [17]. A detailed guidebook was developed which included a descriptor for each unique code. Delivery features are defined as “a broad number of intervention characteristics that relate to how an intervention is delivered” [18]. Delivery features were developed based on the Template for Intervention Description and Replication (TIDieR) checklist [19], including the overarching goal, target population, materials provided, procedures used, who delivered the intervention, delivery mode, intervention setting and dose, as well as any tailoring, modifications and fidelity measures. We also summarised the number and range of different outcome assessment procedures, as these may unintentionally deliver important messages about the aim and intended outcome of the intervention in addition to the planned intervention content. Categories under each delivery feature item/cluster were developed for this project drawing on relevant examples from child obesity prevention [18] and the Human Behaviour Change Project ontologies [20,21]. Intervention strategies is the broad term used to describe the behaviour change content of interventions grouped under key categories (i.e., highest level grouping) relevant to weight-management interventions. Clusters (i.e., mid-level grouping) of intervention strategies were captured under the following categories: intervention intent, framing and outcomes, dietary strategies, eating behaviours/disorder eating, movement and sleep related strategies, and psychological health-related strategies. There were 86 unique intervention strategies (i.e., lowest-level grouping) across these five clusters. ## 2.2. Eligibility Criteria for the Pilot Trials eligible for inclusion in the EDIT Collaboration were randomised controlled trials of behavioural weight-management interventions recruiting adolescents (aged 10 to <19 years at baseline) or adults (aged ≥18 years at baseline) with overweight or obesity defined as body mass index (BMI) z-score > 1 in adolescents and BMI ≥ 25 kg/m2 in adults [22]. Trials must measure eating disorder risk at baseline and post-intervention or follow-up using a validated assessment tool. Purposeful sampling methods were used to select studies for piloting. Eligible studies ($$n = 73$$ as at May 2022) were grouped by decade of publication, with each group weighted by the total number of studies to determine how many studies should be selected from each decade. Both random and purposeful approaches guided the final selection of studies for piloting, ensuring diversity of target population (adolescents, adults), availability of a published protocol and study country. For pragmatic reasons, any studies identified that declined to join the EDIT Collaboration were replaced with a study of a similar profile. ## 2.3. Pilot Process Published intervention descriptions, from trial registries, protocol and main results publications, were used to code intervention components using a standardised procedure following a brief training session. The training session involved familiarisation with the coding framework and practising coding to assist with consistency. Each unique intervention arm was coded by two independent coders (RK and SP, with a background in dietetics, and psychology, respectively), conflicts were identified and resolved through discussion (all authors). Duration of coding time was recorded. Following initial coding and consensus of all studies, all authors critically discussed and reviewed the coding framework to ensure adequate coverage and clarity of delivery features and intervention strategies. Existing codes and descriptors were refined and additional codes included from discussion. Studies were then recoded using the updated codes. ## 2.4. Synthesis of Results Descriptive statistics were used to summarise coding conflicts, number of modifications to the codebook, and to calculate the average and standard deviation (SD) of the time required to code components of each study. Results were synthesised separately for delivery features and intervention strategies, and examined by cluster. ## 3. Results The characteristics of the selected studies are available in Supplementary Table S1. Six studies consisting of a total of 14 active intervention arms were selected. The included studies originated from the United States of America ($$n = 3$$), Australia ($$n = 1$$), Brazil ($$n = 1$$), and the United Kingdom ($$n = 1$$). The average time to code each study was 78 min (SD: 48) for delivery features and 54 min (SD: 29) for intervention strategies. There was a greater number of coding conflicts in the intervention strategies ($$n = 237$$, $19.7\%$) compared to the delivery features ($$n = 156$$, $13.9\%$) (Table 1). The number of conflicts for each intervention arm ranged between 7 and 16 (8.9–$20.5\%$) for delivery features and 2 and 34 (2.3–$39.5\%$) for intervention strategies. When coding the delivery features, unclear definitions were the most common reason for conflicts and updating these definitions was required to achieve consensus. For instance, “duration of contact” was redefined to duration in minutes rather than brief, moderate or extended contact, which was subjective and difficult to code. In contrast, the consensus discussions for the intervention strategies revealed varying interpretations of definitions due to differing coder backgrounds. For example, the cluster “delivery of dietary intervention” had the greatest proportion of conflicts due to one coder having a comprehensive knowledge of clinical dietetic interventions. A total of 43 code definitions were modified, 15 new variables were added, and 7 were removed following the consensus meetings (Supplementary Table S2). Consensus procedures for the new code resulted in 28 conflicts ($13.3\%$) which were resolved through discussion and no further updates to variable definitions were required. The final revised coding framework included 86 delivery features (Table 2) and 88 intervention strategies (Table 3). ## 4. Discussion and Conclusions The EDIT Collaboration aims to identify intervention components that increase or decrease eating disorder risk as part of weight-management interventions [9]. We developed a framework for identifying intervention components, by deconstructing complex interventions into well-defined delivery features and intervention strategies. Using an established coding framework reduces the subjectivity of intervention deconstruction. In addition, this pilot study demonstrates the resourcing required to conduct a comprehensive deconstruction of complex interventions. Our study highlights the need for utilising established and tested definitions to appropriately deconstruct complex behavioural interventions. The taxonomic deconstruction of interventions is useful for examining which components of an intervention may be driving outcomes. However, previous studies of deconstruction behavioural weight-management interventions have predominately focussed on weight outcomes [14,15,16,18] or other measures of effectiveness [23,24,25]. Future research should consider whether interventions, or components of interventions produce unintended effects, such as increasing eating-disorder risk [5]. The strengths of this study include the use of a systematic coding process to validate the codes and definitions within the framework developed through extensive stakeholder consultation involving expertise from both the obesity and eating disorder fields. The framework was tested on a range of studies, varying in population (adolescents, adults), countries, and publication date. Coders were from differing disciplines (dietetics and psychology). The limitations include reliance on published or publicly available intervention descriptions, which may provide insufficient detail in the reporting of intervention components [26]. Further, we did not quantify the frequency or intensity of the intervention strategies. Our coding framework will be implemented for all trials included in the EDIT Collaboration to examine eating-disorder risk during weight management [27]. Moreover, this framework can provide insight into a broad range of weight-management interventions and can be transferred or adapted to examine other safety or effectiveness outcomes (e.g., weight regain, health-related QOL, depression, etc). Coding frameworks, such as the one developed in this study, can assist in the transparent and systematic coding of existing interventions to enhance our understanding of the components of complex interventions. ## References 1. Semlitsch T., Stigler F.L., Jeitler K., Horvath K., Siebenhofer A.. Management of overweight and obesity in primary care—A systematic overview of international evidence-based guidelines. Obes. Rev. (2019.0) 20 1218-1230. DOI: 10.1111/obr.12889 2. Alman K.L., Lister N.B., Garnett S.P., Gow M.L., Aldwell K., Jebeile H.. Dietetic management of obesity and severe obesity in children and adolescents: A scoping review of guidelines. Obes. Rev. (2020.0) 22 e13132. DOI: 10.1111/obr.13132 3. 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--- title: 'Association between the Inflammatory Potential of the Diet and Biological Aging: A Cross-Sectional Analysis of 4510 Adults from the Moli-Sani Study Cohort' authors: - Claudia F. Martínez - Simona Esposito - Augusto Di Castelnuovo - Simona Costanzo - Emilia Ruggiero - Amalia De Curtis - Mariarosaria Persichillo - James R. Hébert - Chiara Cerletti - Maria Benedetta Donati - Giovanni de Gaetano - Licia Iacoviello - Alessandro Gialluisi - Marialaura Bonaccio journal: Nutrients year: 2023 pmcid: PMC10056325 doi: 10.3390/nu15061503 license: CC BY 4.0 --- # Association between the Inflammatory Potential of the Diet and Biological Aging: A Cross-Sectional Analysis of 4510 Adults from the Moli-Sani Study Cohort ## Abstract Chronological age (CA) may not accurately reflect the health status of an individual. Rather, biological age (BA) or hypothetical underlying “functional” age has been proposed as a relevant indicator of healthy aging. Observational studies have found that decelerated biological aging or Δage (BA-CA) is associated with a lower risk of disease and mortality. In general, CA is associated with low-grade inflammation, a condition linked to the risk of the incidence of disease and overall cause-specific mortality, and is modulated by diet. To address the hypothesis that diet-related inflammation is associated with Δage, a cross-sectional analysis of data from a sub-cohort from the Moli-sani Study (2005–2010, Italy) was performed. The inflammatory potential of the diet was measured using the Energy-adjusted Dietary Inflammatory Index (E-DIITM) and a novel literature-based dietary inflammation score (DIS). A deep neural network approach based on circulating biomarkers was used to compute BA, and the resulting Δage was fit as the dependent variable. In 4510 participants (men $52.0\%$), the mean of CA (SD) was 55.6 y (±11.6), BA 54.8 y (±8.6), and Δage −0.77 (±7.7). In a multivariable-adjusted analysis, an increase in E-DIITM and DIS scores led to an increase in Δage (β = 0.22; $95\%$CI 0.05, 0.38; β = 0.27; $95\%$CI 0.10, 0.44, respectively). We found interaction for DIS by sex and for E-DIITM by BMI. In conclusion, a pro-inflammatory diet is associated with accelerated biological aging, which likely leads to an increased long-term risk of inflammation-related diseases and mortality. ## 1. Introduction Aging is a complex process that results from a wide variety of molecular and cellular damage over time that therefore varies across individuals [1]. Globally, the proportion of people aged over 60 years is increasing, thus placing burdens on health systems across the world [2]. In unhealthy aging, “inflammaging”, defined as low-grade chronic inflammation in the absence of known infections or other established causes, occurs [3]. Inflammaging constitutes a marker of accelerated aging and increased morbidity [4,5,6] and disability [7]. Several mechanisms are involved, including the accumulation of cellular damage [8], changes in the gut and oral microbiota [9], and cellular senescence [10], which causes an increase in inflammatory cytokines, particularly in visceral fat [11]. Chronological age (CA) is limited in capturing the heterogeneity of aging events and their impact on health. The concept of biological age (BA)—namely, the actual underlying biologically relevant age of an organism—has been proposed to provide a better understanding of the heterogeneity of the aging process across individuals. BA can be estimated through multiple algorithms and biomarkers [12,13,14]. The resulting discrepancy between BA and CA is usually indicated by Δage, which may suggest either accelerated (Δage > 0) or decelerated biological aging (Δage < 0) [13]. Negative values of Δage (i.e., where BA is less than CA) are associated with the deceleration of aging and a lower risk of morbidity, hospitalization, and mortality [15,16]. One of the most innovative ways to estimate biological aging is by applying deep neural networks to circulating biomarkers [16,17,18,19]. Indeed, although this represents only a generic marker of biological aging and other markers or scales such as frailty and cognitive performance may better tag organ-specific aging [20] or the intrinsic aging capacity [21,22], blood-based estimates of BA can provide information on several aging domains within the human body because it can be based on a range of different circulating biomarkers. Indeed, previous studies identified prominent roles of glucose homeostasis, liver and kidney functionality, and inflammation, among other biomarkers [16,17,18,19]. Moreover, the wide availability of routine blood tests resulting from common clinical practice makes this a cost-effective estimator of biological aging, which could be used as a public health and healthy aging screening tool in the general population [16]. Despite evidence suggesting a prominent role of healthy dietary patterns in modulating healthy aging, the association between dietary exposures and biological aging parameters remain understudied. However, previous observations suggest a central role of diet in the regulation of subclinical inflammation, a precursor of chronic diseases [23,24], which is also inherently linked to inflammaging [3]. Plant-based, whole-food dietary patterns characterized by food rich in compounds with anti-inflammatory activity, e.g., the Mediterranean diet, appear to promote healthy aging [25]. By contrast, a pro-inflammatory diet leads to low-grade inflammation and, consequently, an increased risk of chronic conditions, such as cancer, metabolic disorders, and depressive symptoms [23,26,27]. The dietary inflammatory index (DII®) [27]; and the energy-adjusted version (E-DIITM) [28] are literature-based tools widely used to assess the inflammatory potential of the diet associated with health outcomes including cancer, cardiovascular diseases, adverse mental health, cardiometabolic risk, and frailty [29,30,31,32,33,34]. The DII, which is based on existing literature, includes up to 45 food parameters, including 35 nutrients and 10 whole foods. Many data sets do not include whole foods because programs often compute and output only nutrients; so, most will have fewer than 45 parameters. Because the E-DII includes energy in the denominator, it will have one fewer parameter than the DII. Because, in most datasets, the DII is based on nutrients, it might be useful to consider additional whole foods that contain multiple interacting substances and nutrients [35]. To address this concern, Byrd et al. developed and validated a novel FFQ-based dietary inflammation score (DIS) that includes whole foods, beverages, and micronutrient supplements. In a validation study within three populations, the use of the DIS suggests stronger associations with plasma inflammation biomarkers than DII [36]. Moreover, a pro-inflammatory DIS value has been associated with all-cause mortality [37] and with an increased risk of colorectal cancer [38]. It should be noted that with over 900 publications, the DII/E-DII literature is much more robust [36]. We performed a cross-sectional analysis in a sub-cohort from the Moli-sani Study (2005–2010, Italy) to examine the potential association of pro-inflammatory diets with biological aging. We hypothesized that a proinflammatory diet is directly associated with accelerated biological aging, estimated using a blood-based deep learning algorithm. ## 2.1. Study Population We analyzed data from the Moli-sani Study, a large population-based cohort designed to investigate genetic and environmental risk factors associated with cardiovascular and cerebrovascular diseases and cancer. At the baseline survey performed between 2005 and 2010, 24,325 subjects (aged ≥ 35 years) were recruited from city-hall registries of the Molise region. Exclusion criteria were pregnancy at the time of recruitment, mental impairments, current poly-traumas or coma, or refusal to sign the informed consent form. The Moli-sani Study complies with the Declaration of Helsinki and was granted the approval of the Ethics Committee of the Catholic University in Rome, Italy. Additional details of the study design are available elsewhere [39]. For the present analyses, we excluded individuals with missing data on diet ($$n = 20$$) or with implausible energy intake (<800 or >4000 kcal/d in men; <500 or >3500 kcal/d in women) ($$n = 126$$), or individuals with medical ($$n = 43$$) or dietary questionnaires judged as unreliable ($$n = 179$$). ## 2.2. Computation of Biological Age To compute biological age, we used a supervised machine learning algorithm called a deep neural network (DNN). From the initial 24,325 participants, Δage was calculated in a test set of 4772 subjects as described below [16]. We deployed a DNN for the prediction of BA using 36 circulating biomarkers, using recruiting center and sex as input features, and the CA of each participant as a label. Biomarkers included were (a) glucose metabolism: glucose, C-peptide, and insulin; (b) lipids: triglycerides, high and low-density lipoprotein-cholesterol, lipoprotein a and apolipoprotein A1 and B; (c) liver enzymes: aspartate transaminase and alanine aminotransferase; (d) renal function: uric acid, albumin, creatinine, cystatin-C; (e) vascular and cardiac: NT-proB-type Natriuretic Peptide and high-sensitivity cardiac troponin I; (f) hormones: testosterone and vitamin D; (g) hemostasis: D-Dimer; (h) inflammation: high-sensitivity C-reactive protein; (i) haemachrome: red blood cell count and distribution width, hematocrit, hemoglobin levels, mean corpuscular volume, mean corpuscular hemoglobin concentration, total white blood cells, lymphocytes, monocytes, granulocytes, neutrophils, basophils, and eosinophils; platelet count, mean platelet volume, and platelet distribution width. The DNN was built in R v3.9 through the Keras package v2.4.0 (https://www.r-project.org/; https://cran.r-project.org/web/packages/keras/index.html; accessed on 15 September 2022). We split the available dataset passing quality control ($$n = 23$$,858) into a random training and test set (80:20 ratio), then trained the algorithm over 1000 epochs in the training set and evaluated the accuracy in the test set. For each participant, BA and the resulting discrepancy with CA were computed (∆age = BA–CA) within the training set ($$n = 4772$$), which was used within the study population (i.e., the test set. A permutation feature importance analysis revealed that the most influential features on BA (hence ∆age) estimates—namely those showing a loss-drop after permutations of at least $5\%$ compared to the original non-permuted model—were cystatin-C, NT-proBNP, sex, creatinine, glucose, ALT, AST, triglycerides and D-Dimer [16]. Other details on quality control, DNN architecture, and performance are reported elsewhere [16]. The final analysis was carried out in the remaining test sample of 4510 subjects after applying the exclusion criteria mentioned above. ## 2.3. Dietary Assessment Food intake during the year before enrolment was assessed through an interviewer-administered EPIC 188-item food frequency questionnaire (FFQ) [40], which was validated and adapted to the Italian population. The food items were classified into 45 predefined food groups based on similar nutrient characteristics or culinary usage. Frequencies and quantities of each food were linked to Italian Food Tables using specialized software [41,42] to estimate energy, macro-, and micro-nutrient intake. ## 2.4. Computation of DII and E-DII Scores We calculated the DII and E-DII scores for all subjects using FFQ-derived dietary information, as mentioned above and described in detail elsewhere [27,28]. The dietary data for each study participant were first linked to the regionally representative global database that provided a robust estimate of a mean and standard deviation for each of the food parameters (i.e., foods, nutrients, and other compounds such as flavonoids). A z-score was derived by subtracting the “standard global mean” from the amount reported and dividing this value by the standard deviation (SD). The z-score was converted to a centered proportion and then multiplied by the respective food parameter inflammatory effect score (derived from a literature review and scoring of 1943 “qualified” articles) to obtain the subject’s food parameter-specific DII score. To compute the overall DII score for every subject in the study, all the food parameter-specific DII scores were summed. We repeated this procedure for the E-DII using calorie-adjusted values for intake and using a calorie-calorie-adjusted global comparative database to compute Z scores and, ultimately, the overall E-DII score. For the current analysis, data were available for a total of 34 food parameters (carbohydrate, protein, total fat, alcohol, fiber, cholesterol, saturated fat, monounsaturated fat, polyunsaturated fat, omega-3, omega-6 fatty acid, niacin, thiamin, riboflavin, vitamin B12, vitamin B6, iron, magnesium, zinc, vitamin A, vitamin C, vitamin D, vitamin E, folic acid, β-carotene, anthocyanidins, flavan-3-ols, flavones, flavanols, flavonones, isoflavones, garlic, onion, tea). The DIS was calculated using the method described by Byrd et al. [ 36], consisting of 19 food groups (18 whole foods and beverages and 1 composite micronutrient supplement group) that were selected a priori based on biological plausibility and previous literature (Supplemental Table S1). The DIS components (dietary and supplemental intakes) were acquired from FFQ used in our cohort [40]. An individual’s DIS score was then calculated as the sum of their weighted components. For comparison purposes, both scores were standardized. ## 2.5. Ascertainment of Covariates Information about sociodemographic factors, lifestyles, and clinical variables was obtained at baseline via interviewer-administered questionnaires. Personal history of cancer and cardiovascular disease (angina, myocardial infarction, revascularization procedures, peripheral artery diseases, and cerebrovascular events) and drug treatment were self-reported and confirmed by medical records. Participants were considered to have hypertension, hyperlipidemia, or diabetes at baseline if they reported having been treated with disease-specific drugs. Leisure-time physical activity (PA) was expressed as daily energy expenditure in metabolic equivalent task hours (MET-h/d) for sport, walking, and gardening. Height and weight were measured, and body mass index (BMI) was calculated as weight (kg)/height (m)2 and grouped into three categories normal (≤25 kg/m2), overweight (>25 < 30 kg/m2), or obese (≥30 kg/m2). Subjects were classified as never, current, or former smokers (reported not having smoked at all over the previous 12 months or more). Education was based on the highest qualification attained and was categorized as up to lower secondary (approximately ≤8 years of study), upper secondary school (9–13 years of study), and post-secondary education (>13 years of study). Housing tenure was classified as rented, ownership of one dwelling, and ownership of more than one dwelling. Urbanization was classified as living in either an urban or rural area based on the urbanization level (defined by the European Institute of Statics, EUROSTAT) and obtained by the tool “Atlante Statistico dei Comuni” provided by the Italian National Institute of Statistics [43]. ## 2.6. Statistical Analysis Characteristics of the study population are presented as number and percentage or mean and standard deviation (±SD) for continuous variables. Multivariable-adjusted linear regression models were fit to estimate β-coefficients and corresponding $95\%$ confidence interval ($95\%$ CI) for the relation between the E-DII and the DIS scores (independent variables, scores were standardized for comparison purposes) with Δage (PROC REG in SAS). Missing values for covariates, i.e., history of CVD ($$n = 68$$), cancer ($$n = 19$$), diabetes ($$n = 62$$), hyperlipidemia ($$n = 45$$), hypertension ($$n = 42$$), menopausal status ($$n = 6$$), education ($$n = 1$$), housing ($$n = 3$$), smoking habits ($$n = 5$$), hormone replacement therapy ($$n = 102$$), leisure-time PA ($$n = 42$$) and BMI ($$n = 4$$) were handled using a multiple imputation technique (SAS PROC MI and PROC MIANALYZE). To maximize data availability for all variables and to avoid bias introduced by data not missing-at-random, multiple imputation was performed ($$n = 10$$ imputed datasets). Potential confounders were defined a priori based on the literature on associations with both diet and biological age [44,45]. Two models were fit: one with just age, sex, and energy intake adjusted l (not for analyses with E-DII); and a multivariable model additionally adjusted for education, housing, urban, leisure-time physical activity, smoking habit, BMI, CVD, cancer, diabetes, hypertension, hyperlipidemia, menopausal status, and hormone replacement therapy. In sensitivity analysis, we removed one comorbidity at a time from the principal model. Several subgroup analyses were conducted to test the robustness of the findings according to potential effect modification factors: age, sex, BMI (normal weight, overweight and obese), smoking status, and comorbidity [44]. We tested interaction using multiplicative terms. Statistical tests were two-sided, and P values of less than 0.05 were considered to indicate statistical significance. Data analyses were generated using SAS/STAT software, version 9.4 (SAS Institute Inc., Cary, NC, USA). ## 3. Results The analytical sample consisted of 4510 participants (men $52.0\%$) with a proportion of $52.8\%$ of participants in the lower education level and $63.1\%$ with no comorbidities. The average ± SD of the biological age of participants was 54.8 ± 8.6 y, CA 55.6 ± 11.6 y, and Δage −0.77 ± 7.7. At baseline, the median score (interquartile range; IQR) for E-DII TM was 1.5 (0.2–1.6), and for DIS, −0.12 (−0.6–0.6); higher E-DII or DIS indicate a more proinflammatory diet. Participants in the higher quartile of E-DII and DIS were more likely to have no comorbidities and normal weight than those in the lowest quartile. Daily energy intake and macronutrients were similar across quartiles of the inflammatory potential of diet scores. On average, participants with more pro-inflammatory diets, according to DIS or E-DII, had a lower intake of fiber, fruits, and vegetables per day (Table 1). In the multivariable-adjusted analyses, an increase in the E-DII score was associated with acceleration in Δage (β = 0.22; $95\%$CI 0.05, 0.38). For DIS, we observed the same direction, although slightly greater magnitude (β = 0.27; $95\%$CI 0.10, 0.44) (Table 2). Subgroup analyses confirmed the association of pro-inflammatory diet and acceleration of biological aging only for DIS by sex: men (β = 0.08; $95\%$CI—0.17, 0.33); women (β = 0.43; $95\%$CI 0.21, 0.65); p-value for interaction = 0.03. We found an interaction between E-DII and BMI, participants with normal weight had an increase in acceleration of biological aging (β = 0.27; $95\%$CI—0.05, 0.60 p-value for interaction = 0.001). Increased accelerated aging was also observed among smokers when we analyzed DIS (β = 0.58; $95\%$CI 0.24, 0.93), although the p-value for interaction was not significant (0.16; Table 3). When we excluded comorbidities, the associations remained similar (Supplemental Table S2). In sensitive analyses with two cut-offs of age >65 and >70 years, the association was apparently weaker in older compared to younger groups (Supplemental Table S3). ## 4. Discussion In a large Italian cohort of adults, a positive association was observed between pro-inflammatory diets and biological aging, as measured by a deep learning-based assessment based on many circulating biomarkers. The findings suggest that a large proportion of foods with high pro-inflammatory potential may promote an acceleration of aging, which is an independent risk factor for numerous chronic diseases and mortality [30,31,33,34,46]. Inflammation underlies many different biological aging clocks, even those not strictly based on inflammatory markers, as supported by recent bioinformatic evidence showing an association of inflammation-related gene products in aging-related molecular networks [47]. In line with this evidence, our deep learning aging clock was only partly based on inflammatory or inflammation-related markers [16]. Therefore, the findings reported here suggest that a pro-inflammatory diet may influence aging-related biological pathways (or molecular networks) not strictly related to the inflammatory response. Moreover, sensitivity analysis revealed that older subjects (above the age of 65 or 70 years) show a notably reduced association between inflammatory potential of diet and biological aging, suggesting that adopting healthful diets at an early age may be critical to reducing the future burden of aging. Further longitudinal studies are warranted to ascertain this hypothesis. Our results are in accordance with prior observations where a high adherence to well-known anti-inflammatory dietary patterns (e.g., Mediterranean Diet and DASH) and dietary polyphenols consumption were associated with delayed biological aging [48,49]. In cross-sectional studies, a pro-inflammatory diet, as reflected by a higher DII/E-DII score, was associated with increased levels of inflammatory markers [50] and metabolic syndrome [51]. Moreover, in a middle-aged Korean cohort, a vegetable-based dietary pattern rich in anti-inflammatory foods was inversely associated with a higher level of C-reactive protein, a biomarker of persistent low-grade inflammation [52]. In our study, the E-DII, based mainly on nutrients (as that is where the evidence exists in the biomedical literature), and the DIS, which is based exclusively on foods, were both associated with biological aging [36]. Biological aging is defined as an increased state of cellular vulnerability characterized by senescence, mitochondrial dysfunction, genomic and epigenomic instability, and telomere shortening. Telomere shortening is an important cause of stem cell decline in aging in multiple tissues [1]. In a 5-year longitudinal study on the Mediterranean diet (PREDIMED), a pro-inflammatory diet assessed through the DII was associated with telomere length [53]. In a 5-year longitudinal study (PREDIMED), a more pro-inflammatory diet, assessed using the DII, was associated with telomere shortening [53,54] In a cross-sectional study, higher adherence to a healthy diet with the DASH approach may be involved with slower epigenetic age acceleration [55]. In addition, in a pilot randomized clinical trial, plant-centered diet and lifestyle interventions, including relaxation techniques and exercise, may have a role in decreased epigenetic age [56]. The mechanisms underlying diet-related inflammation and its link with biological aging are still unclear. An unhealthy microbiota and its metabolites possibly are involved in the acceleration of age-related decline and the occurrence of an extensive number of diseases [57]. Moreover, reduced gut microbiota in older adults may play a role in the induction and maintenance of the inflammaging process, cognitive performance, and frailty [58]. The composition of gut microbiota is readily modified by diet [59,60,61,62]. Consistent with this observation, high consumption of food rich in anti-inflammatory compounds (e.g., polyphenols) has demonstrated a positive effect on gut microbiota [63]. By contrast, a Western-type diet rich in fat, sugar, and processed foods and low in fiber may lead to a decrease in gut-beneficial bacteria [63]. The NU-AGE trial, with the objective of reducing inflammaging and preventing cognitive decline in apparently healthy subjects, found that the Mediterranean diet approach may prevent cognitive decline [64]. Additionally, an increased intake of fresh fruits, nuts, seeds, and peanuts (important sources of polyphenols and compounds with anti-inflammatory properties) has been associated with cognitive function, probably through modulating gut intestinal microbiota [65] and suppression of neuroinflammatory process by inhibiting free radicals [45]. Dietary lignans are converted through gut bacteria into enteric lignans, a family of polyphenols with therapeutic activity, including anti-inflammatory and apoptotic effects [66]. Urinary enterolignans may be potential markers for microbiota diversity and have been directly associated with dietary inflammatory potential using the DII [67]. However, further studies are warranted to deepen understanding of the association between pro-inflammatory diets and microbiota. When we analyzed the population according to the main characteristics predisposing to inflammation, we observed differences by sex and BMI categories. In our study, the association between DIS and biological aging was stronger in women than in men. In previous studies, some diseases, such as inflammatory bowel disease and autoimmune diseases with a strong inflammatory component, were more prevalent in females than in men [68,69]. This differential association between men and women could be explained through differences in sex hormones [70], gastrointestinal characteristics, body composition [71], and differences in gut microbiota [72]. Earlier lifestyle may play a role in aging in adulthood [73] To fully understand the role of the cumulative effect of dietary and lifestyle patterns with biological aging would require access to longitudinal data. The DII has been associated with biomarkers of inflammation [50] and with an increased risk of comorbidities hypothesized to be related to inflammation [74]. Diabetes has been associated positively with high DII scores [75,76] and many neurological symptoms that may indicate an acceleration in cerebral aging [77]. The DIS literature, though much smaller, has been associated with inflammation-related diseases, such as sporadic colorectal adenoma [38] and colorectal cancer [78], and with all-cause mortality, including cardiovascular disease and cancer [37]. In the present analysis, “apparently healthy” people (i.e., without evident or self-reported comorbidities) were more likely to have pro-inflammatory diets. Additionally, the subgroup analysis among healthy participants suggested a stronger association between E-DII, DIS, and accelerated biological aging. These results should be considered in light of the cross-sectional design, which may suffer from reverse causality bias. It is conceivable that people with no obvious comorbidities may have a lower perception of risk and, consequently, be more likely to indulge in unhealthy dietary behaviors and other aspects of a lifestyle than people with comorbidities [79]. ## Strengths and Limitations To the best of our knowledge, no analysis has been conducted to link the inflammatory potential of the diet and accelerated biological aging. We uniquely evaluated two different methods to estimate the inflammatory potential of the diet and biological aging through an innovative, deep learning-based measure of BA using circulating biomarkers. Additionally, our results support the use of DIS as an accurate tool for studying associations with the inflammatory potential of the diet, using data from FFQs in observational studies. However, its use would be limited to populations with patterns of intakes similar to those observed in Europe and North America—and not those in other regions such as East, South, and Southeast Asia and East, West, and South Africa. The DII was designed for use in all these populations, including the USA, Bahrain, Denmark, India, Kapan, New Zealand, Taiwan, South Korea, Mexico, and the UK [27]. As of publication, the DII or E-DII has been used in over 900 studies in over 70 countries around the world—vastly more than have used the DIS. Despite this study’s strengths, several limitations need to be acknowledged. First, in a cross-sectional design, a causal association cannot be established, and reverse causation must be considered. Longitudinal studies are needed in the future to help clarify these aspects. Second, although we adjusted for an extensive list of lifestyle and risk factors, residual and unmeasured confounding cannot be fully excluded. Third, dietary data collected through an FFQ may lead to recall and measurement bias, e.g., lack of accuracy in reported portion sizes and in food composition tables. We partially mitigated this limitation by the exclusion of participants with implausible energy intakes and by energy adjustment [80,81]. Our findings have uncertain generalizability because the cohort originated from a southern Italian region. However, the main characteristics of the Moli-sani cohort are comparable with those in the Italian Cardiovascular Epidemiological Observatory, representative of the Italian population [82]. ## 5. Conclusions Results from a Mediterranean cohort indicate that a pro-inflammatory diet, evaluated through two diverse indices, is directly associated with blood-based markers of biological aging. 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--- title: Serum Fibroblast Growth Factor 21 Is Markedly Decreased following Exercise Training in Patients with Biopsy-Proven Nonalcoholic Steatohepatitis authors: - Jonathan G. Stine - Jaclyn E. Welles - Shelley Keating - Zeba Hussaini - Christopher Soriano - J. Wes Heinle - Nathaniel Geyer - Vernon M. Chinchilli - Rohit Loomba - Scot R. Kimball journal: Nutrients year: 2023 pmcid: PMC10056327 doi: 10.3390/nu15061481 license: CC BY 4.0 --- # Serum Fibroblast Growth Factor 21 Is Markedly Decreased following Exercise Training in Patients with Biopsy-Proven Nonalcoholic Steatohepatitis ## Abstract Background and Aims: Exercise remains a key component of nonalcoholic fatty liver disease (NAFLD) treatment. However, mechanisms underpinning the improvements in NAFLD seen with exercise are unclear. Exercise improved liver fat and serum biomarkers of liver fibrosis in the NASHFit trial. We investigated exercise’s mechanism of benefit by conducting a post hoc analysis of these data to determine the relationship between serum fibroblast growth factor (FGF) 21, which is implicated in NAFLD development, and exercise. Methods: In the 20 wk NASHFit trial, patients with nonalcoholic steatohepatitis (NASH) were randomized to receive moderate-intensity aerobic exercise training or standard clinical care. Mediterranean-informed dietary counseling was provided to each group. Change in serum FGF21 was measured after an overnight fast. Results: There was a significant improvement in serum FGF21 with exercise training compared to standard clinical care ($$p \leq 0.037$$) with serum FGF21 reducing by $22\%$ (−243.4 +/−349 ng/mL) with exercise vs. a $34\%$ increase (+88.4 ng/mL +/−350.3 ng/mL) with standard clinical care. There was a large inverse association between change in serum FGF21 and change in cardiorespiratory fitness (VO2peak) (r = −0.62, $95\%$ CI −0.88 to −0.05, $$p \leq 0.031$$), and on multivariable analysis, change in VO2peak remained independently associated with change in FGF21 (β = −44.5, $95\%$ CI −83.8 to −5.11, $$p \leq 0.031$$). Conclusions: Serum FGF21 is markedly decreased in response to aerobic exercise training, offering a novel mechanism to explain the observed reduction in liver fat and improvement in serum biomarkers of liver fibrosis in patients with NASH who do exercise. ## 1. Introduction Globally, nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease and affects between $25\%$ and $30\%$ of the world population [1]. Beyond the vast prevalence rates, NAFLD is also independently associated with lower overall survival [2]. While multiple factors contribute to NAFLD development, physical inactivity plays a major role and leads to abnormal accumulation of fat in the liver [3,4]. NAFLD incorporates two distinct histologic processes including NAFL, the non-progressive type, and nonalcoholic steatohepatitis (NASH) [5]. If uncorrected, NASH can lead to the development of liver fibrosis and disease progression to cirrhosis or hepatocellular carcinoma, often necessitating liver transplantation [6,7]. In the absence of a regulatory-agency-approved drug therapy, lifestyle modification through dietary change and increased physical activity remain the most effective treatment for NAFLD and NASH [8]. Physical activity, including exercise training which is a type of physical activity that is planned, structured, and repetitive [9], has many beneficial effects on the liver, including a reduction in liver fat, and when coupled with modest weight loss may halt or reverse liver fibrosis [8,10,11,12]. Physical activity also has many extrahepatic benefits, including improvement in cardiorespiratory fitness, favorable change in body composition with loss of body fat and gain of skeletal muscle, improvement in hemostasis, gain in bone density, and reduction in metabolic risk [8,11,12]. However, despite our longstanding knowledge about the extensive clinical benefits of physical activity and exercise training, the mechanism explaining exercise’s benefit remains unknown. Fibroblast growth factor (FGF) 21 is a hepatokine that regulates carbohydrate, lipid, and energy metabolism [13]. FGF21 is implicated in NAFLD development and disease progression to NASH; in fact, NAFLD and NASH are felt to be FGF21-resistant states, where an elevated serum FGF21 is observed as a compensatory response [14,15]. FGF21 has been identified as a therapeutic target, and early-phase trials in patients with NASH are underway or have been completed [16,17,18]. Importantly, FGF21 is also impacted by physical activity, and this intervention may reverse the FGF21-resistant state characteristic of NAFLD. While acute exercise may increase serum FGF21 owing largely to skeletal muscle stimulated production, over time, exercise training leads to physiologic adaptation, and a different impact on serum FGF21 is seen [19]. In fact, animal models of NAFLD have demonstrated that exercise training can significantly decrease serum FGF21 [20]. The improvement in FGF21 with aerobic exercise was confirmed in a recent study in elderly Japanese men without established NAFLD who underwent a short-term, five-week moderate-to-vigorous intensity aerobic exercise training program [21]. Whether this improvement is sustained with long-term aerobic exercise training in patients with established NAFLD/NASH is unknown, as the only study performed to date in patients with NAFLD that demonstrated a reduction in serum FGF21 utilized a 12-week resistance training program and not aerobic exercise [22]. Exercise training may also impact the FGF21–adiponectin axis, where an elevation in this ratio is observed in patients with NAFLD and indicative of metabolic dysfunction. In fact, FGF21/adiponectin ratio has been proposed as a NAFLD biomarker [23]. While animal models suggest that exercise training can protect against FGF21–adiponectin axis impairment, this remains largely unexplored in human subjects [24]. In the NASHFit trial, aerobic exercise training was shown to improve liver fat, serum biomarkers of liver fibrosis, as well as cardiorespiratory fitness [12], and to further investigate this significant, unanswered question, we conducted a post hoc analysis of these data to determine the relationship between serum FGF21 and long-term aerobic exercise training in patients with NASH. We also investigated the impact of aerobic exercise training on the FGF21–adiponectin axis. ## 2.1. Study Design and Population This is a post hoc analysis of the NASHFit trial (NCT03518294) for which data on primary and secondary outcome measures were previously published [12]. The NASHFit trial compared the efficacy of 20 weeks of moderate-intensity aerobic exercise training to standard clinical care. Of the 28 patients enrolled, 24 patients completed the trial between May 2018 and February 2021. All patients provided informed consent prior to being included in the study. The study was approved by the Penn State Health Institutional Review Board (Study 8507). All research methods were in accordance with the Declaration of Helsinki, Good Clinical Practice guidelines, and local regulatory requirements. Specific details about subject recruitment, randomization, sample size, and other methods were provided in previous papers [12,25]. Inclusion criteria included sedentary adults with biopsy-confirmed NASH using the NASH Clinical Research Network histological scoring system [26]. Patients were excluded for uncontrolled diabetes, other chronic liver disease, excessive alcohol consumption, or an inability to perform regular exercise. Full eligibility criteria were previously published [12,25]. Patients were randomized 2:1 to intervention with exercise training or a standard of care group using a list generated by computer randomization (REDCap, Vanderbilt University) [27]. No stratified randomization was performed. Patients in the intervention group performed five moderate-intensity aerobic exercise sessions (45–$55\%$ VO2peak) per week, each lasting 30 min. Standard of care control subjects were instructed to continue their current clinical care. Compliance was ensured by remote monitoring with fitness activity trackers and direct supervision of exercise training sessions. Both study groups received Mediterranean-based dietary counseling. Despite this counseling, no change in dietary practices was observed in the NASHFit trial, with similar macronutrient intake reported before and after intervention, and no clinically significant changes in body weight were observed for either group [12]. ## 2.2. Laboratory Methods Blood samples from the NASHFit trial were collected after an overnight fast and, after processing, immediately stored at −80 °C. FGF21 levels were assessed using a Human FGF21 Quantikine enzyme-linked immunoassay (ELISA) kit (catalog number DF2100) from R&D Systems (Minneapolis, MN, USA) following the manufacturer’s instruction. ## 2.3. Statistical Analysis For this post hoc study, the main outcome of interest was change in serum FGF21. This was performed with the use of paired and two-sample t-tests. Both between-group and within-group comparisons were performed where appropriate. Statistical significance was defined by two-sided p-values of <0.05. Secondary outcomes are presented as geometric means with $95\%$ confidence intervals (CIs) or median with interquartile ranges, if negative or zero values. Both between- and within-group comparisons were performed. Continuous endpoints were analyzed with the use of paired and two-sample t-tests and categorical endpoints by the chi-squared test and Fisher’s exact test, where appropriate. Pearson’s correlation coefficients were calculated between FGF21 and routinely captured clinical variables from the NASHFit study. Linear regression modeling was performed to determine predictors of FGF21 change. Variables included in the final model were change in visceral adipose tissue, magnetic resonance imaging proton density fat fraction (MRI-PDFF), and maximal oxygen uptake (VO2peak). SAS (Cary, NC, USA) Version 9.4 was used for all statistical analysis. ## 3.1. Baseline Characteristics Of the 24 patients who completed the NASHFit trial, 20 were included in this analysis (12 exercise, 8 standard of care controls) where serum FGF21 could be measured. The four patients without serum FGF21 were similar in baseline characteristics to the included patients. For patients with measurable serum FGF21, mean patient age was 52 +/−12 years (range 25 to 69 years). Mean body weight was 100.1 +/−18.5 kg, and mean body mass index (BMI) was 32.8 +/− 5.2 kg/m2. The majority of patients were female ($55\%$). In terms of metabolic comorbidities, $80\%$ had hypertension, $60\%$ had hyperlipidemia, and $45\%$ had diabetes. Liver fibrosis stage was as follows: $55\%$ ($$n = 11$$) F0/F1 fibrosis, $25\%$ stage F2 ($$n = 5$$), $15\%$ stage F3 ($$n = 3$$), and $5\%$ stage F4 ($$n = 1$$). Demographic and baseline clinical characteristics were similar between the exercise and the standard of care control group (Table 1). Importantly, the two groups were well matched for age, sex, BMI, metabolic risk, and NASH phenotyping, to include both serum and imaging biomarkers as well as liver histology. ## 3.2. Change in Serum FGF21 following Exercise Training There was a significant improvement in serum FGF21 with exercise training compared with standard clinical care ($$p \leq 0.037$$). In patients who underwent exercise training, serum FGF21 was reduced by −$22\%$ (−243.4 ng/mL, $95\%$ CI −441.4 to −45.5 ng/mL), whereas standard clinical care patients had a +$34\%$ increase (+88.4 ng/mL, $95\%$ CI −90.0 to + 314.5 ng/mL, $$p \leq 0.037$$) (Figure 1). Serum FGF21 reduction was significantly correlated with VO2peak gain (r = −0.62, $95\%$ CI −0.88 to −0.05, $$p \leq 0.031$$), liver volume reduction ($r = 0.68$, $95\%$ CI 0.18 to 0.89, $$p \leq 0.016$$), and PAI-1 reduction ($r = 0.62$, $95\%$ CI 0.04–0.87, $$p \leq 0.033$$) (Figure 2). On multivariable analysis, VO2peak improvement remained independently associated with FGF21 decrease (β = −44.5, $95\%$ CI −83.8 to −5.11, $$p \leq 0.031$$). This means that for every one unit increase in VO2peak, serum FGF21 will reduce by 44.5 ng/mL. ## 3.3. Change in Non-Invasive Tests for NASH Several significant changes were seen in serum and imaging biomarkers following exercise training (Table 2). MRI-PDFF was reduced by −$5.0\%$ ($95\%$ CI −8.2 to −$1.8\%$) following exercise training, while patients in the standard of care arm experienced a +$1.2\%$ ($95\%$ CI −0.7 to +$3.1\%$) gain in liver fat ($$p \leq 0.011$$). In all, $33\%$ of exercise training patients met the minimal clinically important difference of at least $30\%$ relative reduction in MRI-PDFF [28,29], which surrogates for improvement in histologic NASH activity and liver fibrosis, compared to $13\%$ of standard of care patients ($$p \leq 0.008$$). In total, $58\%$ of exercise training patients achieved at least a 17 IU/L reduction in alanine aminotransferase (ALT) [30], which also surrogates for liver fibrosis improvement, compared to $13\%$ of standard clinical care patients ($p \leq 0.001$). All four of the patients who achieved at least $30\%$ relative reduction in MRI-PDFF also had at least a 17 IU/L reduction in ALT. Serum biomarkers were also improved, including a reduction in plasminogen activator one (PAI-1) of −45 ng/mL ($95\%$ CI −106 to +15 ng/mL) compared to a +70 ng/mL ($95\%$ CI +19 to +106 ng/mL) increase in the standard of care condition ($$p \leq 0.020$$) as well as a reduction in cytokeratin (CK)−18 of −59 IU/L ($95\%$ CI −86 to −30 IU/L) versus a +70 IU/L gain ($95\%$ CI −28 to +168 IU/L) with standard clinical care ($$p \leq 0.062$$). The FGF21/adiponectin ratio in the exercise group was reduced compared to the standard of care group (−0.07, $95\%$ CI −0.13 to −0.01 vs. +0.02, $95\%$ CI −0.07 to +0.11, respectively, $$p \leq 0.099$$). No statistically significant change was observed in serum biomarker adiponectin or clinical decision aids NAFLD Fibrosis Score or the Fibrosis-4 index. ## 3.4. Change in Non-Hepatic Clinical Outcomes Cardiorespiratory fitness and glycemic control were significantly improved following exercise training where patients in the exercise arm experienced a +2.8 mL/kg/min gain ($95\%$ CI +0.1 to +5.5 mL/kg/min) in VO2peak compared to a −1.9 mL/kg/min loss ($95\%$ CI −5.4 to +1.7 mL/kg/min) with standard clinical care ($$p \leq 0.057$$) (Table 3). In all, $50\%$ of patients achieved a clinically significant improvement in VO2peak of at least $10\%$ following exercise training compared to $0\%$ of patients in the standard of care arm. Hemoglobin A1c was improved by −$0.5\%$ ($95\%$ CI −0.8 to −$0.2\%$) with exercise training compared to a +$0.4\%$ ($95\%$ CI $0.0\%$ to +$0.8\%$) gain in the standard of care arm ($$p \leq 0.006$$), corresponding to similar changes in fasting serum glucose (−19.5 mg/dL, $95\%$ CI −38.2 to −0.8 mg/dL exercise vs. +20.2 mg/dL, $95\%$ CI −9.3 to +49.7 mg/dL standard clinical care, $$p \leq 0.030$$). While not statistically significant, insulin resistance as measured by homeostatic model assessment for insulin resistance (HOMA-IR) improved following exercise training (−5.5, $95\%$ CI −13.3 to +2.3, $$p \leq 0.148$$). Modest weight change was observed following exercise training −2.1 kg ($95\%$ CI −4.4 to +0.2 kg), but this was not statistically significant ($$p \leq 0.705$$) nor clinically meaningful, as this was a <$3\%$ relative reduction. ## 4. Discussion This post hoc analysis of the NASHFit trial found serum FGF21 to be markedly decreased following 20 weeks of moderate-intensity aerobic exercise training and without clinically significant body weight loss compared with standard clinical care. This finding supports a novel mechanism to explain the observed reduction in MRI-measured liver fat and improvement in serum biomarkers of liver fibrosis in patients with NASH who do undertake aerobic exercise. This is the first study to show that improvement in serum FGF21 is sustained with a long-term exercise training in patients exclusively with NASH, extending the findings of previous study by Taniguchi et al. of 27 elderly Japanese men without established NAFLD which found a short-term five-week aerobic exercise program to decrease serum FGF21 [21]. Taken together, regular aerobic exercise appears to improve the FGF21 resistant-state characteristic of NASH and lead to measurable clinical benefits at or above the thresholds of meaningful response. FGF21 regulates energy metabolism and is largely expressed in the liver, although it can be found in other tissues, including adipose tissue [31]. In the liver, FGF21 stimulates fatty acid oxidation while simultaneously inhibiting de novo lipogenesis [32], an effect that may be mediated through changes in the AMP-activated protein kinase (AMPK) pathway [33,34]. It is plausible that exercise-induced reduction in serum FGF21 may feed back to the liver and potentially stimulate hepatic expression of FGF21, leading to these favorable changes in metabolism and liver fat. FGF21 is also closely related to insulin resistance. Whether the benefits of an exercise-induced reduction in serum FGF21 act directly on the liver or are instead mediated through improvement in insulin resistance, which we observed with measurable improvement in glycemic control and reduction in HOMA-IR, is not possible to answer through this study yet offers an intriguing avenue for future study and would require liver histology. The results of a recent study [23] show that the ratio of FGF21/adiponectin is higher in subjects with NAFLD than in those without NAFLD, and that there was a positive relationship between change in the ratio and liver fat percentage in individuals enrolled in a clinical weight loss program. Based on these results, the authors propose that the FGF21/adiponectin ratio is a potential biomarker to monitor changes in liver fat content. Interestingly, in the present study, there was a trend towards a decrease in the FGF21/adiponectin ratio in the exercise group compared to the standard of care group, consistent with the observed reduction in MRI-measured liver fat. Future studies are warranted to assess the FGF21/adiponectin ratio as a biomarker for exercise-induced reductions in liver fat and to better understand if exercise training restores normal function in the FGF21–adiponectin axis. The relationship between FGF21 and cardiorespiratory fitness is also of great interest given that VO2peak is associated with histologic NASH activity and liver fibrosis [35,36] and also overall mortality in the general population [37] as well as NAFLD [38]. Previous studies have not only demonstrated an association between cardiorespiratory fitness and serum FGF21 [39] but also that FGF21 is predictive of future adverse cardiovascular disease events [40]. Whether a reduction in serum FGF21 and, in effect, the prevention of FGF21 resistance in patients with NASH lowers the risk of future cardiovascular disease events remains unknown but of great significance given that cardiovascular disease is a leading cause of morbidity and mortality in patients with NAFLD and NASH [2]. The multivariable regression demonstrated that a 1 mL/kg/min improvement in VO2peak was associated with a 44.5 ng/dL decrease in serum FGF21. This magnitude of change in VO2peak is frequently observed with even modest amounts of aerobic exercise. Moderate-intensity aerobic exercise training studies of 4–16 weeks duration have shown an average improvement of 3.6 mL/kg/min in people with NAFLD [41]. While the minimally clinically important difference for change in FGF21 is unknown, this magnitude of change in VO2peak would lead to a ~$30\%$ improvement in serum FGF21 in the NASHFit cohort. FGF21 is also implicated in the development of extrahepatic cancers found more commonly in patients with NAFLD, including breast, colorectal, esophageal, and pancreatic, as well as hepatocellular carcinoma [42]. Because extrahepatic and hepatic cancers are also leading causes of death in patients with NAFLD and NASH, the ability of exercise to improve FGF21 resistance offers promise as a mechanism of interest to explore as we continue to tease out the protective benefit of regular physical activity on oncologic risk [43,44]. This study has multiple strengths in that it uses paired samples from a highly rigorous clinical trial conducted in a population of patients with NASH who were well phenotyped and studied systematically. Possible limitations include the sample size (although this study is powered similar to previously published exercise trials in patients with NAFLD), the lack of liver histology, the inability of the study design to evaluate long-term clinical outcomes, and the fact that serum FGF21 was not possible to measure for each patient who completed the NASHFit trial. ## 5. Conclusions Serum FGF21 is markedly decreased in response to exercise training, offering a novel mechanism to explain the observed reduction in liver fat, improvement in serum biomarkers of liver fibrosis, and gains in cardiorespiratory fitness in patients with NASH who do exercise. Future studies are required to determine if exercise training can directly impact patient outcomes by ameliorating the FGF21-resistant state that is characteristic of NASH. ## References 1. Younossi Z.M.. Non-alcoholic fatty liver disease—A global public health perspective. J. Hepatol. (2019) 70 531-544. DOI: 10.1016/j.jhep.2018.10.033 2. 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--- title: Resveratrol Ameliorates Vancomycin-Induced Testicular Dysfunction in Male Rats authors: - Fahad S. Alshehri journal: Medicina year: 2023 pmcid: PMC10056352 doi: 10.3390/medicina59030486 license: CC BY 4.0 --- # Resveratrol Ameliorates Vancomycin-Induced Testicular Dysfunction in Male Rats ## Abstract Background and Objectives: Numerous studies have indicated that antibiotics may adversely affect testicular and sperm function. As an alternative to penicillin, vancomycin is a glycopeptide antibiotic developed to treat resistant strains of Staphylococcus aureus. A few studies have suggested that vancomycin could cause testicular toxicity and apoptosis. Vancomycin, however, has not been investigated in terms of its mechanism of causing testicular toxicity. Materials and Methods: An experiment was conducted to investigate the effects of resveratrol (20 mg/kg, oral gavage) against vancomycin (200 mg/kg, i.p.) on the testicular function of Wistar rats for one week (7 days). There were three subgroups of animals. First, saline (i.p.) was administered to the control group. Then, in the second group, vancomycin was administered. Finally, vancomycin and resveratrol were administered in combination in the third group. Results: After seven days of vancomycin treatment, testosterone levels, sperm counts, and sperm motility were significantly reduced, but resveratrol attenuated the effects of vancomycin and restored the testosterone levels, sperm counts, and sperm motility to normal. In the presence of resveratrol, the vancomycin effects were attenuated, and the luteinizing hormone and follicular hormone levels were normalized after seven days of treatment with vancomycin. Histologically, vancomycin administration for seven days caused damage to testicular tissues and reduced the thickness of the basal lamina. However, the resveratrol administration with vancomycin prevented vancomycin’s toxic effects on testicular tissue. Conclusion: Resveratrol showed potential protective effects against vancomycin-induced testicular toxicity in Wistar rats. ## 1. Introduction There has been a growing body of research indicating that antibiotics may adversely affect sperm and testicular function [1,2,3,4]. Generally, vancomycin is used for infections caused by Methicillin-resistant Staphylococcus aureus (MRSA) and for patients allergic to penicillin or cephalosporins [5,6]. It has been reported that higher vancomycin trough concentrations should be achieved because MRSA-related infections require higher minimum inhibitory concentrations due to the difficulty in penetrating vulnerable sites such as the lungs, brain, and bone [7,8,9]. However, vancomycin treatment is associated with kidney- and liver-related side effects [10,11,12,13], making it unsuitable for patients with impaired renal and hepatic function [10,11]. There has been significant evidence that various antibiotics can impair the motility of sperm, reduce the weight of the reproductive organs, and cause apoptosis in the testes, eventually leading to testicular failure [12]. A large body of research has shown that the use of antibiotics can disrupt the normal functioning of the male reproductive system by interfering with the hormones that regulate spermatogenesis and sperm motility. Additionally, antibiotics can damage sperm cells, leading to reduced sperm count and motility, which can eventually cause infertility [2,13,14,15]. Several recent studies have reported that vancomycin may adversely affect the testes and cause apoptosis in some cases. [ 16,17]. It is still unclear how vancomycin causes testicular toxicity. It has been shown that vancomycin increases oxidative stress, such as elevated lipid peroxide levels, and reduces antioxidant enzymes, such as glutathione (GSH) and mitochondrial damage [18,19]. Furthermore, vancomycin stimulates reactive oxygen species (ROS) and inhibits DNA synthesis [20,21]. The presence of ROS is physiologically essential in semen; however, higher levels of ROS production exceed the natural sperm antioxidant ability to prevent ROS damage [22]. Thus, higher levels of free radicals can reduce spermiogenesis resulting in loss of motility and DNA damage in the sperm nucleus [23]. Resveratrol (3,4′,5-trihydroxy-trans-stilbene) is a natural polyphenolic compound found in vegetables such as grapes, berries, and peanuts. Resveratrol has continuously been reported to have a growing number of benefits. For example, cumulative reports have suggested that resveratrol has potential benefits as an anti-inflammatory, antidiabetic, anticancer, and protective effect against cardiovascular disease. Moreover, resveratrol could improve stress resistance, extend human lifespans, and prevent the progression of many illnesses, including cancer, cardiovascular disease, and ischemic injuries [24,25,26]. As a result, resveratrol’s antioxidant properties have effectively protected cells from hydrogen-peroxide-induced oxidative stress and UV-irradiation-induced cell death after pretreatment with resveratrol [27,28,29]. Furthermore, in pharmaceutical products, resveratrol can delay the oxidation of lipids, reduce the toxic byproducts of oxidation, and extend the shelf life while maintaining the nutritional quality [30,31]. The structure of resveratrol is similar to that of estradiol, which suggests that it may play a similar role in the testes [32]. In humans and domestic animals, resveratrol has been shown to improve sperm quality [33]. This appears to be possible as a result of its ability to pass through the blood–testis barrier and impart its protective properties to the testes [34]. The use of resveratrol to treat infertility in vivo has been found to be effective. Resveratrol has been found to be effective in treating men afflicted with dyszoospermia because it ameliorated the effect induced by 2,5-hexanedione on spermatogenesis [32]. It has been shown that oral administration of resveratrol and coenzyme Q10 protects against radiation-induced spermatogenesis injuries, suggesting that the combination may be beneficial for promoting male fertility [35]. Even though considerable research has been conducted, it is unclear what role resveratrol plays in male reproductive function. Therefore, many attempts have been implemented to reduce antibiotics’ effects on spermatogenesis [36,37]. It is well known that resveratrol is an antioxidant that can scavenge ROS, preventing the damage of cells in tissues. There is promising evidence that antioxidants effectively preserve spermatogenesis in an animal model and treat male factor infertility. Several studies have shown that vancomycin can cause testicular atrophy and impaired sperm quality in animals and humans. However, limited information about vancomycin’s effect on men with reproductive disorders is available. This study aimed to determine whether high doses of vancomycin induce testicular or spermatotoxicity in rats and to investigate whether resveratrol might have a modulatory effect on the development of testicular damage induced by high doses of vancomycin. ## 2.1. Drugs During the study, resveratrol was obtained from ProHealth in the USA (B094XH3W98), mixed with saline ($0.9\%$ NaCl) as suspension, and given to animals immediately, and vancomycin was obtained from Medis in Tunisia (AMM$\frac{12}{96860}$), dissolved in saline ($0.9\%$ NaCl). The dose of resveratrol was selected based on several studies that used 20 mg/kg to show its antioxidant and anti-inflammatory effects [38,39,40]; the vancomycin dose was selected based on its ability to produce toxic effects on several organs, including testicular tissues [41,42,43]. ## 2.2. Animals Twenty-one adult male Wistar rats weighing 160–200 g (7 weeks old) were used in the study. The animals were housed in plastic cages in a humidity-controlled room under a 12 h light/12 h dark schedule for the experiment. Daily monitoring was performed to ensure the wellbeing of the animals, who had access to food and water ad libitum. All animals were obtained from the King Abdulaziz University animal house. ## 2.3. Experimental Design The experiment was conducted for eight days. The rats were divided into three groups: [1] control group ($$n = 7$$), rats injected with saline (i.p) for seven days; [2] vancomycin group ($$n = 7$$), rats injected with vancomycin (200 mg i.p) for seven days; [3] vancomycin + resveratrol group ($$n = 7$$), rats injected with vancomycin (200 mg i.p) and resveratrol (20 mg/kg, oral gavage) for seven days (Table 1). On day eight, all rats were euthanized using CO2; then, serum and tissue samples were collected. The blood samples were collected from their retroorbital plexus and tail vain. For the separation of serum from plasma, a blood sample was collected and centrifuged at 3000× g for 15 min at 4 °C. For testing, all samples were centrifuged and analyzed immediately. ## 2.4. Determination of Testosterone, Follicle Stimulating Hormone (FSH), and Luteinizing Hormone (LH) by ELISA Serum samples were collected on day 8. Each sample was centrifuged, and each animal’s serum was analyzed separately. The ELISA technique was used to determine the serum levels of testosterone, FSH, and LH according to the manufacturer’s protocol (MyBioSource, Inc San Diego, CA 92195-3308, USA). ## 2.5. Determination of the Sperm Motility and Counts The male rats were euthanized by CO2 inhalation. The sperm samples were diluted with physiological solution (10 μL), pipetted with TL-HEPES solution containing 3 mg/mL bovine serum albumin, and then used to buffer HEPES-buffered Tyrode lactate (TL-HEPES) solution. The cauda epididymis was cut at several points at 37 °C to allow the sperm to flow out. The sperm motility percentage and sperm count (Cells/mm3) were obtained using computer-assisted semen analysis to measure the sperm motility and forward motility with the SpermVision™ CASA System (MiniTub, Tiefenbach, Germany). Following Zemjanis’ method, the sperm motility of rats was measured within 2–4 min of sacrifice [44]. ## 2.6. Histological Examination The testicular tissues were used for histopathological assessment and prepared in $10\%$ formalin solution for two days. The tissue was embedded in paraffin blocks and stained with Hematoxylin and Eosin (H&E). The thickness of the slices was between 3 and 5 mm. An OMAX 3 MP Digital Compound Microscope was used to observe the stained sections at various magnifications and to take photographic micrographs. Under a light microscope at ×400, 20 seminiferous tubules from each animal section were evaluated for histomorphometric changes. In addition, histopathological changes in the testes were examined. ## 2.7. Statistical Analysis The serum testosterone, follicle-stimulating hormone, luteinizing hormone, and sperm motility were analyzed using a one-way analysis of variance (ANOVA) followed by Tukey’s post hoc tests. The data were analyzed using Prism version 9.4.1 (p-value < 0.05). ## 3.1. Body Weight There was no significant difference in the body weight of all the treated groups compared to the controls on day 1. However, repeated measure two-way ANOVA analysis showed a significant main effect in days, as shown in the ANOVA table [F [1, 6] = 178.5, $p \leq 0.0001$], treatment [F [2, 12] = 4.026, $$p \leq 0.0459$$], the days x treatment [F [2, 12] = 126.1, $p \leq 0.0001$] (Figure 1). In addition, multiple comparison tests using the Tukey post hoc test revealed a significant reduction in body weight in the vancomycin group on day 8 compared to the vancomycin group on day 1 ($p \leq 0.0001$). Moreover, there was a reduction in body weight in the vancomycin group compared to the control group on day 8 ($p \leq 0.0001$). However, there was an increase in the body weight of the vancomycin + resveratrol group compared to the vancomycin group ($p \leq 0.0001$). ## 3.2. Testosterone, Follicle Stimulating Hormone, and Luteinizing Hormone in the Blood Levels The testosterone level in the blood was determined on day 8 of the experiment. One-way ANOVA revealed significant changes in the level of testosterone (ng/dl) between the treatment groups, as shown in the ANOVA table [F [2, 18] = 17.02, $p \leq 0.0001$, Figure 2]. Further analysis using Tukey’s multiple comparison tests showed a significant reduction in the testosterone levels in the vancomycin group compared to the control group ($$p \leq 0.0002$$) and the vancomycin + resveratrol group ($$p \leq 0.0003$$). However, no significant changes were found between the control group and the vancomycin + resveratrol group ($$p \leq 0.9977$$). The FSH level in the blood was determined on day 8 of the experiment. One-way ANOVA revealed significant changes in the level of FSH (mIU/mL) between the treatment groups, as shown in the ANOVA table [F [2, 18] = 41.43, $p \leq 0.0001$, Figure 3]. Further analysis using Tukey’s multiple comparison tests presented a significant increase in the FSH levels in the vancomycin group compared to the control group ($p \leq 0.0001$) and the vancomycin + resveratrol group ($p \leq 0.0001$). However, no significant changes were found between the control group and the vancomycin + resveratrol group ($$p \leq 0.8106$$). The LH level in the blood was determined on day 8 of the experiment. One-way ANOVA revealed significant changes in the level of LH (mIU/mL) between the treatment groups, as shown in the ANOVA table [F [2, 18] = 50.58, $p \leq 0.0001$, Figure 4]. Further analysis using Tukey’s multiple comparison tests showed a significant increase in the LH levels in the vancomycin group compared to the control group ($p \leq 0.0001$) and the vancomycin + resveratrol group ($p \leq 0.0001$). However, no significant changes were found between the control group and the vancomycin + resveratrol group ($$p \leq 0.8745$$). ## 3.3. Sperm Motility and Counts The sperm motility was determined on day 8 of the experiment. One-way ANOVA revealed significant changes in the sperm motility between the treatment groups, as shown in the ANOVA table [F [2, 18] = 21.70, $p \leq 0.0001$, Figure 5]. Further analysis using Tukey’s multiple comparison tests showed a significant reduction in the sperm motility in the vancomycin group compared to the control group ($p \leq 0.001$) and the vancomycin + resveratrol group ($p \leq 0.0001$). However, no significant changes were found between the control group and the vancomycin + resveratrol group ($$p \leq 0.9314$$). The sperm counts were determined on day 8 of the experiment. One-way ANOVA revealed significant changes in the sperm counts (Cells/mm3) between the treatment groups, as shown in the ANOVA table [F [2, 18] = 29.55, $p \leq 0.0001$, Figure 6]. Further analysis using Tukey’s multiple comparison tests showed a significant reduction in the sperm motility in the vancomycin group compared to the control group ($p \leq 0.0001$) and the vancomycin + resveratrol group ($$p \leq 0.0001$$). However, no significant changes were found between the control group and the vancomycin + resveratrol group ($$p \leq 0.1148$$). ## 3.4. Histological Examinations of the Testicles The histology of the testicles was not significantly different between the rats given daily saline alone and the controls (Figure 7A–C). Hence, it was found that normal spermatogenesis had taken place, that the Sertoli cells had been preserved well, and that the tubular basement membrane had been clearly defined. Furthermore, the interstitial space between the tubules and the Leydig cells also appeared intact. However, the vancomycin-treated group showed a significant difference in the histology of the testes, where the seminiferous tubules were observed to be swallowed up completely. The tubular basement membranes of the seminiferous tubules were identified in other areas of the section. While most germ cells, including highly differentiated germ cells and deformed sperm, were degenerating, a small percentage were flourishing. It is also important to note that the ground substance within the interstitium partially disappeared and was replaced by fibroblasts and inflammatory cells. There was an improvement in these toxic effects in the group treated with vancomycin and resveratrol. ## 4. Discussion A growing concern has been raised over the possibility that antibiotics may adversely affect human fertility [3,13,45]. Vancomycin is considered one of the most common antibiotics used globally for treating severe Gram-positive infections caused by meticillin-resistant S aureus (MRSA) [5,46]. Nevertheless, vancomycin has long been recognized as one of the most commonly encountered drugs that induces nephrotoxicity and hepatotoxicity. In addition to its toxic effects, vancomycin can also cause testicular toxicity [43]. Hence, the present study was conducted to investigate the protective effects of resveratrol against the toxic effects of vancomycin on the testicular functions of male Wistar rats, through analysis of the histopathological and biochemical profiles. The present study demonstrated the toxicological effects of vancomycin in male Wistar rats. There was a significant decrease in the serum testosterone levels after administering vancomycin. Low intratesticular testosterone concentrations may result in germ cell degeneration due to vancomycin exposure. It has been suggested that the testosterone level in the testes is essential for spermatogenesis and maintaining the seminiferous tubules’ structural morphology and physiology [47,48,49]. Vancomycin administration was associated with the degeneration of germ cells, including highly differentiated germ cells and deformed sperm. Moreover, changes in the testes’ morphological characteristics in the vancomycin treatment groups were observed. Epithelium, tubular shrinkage, and atrophy were manifestations of these changes. In addition, several studies have demonstrated that the sperm count and motility are the most valuable indicators of male fertility. Research shows that the sperm count and motility are positively associated with pregnancy rates. Based on the findings of our study, the vancomycin-induced structural damage to rat testicular tissues resulted in a severe reduction in the sperm count and motility. Furthermore, it has been suggested that administering vancomycin can lead to various biochemical malfunctions [50,51]. Unfortunately, there is a lack of understanding the mechanisms through which vancomycin produces these effects. There is, however, evidence that reactive oxygen species (ROS) are involved. In fact, the rats exposed to vancomycin showed an elevation in oxidative stress caused by a reduction in antioxidant enzymes, such as the glutathione levels, coupled with an increase in the lipid peroxide levels, which resulted in oxidative stress in the animals [16,52]. Other antibiotics have shown similar patterns regarding testicular dysfunction. In vivo and in vitro, it has been well established that gentamicin is capable of causing ROS formation and oxidative damage. In the testes of rats treated with gentamicin, a similar reduction in enzymatic and nonenzymatic antioxidant activity was observed, along with increased lactoperoxidase levels [53]. As a result of the treatment with gentamicin, the MDA concentrations increased, and the GSH levels decreased. Consequently, the oxidative stress caused by the gentamicin treatment could be linked to increased lactoperoxidase levels and decreased antioxidant activity and GSH levels in the testes [54]. Therefore, it is essential to monitor antibiotic use to minimize the potential risks. Moreover, there was a significant increase in the serum LH and FSH levels after administering vancomycin. The LH level was also significantly higher in the treatment group, indicating that the hypophyseal–pituitary axis was affected [55]. In fact, male interstitial cells produce testosterone in response to LH stimulation [56]. Additionally, the FSH level was significantly higher than that of the controls. It has been reported that there is a direct interaction between FSH and Sertoli cells; therefore, FSH binds to its receptor and stimulates its signaling pathway, which leads to Sertoli cell differentiation [57]. It is possible that an elevated level of FSH indicates abnormal spermatogenesis and may indicate testicular failure [58]. Conversely, a low level of testosterone and raised FSH and LH levels have been associated with insufficient sperm production by the testicles [59]. As a result of these changes, this study suggests that using vancomycin in high doses is likely to lead to infertility. Known for its antioxidant properties, resveratrol is a natural polyphenolic compound present in vegetables such as grapes, berries, and peanuts [60]. In recent years, researchers have extensively studied the antiaging properties of resveratrol and its potential to help prevent aging-related diseases, such as Alzheimer’s and diabetes [61,62]. Furthermore, resveratrol may play a role in preventing heart disease and stroke [63] and improving cognitive function [64]. According to cumulative reports, resveratrol has potential anti-inflammatory and protective effects against cancer [65]. Moreover, resveratrol could improve stress resistance, extend human lifespans, and prevent the progression of many illnesses [66]. Further, it has recently been shown that in vivo treatment with resveratrol prevents oxidative stress in the testes of hyperthyroid rats and rats treated with a chemotherapy drug [33,67]. Although resveratrol appears to have an antioxidant effect on male reproduction, its exact mechanism of action is unknown. As part of this study, resveratrol was examined for its protective effects against damage caused by vancomycin on rats’ spermatozoa. The protective effects of resveratrol on the glutathione levels have been reported, particularly those conducted in a testicular ischemia model. Furthermore, resveratrol stimulates spermatogenesis and testicular regeneration in adults [32]. In addition, rats treated with resveratrol produced more spermatozoa [68]. A similar increase in spermatozoa production was observed in the resveratrol groups compared to the vancomycin groups. In the present study, we observed that resveratrol prevented the sperm reduction caused by vancomycin, suggesting that it has antioxidant properties. The oxidative stress produced by vancomycin, including hydroxyl radicals, can be scavenged effectively by resveratrol [69]. However, several possible molecular mechanisms could explain the effects of resveratrol. It has been reported that resveratrol maintains the integrity of mitochondrial membranes and provides sufficient energy to spermatogonial stem cells through modulating the SIRT1 protein and deacetylating FOXO1 in vitro [70]. Moreover, a study conducted in rats showed that resveratrol increased the expression of sirtuin-1, neuronal nitric oxide synthase (nNOS), decreased the rate of cell death, and stimulated the differentiation of germ cells in rats [35,71,72,73]. 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--- title: Vascular Damage and Glycometabolic Control in Older Patients with Type 2 Diabetes authors: - David Karasek - Jaromira Spurna - Dominika Macakova - Ondrej Krystynik - Veronika Kucerova journal: Metabolites year: 2023 pmcid: PMC10056387 doi: 10.3390/metabo13030382 license: CC BY 4.0 --- # Vascular Damage and Glycometabolic Control in Older Patients with Type 2 Diabetes ## Abstract Diabetes is one of the main risk factors for vascular damage, including endothelial dysfunction and arterial stiffness. The aim of this study was to compare selected parameters of vascular damage in patients with type 2 diabetes (T2D) in different age categories and to determine their relationship to indicators of glycometabolic control. A total of 160 patients with T2D were included in this cross-sectional study. They were divided into four age quartiles (with mean ages of 42.1 ± 4.5, 51.6 ± 1.4, 59.2 ± 3.0, and 69.8 ± 3.8, respectively). All subjects were evaluated for indicators of glycometabolic control and for arterial stiffness parameters along with markers of endothelial damage—tissue plasminogen activator (tPA), plasminogen activator inhibitor-1 (PAI-1) and von Willebrand factor (vWF). The oldest compared to the youngest participants showed significantly increased parameters of arterial stiffness (augmentation pressure 13.4 ± 8.6 vs. 6.7 ± 4.4 mm Hg, augmentation index 26.2 ± 11.3 vs. 19.6 ± 9.2 mm Hg, aortic pulse pressure 47.7 ± 17.1 vs. 33.7 ± 10.4 mm Hg, and pulse wave velocity 11.9 (10.1–14.3) vs. 8.2 (7.7–9.8) m/s) despite having similar glycometabolic control. Arterial stiffness parameters were mainly associated with age and blood pressure. Age and systolic blood pressure were major determinants of arterial stiffness regardless of glycometabolic control. The oldest patients also had the highest levels of vWF (153.7 ± 51.9 vs. 121.7 ± 42.5 %) but the lowest levels of PAI-1 (81.8 ± 47.5 vs. 90.0 ± 44.9 ng/mL). Markers of endothelial dysfunction correlated with metabolic parameters, but did not correlate with arterial stiffness. Age and systolic blood pressure are major determinants of arterial stiffness in patients with T2D regardless of glycometabolic control, whereas an unfavorable metabolic profile is mainly related to endothelial dysfunction. These results suggest a differential contribution of cardiometabolic risk factors to vascular damage in T2D patients over their lifetime. ## 1. Introduction In recent decades, the incidence of diabetes has been increasing worldwide, especially among the elderly. An aging population is considered one of the most significant contributors to the increasing prevalence of diabetes. In the US, more than one-third of the adult population with diabetes is currently 65 years of age and older [1]. In high-income countries, the prevalence of diabetes peaks ($22\%$) in the 75–79 age group and in middle-income countries in the 60–74 age group ($19\%$) [2]. Diabetes and the aging process independently increase the risk of cardiovascular disease (CVD). Elderly diabetic patients show higher vascular damage and CVD risk than those without diabetes. Vascular inflammation and oxidative stress appear to play a major role in the mechanisms of aging, diabetes and CVD [3]. However, the precise mechanisms underlying age- and diabetes- related CVD remain poorly understood, including the contribution of glycometabolic control. Vascular stiffness represents a subclinical marker of CVD risk. Both age and diabetes are important determinants of vascular damage [4,5,6]. Several studies have found that arterial stiffness may be a predictor of future CVD morbidity and mortality in the diabetic population [7,8]. It is important to note that an independent relationship between arterial stiffness and diabetes has not been consistently demonstrated in all studies. Diabetes does not appear to be a major determinant for this type of vascular damage, especially in older, hypertensive patients [7,9]. On the other hand, some findings suggest that vascular stiffness in diabetic patients may be attributed to the role of diabetes itself rather than aging and higher blood pressure [4]. These possible metabolic mechanisms include non-enzymatic advanced glycation of proteins with production of advanced glycation end products leading to an abnormal extracellular matrix, endothelial dysfunction with nitric oxide dysregulation associated with a tendency to vasospasm, and/or chronic vascular inflammation accompanied by accelerated arterial calcification [7,10]. Both arterial stiffness and endothelial dysfunction represent surrogate markers for CVD; however, they reflect different aspects of vascular damage [11]. Arterial stiffness results mainly from arteriosclerosis (primary disease of the media); endothelial dysfunction contributes to atherosclerosis (primary disease of the intima). Traditional CVD risk factors, such as hypertension, dyslipidemia or diabetes, may differentially affect vascular involvement. Some drugs, such as statins and angiotensin-converting enzyme (ACE) inhibitors, can simultaneously improve endothelial function and reduce arterial stiffness. Glucagon-like peptide 1 (GLP-1) receptor agonists and gliflozins also have a positive effect on arterial stiffness and restore endothelial function [12]. Recently, GLP-1 receptor agonists, gliflozin, and especially their combination have shown greater reductions in markers of endothelial dysfunction and arterial stiffness than insulin in patients with T2D, despite similar reductions in glycosylated hemoglobin [13]. Thus, the contribution of glycometabolic control to vascular damage in T2D is not always the same and may change throughout life. The aim of this study was to compare markers of glycometabolic control and vascular damage in patients with type 2 diabetes (T2D) according to age and to determine whether the parameters of arterial stiffness and/or endothelial dysfunction are influenced by the glycometabolic control. ## 2.1. Study Design and Subjects This cross-sectional study comprised T2D patients consecutively examined during their visits in an outpatient diabetic clinic. The principles of the Declaration of Helsinki for human experiments were respected. The study design and informed consent were reviewed and approved by the Ethics Committee of the Faculty of Medicine and University Hospital Olomouc. All participants were asked about their previous medical history, especially their cardiovascular status, medication, diabetic complications and diabetes duration. We used the following criteria to diagnose diabetes: fasting plasma glucose level ≥7 mmol/L and/or oral antidiabetic drugs (OADs) or insulin administration. Subjects with type 1 diabetes, secondary or genetic diabetes, infection, active cancer, and trauma were not included in this study. Body mass index (BMI), waist circumference, systolic and diastolic blood pressure (SBP and DBP) were also measured. BMI was calculated as body weight/body height2 (kg/m2). Waist circumference was measured while standing, in the middle between the anterior iliac crest and the lower border of the ribs. ## 2.2. Arterial Stiffness Measurements These markers of arterial stiffness were used: augmentation index (AIx), augmentation index normalized for a heart rate of 75 beats per minute (AIx-75), augmentation pressure (AP), aortic systolic pressure (Aortic SP), aortic pulse pressure (Aortic PP), and pulse wave velocity (PWV) [14,15]. The measurement was performed with the SphygmoCor system (AtCor Medical Pty Ltd. Head Office, West Ryde, Sydney, Australia). At least 12 h before the examination, patients were not allowed to smoke or drink alcohol or caffeinated beverages. They were examined in the morning after at least 10 min of rest in a quiet, temperature-controlled room. The examination first took place in a sitting position with a sensor on the radial artery to estimate the aortic pulse wave. PWV was then measured in the supine position; carotid and femoral artery pulse waves were analyzed, and the delay with respect to the ECG wave was detected. Integral software was used to process each pulse wave and ECG data set to analyze the average time difference between the R-wave and the pulse wave over about 10 consecutive cardiac cycles. Distance measurements were made using a tape measure from the sternum (carotid site) to the femoral arteries at the sensor site. Subsequently, PWV was calculated using the distance and average time difference between the two recorded sites according to the formula: PWV (m/s) = carotid–femoral distance (m)/carotid–femoral transit time (s). ## 2.3. Laboratory Analyses Venous blood samples were drawn in the morning after a 12 h fast. Routine serum biochemical parameters were analyzed on the day of blood collection. The modular system SWA (Serum Work Area, Roche, Basel, Switzerland) was used for biochemical examinations. Total cholesterol (TC), TG, and high-density lipoprotein cholesterol (HDL-C) were determined enzymatically. Low-density lipoprotein cholesterol (LDL-C) was calculated according to the Friedewald formula (LDL-C = TC—TG0.4537—HDL-C for TG < 4.5 mmol/L). Non-HDL cholesterol (non-HDL-C) was calculated as follows: non-HDL-C = TC—HDL-C. Glucose was determined by the GOD-PAP method (Roche, Basel, Switzerland) and apoB by immunoturbidimetric method (Tina-quant apoB kits by Roche, Basel, Switzerland). Glycated hemoglobin (HbA1C) levels were measured by ion-exchange chromatography using the ADAMS A1c HA-8180V analyzer (Arkray Corporation, Kyoto, Japan). High-sensitive C-reactive protein (hs-CRP) was assessed by the ultra-sensitive latex immunoturbidimetric method (Tina-quant CRP latex kit by Roche, Basel, Switzerland). Specific antibodies and an immunoradiometric assay in commercially available kits (Immunotech, Marseille, France) were used for insulin and C-peptide concentrations. Tissue plasminogen activator (tPA), plasminogen activator inhibitor-1 (PAI-1) and von Willebrand factor (vWF) were chosen as humoral markers of endothelial damage. VWF antigen was measured by immunoturbidimetric assay (vWF-a, Instrumentation Laboratory, Milan, Italy). Concentrations of t-PA and PAI-1 were determined from human plasma by using ELISA (both by Technoclone, Vienna, Austria). ## 2.4. Statistical Analysis All values are expressed as means ± standard deviation (SD) or medians and interquartile ranges (Q25–Q75; for data with non-normal distribution). Non-normal distribution was tested by the Shapiro–Wilk test. Differences in variables between the groups were analyzed with the t-test and ANOVA for normally distributed variables, the Mann–Whitney U-test and the Kruskal–Wallis test for non-normally distributed variables and the chi-squared (χ2) test for categorical variables. Spearman’s coefficient (ρ) was used to express the value of correlation. Multiple regression analysis was used to estimate the relationship between independent and dependent variables. $p \leq 0.05$ was considered as significant. Statistical analyses were performed using Statistica 12.0 (StatSoft Software Inc., Tulsa, OK, USA). Probability values of $p \leq 0.05$ were considered as statistically significant. ## 3.1. Basic Characteristic A total of 160 patients with T2D participated in this study (109 men, 51 women; age = 58.2 ± 11.7 years). All T2D patients were treated with diet. Of the total number of participants, $72\%$ were on insulin and $91\%$ were on oral antidiabetic drugs (OADs), as follows: metformin $86\%$, dipeptidyl peptidase-4 inhibitors $36\%$, gliflozin $14\%$, sulfonylureas $9\%$ and GLP-1 receptor agonists $7\%$. Eighty-three percent of subjects were treated with antihypertensive therapy (ACE inhibitors $62\%$, angiotensin receptor blockers $19\%$, calcium channel blockers $39\%$, diuretics $43\%$, and beta-blockers $35\%$). Hypolipidemic drugs were administered to $60\%$ of patients (specifically: statins $56\%$, ezetimibe $16\%$ and fibrates $18\%$). Among all participants, $35\%$ were smokers. Table 1 shows the basic characteristics of the participants divided by age into individual quartiles. The oldest patients had, significantly, the highest SBP, prevalence of hypertension, and CVD, but the lowest levels of LDL-C, and hs-CRP. ## 3.2. Vascular Damage Parameters The results are shown in Table 2 and Figure 1. The oldest patients had, significantly, the highest arterial stiffness parameters, namely AP, AIx-75, Aortic PP and PWV. They also had the highest elevation of vWF and the lowest PAI-1 levels. No significant differences were detected in t-PA. All markers of arterial stiffness correlated with age (for: AIx-75 ρ = 0.27, AP ρ = 0.43, aortic SP ρ = 0.25, aortic PP ρ = 0.48, and PWV ρ = 0.43). Some parameters correlated with SBP (for: AP ρ = 0.22, aortic SP ρ = 0.75, aortic PP ρ = 0.50, and PWW ρ = 0.27). Aortic SP, in addition, correlated with DBP (ρ = 0.54) and waist circumference (ρ = 0.18); moreover, aortic PP and PWV correlated with insulin levels (ρ = 0.21, ρ = 0.19, respectively). There were no associations between arterial stiffness and parameters of glycometabolic control. PAI-1 correlated positively with BMI (ρ = 0.39), waist circumference (ρ = 0.38), hs-CRP (ρ = 0.23), TG (ρ = 0.40), non-HDL-C (ρ = 0.39), apoB (ρ = 0.35), fasting glucose (ρ = 0.20), C-peptide (ρ = 0.37), and negatively with HDL-C (ρ = −0.27). VWF correlated positively with TG (ρ = 0.26), and negatively with HDL-C (ρ = −0.36). Levels of t-PA correlated only with C-peptide (ρ = 0.20) and PAI-1 (ρ = 0.24). There were no significant correlations between endothelial markers and parameters of arterial stiffness. Multiple regression analyses were performed to identify independent predictors for markers of vascular damage—see Table 3. Age was the only independent predictor for AP. SBP predicted aortic PP, aortic SP (together with DBP), and PWV. Von Willebrand factor was independently predicted by TG and HDL-C; PAI-1 was predicted by C-peptide, and BMI. ## 4. Discussion Patients with T2D in the highest age quartile have shown the most striking signs of vascular damage. Parameters of arterial stiffness (AP, AIx-75, aortic PP and PWV) were significantly increased in this group compared to younger individuals regardless of glycometabolic control. Arterial stiffness was mainly associated with age and systolic blood pressure. The oldest patients also had the highest levels of vWF, but the lowest levels of PAI-1. Markers of endothelial dysfunction correlated with metabolic parameters, but did not correlate with parameters of arterial stiffness. Age and blood pressure are among the strongest predictors of arterial stiffness [3,4,7]. Both were significantly and independently associated with PWV in $91\%$ and $90\%$ of conducted studies, respectively, whereas the presence of diabetes was associated with PWV only in $52\%$ of studies. However, even within the studies in which a positive association between diabetes and arterial stiffness was seen, diabetes accounted for only $5\%$ of the variation in PWV [9]. Potential mechanisms of arterial stiffness in diabetes may include the role of advanced glycation end products (AGEs) and nitric oxide (NO) [7]. In the present study, there were no significant differences between age-defined quartiles in glycemic control or duration of diabetes, which primarily determine the development of AGEs. Only blood pressure and age were independently predictors for markers of arterial stiffness. Therefore, we believe that age and systolic blood pressure (still significantly elevated in older patients) are major contributors to arterial stiffness in this cohort of T2D patients. This is consistent with the results of previous studies where diabetes per se (in contrast to hypertension) was only a weak predictor of arterial stiffness [9]. Reduced bioavailability of NO leading to endothelial dysfunction results in impaired vasodilation, increased vascular fibrosis and arterial stiffness [4]. Age-related endothelial dysfunction may affect the arterial network differently depending on the location and type of vessel. Aging results in endothelial dysfunction, particularly in large conduit arteries [16]. It is the involvement of these arteries that most influences the selected parameters of arterial stiffness. In addition to impaired NO-dependent vasodilation, endothelial dysfunction is also manifested by increased production of pro-inflammatory, pro-adhesive and pro-thrombotic molecules, such as vWF, and PAI-1. Although these indicators did not correlate with the examined parameters of arterial stiffness, the oldest patients had the highest levels of vWF, and on the contrary, the lowest levels of PAI-1. Von Willebrand factor is known to be a more specific marker of endothelial dysfunction than PAI-1 because plasma levels of vWF are exclusively produced by endothelial cells [17,18,19,20], whereas plasma levels of PAI-1 reflect its production not only in the endothelium, but also in adipose tissue and other cells such as megakaryocytes, smooth muscle cells, fibroblasts, monocytes and macrophages [21,22]. Von Willebrand factor plays a key role in platelet adhesion and aggregation, and numerous studies have investigated the relationship between VWF plasma levels and thromboembolic cardiovascular events. VWF typically rises during an acute coronary syndrome, and the extent of this VWF release is an independent predictor of adverse clinical outcomes in these patients. Many lines of evidence suggest that VWF is not only a marker, but also a truly important effector in the pathogenesis of myocardial infarction [18]. A recent meta-analysis showed that plasma vWF levels were significantly higher in T2D patients with CVD than those without CVD [23]. This is consistent with the results of this study; the oldest patients with the highest vWF levels had the highest prevalence of CVD. However, vWF did not correlate with age, but was independently associated with indicators of mixed dyslipidemia (TG and HDL-C levels). Several studies have observed an association between dyslipidemia and VWF in patients with T2D and may point to how dyslipidemia contributes to endothelial damage in this population [24,25]. The lowest PAI-1 levels found in the oldest patients with T2D probably reflect the relatively smaller proportion of adipose tissue in these individuals. Surprisingly, the existence of the lowest PAI-1 levels in the oldest T2D patients of this study contradicts some previous observations [26]. This may be related to the treatment given for diabetes, hypertension, or dyslipidemia, or it might reflect the relatively smaller proportion of adipose tissue in these individuals [27,28]. They also had significantly lower BMIs and hs-CRP levels compared to the youngest patients. PAI-1 correlated with parameters of atherogenic dyslipidemia, abdominal obesity, inflammation, fasting glucose, and C-peptide. C-peptide levels and BMI were independently associated with PAI-1. PAI-1 is produced not only by endothelial cells; a significant amount of PAI-1 is secreted by adipose tissue [22,28,29]. Significant correlations of PAI-1 with different indicators of visceral obesity (e.g., BMI, waist circumference, waist-to-hip ratio, etc.), markers of insulin resistance, and adverse metabolic profile have been reported [30,31]. Elevated plasma PAI-1 levels in obese subjects can be normalized by weight-loss diet or bariatric surgery [32,33,34]. Adipose tissue is responsible for the secretion of various pro-inflammatory cytokines, adipokines and markers of chronic inflammation, which are associated with the development of insulin resistance. Thus, the association of PAI-1 with obesity and diabetes may reflect a confounding association of one or more other inflammatory markers. However, growing evidence supports a potential association between PAI-1 and the development of T2D, regardless of other established risk factors for diabetes [35]. This can apply especially to younger individuals who are more obese and have more pronounced chronic low-grade inflammation. A limitation of this study is its cross-sectional and non-randomized design, especially in relation to drugs that potentially affect both parameters of arterial stiffness and indicators of endothelial dysfunction. Only $14\%$ of study participants were treated with gliflozins and $7\%$ with GLP-1 receptor agonists, which did not allow statistical evaluation with significant power. Conversely, a relatively high number of participants were treated with drugs (ACE inhibitors, sartans, statins, or their combination), potentially affecting arterial stiffness and/or endothelial function. Moreover, the effect of drugs on vascular damage is likely to be time- and dose-dependent. Thus, larger prospective studies are needed to determine whether better glycometabolic control can improve arterial elasticity in elderly patients with T2D and to find the possible role of different classes of other antidiabetic drugs. ## 5. Conclusions Age, together with systolic blood pressure, seems to be the main determinant of arterial stiffness in patients with T2D, regardless of glycometabolic control. This is consistent with the results of previous studies where diabetes per se was only a weak predictor of arterial stiffness. An unfavorable metabolic profile is mainly related to endothelial dysfunction. The lack of correlation between markers of endothelial dysfunction and arterial stiffness suggests a differential contribution of cardiometabolic risk factors to various markers of vascular damage in T2D patients throughout their lifetime. This may require a different, age-specific therapeutic approach. ## References 1. 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--- title: Inhibition of Expression of the Circadian Clock Gene Cryptochrome 1 Causes Abnormal Glucometabolic and Cell Growth in Bombyx mori Cells authors: - Jianfeng Qiu - Taiming Dai - Hui Tao - Xue Li - Cheng Luo - Yanghu Sima - Shiqing Xu journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10056408 doi: 10.3390/ijms24065435 license: CC BY 4.0 --- # Inhibition of Expression of the Circadian Clock Gene Cryptochrome 1 Causes Abnormal Glucometabolic and Cell Growth in Bombyx mori Cells ## Abstract Cryptochrome is the earliest discovered photoreceptor protein in organisms. However, the effect of CRY (BmCRY), the clock protein in Bombyx mori, on the body or cell metabolism remains unclear. In this study, we continuously interfered with the expression of the BmCry1 gene (Cry1-KD) in the silkworm ovary cell line (BmN), and the BmN cells developed abnormally, with accelerated cell growth and a smaller nucleus. Metabolomics was used to identify the cause of the abnormal development of Cry1-KD cells based on gas chromatography/liquid chromatography-mass spectrometry. A total of 56 differential metabolites including sugars, acids, amino acids, and nucleotides were identified in wild-type and Cry1-KD cells. KEGG enrichment analysis showed that BmCry1 knockdown resulted in significantly upregulated glycometabolism in BmN cells, indicated by glucose-6-phosphate, fructose-6-phosphate, and pyruvic acid levels. The activities of key enzymes BmHK, BmPFK, and BmPK as well as their mRNA levels further confirmed that the glycometabolism level of Cry1-KD cells was significantly increased. Our results show that a possible mechanism of BmCry1 knockdown leading to abnormal cell development is the elevated level of glucose metabolism in cells. ## 1. Introduction Cryptochrome (CRY), a photoreceptor protein, was first discovered in Arabidopsis thaliana [1]. CRY has been found in three kingdoms: plants, mammals, and prokaryotes (CRY-DASH). The insect Cry gene has two families: Cry1 and Cry2 [2]. Drosophila melanogaster has a single Cry1 (DmCry), Hymenoptera Apis mellifera and Coleoptera Tribolium castaneum have a single Cry2 (AmCry and TcCry), while in Lepidoptera insects such as Bombyx mori and Antheraea pernyi, both Cry1 and Cry2 genes (BmCry$\frac{1}{2}$ and ApCry$\frac{1}{2}$) were discovered [3,4,5,6]. Research has shown that DmCRY proteins in D. melanogaster initiate a similar response to blue light as CRY proteins do in A. thaliana (AtCRY). DmCRY proteins receive photo signals and directly guide the circadian reset. DmCry gene expression is also affected by light duration and intensity [3,7,8]. In lepidopteran Danaus plexippus, knockout of the DpCry2 gene resulted in the impairment of the molecular rhythm of core circadian clock members and the destruction of the emergence rhythm [9]. Using anti-DmCRY, a CRY-like protein appears in the cephalic nervous system of B. mori moths [10]. Wang et al. [ 2011] reported sequences of BmCry1 and BmCry2 in silkworms [4]. The results showed that under the induction of an oscillating rhythm of light and temperature, the transcriptional activity of BmCry$\frac{1}{2}$ in both adults and embryos showed similar oscillation patterns [11,12]. Our recent findings suggest that cyclical changes in temperature with differences from 2 °C to 10 °C reset and synchronized the circadian clock in silkworm B. mori ovary cell line (BmN) cells. Transcription BmCry2 and period (BmPer) showed a better reset and synchronization with cyclical temperature changes [13]. Metabolic pathways are important for the output of circadian clock signals, and their relationship with circadian clocks has been extensively investigated [14,15,16]. It is clear that circadian clocks and metabolic processes interact [17,18], and such interactions exist in various species ranging from yeast to humans [19]. In recent years, many studies have shown that several important metabolic processes are regulated by circadian clocks, including glucose metabolism and lipid metabolism [17,20,21]. Breaks in circadian rhythms, such as genetic disruption of the circadian clock, sleep abnormalities, and shift work can lead to metabolic disorders and diseases [22,23,24]. Therefore, it is believed that an understanding of the impact of circadian clocks on metabolism will aid in the development of new methods for circadian clock-based intervention treatments for specific metabolic disorders [25]. Metabolic function determines the rate of cell growth and proliferation. Studies have shown that cell proliferation is not merely a passive consequence of increased cell cycle activity, and must be regulated simultaneously with cell metabolism [26]. The metabolic cycles of yeast cells are synchronized with the cell cycle phases [27], and the production of acetyl-CoA affects cell cycle progression [28]. Even before cell division, extensive metabolic reprogramming has occurred to enable cells to obtain sufficient nutrients, such as glucose, amino acids, lipids, and nucleotides [29,30]. Mitochondrial dysfunction leads to cell cycle arrest through signaling to cyclin E and CDK inhibitor dacapo [31]. Conversely, cell proliferation also affects metabolic changes. Multiple signaling pathways that influence proliferation and differentiation are also involved in the control of cellular metabolism [32]. Glycolysis and glutaminolysis might be activated during the late G1 and S phases of cell division [33]. Thus, the metabolism interacts with processes of cell proliferation such as the cell cycle. However, much is still unknown about the timing mechanism of Cry1 and Cry2 on lepidopterous circadian clocks and studies linking BmCry$\frac{1}{2}$ with metabolic regulation are scarce. Recent findings suggest that inhibition of BmPer expression causes glycometabolism repression in BmN cells [34]. Here, we continuously knocked down BmCry1 protein expression in BmN cells in vitro to eliminate any potential endocrine effects. We performed metabolomic analysis using gas chromatography/liquid chromatography-mass spectrometry (GC-MS and LC-MS/MS), and detected the key enzymes of the glycolytic pathway, the transcript levels of their coding genes, and levels of glycolytic metabolites to evaluate the impact of the peripheral circadian clock on the glycometabolism pathways of silkworm cells. ## 2.1. BmCry1 Knockdown Results in Accelerated Cell Growth and a Smaller Nucleus The mRNA and protein levels of BmCry1 were measured in Cry1-KD cells. The results showed that compared with WT cells, the mRNA level of BmCry1 in Cry1-KD cells was reduced by more than $73\%$ (Figure 1A) and the protein level was reduced by $42\%$ (Figure 1B). This suggests that shRNA effectively knocked down BmCry1 expression in the Cry1-KD cell line. We then further investigated the effect of BmCry1 knockdown on other major clock genes in the transcriptional-translation feedback loop. After cell development synchronization (dexamethasone treatment), the mRNA levels of BmCry2, BmPer, BmTim, BmClk, and BmCyc genes in Cry1-KD cells were significantly reduced compared with the WT cells (Figure 1C–G). Moreover, the amplitudes of these genes’ expression also appeared to be significantly reduced. The JTK_CYCLE method was used to determine the rhythmicity of clock gene expression, as well as the peak phase and amplitude. The results showed that the circadian rhythm of BmPer, BmClk, and BmCyc gene expression was lost (JTK $p \leq 0.5$), and the amplitudes of these clock genes were reduced (Figure 1C–G and Table S2). These results indicated that BmCry1 knockdown leads to the attenuation and destruction of the circadian clock system oscillations in BmN cells. During the cell culture, we found that Cry1-KD cells grow faster than WT cells. The MTT assay showed that the growth rate of Cry-KD cells was significantly faster ($72\%$) than that of WT cells (Figure 2A). Interestingly, the cell volume and nuclear size of Cry1-KD cells were smaller than those of WT cells. The average diameter of Cry1-KD cell nuclei (8 μm) was reduced by 3 μm compared with that of WT nuclei (11 μm; Figure 2B,C). BmCRY1 protein was remarkably reduced in Cry1-KD cells (Figure 2C). Staining patterns of cell membranes also clearly showed that Cry1-KD cells were significantly smaller than WT cells (Figure 2D). ## 2.2. GC-MS and LC-MS/MS Assay Showed That the Knockdown of BmCry1 Caused Changes in Cell Metabolites To determine how BmCry1 knockdown resulted in faster BmN growth and smaller cell size, we used GC-MS and LC-MS/MS to determine and analyze the metabolic differences between Cry1-KD and WT cells. The ion chromatograms of GC-MS (Figure S1) and LC-MS (Figure S2) showed a clear difference in chromatographic peaks between the two samples, suggesting that BmCry1 knockdown may have led to variations in metabolites of BmN cells. The principal component analysis (PCA) of GC-MS showed that the automatic simulation obtained two principal components. The model fitting parameter R2X was 0.644, and parameter Q2 was 0.502, indicating that the two principal components of the model could explain $64.4\%$ of the X variables (Figure S3A). Similarly, the PCA of LC-MS showed that the model fitting parameter R2X was 0.644, and Q2 was 0.278 for positive ion mode (Figure S3B), and model fitting parameter R2X was 0.648, and Q2 was 0.349 for negative ion mode (Figure S3C). These results indicated that the metabolome of WT and Cry1-KD samples have a clear separation trend. To eliminate the noise irrelevant to the classifications, a partial least-squares discriminant analysis (PLS-DA) was used to further confirm that there were significant differences in metabolites between WT and Cry1-KD (Figure S3D–F). We analyzed the VIP ≥ 1 (first principal component variable importance projection value) from PLS-DA analysis with p ≤ 0.05 from t-test thresholds to screen differential metabolites in GC-MS measurement data. A total of 45 differential metabolites of Cry1-KD and WT cells were screened (Table S3). For LC-MS data, we used the first principal components of the PLS-DA model VIP ≥ 1, S-plot p (corr) value ≥ 0.8, and t-test with a p ≤ 0.05 as criteria for screening differential metabolites. The 11 metabolites with large differences were examined with secondary MS (Table S4). These results showed that glucose-6-phosphate, pyruvic acid, aconitic acid, and other glycometabolism-related substances were increased; lipid-metabolizing related substances including 4-hydroxybutanoic acid and cholesterol were also increased, as were nucleotide metabolites including guanosine, guanine, ADP, and FAD. However, amino acids including glutamic acid, ornithine, and aspartic acid were decreased. In addition, some secondary metabolites, including phosphatidylinositol, nicotinamide, ascorbic acid, and other vitamins showed significant decreases (Tables S3 and S4). Combining the differential metabolites in LC-MS and GC-MS/MS, the eight replicates of the two samples were clustered in PCA, and the two principal components together explained more than $80\%$ of the difference (Figure 3A). Differential genes were selected under more stringent conditions (fold change > 1.5 and FDR > 0.05). The volcano plots showed that 11 metabolites were significantly downregulated and 19 metabolites were significantly upregulated after BmCry1 knockdown (Figure 3B). Further comparison of the heatmap of the top 50 differential metabolites showed that the content of 21 metabolites significantly decreased and 29 metabolites significantly increased in the Cry1-KD cells (Figure 3C). Amino acids were the major metabolites that were significantly downregulated, and nucleotides, organic acids, and fats were the major metabolites that were significantly upregulated. All differential metabolites were used for subsequent analysis. After the analysis of differential metabolites, we used metabolite network analysis to investigate the correlation among differential metabolites. Figure 4 shows that although the correlation threshold is set to a high level (0.8), there are 51 related metabolites. To verify the important regulatory role of circadian clocks in glucose metabolism, we selected pyruvate and glucose-6-phosphate acid, which had the most obvious metabolic changes, and clustered them with positively and negatively related substances, respectively. The results showed that substances that were positively related to pyruvate and glucose-6-phosphate acid were mainly organic acids and nucleotide metabolites, while negatively related substances were mainly amino acids, carbohydrates, and other metabolites that could not be classified. The results showed that after knockdown of BmCry1, the metabolic intermediates of glycolysis, pyruvate, and glucose-6-phosphate acid changed significantly, and were correlated with other differential substances. Furthermore, we performed KEGG analysis using all differential metabolites. Figure 5 shows the top 25 KEGG pathways with the greatest changes in cells after BmCry1 knockdown. Notably, there were changes in glycolysis/gluconeogenesis pathways, which are consistent with the overall upregulation of metabolites in the glucose metabolism pathway (Figure 3). In addition, energy metabolic pathways such as citrate cycle and pyruvate metabolism also produce significant changes. Specifically, the comprehensive analysis of KEGG pathways showed that BmCry1 knockdown resulted in extensive and comprehensive metabolic changes in BmN cells. ## 2.3. Glucose Metabolism Was Increased in BmCry1 Knockdown Cells Figure 6A lists the results of metabolomic measurements of the main glycometabolism products. Results showed that BmCry1 knockdown initially resulted in decreased glucose levels in glycometabolism, but the levels of subsequent metabolites, including glucose-6-phosphate, fructose-6-phosphate, pyruvic acid, and citric acid (a metabolite necessary for the initial stages of the TCA cycle), as well as lactic acid (the final product of glycolytic anaerobic metabolism), were all significantly elevated. This indicated that after BmCry1 knockdown, cellular glycometabolism levels, especially glycolysis levels, were significantly altered. To further demonstrate these changes, we measured the key enzyme activities of the glycolytic pathway and the mRNA levels of their coding genes. The results showed that among three rate-limiting steps of glycolysis, there were no significant changes in transcription levels of BmHk; transcription levels of BmPfk were significantly downregulated; and transcription levels of BmPk were significantly upregulated (Figure 6B). Similar results were found with regard to enzyme activity. There were no significant changes in the enzymatic activities of BmHK; enzymatic activities of BmPFK were significantly downregulated; and enzymatic activities of BmPK were significantly upregulated (Figure 6C). It is worth noting that the upregulation of the enzymatic activity of BmPK was greater than the upregulation of its gene expression. Due to the ubiquitous activity changes caused by kinase post-translational modifications, we suggest that metabolic pathways may also be impacted by these post-translational modifications. These results indicated that BmN cell glycolytic metabolism was changed through the regulation of both the expression and enzymatic activity of key enzymes associated with the glycolysis pathway after the knockdown of BmCry1 expression (Figure 6D). ## 3. Discussion Numerous studies have shown that the destruction and interruption of circadian rhythms are associated with many diseases, such as sleep disorders, obesity, diabetes, depression, metabolic syndrome, and cancers [33,35,36,37]. Aging has also been shown to be related to the decline in circadian rhythm levels and the destruction of overall metabolic and energy homeostasis [38,39,40]. Desynchronization of the circadian rhythm of rats and the external environment caused the acceleration of cell proliferation [41]. At the cellular level, mClock gene knockout resulted in decreased proliferation and increased apoptosis of embryonic stem cells [42]. We previously knocked down the BmPer gene in BmN cells and found that cell proliferation slowed down and cell division was arrested in the G0/G1 phase [34,43]. In this study, BmN cells also showed phenotypic changes with a faster growth rate and smaller size upon BmCry1 knockdown, which may be related to cellular metabolic changes. Studies have shown that decreased Cry1 mRNA levels increase protein content through post-transcriptional regulation [44,45]. AU-rich element RNA binding protein 1 (AUF1) binds the Cry1 3′UTR to promote Cry1 translation [45,46]. Although the mRNA level of BmCry1 was reduced by $73\%$ after shRNA interference, the knockdown efficiency at its protein level was insufficient. We speculate that it may be affected by post-transcriptional regulation, resulting in strong stability or high translational efficiency of BmCry1 mRNA. It is necessary to directly knock out BmCry1 at the cellular level in the future. ## 3.1. Possible Functions of Cryptochrome in Bombyx mori Rhythmic expression is a characteristic of clock genes. The knockout or knockdown of silkworm clock genes mainly affected the expression level and oscillatory rhythm of other clock genes, leading to the disorder of circadian rhythm [34,47,48]. Knockdown of the BmCry1 gene led to decreased mRNA levels of BmClk and BmCyc genes, which inevitably reduced the expression levels of BmCry2, BmPer, and BmTim genes. The reason for this is that BmCLK/BmCYC heterodimers regulate the transcription of different clock genes, such as BmCry2, BmPer, and BmTim, via E-box in the gene promoter region. Similarly, the knockdown of BmCry1 resulted in a loss of rhythmic expression of clock genes (Figure 1C–G). Under the light cycle of LD 12:12, the mRNA levels of BmCry2, BmTim, and BmClk in Cry1-KD cells remained low, while the mRNA levels of BmPer and BmCyc recovered, and the amplitudes of clock genes increased (Figure S4). More importantly, the expression of BmCry2, BmPer, BmTim, and BmClk in Cry1-KD cells resumed oscillatory rhythm due to photoentrainment, which is consistent with the observations of several instances of clock gene knockout in silkworms [47,48,49,50,51]. We speculate that there are two possible reasons for this: [1] there is still a small amount of remaining BmCRY1 protein in Cry1-KD cells, which responds to photo signals and resets circadian rhythms; [2] other clock genes, such as BmCRY2, have the function of receiving photo signals. DpCRY1 functions in Danaus plexippus (Lepidoptera) resemble that of CRY in Drosophila, whereby it can receive photo signals and thus direct the reset of circadian rhythms. Moreover, DpCRY1 can promote the degradation of the DpTIM protein [7]. DpCRY2 functions resemble mammalian CRY$\frac{1}{2.}$ DpCRY2 and DpPER form a dimer, enter the nucleus and participate in feedback inhibition in the monarch butterfly [5,8,52]. Our results regarding molecular evolution analysis of silkworm CRY also showed that the evolutionary distance between BmCRY1 and DpCRY1, as well as between BmCRY2 and DpCRY2 were closely related (Figure S5). The function of BmCRY2 is unclear. Our experiments preliminarily showed that BmCRY1 could not enter the nucleus, and BmCRY2 in the cytoplasm bound BmPER to enter the nucleus (unpublished data). These results suggested that the function of BmCRY1 may be similar to that of DpCRY1 in the circadian clock. Therefore, we believe that the small amount of remaining BmCRY1 protein in Cry1-KD cells responds to photo signals under the LD cycle. Taken together, we speculate that the function of BmCRY1 is to receive photo signals and reset the circadian clock, and the function of BmCRY2 is involved in the inhibition of the feedback loop. ## 3.2. The Interaction of Circadian Clock, Cellular Glycometabolism and Cell Proliferation Studies have shown that important metabolic processes such as glycometabolism are controlled by the circadian clock [20,53]. Indeed, the metabolic regulation of organisms is the most important function of circadian clock signals [16,54]. MmCry1 or MmCry2 knockdown also increased hepatic gluconeogenesis gene expression and glucose production [55]. Furthermore, these knockout mice also showed decreased glucose tolerance [56]. mCry1 knockout mice developed symptoms similar to those found in diabetes [57], while mCry2 knockout mice had alterations in their blood sugar levels [58]. Conversely, mCry1 overexpression reduced fasting blood sugar levels and increased insulin sensitivity in mice [55]. Mice lacking mCry$\frac{1}{2}$ showed continual increases in the activity of key enzymes controlled by mineralocorticoid aldosterone from the adrenal gland, which induced increased synthesis of aldosterone, thus leading to hypertension [59]. The above results indicated that CRY plays an important role in regulating glucose metabolism in mammals. Interaction between CRY proteins and metabolic pathways has been reported previously [60,61]. AMPK can sense cellular metabolic signals in circadian clock systems, phosphorylate CRY proteins, stimulate the ubiquitination of FBXL to CRY proteins and further degrade CRY [62], thus impacting the homeostasis of glycometabolism [56,58,63,64]. We knocked down the BmCry1 gene of BmN, and the glucose metabolism of cells was affected. Glycolytic pathway metabolites glucose-6-phosphate, pyruvic acid, and aconitic acid were significantly increased, and rate-limiting enzymes BmPFK and BmPK mRNA levels and enzyme activities were also affected (Figure 6). Studies have shown that circadian clocks can regulate the conversion of glucose and lipids to energy by regulating the expression of key metabolic enzymes [53]. For example, knockout of Bmal1 results in lipid oxidation and downregulation of key enzymes associated with the TCA pathway and the oxidation respiratory chain in the liver [65]. Bmal1 is also known to control PBP4, a key hepatokine, and thus participates in regulating glucose homeostasis in the liver [66]. Mitochondrial proteomics also shows that rate-limiting enzymes for lipid and carbohydrate accumulation depend on PER$\frac{1}{2}$ [67]. However, there is no documented evidence showing that CRY1 regulates the metabolism through a similar pathway in insects. In this study, after BmCry1 knockdown, the mRNA levels of other circadian clock genes in cells were downregulated or lost rhythm (Figure 1). Therefore, the knockdown of BmCry1 may indirectly affect glucose metabolism by affecting the expression of other clock genes such as BmCycle, BmClock, or BmPer. The mechanism of the circadian clock and the cell cycle system has been widely reported. In healthy cells, the coupling of the clock and cell cycle leads to timed mitosis and rhythmic DNA replication [68]. Disruption or disorder of biological rhythms leads to uncontrolled cell proliferation. Studies have shown that expression of downregulated mammalian clock genes Bmal1, Clock, or Tim arrests the cell cycle in G0/G1 or G2/M phase [69,70]. Similarly, interference with BmPer genes in BmN cells also led to cell cycle arrest in G0/G1 phase, and decreased BmC-myc and BmCdc2 expression [43]. The mClk knockout mutant changes the pattern of cell cycle gene expression and inhibits cell growth and proliferation [71,72]. Conversely, some studies have shown that Bmal1 deficiency or circadian photoperiod disruption increased tumor initiation [73]. Mice lacking the mPer2 gene had an increased incidence of lymphoma [42]. Downregulation of mammalian Per2 gene expression will promote the expression of cell cycle-related proteins Cyclin A, Mdm2, C-MYC, etc., thus promoting cell proliferation [42,74,75]. MYC/MIZ1 inversely regulated the expression of circadian clock genes and affected the cell cycle and proliferation [76]. These results indicated that the circadian clock mainly regulates cell proliferation by blocking the cell cycle process or affecting the expression of cell cycle activators/cell cycle suppressors. Overexpression of the cell cycle activator or inhibitor changes the cell number but also leads to reverse changes in cell size [77]. In the Xenopus nervous system, inhibition of progenitor proliferation results in fewer but bigger neurons [78]. Knockdown of the BmCry1 gene in BmN cells resulted in accelerated cell proliferation and a smaller nucleus, which may be related to cell cycle changes; however, we were more concerned about the metabolic changes of Cry1-KD. Many cells, ranging from microbes to mammals, use aerobic glycolysis during rapid proliferation, suggesting that it may play a fundamental role in supporting cell growth [79]. In healthy cells, the PI3K/AKT/mTOR signaling pathway stimulates glucose input and glycolysis, promoting cell proliferation [80,81]. AMPK inhibits cell proliferation by inhibiting anabolic and catabolic pathways [82]. BmN cells with knockdown of the BmPer gene had inhibited glucose metabolism and slowed cell proliferation [34]. In the study of cancer cells, it is also found that the changes in glucose metabolism are always positively correlated with cell proliferation [83]. Cannabinoid receptor 2 (CB2R) inhibits glycolysis by downregulating HIF-1α, thereby inhibiting the proliferation of mouse liver macrophages [84]. Inhibition of genes related to glucose metabolism OIP5, SMC4, and NUP107 also inhibited cell cycle progression [85]. These studies indicated that glucose metabolism is an important target for regulating cell proliferation. The proliferation of Cry1-KD cells was accelerated, which may be related to the upregulation of glucose metabolism. ## 3.3. Metabolic Difference between Knockdown BmCry1 and BmPer in BmN Cells Previously, we reported the impact of the knockdown of BmPer on the metabolism of BmN cells [34]. We also investigated the reason that BmN cell proliferation slowed down after the knockdown of the BmPer gene [43]. In contrast to slowed growth of Per-KD cells, BmCry1 knockdown significantly increased cell proliferation. We focused on metabolomics data and summarized the differences in the impacts of knockout of BmCry1 and BmPer on BmN cell growth and metabolism (Figure S6). The results showed that carbohydrates and most organic acids and amino acids involved in the metabolism of glucose and lipids showed opposite trends. The overall level of glycometabolism was upregulated in Cry1-KD, whereas they were upregulated in Per-KD cells. Even though PER and CRY both play important roles in inhibiting expression in the feedback loop of circadian clocks, PER protein protects the phosphorylation of CLOCK proteins by preventing damage from CRY proteins [86]; this is closely related to the entire clock oscillation and transcriptional rhythm of the output system [87]. Previous studies also showed that PER proteins have an opposite mechanism to CRY proteins, which is different from the traditional model, wherein PER proteins are transcriptional repressors of Per and Cry [88,89,90]. In addition, studies also showed that PER2 may have a positive effect on transcription in a promoter-specific manner [88,91]. Moreover, at certain stages and tissues, PER may play an antagonistic role with CRY [92,93]. It is therefore reasonable to conclude that CRY and PER proteins may play an antagonistic role in affecting metabolism in BmN cells. In zebrafish, computer simulations have demonstrated that many purine and pyrimidine metabolites exhibit the same rhythm phase, and de novo purine synthesis pathways have a great impact on the cell cycle [16]. In Danaus plexippus, the CRY1 protein transmits photo signals to PER and CRY2 proteins; these proteins then further regulate the transcription level genes by inhibiting CLK/CYC transcriptional activation, thereby implementing signal output [5]. Therefore, we conclude that the knockdown of BmCry1 and BmPer may partially relieve the transcriptional repression of clock-controlled genes. Our study found that both Cry-KD and Per-KD cell lines showed similar changes in regard to decreased cell nuclei diameters and decreased cell volume, as well as upregulation of nucleotide metabolism levels. We were unable to explain why the nuclei were smaller after the knockdown of BmPer and BmCry, or why the levels of nucleotide and purine metabolites were upregulated. We suppose that these changes may be related to changes in the transcriptional repression of clock-controlled genes. ## 4.1. Preparation of the BmCry1 Knockdown (Cry1-KD) Cells The B. mori ovary cells (BmN) were cultured in Grace’s insect medium (Corning, NY, USA) with $10\%$ FBS (Corning, NY, USA) at 26 °C in constant darkness (DD). Pre-microRNA with mir-30 was used to construct transfection plasmid short hairpin RNAs (shRNAs). We designed three interference sites of BmCry1, and the mRNA levels of BmCry1 were detected by qRT-PCR after transfection. We found that the Cry1-shRNA3 had a knockdown efficiency of $73\%$ for BmCry1 transcript levels, while Cry1-shRNA1, Cry1-shRNA2 or Cry1-shRNA1 + 3 had a lower knockdown efficiency ($50\%$) (Figure S7A). The BmCry1 cDNA sequence 1856AAGAACGTGCCAACTGTATAA1876 was chosen for siRNA Cry1 and inserted into shRNA to obtain shRNA-Cry1 (GCGACTTATACAGTTGGCACGTTCTTCTGTGAAGCCACAGATGGGAAGAACGTGCCAACTGTATAAGCTGC), which was then inserted into the OpIE2 promotor driven pIZT/V5-His/Cat vector between SacI578 and SpeI651 to generate the shRNA-Cry1 expression vector (Figure S7B). The preparation of BmCry1 knockdown cells was performed as described previously [34]. Briefly, BmN cells were cultured in Grace’s Insect Medium with $10\%$ FBS at 26 °C in DD. We diluted 2 μL of Lipofectamine LTX reagent and 0.5 mg DNA plus reagent vector in 25 μL of Grace’s Insect Medium and mixed after a 10 min incubation. The mixture was then used to transfect BmN cells in 24-well plates for 48 h, followed by the addition of 100 μg/mL bleomycin. The transfection efficiency was counted by fluorescence microscopy. The shRNA-Cry1 expression vector transfected BmN cells for 2 days, and the number of green-labeled cells was about $50\%$ (Figure S8A). The medium was changed every 48 h and the bleomycin concentration in the medium was maintained at 100 μg/mL. The interference plasmid was continuously transfected, cells were screened with bleomycin for 2 weeks, and the number of green-labeled cells was about $90\%$ or more (Figure S8B). The efficiency of shRNA-mediated BmCry1 knockdown was determined using qRT-PCR and Western blotting. Prior to qRT-PCR and Western blotting, both cell lines were synchronized using Dex (0.1 μM) for 2 h in darkness. The BmN cells were identified as BmCry1 knockdown cells (Cry1-KD) after stable interference of the BmCry1 gene, and cultured in the medium containing 100 μg/mL bleomycin at 26 °C in DD. ## 4.2. Cell Proliferation Assay The 3–[4, 5]–dimethylthiazo (–z–y1) -3, 5-diphenytetrazoliumbromide (MTT) method was used to determine cell growth, according to the manufacturer’s instructions (C0009, Beyotime, Nantong, Jiangsu, China). Briefly, 100 μL of cell solution (~2 × 103 cells) and 10 μL of MTT (5 mg/mL) solution were added to each well of a multi-well plate and incubated for 4 h at 37 °C. Formazan solution (100 μL) was then added and the mixture was incubated for a further 4 h at 37 °C. The absorbance at 570 nm was then measured using a microplate reader (Eon, Biotek, Winooski, VT, USA). All experiments were performed in triplicates. ## 4.3. Cell Staining WT or Cry1-KD BmN cells were inoculated into culture plates for 12 h, washed with 1 mL PBS, and then fixed with $4\%$ paraformaldehyde for 15 min. They were further treated with $0.1\%$ Triton X-100 and stained with Dil staining solution (Beyotime, Nantong, Jiangsu, China) for 1 min. Finally, cells were stained with 4′–6-diamidino–2–phenylindole (DAPI; Beyotime) for 5 min at room temperature. It is important to ensure that PBST (PBS with $0.05\%$ Tween-20) is used to fully wash after each fixation, permeabilization, and staining. After staining, the cell nuclear size (the average value of the longest and shortest diameters) was surveyed promptly using Image-Pro Plus software on a fluorescence microscope (Olympus BX51, Tokyo, Japan). Five independent fields were analyzed for each plate, and all experiments were performed in triplicate. ## 4.4. Determination of Metabolomics According to the reported method [94], 1 × 107 cells were counted, then obtained by quenching and centrifugation at 1000× g for 1 min. Based on the method reported by Tao et al. [ 2017], cell samples to be determined with GC/LC-MS were pretreated. Samples were resuspended in 500 μL of methanol, precooled to −80 °C and centrifuged twice (15,000× g for 1 min). The supernatant was further resuspended in ultrapure water, frozen in liquid nitrogen, and centrifuged (15,000× g, 1 min). The supernatant of the treated samples was vacuumed and dried at 30 °C to generate the specimen. Spectroscopic parameters of GC-MS (Agilent 7890A/5975C, Agilent, Santa Clara, CA, USA) and LC-MS (Waters UPLC, Waters, Milford, MA, USA) are shown in Figures S1 and S2, respectively. An HP-5 MS capillary column ($5\%$ phenyl methyl silox: 30 μm × 250 μm internal diameter, 0.25 μm; Agilent J &W Scientific, Folsom, CA, USA) was used for GC-MS, and a C18 column (1.7 μm, 2.1 × 100 mm; (BEH, Waters, Milford, MA, USA) was used for LC-MS. Metabolites from LC-MS detection were further confirmed using secondary MS. We first confirmed and obtained the empirical formula of the metabolites based on the exact molecular weight (molecular weight error < 30 ppm). We further used the exact molecular weight according to MS/MS fragment patterns to search and confirm potential biomarkers in the Human Metabolome Database http://www.hmdb.ca website (accessed on 15 October 2022), Metlin http://metlin.scripps.edu/website (accessed on 27 October 2022), massbank http://www.massbank.jp/ (accessed on 5 November 2022) and LipidMaps http://www.lipidmaps.org (accessed on 8 December 2022) databases. A total of 20 μL of the sample was taken from all tested samples to correct errors that may have occurred during the tests. GC-MS and LC-MS assays were carried out at BioNovoGene Co., Ltd. (Suzhou, China). ## 4.5. qRT-PCR Analysis WT and Cry1-KD cells were sampled every 4 h for 24 h for qRT-PCR. Total RNA was isolated with RNAiso™ Plus (TaKaRa, Dalian, China) from WT and Cry1-KD BmN cells, and cDNA was synthesized with the PrimeScript RT (Perfect Real Time) Reagent Kit with gDNA Eraser (TaKaRa), according to the manufacturer’s instructions. All reactions were carried out in a total reaction volume of 20 μL using an ABI StepOnePlus™ PCR system (Ambion, Foster City, CA, USA) and the fluorescent dye SYBR Premix Ex Taq (TaKaRa). Transcript levels of BmCry2, BmPer, BmTim, BmClk, and BmCyc were obtained under the following reaction conditions: 95 °C for 30 s, then 40 cycles at 95 °C for 5 s and 60 °C for 30 s. After PCR, we used a melting curve analysis to confirm the amplification of the specific products. The data were normalized with endogenous BmRp49. All the experiments were performed in triplicate. The gene-specific primers used in this study are shown in Table S1. ## 4.6. Determination of Enzyme Activity Soluble proteins from WT and Cry1-KD cells were extracted for enzyme activity assay. As mentioned in Tao et al. [ 2017], enzyme activities of HK, PFK, and PK were determined with the corresponding enzyme activity assay kit (Jiancheng, Nanjing, China), with glucose, glucose-6-phosphate, and phosphoenolpyruvate as substrates, respectively, according to the manufacturer’s instructions. One unit of HK, PFK, and PK, respectively, was defined as the consumption of 1 nM nicotinamide adenine dinucleotide/min/mg of cell protein (U/mg protein). Protein concentrations were measured with a BCA kit (Beyotime). ## 4.7. Western Blotting Proteins were extracted from WT and Cry1-KD cells, and concentrations were measured with a BCA kit (Beyotime). Western blotting was performed according to the method described by Tao et al. [ 2017]. Resultant protein bands were quantified using a ChemiDoc Touch Imaging System (Bio-Rad, Hercules, CA, USA). The BmCRY1 protein amino acid sequence was acquired from the NCBI (GenBank accession number ADM86934). After peptide sequence design, synthesis, and purification, the peptide sequence of the CRY1 protein, NH2-RLDPSGEYVRRYVPECCONH2, was used as an antigen to immunize New Zealand rabbits. The antigenic epitopes used for raising the polyclonal antibodies for CRY1 were at the amino acid residues of 432–446. The NH2 at the N-terminal and the CCONH2 at the C-terminal were used to conjugate with the carrier protein KLH. An enzyme-linked immunosorbent assay (ELISA) was used to detect the antibody titer. The ELISA result was about 3 when they were diluted 1000 times. As the titers of the antisera were more than 1, we affirmed that they were efficient antibodies. The antibodies were purified using affinity purification. All of these procedures were completed by Abgent Biotechnology Co., Ltd. (Suzhou, China). ## 4.8. Immunohistochemistry WT or Cry1-KD BmN cells were inoculated in culture plates for 12 h and washed with 1 mL phosphate-buffered saline (PBS). Cells were fixed with $4\%$ paraformaldehyde solution for 15 min at room temperature and washed three times with PBS. Then, the cells were treated with permeabilization buffer ($0.1\%$ sodium deoxycholate and $2\%$ Tween-20 in PBS solution) for 30 min at room temperature and washed three times with PBS. The blocking solution was added and shaken for 1 h at 37 °C. Cells were incubated with CRY1 polyclonal antibody for Bombyx mori, and diluted 1: 100 with TBS (PBS with $0.05\%$ Tween-20) at 4 °C for 12 h. After further washing three times with TBS, cells were then added to 500 μL TBS and 0.5 μL goat anti-rat IgG(H + L) FITC (MultiSciences, Hangzhou, China) and incubated at 37 °C for 2 h in the dark. Cells were washed three times again with TBS and observed using a fluorescence microscope (Olympus BX51, Tokyo, Japan). ## 4.9. Statistical Analysis Raw data from GC-MS and LC-MS were processed as described in Tao et al. [ 2017], using XCMS www.bioconductor.org/ (accessed on 27 October 2022). We used a partial least-squares discriminant analysis (PLS-DA), first principal component variable importance in projection (VIP) values (VIP ≥ 1), and Student’s t-test ($p \leq 0.05$) to screen the differential metabolites. Analysis of the above data as well as PCA and heat map functions were all implemented using R 3.0.3 www.r-project.org (accessed on 22 December 2022). Metabolite association analysis and statistical tests using Pearson’s correlation coefficients with cor() and cor.test() functions were analyzed using the R package. The metabolite correlation threshold was set to 0.8, and a false discovery rate of p ≤ 0.05 was considered significant; the relationships among metabolites for constructing the metabolic pathways were based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database http://www.genome.jp/kegg/ (accessed on 27 December 2022). A univariate analysis of variance (ANOVA) was used to determine the significance of differences in relative contents between different groups. Pathway activity profiling (PAPi) was used to predict and compare the relative activity of different metabolic pathways during different comparisons. Throughout the analysis process, data normalization and related statistical analysis were carried out in MetaboAnalyst https://www.metaboanalyst.ca (accessed on 14 January 2023). 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--- title: Gender-Related Discrepancies in Short-Term Outcomes in Patients Undergoing Off-Pump Coronary Artery Bypass Grafting Surgery authors: - Ihor Krasivskyi - Ilija Djordjevic - Borko Ivanov - Kaveh Eghbalzadeh - Clara Großmann - Stefan Reichert - Medhat Radwan - Rodrigo Sandoval Boburg - Anton Sabashnikov - Christian Schlensak - Thorsten Wahlers - Christian Jörg Rustenbach journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10056417 doi: 10.3390/jcm12062202 license: CC BY 4.0 --- # Gender-Related Discrepancies in Short-Term Outcomes in Patients Undergoing Off-Pump Coronary Artery Bypass Grafting Surgery ## Abstract The sex differences in patients undergoing off-pump coronary artery bypass grafting (OPCAB) surgery are still unclear. Our aim was to investigate the impact of gender on short-term outcomes in males and females after off-pump bypass procedures. Our research was designed as a double-center retrospective analysis. Generally, 343 patients (men ($$n = 255$$) and women ($$n = 88$$)) who underwent an OPCAB procedure were included in our study. To provide a statistical analysis of unequal cohorts, we created a propensity score-based matching (PSM) analysis (men, $$n = 61$$; women, $$n = 61$$). The primary endpoint was all-cause in-hospital mortality. Dialysis, transient ischemic attack (TIA), low cardiac output syndrome (LCOS), reoperation due to postoperative bleeding, wound infection and duration of hospital stay were secondary outcomes in our analysis. No significant differences were detected within the male and female groups regarding age ($$p \leq 0.116$$), BMI ($$p \leq 0.221$$), diabetes ($$p \leq 0.853$$), cardiogenic shock (0.256), STEMI ($$p \leq 0.283$$), NSTEMI ($$p \leq 0.555$$) and dialysis ($$p \leq 0.496$$). Males underwent significantly more frequently ($$p \leq 0.005$$) total-arterial revascularization with T-graft technique ($$p \leq 0.005$$) than females. In contrast, temporary pacer use was significantly higher ($$p \leq 0.022$$) in females compared to males. The in-hospital mortality rate was not significantly higher ($$p \leq 0.496$$) in the female group compared to the male group. Likewise, secondary outcomes did not differ significantly between the non-adjusted and the adjusted groups. Based on our findings, gender has no impact on short-term outcomes after OPCAB surgery. ## 1. Introduction Cardiovascular diseases, including coronary artery disease, are associated with the highest mortality rate in western Europe [1]. Despite prophylactic and therapeutic efforts in the last decades, the prevalence of this disease continues to grow [2]. On-pump and/ or off-pump coronary artery bypass grafting surgery is a common and effective procedure used to treat triple vessel disease [1,2]. The discrepancies between both procedures regarding morbidity and mortality have been critically discussed in the last decades [3,4,5]. Woman generally present a higher prevalence of cardiovascular risk factors, including arterial hypertension, diabetes, dyslipidemia, and peripheral vascular disease (PVD), compared to men [6,7,8]. This could be explained by the fact that females develop coronary artery disease much later than males due to the protective role of sex hormones [9]. In addition, a smaller coronary artery diameter in women compared to men could influence the higher mortality rate after cardiac surgery [10,11]. Moreover, various studies have shown that coronary hyperreactivity, microvascular dysfunctions, plaque erosions, and distal microembolizations are more common among women compared to men [12,13]. A lower use of arterial grafts in females has also been associated with adverse outcomes and could affect survival [13]. Likewise, several studies have shown a significantly higher morbidity rate in the female group compared to the male group after bypass surgery [14,15]. Female gender was also found to be an independent predictor of higher mortality after a bypass procedure [16]. In contrast, other authors have mentioned that female gender was not associated with adverse outcomes and higher mortality after a CABG procedure [17,18]. However, the majority of the above-mentioned trials referred to patients who underwent both on-pump and off-pump CABG procedures [14,15,16,17,18]. Differences in gender outcomes in patients undergoing only OPCAB surgery are controversial [9,17,19]. Fu et al. [ 9] mentioned that early mortality did not differ significantly between both sex groups after OPCAB procedure. Likewise, further authors analyzed 776 patients who underwent an OPCAB procedure and showed no difference regarding mortality between males and females postoperatively [20]. In contrast, Puskas et al. [ 21] found that off-pump bypass surgery was associated with significantly lower ($$p \leq 0.001$$) mortality in women compared to men. Consequently, our aim was to evaluate whether the OPCAB technique could impact early outcomes in both sex groups. Thus, we investigated gender-related discrepancies in short-term outcomes in patients who underwent the OPCAB procedure in our heart centers. ## 2. Materials and Methods Our study was a retrospective analysis of a double center-retrospective OPCAB cohort, which included 343 patients who underwent off-pump coronary artery bypass procedure for coronary artery disease from January 2017 until November 2022 in the University hospitals in Cologne and Tuebingen. We formed two groups to investigate potential sex-related differences in early clinical outcomes: men ($$n = 255$$) and women ($$n = 88$$). To account for the unequal cohort sizes, we performed a propensity score-based matching (PSM) analysis using the methods previously described [22]. The details of the analysis are provided in Figure 1. ## 2.1. Surgical Procedure All surgical procedures were performed using the OPCAB technique. Patients who underwent on-pump coronary artery bypass grafting surgery were excluded from the analysis. All patients who presented with symptoms such as chest pain (angina pectoris) lasting for 20 min or longer despite the use of nitroglycerin were operated on within the first 24 h after admission to our hospital. We used the left internal mammary artery (LIMA), right internal mammary artery (RIMA), radial artery, great saphenous vein and/or small saphenous vein for future anastomosis, based on previous risk factors. Intravenous heparin was administered in all cases to achieve an activated clotting time of more than 300 s. An automatic pod spreader was used for a better visualization of the anastomotic site. Afterwards, the coronary arteries were longitudinal incised. The coronary arteries were then longitudinally incised, and temporary shunts were placed into the lumen of the targeted arteries to allow for continuous blood flow during anastomosis and to decrease the possibility of bleeding. Anastomosis was performed using 7-0 and 8-0 monofilament sutures. The decision to use temporary epicardial pacing was made by each surgeon. Standard anticoagulation protocols were used by all patients after the OPCAB procedure, which were similar in Cologne and Tuebingen. Our methods were previously described [22]. ## 2.2. Data Collection The data were withdrawn from the institutional database of the University hospital Cologne and the University hospital Tuebingen. All information was collected during patients’ hospital stay and analyzed retrospectively. ## 2.3. Outcome Analysis All-cause in-hospital mortality after OPCAB surgery was the primary endpoint of our study. The secondary outcomes analyzed included dialysis, transient ischemic attack (TIA) with symptom duration less than 60 min, low cardiac output syndrome (LCOS), reoperation due to postoperative bleeding, wound infection, and duration of hospital stay. ## 2.4. Ethics This research was conducted in accordance with the Declaration of Helsinki (revised version of 2013). The Ethics Committee of the Medical Faculty of the University of Cologne and the Ethics Committee of the Medical Faculty of the University of Tuebingen confirmed that under German law, the authors did not require a separate ethical approval. All purely retrospective clinical studies can be conducted without an ethical statement. ## 2.5. Statistical Methods Statistical analysis was conducted using Student’s t-test or Mann–Whitney-U test, depending on whether continuous variables were normally distributed or not. The Chi-square test was used for categorical variables. Normally distributed samples were presented as the mean ± standard deviation (SD). Fisher’s exact test was used when the minimum expected count of cells was less than 5. The optimal cut-off values were defined as values that provided highest sensitivity and specificity. Logistic regression was used to create the predicted variable. The PSM analysis was applied to even groups and provide statistical comparison. A p-value of less than 0.05 was considered significant. Statistical analysis was performed using Statistical Package for Social Sciences, version 28.1 (SPSS Inc., Chicago, IL, USA). ## 3.1. Preoperative Data before and after PSM Preoperative characteristics of the two groups (prior to PSM) are presented in Table 1 for men ($$n = 255$$) and woman ($$n = 88$$). Following PSM, the groups were reduced to 61 men and 61 women. The mean age was 66 ± 9 years for men and 71 ± 8 years for women. The majority of patients were classified as overweight or obese, with mean BMIs of 29.3 ± 9.7 and 30.2 ± 5.1 kg/m2 for males and females. After 1:1 propensity score matching, the groups were well balanced. No significant differences were observed between the male and female groups in terms of age ($$p \leq 0.116$$), BMI ($$p \leq 0.221$$), diabetes ($$p \leq 0.853$$), cardiogenic shock (0.256), STEMI ($$p \leq 0.283$$), NSTEMI ($$p \leq 0.555$$), or dialysis ($$p \leq 0.496$$). ## 3.2. Intraoperative Characteristics before and after PSM Intraoperative data for both groups before (men, $$n = 255$$; women, $$n = 88$$) and after PSM (men, $$n = 61$$; women, $$n = 61$$) are presented in Table 2. The use of both internal thoracic arteries (ITA) grafts was significantly higher ($$p \leq 0.043$$) in the male group compared to the female group before PSM. However, after PSM, the use of both ITA grafts was similar in both groups ($$p \leq 0.412$$). Additionally, male patients underwent total-arterial revascularization with the T-graft technique significantly more frequently than female patients before PSM ($p \leq 0.001$) and after PSM ($$p \leq 0.005$$). The mean operation time was significantly longer ($$p \leq 0.001$$) in the male group than in the female group. Other data did not differ significantly between the two groups. ## 3.3. Primary and Secondary Outcomes Primary and secondary outcomes for both groups before (men, $$n = 255$$; women, $$n = 88$$) and after PSM (men, $$n = 61$$; women, $$n = 61$$) are summarized in Table 3. The in-hospital mortality rate was not significantly higher ($$p \leq 0.496$$) in the female group compared to the male group. Furthermore, there was no significant difference in the mean length of ICU ($$p \leq 0.529$$) or hospital stay ($$p \leq 0.930$$) between both groups. Likewise, in the secondary outcomes (transient ischemic attack ($$p \leq 0.365$$), low cardiac output syndrome after surgery ($$p \leq 0.644$$), dialysis ($$p \leq 0.496$$), reoperation due to postoperative bleeding ($$p \leq 0.691$$), and wound infection rate ($$p \leq 0.187$$)), no significant differences were observed between the unadjusted and adjusted groups. ## 4. Discussion We investigated gender-related discrepancies in short-term outcomes in patients who underwent OPCAB surgery only. Our analysis showed that all-cause in-hospital mortality rate was not significantly higher ($$p \leq 0.496$$) in the female group compared to the male group. Likewise, in the secondary outcomes (transient ischemic attack ($$p \leq 0.365$$), low cardiac output syndrome after off-pump surgery ($$p \leq 0.644$$), dialysis ($$p \leq 0.496$$), reoperation due to postoperative bleeding ($$p \leq 0.691$$), wound infection rate ($$p \leq 0.187$$), and duration of hospital stay ($$p \leq 0.930$$)), no significant differences were observed between the two above-mentioned groups. Studies on gender differences after OPCAB surgery are scarce [16,17,18,19,20]. Previous retrospective trials have shown that female gender might be an independent risk factor for operative mortality [14,23]. Similarly, Alam et al. [ 24] found increased operative and 30-day mortality rates in females compared to males after bypass surgery. However, the authors did not analyze the potential impact of the operative technique on patient’s survival, which could affect results and lead to potential bias [24]. In general, females present more often relevant comorbidities such as diabetes mellitus, hyperlipidemia, arterial hypertension and peripheral vascular disease compared to males [8,25]. In addition, women suffered more commonly from chronic renal insufficiency [14]. All of the above-mentioned factors could lead to poor outcomes during the postoperative period [14,25]. In contrast, we could not find any statistically significant differences regarding preoperative comorbidities between both groups in our study. Generally, women undergo less frequent and significantly later coronary revascularization due to protective hormone levels in the premenopausal period [26]. These factors could explain the higher mortality rate by women compared to men by cardiogenic shock after CABG procedure [27]. In contrast, Amato et al. [ 28] stated that female gender was not an independent predictor for higher mortality after CABG surgery. Ennker et al. [ 29] analyzed 12,606 patients who underwent bypass surgery in their study. After adjusting for preoperative risk factors, the authors could not find any significant difference in the mortality rate between male and female groups [29]. Likewise, the mortality rate was not statistically significantly different ($$p \leq 0.496$$) in our analysis. Moreover, further authors showed that the operative technique had no impact on in-hospital mortality after OPCAB surgery [30]. However, difficulties with anastomoses due to a smaller size of the coronary artery by women could affect results [31]. The increased risk of thrombosis, especially near the suture line, should be taken into consideration [30,31]. The use of multiple arterial grafts to compensate for these risk factors remains controversial [11,30,31]. Rocha et al. [ 31] showed improved outcomes in females despite a significantly lower use of arterial grafts compared to males. Moreover, Kurlansky et al. [ 32] showed no difference in survival after 12 years using propensity-matched analysis of the female group undergoing cardiac surgery with multiple artery grafting versus single artery grafting. The authors suggested that multiple artery grafting surgery might benefit results by younger patients [32,33]. Likewise, in our study, the use of arterial grafts was significantly higher ($$p \leq 0.005$$) in the male group compared to the female group. Despite all of the above-mentioned factors, we were unable to identify a higher survival rate in the male group compared to the female group in our sample. Based on the information provided, the use of OPCAB technique to improve outcomes has been controversially discussed in recent years [9,14,21]. Puskas et al. [ 21] found a decreased rate of major adverse events among female patients undergoing OPCAB surgery compared to on-pump CABG procedure. Others, such as one by Fu et al. [ 9] showed an increased risk of major cardiac and cerebral events by female patients after the OPCAB procedure. The authors mentioned that incomplete revascularization due to the smaller intraluminar diameter of coronary arteries could explain an increased rate of late adverse events in females [9]. However, the survival rate was similar in both gender groups [9,21]. Similarly, in our sample, we did not find any benefits of the OPCAB procedure with regard to morbidity and mortality between both sexes. Moreover, several studies stated that female patients suffered more often from postoperative complications after bypass surgery [9,34]. The authors stated that female sex could be a significant risk factor for acute renal failure [9,34]. Chou et al. [ 34] showed that women underwent dialysis significantly earlier compared to men after bypass procedure. In contrast, the observational animal studies showed that male sex might be associated with an increased incidence of acute kidney injury requiring dialysis [35,36]. Moreover, Neugarten et al. [ 36] highlighted the protective role of female sex in the development of acute renal failure in patients after open-heart surgery. The authors mentioned that this protective effect could be explained by effects of sex hormones on cellular processes in the pathogenesis of acute renal failure [35,36]. In contrast, we could not identify any significant differences regarding acute kidney failure ($$p \leq 0.956$$) and dialysis ($$p \leq 0.456$$) between the male and female group in our study. Female gender was associated with a higher prevalence of wound infection after bypass surgery compared to male gender [37]. Additionally, the authors reported that female gender remained a strong predictor of wound infection after the CABG procedure [38]. Furthermore, Patel et al. [ 20] observed that females had a significantly higher incidence of wound infection ($$p \leq 0.028$$) compared to males after the off-pump procedure only. However, we found no significant differences ($$p \leq 0.187$$) in wound infection between both genders after OPCAB surgery. No significant differences in primary and secondary outcomes were observed between men and women after OPCAB surgery. Therefore, further prospective trials with larger sample sizes are needed to identify any potential sex differences in patients after off-pump bypass procedure. ## 5. Study Limitations This purely retrospective clinical research has several limitations. Firstly, it is a retrospective double-center analysis with a relatively small patient cohort. Secondly, we focused on short-term outcomes and did not pay enough attention to the long-term results. Thirdly, specific pathophysiological conditions were not evaluated in our study. 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--- title: A Comparative Study of Cancer Cells Susceptibility to Silver Nanoparticles Produced by Electron Beam authors: - Evgenii V. Plotnikov - Maria S. Tretayakova - Diana Garibo-Ruíz - Ana G. Rodríguez-Hernández - Alexey N. Pestryakov - Yanis Toledano-Magaña - Nina Bogdanchikova journal: Pharmaceutics year: 2023 pmcid: PMC10056419 doi: 10.3390/pharmaceutics15030962 license: CC BY 4.0 --- # A Comparative Study of Cancer Cells Susceptibility to Silver Nanoparticles Produced by Electron Beam ## Abstract Introduction: Silver nanoparticles (AgNPs) have a wide range of bioactivity, which is highly dependent on particle size, shape, stabilizer, and production method. Here, we present the results of studies of AgNPs cytotoxic properties obtained by irradiation treatment of silver nitrate solution and various stabilizers by accelerating electron beam in a liquid medium. Methods: The results of studies of morphological characteristics of silver nanoparticles were obtained by transmission electron microscopy, UV-vis spectroscopy, and dynamic light scattering measurements. MTT test, alamar blue test, flow cytometry, and fluorescence microscopy were used to study the anti-cancer properties. As biological objects for standard tests, adhesive and suspension cell cultures of normal and tumor origin, including prostate cancer, ovarian cancer, breast cancer, colon cancer, neuroblastoma, and leukemia, were studied. Results: The results showed that the silver nanoparticles obtained by irradiation with polyvinylpyrrolidone and collagen hydrolysate are stable in solutions. Samples with different stabilizers were characterized by a wide average size distribution from 2 to 50 nm and low zeta potential from −7.3 to +12.4 mV. All AgNPs formulations showed a dose-dependent cytotoxic effect on tumor cells. It has been established that the particles obtained with the combination of polyvinylpyrrolidone/collagen hydrolysate have a relatively more pronounced cytotoxic effect in comparison to samples stabilized with only collagen or only polyvinylpyrrolidone. The minimum inhibitory concentrations for nanoparticles were less than 1 μg/mL for various types of tumor cells. It was found that neuroblastoma (SH-SY5Y) is the most susceptible, and ovarian cancer (SKOV-3) is the most resistant to the action of silver nanoparticles. The activity of the AgNPs formulation prepared with a mixture of PVP and PH studied in this work was higher that activity of other AgNPs formulations reported in the literature by about 50 times. Conclusions: The results indicate that the AgNPs formulations synthesized with an electron beam and stabilized with polyvinylpyrrolidone and protein hydrolysate deserve deep study for their further use in selective cancer treatment without harming healthy cells in the patient organism. ## 1. Introduction Nanoparticles are widely used in various technology fields, but from a medical point of view, their potential is still far from being discovered. Currently, nanoparticles are used for the visualization of some molecular markers of diseases, diagnosis, malignant tumors treatment, and targeted delivery of drugs with controlled release and accumulation in tissues and organs. Nanoparticles are used as active components, for example, photosensitizers in photodynamic therapy of cancers or hyperthermic tumor destruction by heating nanoparticles [1]. However, the toxicity of nanoparticles for living organisms limits their medical use [2]. The biological properties of nanoparticles significantly depend on their size, shape, stabilizer type, and method of preparation [3,4]. In addition, particle nanosizing often leads to the appearance of new material properties or the enhancement of existing ones. However, this can also increase the potential hazard to human health [2]. The small sizes of nanoparticles (1–100 nm) allow them to penetrate through the epithelial and endothelial layers into the internal environment and body fluids, while migrating and being carried by the blood, penetrating even through dense histohematological barriers including the blood–brain barrier [5]. In this regard, the toxicity of nanoparticles is mainly realized through the following mechanisms: mechanical impact of nanoparticles and, in some cases, the formation of their aggregates with biological molecules; membrane integrity alteration and perforation; catalytic action of nanoparticles; enzymes damage and inhibition with cell metabolism disruption; deactivation of antioxidants and oxidative stress induced by nanoparticles; damage to cell cytoskeleton and internal organelles, primarily mitochondria; tissue inflammatory response and tissue damage due to immune response [6]. In many cases, toxicity is determined by the metal ions’ action during the dissolution of nanoparticles [7]. Among the wide range of nanoparticles, AgNPs occupy a special place due to their antibacterial, antifungal, antiviral, anticancer, etc., properties. Interest in AgNPs was mainly due to their outstanding antimicrobial activity, which allows them to be used in medicine and industry, where microflora suppression is required. Many works describe the antiviral, anti-inflammatory, antioxidant, and even hormetic stimulating effects of AgNPs [8,9,10,11,12,13]. Novel ways to use AgNPs in medicine continue to be sought; therefore, new risks appear. They are mainly associated with AgNPs toxic effects in human organisms [14]. Based on various data, it was confirmed that the biological properties of AgNPs can vary widely even when the particles have the same chemical composition [15]. The advancement of new methods of AgNPs preparation serves to develop nanoparticles with higher biomedical activity, higher stability, and lower toxicity [16,17,18,19]. Usually, various organic solvents and reducing agents are used for AgNPs production, and as a result, their traces could remain in the final AgNPs. These residual components are difficult to remove, which increases the toxicity of the obtained compositions based on AgNPs. In addition to the nanoparticles of the desired size, by-products of oxidized silver and its salts can also be formed, which also changes the bioactivity of the final product. One of the promising high-tech and waste-free methods for AgNPs fabrication is the reduction of silver ions by their exposure to an accelerated electron beam in an aqueous solution containing a stabilizer and silver nitrate [20]. Variations of this method have been developed, where the effect of the accelerated electron beam on the aqueous solution of silver nitrate was accompanied by adding the different stabilizers, including polyvinylpyrrolidone (PVP) [20] or protein hydrolysate (PH) [21]. This approach ensures the production of standardized AgNPs with high bioactivity and high stability in solution [22], which was used in biotechnology, medicine, veterinary, and agriculture [23,24,25]. At the same time, AgNPs synthesized with an electron beam and stabilized with PVP and PH showed low toxic effects towards hemolysis [26], and human lymphocytes [27]. The effect of AgNPs on tumor cells is of particular interest. The results obtained by our group for AgNPs synthesized with an electron beam and stabilized with PVP and PH allows us to consider AgNPs as potential agents for cancer therapy [28,29]. This study aimed to demonstrate that AgNPs formulations synthesized with an electron beam and stabilized with PVP and PH hydrolysate are promising agents as an alternative for cancer treatment. Characterization of AgNPs samples, cell growth inhibition, and cell death pathway were evaluated in SKOV-3, HCT-116, PC-3, SH-SY5Y, and Jurkat cell lines. ## 2.1. Synthesis of Silver Nanoparticles Nanoparticles were obtained according to the methods described in patents [20,21]. Briefly, the method includes the following steps. First, a solution of collagen hydrolysate 18.8 wt$.\%$ (to obtain sample No. 1) or polyvinylpyrrolidone with a concentration of 18.8 wt$.\%$ (for samples No. 2 and No. 3). Then, silver nitrate solution necessary to reach $1.2\%$ wt. of AgNPs (12 mg/mL of metallic silver) was prepared and stirred at room temperature until completely dissolved. The resulting silver salt solution was added to a vessel with the appropriate amount of stabilizer solution, intensively mixed, and exposed to an accelerated electron beam (voltage 30 kV) of high-energy (2–2.5 MeV) electrons with an absorbed dose of 15 kGy generated on a linear accelerator ILU-10 (Institute of Nuclear Physics, Novosibirsk, Russia). Electron beam treatment led to stable AgNPs formation. In general, the accelerated electrons have a relatively low damaging effect on organic polymers compared to gamma radiation [30]. For the comparative test of biological activity, all samples were diluted with distilled water. The samples for all tests were denominated as sample #1 (with collagen hydrolysate stabilizer), sample #2 (with polyvinylpyrrolidone stabilizer), and sample #3 (with a mixture of $70\%$ of collagen hydrolysate and $30\%$ of polyvinylpyrrolidone). ## 2.2.1. UV-Vis Spectroscopy The optical properties of silver nanoparticles were characterized by measuring their absorption spectrum at the wavelength range from 200 to 800 nm at room temperature (25 °C) by UV−vis spectroscopy (Cary 60 UV-Vis Spectrophotometer, Agilent Technologies, Santa Clara, CA, USA). The absorption spectra of all samples were recorded for dilute aqueous solutions of the corresponding samples. Distilled water was used as a reference sample. ## 2.2.2. Hydrodynamic Diameter and Zeta-Potential Analysis AgNPs samples charge and hydrodynamic diameter distribution were determined by dynamic light scattering (Nano-ZS (Malvern Instruments Ltd., Malvern, UK)). The size distribution characteristics and Zeta-potential were measured in aqueous solutions at room temperature 25 °C with an equilibration time of 2 min. All samples were analyzed in triplicate. ## 2.2.3. Transmission Electron Microscopy Analysis (TEM) The transmission electron microscopy study was carried out on a JEM-2100F instrument (Jeol). A suspension was prepared based on ethanol and AgNPs lots and processed in an ultrasonic bath for 1 min. After that, the suspension was applied to a special copper mesh with a layer of formvar and a thin carbon film. Dried at room temperature for ~10 min. ## 2.3.1. Cell Cultures The evaluation of the biological properties of the AgNPs was carried out on cell cultures in vitro. Standard tumor cell lines, including Jurkat (T-lymphoblastic leukemia), SH-SY5Y (neuroblastoma), HCT-116 (colon cancer), MCF-7 (breast cancer), MDA-MB-231 (breast cancer), SKOV-3 (ovarian cancer), and PC-3 (prostate cancer) (LLC “PrimeBioMed”, Russia”), were used. All the studied lines were brought into the phase of stable growth and, after 2–3 passages, were applied in the experiment. Adhesive cell line cultivation and subsequent cell experiments were performed using DMEM cell culture medium (Gibco, Billings, MT, USA) with GlutaMAX (cell supplement #35050061, Gibco, Billings, MT, USA), $10\%$ FBS (fetal bovine serum, One Shot™ format, Brazil, Thermo Fisher Scientific, São Paulo, Brazil) and a mixture of antibiotics (penicillin/streptomycin mixture, Paneco, Moscow, Russia). Suspension cells were cultivated in RPMI 1640 medium with the same supplements. The preparation procedure included the following steps. Twenty-four hours before testing, 5000 cells of the corresponding cell line were seeded into each well of the 96-well plate and incubated for 24 h for cell adhesion and the start of cell growth and proliferation. After that, AgNPs with concentrations from 0.05 to 125 µg/mL (prepared by the serial dilution method) were added to the same plate. These concentrations refer to the concentration of metallic silver. The plate was incubated at 37 °C and under a $5\%$ CO2 atmosphere for 24 h. Before starting the experiment, a visual check of morphological changes and living conditions of the cells was performed. ## 2.3.2. Cytotoxicity Assay To assess the cytotoxic effect of nanoparticles, an MTT test was performed. For the test, AgNPs with a final concentration of 0.05–125 µg/mL was added into a pre-seeded with cancer cells 96-well plate. Cells without exposure to AgNPs were used as a negative control. Cells incubated in a medium supplemented with $0.3\%$ hydrogen peroxide were used as a positive control (dead cells). After 24 h of incubation, the medium from all wells was aspirated, and replaced with the fresh medium of the same composition containing 0.5 mg/mL MTT reagent (Paneco, Russia) was added and kept for 4 h at 37 °C in a CO2 incubator. The optical density was measured on a spectrophotometer (Multiskan FC, TermoFisher, Waltham, MA, USA) at a wavelength of 570 nm. The calculation was performed by subtracting the optical density of the background and the optical density of the positive (dead cells) control. Calculation of cell viability after exposure was performed as a percentage of alive cells in the experiment towards the viability control (cells without exposure to AgNPs). ## 2.3.3. Fluorescent Microscopy Cells morphological changes assessment was performed by optical bright-field and fluorescent microscopy with differential staining according to the standard protocol (CalceinAM-Propidium iodide). For this, a stain solution was prepared with 0.5 μg/mL (CalceinAM) and 5 μg/mL (propidium iodide), and then it was added to the corresponding wells with cells. Incubation was carried out for 15 min at 37 °C, after which cultures were observed under a fluorescent microscope (AxioVert. A1, Zeiss, Jena, Germany). ## 2.3.4. Flow Cytometry The cytotoxic effect and the cell death pathway, viable, necrotic, and apoptotic cells at different phases were determined by flow cytometry. The cells were incubated with AgNPs for 24 h at 37 °C, and then they were washed and removed from the plate by exposure to trypsin solution. After that, they were precipitated by centrifugation (5 min, 200 g) and resuspended in a staining buffer containing a mixture of annexin V–FITC and propidium iodide. Next, live cells (negative staining), stained cells in the state of early apoptosis (annexin V-FITC-positive), late apoptosis (positive for both stains), and (necrotic) dead cells stained only with propidium iodide were counted by cytometer CytoFlex (Beckman Coulter, Brea, CA, USA) ## 2.4. Statistical Analysis The experiments were carried out in at least 6 replicates. The experimental results were statistically processed using the software GraphPad Prism 9 (GraphPad Software, Inc., San Diego, CA, USA). Results are presented as mean value with standard deviation. Differences between groups were considered significant at $p \leq 0.05.$ ## 3.1.1. Transmission Electron Microscopy (TEM) TEM is the main method for objective assessment of the morphology and size of nanoparticles. Micrographs of AgNPs samples showed that all samples contain detectable particles, located both in an isolated and grouped order (Figure 1). According to Figure 1, all samples showed that three studied samples have very similar particle size distribution, and they mainly consist of separated, spherical in shape, single particles with size 2–50 nm. Some of the nanoparticles have contact and produce aggregates with a size close to 100 nm. ## 3.1.2. UV-Vis Spectroscopy Optical characterization of the solution with the UV-visible spectrum is a simple method to confirm the presence of AgNPs. The absorption spectra of AgNPs samples obtained by electron beam irradiation are shown in Figure 2. The spectrum of sample #1 has an absorption peak with a maximum at 426 nm. The spectrum of Sample #2 has an absorption band at 436 nm with a shoulder at 421 nm and a low intensive band at 520 nm (Figure 2). The spectrum of sample #3 has a band with a wide maximum in the interval of 420–460 nm. Peaks close to 400 nm are typical for AgNPs. The peak at 520 nm is attributed to large aggregated AgNPs [31,32]. These aggregates with a size of 100 nm were observed in TEM more frequently for sample 2 than for other samples (Figure 1). ## 3.1.3. Dynamic Light Scattering The results of AgNPs hydrodynamic diameter distribution are shown in Figure 3A–C. As can be seen, there is some polydispersity in each sample, although the main fraction is always more than 60 percent. The peak between 1000 and 10,000 nm in Figure 3A indicates that in sample #1, the small part ($9\%$) of nanoparticles was coagulated into microparticles. The data showed that the average hydrodynamic diameter of AgNPs samples was mainly in the range of 110–140 nm: 114.1 ± 0.340, 141.0 ± 0.189, and 142.6 ± 0.241 nm for samples #1, 2, and 3, respectively. The sample’s polydispersity index (PDI) ranged from 0.189 to 0.340, which indicates a relatively wide particle size distribution. The Zeta potential of the samples also varies significantly due to different stabilizers. The average zeta potentials were −7.3 ± 3.62, −3.8 ± 3.73, and +9.4 ± 6.28 mV for samples 1, 2, and 3, respectively. Usually, a large value of the zeta potential indicates the stability of the nanoparticles. When nanoparticles have a high charge, they repel, which increases their stability. For nanoparticles with zeta potential values less than ±25 mV, the aggregate formation increases, and the overall stability in suspension decreases [33]. In our case, surprisingly, despite the low charge (from −7.3 to +9.4 mV), after the electron beam, AgNPs remain stable probably due to the specific high protection by polyvinylpyrrolidone and collagen hydrolysate occurring at this method of sample preparation. ## 3.2. Cytotoxicity Properties of Silver Nanoparticles The AgNPs obtained by electron beam irradiation were stable in water without any precipitation. AgNPs of samples #1–3 were added to the cell culture medium to obtain the desired concentration and used in cell biological activity tests. All samples were stable in cell media during experiments. ## 3.2.1. Cytotoxicity Assay Figure 4 shows the significant cytotoxic effect of the studied AgNPs on all studied cancer cell lines. However, cytotoxicity differs significantly for samples with different stabilizers. The 3T3L1 line fibroblasts were the most resistant to silver nanoparticles. The tumor cell lines showed an AgNPs susceptibility higher than to 3T3L1 line fibroblasts (Figure 4). The overall resistance to AgNPs of tumor cell lines increases as follows: SH-SY5Y, MCF-7, Jurkat, MDA-231, HCT-116, PC-3, SKOV-3 (Figure 4). Thus, it was found that among the studied tumor cell lines, the most sensitive cell culture is neuroblastoma (SH-SY5Y), and the most resistant is ovarian carcinoma (SKOV-3). At the same time, it was found that AgNPs exhibit different cytotoxicity depending on the stabilizer used. All AgNPs samples contain the same concentration of silver and stabilizer, have very similar particle sizes measured with TEM and DLS, and differ only in the stabilizer. Half-maximal inhibitory concentrations for all studied tumor cells are compared for three investigated samples in Figure 5A. In addition, the selectivity index (SI) was calculated for each sample using the formula: SI = (IC50 for normal cell line 3T3L1)/(IC50 for each cancerous cell line) (Figure 5B). The samples efficacy against tumor cells is given by a SI > 1.0; in this case, all tumor cell lines present a SI > 1.0. The difference in cell viability for the most sensitive (SH-SY5Y, neuroblastoma) and the most resistant (SKOV-3, ovarian cancer) cell tumor lines reach 1 order magnitude (Figure 6A–C). ## 3.2.2. Fluorescent Microscopy The growth cell density and the number of viable and dead cells exposed to AgNPs in a wide range of concentrations were revealed by fluorescence microscopy (Figure 7). Figure 7A,B show for the most cytotoxic sample #3 live and dead cell densities for the most resistant tumor cell line SKOV-3 and the most susceptible SH-SY5Y neuroblastoma cells, respectively. The marked transition in cell viability is noted at concentrations 1.6 µg/mL for ovarian cancer and at concentrations 0.4 µg/mL for neuroblastoma. The rapid live-to-death transition within adjacent two-fold concentrations under the impact of AgNPs for all tested cell lines was observed. ## 3.2.3. Flow Cytometry The results of flow cytometry testing are shown in Figure 8. The cell suspension after AgNPs exposure was divided according to the viability status into the following fractions: live, early apoptosis, late apoptosis, and necrosis. Figure 8 illustrates that the main mechanism of cell death is apoptosis. The main fraction of cells after damage by AgNPs within 24 h starts the process of apoptosis, programmed cell death. In this experiment, an externally induced apoptosis (induced by AgNPs) pathway predominates. More than $90\%$ of the cells are in a state of early and late apoptosis at concentrations of AgNPs above 1.6 μg/mL for all samples (Figure 8A–C). However, some necrotic cells (≤$9\%$) were detected at high AgNPs concentrations. ## 4. Discussion Our results revealed that AgNPs samples evaluated have a selective and cytotoxic effect against cancer cell lines (Figure 1). Selectivity of AgNPs against tumor cell lines was shown by comparing the cytotoxic effect on non-cancer fibroblasts 3T3L1 line, which turned out to be up to 16 times more resistant to AgNPs samples than cancer cell lines. Furthermore, the three AgNPs samples evaluated have a SI > 1, demonstrating the need for further research regarding their use to treat cancer. In general, non-tumor cells are more resistant to the cytotoxic effect of silver. It is likely that tumor cell lines are more susceptible due to the increased cell replication rate [34,35,36]. Unlike ionic silver, nanoparticles could induce cell death primarily through mechanical impact and catalytic action enhancing lipid peroxidation, leading to proteotoxicity and necrotic cell death [35]. At the same time, AgNPs’ classical variants induced oxidative stress and apoptotic cell death which plays a significant role in these effects [37]. Surprisingly, even though neuroblastoma SH-SY5Y is characterized by not rapid growth with a doubling time of approximately 27 h [38], it was the most susceptible to AgNPs among the tumor cell lines used in the present study. This could be explained by the mechanism reported in which AgNPs induce endoplasmic reticulum stress and alter calcium metabolism, changing inositol phosphate function by the increased levels of phosphatase, which eventually leads to disrupted homeostasis in the mitochondria and apoptotic cell death [39]. Thus, the high degree of development of the protein-synthesizing apparatus of neuroblastoma cells makes them especially sensitive to such induced endoplasmic reticulum damage. Another non-specific mechanism of AgNPs on cells is oxidative stress. Even short-time exposure to AgNPs leads to reactive oxygen species production [37]. However, susceptibility to induced oxidative stress is highly variable among different types of tumor cell lines. This partly explains the different sensitivities found in our study. Summarizing the results, AgNPs cytotoxicity towards cancer cell lines decreases in the row of samples #3 > #1 > #2 (Figure 5). Sample #3, stabilized with a mixture of PVP and PH, showed a higher damaging effect compared to the studied tumor cells. However, the selectivity indexes are comparable for the three AgNPs samples except for MCF-7 and SH-SY5Y tumor cell lines. Moreover, the main mechanism of cell death upon exposure to three samples of AgNPs formulations is apoptosis (Figure 8). The contribution of primary necrotic cells increases only at high AgNPs concentrations (but even in this case, it does not exceed $9\%$). It indicates the increase in critical cancer cell damage under an excess of AgNPs, when the viability decreases so rapidly that the internal systems cannot respond adequately. The exposure to AgNPs is characterized by a very narrow critical concentration range between the state of cell death, partial viability, and normal cell culture growth, as shown in Figure 8. This pattern was detected for all studied cell lines. In accordance with the reported in the literature, our results showed that tumor cell lines are more sensitive to AgNPs effect than normal 3T3L1 fibroblasts that exhibits noticeably greater resistance (Table 1). Thakore S. and co-workers reported a very low cytotoxicity of nanoparticles in relation to healthy cells, when the death of the fibroblast cell population did not exceed $30\%$ at the maximum studied concentration 100 µg/mL [40]. The authors note that fibroblasts were significantly more resistant than A549 lung cancer cells. Half-maximal inhibitory concentration for the studied samples on different tumor cell lines are in the range of 0.145–2.649 µg/mL, while the sensitivity of different cell types to the same AgNPs sample can differ up to 10 times (Figure 5). A significant scatter in the estimates of cytotoxicity is also observed according to the literature data (Table 1). Analyzing the results for AgNPs formulations obtained by different methods, a huge difference in the CI50 for one cancer cell lines can be clearly seen (Table 1). All authors confirm that AgNPs are toxic to cancer cells and cause a decrease in cell viability. However, effective doses causing similar cytotoxic effects in some cases differ by an order of magnitude due to the properties differences in different AgNPs formulations. The results in Table 1 summarize the IC50 reported for diverse AgNPs, with the AgNPs sample #3 evaluated in this paper being the one that has the highest activity: 66, 205 times (SH-SY5Y); 4.4, 14.5 times (MDA-231); 2.4, 14.6, 23.4, 86, 226.8 times (MCF-7); 7.9, 44.6 times (HCT-116); 1.12, 3.25 times (PC-3); 130, 29.7 times (Jurkat); 7.7, 99.1 (SKOV-3); >57.8, 8.7–11.6 times (3T3L1). So, sample #3 is more active than AgNPs samples reported in the literature and is presented in Table 1 by an average of 52 times ($5.220\%$). Only one AgNPs formulation showed very similar activity with sample #3, namely, the samples synthesized with green method [49] (Table 1), for PC-3 AgNPs in this work [49]. IC50 was just $12\%$ higher than IC50 obtained in our work for sample #3. The IC50 for other AgNPs formulations of Table 1 were 2–200 times higher than IC50 of sample #3. The extremely high anticancer activity of sample #3 compared with other AgNPs formulations described in the literature (higher 52 times) and the selectivity index that shows it would be a safe formulation indicate its perspective and the necessity to continue the study of this formulation. The comparison of our results with the literature data of Table 1 showed that Argovit AgNPs formulation is on average 52 times more active than the other sixteen AgNPs formulations presented in Table 1. AgNPs is not a molecule; it is very wide class of different formulations. Every formulation of AgNPs has different biological activity and toxicity, which depends on AgNPs size, shape, charge, stabilizer nature, method of preparation, impurities, etc. [ 56]. In our previous publication [57], it was shown that the activity of AgNPs formulations in relation to erythrocytes was very different, and Argovit AgNPs showed 40 times less toxicity (measured with AgNPs concentrations corresponding to $5\%$ hemolysis) than the most toxic AgNPs formulation cited in [57]. The present work is another excellent illustration of great activity variation of different AgNPs formulations towards human cells. Some AgNPs formulations, described in Table 1, have very monodispersed AgNPs distributions, but all of them have lower activity than our AgNPs formulation. As indicated above, they were 2.4 to 250 times (240–$2500\%$) less active than our formulation. Additionally, just one of them was only $12\%$ less active. We think that the wide particle size distribution of our formulation is responsible for the fact that it has the highest activity for different cancer cell lines between seventeen various AgNPs formulations. These results allow us to hypothesize that if every cancer cell line needs a specific optimal AgNPs size, and if other formulations previously published and summarized in Table 1 do not have this specific size, then they will show low activity. In contrast, the wide size distribution of Argovit AgNPs formulation successfully provides the activity higher than activity of other AgNPs with monodispersed size, since at least part of AgNPs in this formulation has the optimized size for every specific cancer cell line that is provided by a wide particle size distribution. Obviously, this hypothesis needs further experimental verification. Optical spectra showed that the more monodispersed samples #1 and #2 have narrow peak at 420–440 nm, and sample #3 was characterized by the wider peak with a maximum at 420–460 nm, indicating the higher polydispersity of sample #3 than that of samples #1 and #2 (Figure 2). Thus, sample #3 presented in optical spectra the widest peak indicating the highest polydispersity showed the highest activity in cancer cell proliferation. In the previous work of our group, it has been shown that AgNPs prepared with electron beam and stabilized with PVP do not have a noticeably damaging effect on primary cells cultures even at concentrations more than 100 times higher than inhibitory concentrations for tumor cell lines [28]. The authors noted that such nanoparticles showed 34.5 times greater activity against tumor cells than the well-known platinum-based cytostatic carboplatin. These results [28] directly indicate the selective nature of electron beam AgNPs formulation activity. Their significantly increased effect on tumors compared with healthy cells was obviously shown, and this creates certain prospects for their use as cytostatic agents. AgNPs reported in this paper, particularly sample #1 (stabilized with protein hydrolysate), showed an IC50 of 2.3 µg/mL on the highly aggressive human adenocarcinoma HCT-15, which is about 10 times more potent than carboplatin [29], while at a 260 times higher concentration (600 µg/mL), neither cytotoxic nor genotoxic damage was produced on human peripheral blood lymphocytes. Lymphocytes toxicity test is a sensitive, accurate, fast, and economical tool to evaluate whether materials are worthy of continued study of their effectiveness and toxicity for biomedical uses [27]. The main death pathway, elicited by sample #1 on HCT-15, was also apoptosis as it was determined for cancer cells here. Moreover, sample #1 acute oral toxicity on mice showed a lethal dose (LD50) of 2618 mg of Ag/Kg body weight determined in accordance with the OECD guideline 420 for Acute Oral Toxicity Assay, which classified it as practically nontoxic (Category 5) in accordance with the Globally Harmonized System of Classification and Labelling of Chemicals [29]. Thus, the aim set in the present work was achieved. All of the above results showed that the studied AgNPs formulations, prepared by electron beam and stabilized with PVP and protein hydrolysate, are characterized by high anticancer activity, low toxicity, and high stability, which are relevant characteristics for any pharmaceutical agents. These properties open new perspectives in the development of effective, selective, and safe AgNPs formulations for the cancer treatment and promise significant side effect reduction. ## 5. Conclusions In this work, we present the cancer cell growth inhibition induced by three AgNPs formulations prepared by an accelerated electron beam of high-energy electrons which confers their unique biological properties. For the three studied AgNPs formulations, it was revealed that cancer cell inhibition by AgNPs was dose dependent, and the main mechanism of cell death was apoptosis. Moreover, the half-max inhibitory concentration for the seven studied cancer cell cultures varies by more than an order of magnitude, possibly due to different proliferation rates and tissue specificity of different tumor cell types. For all three studied AgNPs formulations, the sensitivity of seven cancer cell lines towards AgNPs decreases in the following order: SKOV-3 > PC-3 > HCT-116 > MDA-231 > Jurkat > MCF-7 > SH-SY5Y. The most sensitive to AgNPs were neuroblastic cells (SH-SY5Y), while the less sensitive ones were ovarian cancer cells (SKOV-3). The activity of the AgNPs formulation prepared with a mixture of PVP and PH, studied in this work, was about 50 times higher than the activity of other AgNPs formulations reported in the literature. The 3T3L1 fibroblasts cell line used as a control of normal cell (not cancer), was up to 16 times more resistant to AgNPs formulations than the tested tumor cell lines. These results, together with previous ones published by our group, indicate the selectivity of these AgNPs formulations, which are electron beam synthesized and stabilized with polivinilpirrolidone and protein hydrolysate, against cancer cells compared to healthy cells, with a selectivity index greater than 1 for all samples and tumor cell lines evaluated (SKOV-3, PC-3, HCT-116, MDA-231, Jurkat, MCF-7, and SH-SY5Y). The results indicate that these AgNPs formulations deserve deep study for their further use in selective cancer treatment without harming healthy cells in the patient organism. ## References 1. 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--- title: Immune Factors Drive Expression of SARS-CoV-2 Receptor Genes Amid Sexual Disparity authors: - Ashutosh Vashisht - Pankaj Ahluwalia - Ashis K. Mondal - Harmanpreet Singh - Nikhil S. Sahajpal - Sadanand Fulzele - Vamsi Kota - Gagandeep K. Gahlay - Ravindra Kolhe journal: Viruses year: 2023 pmcid: PMC10056434 doi: 10.3390/v15030657 license: CC BY 4.0 --- # Immune Factors Drive Expression of SARS-CoV-2 Receptor Genes Amid Sexual Disparity ## Abstract The emergence of COVID-19 has led to significant morbidity and mortality, with around seven million deaths worldwide as of February 2023. There are several risk factors such as age and sex that are associated with the development of severe symptoms due to COVID-19. There have been limited studies that have explored the role of sex differences in SARS-CoV-2 infection. As a result, there is an urgent need to identify molecular features associated with sex and COVID-19 pathogenesis to develop more effective interventions to combat the ongoing pandemic. To address this gap, we explored sex-specific molecular factors in both mouse and human datasets. The host immune targets such as TLR7, IRF7, IRF5, and IL6, which are involved in the immune response against viral infections, and the sex-specific targets such as AR and ESSR were taken to investigate any possible link with the SARS-CoV-2 host receptors ACE2 and TMPRSS2. For the mouse analysis, a single-cell RNA sequencing dataset was used, while bulk RNA-Seq datasets were used to analyze the human clinical data. Additional databases such as the Database of Transcription Start Sites (DBTS), STRING-DB, and the Swiss Regulon Portal were used for further analysis. We identified a 6-gene signature that showed differential expression in males and females. Additionally, this gene signature showed potential prognostic utility by differentiating ICU patients from non-ICU patients due to COVID-19. Our study highlights the importance of assessing sex differences in SARS-CoV-2 infection, which can assist in the optimal treatment and better vaccination strategies. ## 1. Introduction The emergence of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic has posed a global health emergency with approximately 649 million people infected and 6.6 million deaths as of 21 December 2022 [1]. As the COVID-19 pandemic continues to evolve, there is a growing need to better understand the risk factors associated with disease severity. Advanced age and comorbidities such as chronic respiratory disease, cardiovascular disease, diabetes, and hypertension have been identified as primary risk factors for severe COVID-19 [2,3,4,5]. Furthermore, several studies have demonstrated sex-related differences in COVID-19 cases and mortality rates, with males exhibiting higher rates of severe and fatal cases [6,7,8,9,10]. Clinical studies on COVID-19 have also established that males are at a greater risk of poor prognostic outcomes and higher mortality rates [10,11,12]. However, the molecular-level sex-specific differences in the host response to SARS-CoV-2 have not been clearly defined yet. SARS-CoV-2 is a β-coronavirus with positive-sense single-stranded RNA as the genetic material (subgenus: sarbecovirus, subfamily: Orthocoronavirinae) [11]. The genome sequence analysis of SARS-CoV-2 was found to be $96.2\%$ identical to the bat coronavirus RaTG13, whereas it was $79.6\%$ identical to SARS-CoV [12]. The deep meta-transcriptomic sequencing results showed that the receptor-binding domain (RBD) of the spike (S) glycoprotein of SARS-CoV-2 was one amino acid longer than that of SARS-CoV [13]. An analysis of bronchoalveolar lavage fluid (BALF) from a COVID-19 patient along with infectivity studies in HeLa cells concluded that just like SARS-CoV, SARS-CoV-2 also uses angiotensin-converting enzyme 2 (ACE2) as the cellular entry receptor, and hence is capable of direct human transmission [12,14]. Transmembrane serine protease 2 (TMPRSS2) forms a receptor-protease complex by associating with ACE2 and allows for the successful entry of the virion into the cell [15]. SARS-CoV-2 RNA has been reliably detected in bronchoalveolar lavage fluid, nasopharyngeal swabs, sputum, blood, and stool samples, although with differential sensitivity [16]. The severity of COVID-19 disease is thought to be linked to virus-induced damage to cells and the ability of the virus to evade the host immune system [17]. In COVID-19 patients, the immune system can induce a lethal inflammatory condition known as cytokine release syndrome (CRS) [18]. This phenomenon involves an extreme inflammatory response, where large amounts of inflammatory cytokines are rapidly secreted in response to infective stimuli. Patients with severe COVID-19 exhibit higher levels of pro-inflammatory cytokines (such as IL-6 and IL-8) in their bronchoalveolar lavage fluid as well as increased expression of inflammatory chemokines (such as CCL2) in macrophages compared to those with mild COVID-19 [19]. The unconstrained cytokine storm is particularly severe in patients requiring admission to an intensive care unit (ICU) [20]. However, defining the clinical criteria for CRS remains challenging, and the mechanisms responsible for the unchecked release of inflammatory factors are still unclear. Several studies suggest that viruses have developed strategies to counteract the host defense system to ensure their survival and propagation. The first viral protein that was found to interfere with the host immune system by targeting TLRs was A46R of the vaccinia virus (VACV) [21]. The A46R protein directly regulated the TIR-domain-containing adaptor proteins. Additionally, studies have shown that pathogen recognition receptors play an important role in viral entry to the cells. In a study conducted on CXCR4 (a chemokine receptor acting as an HIV co-receptor), it was found that CXCR4 was a component of the TLR oriented receptor complex active in LPS recognition [22]. In a similar study, the oral/systemic pathogen P. gingivalis, which causes periodontal/systemic infections, evades TLR-mediated immunity by binding to CXCR4, which induces PKA signaling. This, in turn, inhibits TLR2-mediated proinflammatory and antimicrobial responses, and thus resists its clearance from the body [23,24]. In another study on Epstein–Barr virus (EBV), it was suggested that the virus modulates the TLR7 pathway to regulate IRF-5, which has antiviral activity, to minimize the immune response [25]. EBV was found to induce a new negative regulatory IRF-5 splice variant, V12, which had no activation domain but was able to code for a DNA binding domain. This led to the inactivation of the immune response in the presence of EBV. The effect of sex steroid hormones has resulted in sex-specific disease outcomes in many diseases [26,27]. While testosterone suppresses innate immune response [28], the presence of estrogen (ESR) at higher concentrations is immune-suppressive and at lower concentrations, is immunomodulatory [29]. Estrogen signaling can also promote adaptive T-cell response [30] and can limit influenza infection by modulating genes that regulate the cells’ metabolic function [31]. Additionally, androgen receptors (AR) are involved in the severe outcome of viral infections such as COVID-19 and hepatitis B virus (HBV) [32,33]. A lot of information is known about the immune response generated by SARS-CoV-2 infection, however, key issues related to SARS-CoV-2 pathogenesis and its sex-specific outcome in humans are still to be resolved. In this manuscript, we have offered a perspective on the possible interaction of SARS-CoV-2 with the host immune system and discussed scenarios through which these interactions have resulted in increased severity of the infection and its association with sex-specific outcomes. While previous analysis of the host response to SARS-CoV-2 highlighted a role for TLRs, IFNs, IL6, and other immune factors, the cell-specific studies elucidating their association have not been explored yet. Single-cell RNA-sequencing (scRNA-Seq) is reshaping our potential to comprehensively analyze numerous types of cells during healthy and infectious states. The global single-cell sequencing databases such as single-cell portals have enhanced our understanding of infections during such pandemics [34,35]. In this paper, we have used such datasets (all from non-SARS-CoV-2 infected samples) to analyze mouse cells that have been assigned as SARS-CoV-2 targets and investigated the co-expression of the above-mentioned host immune factors. We focused our analysis on airway epithelial cells, which were found to be the primary target of SARS-CoV-2 spread in the lungs. After analyzing the single-cell RNA databases, we found that the expression of TLR7, IRFs, IL6, estrogen receptors, and the androgen receptors were associated with SARS-CoV-2 cell receptors. Additionally, the genome-related analysis showed that the estrogen receptor subtype was under the immediate control of the ACE2 promoter and androgen receptor (AR) under the IRF5 promoter region. To further understand the variations in the gene expression level, we utilized two independent external datasets. The comparison of a 6-gene signature (ACE2, TMPRSS2, AR, TLR7, IL6, and IRF5) was made to explore the sex-based differences associated with COVID-19. These findings point to a potential sexual disparity and a strong influence of underlying immunological components in determining the susceptibility to COVID-19 associated illness. ## 2. Results Human Cell Atlas datasets have shown that ACE2 and TMPRSS2 are expressed in the nasal, lung, and gut epithelial cells [36]. The highest expression of both these genes has been found in nasal goblet cells and multi-ciliated cells, which showed that these cells have a significant role in reservoiring viral load. Furthermore, in the distal lung, co-expression has been found in alveolar type-2 (AT2) cells [37,38]. However, these studies have not included the data related to immune factors and thus lack the results that could explain the possible role of immune response in regulating the expression of SARS-CoV-2 receptor genes. To address this limitation, we focused on immune genes that play a role in the initial response following the detection of viral RNA such as Toll-like receptors (TLRs), interferon regulators, and cytokines as well as SARS-CoV-2 host receptor proteins (Table 1). We further examined whether immune responses to SARS-CoV-2 vary between males and females and whether these differences are associated with the observed disparities in the disease course of COVID-19. To accomplish this, we analyzed the AR and ESSR genes in our datasets. Only single-cell RNA studies were chosen in this study, which included the expression data of the desired candidates. ## 2.1. SARS-CoV-2 Receptor Genes and Host Immune Factors Co-Expression in Airway Epithelium To investigate whether candidate immune factors are directly associated with the SARS-CoV-2 receptor genes and if there is any co-localization and expression pattern present among these, we performed a set of bioinformatics analysis. String analysis of the host genes including immune factors-TLR7, IRF5, IRF7, and IL6 along with the SARS-CoV-2 receptor genes (ACE2, TMPRSS2) was performed to analyze any functional interaction between the target proteins. The string analysis result showed that the immune factors were actively associated with ACE2 and TMPRSS2 through IL6 (Figure S1). The STRING parameters showed the following: the number of nodes, 6; number of edges, 8; average node degree, 2.67; average local clustering coefficient, 0.75: expected number of edges, 1; PPI enrichment p-value, 3.54 × 10−6 (Table S1). Furthermore, to determine whether the candidate genes were co-localized with the SARS-CoV-2 receptor genes, we performed a targeted analysis of Ace2, Tmprss2, Tlr7, Il6, Irf5, and Irf7 gene expression in the mouse lung airway epithelium. We examined these genes in a curated dataset of 7193 airway epithelial cells for gene expression from the tracheal portion of the lung. These cells were investigated broadly in the regions of the basal, ciliated, club, goblet, ionocyte, neuroendocrine, and tuft cells (Figure 1a). All candidate genes were expressed in the airway with the highest levels observed for Tmprss2, and Irf7 and the lowest level observed for Il6 and Tlr7 (Figure 1b). Club cells showed the highest expression of candidate proteins. Irf7 and Tmprss2 showed expression in almost all of the cells, whereas Tlr7 expression was confined to club cells only. Ace2, Il6, and Irf5 were variably expressed in the basal, ciliated, and club cells. Together, the results of the STRING analysis and curated scRNA sequencing showed that immune factors could regulate the expression of Ace2 and Tmprss2 as they tend to share co-expression and localization patterns across the airway epithelial cells. ## 2.2. X-Chromosome-Related Genes May Influence the Severe Outcome of SARS-CoV-2 in Males via IRF Expression We next sought to understand how the expression of X-chromosome-related genes may relate to covariates that have been associated with disease severity in a particular gender during COVID-19. Among the shortlisted gene candidates, AR, ACE2, and TLR7 were found to be present on the X chromosome. Additionally, the co-localization and expression analysis described above identified an association between the IFN signaling pathway and the SARS-CoV-2 receptor genes. This prompted us to investigate whether IFNs may play an active role in regulating the ACE2 expression levels and thus potentially allow for a positive host response for viral entry. We performed the string analysis of genes including AR, ACE2, TMPRSS2, and the above-mentioned immune factors. It was observed that the SARS-CoV-2 receptor genes were associated with IRF5 via IL6 and AR (Figure S2). The STRING parameters showed the following: the number of nodes, 6; number of edges, 7; average node degree, 2.33; average local clustering coefficient, 0.361: expected number of edges, 1; PPI enrichment p-value, 0.00022 (Table S2). Furthermore, the human promoter region analysis showed that the AR promoter at the chrX:66704897-66705917 and TLR7 promoter at position chrX:12795111-12795128 might regulate the expression of IRFs (IRF1, IRF2, IRF7) at chrX:66705521-66705540 (+strand) and chrX:12795245..12795264 (+strand), respectively (Figure 2). In a sex-based meta-analysis study conducted on COVID-19 patients by Huang et al. [ 3], the results showed that AR could regulate the expression of ACE2 and TMPRSS2, which can dictate the possible role of sex hormones in disease outcome. However, our analysis tried to decipher the hidden link of immune factors in this cascade of reaction, which may influence the expression of SARS-CoV-2 receptor genes. Based on these analyses, it can be hypothesized that SARS-CoV-2 can use the upregulation of IRF7 and IL6 as a double-edged sword by increasing the pro-inflammation amid tissue injury and by increasing the expression of ACE2 and TMPRSS2 for its entry to infect the cells. This further strengthened our hypothesis that X-related genes can be influenced to accelerate the SARS-CoV-2 infection in the cells. ## 2.3. Estrogen Expression under the Influence of ACE2 in Females Can Be Protective during SARS-CoV-2 Infection Various studies have mentioned the protective nature of estrogen in females during viral infections, however, the underlined mechanism has yet to be explored. In a quest to investigate the role of estrogen during SARS-CoV-2, we first analyzed the sRNA sequencing data of the same mouse airway epithelial cells to observe the co-localization and expression of the female sex hormone receptors estrogen-related receptor alpha (ESRRA) and estrogen-related receptor beta (ESRRB) among the SARS-CoV-2 receptor genes. The results showed that ESSRA was expressed in all of the cells (higher in the basal cells), but ESSRB was not detected (Figure 3a). It is important to mention here that the basal cells also showed a higher expression of ACE2. This prompted us to perform further investigation, which was based on human promoter region analysis. The ChiP sequence analysis showed that ESSRA at chrX:15529756.15529769 (-strand) was under the regulation of ACE2 promoter region chrX:15530112..15530198 (-strand) (Figure 3b). Additionally, the zinc finger protein at chrX:15529667..15529678 (-strand) was found to be downstream of ESSRA during this analysis, and zinc fingers have been associated with antiviral properties [46]. Based on these results, it can be hypothesized that during the viral infection, ACE2 overexpression upregulates ESSRA, which in turn accelerates the expression of antiviral, zinc finger proteins. This cascade could be a major reason why females have less severe outcomes during SARS-CoV-2 compared to males. ## 2.4. Clinical Implication of Gene Signature in Human Infection We further explored the clinical implications of our gene signature in two independent datasets. The expression of the 6-gene signature was found to be higher in males compared to females (Figure 4a). In another dataset, hierarchical clustering identified two clusters with significant variation in gene expression. Cluster 1 had a mean of 0.578 and cluster 2 had −0.611 (z-score) (Figure 4b,c). Chi-square analysis identified an association of higher expression of the 6-gene signature with non-ICU patients, along with a significant sexual disparity based on gender. ## 3. Discussion Understanding the potential gene interactions across different cells is important to interpret viral infection and the immune response activated against it. Several studies have been performed to understand the tissue-level expression of SARS-CoV-2 receptors in the human epithelia of the lung [47,48,49]. However, unlike ACE2 and TMPRSS2, the expression of host immune factors and sex genes related to SARS-CoV-2 remain unknown. Identifying the association of SARS-CoV-2 with host factors is critical for understanding and modulating host defense mechanisms and viral pathogenesis. In this study, we investigated the target genes that communicated with the SARS-CoV-2 receptor genes in the mouse airway epithelial cells. Our analysis illustrated the crosstalk between various host factors with ACE2 and TMPRSS2 expression. The above-mentioned results describe how the viral proteins have affected the immune response by binding to the immune components before or after the expression of nuclear factors. Through this study, we suggest an alternate hypothesis that the virus does not inhibit the immune response but thrives on it. The host immune response plays a crucial role in the fight against viruses. However, this response can be manipulated by viral genes for survival and propagation. Our analysis found that SARS-CoV-2 can use host immune factors to upregulate ACE2 and TMPRSS2. Our study showed that the mouse airway epithelium enriched for ACE2 and TMPRSS2 had the highest expression of IRFs genes. We extended our investigation through ChiP and promoter region analysis and observed that IRFs could regulate ACE2 expression via AR in males. This could be linked to the higher CFR in males due to SARS-CoV-2. Our analysis is in line with the other studies that showed that the coronavirus has evolved to take advantage of immune response by hampering the IFN signaling pathway for promoting efficient viral entry to the cell [50,51]. In a COVID-19 hypercytokinemia-based study, patients were observed to have upregulation of IL-1, IL-2, IL-4, IL-7, IL-10, IL-12, IL-13, IL-17, GCSF, macrophage colony-stimulating factor (MCSF), IP-10, MCP-1, MIP-1α, hepatocyte growth factor (HGF), etc. [ 36]. I-sHLH, a type of hemophagocytic lymphohistiocytosis (HLH) is caused by microbial infections (mainly viruses) and is a hyperinflammatory syndrome that leads to multiple organ failure [52]. Crucial features of I-sHLH include high fever, hyperferritinemia, and ARDS (acute respiratory distress syndrome) [53]. ARDS has been the leading cause of death in COVID-19 patients. In a recent study comprising 150 patients of COVID-19 in Wuhan, China, higher levels of ferritin and IL6 were observed in the non-survivors compared to the survivors [54]. It can be hypothesized by analyzing these results and our analysis that IL6 upregulation not only damages the organs of the patients but also helps in the increase of viral load by providing higher expressions of ACE2 and TMPRSS2. All of these targets are exploited by SARS-CoV-2 as a component of a bigger complex for its propagation. In a mouse-adapted SARS-CoV virus (MA15) study, it was observed that gonadectomized female mice had a gradual weight loss and a higher death rate than the control group. In a different experiment, the treatment of female mice with ICI-182, 780 (Faslodex; an estrogen receptor antagonist) made the group more vulnerable to MA15 infection in contrast to the female mice that were given estrogen agonists [55]. Our analysis also provided the data that ACE2 can enhance estrogen regulation in females during COVID-19 and thus counteract SARS-CoV-2 infection. Our exploration of gene signatures in human datasets identified significant perturbations associated with sex. Additionally, higher gene expression was associated with non-ICU patients, signifying the classification potential of our gene signature. Recently, several gene signatures that can classify COVID-19 patients have been identified [56,57,58]. These studies, along with our study focusing on sex-based differences, can help in the clinical management of infected patients. As the present study included the single-cell RNA sequencing data of mice, analyzing these targets in human airway epithelial cells and the careful consideration of appropriately selected gene lists and model cellular systems to understand the pathophysiology of SARS-CoV-2 are highly required. Our results anticipate that transcriptional response to the virus will need to be rigorously characterized in appropriate model systems. Whether the immune factors and X-chromosome-related genes are net protective or detrimental to the host may also depend on the stage of viral infection in a specific host. ## 5.1. Analysis of Protein Network The search tool for retrieval of interacting genes (STRING) database, which integrates both known and predicted protein–protein interactions (PPIs), was applied to investigate the co-expression network of the immune markers [59]. The active interaction sources including text mining, experiments, databases, and other co-expression analysis were used by STRING to construct the PPI networks and the outcomes represented as nodes and edges. The nodes correspond to the proteins and the edges represent the interactions. The following steps were taken to conduct the functional enrichment analysis: the multiple protein mode was selected as a total of seven target proteins (ACE2, TMPRSS2, AR, TLR7, IL6, IRF5, and IRF7) taken as input proteins, the interaction score was in the range between 0.400 and 0.700 (medium to high confidence), the maximum number of interactors for the first and second shell was taken as the query protein only, all the active interaction sources such as text mining, experiments, databases, co-expression, neighborhood, gene fusion, and co-occurrence were checked. ## 5.2. Target Localization Furthermore, to have a comprehensive understanding of immune markers in the different compartments of mouse airway epithelial cells, a single-cell portal was used. Since the number of the cluster-based algorithms relied on gene signatures, we made a concerted effort to find datasets that contained our target gene signatures. Additionally, we looked for the cell type labels and raw counts of the cells. The data from gene expression experiments using Illumina-based single-cell RNA sequencing were obtained. The t-distributed stochastic neighbor embedding (t-SNE) graphs containing the scRNA-Seq expression of 7193 mouse trachea cells which included 3845 basal cells, 425 ciliated cells, 2578 club cells, 65 goblet cells, 26 ionocytes, 96 neuroendocrine cells, and 158 tuft cells were taken for further analysis. ## 5.3. Target Regulation Analysis To analyze the transcriptional regulation and to extract the precise positional information of the transcriptional start sites of eukaryotic mRNAs, the database of transcription start sites (DBTSS) was used [60]. All of the targets (ACE2, TMPRSS2, AR, TLR7, IL6, IRF5, IRF7, ESSRA, and ESSRB) were individually analyzed to check whether one of these were under the promoter region of the other. Additionally, the Swissregulon portal was used to access the genome-wide annotations of the regulatory sites and motifs [61]. The reference-seq transcripts, transcription factor, and promoters were marked as the available filters for the analysis. ## 5.4. Analysis of Gene Expression Variations in Humans To evaluate the variation in gene expression and sexual disparity, we explored two datasets, GSE161731 ($$n = 198$$ samples) and GSE157103 ($$n = 126$$ samples). The datasets were normalized to counts per million for the analysis. Z-score was calculated for each sample based on the standard deviation from the mean for the combined gene signature. Hierarchical clustering of the 6-gene signature was performed and divided patients into high and low clusters. To correlate the gene signature with clinical variables, the chi-square test was used. All statistical analyses were performed using JMP (JMP Statistical Discovery Software [2022] Version 16. SAS Institute Inc., Cary, NC, USA) and R software (R Core Team [2022] R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria). ## 6. Conclusions The pandemic caused by COVID-19 highlights the need to understand molecular features associated with its pathogenesis and its association with the clinical and ethno-demographic variables. To understand the pathophysiology of COVID-19, it is essential to correlate the findings from mouse models to better understand the clinical outcomes in humans. In this study, we attempted to explore the sexual differences at the molecular and gene expression levels in both the mice and humans. We identified a 6-gene signature that showed significant variation at the gene expression levels between the males and females along with hospitalization risk groups. The observed variation in gene expression between the males and females can provide insights into the underlying mechanisms that contribute to the varying degrees of COVID-19 susceptibility and severity. The approach can further assist in patient classification, leading to improved clinical management and treatment strategies. Further research is required to investigate the role of other important covariates such as hormones, age, BMI, co-morbidities, vaccination status, and other host factors on SARS-CoV-2 susceptibility and severity. The emergence of new variants and vaccine efficacy may also affect the clinical presentation of sex-specific differences in COVID-19 pathology. 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--- title: 'Exploring the Effects of Cancer as a Traumatic Event on Italian Adolescents and Young Adults: Investigating Psychological Well-Being, Identity Construction and Coping Strategies' authors: - Chiara Ionio - Francesca Bigoni - Maddalena Sacchi - Marco Zecca - Elena Bergami - Marta Landoni - Giulia Ciuffo - Anna Rovati - Damiano Rizzi journal: Pediatric Reports year: 2023 pmcid: PMC10056441 doi: 10.3390/pediatric15010021 license: CC BY 4.0 --- # Exploring the Effects of Cancer as a Traumatic Event on Italian Adolescents and Young Adults: Investigating Psychological Well-Being, Identity Construction and Coping Strategies ## Abstract Cancer in adolescence is considered a family disease that can have numerous negative psychological consequences for adolescents and the entire household. The aim of this study was to investigate the impact of oncological disease in adolescence, with particular reference to the psychological and post-traumatic consequences for the adolescents themselves and the family system. An explorative case–control study was conducted with 31 adolescents (mean age 18.03 ± 2.799) hospitalised for cancer at IRCCS San Matteo Hospital in Pavia and 47 healthy adolescents (mean age 16.17 ± 2.099). The two samples completed a survey that included sociodemographic information and questionnaires assessing psychological well-being, traumatic effects of the disease, and adequacy of the relationship with parents. $56.7\%$ of oncology adolescents scored below average in psychological well-being, and a small proportion of them fell within the range of clinical concern for anger ($9.7\%$), PTS ($12.9\%$), and dissociation ($12.9\%$). Compared with peers, there were no significant differences. However, in contrast to peers, oncology adolescents showed a strong influence of the traumatic event on the construction of their identity and life perspectives. A significantly positive correlation also emerged between adolescents’ psychological well-being and the relationship with their parents (mothers: $r = 0.796$; $p \leq 0.01$; fathers: $r = 0.692$; $p \leq 0.01$). Our findings highlight how cancer in adolescence could represent a central traumatic event that can shape the identity and life of teenagers who are in an intrinsically delicate and vulnerable stage of life. ## 1. Introduction In 2020, approximately 10 million people worldwide will have died from cancer, making it one of the leading causes of death [1]. A total 400,000 children and adolescents aged 0 to 19 years are diagnosed with cancer each year, and the incidence of the disease tends to increase with age (Lam et al., 2019; World Health Organization, 2021). The third volume of the International Childhood Cancer Incidence (IICC-3) published data showing worldwide differences in overall cancer incidence by geographic region, with southern Europe having the highest rates in the 15- to 19-year-old age group [2,3]. With a standardized incidence rate of 275.4 per million person-years for the 15- to 19-year-old age group, data collected by the Italian Network of Cancer Registries (AIRTUM) from 1992 to 2013 from the 26 local Italian cancer registries confirmed that Italy is one of the European countries with the highest incidence rates [2,3]. Recently, the Italian Association of Cancer Registries (AIRTUM) estimated that 4000 cases will be diagnosed in adolescents (15–19 years) in Italy between 2016 and 2020. The estimated annual average is 1400 cases in the 0–14 years age group and 900 cases in the 15–19 years age group [4]. However, cancer diagnosis and treatment can cause significant psychological and emotional distress (Akimana et al., 2019). Compared to the adult population, adolescent patients present with different cancer patterns with unique biological and psychological characteristics. In fact, adolescence is already characterized by physical, intellectual, affective, and social changes that are of great importance to individuals [5]. Since puberty itself is a very complex and delicate developmental period, an illness occurring during this time can be a traumatic experience for the child and the entire family, with many short- and long-term psychological consequences [6]. Post-traumatic stress disorder (PTSD) is described as “a psychological disorder that can occur in individuals who have experienced or witnessed a horrific event” (American Psychiatric Association, 2020). When considering the perception of cancer as trauma in adolescence, it is also important to consider the risk factors that may contribute to additional stress and perception of trauma, such as intense and painful treatments, hospitalization, and daily separation from family and friends [7]. In addition, the neoplasm represents another challenge that is added to normal developmental tasks [8]. For example, during the disease, young people experience the side effects of treatment: hair and eyelash loss, surgical scars, weight changes, and eventually persistent fatigue. These threaten the young person’s self-image and self-esteem [9]. Recently, the pandemic COVID-19 has raised additional difficulties. The COVID-19 pandemic has placed a heavy burden on health care systems and has led to widespread disruptions in cancer care. This has led to delays in the diagnosis and treatment of cancer patients [10,11,12]. In addition, cancer patients have a higher risk of experiencing complications due to their weakened immune system and pre-existing conditions related to their cancer and its treatment COVID-19 [13]. The impact of changing dynamics in cancer care is felt by adolescents and young adults (AYAs) aged 15 to 39 years; however, their unique developmental, educational, social, and emotional needs may make them more vulnerable to the negative effects of this pandemic [14]. However, research has shown that there are both negative and positive effects of cancer. Many people who have undergone cancer treatment have reportedly expressed positive feelings about their cancer diagnosis [5]. Post-traumatic growth (PTG) can be defined as the cognitive process undertaken to make sense of a traumatic event by reinterpreting the traumatic event in a positive way [5,7]. The role of coping strategies is fundamental in this process. According to a recent review, knowledge of how children and adolescents cope with chronic illnesses such as cancer has generally improved [15]. Three coping strategies can be distinguished: primary control coping, secondary control coping, and disengagement coping. These strategies are based on the model of perceived control in children and adolescents developed by Weisz and colleagues [16,17,18,19,20]. Problem solving or changing one’s emotional responses to the stressor are examples of primary control coping techniques (e.g., emotional expression and emotional modulation). Attempts to manage stress are referred to as secondary control (e.g., cognitive reappraisal, positive thinking, acceptance). In addition, last but not least, disengagement coping involves attempts to divert attention from the cause of the stress or one’s reactions to it (e.g., avoidance, denial, wishful thinking). Of all the coping strategies, the most important one highlighted in the literature is social support from parents, friends, and health care providers [21], depending on the developmental stage. An important aspect of an adolescent’s life is belonging to a peer group. Close relationships with peers are also an important source of support for chronically ill adolescents at a time when they are coping with both developmental tasks and illness-related challenges. While parents continue to play the role of primary caregivers, friends and peers provide emotional support by accepting their ill friend with his or her physical limitation. ## 2.1. Data Collection This study was conducted by the Soleterre Onlus Foundation, the Trauma Psychology Research Unit of the Catholic University of Milan, and the IRCCS San Matteo Hospital in Pavia to investigate risk and protective factors for quality of life and psychological and relational adjustment in children and adolescents with a cancer diagnosis. The study was approved by the Ethics Committee of IRCCS San Matteo Hospital in Pavia, Italy (prot. no. 20200063196). Minor participants were required to provide informed consent signed by both parents. Adult participants were asked to sign the same informed consent. The survey took place from October 2019 to October 2021, with an interruption during the health emergency due to the pandemic COVID-19. The survey was administered to adolescents with oncological disease by Soleterre staff at the Paediatric Oncohematology Unit of the IRCCS Policlinico San Matteo Foundation in Pavia. The survey of participants in the control sample, on the other hand, began in November 2021 and ended in March 2022. Healthy adolescents were recruited in schools and answered the questionnaire online through the Qualtrics platform. All data collected were entered into a dedicated database, with participants identified only by a unique ID number. The database was stored on a secure server, and access to the information was limited to members of the research team. ## 2.2. Participants Our clinical sample consisted of 31 adolescents and young adults aged 13 to 24 years (18.03 ± 2.799) who had been diagnosed with cancer and were undergoing treatment at the Paediatric Oncohematology Unit of the IRCCS Policlinico San Matteo Foundation in Pavia. The group consisted of $54.8\%$ males and $45.2\%$ females. Most of them ($73.3\%$) were attending secondary school. Of the group, $45.2\%$ reported living with their parents and siblings, $16.1\%$ lived with their parents, and the remaining $38.7\%$ reported living with only one parent (mother or father). The most common diseases represented in the sample were Hodgkin’s lymphoma ($14.3\%$) and acute lymphoblastic leukemia (LLA) ($14.3\%$). Inclusion criteria were both sexes, with an oncological disease, with a good knowledge of Italian, and recruited from the Department and Day Hospital of Oncohematology. Our control group consisted of 47 adolescents aged 11 to 18 years (16.17 ± 2099). It consisted of $36.2\%$ males and $63.8\%$ females. Most of them ($74.5\%$) were attending secondary school. Regarding the clinical sample, most ($68.1\%$) reported living with their parents and siblings, $25.5\%$ lived with their parents, and the remaining $6.4\%$ reported living with only one parent (mother or father). Inclusion criteria were both sexes, without oncological disease, with a good knowledge of the Italian language. ## 2.3. Ethics All study procedures were reviewed and approved by the Ethics Committee of the IRCCS Policlinico San Matteo Foundation in Pavia (protocols no. 20200063196). Informed consent was obtained from all subjects involved in the study. Data were collected in accordance with the principles of the Declaration of Helsinki and in compliance with the IRB ethical guidelines. ## 2.4. Measures The adolescents and young adults were asked to complete the following questionnaires:TRI. Test of Interpersonal Relations (Bracken, 1993; Italian validation by Inaes, 1996): It was designed to assess the adequacy of children’s interpersonal relationships in the social domain, i.e., in relation to peers, in school, in relation to teachers, and in the family in relation to the relationship with parents. It consists of 35 items, each rated on a 5-point Likert scale from “strongly agree” to “strongly disagree”. In this work, we specifically used scales related to adolescents’ perceptions of the quality of their relationship with their mother and father. The questionnaire had good internal consistency (Cronbach’s alpha ranged from 0.93 to 0.96).KIDSCREEN-27 (Italian validation by The KIDSCREEN GROUP, 2004): Allows the assessment of well-being and health related to quality of life. It consists of 27 items and measures five Rasch-scaled dimensions: [1] Physical well-being, [2] Psychological well-being, [3] Autonomy and relationship with parents, [4] Peers and social support, [5] School environment. Each item is scored on a 5-point Likert scale ranging from 1 for “not at all” to 5 for “very much”. Higher scores indicate better quality of life and social support. Construct validity was assessed by calculating Cohen’s effect size (ES = 0.54). The questionnaire had good internal consistency (Cronbach’s alpha > 0.70).Centrality of Events Scale (CES; Berntsen and Rubin, 2006; Italian validation by Ionio, Mascheroni, and Di Blasio, 2018): a self-report measure designed to assess the extent to which the memory of a stressful and traumatic event was central to one’s (a) life history, (b) personal identity, and (c) attribution of meaning to other personal life events. These three factors are assessed using 20 items on a 5-point Likert scale ranging from 1 for “strongly disagree” to 5 for “strongly agree”. The questionnaire has good internal consistency (Cronbach’s alpha = 0.94).Trauma Symptom Checklist for Children (TSCC-A; Briere, 2011; Italian validation by Di Blasio, Piccolo, Traficante, 2011): used to assess post-traumatic stress and related psychological symptoms. This instrument is particularly suitable for assessing children and adolescents aged 11 to 16 years who have experienced traumatic events. Each item is rated on a 4-point Likert scale ranging from 0 for “never” to 3 for “almost always”. We used the 44-item version, which does not include references to sexual issues. The questionnaire consists of the following five clinical scales: [1] Anxiety, which captures general fear, overexcitement, worry, specific fears (e.g., of men, women, or both, of the dark, of being killed), episodes of free-floating fear, and a sense of impending danger. [ 2] Depression: feelings of sadness, unhappiness, and loneliness, episodes of weepiness, depressive cognitions such as guilt and self-denial, and self-harm and suicidality. [ 3] Anger, which deals with angry thoughts, feelings, and behaviors, such as feeling angry, being mean and hating others, having difficulty de-escalating anger, wanting to yell at or hurt people, arguing, or fighting. [ 4] Post-traumatic stress, which captures post-traumatic symptoms such as intrusive thoughts, sensations, and memories of painful past events, nightmares, anxiety, and cognitive avoidance of painful feelings; and [5] Dissociation, which examines dissociative symptoms such as derealization, thought emptiness, emotional numbing, pretending to be another person or place, daydreaming, memory problems, and dissociative avoidance. We chose to use the TSCC-A because our clinical sample was not in treatment, and this may have influenced the results of the Sexual Concerns scale. The questionnaire showed good internal consistency (Cronbach’s alpha = 0.83). ## 2.5. Analysis SPSS Statistics version 27.0 was used to analyze the data. Descriptive analyses were performed first to examine the sociodemographic characteristics of the sample. The t-test for independent samples was used to compare the two groups in terms of psychological well-being and possible traumatic effects of the disease. Pearson correlations were used to examine the association between an appropriate relationship with parents and adolescents’ psychological well-being. ## 3.1. Psychological Well-Being and Effects of the Traumatic Event on Adolescents To examine the psychological well-being of the adolescents in our sample, we converted the raw scores of the KIDSCREEN-27 into T-scores, with a mean of 50 and a standard deviation of 10, based on the parameters of the Rasch person. The results of the descriptive analysis performed show that the mean score of our subjects in relation to oncological adolescents was 48.64 (SD 10.30). In fact, $56.7\%$ of our subjects scored below average for psychological well-being. In the control sample, the average score of our subjects was 50.91 (SD 9.80). Only $44.5\%$ of our sample had below average scores for psychological well-being. We then compared the scores of our two samples using independent samples t-tests and found no significant differences in psychological well-being (t = −0.954; $$p \leq 0.344$$; gl = 60.111). Using the Trauma Symptom Checklist for Children, we then specifically examined anxiety, depression, anger, post-traumatic stress, and dissociation. Most subjects in the clinical sample were within the normal range on all five clinical scales (anxiety $87.1\%$; depression $96.8\%$; anger $90.3\%$; PTS $83.9\%$; dissociation $83.9\%$). However, a small portion of the sample fell within the range of clinical concern (Anger $9.7\%$; PTS $12.9\%$; Dissociation $12.9\%$). The results of the control sample show that most of them fell within a normal range (anxiety $89.4\%$; depression $85.1\%$; anger $80.9\%$; PTS $87.2\%$; dissociation $80.9\%$). In contrast, a small proportion of the sample had clinically worrisome scores (depression $8.5\%$; anger $12.8\%$; PTS $10.6\%$; dissociation $10.6\%$). We then compared the results of our two samples of adolescents using independent samples t-tests and found a significant difference on the clinical depression subscale of the TSCC (t= −2.092; $$p \leq 0.040$$; gl = 69.752). To examine the centrality of the traumatic event (oncologic disease) in the adolescents’ lives, we converted the raw scores of the CES into T-scores, with a mean of 50 and a standard deviation of 10, based on the Rasch person parameter. For oncology adolescents, the mean score of the first subscale (Factor I) was 53.36 (SD 8.98). Most of the sample ($75.8\%$) scored above average on the impact of traumatic memories on daily life (Factor I). The average score of these adolescents in the second subscale (Factor II) was 55.86 (SD 8.11). $72.2\%$ of the oncology sample scored above average on the impact of traumatic memories on personal identity (Factor II). In addition, their mean score on the third subscale (Factor III) was 54.39 (SD 9.88). $68.8\%$ of the clinical sample scored above average on the impact of traumatic memories on life perspective. The average total score (impact of traumatic memories) of our adolescents was 55.24 (SD 8.42), and $79.6\%$ of the sample scored above average. In contrast, the average score of our subjects in the control group in the first subscale (Factor I) was 47.88 (SD 10.11). Most of the sample ($52\%$) scored below average on the impact of traumatic memories on daily life (Factor I). The average score of these adolescents on the second subscale (Factor II) was 46.30 (SD 9.34). $69.7\%$ of the adolescents scored below average on the impact of traumatic memories on personal identity (Factor II). ( Factor II). In addition, their mean score on the third subscale (Factor III) was 47.23 (SD 9.13). $60.8\%$ of our adolescents scored below average on the impact of traumatic memories on life perspective. The average total score (impact of traumatic memories) of our adolescents was 46.69 (SD 9.55) and $69.5\%$ of them scored below average. We then compared the results of the two samples using independent samples t-tests. As shown in Table 1, the results showed significant differences between the two groups for all subscales and the total scale. ## 3.2. Effects of the Traumatic Event on the Family System To examine the adequacy of the relationship between adolescents with cancer and those without cancer and their parents, we used the Interpersonal Relationship Test (TRI). In the oncology sample, most adolescents had an average relationship with both their mother and father ($41.9\%$ and $51.6\%$, respectively). Of the sample, $38.7\%$ had a positive relationship with their mother, while only $16.1\%$ had a positive relationship with their father. In the control group, most adolescents had an average relationship with their mother and father ($36.2\%$ and $44.7\%$, respectively). Of the control group, $27.7\%$ of adolescents had a positive relationship with their mother and $29.8\%$ with their father, while $19.1\%$ of adolescents had a negative relationship with both. The results of descriptive analysis of TRI are shown in Figure 1. In addition, the results of the correlation analyses performed show that there is a significant positive relationship between the adolescents’ psychological well-being (Kidscreen-27) and their relationship with their parents (TRI) (mothers: $r = 0.796$; $p \leq 0.01$; fathers: $r = 0.692$; $p \leq 0.01$). ## 4. Discussion The purpose of this study was to examine psychological well-being and parent-child relationships in adolescents and young adults with cancer compared with healthy peers, focusing on how cancer is perceived as a traumatic event. ## 4.1. Psychological Well-Being and Effects of the Traumatic Event on Adolescents While research on psychological well-being and quality of life during cancer treatment has focused primarily on childhood cancer survivors [22], studies examining adolescents’ and young adults’ psychological adjustment during treatment have yielded inconsistent results. The results of analyses conducted on our sample of adolescents and young adults partially confirm the findings of the recent literature. A significant proportion of our clinical sample exhibited below-average psychological well-being, indicating a lack of positive emotions and life satisfaction, as well as a low experience of positive emotions such as happiness, joy, and cheerfulness. In addition, a small but still consistent proportion of adolescents fell into the range of clinical concerns for anger, PTS, and dissociation. These findings appear to be consistent with the work of Briere [23], suggesting that there are many different maladaptive outcomes that can occur as a result of cancer. However, contrary to expectations, we found no significant differences between the psychological well-being of adolescents with cancer and that of their peers, with the exception of depression. Some studies suggest that the majority of these patients do not have significantly higher levels of anxiety or depression compared with their peers [24]. However, a minority of them (17–$30\%$) were found to have symptoms of depression and anxiety [25]). There are several possible explanations for the lack of significant differences in psychological well-being between our samples of adolescents and AYA. First, the samples are unfortunately not perfectly balanced in terms of age and size. In addition, previous research has found that youth resilience increases as a result of their trauma exposure, leading to better adjustment during the process [26]. Indeed, cancer may have led to post-traumatic growth in these adolescents, attenuating the differences between the two samples. In addition, we need to consider that the COVID-19 pandemic also has implications for the mental health of adolescents and young adults whom we consider “healthy”. Healthy adolescents and young adults may have reported more symptoms during this time because COVID-19 affected their lives more than patients who are already accustomed to being isolated because of their illness. Finally, our findings underscored the central role of cancer as a traumatic event in the personal identity and life history of adolescents compared with peers, suggesting that this event may have maladaptive psychological consequences that are also felt in the medium and long term. These findings are consistent with previous research [27,28] reporting how many adolescents and AYA described cancer or cancer-related events as the most stressful and traumatic experiences of their lives. These data highlight the importance of mental health professionals paying attention not only to the physical health but also to the mental health of these young patients who are facing the disease at such a sensitive time in their development. ## 4.2. Effects of the Traumatic Event on the Family System The literature suggests that adolescent illness can lead to withdrawal and ambivalence toward parents, while parents remain an important protective factor during this sensitive life event [7]. The results of our clinical sample seem to confirm these findings, as most adolescents and young adults have an adequate relationship with their parents. The fact that the relationship with the mother is more positive could be due to her role as the main caregiver. Indeed, the mother often takes time off from work or chooses a part-time solution so that she can devote all her time to her child [29]. As expected, most healthy adolescents and AYA had an average relationship with both parents. The latter could be explained by the ambivalence typical of this stage of life, as adolescents begin a process of emancipation from the family to gain autonomy and independence [5]. Moreover, our correlation analysis revealed a positive association between adolescents’ psychological well-being and their relationship with their parents, suggesting that parental support may help mitigate the negative effects of cancer and positively influence adolescents’ well-being [30,31]. In addition, separating these young patients from their families due to hospitalization at a time when they are very vulnerable may contribute to increasing their dependence and desire for closeness with parental caregivers, which is not usually the case at this developmental stage [8]. These findings shed light on the protective role of parental involvement and support for adolescents and young adults diagnosed with cancer and underscore the importance of parental involvement in treatment by health care providers. ## 5. Limitations Despite the significant results, we cannot overlook some limitations of the present study. First, our samples were quite small, and not perfectly balanced in terms of age or size. Further studies are needed to examine differences and potential risk factors related to psychological well-being in adolescents and young adults with cancer compared with their peers. In addition, it is imperative to keep in mind that these data were collected during the COVID-19 pandemic. Therefore, as mentioned earlier, it is important to recognize that healthy adolescents and young adults were also psychologically affected by the pandemic. They may even have experienced more symptoms than those who were already accustomed to disease-induced isolation, which may have partially influenced our results. ## 6. Conclusions The research hypotheses of the present study were well supported by the analysis performed and, despite the current limited sample size, provide important support for what is already known on this topic. Our findings highlight that cancer in adolescence may represent a key traumatic event that can shape the identities and lives of teens who are at an inherently delicate and vulnerable stage of life. These findings underscore the importance of timely and targeted intervention by clinicians and therapists. As we mentioned earlier, hospitalization often causes these teens to feel isolated from friendships and the peer group at a developmental stage when peers play a critical role. For this reason, it can be beneficial for teens and young adults to be part of a support group where they can meet others who are going through similar experiences, share their problems, and find comfort in knowing that they are not alone. In addition, the hospital school could help maintain that sense of normalcy during a difficult time in their lives. The opportunity to attend school and interact with peers may help adolescents maintain social skills and avoid feelings of isolation. In addition, our findings suggest the protective role of parents in mitigating the psychological consequences of the disease. These findings provide useful guidance for designing interventions that involve parents and target the well-being of the entire family system, not just the individual adolescent. 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--- title: Design and Modelling of Graphene-Based Flexible 5G Antenna for Next-Generation Wearable Head Imaging Systems authors: - Asad Riaz - Sagheer Khan - Tughrul Arslan journal: Micromachines year: 2023 pmcid: PMC10056467 doi: 10.3390/mi14030610 license: CC BY 4.0 --- # Design and Modelling of Graphene-Based Flexible 5G Antenna for Next-Generation Wearable Head Imaging Systems ## Abstract Arguably, 5G and next-generation technology with its key features (specifically, supporting high data rates and high mobility platforms) make it valuable for coping with the emerging needs of medical healthcare. A 5G-enabled portable device receives the sensitive detection signals from the head imaging system and transmits them over the 5G network for real-time monitoring, analysis, and storage purposes. In terms of material, graphene-based flexible electronics have become very popular for wearable and healthcare devices due to their exceptional mechanical strength, thermal stability, high electrical conductivity, and biocompatibility. A graphene-based flexible antenna for data communication from wearable head imaging devices over a 5G network was designed and modelled. The antenna operated at the 34.5 GHz range and was designed using an 18 µm thin graphene film for the conductive radiative patch and ground with electric conductivity of 3.5 × 105 S/m. The radiative patch was designed in a fractal fashion to provide sufficient antenna flexibility for wearable uses. The patch was designed over a 1.5 mm thick flexible polyamide substrate that made the design suitable for wearable applications. This paper presented the 3D modelling and analysis of the 5G flexible antenna for communication in a digital care-home model. The analyses were carried out based on the antenna’s reflection coefficient, gain, radiation pattern, and power balance. The time-domain signal analysis was carried out between the two antennas to mimic real-time communication in wearable devices. ## 1. Introduction In the modern era, communication technology has reached its peak, whereas the delivery of ultra-high peak data speeds, lower latency, enhanced network efficiency, high transmission reliability, data integrity, massive network capacity, users flexibility, and a more uniform availability to the users has brought a revolution in wireless technology [1,2]. This led to the tremendous innovation of 5G wireless technology which established an innovative unified network for universal connectivity of everyone and everything including people, machines, and devices. Thus, 5G is connecting the world in a universal Wi-Fi zone in the next decade. The user traffic is expected to burst to 10,000 times more compared to the current traffic, as millions of new devices will be connected using a 5G network. The United States (US) agency of the Federal Communications Commission (FCC) adopted frequencies ranging from 28 to 38 GHz for 5G standards that fell under the Ka-band, promising a lower rate of absorption, reduced path-loss, and minimal signal fading [3,4]. Similarly, in the U.K., the Office of Communications conducted 5G test beds on 26 GHz [5]. According to the next generation mobile network (NGMN) white paper [2], the latency in a two-way end-to-end (E2E) access is 10 ms in dense areas or 50 ms in areas influenced by environmental factors such as core networks (CNs), proxy servers, etc. The 5G deployment was started and based on two types, i.e., standalone and non-standalone. Most of the current deployments are non-standalone, which is the first stage of 5G deployment. At this stage, a new 5G network radio utilizes the existing 4G long-term-evolution (LTE) radio access and core networks (CNs) for mobility and coverage management of the connected devices [6]. ## 2. Latest Healthcare Applications In healthcare, 5G is mostly used for remote diagnosis, long-term monitoring, remote intervention, etc. There are numerous emerging applications of the 5G technology in healthcare’s robot-assisted remote surgeries, augmented reality (AR) assisted care services, hospital logistics automation, remote patient care, etc. [ 7]. Some of the up-to-date advancements are as follows. The above discussion provides a deep insight into the potential applications and advantages of 5G in healthcare. The usage of 5G in wireless communication, remote patient diagnosis, and continuous remote monitoring gained a growing interest in telemedicine [22,23,24]. The basic concept of telemedicine is that the patient’s biological parameters are monitored using multiple sensors. These sensors can be both on-body or implanted. The signals from these sensors are collected by receivers such as a mobile phone or a PC, whereas signals recorded are transmitted to diagnostic centres, doctors, or clinical facilities [25,26]. The idea of telemedicine is very beneficial as many lives can be saved by real-time monitoring and communication of sensitive patient data [27,28]. Several efforts were made in this area [29]. However, these solutions are still far from being adopted in routine medical investigations nowadays. The implementation technology still needs maturity, whereas many constraints that are limiting clinical implementation remain. ## 3. Flexible Electronics and Portable Systems Flexible electronics generally refers to the class of electronic devices built on stretchable comfortable substrate materials, e.g., mostly plastics but also metal foils, flex glass, and paper as well. Flexible electronics received great attention in the past decade because of their potential to revolutionize human lives. Flexible electronics have whole application sectors including, telecom, solar cells, logic memories electronics, flexible sensors, displays, and medical devices. Apple inc. is on the verge of releasing some revolutionary wearable electronics, which will be more stylish, lightweight compatible, and mechanically durable including flexible wearable smart watches and smart bracelets [30]. Other such applications include the integration of ferroelectric oxide-based into flexible devices for very sensitive applications such as eyeglasses, 3D printing technology, and smart eardrop applications [31]. Similarly, daily devices such as cell phones, flexible smart watches, flexible smart bracelets, smart laptops, and computers need flexible wireless electronics. By 2023, it is estimated that the market share of flexible electronics will reach 40 billion [32,33]. Various flexible conductive materials are used for wearable applications purposes depending upon their dielectric properties, electrical conductivity, mechanical strength, accommodation to miniaturization, tolerance, etc. [ 34], including polymers, polyesters, and textiles, etc. [ 35,36]. Metal nanoparticles such as Ag (conductivity of 2.1 × 107 S/m), and Cu (×106 S/m) are used for designing flexible and stretchable electric circuits [37,38]. C additive polymers and their additives including polyaniline (PANI) [39], polypyrrole(PPy) [40], and C nanotubes [41] have been deployed with low to medium conductivity values. Graphene is becoming very popular for flexible electronics because of its extraordinary properties including exceptional mechanical strength, high thermal stability, high electrical conductivity, and biocompatibility. Graphene-based materials such as nano-flakes (6 × 105 S/m) [42], graphene paper (4.2 × 105 S/m) [43], and graphene-based fabric (2 × 105 S/m) [44] have relatively good conductivity values and were, thus, used for flexible structure based antennas and wearable electronics. Portable imaging systems are wearable anytime anywhere and are easy-to-move devices, which are contributing a great deal to the early detection and continuous monitoring of numerous fatal diseases such as strokes, heart diseases, neurodegenerative diseases, diabetes, arthritis, Alzheimer’s disease, etc. Currently used imaging systems widely used in medical healthcare such as CT scans, MRI, ultrasound, and X-rays are very bulky, costly, and not accessible in rural areas. These bulky systems are not movable to specific locations for diagnosis purposes [45]. Portable and wearable health-monitoring devices are the only solution to minimize the distance between the patient and the physician. These portable devices can be used for monitoring patients with critical diseases for long periods of time, and the data collected must be processed, stored and analyzed. Wearable devices used for medical diagnostics purposes need high bandwidths specifically for real-time patient monitoring. In [46,47], the authors detailed the possibility of adding some extra features to the 5G architecture in the future. These would enhance the communication bandwidth, solve addressing issues, and security improvement. The wireless integration of 5G into portable devices would allow the establishment of high-capacity, unified networks for versatile applications with the added advantages of device compactness, flexible structures, and resourceful fabrication [48]. It will increase the patient’s comfort through highly efficient and seamless collection and communication of the patient’s data [49]. Similarly, 5G technology helps in establishing a personalized database of individual patients based on personalized physiological indicators for the prevention and treatment of diseases. Nonetheless, the use of 5G makes imaging technology more sustainable by continuously collecting and analyzing the data and sharing it with the patient’s friends and family, doctors, etc. This keeps the patient self-motivated to implement the treatment in time while maintaining a good mood [50]. Thus, the 5G integration into medical diagnostic systems makes it more cost-effective in two ways, firstly by preventing the users from getting the disease in the early stage by continuous monitoring and secondly by providing out-of-hospital treatment, thereby reducing the on-the-spot treatment cost and long-term hospitalization cost [51]. The feasibility of using flexible electronics for a portable device is that these are based on organic and inorganic nanostructured materials [52], thereby providing the exact geometrical and performance features as required by a specific application [53]. Due to the nano-structure-based structure of the flexible materials, they can help design flexible devices on textile materials such as paper, silk plastic, etc. [ 54], with high mechanical flexibility and strength. An added advantage is that flexible materials are mostly inexpensive, specifically organic materials [55]. One such example is high-performance organic crystalline materials (OCMs) [56], which are widely used in the advanced electronics of the current era such as displays, image detection sensors, and flexible electronics-based artificial skin [57]. They not only provide good flexibility but also demonstrate excellent molecular diversity because of their nanoscale structures resulting in minimal gain defects required for smooth and uniform characteristics across the whole device structure [58]. The fabrication processes of flexible electronics, such as solution processing, inkjet printing, and even roll-to-roll, are inexpensive and easy to implement [59]. Flexible electronics have a low environmental impact and are biocompatible, especially organic flexible materials that are biodegradable and, thus, easily disposable after use [60]. In recent years, flexible antennas gained popularity for mm-wave based 5G architecture in different applications including cellular, vehicular, and wearable portable electronics, etc. [ 61]. There exists a shared interest among government departments, federal agencies, corporations, industries, and academia, in developing flexible antennae for deployment in extreme conditions. A few high-temperature applications include developing flexible antennas for monitoring the H2 safety in high-temperature gas-cooled reactors, development of communication solutions for non-line-of-sight (NLoS) communication in unmanned aircraft NASA, etc. A recent extensive study previewed the materials used for the flexible antennas, their fabrication methods and processes as well as their applications [62,63,64,65,66,67]. In [62], a practical report was presented in which the fabrication processes of both the textile and non-textile-based antennas were described. In [63,64], a comprehensive survey of different materials used for the fabrication of flexible antennas in the frequency range of mm-wave to very high frequencies was presented. In [65], the flexible antennas were covered in detail, focusing on the materials and fabrication techniques along with specific applications and limitations. The wearable flexible antennas operating in ultra-wide band (UWB) for frequencies ranging from 3 GHz to 10 GHz were detailed in [66] along with their applications in the wireless body area network (WBAN) systems. A survey on the various types of implantable antennas, their specific design requirements for specific applications, and performance analysis of different implantable antennas was presented in [67]. The choice of flexible wearable antennae for wireless applications depends mainly upon the channel characteristics of the wireless environment, the transmission capability of the channels as well as the operating frequency [68]. In addition, the major factors defining the antenna performance include the type of flexible material used, the fabrication technique as well as the electrical, and mechanical properties, and the physical geometry of the antenna. The substrate material for the antenna is chosen based on the material’s minimal dielectric loss, low relative permittivity, and low value of the thermal coefficient of expansion as well as high thermal conductivity [69]. A tradeoff between the antenna size and increased efficiency is taken for meeting these constraints. There are three types of substrates used for the fabrication of flexible antennas including thin glass, polymers, and metal foils [70]. Polymers or plastic-based materials are becoming very popular for flexible antennas because of their robustness, flexibility, wettability, and stretchability. One such example is Kapton polyimide with a dielectric constant of 2.91, a loss tangent of 0.005, and high transition temperature (Tg), which is one of the most preferred substrate materials, for flexible antennas in the previous literature [71,72,73,74,75]. The authors in [76] reported a flexible, washable fully textile-based antenna which was wearable and reusable operating at frequency bands between from 3 to 20 GHz. The authors had undertaken the design and analysis of the antenna for smart garments and wearable medical monitoring purposes in detail. For instance, flexible graphene-based antennas and arrays were designed using the flexible polyimide substrate with a wide bandwidth, operating at 15 GHZ [77]. This antenna supported high-speed transmission; however, the bandwidth was limited and not sufficient for large data applications such as portable health monitoring systems in telemedicine. This problem can be solved by designing a wearable flexible antenna for 5G frequency ranges between 26 and 60 GHz. In another study, a flexible wideband slotted monopole antenna was designed for a millimeter wave range [78]. The antenna exhibited an ultra-high bandwidth of 26 GHz, i.e., from 18 to 44 GHz; however, the radiation efficiency was limiting at $55\%$ and the gain maximum value was 1.45 dBi. Similarly, in [79], a comparison was made between CPW-fed antennas upon PET and Epson paper at 20 GHz. In [80], the modelling of flexible coplanar waveguide fed (CPW) antennas over the 5G range of 23 to 29.5 GHz was performed. The substrate material utilized was transparent PET. An LCP substrate-based proximity antenna for the 24 GHz range was designed using inkjet-printing technology [81]. These antennas operated at different frequencies owing to the fact that these were reconfigurable antennas. Another reconfigurable wearable antenna operating at 20.7–36 GHz was designed using inkjet printing [82]. This antenna incorporated various switch configurations for operation purpose. A flexible micromachined patch antenna operating at 60 GHz was designed using the PDMS substrate for assessing this technology with other market-available technologies [83]. A high-gain flexible mm-wave antenna was designed with a peak gain of 11.35 dBi at 35 GHz [84]. This antenna was unique in a way as it maintained a consistently high gain of above 9 dBi over the complete Ka-band. Another study proposed the design and analysis of an electromagnetic bandgap (EBG) based mm-wave MIMO antenna at 24 GHz [85]. The authors in this paper presented the design of a flexible Rogers-based substrate for on-body wearable applications with bending ability. Plenty of research was published on flexible antennas over UWB and lower frequency ranges, whereas a small amount of literature was present on flexible antennas at mm-wave spectrum for integration of 5G and beyond technologies for the healthcare portable wearable systems [86]. Thus, a need for a high bandwidth antenna arises that could be used for 5G front ends in telemedicine systems, which communicate continuous and real-time monitoring data over the 5G frequency spectrum with good efficiency and gain values. The antenna should be flexible enough to be integrated into flexible wearable devices. In order to minimize the attenuations of the antenna, gain should be optimized in comparison to the bandwidth due to the gain-bandwidth tradeoff. The motivation behind this paper is to design a novel flexible antenna for 5G communication in Ka-band, for telemedicine purposes. This antenna will enable the wearable portable device to perform the real-time monitoring of the patient and communicate the collected data over the 5G frequency band to the desired location for remote analysis purposes. The antenna has a graphene-based radiating patch on a Kapton polyimide substrate that makes it flexible and wearable. The antenna is simulated and analyzed for individual performance in free space, and the time domain signal analysis for the front-to-front antenna’s communication. The antennas were analyzed for their performance and their ability to operate in a stationary care-home model for telemedicine purposes. The analysis was carried out in the presence of a human model in the home-care scenario by keeping the antenna in line-of-sight (LOS) and non-line-of-sight (NLOS) at variable distances, positions, and locations from each other. ## 4. Antenna Design The antenna design was carried out using the CST Microwave Studio software. Flexible substrate Kepton HN polyamide with a thickness of 1.5 mm was used to make the structure flexible and strong with a dielectric strength rating of 7700 V/mil for a 0.001 thick film and a tensile strength of 221 MPa. The antenna was fed using coaxial feeding, i.e., the inner conductor of the coaxial cable was soldered into the radiating patch, extending through the dielectric, while the outer conductor was attached to the ground. This technique helped in matching the cable impedance with the input impedance of the antenna by placing the feed at any suitable location on the patch. This technique was easy to fabricate with minimum spurious radiation. Its major aim was to enhance the antenna gain, narrow bandwidth, and impedance matching [87]. The central strip of the coaxial cable had a 4.88 mm width which was equivalent to the 50 Ω impedance line for this material. The patch was designed using a fractal structure that increased the conductive path without affecting the antenna’s radiation properties. The antenna front view is shown in Figure 1 below. The antenna was made of an 18 µm thin graphene film for the conductive patch and ground with electric conductivity of 3.5 × 105 S/m, as shown in Figure 1a. The radiative patch was designed in a fractal geometry that brings many benefits over a plane structure. The fractal patch made the design operate consistently over a wideband with a bandwidth of 14 GHz with faster communication. The fractal design allowed instantaneous spectrum access, which meant a single antenna could be used instead of many. The fractal structure survived in the harshest conditions which made it very useful for frequently used flexible wearable portable devices. The substrate was designed over a 1.5 mm thick Kapton-based flexible polyamide material with a permittivity of 3.5 F/m, as shown in Figure 1b. The design dimensions are summarized in Table 1 in detail. The width of the feedline was kept at 4.88 mm to obtain an optimal value of radiation efficiency ($62\%$) and gain (9.1 dB at 33 GHz) which decreased as the width was increased. A tradeoff was made between the height of the substrate (1.575 mm) and a minimum graphene patch height (0.018 mm) which helped in increasing the radiation efficiency as well as maximizing the antenna’s bandwidth at around 14 GHz. The patch fractal geometry consisted of a larger triangle and ten smaller triangles with side lengths in a ratio of 1 to ½, i.e., 6.6 mm to 3.3 mm designed in a symmetric combination to provide radiation efficiency and structural flexibility. ## 5. Simulation Results In this section, the simulation results of the proposed antenna are presented in detail. CST Microwave Studio by Dassault Systèmes’ was used for our design which was based on the finite integration technique (FIT) in the time domain. The graphene-based patch elements were embedded into the fractal geometry to maintain optimal antenna performance. The antenna geometry was optimized for analysis in terms of return loss, voltage standing wave ratio (VSWR), and gain of the antenna. The key performance parameters of the design were described as follows. ## 5.1. Return Loss (S11) and Voltage Standing Wave Ratio (VSWR) Figure 2 shows the return loss of the antenna which is represented by the scattering parameters (S-parameters) graph. When the antenna was energized, some of the antenna power was transmitted while another part of the input waves was reflected or lost in the environment. The value of return loss shows the number of reflected waves in the antenna structure. This reflection occurred due to many factors both inside the antenna structure and because of environmental factors. The two main reasons that caused the return loss to occur include (a) discontinuities at connections and (b) impedance mismatches. The value of the return loss of the antenna should be less than −10 dB for proper operation. This is because at this value the antenna’s VSWR will be >2 which means that around $30\%$ of the transmitted power will be reflected, whereas $70\%$ of the input power will be transmitted successfully. The simulated return loss of the proposed antenna had the minimum value at −38.9 dB at 34.6 GHz resonant frequency, as shown in Figure 2. The antenna had a wide operating bandwidth of around 14 GHz that ranged from 27.3 GHz to around 41.5 GHz, with S11 below −10 dB. The practical influence of the return loss on telemedicine is that lower the value of return loss, higher will be the power transmission towards the receiving end in the telemedicine system. ## 5.2. Voltage Standing Wave Ratio (VSWR) VSWR is a measure of the amount of mismatch between the antenna and the feedline connecting to it. The smaller the value of VSWR, the better the antenna is matched the transmission line and more power is transmitted. For an antenna to operate with a good power transfer value, the antenna’s VSWR should be <2 [88]. The graph in Figure 3 shows the value of VSWR of the antenna with values < 2 for the frequency range of 27.2 GHz < f < 41.5 GHz, which confirmed that the proposed antenna will operate efficiently between the range of 27.3 GHz and around 41.5 GHz. ## 5.3. The Practical Influence on the Telemedicine System The practical influence of the return loss and VSWR on telemedicine is that the lower the value of return loss and VSWR, the higher the power transmission towards the receiving end in the telemedicine system will be. In this design, the return loss value of −38.9 dB and a VSWR of 1.02 at 34.6 GHz resonant frequency results in a small value of Reflection co-efficient (Γ) of 0.011. This shows that a good amount of power was transmitted towards the receiving end. Similarly, the reflected power was only a minor percentage, i.e., $0.013\%$, with a high transmitted power of $99.987\%$. In a nutshell, the overall mismatch of the antenna was a minute value of 0.0056 dB. ## 5.4. Radiation Pattern, Directivity, and Gain The gain of an antenna represents its ability to radiate less or more in any direction compared to the hypothetical antenna. For example, if, in theory, an antenna can be made perfectly spherical, it would radiate equally in all directions. The higher the gain of the antenna, the better the antenna’s ability to transmit in a particular direction. Similarly, the directivity of an antenna shows the power density of an antenna in the maximum radiation direction concerning the average power density of the antenna in all directions. The higher the antenna’s directivity value, the better the radiation concentration in a particular direction and, therefore, the farther the radiation beam will travel. Figure 4 shows the 2D pattern representing the directivity and gain of the flexible antenna. Figure 4a shows that the maximum gain of the radiator increased from 27 GHz to 41 GHz with the highest value of 8.89 dB at 33 GHz. Figure 4b shows the maximum value of directivity at 9.51 dB. ## 6. Time Domain Communication Analysis A front-to-front antenna model is presented in Figure 5a, in which the two prototypes of the flexible 5G antennas were simulated and the time domain communications were analyzed. The return loss value of −35 dB shows that there was a good amount of power transfer between the antennas, as shown in Figure 5b. The communication bandwidth was a wide bandwidth of around 7 GHz ranging from 29 GHz to 36 GHz. This made our design a good candidate for use in telemedicine, which is simulated in the form of a care-home model in the next section. The graph comparison of various input and output signals is shown in Figure 6a,b as follows. ## 7. Care-Home-Model in CST for Telemedicine Simulation Today’s era can truly be called the era of data-centric computing with petabytes of data generated every day. The Internet of Things (IoT) caused a revolution in smart-health-care research by connecting humans and devices [89]. The authors in [90] proposed a new name for this research area: the Internet of Health Things (IoHT). Innovative data processing models and tools are the need of the day for the collection, storage, and processing of the increased amount of data from IoTs and information sources. Big data analytics (BDA), on the other hand, has left both the public and private healthcare sectors with a massive amount of data that they never had access to before [91]. AI integration into the healthcare sector is increasing gradually in order to cope with the global challenges in the healthcare sector including the increasing ageing population [92], shortage of medical healthcare staff [93], and soaring costs [94]. Patients suffering from chronic diseases such as heart diseases, neurodegenerative diseases, diabetes, arthritis, Alzheimer’s disease, brain and heart strokes, Parkinson’s disease, etc., need early detection and continuous monitoring. The currently available detection and monitoring devices are not only static and bulky but costly as well. Therefore, portable and flexible wearable healthcare devices equipped with 5G communication technology are the need of the day. CST Microwave studio was used to design a digital model for a home-care scenario with the flexible 5G antenna placed in the two different rooms. Figure 7 shows that Antenna.1 was placed in a portable wearable device on the patient’s head, whereas antenna.2 was placed at the receiving end in a doctor’s room with a brick wall separation between the two antennas. The digital model was created in SolidWorks (properties of each item in the model were assigned in SolidWorks), and imported into the CST studio for simulation purposes. The human/patient model carried the properties of the human skin available in CST studio, the table at the receiving end was made of wood, and the wall was made of bricks all with different dielectric properties to imitate a real-case scenario. The dimensions of VHCM are summarized in Table 2 and in Figure 8 as follows. The room dimensions were kept at 7.7 m × 5.5 m × 3.3 m. The human model height was taken as 1.6 m, i.e., an average height of a normal human being. The two antennas were kept 3 m apart from each other in separate rooms mimicking both the LoS and NLoS scenarios. The table size was taken as 0.75 m × 1.5 m. The thermal resistance (R) of the brick wall used was 2.63 and the thermal transmittance (U) was 0.38. The permittivity of the room wall was 3.56, whereas the permeability of the brick wall was $0.79\%$. The value of return loss obtained after the simulation not only depended upon the dielectric properties of the objects present but also upon the position of the patient and antennas both at the transmitting and receiving end. Figure 9 shows the return loss and power values when the two antennas were facing front-to-front in line-of-sight (LoS) in the home care model. Figure 9a shows a good power transfer with the S11 of −51.4 dB, whereas Figure 9b shows that the power transfer between the antennas shoots to a maximum after 27 GHz of frequency and stays at its maximum value till 42 GHz. Both of these graph depict that a good value of power was transmitted from the transmitter antenna to the receiver antenna in the line-of-sight (LOS) communication in the virtual home care model (VHCM). A comparative analysis of the return loss of the LOS versus NLOS communication was performed in Figure 10. Figure 10a shows that the S11 of the antennas placed in the NLOS was almost the same value of −55.5 dB as compared to the S11 of the antennas in the LOS situa, as shown in Figure 10b. Thus, the simulation shows that the proposed antennas performed equally well when they were simulated in different scenarios of LOS and NLOS in the VHCM. ## 8. Conclusions The fusion of 5G into healthcare systems has a huge implementation potential both for the public and private healthcare sectors. Unobtrusive mm-wave communication is an effective method for collecting and transmitting useful data from healthcare devices. This research focused on the design and integration of flexible 5G antennae for wearable portable devices used for healthcare purposes. Firstly, the time domain signal communication of the two front-to-front antennas in free space was analyzed and optimized. Secondly, the antennas were analyzed for their performance and their ability to operate in a stationary care-home model for telemedicine purposes. The analysis was carried out in the presence of a human model in the home-care scenario, by keeping the antenna in line-of-sight (LOS) and no-line-of-sight (NLOS) at variable distances, positions, and locations from each other. The analysis showed that data transmission between the transmitting and receiving antennas was varied by the antenna’s position, location, and distance. The value of return loss remained almost the same in both the LOS and NLOS scenarios. However, the power transmitted was maximum in the LOS scenario with a minimum distance (in our case) in between the two antennas. Future work should aim to fabricate the 5G flexible antenna and verify the results in a real-time scenario. 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--- title: Nasal Bacteriomes of Patients with Asthma and Allergic Rhinitis Show Unique Composition, Structure, Function and Interactions authors: - Marcos Pérez-Losada - Eduardo Castro-Nallar - José Laerte Boechat - Luis Delgado - Tiago Azenha Rama - Valentín Berrios-Farías - Manuela Oliveira journal: Microorganisms year: 2023 pmcid: PMC10056468 doi: 10.3390/microorganisms11030683 license: CC BY 4.0 --- # Nasal Bacteriomes of Patients with Asthma and Allergic Rhinitis Show Unique Composition, Structure, Function and Interactions ## Abstract Allergic rhinitis and asthma are major public health concerns and economic burdens worldwide. However, little is known about nasal bacteriome dysbiosis during allergic rhinitis, alone or associated with asthma comorbidity. To address this knowledge gap we applied 16S rRNA high-throughput sequencing to 347 nasal samples from participants with asthma (AS = 12), allergic rhinitis (AR = 53), allergic rhinitis with asthma (ARAS = 183) and healthy controls (CT = 99). One to three of the most abundant phyla, and five to seven of the dominant genera differed significantly ($p \leq 0.021$) between AS, AR or ARAS and CT groups. All alpha-diversity indices of microbial richness and evenness changed significantly ($p \leq 0.01$) between AR or ARAS and CT, while all beta-diversity indices of microbial structure differed significantly ($p \leq 0.011$) between each of the respiratory disease groups and controls. Bacteriomes of rhinitic and healthy participants showed 72 differentially expressed ($p \leq 0.05$) metabolic pathways each related mainly to degradation and biosynthesis processes. A network analysis of the AR and ARAS bacteriomes depicted more complex webs of interactions among their members than among those of healthy controls. This study demonstrates that the nose harbors distinct bacteriotas during health and respiratory disease and identifies potential taxonomic and functional biomarkers for diagnostics and therapeutics in asthma and rhinitis. ## 1. Introduction Asthma is a chronic inflammatory disorder of the airways induced by complex interactions between the environment and the individual’s genetic and clinical background [1,2]. The onset of asthma results in airway inflammation and mucous production with bronchial obstruction and hyperresponsiveness [3,4,5]. Asthma is a global economic burden with high direct and indirect medical costs [6,7]. It affects people of all ages, being the most common chronic disease among children [8,9]. Over 300 million patients worldwide have been diagnosed with asthma, corresponding to more than 495,000 deaths per year [3,8,10,11]. In Portugal there are almost 695,000 individuals with asthma, corresponding to a prevalence of $8.4\%$ in children and adolescents and $6.8\%$ in adults [12,13,14]. Allergic rhinitis is also a common chronic airway disease worldwide with a substantial economic impact mainly attributable to prescription medications [7,15]. Allergic rhinitis refers to nasal symptoms resulting from inflammation or dysfunction of the nasal mucosa caused by an increase in Th2 cytokines that interfere with the nasal epithelial barrier integrity [16,17,18]. Allergic rhinitis is diagnosed by the observations of its typical symptoms (i.e., rhinorrhea, nasal obstruction, sneezing and nasal pruritus) and the demonstration of IgE-mediated sensitization to aeroallergens [19]. Nearly 400 million people suffer from allergic rhinitis worldwide [20]. In Portugal allergic rhinitis has a prevalence of 9–$10\%$ in children and adolescents and $26.1\%$ in adults [12,21,22]. Over the last years evidence has been accumulating on the association between asthma and rhinitis [23,24,25,26]. They appear to be interrelated at the epidemiologic and pathophysiologic levels [23,27,28], and when co-existing in the same patient, asthma prevalence and severity are increased by allergic rhinitis [19,29]. In Portugal more than $46\%$ of the patients with asthma also present allergic rhinitis, which is higher than the worldwide estimate of $38\%$ [19,29]. Multiple studies using high-throughput sequencing (HTS), mainly of the 16S rRNA gene, have already demonstrated that the bacterial communities living in the respiratory airways (i.e., airway bacteriome) play a significant role in the onset, development and severity of both asthma [30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46] and allergic rhinitis [47,48,49,50,51,52]. Microbial HTS has also shown that the nasal cavity is a major reservoir for opportunistic pathogens (e.g., Moraxella, Streptococcus, Haemophilus, Neisseria, and Staphylococcus), which can spread to other sections of the respiratory tract and potentially induce asthma, rhinitis and other respiratory illnesses [31,35,36,37,38,39,41,48,49,50,51,53,54,55,56,57,58,59]. The importance of the nasal microbiota as a gatekeeper to respiratory health is well known, and their intimate links to chronic airways disease are beginning to be elucidated [60,61,62]. Several studies (see previous references) have already characterized the nasal microbiome and shown that airway bacteriome composition and structure vary between healthy and asthmatic individuals; less is known, however, about the nasal microbiome of individuals with allergic rhinitis with and without asthma comorbidity [48,52,63]. This is particularly remarkable in some countries like Portugal, where despite the high incidence of these respiratory conditions (see statistics and references above), no study has yet characterized the airway microbiomes of healthy, asthmatic or rhinitic individuals. Hence, whether taxonomic and functional characteristics of the nasal microbiota could contribute to asthma or allergic rhinitis in Portugal remains to be determined. Moreover, defining the relationships between the nasal bacteriomes in healthy and respiratory disease individuals could ultimately improve our understanding of asthma and rhinitis pathophysiology and help identifying broadly applicable prognostic markers [64,65]. In this study we have used 16S rRNA HTS to characterize the nasal bacteriomes of 347 participants from northern Portugal with asthma and allergic rhinitis (with and without comorbid asthma) and healthy controls. We sought to identify distinct bacterial taxonomic and functional profiles (i.e., biological markers) across those four clinical groups and compare their microbial composition, diversity, metabolic functions, and microbe-microbe interactions. ## 2.1. Studied Cohort ASMAPORT was a cross-sectional study of children and adults designed to find associations between airway microbes and clinical manifestations of asthma and rhinitis. ASMAPORT represents a unique sample of otherwise healthy individuals recruited from northern Portugal attending the outpatient clinic of the Serviço de Imunoalergologia in the Centro Hospitalar Universitário São João from July 2018 to January 2020. Patients suspected to have allergic rhinitis or asthma were enrolled at their first visit and after completing a questionnaire on their clinical history. Individuals showing severe inflammation of the nasal cavity, polyps/mass or nasal crusts, “chronic dry mouth”, periodontal lesions greater than 4 mm, oral abscesses, evidence of precancerous lesions or candidiasis were ineligible. Healthy volunteers from the Porto area with no history of respiratory illness were also enrolled but did not complete the questionary or provided clinical information. All participants in this study were part of the ASMAPORT Project (PTDC/SAU-INF/$\frac{27953}{2017}$). This study was approved by the “Comissão de Ética para a Saúde” of the Centro Hospitalar Universitário São João/Faculdade de Medicina (Porto) in March 2017, Parecer_58-17. Written consent was obtained from all independent participants or their legal guardians using the informed consent documents approved by the Comissão de Ética. The diagnosis of allergic rhinitis was confirmed by an allergy specialist based on clinical criteria (sneezing, rhinorrhea and nasal congestion) and a positive skin prick or specific IgE (ImmunoCAP™ ThermoFisher) test to at least one common inhalant allergen in the region (mites, pollens, molds, cat or dog dander) [66,67]. Diagnosis of asthma was established by the attending physician based in the presence of typical symptoms (wheeze, chest tightness, and cough) in the previous 12 months or a positive bronchodilator responsiveness testing with salbutamol (FEV1 reversibility of at least $12\%$ and 200 mL) [68]. ## 2.2. Sample Collection A total of 347 individuals participated in this study (Table S1). They were distributed into four clinical groups: healthy controls (CT = 99 individuals), asthma (AS = 12), allergic rhinitis (AR = 53), and allergic rhinitis with asthma (ARAS = 183). Samples were collected by swabbing the right and left nostrils. We tilted the patient’s head back 70 degrees, inserted the swab less than one inch into the nostril and rotated several times against the nasal wall for about 30 s. We then repeated the process in the other nostril using the same swab. Sample swabs were then preserved in tubes containing DNA/RNA Shield (Zymo Research) and stored at −20 °C until further analysis. Because of the sample size of the AS group, we have only used AS in some of the pairwise comparisons and applied statistical tests that are moderately robust to small sample sizes (see below). Similar considerations were also implemented in other microbiome studies of asthma and rhinitis including groups of ≤12 participants [34,39,50,55,69]. ## 2.3. 16S rRNA High-Throughput Sequencing Total DNA was extracted from swabs using the ZymoBIOMICS™ DNA Miniprep Kit D4300. All extractions yielded <2 ng/μL of total DNA, as indicated by NanoDrop 2000 UV-Vis Spectrophotometer measuring. DNA extractions were prepared for sequencing using the Schloss’ MiSeq_WetLab_SOP protocol in Kozich et al. [ 70]. Each DNA sample was amplified for the V4 region (~250 bp) of the 16S rRNA gene and libraries were sequenced in a single run of the Illumina MiSeq sequencing platform at the University of Michigan Medical School. Negative controls processed as above showed no PCR band on an agarose gel. We used 10 water and reagent negative controls and 7 mock communities (i.e., reference samples with a known composition) to detect contaminating microbial DNA within reagents and measure the sequencing error rate. We did not find evidence of contamination and our sequencing error rate was as low as $0.0062\%$. ## 2.4. Microbiome Analyses 16S rRNA–V4 amplicon sequence variants (ASV) in each sample were inferred using dada2 version 1.18 [71]. Exact sequence variants provide a more accurate and reproducible description of amplicon-sequenced communities than is possible with operational taxonomic units defined at a constant level ($97\%$ or other) of sequence similarity [71]. Reads were filtered using standard parameters, with no uncalled bases, maximum of 2 expected errors and truncating reads at a quality score of 2 or less. Forward and reverse reads were truncated after 150 bases, merged and chimeras were identified. Taxonomic assignment was performed against the Silva v138.1 reference database using the implementation of the RDP naive Bayesian classifier available in the dada2 R package [72,73]. ASV sequences (226 to 260 bp) were aligned in MAFFT [74] and used to build a tree with FastTree [75]. The resulting ASV tables and phylogenetic tree were imported into phyloseq [76] for further analysis. Sequence files and associated metadata and BioSample attributes for all samples used in this study have been deposited in the NCBI (PRJNA913468). Metadata and ASV abundances with corresponding taxonomic classifications are presented in Table S1 and Table S2, respectively. We normalized our samples using the negative binomial distribution as recommended by McMurdie and Holmes [77] and implemented in the Bioconductor package DESeq2 [78]. This approach simultaneously accounts for library size differences and biological variability and has increased sensitivity if groups include less than 20 samples [79]. Taxonomic and phylogenetic alpha-diversity (within sample) were estimated using Chao1 richness and Shannon, ACE, and Phylogenetic (Faith’s) diversity indices. Beta-diversity (between-sample) was estimated using phylogenetic Unifrac (unweighted and weighted), Bray–Curtis and Jaccard distances, and dissimilarity between samples was explored using principal coordinates analysis (PCoA). Differences in taxonomic composition (phyla and genera) and alpha-diversity indices between respiratory disease groups (AS, AR and ARAS) and healthy individuals (CT) were assessed using the Wilcoxon and the Kruskal–Wallis rank sum tests and the Wald test with Cook’s distance correction for outliers (DESeq2 package), while accounting for covariables (age, season and sex). Beta-diversity indices were compared using permutational multivariate analysis of variance (adonis) as implemented in the vegan R package [80], while also accounting for covariables. None of the covariables were significant for any of the taxonomic and diversity indices compared. We applied the Benjamini–Hochberg method at alpha = 0.05 to correct for multiple hypotheses testing [81,82]. All the analyses were performed in R [83] and RStudio [84]. A full record of all statistical analyses was created in R studio and is included in Figure S1. All data files and R code used in this study with instructions can be found here GitHub (https://github.com/mlosada323/asmaport_bacteriome_nasal, accessed on 5 January 2023). ## 2.5. Functional Analyses The metagenome functional component of the nasal bacteriome was predicted by coupling Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) [85] and the Integrated Microbial Genomes & Microbiomes (IMG/M) database [86]. The hsp.py script was executed with default parameters (maximum parsimony). ASVs abundances were normalized by 16S rRNA gene copy number. Gene abundances were estimated by multiplying the normalized ASV counts by the predicted gene copy numbers using the metagenome_pipeline.py script. From gene abundance predictions, metabolic pathways were predicted using the MetaCyc database [87,88] and the PICRUSt2 pathway_pipeline.py script with default parameters. Differentially abundant metabolic pathways along the different clinical groups were analyzed using the DESEq2 Wald’s test with a p-value cutoff of 0.05 and an absolute fold change acceptance criterion of two units. ## 2.6. Network Analyses To gain insight into community interactions among bacterial taxa in the nasal bacteriome, we generated microbial association networks using the SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference) R package [89]. All ASVs were classified to their best-hit taxonomic assignment and then agglomerated by identical taxonomic rank. The most parsimonious network structures were detected using LASSO regularized regression by calling the neighborhood selection method (method = “mb”) on the inverse covariance matrix. In order to capture the optimal network links, optimal lambda values were chosen by nlambda = 50 and lambda.min.ratio = 0.01 using 50 subsamples for graph re-estimation (rep.num = 50). Count data were centered log-ratio transformed. The number of links per node was chosen as the centrality metric to detect hub nodes (Degree centrality metric) and the clustering/modulation stage was performed with the default method for the association matrices (cluster_fast_greedy) using the NetCoMi R package [90]. All nodes whose normalized degree centrality metric was greater than the 90 percentile were defined as hub nodes. Finally, network visualizations were generated using the NetCoMi plot function. ## 3. Results We collected nasal swabs from a cohort of 347 participants (248 individuals with respiratory disease and 99 healthy controls) from northern Portugal comprised mainly of children and young adults (Table S1). The median age of the participants was 12.6 ± 5.2 years and $52.7\%$ were female. Subjects with respiratory disease were subdivided into three groups: AS (12 subjects), AR [53] and ARAS [183]. We sequenced the variable V4 region of the 16S rRNA gene to characterize the nasal bacteriome of each participant. ASV singletons and two CT samples and one ARAS sample with < 1771 reads were eliminated. ## 3.1. Bacteriome Taxonomic Diversity and Structure The nasal microbiome (344 samples after quality control) comprised 6,515,609 clean reads, ranging from 1771 to 82,430 sequences per sample (mean = 18,940.7) and comprising 6195 ASVs (Table S2). CT samples had 651 unique ASVs, AS samples had 181, AR samples had 927 and ARAS samples had 2987 (Figure S2). The four groups shared 268 ASVs, while other pairs and trios shared a variable number, ranging from 1 to 363 ASVs (Figure S2). The nasal bacteriome sequences across all 344 filtered samples were classified into four dominant (<$2\%$ abundance) Phyla: Firmicutes ($44.9\%$), Actinobacteriota ($27.7\%$), Proteobacteria ($20.3\%$) and Bacteroidota ($4.6\%$) (Figure 1). Those Phyla comprised 10 dominant (<$2\%$) genera: Corynebacterium ($21.9\%$), Staphylococcus ($18.3\%$), Dolosigranulum ($10.6\%$), Moraxella ($8.8\%$), Streptococcus ($5.2\%$), Lawsonella ($3.9\%$), Anaerococcus ($2.8\%$), Haemophilus ($2.8\%$), Neisseriaceae sp. ( $2.7\%$) and Peptoniphilus ($2.4\%$) (Figure 1). All the other detected phyla and genera accounted for <$2\%$ of the total 16S rRNA sequences each. Two ASVs (ASV1 and ASV2) of the species Streptococcus oralis and Staphylococcus aureus comprised the nasal core microbiome (prevalence < $90\%$) and accounted for $3.5\%$ and $17.1\%$ of the total reads, respectively. The same two ASVs and species comprised the nasal core microbiome of respiratory disease patients and accounted for $4.0\%$ and $17.4\%$ of their total reads, respectively; while only Staphylococcus aureus (ASV2) composed the nasal core microbiome of healthy individuals and accounted for $16.6\%$ of the reads. These two core ASVs may represent the more stable and consistent members of the nasal bacteriomes [91,92]. We also compared the mean relative abundance of specific taxa in subjects with respiratory disease and healthy controls. Of the four dominant bacterial phyla comprising the nasal microbiome (Figure 1), one to three phyla showed significant differences in their mean relative proportions between a respiratory disease group (AS, AR or ARAS) and healthy controls (CT), while only Firmicutes varied significantly between AR and ARAS (Table 1). Similarly, of the 10 dominant bacterial genera comprising the nasal microbiome (Figure 1), 5 to 7 genera showed significant differences in their mean relative proportions between a respiratory disease group (AS, AR or ARAS) and CT. However, only two genera (Anaerococcus and Staphylococcus) varied significantly between AR and ARAS (Table 1). All these significant associations (Wilcoxon test) between phyla and genera and clinical groups were confirmed by the Wald test with Cook’ s distance correction for outliers (0.02 ≤ p ≤ 0.0001). Alpha-diversity indices (Shannon, Chao1, ACE, and PD) of microbial community richness and evenness varied among clinical groups (Figure 2 and Table S3). AR showed the highest diversity for all indices, while CT showed the lowest. ARAS–CT and AR–CT comparisons were significantly distinct for the four indices (Wilcoxon test; p ≤ 0.0026). All the other pairwise comparisons were not significant. To characterize the structure of the nasal bacteriomes (beta diversity), we applied principal coordinates analysis (PCoAs) to Unifrac (unweighted and weighted), Bray–Curtis and Jaccard distance matrices. All the PCoAs showed partial segregation of the bacteriotas from each clinical group (Figure 3). Subsequently, the adonis analyses detected significant differences ($p \leq 0.011$) in beta-diversity between each of the respiratory disease groups (AS, AR and ARAS) and the healthy controls for all the distances. None of the pairwise comparisons between respiratory disease groups resulted significant. This suggests that the bacteriomes of AS, AR and ARAS participants may differ from those of healthy individuals in a similar compositional manner. ## 3.2. Bacteriome Functional Diversity We predicted bacterial functional profiles for the AR, ARAS and CT groups in the nasal mucosa (Table S4)—the AS group was excluded due to its inadequate sample size for this analysis. We then compared AR and ARAS against control subjects and inferred differentially abundant pathways with $p \leq 0.05$ and log2FC < 2 (Figure S3). We detected 72 (55 upregulated and 17 downregulated) pathways in AR vs. CT and 72 (50 upregulated and 22 downregulated) pathways in ARAS vs. CT, but only 18 (2 upregulated and 16 downregulated) pathways in ARAS vs. AR. The first two comparisons shared 49 upregulated and 16 downregulated pathways out of 72 differentially expressed pathways; this, again, may suggest that bacteriomes of AR and ARAS participants deviate from those of healthy individuals in a similar manner. Most of those pathways were related to degradation (32–33 pathways) and biosynthesis (23–24 pathways) processes. The AR vs. ARAS comparison was dominated by degradation (9 pathways), fermentation (4 pathways), and biosynthesis (3 pathways) processes. ## 3.3. Bacteriome Network Interactions We inferred potential interactions among nasal bacteria in the AR, ARAS, and CT groups—AS was again excluded due to its limited sample size. The inferred co-occurrence SPIEC-EASI networks included the following parameters: modules (subnetworks), nodes and hub nodes (key taxa), and connected nodes (Figure 4). The nasal microbial networks of respiratory disease groups (AR and ARAS) were more complex than that of the control group, in accordance with observed trends in intra-group diversity (Figure 2; Table S3). The CT network included six modules, three hub taxa (Neisseria, Leptotrichia and Novosphingobium), 53 nodes, and 39 connected nodes. The AR network included 17 modules, six hub taxa (Leptotrichia, Novosphingobium, Ezakiella, Veillonela, Actinomyces and Corynobacterium 1 kroppenstedtii), 119 nodes, and 92 connected nodes. This network shared two hub taxa with the CT network (Leptotrichia and Novosphingobium) and also included a subnetwork between Moraxella and Dolosigranulum pigrum, two taxa usually associated with inflammation [45,50,93]. The ARAS network included 12 modules, nine hub taxa (Aliterella_CENA595, Deinococcus, Leptotrichia, Neisseria, Veillonela, Gemella, Actinomyces, Finegoldia magna and Johnsonella), 109 nodes, and 68 connected nodes. Two hub taxa were also shared with the CT network (Leptotrichia and Neisseria) and three with the AR network (Leptotrichia, Veillonela and Actinomyces). Of the 10 dominant genera in the nasal bacteriome (Table 1), 6 formed subnetworks in CT, 8 in AR and 10 in ARAS. Interestingly, Moraxella and Staphylococcus only appeared in the networks of rhinitic patients alone or connected to the opportunistic pathogen Dolosigranulum pigrum [94]. Similarly, other commensal genera (e.g., Corynebacterium and Veillonella) were also associated in separate modules, suggesting a robust relationship. ## 4. Discussion Asthma and allergic rhinitis are two conditions that, either when occurring together or separately, impart a health and economic burden to persons and society [6,7,15,95,96,97]. Emerging evidence has suggested that both airway diseases are intimately linked to alterations of the nasal bacteriome [45,52,56,93,98,99,100,101,102]. In this cross-sectional study, we apply 16S rRNA amplicon HTS to a large cohort of individuals from northern Portugal with asthma or allergic rhinitis (with and without comorbid asthma) and healthy controls to characterize their nasal bacteriotas. We identified distinct taxonomic and functional bacterial profiles and co-occurrence networks associated with chronic respiratory disease. The nasal bacteriomes of the studied samples were composed of four dominant phyla and 10 dominant genera (Figure 1 and Table 1). All these taxa have been previously described in the nasal cavity of asthmatic, rhinitic or healthy individuals [35,37,38,47,48,49,51,52,69,103,104], where they are considered normal residents. The characterized bacteriotas were mainly comprised of commensal taxa [103,104], but some genera (e.g., Moraxella, Streptococcus, Haemophilus, Neisseria and Staphylococcus) including pathogenic species associated to asthma [33,34,35,36,39,40,55,57,58,59,105] and allergic rhinitis [47,48,49,50,51,52] were also detected. Hence, overall, the nasal bacteriome of children and young adults from northern Portugal resembled those described in other studies of cohorts from USA, Europe, Australia and Asia. Both heathy participants and those with a chronic respiratory disease harbored unique microbial taxa in their nasal mucosa. The healthy nasal bacteriome contained $10.5\%$ unique ASVs, while the AS, AR and ARAS bacteriomes contained $2.9\%$, $15\%$ and $48.2\%$ unique ASVs, respectively (Figure S2). These ASVs may represent fingerprints or biomarkers in those patients with asthma and allergic rhinitis with and without comorbid asthma. Future microbiome studies will need to confirm their consistency across other cohorts and nasal microenvironments [41,106], and their potential as targets for new therapeutic strategies in asthma and rhinitis [34,39,107]. The proportions of most of the dominant bacterial phyla and genera in the nose varied significantly between healthy and respiratory disease groups (Table 1). The most significant differences in phyla (three out of four) and genera (7 out of 10) and relative mean abundance (Figure 1) were observed in CT vs. AR and CT vs. ARAS. Nonetheless, one phylum and five genera varied significantly between AS and CT, despite the small sample size of the AS group. Actinobacteriota was more abundant in CT, while Firmicutes, Proteobacteria and Bacteroidota were more abundant in the disease groups. Similarly, Corynebacterium and Lawsonella were more abundant in healthy subjects, while Dolosigranulum, Haemophilus, Moraxella and Streptococcus were more abundant in all the respiratory disease groups. Firmicutes, Anaerococcus and Staphylococcus increased significantly in rhinitic participants with comorbid asthma compared to those without (Table 1). A similar study in Chinese adult participants showed the opposite trend for the phylum, but the same result for Staphylococcus [48]. The compositional patterns observed here agree well with some previous studies of asthma and allergic rhinitis [48,49,69,108,109], and confirm the pathogenic potential of some of these genera (e.g., Haemophilus, Moraxella and Streptococcus) via host inflammatory or immune response [34,39,45,109]. Changes in these bacterial groups may then provide insight into the pathobiology of asthma and allergic rhinitis. Nonetheless, given the diversity (asthma) and limitation (allergic rhinitis) of microbiome studies so far, intrasubject variation, lack of biological and longitudinal replicates, and limited resolution of 16S rRNA HTS, the relationships between specific bacterial colonization, dysbiosis and chronic inflammatory disease may still remain elusive [50,93]. Bacterial alpha-diversity (species richness and evenness) varied significantly between samples from healthy controls and those from participants with allergic rhinitis, with and without comorbid asthma (Figure 2). No differences were observed for the four indices between AR and ARAS. Alpha-diversity has shown inconsistent patterns in the upper airway bacteriome. Some studies have revealed less within-sample diversity in healthy controls compared to asthma [69,110,111] or allergic rhinitis with and without comorbid asthma [49,51,112]; while others have shown the opposite trend across those same groups [48,50,93,109,113] or across metrics of richness and evenness [34,109]. A recent study has suggested that asthma may substantially affect alpha-diversity more than AR in the upper airway, since AR values are not as low as AS values compared to CT [48]. Our results seem to confirm that statement (Figure 2; Table S3). Nonetheless, given the discrepancy in alpha-diversity patterns in microbial studies of asthma and allergic rhinitis, this metric may not be a good proxy of disease status or pathogenesis in the nose. All nasal bacterial communities in samples from respiratory disease participants (AS, AR and ARAS) were significantly restructured compared to those from healthy controls (Figure 3). No differences were observed, however, between AR and ARAS groups. This pattern held irrespective of the distance metric used, whether accounting for phylogenetic diversity or not. Previous studies also showed specific community structuring associated with distinct bacterial composition among these same groups [48]; while others also confirmed structural differences between healthy controls and patients with asthma [34,69,109,113] or allergic rhinitis [47,50,51]—although one study reported conflicting results for the latter [49]. It is well established that altered bacterial diversity increases the risk of immune-mediated diseases like asthma and allergic rhinitis [35,45,114], but while alpha-diversity might not be a consistent predictor of disease status in the nasal microbiome, beta-diversity indices may be more reliable indicators of heterogeneity/stochasticity associated to dysbiosis [115,116]. As reported for the human gut microbiome, we speculate that the human airway bacteriome may also follow the Anna Karenina principle, i.e., “all healthy microbiomes are alike, but each disease-associated microbiome (i.e., asthma, allergic rhinitis and their combined occurrence) is sick in its own way”. The airway microbiota can influence host metabolism and homeostasis, including epithelial cell growth and repair, and inflammatory and immune responses, thereby impacting chronic disease onset and progression [34,39,45,99,100,101,102,109,117,118,119]. Compared to healthy controls, our PICRUSt2 analyses predicted 50–55 pathways upregulated in rhinitic patients (Figure S3). A similar array of differentially expressed pathways was also observed in a previous study comparing AR to CT participants [109]. As far as we know, that and ours are the only two studies so far using PICRUSt2 to predict metabolic functions in the nasal microbiome during allergic rhinitis. Several of the inferred metabolic pathways (e.g., tryptophan, tyrosine, histidine, nicotinate, acetate or glycerol metabolism) have been associated with allergic sensitization and inflammation of the airways [39,120,121,122,123]. Our study, thus, suggests that dysbiosis of the nasal bacteriome may influence these bacterial metabolic pathways, thereby affecting the development of allergic rhinitis. Nonetheless, since microbial function here has been predicted using 16S rRNA amplicons, more powerful dual-transcriptomic studies, e.g., [34,39], should be performed to confirm our predictions and decipher the interplay between host and microbiota. Finally, co-occurrence network analyses revealed distinct and specific connectivity patterns (i.e., interactions) in rhinitic groups compared to healthy controls (Figure 4). The AR and ARAS networks were more intricate than that of the control group including more and larger modules and connected taxa. Different modules in each network represent different co-regulated bacteria that, in turn, suggest distinct community partitions [124]. Many microbes of variable abundance were embedded in the networks, highlighting their importance individually and also in the community (interactions). Highly connected taxa (e.g., Veillonella and Leptotrichia), even if in low abundance, may still play key roles in the functionality of the nasal community [124,125]. Microbes that are both prevalent and abundant (e.g., core taxa Streptococcus and Staphylococcus) in the nose of patients with AR and ARAS, but are also highly connected, might serve as better indicators of disease [126,127,128]. Similarly, understudied bacteria in AR and ARAS patients connected to well-known pathogenic groups may also be drivers of disease [129]. Microbe–microbe interactions have not been investigated in rhinitis. However, a few studies in asthma have also revealed striking differences between networks of asthmatic groups and healthy controls, although with opposite trends in connecting density [38,124,125,129]. Further research is still needed to assess the role of microbial networks and their biomarker potential in the pathogenesis of inflammation [98]. The bacteriomes of patients with AR and ARAS showed some differences in composition for specific taxa (see comments above and Table 1), but no differences in alpha- and beta-diversity were observed at the community level. A previous study [48] described differences in intra-and inter-sample diversity between AR and ARAS bacteriomes, although all the enrolled participants were adults (mean age/group < 37 years). Despite few significant changes in composition, we observed some significant variation in the functionality of AR and ARAS (Figure S3) and a richer pattern of microbe–microbe interactions in AR (Figure 4), but not as remarkable as the difference observed between rhinitic patients and controls. Therefore, by comparison, nasal bacterial communities across respiratory disease groups varied much less, which may suggest that, at least in our cohort, the etiology and pathophysiology of these two chronic respiratory illnesses may be driven by a shared group of bacteria. ## 5. Conclusions We characterized for the first time in Portugal the nasal bacteriomes of individuals with asthma and rhinitis (with and without comorbid asthma) and healthy controls. We demonstrated that several of the most abundant bacterial phyla and genera in the nose varied significantly between healthy and respiratory disease participants (i.e., potential biomarkers of disease). 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--- title: Exploring the Potential of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 as Promising Psychobiotics Using SHIME authors: - Fellipe Lopes De Oliveira - Mateus Kawata Salgaço - Marina Toscano de Oliveira - Victoria Mesa - Adilson Sartoratto - Antonio Medeiros Peregrino - Williams Santos Ramos - Katia Sivieri journal: Nutrients year: 2023 pmcid: PMC10056475 doi: 10.3390/nu15061521 license: CC BY 4.0 --- # Exploring the Potential of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 as Promising Psychobiotics Using SHIME ## Abstract Psychobiotics are probiotics that have the characteristics of modulating central nervous system (CNS) functions or reconciled actions by the gut–brain axis (GBA) through neural, humoral and metabolic pathways to improve gastrointestinal activity as well as anxiolytic and even antidepressant abilities. The aim of this work was to evaluate the effect of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on the gut microbiota of mildly anxious adults using SHIME®. The protocol included a one-week control period and two weeks of treatment with L. helveticus R0052 and B. longum R0175. Ammonia (NH4+), short chain fatty acids (SCFAs), gamma-aminobutyric acid (GABA), cytokines and microbiota composition were determined. Probiotic strains decreased significantly throughout the gastric phase. The highest survival rates were exhibited by L. helveticus R0052 ($81.58\%$; $77.22\%$) after the gastric and intestinal phase when compared to B. longum ($68.80\%$; $64.64\%$). At the genus level, a taxonomic assignment performed in the ascending colon in the SHIME® model showed that probiotics (7 and 14 days) significantly ($p \leq 0.005$) increased the abundance of Lactobacillus and Olsenella and significantly decreased Lachnospira and Escheria-Shigella. The probiotic treatment (7 and 14 days) decreased ($p \leq 0.001$) NH4+ production when compared to the control period. For SCFAs, we observed after probiotic treatment (14 days) an increase ($p \leq 0.001$) in acetic acid production and total SCFAs when compared to the control period. Probiotic treatment increased ($p \leq 0.001$) the secretion of anti-inflammatory (IL-6 and IL-10) and decreased ($p \leq 0.001$) pro-inflammatory cytokines (TNF-alpha) when compared to the control period. The gut–brain axis plays an important role in the gut microbiota, producing SCFAs and GABA, stimulating the production of anti-anxiety homeostasis. The signature of the microbiota in anxiety disorders provides a promising direction for the prevention of mental illness and opens a new perspective for using the psychobiotic as a main actor of therapeutic targets. ## 1. Introduction Mental health is a growing concern worldwide, especially after the COVID-19 pandemic era, in which rates of common disorders such as anxiety have increased [1]. Anxiety disorders constitute a group of pervasive, persistent, disabling conditions, and are associated with a high onus of the disease, ranked among the top 25 leading causes of burden worldwide [2]. In 2019, 301 million people were living with an anxiety disorder, including 58 million children and adolescents [3]. Although adherence to psychotherapeutic and psychotropic treatments has increased over the past years [4], the current treatment modalities are still challenging in efficacy regarding remission, prognosis, and relapse prevention [5]. Available drugs that ameliorate mood and anxiety, such selective serotonin reuptake inhibitors (SSRI), selective serotonin and noradrenaline reuptake inhibitors (SNRI) and tricyclics (TCA) frequently have negative secondary effects, such as dizziness, weight gain, sexual impairment and gastrointestinal consequences. Benzodiazepines, largely used to treat anxiety as well, by its turn, can impair cognition, vigilance and may cause tolerance and dependence [6]. Furthermore, there is a massive variation in response to existing treatments, which are overall efficacious in less than half of the diagnosed patients [7,8]. Therefore, developing alternative treatments or adjunctive therapies is an urgent need in this scenario. One such promising area of research is the microbiota–gut–brain axis. The human gut microbiome is inhabited by 1013 to 1014 microorganisms (bacteria, viruses, archaea, and fungi), which outnumber the 20,000–25,000 human genes by 10 million genes in the entire human microbiota [9,10]. The gut microbiota plays an important role in distinct physiological processes, including multiple functions (e.g., by producing vitamins and neurotransmitters, contributing to food digestion, regulating neuronal feeding circuits, drug biotransformation, expression of genes and others) [11,12], as well as immune functions (e.g., providing defense against pathogenic strains) [13]. The gut microbiota and the brain communicate with each other in a bidirectional way via multiple mechanisms, including directly through the vagus nerve, via endocrine and via immune systems [14,15]. This reciprocal interaction between the brain and gut microbiota is known as the “gut–brain-axis”. Gut microbiota thereby have the potential to influence brain activity and mental health [16]. Recent scientific evidence suggests that brain functions including mood, cognitive function, and stress-associated anxiety or depression in humans may be related to the gut microbiome axis [17,18]. A range of high quality preclinical and clinical studies have shown that the disruption of the gut microbiota modulates stress reactivity [19,20,21] and is connected to mental health outcomes [22]. One of the first studies that showed the brain–microbiota-axis was carried out by Sudo et al. [ 23] using germ-free (GF) male mice that have a hyperreactive HPA axis, leading to increased concentrations of corticosterone and adrenocorticotropic hormones (ACTH) after a stressful stimulus. Another interesting study was performed with the transplantation of depressed patients to microbiota-depleted rats, which induced the development of some of the behavioral and physiological features of the depressive phenotype [24]. Thus, these findings suggest that the gut microbiota exerts an important influence on mental health, shaping host immunity and stress responses [25]. Hence, intervention in the microbiota may hold promise for the treatment of mental illnesses. In this way, in 2013, Dinan et al. [ 11] introduced a psychobiotics concept, defined as a probiotic yielding neurobehavioral or psychiatry benefits. The possible mechanisms of psychobiotics in mental illness have been investigated. In preclinical studies, the psychobiotics are proved to increase the integrity of the intestinal epithelial barrier, reducing permeability, inhibiting endotoxemia and presenting anti-inflammatory signals to immune cells [25]. For example, pretreatment with Lactobacillus helveticus and Bifidobacterium longum restored the tight junction barrier integrity in mice facing water avoidance stress [26]. Lactobacillus rhamnosus significantly reduced corticosterone levels in mice subjected to stress-induced hypothermia attenuating stress response, demonstrating the effects on anxiety- and depressive-like behaviors [20]. Bifidobacterium breve reduced anxiety-like behavior during an elevated maze test in the same anxious mouse strain [27]. Beneficial effects of probiotics have also been seen in clinical trials. L. rhamnosus and Lactobacillus reuteri reduced small intestinal permeability in children with eczema [28]. Probiotic strains, particularly the Bifidobacterium genus and Lactobaciliaceae family, help relieve stress, anxiety, and depression symptoms [29,30,31]. In human volunteers, the administration of a probiotic formulation consisting of L. helveticus R0052 and B. longum R0175A significantly attenuated psychological distress and reduced anxiety-like behavior, respectively [32]. Furthermore, the potential of L. rhamnosus to alter the expression of central GABA receptors, mediating depression and anxiety-like behavior, has been demonstrated [6]. GABA is the predominant inhibitory neurotransmitter in the nervous system and plays important physiological roles in humans, mediating several immunological [33] as well as intestinal neurophysiological processes [34,35]. This neurotransmitter can be synthesized by the gut microbiota residents, such as the Lactobacillaceae family and Bifidobacteria genus [6,36]. A recent metanalysis showed that probiotic intervention can improve the emotional state of depressed patients [32]. According to guidelines from The World Federation of Societies of Biological Psychiatry (WFSBP) and the Canadian Network for Mood and Anxiety Treatments (CANMAT) Taskforce, probiotic strains (at doses of 1–10 billion units per day) are provisionally recommended for adjunctive use and weakly recommended for monotherapy use in major depressive disorders [37]. However, probiotics with neurological effects are not universal, and it depends on strain type (isolated or combined), the administration method, intervention time, and the subjects’ physiological status [38]. In this way, given this ability of a probiotic to function by conferring benefits to the host, especially concerning mental health, the knowledge of their mechanisms and applications is imperative. Therefore, this study aimed to evaluate the effect of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on the gut microbiota composition of individuals with mild anxiety using the validated microbiome model (Simulator of Human Intestinal Microbial Ecosystem® (Shime®)) and measurement of metabolites. ## 2.1. Dynamic Colonic Microbiome Model The Simulator of the Human Intestinal Microbial Ecosystem (SHIME®) consists of a dynamic model of the human gastrointestinal tract connected to a software, composed of five connected reactors that represent the stomach (R1), small intestine (R2) and the ascending colon (R3), transverse colon (R4) and descending colon (R5), with their respective pH values, residence time, temperature, and volumetric capacity. In this experiment, R4 and R5 were transformed into R3 to conduct a triplicate study of the ascending colon with a pH range from 5.6 to 5.9 and retention time of 20 h (Figure 1). For the conditions of the stomach, the pH ranged from 2.3 to 2.5 and with a retention time of 2 h. To simulate the passage through the duodenum, we used 60 mL of artificial pancreatic juice (12.5 g/L NaHCO3, 3.6 g/L Oxgall, 0.9 g/L pancreatin, Sigma-Aldrich, St. Louis, MO, USA) at a flow rate of 4 mL/min and time retention of 4 h. The pH of the reactors was automatically adjusted with the addition of sodium hydroxide or hydrochloric acid and the whole simulator remained at 37 °C. The anaerobiosis conditions of the simulator were performed with a nitrogen injection of 30 min/day [39,40,41]. Initially, ascending colons were added to 500 mL of sterile feed medium (1.0 g/L arabinogalactan (Sigma), 2.0 g/L pectin (Sigma), 1.0 g/L xylan (Roth, Karlsruhe, Germany), 3.0 g/L starch (Êxodo, Hortolândia, Brazil), 0.4 g/L glucose (Synth, Diadema, Brazil), 3.0 g/L yeast extract (Neogen, Lansing, MI, USA), 1.0 g/L peptone (Kasvi, Italy), 4.0 g/L mucin (Sigma) and 0.5 g/L cysteine (Sigma)) along with 40 mL of fecal inoculum supernatant [43]. Inclusion criteria for donors were young people, ages 20 to 30 years old, with mild anxiety. The Hamilton Anxiety Rating Scale [44], Bristol stool form scale (BSFS) [45] and constipation assessment scale (CAS) were used by a nutritionist to select the fecal donors (Table 1). Exclusion criteria were use of antibiotics in the last 6 months, and prebiotics or probiotics, medication for gastrointestinal or metabolic disease, and dietary supplements in the last 3 months. ## 2.2. Experimental Protocol The experiment was conducted for 5 weeks in SHIME®, which were divided into: 2 weeks of microbiota stabilization (300 mL of the feed medium once a day) [46], 1 week of control period (240 mL of the feed medium plus 60 mL of pancreatic juice once a day) and 2 weeks of probiotic treatment (240 mL of the feed medium, 60 mL of pancreatic juice plus 3 pills of the probiotics (Lactobacillus helveticus R0052, 3.0 × 109 cfu.log/pill and Bifidobacterium longum R0175, 3.0 × 108 cfu.log/pill, Lallemand Health Solutions Inc., Montreal, QC, Canada)). Samples were collected every 7 days after the start of the control period, stored at −20 °C, and the experiment was carried out in biological triplicate. ## 2.3. Metabolic Activity: Ammonia (NH4+), Short-Chain Fatty Acids (SCFAs) and Gamma-Aminobutyric Acid (GABA) Production A specific ammonia ion electrode (Model 95–12, Orion) is used for ammonium ions quantification [47]. The methodology to quantify SCFAs (acetic, propionic and butyric acids) was previous described by Dostal et al. [ 48]. Briefly, 2 mL of colonic fermented samples were centrifuged (14,000 rpm for 5 min), and 1 mL of the supernatant were diluted 1:1 with MilliQ water, filtered in Millex® (0.45 μm), and then injected into an Agilent gas chromatograph (model HP-6890, Santa Clara, CA, USA), equipped with an Agilent selective mass detector (model HP-5975) using a DB-WAX capillary column (60 m × 0.25 mm × 0.25 μm) under the following conditions: temperature of injector = 220 °C, column = 35 °C, 2 °C/min, 38 °C; 10 °C/min, 75 °C; 35 °C/min, 120 °C (1 min); 10 °C/min, 170 °C (2 min); 40 °C/min, 170 °C (2 min) and detector = 250 °C. Helium was used as a drag gas at a flow rate of 1 mL/min. Stock solution of acetic, propionic, and butyric acids were used to construct the analytical curves. The samples were analyzed in triplicate per ascending colon replica, before and after treatment. Data was expressed in mmol/g [48]. GABA production was measured by a colorimetric test using GABA as the standard. The reactions occurred with 40 µL of supernatant 108 µL of Tris/HCl buffer (160 mM (Sigma)), 6.6 µL of 2-mercaptoethanol (100 mM (Sigma)), 10 µL of α-cetoglutarato (100 mM (Sigma)), 10 µL of NADP (25 mM (Sigma)), 5.5 µL of ultrapurewater and 20 µL of GABase (Sigma). The absorbance was measured at 340 nm every 15 s for 5 min in the centering of plates with 96 cups and results were expressed as mM of the GABA [49]. ## 2.4. Survival of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 under Simulated GI Conditions To assess the survival of probiotics under simulated GI conditions, samples were collected from R1 and R2 during the treatment period. For lactobacilli, the microdilution technique was used [50], followed by plating on MRS agar (Merck®). For bifidobacteria, one mL of the serial dilution was plated in MRS agar supplemented with lithium chloride (2 g/L (Merck®, Darmstadt, Germany)) and sodium propionate (3 g/L (Merck®, Germany)). Both samples were diluted in sterile peptone water. After, plates were incubated in anaerobic conditions for 72 h at 37 °C [51]. ## 2.5. Microbiological Analysis Employing 16S rRNA Gene Sequencing The extraction of bacterial DNA was performed using the DNeasy® PowerSoil® Pro Kit (QIAGEN, Hilden, Germany) following the manufacturer’s instructions. The DNA samples were then immediately frozen at −80 °C until molecular analysis. The library was prepared using primers for the V3–V4 region of 16S rRNA (~470 bp, amplified with primers 341F-806R), and bacteria amplicons were sequenced by Illumina platform (Novaseq6000 PE 250). Primers’ sequence 341F 5′-CCTAYGGGRBGCASCAG-3′ and 806R 5′-GGACTACNNGGGTATCTAAT-3′ [52]. Sequence data were processed and analyzed with QIIME (Quantitative Insights Into Microbial Ecology, version 2022.2.0 (https://qiime2.org/ (accessed on 15 December 2022)). On average, a total of 181,443 raw reads per sample were sequenced. Initially, during the demultiplex and trimming steps, the low-quality readings were removed, such as reads up to Q30, reads with unsatisfactory length and chimeras were removed with QIIME [53]. After this process, the data set contained an average of 27,154 raw reads per sample. The clean reads were used in the definition of the ASV (Amplicon Sequence Variant). To measure the rates present in the samples, a predictor model of the V3 and V4 regions was used (SILVA $138.99\%$ OTUs from 515F/806R region of sequences). The operational taxonomic units (OTUs) were grouped by cluster readings with $99\%$ similarity. The taxonomy was assigned to OTUs using SILVA 138 reference database (https://www.arb-silva.de/). Heatmaps and barplots of relative abundance of OTUs were generated with Python (version 3.7) through codes developed by the company ByMyCell Inova Simples Ltd., Ribeirão Preto, Brazil. The rarefaction curve was made using QIIME. Alpha diversity was calculated by applying different metrics. The Shannon, Simpson and Fisher indices representing the species diversity were calculated. The Chao1 and ACE indices representing species richness were calculated. To evaluate the similarity of microbiota from different groups, the beta diversity was reported using weighted and unweighted UniFrac distances. A Permutational multivariate analysis of variance (PERMANOVA) test was employed using Adonis test, to evaluate differences of beta diversity groups. Predictive functional profiling of bacteria communities was identified by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2, version 2.4.2). The bacterial OTUs exported from QIIME2 in the standard format are imported into PICRUSt2. Exploration analysis of genomic data was carried out in Python (version 3.7) [54]. ## 2.6. Co-Culture of Caco-2 and THP 1 Cells The immunomodulatory effects of probiotic treatment were realized using a co-culture model of Caco-2 cells (HTB-37; American Type Culture Collection, Rio de Janeiro Cell Bank, Duque de Caxias, Brazil) and THP1 cells (TIB-202, Rio de Janeiro Cell Bank, Brazil). This co-culture model of Caco-2/THP1 cells was previously described by Daguet et al. [ 55]. Briefly, Caco-2 cells at passage 37 were seeded into 24-well semipermeable inserts (0.4 m Thincerts, Greiner Bio-one, São Paulo, Brazil) at a density of 80,000 cells/insert. Caco-2 cell monolayers were cultured for 14 days, with three medium changes/week, until a functional monolayer of cells with a transepithelial electrical resistance (TEER) of more than 300 Ω.cm2 was achieved. Cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM; Life Technologies, São Paulo, Brazil) and supplemented with 10 mM HEPES (Life Technologies, São Paulo, Brazil) and $10\%$ (v/v) heat-inactivated Roswell Park Memorial Institute (RPMI) 1640 medium containing fetal bovine serum (FBS; Life Technologies). THP1 cells were maintained in 11 mM glucose and 2 mM glutamine and supplemented with 10 mM HEPES, 1 mM sodium pyruvate and $10\%$ (v/v) heat-inactivated FBS. THP1 cells at passage 29 were seeded in 24-well plates at a density of 500,000 cells/well and treated with 50 ng/mL phorbol 12-myristate 13-acetate (PMA; Sigma-Aldrich) for 48 h [55,56]. Prior to co-culture, the TEER of Caco-2 monolayers was evaluated using a Millicell ERS-2 (Merck Millipore, São Paulo, Brazil) Volt-Ohm epithelial meter (0 h time). The TEER of an empty well was subtracted from all readings to account for the residual electrical resistance of the insert. Then, Caco-2 pellets were placed on top of PMA differentiated THP1 cells for further experiments as described previously [56,57]. The apical compartment (containing the Caco-2 cells) was filled with sterile SHIME® colonic suspensions (membranem, 0.22 µm) in Caco-2 culture media. The basolateral compartment (containing THP1 cells) was filled with Caco-2 culture media only. Cells were also exposed to Caco-2 culture media alone in both chambers as a control. The cells were treated for 24 h, after which the TEER was measured (24 h time point). After subtracting the TEER of the empty insert, all 24 h values were normalized to their own 0 h value (to account for differences in TEER of the different inserts). Then, the basolateral supernatant was discarded and cells were stimulated basolaterally with Caco-2 media culture containing 100 ng/mL LPS (Escherichia coli K12, InvivoGen, San Diego, CA, USA). Cells were also stimulated with media without LPS as a control. After 6 h of LPS stimulation, basolateral supernatant was collected for the measurement of cytokines such as human IL-6, IL-10 and TNF-α (Tumor necrosis factor alpha) by ELISA (eBioscience, Vienna, Austria). All treatments were performed in triplicate [56]. ## 2.7. Statistical Analysis We used paired t-test, one-way analysis of variance (ANOVA), and Tukey’s test to analyze the results of metabolic activity (Ammonia (NH4+), short-chain fatty acids (SCFAs) and gamma-Aminobutyric Acid (GABA) production) and survival of L. helveticus R0052 and B. R0175 under simulated GI conditions, with $p \leq 0.05$ considered statistically significant. A statistical analysis was performed using GraphPad Prism software, version 8.0 (La Jolla, CA, USA). Next, 16S rRNA gene sequence analyses were performed in RStudio, version 3.2.4 [58] using the phyloseq package [59] to import sample data and calculate alpha and beta diversity metrics. The significance of non-parametric variables (alpha diversity) was determined using the non-parametric Wilcoxon test for two category comparisons or the Kruskal–Wallis test when comparing three or more categories. Principal coordinate plots were based on the PERMANOVA test to estimate p-values [60]. The p-values were adjusted for multiple comparisons using the FDR algorithm [61]. ## 3.1. Survival of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 under Simulated GI Conditions We demonstrated the survival of L. helveticus R0052 and B. longum R0175 in vitro gastrointestinal conditions. The L. helveticus R0052 and B. longum R0175 probiotic strains decreased significantly over the course of the gastric phase. The highest survival rates were exhibited by L. helveticus R0052 ($81.58\%$; $77.22\%$) after the gastric and intestinal phases when compared to B. longum ($68.80\%$; $64.64\%$). However, both strains reached the large intestine at least >7 cfu/mL (Figure 2). ## 3.2. Microbiota Composition in Long-Term SHIME® Run It is possible to notice that a total of 4,205,262 million high-quality reads were achieved from the microbiota samples collected over the periods of control ($$n = 9$$) and treatments ($$n = 9$$). Then, 3,217,336 million sequences were generated after normalizing the data. Figure 3 shows the prevalent phyla in the microbiota of individuals with mild anxiety before and after dosing 7 and 14 days of L. helveticus R0052 and B. longum R0175. The predominant phyla before the probiotic treatment were Actinobacteria, Firmicutes, Bacteriodetes and Proteobacteria. However, after the dosing of probiotic, the increase ($p \leq 0.001$) of Firmicutes was observed (Figure 3). We did not observe significant differences in alpha diversity between the control period and the probiotic treatment (7 and 14 days). However, beta diversity showed a significant difference ($p \leq 0.001$) between the control period and the probiotic treatment (7 and 14 days). Therefore, a principal-coordinate analysis showed different clustering in the control period, 7 days and 14 days after the probiotic treatment (Figure 4). At the genus level, the taxonomic assignment performed in ascending colon vessels in the SHIME® model showed that probiotics (7 and 14 days) significantly enhanced ($p \leq 0.005$) the abundance of Lactobacillus and Olsenella and significantly decreased Lachnospira and Escheria-Shigella (Figure 5). ## 3.3. Metabolic Activity: Ammonia (NH4+), Short-Chain Fatty Acids (SCFA) and Gamma-Aminobutyric Acid (GABA) Production Metabolic activity involving NH4+ and SCFAs production is shown in Figure 6. The probiotic treatment (7 and 14 days) decreased ($p \leq 0.001$) the NH4+ production when compared with the control period. For the SCFAs, we observed after treatment with probiotics (14 days) an increase ($p \leq 0.001$) of acetic acid and total SCFAs when compared with the control period. However, for butyric and propionic acids, no statistical difference was observed in relation to the control period. In addition, after 14 days of probiotic treatment, we observed an increase ($p \leq 0.001$) of GABA production when compared with the control period (Figure 7). ## 3.4. Potential Modulation of the Gut-Epithelial Function and Immunity The values of transepithelial electrical resistance (TEER) for Caco-2/THP1 cells treated with colonic media with and without treatment without probiotics (control) were lower (184. 8 ± 19.8) than with probiotics (7 days: 208.56 ± 35.7 and 14 days: 205.55 ± 31.1), but without statistical significance ($$p \leq 0.06$$). Figure 8 shows the concentrations of anti-inflammatory cytokines IL-6 and IL-10 and pro-inflammatory cytokine TNF-α following exposure of the cells to colonic media after dosing with probiotics for 7 and 14 days. The treatment with probiotics increased ($p \leq 0.001$) the secretion of the anti-inflammatory and decreased ($p \leq 0.001$) proinflammatory cytokines when compared to the control period. ## 4. Discussion In this study, we showed, for the first time, using a dynamic microbiome model, the positive influence of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on gut microbiota of individuals with mild anxiety. Therefore, the probiotic ability to survive in adverse conditions in the gastrointestinal tract proves fundamental for probiotics to exert their beneficial effects on the colon [62]. Both strains evaluated reached the large intestine at least >7 cfu/mL. The international recommendation is that probiotics should be present in supplements in an amount of 8–9 log cfu/g in the daily recommendation of the product [63], before ingestion, to ensure that a sufficient therapeutic minimum of 6–7 cfu/g can reach the colon [38]. In the last 10 years, with the development and improvement of molecular tools, many researchers have been associating changes in the composition of the microbiota to an increase in potentially pathogenic groups and a decrease in beneficial ones, with different types of intestinal diseases and extra intestinal diseases, such as autism, depression, Alzheimer, dementia, Parkinson and others [64,65,66]. The researchers seek to know a signature of the microbiota to establish personalized treatment strategies. In this way, the high abundance of the Actinobacteria phylum in the microbiota of individuals with mild anxiety before the intervention with probiotics called our attention. However, after 14 days of probiotic treatment, we observed a decreased ($p \leq 0.001$) of this phylum. Interestingly, Simpson et al. [ 67], in a systematic review, showed that the increase of Actinobacteria in patients with anxiety positively correlated with depression symptoms. However, understanding and characterizing the gut microbiota involves a multiple approach, often including a measure of alpha and beta diversity [68]. The alpha diversity index summarizes the structure of an ecological community with respect to its richness (number of taxonomic groups), evenness (distribution of abundances of the groups), or both [69]. We observe a decrease in alpha diversity, but without statistical differences, after probiotic doses when compared with the control period. However, many studies that investigate the gut microbiota of patients with anxiety symptoms did not observe an association between the alpha diversity index and anxiety symptoms [70,71,72]. Conversely, after probiotic treatment, we observed a significant difference between the control period and the probiotic treatment in beta diversity, showing the influence of probiotics on microbiota composition. Normally, beta-diversity is measured by end-to-end comparison of microbiome pairs using distance metrics such as UniFrac or Bray–Curtis. Therefore, beta-diversity-based status identification and classification relies on an assumption that most members of the community, or at least the highly abundant members, are associated with the status of interest [73]. At genus level the increase of Olsenella is remarkable because this genus has been previously associated with a reduction in gut inflammation [74]. On the other hand, the increase of Lactobacillus spp. was expected, due to the high survival rate observed after gastric and duodenal digestion. Conversely, the decrease of Lachnospira and Escherichia-Shigella was an important result, since Escherichia-Shigella is known to be an opportunistic pathogen for humans, being able to produce several pro-inflammatory components, such as lipopolysaccharides and peptidoglycans, and being able to trigger the host immune response and lead to intestinal inflammation in varying degrees. In addition, Chen et al. [ 75] correlated a high abundance of Escherichia/Shigella with anxiety symptoms. The gut microbiota composition and microbial metabolites have been associated with human health [76]. Hyperammoniac patients have a motor and cognitive dysfunction, indicating that the brain function can be affected by NH4 through underlying mechanisms [77]. Therefore, excessive production of ammonia by gut bacteria could contribute to increased ammonia levels in the blood and astrocyte edema [78]. We observed a strong decrease of NH4 after the probiotic treatment (7 and 14 days). Interestingly, Kurtz et al. [ 79] showed that engineered probiotic Escherichia coli Nissle 1917, which can convert NH3 to l-arginine, reduced systemic hyperammonemia in the preclinical model. However, the most crucial metabolic activity of gut bacteria is the production of SCFAs formed by the fermentation of gut bacteria [80]. The main SCFAs are acetic, propionic, and butyric acids. They contain one to six saturated carbons in their structures [81]. After the probiotics treatment, an increase of acetic acid was observed. The acetic acid has been reported to decrease a stimulate leptin, inflammatory cytokines and free fat acids to the liver [82]. In addition, SCFAs can enter the circulatory system; thus, this could probably be a signaling pathway between the microbiota and the host brain [83]. In this way, Zheng et al. [ 84] demonstrated that bacterial acetate production increased synaptophysin (SYP) in the hippocampus as well as learning and memory impairments level, improving cognitive decline in mice. In addition, Burton et al. [ 85] showed in clinical trials that SCFA increases are correlated with decreases of depressive and anxiety symptoms. We observed in our study after 14 days of probiotic treatment an increase of gamma-aminobutyric acid (GABA). GABA is an important and well-known neurotransmitter in the central nervous system (CNS). Some species of Lactobacillaceae family and Bifidobacterium genus can produce GABA, among them we can highlight L. helveticus [12] and B. longum [86]. Additionally, Chen et al. [ 75] showed that L. helveticus upregulated the expression of GABA receptors in animal models. Interestingly, GABA serves as an acid resistance mechanism for some Lactobacillaceae and Bifidobacterium species. At a low pH (<5.0), glutamate decarboxylation is induced and produces GABA, which is then exported from the cell in a protonated form, alkalizing the cytoplasm [87]. In addition, the influence of the ingestion of some strains of probiotics and the improvement of behavior has been described. Patrick et al. [ 88] demonstrated that the probiotic L. helveticus R0052 and B. longum R0175 reduces the anxiety-like behavior in Syrian hamsters. In clinical trials, strains of L. helveticus and B. longum have been reported to improve psychological symptoms [89]. Probably, the outcomes observed in clinical trials with the strains L. helveticus R0052 and B. longum R0175 may be correlated with the GABA production observed in this study. Interestingly, a clinical trial found that the transplantation of a fecal microbiota from lean to obese individuals showed increased plasma GABA levels, showing that manipulating the microbiome can alter GABA levels [90]. Studies have shown that the gut microbiota can exert an effect on the hypothalamic– pituitary–adrenal axis [91,92]. Anxiety and depression disorders are correlated with dysregulation of the HPA axis, which is normally associated with higher levels of cortisol and inflammation [93]. The composition of the gut microbiota, mainly in dysbiosis, can contribute to the increase of cortisol and inflammation [94], but this effect is bilateral since pro-inflammatory states can aggravate the changes in microbiota composition through deleterious effects on gastrointestinal health [95]. Thus, excessive levels of circulating cortisol and inflammatory mediators increase intestinal permeability, allowing Gram-negative bacteria, which have lipopolysaccharides (LPS) in their membranes, to induce inflammation in the intestinal mucosa, thus being a trigger for a leaky gut. LPS can also translocate into the bloodstream, inducing chronic CNS inflammation [96]. We observed in our study the increase of anti-inflammatory cytokines (IL-6 and IL-10) and the decrease of TNF-α (pro-inflammatory) and a strong reduction of pro-inflammatory bacteria Escherichia-Shigella after 14 days of treatment with probiotics. IL-6 is known to associate with and aggravate an inflammation by differentiation directly/indirectly through the nervous system and lymphocytes proliferation [97]. However, the anti-inflammatory functions of IL-6 have been revealed. In this way, we highlight that IL-6 can help maintain mucosal integrity and facilitate intestinal barriers repair with an increased claudin-2 expression [98]. Interestingly, we observed an improvement trend in the transepithelial electrical resistance of Caco-2/THP1 cocultures, suggesting an increase in mucosal integrity. ## 5. Conclusions The results found in SHIME showed that the combination of L. helveticus R0052 and B. longum R0175 can modulate the gut microbiota, increasing gamma-aminobutyric acid (GABA) and short chain fatty acids, decreasing pro-inflammatory cytokines and increasing the anti-inflammatory one. Therefore, L. helveticus R0052 and B. longum R0175 showed it is a promising psychobiotic for the adjuvant treatment of patients with anxiety. Finally, the microbiota signature in anxiety disorders provides a hopeful direction for the prevention of mental diseases and opens a new perspective of the use of psychobiotics as a main player of therapeutic targets. ## 6. Commentary of Expert: Dr. Antonio Medeiros Peregrino (Psychiatrist, Master in Neuropsychiatry and Behavioral Sciences and PhD in Tropical Medicine) In recent decades, psychiatry has developed numerous lines of research for a better understanding of the etiopathogenesis and pathophysiology of anxious and depressive states. The aim is to achieve constant improvements in pharmacological therapy, in biological or physical interventions (e.g., electroconvulsive therapy, transcranial magnetic stimulation) and even in psychotherapies. As it has been observed that up to one third of depressed patients may not respond adequately to conventional pharmacological treatments, it is possible to assume that different pathophysiological mechanisms are involved in the genesis of depression. The same can be supposed for anxious pictures. The finding that intestinal dysbiosis may constitute one of the etiopathogenic factors in anxiety-depressive conditions directs psychiatry to pay attention to new areas of clinical activity. The use of psychobiotics (probiotics with neuropsychiatric action), especially L. helveticus and B. longum, has been an area of interest in the search for better monoamine homeostasis and, likewise, for the reduction of inflammatory processes underlying the disorders. 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--- title: Prevalence and Characteristics of Inspiration-Induced Negative Left Atrial Pressure during Pulmonary Vein Isolation authors: - Takenori Ikoma - Yoshihisa Naruse - Yutaro Kaneko - Tomoaki Sakakibara - Taro Narumi - Makoto Sano - Satoshi Mogi - Kenichiro Suwa - Hayato Ohtani - Masao Saotome - Tsuyoshi Urushida - Yuichiro Maekawa journal: Journal of Cardiovascular Development and Disease year: 2023 pmcid: PMC10056480 doi: 10.3390/jcdd10030101 license: CC BY 4.0 --- # Prevalence and Characteristics of Inspiration-Induced Negative Left Atrial Pressure during Pulmonary Vein Isolation ## Abstract Background: Atrial fibrillation (AF) ablation is performed under deep sedation, which may cause inspiration-induced negative left atrial pressure (INLAP) associated with deep inspiration. INLAP could be the cause of periprocedural complications. Methods: We retrospectively enrolled 381 patients with AF (mean age, 63.9 ± 10.8 years; 76 women; 216 cases of paroxysmal AF) who underwent CA under deep sedation using an adaptive servo ventilator (ASV). Patients whose LAP was not obtained were excluded. INLAP was defined as <0 mmHg of mean LAP during inspiration immediately after the transseptal puncture. The primary and secondary endpoints were the presence of INLAP and the incidence of periprocedural complications. Results: Among 381 patients, INLAP was observed in 133 ($34.9\%$). Patients with INLAP had higher CHA2DS2-Vasc scores (2.3 ± 1.5 vs. 2.1 ± 1.6) and $3\%$ oxygen desaturation indexes (median 18.6 (interquartile range 11.2–31.1) vs. 15.7 (8.1–25.3)), and higher prevalence of diabetes mellitus (23.3 vs. $13.3\%$) than patients without INLAP. Air embolism occurred in four patients with INLAP (3.0 vs. $0.0\%$). Conclusion: INLAP is not rare in patients undergoing CA for AF under deep sedation with ASV. Much attention should be paid to the possibility of air embolism in patients with INLAP. ## 1. Introduction The incidence and prevalence of atrial fibrillation (AF) have increased in recent years. AF is associated with an increased risk of cerebrovascular accidents, heart failure, and all-cause death [1]. Early rhythm control therapy has been reported to improve the prognosis of patients with AF [2]. Additionally, rhythm control therapy with catheter ablation (CA) can improve a patient’s quality of life [3]. Currently, CA is recognized as a reasonable therapeutic option for AF [4]. CA for AF is effective; however, the complication rate of CA in the acute phase is $7.48\%$ [5]. Among the complications, air embolism is a crucial complication that can cause acute systemic embolism [6]. The most common embolisms are coronary air embolisms, with a prevalence of $2.6\%$ in cryoballoon ablations [7]. Negative left atrial pressure (LAP), in conjunction with air-leaking sheaths, is essential for air intrusion into the left atrium (LA) [8]. In particular, chest wall expansion and diaphragm descent due to deep inspiration with airway obstruction induce negative atrial pressure [9]. However, the prevalence and clinical characteristics of inspiration-induced negative LAP (INLAP; Figure 1) in patients undergoing CA for AF are unknown. In this study, we aimed to evaluate the prevalence and clinical impact of INLAP during CA for AF. ## 2.1. Participants This study is a registry-based study and the registry itself prospectively collected the data. We retrospectively enrolled 398 Japanese patients who underwent CA for AF at the Hamamatsu University Hospital between March 2017 and March 2020, using a prospectively collected ablation database (Hamamatsu EPS registry). Patients aged 18 years or older were eligible. Seventeen patients whose LAP was not measured during CA were excluded from this study (Figure 2). The primary endpoint was the presence of INLAP measured immediately after a transseptal puncture, and the secondary endpoint was the incidence of periprocedural complications. Patients were divided into two groups according to the presence or absence of INLAP, and the procedural parameters were compared between the groups. Data on age, sex, body mass index, comorbidities, and medications were collected. Paroxysmal (terminated within 7 days) and persistent AF (lasting > 7 days) were defined according to the current guidelines [1]. Before ablation, patients underwent a blood examination, transthoracic echocardiography, and electrocardiogram-gated contrast-enhanced computed tomography (CT) scanning. The LA volume index was obtained by dividing the LA volume by body surface area (BSA). BSA was calculated by the following formula: BSA = (height)0.725 ×(body weight)0.425 × 0.007184. Overnight pulse oximetry was performed 1 day after CA for AF. Written informed consent was obtained from all the patients before enrollment. The protocol was performed according to the Declaration of Helsinki and approved by the Human Investigations Committee of the Hamamatsu University School of Medicine (approval number #20-361). ## 2.2. Measurement of Intracardiac Pressure and the Definition of INLAP and Inspiration-Induced Negative Right Atrial Pressure Inferior vena cava pressure, right atrial pressure (RAP), and LAP were measured under sedation and adaptive servo-ventilator (ASV) support without the use of the nasal airway by connecting a pressure transducer with a T-shaped stopcock of the SL0TM 8.5 Fr Swartz Braided Sheath (Abbott, St. Paul, MN, USA). These pressures were measured at an average of four to five respiratory cycles. RAP and LAP were measured immediately after the sheath’s tip reached the right atrium (RA) and LA. INLAP or inspiration-induced negative right atrial pressure (INRAP) was defined as a mean LAP or RAP < 0 mmHg during inspiration. Representative cases of patients with and without INLAP are shown in Figure 1. The pressure data during the procedure was obtained by an electrophysiological specialist independent of this study. ## 2.3. Electrophysiological Study and CA Rivaroxaban, apixaban, and edoxaban treatments were omitted on the morning of the ablation procedure; however, dabigatran and warfarin treatments were uninterrupted. Antiarrhythmic drugs prescribed before the ablation were continued. The procedure was performed under intravenous sedation with midazolam (0.1 mg/kg/h) and dexmedetomidine (0.2 µg/kg/h following 10 min of bolus infusion of 4 µg/kg/h). The sedation level was monitored using the Richmond Agitation–Sedation Scale (RASS) and a bispectral index (BIS). The infusion rate of sedatives (midazolam and dexmedetomidine) was adjusted to maintain a BIS level between 50 and 80. Respiration was supported by ASV under the uniform settings (S/T mode, inspiratory positive airway pressure at 8 cm H2O, expiratory positive airway pressure at 4 cm H2O, fraction of inspired oxygen (FiO2) $40\%$, and respirate 15 cycles/min) at the beginning of the procedure. Confirming mask fitting, changing mask size or raising the oxygen concentration level were offered according to the operator’s judgement when there were problems with oxygenation or other problems. Intravenous heparin was administered to maintain an activated clotting time of 300–400 s during the procedure. Electroanatomical mapping was performed in sinus rhythm or atrial pacing, and electrical cardioversion was performed when the patient presented with AF during voltage mapping. Electroanatomical voltage maps of the LA were created using a 20-pole circular mapping catheter (Optima or Advisor-VL, Abbott, St. Paul, MN, or LASSO, Biosense Webster, Diamond Bar, CA, USA) or a multi-electrode high-density mapping catheter (HD grid, Abbott, or PENTARAY, Biosense Webster), with three-dimensional electroanatomical mapping systems (Navx-Ensite Velocity, Abbott, or CARTO 3, Biosense Webster). In both mapping systems, low voltage zones (LVZs) were defined as sites with a peak-to-peak electrogram amplitude of < 0.50 mV. Pulmonary vein isolation (PVI) was performed in all patients with de novo AF procedures using open-irrigated radiofrequency catheters, cryoballoons (Arctic Front Advance, Medtronic, Minneapolis, MN, USA), or laser balloons (HeartLight, CardioFocus, Marlborough, MA, USA). A water bucket developed to prevent air intrusion (AirTray, NISSHO, Shizuoka, Japan) was used in balloon catheter insertion. PVI was confirmed in patients with repeated procedures, and the pulmonary vein (PV) was re-isolated if PV reconnection was observed. Additional ablation, consisting of LA posterior box isolation [10], superior vena cava isolation, or ablation of the spatiotemporal dispersion area [11], was performed at the operator’s discretion. Air embolisms during CA for AF were defined as coronary air embolisms with ST segment elevation in inferior leads confirmed with coronary angiography or air intrusion images (e.g., in the LA or aorta). ## 2.4. Statistical Analysis Continuous variables are expressed as mean ± standard deviation or median (interquartile range (IQR)). Between-group comparisons were performed using an unpaired t-test or Mann–Whitney U test. All categorical variables were presented as numbers and percentages for each group and were compared using the chi-square test or Fisher’s exact test. Logistic regression analysis was performed to detect any independent, significant predictors by adjusting the variables with multivariable models (reported as odds ratios (ORs) with $95\%$ confidence intervals (Cis)). Variables that achieved statistical significance ($p \leq 0.05$) or were close to significance ($p \leq 0.1$) in the Spearman’s rank correlation coefficient and possible factors were included in the multiple linear regression analysis. Correlations between the INLAP and INRAP scores were analyzed using Spearman’s correlation coefficient, and freedom from AF recurrence was assessed using the Kaplan–Meier method. Survival curves were compared between the groups using a log-rank test. To evaluate the predictive value of the mean RAP during inspiration for INLAP, a receiver operating characteristic analysis was performed, calculating the area under the curve and evaluating possible cutoff points. Statistical significance was defined as $p \leq 0.05.$ All statistical analyses were performed using SPSS version 26.0 (IBM, Armonk, NY, USA). Graphs were compiled using Prism 7.03 (GraphPad, La Jolla, CA, USA). ## 3.1. Subject Characteristics A total of 381 patients (76 ($19.9\%$) women; mean age, 63.9 ± 10.8 years) were analyzed. Table 1 presents the patients’ baseline characteristics. There were 216 ($56.7\%$) patients with paroxysmal AF. A history of PVI was present in 40 patients ($10.5\%$). The mean LA diameters assessed by echocardiography and the LA volume index by cardiac CT were 39.1 mm and 71.5 mL/m2, respectively. ## 3.2. Feature of Patients with INLAP Within the entire cohort, 133 patients ($34.9\%$) had INLAP. Patients with INLAP had a higher prevalence of diabetes mellitus ($$p \leq 0.013$$) than those without INLAP. Higher CHA2DS2-Vasc scores ($$p \leq 0.043$$) and $3\%$ oxygen desaturation indexes (ODIs; $$p \leq 0.009$$) were observed in patients with INLAP. However, $3\%$ ODI correlated very weakly with the mean LAP during inspiration (Figure 3A). There were no significant between-group differences regarding medications and the results of laboratory testing, echocardiography, and CT findings (Table 1). No significant differences were observed in inferior vena cava (IVC) pressure during expiration; however, IVC ($p \leq 0.001$), RA ($p \leq 0.001$), and LA pressure ($p \leq 0.001$) during inspiration were lower in patients with INLAP. A higher prevalence of INRAP was detected in patients with INLAP ($p \leq 0.001$). Moreover, the mean RAP during inspiration correlated well with the mean LAP during inspiration (Figure 3B). Additional ablation for the cavotricuspid isthmus line and mitral isthmus was conducted less frequently in patients with INLAP ($$p \leq 0.007$$ and 0.040, respectively). Air embolism occurred in four patients with INLAP. Air embolism was suspected from ST segment elevation in the inferior leads of an electrocardiogram, fall in blood pressure, or bradycardia. Air embolism was observed after inserting the 20-pole circular mapping catheter into the LA of two patients, a multi-electrode, high-density mapping catheter into the LA of one patient, and cryoballoon into the LA of one patient. Emergent coronary angiography showed an air embolism in the right coronary artery. Fortunately, proper treatment, such as air aspiration via a catheter, ensured that there was no residual damage, including focal symptoms of stroke and myocardial wall motion impairment, in any of the cases. In contrast, no patients without INLAP experienced air embolism during CA for AF ($$p \leq 0.014$$). Median LA pressure during the inspiration period did not differ significantly between INLAP patients with or without air embolism (−9.5 (−4.5–−13.8) mmHg vs. −8.0 (−3.5–−14.0) mmHg; $$p \leq 0.0812$$). The incidence of cardiac tamponade tended to be higher in patients with INLAP than in those without ($$p \leq 0.053$$). The RASS and BIS index did not differ between patients with and without INLAP. No significant between-group difference was observed in the type of ablation technology and procedural and fluoroscopic times (Table 2). During a median follow-up period of 8.7 (IQR 6.16–13.3) months, AF recurrence occurred in 92 ($24.1\%$) patients. The Kaplan–Meier survival curve showed no significant difference in AF recurrence between patients with and without INLAP ($$p \leq 0.693$$ by log-rank test; Figure 4). ## 3.3. Prediction of INLAP Multiple linear regression analysis was performed to identify the factors associated with the decrease in LA pressure in the inspiration period. Age, body mass index, diabetes mellitus, INRAP, $3\%$ ODI (categorized into the following three groups according to their value: <5, 5–10, and >10), and history of prior PVI were included as factors. Multiple linear regression analysis demonstrated that the presence of INRAP was independently associated with the decrease in LA pressure in the inspiration period ($p \leq 0.001$; Table 3). The receiver operating characteristic curve used to evaluate the predictive value of the mean RAP during inspiration in distinguishing between those with and without INLAP during PVI revealed an area under the curve of 0.884 ($p \leq 0.001$). An RAP during inspiration that was less than 2.5 mmHg identified the presence of INLAP with a sensitivity and specificity of $83.2\%$ and $82.6\%$, respectively (Figure 5). ## 4.1. Main Findings The main findings of this study were as follows: 1) approximately one third of the patients undergoing PVI under deep sedation with ASV had INLAP during the ablation procedure, 2) the presence of INLAP can be predicted from the RAP, 3) the presence of INLAP could cause air embolisms during PVI, and 4) the presence of INLAP was not associated with AF recurrence after PVI. ## 4.2. Clinical Impact and Importance of Air Embolism The Japanese Heart Rhythm Society emphasized the risk of air embolism during cryoballoon ablation in 2018. In addition, a comprehensive review suggested the importance of detecting and managing air embolism [12]. An ex vivo study revealed that air intrusion, the cause of air embolism, is associated with the following two factors: the catheter system (comprising catheters of different vascular diameters) and the negative pressure in the sheath [13]. It was reported that negative LAP was an important factor in air embolism [8], and ex vivo experiments that evaluated air-tightness in response to negative pressures revealed air aspiration at −11 to −13 mmHg of suction [13]. All four cases of air embolism in this study were observed in patients with INLAP. Air intrusion in the left heart system can lead to more severe conditions or after-effects (i.e., myocardial or brain infarctions) than that of the right heart system (pulmonary embolism). Therefore, much attention should be paid to air embolisms in patients with INLAP. ## 4.3. Possible Mechanisms of INLAP In this study, INLAP was observed in 133 ($34.9\%$) patients under deep sedation with ASV, signifying that INLAP is not rare and could even occur with ASV in patients undergoing PVI. Patients with obstructive sleep apnea (OSA) show negative LA pressure [13]. Although the $3\%$ ODI values were higher in patients with INLAP, the correlation was weak. In addition, this study revealed that INLAP was not associated with LA volume and the presence of LVZs, which indicates LA remodeling [14]. Furthermore, the presence of INLAP did not affect the recurrence of AF after PVI. From the above, it seems that INLAP is not associated with AF’s pathophysiology or PVI’s effectiveness. Additionally, since OSA was associated with the presence of LA enlargement [15] and recurrence of AF after CA [16], the severity of OSA could not be associated with the presence of INLAP. A previous study that reports LAP during atrial septal defect/patent foramen ovale closure revealed that sedation provoked a marked decline in the mean inspiratory LAP compared to non-sedated patients [8]. Deep sedation using propofol or midazolam is associated with a profound drop in LAP [17,18]. In addition, a previous report revealed that patients that require airway management tools due to airway obstruction during CA showed substantial negative esophageal pressure compared to those without airway obstruction [19]. Since our findings showed no significant differences in IVC pressure during expiration, the patients’ circulating plasma volume statuses, including overflow and dehydration, did not affect the presence of INLAP. Thus, airway obstruction due to the decrease in muscle tonus under deep sedation, which could not be removed using ASV, could cause INLAP. A recent report showed that the prevalence of INLAP significantly reduced after ASV (pre-ASV $73\%$ vs. post-ASV $14\%$) [20], which is lower than our result of $35\%$. A possible reason for the difference in the prevalence of INLAP in patients during PVI with ASV between the two studies could be that the BIS level was higher in the previous report than in the present study (68.5 ± 12.7 vs. 59.3 ± 14.4). This finding that deeper sedation was associated with a higher prevalence of INLAP could support this as a suspected mechanism of INLAP rather than the severity of sleep apnea. Deep sedation was mainly responsible for INLAP development in patients undergoing PVI under deep sedation with ASV. ## 4.4. Prevention of Complications Due to INLAP We found that RAP could predict the decrease in LA pressure during the inspiration period, which is a crucial finding. Predicting the presence of INLAP and taking proper provisions before a transseptal puncture enables us to prevent systemic air embolisms. The RAP can be measured easily by connecting a long sheath to a pressure transducer. We should pay great attention to the occurrence of systemic air embolisms if a mean RAP during inspiration < 2.5 mmHg is observed before a transseptal puncture. It has been reported that a water bucket developed to prevent air intrusion (AirTray, NISSHO, Shizuoka, Japan or SAFE BOAT, DVx, Tokyo, Japan) could reduce the incidence of air embolisms [7]. Such equipment should be used for patients with INLAP in the insertion of any catheters, as we reported air embolism even when a catheter other than a balloon catheter was inserted. Furthermore, since the incidence of INLAP was high ($34.9\%$ in this study) under deep sedation with ASV, the routine use of such devices may be recommended. Furthermore, our findings demonstrated that patients with INLAP have an increased risk of cardiac tamponade than those without INLAP. An unstable respiration pattern is often observed in patients with INLAP due to airway obstruction, which could be responsible for the occurrence of cardiac tamponade because of the sudden increase in the contact force of an ablation catheter. Additionally, we hypothesize that the use of the nasal airway in addition to ASV could help to relieve airway obstruction in patients with INLAP under deep sedation with ASV and we are conducting further research to assess this hypothesis. ## 4.5. Study Limitations This study had several limitations. First, this was a single-center retrospective study, and the small sample size limited its predictive power. Second, four cases of air embolism were observed in this study. Although the CA procedures were performed by well-trained operators, the incidence of air embolisms and cardiac tamponade may still be affected by the skill of the operators. In addition, it was reported that the asymptomatic cerebral embolism during cryoballoon ablation of AF was observed in $22.9\%$ of patients [21]. Since brain magnetic resonance imaging was not performed routinely after ablation, silent air embolisms were not evaluated. In addition, as the coronary angiography was conducted for patients with ST elevation in ECG, slight changes that are difficult to recognize, including transient ST elevation, could be underestimated. Third, since all the patients received CA procedures under deep sedation using an ASV, we have no control group without sedation and the use of ASV. Fourth, we did not evaluate the detailed cardiac morphology and hemodynamics of the patients, including interventricular septum thickness, trans mitral flow velocity pattern, and blood pressure that could affect the presence of INLAP. Although we did not find any relationship between INLAP and BNP levels in the present study, there is a report in the literature that discusses the relationship between BNP and left atrial morphology [22]. Further investigation that explores the relationships among LA morphology, biomarkers, and the presence of INLAP is needed. ## 5. Conclusions INLAP during CA was not rare in patients undergoing CA for AF under deep sedation, even with ASV. Much attention should be paid to air embolisms in patients with INLAP. ## References 1. Hindricks G., Potpara T., Dagres N., Arbelo E., Bax J.J., Blomström-Lundqvist C., Boriani G., Castella M., Dan G.-A., Dilaveris P.E.. ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association of Cardio-Thoracic Surgery (EACTS). Eur. Heart J. (2020) 42 373-498. DOI: 10.1093/eurheartj/ehaa612 2. Kirchhof P., Camm A.J., Goette A., Brandes A., Eckardt L., Elvan A., Fetsch T., van Gelder I.C., Haase D., Haegeli L.M.. Early rhythm-control therapy in patients with atrial fibrillation. N. Engl. J. Med. (2020) 383 1305-1316. DOI: 10.1056/NEJMoa2019422 3. Mark D.B., Anstrom K.J., Sheng S., Piccini J.P., Baloch K.N., Monahan K.H., Daniels M.R., Bahnson T.D., Poole J.E., Rosenberg Y.. 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--- title: Supplementation with Flaxseed Oil Rich in Alpha-Linolenic Acid Improves Verbal Fluency in Healthy Older Adults authors: - Toshimi Ogawa - Kento Sawane - Kouta Ookoshi - Ryuta Kawashima journal: Nutrients year: 2023 pmcid: PMC10056498 doi: 10.3390/nu15061499 license: CC BY 4.0 --- # Supplementation with Flaxseed Oil Rich in Alpha-Linolenic Acid Improves Verbal Fluency in Healthy Older Adults ## Abstract The effects of docosahexaenoic acid supplements on cognitive function have long been demonstrated, but the effects of alpha-linolenic acid, a precursor of docosahexaenoic acid, have not been fully tested. The search for functional foods that delay cognitive decline in the older adults is considered a very important area from a preventive perspective. The aim of this study was to conduct an exploratory evaluation of alpha-linolenic acid on various cognitive functions in healthy older subjects. Sixty healthy older adults aged 65 to 80 years, living in Miyagi prefecture, without cognitive impairment or depression, were included in the randomized, double-blinded, placebo-controlled clinical trial. Study subjects were randomly divided into two groups and received either 3.7 g/day of flaxseed oil containing 2.2 g of alpha-linolenic acid, or an isocaloric placebo (corn oil) containing 0.04 g of alpha-linolenic acid for 12 weeks. The primary endpoints were six cognitive functions closely related to everyday life: attention and concentration, executive function, perceptual reasoning, working memory, processing speed and memory function. After 12 weeks of intake, changes in verbal fluency scores on the frontal assessment battery at bedside, a neuropsychological test assessing executive function, in which participants are asked to answer as many words as possible in Japanese, were significantly greater in the intervention group (0.30 ± 0.53) than in the control group (0.03 ± 0.49, $p \leq 0.05$). All other cognitive test scores were not significantly different between the groups. In conclusion, daily consumption of flaxseed oil containing 2.2 g alpha-linolenic acid improved cognitive function, specifically verbal fluency, despite the age-related decline, in healthy individuals with no cognitive abnormalities. Further validation studies focusing on the effects of alpha-linolenic acid on verbal fluency and executive function in older adults are needed, as verbal fluency is a predictor of Alzheimer’s disease development, important for cognitive health. ## 1. Introduction In Japan, a super-aged society, the number of patients with dementia is increasing, and it is estimated to exceed 7 million by 2025 [1]. Dementia not only causes physical impairment, social impairment and a decline in the quality of life for the individual, but also places a heavy burden on family caregivers. This has developed into a social problem, as caregivers have fallen into a state of mental and physical exhaustion [2]. In some cases, this may lead to acts of violence against older adults, and vice versa [3,4]. Although much research has been conducted into the mechanisms and treatment of dementia, no fundamental cure has been found so far, and the importance of preventing dementia and alleviating its symptoms is being emphasized. A growing body of research is showing that nutrients in foods have a positive impact on brain health maintenance suggesting an important role of nutrition in prevention. For example, the effects of diets containing docosahexaenoic acid (DHA), an ω-3 polyunsaturated fatty acid (PUFA) contained in fish oil, on cognitive function are becoming evident. According to a large meta-analysis, higher fish consumption is related to lower dementia risk [5] and a longitudinal study of more than 20 years of follow-up indicated the association between fish intake and cognitive function [6]. Moreover, Boespflug et al. showed in an intervention study that fish oil supplementation increased the bioavailability of DHA and blood supply to the posterior cingulate cortex, which resulted in increased working memory performance, suggesting the possibility of nutrient intervention on cognitive performance improvement [7], despite the decline in DHA levels with age. DHA is found in breast milk, and is necessary for brain development and directly involved in neurological function as an important component of neuronal cell membranes throughout life. Therefore, DHA consumption is required throughout life. In addition to the direct inoculation of DHA, DHA precursors can be supplied as a means of promoting DHA synthesis in the body [8]. Alpha-linolenic acid (ALA) is another ω-3 fatty acid, known as a precursor that can synthesize DHA in the body, and is a component of edible oils, such as flaxseed oil and perilla oil. The mechanism of action is beginning to be clarified, as further evidence indicated that ALA inoculation increased DHA concentration in the rodent brain. ALA administration increases brain-derived neurotrophic factor (BDNF) and acts on neural elongation [9,10]. Although the efficiency of DHA synthesis is limited, the conversion rate of ALA to EPA in humans is approximately 0.1–$21\%$ of ingested ALA, and the conversion rate to DHA is 0.1–$9\%$ [11,12], and ALA can act similarly to DHA [13]. Human studies have shown that consumption of perilla oil (equivalent to 4.3 g/day of ALA) for 12 months, assessed by the frontal assessment battery at bedside (FAB), showed changes in cognitive function after the test, compared to pre-intervention [14]. Studies have also shown that a 1 g/day increase in the intake of ALA reduces mortality from myocardial infarction by $10\%$ [15]. This suggests that as little as 1–4.3 g of ALA intake can exert a health function. Therefore, in the present study, 2.2 g/day of ALA was used. This is approximately twice the intake of omega-3 fatty acids in the Japanese population [16], but within the normal range, and is also based on the fact that the adequate intake of omega-3 fatty acids in the Japanese population is 2.1 g/day for men and 1.8 g/day for women over 75 years of age [17]. Thus, although dietary ALA may affect cognitive function positively, we considered that there is little evidence for an objective ALA intervention effect and this needs to be more clarified. Therefore, we decided to conduct a randomized controlled trial to evaluate ALA in healthy older adults, in an exploratory manner, on various cognitive functions. Flaxseed oil containing high concentrations of ALA ($60\%$ of the fatty acid composition) was determined to be a suitable source of ALA. Assessment of cognitive function focused primarily on attention and concentration, executive function, perceptual reasoning, working memory, processing speed and memory function. ## 2.1. Study Design This intervention study was approved by the ethics committee of the Tohoku University Graduate School of Medicine on 23 March 2020 (Receipt number: 2019-1-957). Written informed consent was obtained from all subjects at the time of enrollment. The study was a 12-week randomized, double-blinded, placebo-controlled clinical trial among healthy regional older adults in Miyagi prefecture, Japan. The primary outcome of the study was the cognitive function, which was evaluated individually in-person at the Tohoku University. This RCT was registered at the University Hospital medical information network (UNIM) Clinical Trial Registry (UMIN000039901). ## 2.2. Participants The local widely-read newspaper (Kahoku weekly) was used for subject recruitment, and 91 women and men living in the Miyagi prefecture, aged 65–80 years, visited the test center for screening. Sixty subjects were recruited according to the inclusion and exclusion criteria. The study was targeted at right-handedness, in order to reduce variations in cognitive ability due to the differences between right- and left-handed individuals [18]. In-person, participant-to-tester interviews were conducted using Mini-Mental State Examination (MMSE) and clinical dementia rating (CDR) to screen cognitive impairment, and using the geriatric depression scale (GDS) to screen mental status together with descriptive questions. An MMSE score of 27 and higher was set as a threshold to avoid participants with mild cognitive impairment. The sample size was set as the criterion for sample size adequacy, which can produce significant differences in cognitive psychological tests based on previous study [19]. ## 2.4. Randomization Sixty selected subjects were randomly divided into two groups and supplemented with 3.7 g/day of flaxseed oil containing 2.2 g ALA (ALA group), or iso-calorie placebo (corn oil) (CONT group) containing 0.04g ALA for 12 weeks. The test sample allocation table was generated randomly, and study subjects were assigned. The study was blinded to both participants and researchers. ## 2.5. Experimental Food and Study Settings Flaxseed oil was provided by NIPPN Corporation (Tokyo, Japan), and corn oil was purchased from J-OIL MILLS, Inc. (Tokyo, Japan), and both were individually packed without any labels. The packages were completely identical in color, shape, and size between the test foods. Compliance was monitored by having the subjects mark the calendar provided by the test center whenever the test food was consumed and asking them to bring the calendar with them when they visited the study center for assessments during the experimental period (pre-, after 6 and 12 weeks of intervention). Subjects were asked not to change their daily habits, including physical activity, diets, and daily behavior during the experimental period, and were free to take medications. Dietary habits were checked using the validated food frequency questionnaire (FFQg Ver. 5) [20], Japan, to ensure that there were no changes in diet, particularly in the intake of ω-3 fatty acids, during the study period. In addition, subjects were instructed to use the test oil as table oil during meals to avoid misuse, such as overheating the oil during cooking before ingestion. ## 2.6. Subject Background Information Collections The background data included the date of birth, comorbidities, medications treated, blood pressure, medical history (including allergies), and lifestyle information (smoking, alcohol consumption, food or dietary supplements and health food consumption). Height and weight were measured physically by a tester, and the body mass index (BMI) was calculated by dividing weight by height squared. BMI can be used as a screening for weight categories, as it correlates moderately with more direct measures of body fat [21,22]. ## 2.7. Assessment of Cognitive Function To explore a broad range of cognitive functions, there were six broad categories, all of which were closely associated with daily life activities: attention and concentration, executive function, perceptual reasoning, working memory, processing speed and memory function. All tests proceeded individually with two trained testers, who are research assistants at the Tohoku University (one tester for screening procedure, another for assessments pre-, after 6 and 12 weeks of intervention). All of the timing and details are described below. Overall cognitive status was measured by MMSE and the Montreal cognitive assessment, MoCA-J, at pre- and after 12 weeks of intervention period. Attention was measured by digit cancellation task (D-CAT). Executive functions were measured by frontal assessment battery at bedside (FAB) and Stroop test. Intelligence was measured using WAIS-IV subscales and assessing in perceptual reasoning using block design (BD), matrix reasoning (MR) and visual puzzles (VP). Working memory was measured by digit span (DS) and arithmetic (AR). Processing speed was measured by coding (CD) and symbol search (SS). The short-term memory was assessed by visual and verbal memory tests, attention and concentration were assessed by visual memory range and digit span and using the subscale of WMS-R. The summary of the assessment tasks is presented in Table 1 and the details of all tasks are described below. ## 2.7.1. Mini-Mental State Examination The MMSE is the most widely used dementia screening test in the world, which detects cognitive impairment in older adults in short time (5–10 min), but is not timed. The MMSE covers orientation, memory and attention, and tests the ability to name, follow verbal and written commands and write a sentence spontaneously [23]. Its score vary with age and education [24]. The MMSE is scored from 0 to 30. Lower MMSE scores indicate more impairment. A total score of 27 or higher is considered within the normal range without possible MCI, which ranges between 23 and 26. The score was not adjusted for age and education. ## 2.7.2. Montreal Cognitive Assessment The MoCA is a screening test with a high ability to discriminate normal cognitive function, and MCI and early onset dementia, providing quick guidance for referral and further investigation of MCI. It provides a comprehensive cognitive test and is designed to assess executive functions, higher-level language abilities, and complex visuospatial processing [25]. An MoCA score of 26 or higher out of 30 is considered within the normal range, and the test takes approximately 10 min to complete, but is not timed. ## 2.7.3. Frontal Assessment Battery at Bedside The FAB [26] assesses executive function. The FAB consists of six subtests exploring the following: conceptualization, mental flexibility, motor programming, sensitivity to interference, inhibitory control and environmental autonomy. The FAB is rated on a scale of 0–18, with lower FAB scores indicating a higher degree of executive dysfunction. ## 2.7.4. Stroop Test This test is to answer the reading of the word and the color name of the ink. First, participants practice once, then do this trial four times. They choose the ink the words represent. Second, the combination of words and ink colors are mismatched, but the ink that the words represent is chosen by not being distracted by the color of the ink. Third, the word that corresponds to the color of the ink is chosen. Fourth, the combination of words and ink colors are mismatched, but the word that corresponds to the color of the ink on which the words are written is chosen. ## 2.7.5. Digit Cancellation Task The D-CAT assesses attention. The test form consists of 12 rows of 50-digit numbers. Each row contains five random pairs of numbers from 0 to 9. Thus, one number appears five times in each row and its neighbor is determined randomly. The D-CAT consists of three such sheets. Participants were instructed to locate the designated target number and remove each number with a slash mark as quickly and as accurately as possible, until the experimenter gave a stop signal. One minute was given for each trial, so the D-CAT took a total of three minutes. On the second and third trials, the emphasis was on cancelling all indicated target numbers without omission. The main indicator for this test was the number of hits (correct answers); only the number of hits was used in the first trial. ## Coding Coding is a sub-test of WAIS-IV. This test measures processing speed. For Cd, the participants were shown a series of symbols paired with numbers. Using a key, the participants drew each symbol under its corresponding number within a 120 s time limit. The primary measure of this test is the number of correct answers. ## Symbol Search SS is a sub-test of WAIS-IV. This test measures processing speed. The SS consists of 60 items. In this test, participants visually scanned two groups of symbols (target group and search group) and indicated whether one of the symbols in the target group matched one of the symbols in the search group. Participants answered as many items as possible within a time limit of 120 s. The main measure of this test is the number of correct answers. ## Digit Span Working memory was assessed with a three-digit span task. A series of digits were read, either in the same order, in reverse order or by rearranging the digits in ascending order. ## Arithmetic Subjects responded to verbally presented arithmetic statements by mental arithmetic within a time limit. The test concerns mental operations, concentration, attention, short-term and long-term memory, numerical reasoning ability and mental agility, and continuous processing, fluid reasoning, quantitative reasoning, logical reasoning and quantitative knowledge. ## Block Design Subjects were to make the same pattern as the model pattern presented to them using building blocks, within a time limit. The blocks consisted of a red and white two-color pattern. ## Matrix Reasoning Matrix reasoning consists of choosing the most appropriate option to complete the presented incomplete matrix from the choices. ## Visual Puzzles Participants selected the three pieces that, when combined, formed the same diagram as the sample, within a time limit. ## 2.8. Wechsler Memory Scale-Revised The Wechsler Memory Scale-R (WMS-R) is a comprehensive test that assesses verbal memory, visual memory, general memory and attention and concentration functions. The verbal memory test was analyzed as the sum of logical memory and verbal versus associative, the visual memory test as the sum of graphic memory, visual versus associative and visual recall, and general memory function (short-term memory) as the sum of verbal and visual memory test results. The attention/concentration was analyzed by summing the mental control, digit span and visual memory span score. Each subtest was used according to the instruction (WMS-R David Wechsler). ## 2.9. Analysis There was no exclusion or dropout during the study, therefore, all participants who were selected during the screening were entered in the statistical analysis. Statistical analyses were performed using IBM SPSS ver.24, and the differences between the ALA group and CONT group were analyzed by an unpaired t-test. The points of change in cognitive function before and after 6 and 12 weeks of evaluation were calculated. All data are expressed as the means ± standard deviations. Significance is inferred if $p \leq 0.05.$ ## 3. Results A clinical trial was conducted to evaluate the functionality of daily consumption of flaxseed oil containing ALA on various cognitive functions. Sixty subjects were selected based on the inclusion and exclusion criteria and participated in the study of 91 screened participants. The study participant demographics and the intake of PUFA based on FFQ are presented in Table 2. It was revealed that several pre-intervention cognitive function scores were statistically different between the groups (Table 3), therefore, all the cognitive function scores were analyzed based on the change score (change between post-intervention score and pre-intervention score) for each of cognitive test. Precisely, the change scores were calculated between pre and 6 weeks after, and pre and 12 weeks after the intervention. After 12 weeks of intake, the change in verbal fluency score, a test in which participants were asked to answer as many words as possible beginning with a particular letter, such as “ka” in the Japanese language (Phonemic fluency [27]), on the FAB, a neuropsychological test that evaluates executive function, was significantly greater in the ALA group (0.30 ± 0.53) than in the control group (0.03 ± 0.49, $p \leq 0.05$) (Table 3, Figure 1). There were no significant differences in all other cognitive function test scores between the groups. ## 4. Discussion The purpose of our study was to explore the effect of 12-week daily consumption of flaxseed oil containing 2.2 g of ALA on various cognitive functions in healthy older adults without cognitive impairments, living in the community. The results of this study showed that consumption of flaxseed oil, which is high in ALA contents, improves cognitive function in healthy older adults. In particular, the results indicated positive improvements in the verbal fluency performance. While vocabulary and other language functions are thought to remain relatively unchanged with age, verbal fluency is known to decline with age and may interfere with conversation with others. In this study, verbal fluency performance was assessed as part of the FAB, which evaluates comprehensive executive function of frontal lobe function. The executive function is a higher-level function of the verbal fluency task [28] and is recognized as one of the key factors associated with the ability to set goals, make plans, modify and adjust while actually performing the actions, and carrying out effective actions in everyday life [29]. Thus, it is related to the ability of daily living/instrumental activities of daily living (ADL/IADLs) [30,31], social frailty [32] and importantly, life satisfaction [33] for older adults. In fact, such a simple verbal fluency task has a rather complex mechanism, as many cognitive functions are interrelated during this task, including semantic memory, dialectical lexical retrieval, information processing speed, inhibition, working memory, shifting performance and cognitive flexibility [29], and it includes a variety of anatomical sites. There are several possible explanations for the effects of ALA in the present study. As suggested in a previous review of the effects of DHA, it may have improved the efficiency of cognitive strategies, which decline with increasing age, by altering the structure of neuronal cell membranes in the broad anatomical regions and improving the fluidity and intercellular connectivity of the neuronal cell membrane, which declines with increasing age [34]. Examples of cognitive strategies include clustering (word generation) and switching (transfer of attention from one subcategory to another) strategies needed in verbal fluency tasks, both which are indeed said to be influenced by age [35,36] and implicated in Alzheimer’s disease [37]. Taken together, as a possible mechanism of the improved verbal fluency task, ALA was effective in a wide range of brain regions, acting broadly on neuronal structures and improving neuronal function from a cellular physiological perspective. ALA is the most commonly consumed PUFA in Japan, with a median PUFA intake of 2.09 g/day for men and 1.83 g/day for women aged 75 years and older [16]. The dietary intake of PUFA during the intervention period in both groups of study participants was only slightly higher than that of the general population and, furthermore, there were no differences between the groups, so the PUFA intake of the participants was not considered to influence the study results. In addition to this, a distinction was made between ω-3 and ω-6 and analyzed pre- and post-intervention, but there were no differences between the groups. Comparatively, the conversion of ALA to DHA in the liver is reported to be inefficient [11,12], but the ALA intake, in this case taken daily, may have had a possible accumulative effect [38] and a small amount may have been sufficient [9,39]. This view is consistent with the consideration that DHA synthesis from ALA in humans is nutritionally adequate, despite the low rate of ALA-to-DHA conversion, as reported in a recent review [13]. Furthermore, in this exploratory study on various cognitive functions, ALA intake had no effect on anything other than the verbal fluency task. Considering the duration of the study, the age of the subjects and the sensitivity of the cognitive tests, it is possible that the decline over time in various cognitive functions was less detectable. Compliance with the intake of the test substance was high during the period of this study, which may have had a positive effect on cognitive function. One of the factors that contributed to maintaining high compliance was the use of pouch containers. The pouch container was light-shielding and had the advantage of being packaged in single servings, which could be left on the table and carried on the go, making it suitable for preventing forgotten intake and maintaining quality. According to the subjects, the oil itself was tasteless, odorless, easy to use, and compatible with meals, so it had the same level of usability as salt and pepper in a meal. We believe that in this study, we were also able to create a mechanism to encourage continued intake. While the results of this study are encouraging, some limitations should be mentioned. Ideally, the recruitment of subjects should be unbiased to the general public when targeting community-dwelling older adults, but the recruitment was limited in some areas to newspaper readers, as opposed to internet users, because newspapers were used for this recruitment. It is also possible that the subjects may have been biased, as those with a particular interest in health, for example those who perceive a benefit from cognitive function tests, may have participated. Primary care centers and family physicians and researchers properly selecting the group of participants is more appropriate. To increase the generalizability of the study’s findings, it is necessary to diversify the target population in future studies. Various races and cultures also need to be considered. Second, although this study was exploratory in nature and subjects were asked to consume the test product daily in order to test its efficacy, the frequency, amount, and duration of consumption should be considered to accommodate a variety of dietary habits. Furthermore, since the effect on verbal fluency detected in this study was only detected using one item, a subscale of the FAB, it is necessary to use more specialized indexes related to verbal fluency in order to establish further evidence, and to increase the sample size, as this experiment is still at the preliminary research stage. Regarding the impact of the results of this study, the importance of maintaining and even extending one’s verbal fluency capacity is one of the most important functionalities for older adults to age healthily. Many people, and the general public are unaware that some cognitive functions do not decline with age. Even the elderly, if they learn, should be able to increase their knowledge and vocabulary, help social interactions with others, and possibly form societal roles. In this context, if ALA intake can support verbal fluency even with increasing age, which is important for communication with others, it could promote social participation among older adults. In summary, this RCT, evaluating the functionality of daily consumption of flaxseed oil containing 2.2 g of ALA, improved verbal fluency in healthy individuals aged between 65 to 80 years, with no abnormalities in cognitive function, despite declines with age. Since the improvement in verbal fluency is significant in cognitive health, as it is one of the factors of Alzheimer’s disease progress [37], further validation studies, with a focus on ALA’s impact on verbal fluency and executive function, are needed to extend the obtained evidence in this study to older adults. ## References 1. Future Aging of the Population 2. 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--- title: The Role of Globularia alypum Explored Ex Vivo In Vitro on Human Colon Biopsies from Ulcerative Colitis Patients authors: - Najla Hajji - Ilaria Russo - Jessica Bianco - Ornella Piazza - Paola Iovino - Antonella Santonicola - Carolina Ciacci journal: Nutrients year: 2023 pmcid: PMC10056518 doi: 10.3390/nu15061457 license: CC BY 4.0 --- # The Role of Globularia alypum Explored Ex Vivo In Vitro on Human Colon Biopsies from Ulcerative Colitis Patients ## Abstract The existing literature indicates that Globularia alypum L. (GA) influences inflammation and oxidative stress modulation in rats and in vitro. The present study aims to investigate the effects of this plant in patients with ulcerative colitis (UC) and normal controls. In our experiments, we pretreated colon biopsies from 46 UC patients and normal controls with GA leaves aqueous extract (GAAE) used at two concentrations (50 and 100 µg/mL) for 3 h, followed by Lipopolysaccharides (from Escherichia coli) stimulation. We analyzed the effects on inflammation by studying the cyclo-oxygenase-2, the intercellular adhesion molecule-1, the nuclear factor kappa B, and p38 mitogen-activated protein kinase expression. Moreover, we assessed the levels of interleukin 6, the superoxide dismutase activity, and nitric oxide release in the supernatant of cultures. Our data showed that GAAE influences UC patients and normal controls for most studied markers and enzymes. These results acknowledge, with some scientific evidence, the traditional belief in the anti-inflammatory properties of GA and represent the first demonstration of its effect in a human in vitro model of inflammatory conditions. ## 1. Introduction Globularia alypum L. (GA) is a Mediterranean-growing plant in the Globulariaceae family [1]. Historically, mainly the leaves of GA were used in traditional medicine to treat various ailments induced or accompanied by inflammation, including rheumatism, infections, gout, typhoid, intermittent fever, constipation, and diabetes [2,3,4]. Some research has demonstrated in vitro that GA has antibiotic properties and antileukemic powers [3,5]. The active principles in the flowers, leaves, and stems of GA methanol and dichloromethane extracts are supposed to antagonize the contractile response induced by different neurotransmitters [6]. More recently, GA methanol extract has shown a potent anti-oxidant effect [1,7,8], while GA petroleum ether extract showed anti-tuberculosis activity against Mycobacterium tuberculosis [7]. Numerous studies have demonstrated that GA is abundant in secondary metabolites, such as polyphenols and iridoids [9,10,11,12]. The aqueous GA leaves extract (GAAE) contains active compounds with androgenic qualities that enhance active spermatogenesis in mice at a daily dose of 100 mg/kg for 15 days, and so has the potential for human male infertility treatment [13]. Another known effect of GAAE is the hypoglycemic role coupled with the hypotriglyceridemic effect, demonstrated in rats by repeated oral administration [6]. GA works in the muscle and kidney by lowering lipid peroxidation and making anti-oxidant enzymes work better [8]. Previous in vivo studies showed a laxative effect of GAAE in constipated rats [14], and an anti-inflammatory effect in rats with induced ulcerative colitis (UC) by decreasing the pro-oxidant enzyme activities, in particular superoxide dismutase (SOD) [11]. Furthermore, an acute toxicity analysis demonstrated that the aqueous extracts are relatively safe because the GA LD50 value in rats was found to be above 14.5 g/kg [15], and a 10 g/kg dosage was not deadly in mice [13]. Despite the plant’s long history of usage as a folk treatment in the Mediterranean, there is currently only a small amount of scientific research evaluating the safety and anti-inflammatory benefits of administering the GAAE to humans. Among human diseases causing inflammation, UC is an idiopathic chronic inflammatory disease of the gastrointestinal tract [16]. Colitis, rectal bleeding, diarrhea, stomach discomfort, and weight loss are all symptoms of UC [17,18]. Even though UC’s pathophysiology is still unclear, experimental and clinical evidence suggests that chronic intestinal inflammation may be caused by a dysfunction of the immune system, smoking, and psychological factors [19]. These factors can cause inflammatory cytokines and chemokines to be released and signaling pathways to be turned on. These include tumor necrosis factor-α, nuclear factor kappa B (NF-κB), mitogen-activated protein kinases p38 (p38 MAPK), interleukin -1β, interleukin 6 (IL-6) and 17, interferon-γ, cyclo-oxygenase-2 (COX-2), and intercellular adhesion molecule 1 (ICAM-1) [20,21]. It is possible that oxidative stress, which is linked to persistent intestinal inflammation, is a key factor in the development of the condition [22]. The current work investigates some of the known anti-inflammatory and anti-oxidant capabilities and effects of GAAE in an ex vivo in vitro model of human gut colonic explants from UC patients and controls. ## 2.1. GA Collection and Extract Preparation GA was gathered in March 2018 from the region of Boussalem (north-west Tunisia). This specimen has been cataloged as GA-ISBB-$\frac{03}{03}$/18 and is housed in the herbarium of the Higher Institute of Biotechnology of Beja at Jendouba University. For 72 h at 40 °C in an incubator, GA leaves ($10\%$, weight/volume) were dried and then ground in an electric mixer. The plant powder was then added to distilled water, and the mixture was incubated at room temperature for 24 h with magnetic stirring. After centrifugation (10 min, 10,000× g), the resulting GAAE was lyophilized and kept at −80 °C [23]. ## 2.2.1. Patients and Collection of Biopsies With their agreement, we gathered data and biopsies from 31 patients with UC who underwent colonoscopy for diagnosis or routine follow-up and 15 controls who also underwent colonoscopy for hemorrhoid bleeding or irritable bowel syndrome but had no abnormal findings. Several biopsies were collected from various areas of the colon throughout the procedure. Every biopsy was cultured right after removal by placing it in a sodium chloride solution ($0.09\%$). ## 2.2.2. Biopsy Culture and GAAE Treatments The cultured intestinal explants provide an ex vivo in vitro model that faithfully recapitulates the intestinal environment down to the individual cell populations and their interdependencies. The biopsies were put villous-side up on a stainless steel mesh and then placed above the central well of an organ culture dish (Falcon, Franklin Lakes, NJ, USA). The biopsies’ slicing surfaces were brought into contact with the culture medium by adding it to each well. DMEM F12 (16 mL), fetal calf serum (3 mL), 50,000 IU penicillin, and 5000 IU streptomycin made up the culture medium. At 37 degrees Celsius, the dishes were gassed with $95\%$ oxygen and $5\%$ carbon dioxide in an anaerobic jar [16]. One well contained just culture medium as a negative control, four wells were pretreated with GAAE (50 and 100 µg/mL), and one well served as a positive control. After 3 h of incubation, each culture was given 1 µg/mL of Escherichia coli-lipopolysaccharides (EC-LPS) and left to sit overnight. We terminated all cultures after the first day. For further analysis, the tissue was flash-frozen in liquid nitrogen and kept at −80 °C. ## 2.3. Immunohistochemistry All the cultured biopsies obtained from the 31 UC and 15 control colons were tested by immunohistochemistry. Cryostat 5 µm thick mucosal slices were created and utilized for immunological labeling. Sections were fixed in acetone for 15 min before being treated separately with the following antibodies: human anti-COX-2 (diluted 1:100 CAYMAN, chemical business, Ann Arbor, MI, USA), human anti-ICAM-1 (diluted 1:100; Santa Cruz Biotechnology, Dallas, TX, USA), Human p38 MAPK (diluted 1:200; Bioss Antibodies, Woburn, MA, USA). However, for the NF-κB staining, after being fixed in $4\%$ formaldehyde for 15 min, sections were permeabilized for 5 min with Triton and washed three times in PBS. Then, they were incubated in block buffer ($0.3\%$ Triton, $10\%$ BSA in PBS) for one hour. The human anti-NF-κB antibody (Bioss Antibodies, Woburn, MA, USA, diluted 1:100) was added and incubated overnight. After the first incubation, all sections were washed three times with PBS. Alexa Fluor 488 conjugated anti-mouse IgG was added to the sections for 60 min at room temperature, and the sections were then washed three times with PBS. The tissue sections were then put in a solution of DAPI (1:1000) for 5 min, after which they were mounted with a solution of $20\%$ glycerol in PBS. A fluorescence examination with a Nikon eclipse was used to examine the data. Object 20X and software for processing images were used to take the pictures. ## 2.4. Western Blot Samples of the intestine were broken up in RIPA buffer in the presence of protease and phosphatase inhibitors. The Bradford (Biorad, Hercules, CA, USA) method measured the amount of proteins in the supernatant. The concentration of proteins was calculated using a standard range of BSA with known concentrations. From each biopsy, 40 μg of total proteins are adjusted with lysis buffer (RIPA), mixed with 10 μL of LAEMMLI buffer, and then denatured at 95 °C for 5 min. Ten percent of sodium dodecyl sulfate/polyacrylamide gel electrophoresis was used to put the lysates on the gel (SDS-PAGE). The migration was performed at 80 V and subsequently at 120 V. The proteins were then transferred from the gel to a nitrocellulose membrane using a transfer cassette (Biorad, Hercules, CA, USA) and incubated for one hour at room temperature with a blocking solution ($5\%$ dehydrated milk powder dissolved in TBST). Monoclonal antibodies against COX-2, NF-κB, ICAM-1, and p38 MAPK were applied to membranes overnight at 4 °C. Following three TBST buffer washes, the membranes were treated for one hour with horseradish peroxidase-linked goat anti-mouse or anti-rabbit secondary antibodies (1:1000). The rabbit antibody-actin and lamin A/C were used as internal controls (1:1000; ABCAM, Cambridge, UK). The Chemidoc was used to visualize the findings of Western blot detection reagents (Clarity Western ECL substrate, Biorad, Hercules, CA, USA). ## 2.5. Nitric Oxide Dosage As previously described [24], nitric oxide (NO) levels in the gas phase were measured using a Sievers NOA 280 A chemiluminescence analyzer. To liberate gaseous NO from dissolved NO and nitrite, 100 µL of culture medium samples were pumped into a nitrogen purge tube containing $1\%$ sodium iodide in glacial acetic acid solution. The sample gas was then exposed to ozone in the reaction vessel, producing activated nitrogen dioxide, which was detected with a red-sensitive photomultiplier tube and recorded with an integrated pen recorder. Using a calibration curve developed by examining a series of sodium nitrite standards, the area under the curve for each sample was converted to picomolar NO. ## 2.6. The SOD Activity The SOD activity was assessed using the Misra and Fridovich technique [25], which is based on SOD’s capacity to convert the superoxide anion to peroxide. At basic pH, hydrogen competes with superoxide anion for the autoxidation of epinephrine. In brief, 5 µL of the sample (biopsy lysate supernatant) was added to the bovine catalase (0.4 U/µL) buffered with carbonate/sodium bicarbonate (62.5 mM; pH 10.2). The optical density was determined at 480 nm and adjusted to zero. The reaction solution was then supplemented with epinephrine (5 mg/mL), and SOD activity was evaluated by measuring changes in absorbance every 30 s for a total duration of 5 min at 480 nm. ## 2.7. ELISA Test for IL-6 Assay The ELISA method was used to measure the amounts of IL-6 in the supernatant, and a commercial kit was utilized for the measurement (MyBioSource, San Diego, CA, USA). The protein levels were determined by employing a microplate reader with the 450 nm wavelength setting (Tecan Sunrise RC, Tecan, Mannedorf, Switzerland). The amounts of protein were adjusted such that they were equivalent to the conventional levels of protein. ## 2.8. Statistical Analysis Data are presented as means SEM and were carried out using Student’s t-test for paired and unpaired data when necessary. Values were considered statistically significant at $p \leq 0.05.$ ## COX-2 and ICAM-1 The expression of ICAM-1 in situ (Figure 1) was greatly enhanced by EC-LPS in normal colons (Figure 1B). However, biopsies pretreated with 50 and 100 µg/mL of GAAE before being treated with EC-LPS exhibited a considerable reduction in ICAM-1 in comparison to EC-LPS alone (Figure 1C,D). Compared to non-treated biopsies, biopsies treated simply with GAAE revealed a normal ICAM-1 expression (Figure 1E). Supplementary Tables S1–S5 summarize the results and the statistical analyses of all experiments. The COX-2 in situ expression is shown in Figure 2. The EC-LPS has increased COX-2 positive cells compared to the control M. Results from GAAE pre-treatment 50 µg/ml were almost like EC-LPS treatment (Figure 2C), but the dose of 100 µg/ml showed a significant decrease of positive cells (Figure 2D). Biopsies treated only with GAAE (100 µg/ml) showed almost no COX-2 positive cells (Figure 2E). ## Effect on NF-κB and p38 MAPK Most controls in situ (Table S1, Figure 3B and Figure 4B) have demonstrated an upregulation of NF-κB (Figure 3) and p38 MAPK (Figure 4) following EC-LPS treatment. Both NF-κB and p38 MAPK were more strongly affected by GAAE at the higher of its two dosages (Figure 3C,D and Figure 4C,D). Biopsies cultured in the absence of treatment or with 100 µg/mL GAAE but without EC-LPS stimulation exhibited reduced NF-κB and p38 MAPK (Figure 3E and Figure 4E) expression (Figure 3A and Figure 4A). The statistical analyses of the immunofluorescence are shown in the Supplementary Tables S1–S5. ## Effect on IL-6 Production The EC-LPS challenge caused an increase in the levels of IL-6 in the biopsies. Still, this rise was suppressed when the colons were pretreated with either 50 or 100 µg/mL of GAAE (Figure 5). The levels of IL-6 in cultures that were simply treated with 100 µg/mL of GAAE were much lower than those in the control M (Medium). In the biopsies derived from UC patients (Figure 12), we observed an over-expression of this cytokine compared to control patient cultures (Figure 5). A production increase was detected in most of the cultures after the EC-LPS stimulation (Figure 12, EC-LPS). GAAE pre-treatment significantly affected EC-LPS response, while GAAE alone did not significantly affect IL-6 production compared to non-treated biopsies M. ## Effect on SOD Activity After EC-LPS stimulation, there was a discernible rise in the measured enzyme activity. This activity decreased after GAAE pre-treatment, especially with 100 µg/mL(Figure 6). The SOD activity was also evaluated from the biopsies of six UC patients (Figure 13). An increase in enzyme activity was detected after EC-LPS stimulation, which was partially reduced after GAAE pre-treatment. ## NO Production The EC-LPS stimulation strongly increased NO production in all biopsies (Figure 7). The pre-treatment with GAAE decreased NO production in controls, especially with 100 µg/mL. The treatment with GAAE without EC-LPS stimulation showed the same level of NO production of control cultures M. ## Effect on COX-2 and ICAM-1 Activity Results from the immunohistochemistry of biopsies from UC patients showed a more interesting effect of EC-LPS on COX-2 and ICAM-1 expression (Figure 8 and Figure 9), respectively. Supplementary Tables S1–S5 summarize the results and the statistical analyses of the immunofluorescence. Western blotting results confirmed the in situ results (Figure 8F and Figure 9F) and proved the strong effect of GAAE in decreasing those markers. GAAE decreased ICAM-1 expression to $49.6\%$ and $33.3\%$ when combined with EC-LPS. GAAE decreased the COX-2 expression by $62.5\%$ and $20\%$ in combination with EC-LPS. ## Effect on NF-κB and p38 MAPK Expression In UC patients, NF-κB and p38 MAPK expressions were mostly higher in patients under EC-LPS stimulation (Table S2; Figure 10B and Figure 11B). On the other hand, GAAE had a visible effect on decreasing p38 MAPK (Figure 11C), and NF-κB (Figure 10C) activity in the presence of EC-LPS and almost attenuated its activity in the absence of EC-LPS stimulation (Figure 10D). The results and the statistical analyses of these figures are presented in the Supplementary Tables S1–S5. The determination of the expression level of both NF-κB and p38 MAPK was also performed by Western blotting, which proved the antagonist effect of GAAE, particularly on NF-κB expression (Figure 10F and Figure 11F). The expression of NF-κB was decreased by $45.1\%$ in the presence of GAAE alone and by $73.6\%$ when GAAE was added with the presence of EC-LPS. Meanwhile, the expression of the p38 MAPK was decreased by $49\%$ under GAAE treatment and by $61\%$ in the co-presence of GAAE and EC-LPS. ## Effect on NO Production The EC-LPS stimulation increased NO production in most cultures (Figure 14). In those cultures, the pre-treatment with GAAE decreased NO production level either in biopsies treated in the presence or absence of EC-LPS. ## 4. Discussion The increased interest in medicinal plant extracts in treating intestinal inflammatory diseases has prompted several clinical studies assessing the potential pharmacological properties of plants, their side effects, and costs [26]. We investigated GAAE treatment in colon explants from controls, and UC patients challenged with EC-LPS, showing a significant control of the inflammation. In our setting, normal control colon biopsy cultures showed an increase in levels of NO, SOD activity, COX-2, NF-κB, ICAM-1, p38 MAPK, and IL-6 expression upon stimulation with EC-LPS. In addition, colon biopsies from UC patients showed an increase in NF-κB and COX-2 expression under the EC-LPS effect. However, in UC, the ICAM-1 and p38 MAPK, already increased for the disease-induced inflammation, were less affected by the EC-LPS exposure than in controls. The GAAE pre-treatment remarkably decreased COX-2 and NF-κB. In addition, IL-6 levels increased with EC-LPS exposure in UC and were partially inhibited after GAAE pre-treatment. Previous studies in healthy rats [27] and human colon biopsies taken from healthy subjects [28] revealed that EC-LPS exposure raised several inflammatory indicators, including IL-6. EC-LPS works as a switch for macrophage activation, as indicated by increased production of IL-6, NO, tumor necrosis factor-α (TNF-α), prostaglandin E2, interleukin-1β, IL-10, inducible nitric oxide synthase (iNOS), and monocyte chemoattractant protein 1 [29,30]. MAPKs and NF-κB signaling pathways were proposed to be the two major intracellular molecular pathways involved in the inflammatory cascade response to EC-LPS activation in RAW264.7 cells [30]. Our data indicate that GAAE pre-treatment reduces the activity of some of those markers in colon biopsies and further demonstrates that NF-κB is primarily involved in EC-LPS inflammatory activation. The observed high level of ICAM-1 before the UC biopsies stimulation may explain why the EC-LPS exposure had no high influence on the ICAM-1 level. On the other hand, GAAE inhibited ICAM-1 much more in normal biopsies than in UC samples. Therefore, GAAE seems to act preventatively against ICAM-1 expression more than curatively. The NF-κB is essential for producing pro-inflammatory genes, such as IL-6 [31]. IL-6 is associated with intestinal epithelial cell proliferation [32]. Recent research suggests a relationship between chronic inflammatory disorders and IL-6 signaling [33,34]. NF-κB suppression may reduce cytokine production and affect ROS/RNS generation in inflammatory bowel disease patients, particularly during the disease’s active phase [35]. A study suggested a positive link between NOS-derived NO and IL-6, IL-17A, and IL-23 plasma levels in inflammatory bowel disease patients [36]. The radical scavenger NO may also mediate pro-oxidant actions that take over when there is inflammation or immunological activity in the gastrointestinal system [37]. Over a prolonged period, the overall quantity of NO in the inflamed gut mucosa seems significantly high [32]. In the present study, UC colon biopsy demonstrated an increase in oxidative stress, as evidenced by the up-regulation of SOD and NO production upon EC-LPS stimulation. The present findings are consistent with prior in vivo investigations in rats, utilizing GAAE as a therapy for acetic acid-induced colitis [11]. The activation of SOD is thought to protect the intestinal tissues from oxidative damage caused by inflammation and oxidative stress. The levels of SOD in the peripheral blood of patients with inflammatory bowel disease are presently being employed as a biomarker of oxidative stress [38]. Our data confirm the GAAE’s ability to scavenge ROS [10] and establish anti-oxidant enzyme levels, protecting against colon inflammation [11]. The study limitations were the small number of individuals included and the limited number of biomarkers investigated. The number of individuals was initially greater than presented here. However, we had to exclude many cases because of inadequate culture conditions or the small size of biopsies, which limited the number of tests, giving incomplete results. As per the limited number of biomarkers assessed, we chose the biomarkers directly involved in illness manifestation and activation by EC-LPS. Other biomarkers, particularly cytokines, might be implicated; however, the size of the biopsies and the amount of the culture media, together with the several repeats for each test, limited the study’s ability to cover a broader range of tests. In conclusion, data from our pilot trial formulate the hypothesis that GAAE should be further investigated as an adjuvant therapy for UC/inflammatory bowel diseases, but more interestingly, to prevent chronic intestinal inflammation. ## References 1. Ghlissi Z., Kallel R., Sila A., Harrabi B., Atheymen R., Zeghal K., Bougatef A., Sahnoun Z.. 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--- title: Role of Insulin Resistance as a Mediator of the Relationship between Body Weight, Waist Circumference, and Systolic Blood Pressure in a Pediatric Population authors: - Simonetta Genovesi - Laura Montelisciani - Marco Giussani - Giulia Lieti - Ilenia Patti - Antonina Orlando - Laura Antolini - Gianfranco Parati journal: Metabolites year: 2023 pmcid: PMC10056524 doi: 10.3390/metabo13030327 license: CC BY 4.0 --- # Role of Insulin Resistance as a Mediator of the Relationship between Body Weight, Waist Circumference, and Systolic Blood Pressure in a Pediatric Population ## Abstract Excess weight and high waist circumference (WC) are associated with increased blood pressure (BP), starting from the pediatric age. Insulin resistance is associated with elevated BP in childhood. The aim of the study was to assess the role of insulin resistance in mediating the relationship between body mass index (BMI), WC, and BP values in a pediatric population referred to a cardio-pediatric center for the presence of one or more cardiovascular risk factors. In 419 children (mean age 10.7 [standard deviation, SD 2.5] years), the following parameters were collected both in basal conditions and after 18.6 (SD 9.3) months of follow-up during which a treatment based on lifestyle and dietary modifications was given: systolic and diastolic BP (SBP and DBP), WC, plasma glucose, and insulin values. The HOMA (Homeostasis Model Assessment)-index was considered as an expression of insulin resistance. At baseline there was a significant correlation between HOMA-index and SBP z-score (β = 0.081, $$p \leq 0.003$$), and insulin resistance was a mediator of the relationship between BMI and SBP z-score ($$p \leq 0.015$$), and between waist circumference to height (WtHr) and SBP z-score ($$p \leq 0.008$$). The effect of BMI z-score modifications on SBP z-score changes from baseline to follow-up was totally mediated by HOMA-index changes ($$p \leq 0.008$$), while HOMA-index only partially mediated the effect of WtHr modifications on SBP z-score changes ($$p \leq 0.060$$). Our study strongly suggests that, in a pediatric population at cardiovascular risk, the HOMA-index is an important mediator of the relationship between BMI, WC and SBP. ## 1. Introduction Several studies have shown an association between excess weight and elevated blood pressure (BP) values in children and adolescents [1,2]. Similar data have been observed regarding the relationship between waist circumference (WC) and BP in this age group. Waist circumference improves the ability of body mass index (BMI) to identify hypertension (HT) in obese (OB) children; moreover, WC correlates with BP values in all weight classes [3,4]. However, not all overweight (OW) and OB children and adolescents have high BP values. In addition, a certain fraction of children and adolescents have elevated BP, even without being in excess weight [5,6,7]. It has been hypothesized that in pediatric populations insulin resistance is associated with HT independently of possible confounding factors [8]. It is also known that OW or OB subjects often have higher values of plasma insulin and insulin resistance (expressed as Homeostasis Model Assessment, HOMA-index) than normal-weight peers, even in pediatric age [9]. In previous studies, we described that non-pharmacological treatment based on lifestyle modifications was associated with a reduction in BP, BMI and WC values in a pediatric population with excess weight and/or elevated BP values [10,11]. The question we wanted to address with this study is whether the association between excess weight (and high WC values) and elevated BP is, at least in part, mediated by insulin resistance levels. To this aim, we evaluated the role of the HOMA-index in determining the relationship between BMI, WC, and BP values in a pediatric population referred to a cardio-pediatric center for the presence of one or more cardiovascular risk factors including elevated BP, and/or excess weight, and/or dyslipidemia. First, we performed a cross-sectional analysis to assess the relationship between systolic (SBP) and diastolic (DBP) BP z-score, BMI z-score, waist-to-height ratio (WtHr) and HOMA-index at recruitment. Next, a longitudinal analysis was conducted to evaluate the relationship between changes in SBP and DBP z-score, BMI z-score and WtHr, and HOMA-index modifications after a period of non-pharmacological treatment based on the correction of dietary and poor lifestyle behaviors. ## 2.1. Subjects The study population consists of a cohort of 419 children and adolescents who, due to the presence of elevated BP and/or excess weight and/or altered lipid profile, were consecutively referred to the Cardiovascular Risk Assessment in Children Unit from December 2012 to October 2022. The children were referred by family pediatricians who follow children of all socio-demographic conditions free of charge. The Unit is public and access is free, as is the care provided. The following exclusion criteria did not allow enrollment: type 1 and type 2 diabetes ($$n = 2$$), secondary hypertension of any type ($$n = 6$$), ongoing antihypertensive drug treatment ($$n = 2$$). Children for whom it was necessary to start pharmacological treatment to reduce BP values during follow-up ($$n = 22$$) were excluded from the final analyses. The Unit for Cardiovascular Risk Assessment in Children consisted of the following members: a pediatrician, a nephrologist, a cardiologist, and a nutrition expert. ## 2.2. Baseline and Follow-Up Assessments In all children, the following parameters were examined at baseline and at the end of the follow-up period: weight, height, WC and BP. Between the baseline and final assessment, a minimum of three to a maximum of six additional periodic visits were made (approximately one every three to four months), during which the same variables were recorded again. At each visit, family members were asked for information to assess adherence to the directions given by the team: change in diet, increase in physical activity, reduction in time spent watching television or playing video games, and the responses were recorded in the participants’ medical records. If a worsening of weight and/or blood pressure was found during a follow-up visit compared with the previous visit, an interview was conducted by the staff (pediatrician, cardiologist, and nutritionist) to ascertain whether and what part of the planned intervention had been disregarded (non-adherence to the diet, lack of physical activity, excess sedentary lifestyle). In particular, a careful dietary history was repeated to understand if the child had particular difficulties in accepting the proposed diet. In this case, modifications and substitutions were suggested, maintaining the total caloric intake and the balance between different macronutrients, but taking into account the patient’s tastes and preferences. It was also checked whether there were any difficulties or errors on the part of the parents in the preparation of meals, in the dosage of foods given, and in the amount of the seasoning. If deemed necessary, more frequent follow-up visits were prescribed. In case of concerns or problems outside of the visits, parents had the option of contacting members of the clinical team (physicians or nutritionists) via e-mail and obtaining the necessary explanations. ## 2.3. Anthropometric Parameters and Blood Pressure The anthropometric parameters measured were height, body weight, and WC. The precision applied for data recording was 100 g for body weight and 1 cm for height. Body mass index was calculated as participants’ weight in kilograms divided by the square of height in meters. Body mass index z-scores were calculated using prevention tables from the Centers for Disease and Control prevention charts available at https://www.cdc.gov/growthcharts/clinical_charts.htm (accessed on 9 January 2023). Weight class was defined according to the International Obesity Task Force classification [12] distinguishing between normal weight (NW), OW and OB individuals. Waist circumference was recorded with an accuracy of 1 cm while the study participant was standing. Waist measurement was performed as recommended by Lohman et al. [ 13,14]. Waist-to-height ratio (WtHr) was obtained by dividing WC by height. Blood pressure was measured with a specific oscillometric device validated for use in children and recommended by the 2016 European Society of Hypertension Pediatric guidelines (Omron 705IT; Omron Co, Kyoto, Japan) [15]; special care was taken to use an appropriately sized cuff. Blood pressure values were recorded after a rest period of at least 5 min and while participants were seated. The measurement was taken 3 times (at intervals of a few minutes), and the mean value of the second and third measurements was recorded. The percentiles and z-scores of SBP and DBP were calculated based on nomograms from the National High Blood Pressure Education Program (NHBPEP) Working Group on High Blood Pressure in Children and Adolescents [13,14]. Children were classified according to the mean of the two measurements as follows: normotensive (NT) if the percentiles of SBP and DBP were both <90th; high normal (HN) if the percentiles of SBP and/or DBP were ≥90th, but both <95th; hypertensive (HT) if the percentiles of SBP and/or DBP were ≥95th. ## 2.4. Biochemical Parameters Blood samples were taken after a 12-h fasting period to measure serum concentrations of total cholesterol, high-density lipoprotein (HDL), triglycerides, glucose, and insulin. Commercial kits, normally used for routine patient examinations, were used for all analyses. [ Cobas Roche colorimetric enzymatic cholesterol Gen.2 test, for total cholesterol assay; homogeneous-phase colorimetric enzymatic test HDL cholesterol Gen.4 Cobas Roche, for HDL cholesterol; colorimetric enzymatic test Triglycerides Cobas Roche, for triglyceride assay; hexokinase enzymatic method Glucose HK Gen.3 Cobas Roche, for glucose assay; immunoassay in ElectroChemiLuminescence Elecsys Insulin Cobas Roche, for insulin assay]. The HOMA index was calculated by dividing the product of serum insulin (µU/mL) and serum glucose (mmol/L) by 22.5 [16]. ## 2.5. Recommended Lifestyle Modifications All participants were encouraged to engage in at least two to three hours of structured physical activity per week [17], to engage in more spontaneous unstructured physical activity, and to reduce sedentary activities such as playing video games or watching TV to a maximum of one hour per day, as recommended by the Italian Society of Pediatrics (https://sip.it/$\frac{2017}{09}$/25/non-solo-sport-ma-anche-gioco-la-piramide-dellattivita-fisica-e-motoria-per-combattere-lobesita/, accessed on 21 February 2023). Children were encouraged to choose sports of their liking for an appropriate number of hours per week. All participants were given general advice on how to achieve a healthy and balanced diet (more fruits and vegetables, low-fat dairy products, lower intake of free sugars and elimination of soft drinks) with proper salt intake (a maximum of 5 g per day near equivalent to 2 g of sodium) following the World Health Organization (WHO) guideline) (www.who.int/data/gho/indicator-matadata-registry/imr-details/3082, accessed on 21 February 2023) At the baseline visit, parents of the children and adolescents were interviewed by an experienced nutritionist to assess the eating habits and physical activity levels of the participants. Based on the information obtained, appropriate and individualized changes in lifestyle and nutritional habits were proposed to each participant. Once it was determined what the appropriate caloric intake was and how it should be divided into protein, glycides, and lipids in the dietary pattern, specific interviews were conducted between the child, parents, and nutritionist to perform a diet that took into account the individual child’s preferences and the families’ needs. A personalized dietary scheme was prepared for all participants with the help of specially designed software (Dietosystem, Ds Medica, Milan, Italy). Depending on each participant’s actual body weight and blood pressure status (excess weight isolated, or high, or a combination of both risk factors), additional specific recommendations were provided. Excess weight. Overweight and obese subjects were subjected to a weekly dietary program, the calorie content of which had been previously calculated based on the Schofield equation [18], which considers basal metabolic rate, and based on functional metabolism [19]. Young children were asked to follow a balanced normocaloric regimen that matched the estimated energy expenditure, while adolescents with severe excess weight were recommended to follow a mildly hypocaloric (−$10\%$) diet. The dietary-behavioral treatment implemented to reduce HOMA-index values coincided with that to reduce excess weight. Qualitatively, consumption of non-starchy fruits and vegetables rich in fiber, vitamins and minerals, citrus fruits, legumes and preferably whole grains, lean meats, fresh cheeses, fish and nuts, and unsweetened dairy products was encouraged. The intake of sugary, carbonated and soft drinks was excluded. Fruit juices, starchy vegetables such as potatoes, squash, and corn, processed snacks and canned foods, sweets, ice cream, and chocolate were limited. Elevated blood pressure. In HN and HT individuals it was proposed to reduce salt intake to less than 5 g per day, following the WHO guidelines. The study protocol was approved by the Local Ethics Committee (Istituto Auxologico, Milano, Italy) (RICARPE 2015_10_20_02) and informed consent was obtained from the children’s parents. ## 2.6. Statistical Analysis Continuous variables were expressed as mean and standard deviation and categorical variables by count and relative frequency (%) of each category. The raw data of BMI and SBP and DBP were converted to z-score. The BMI and SBP z-scores are a function of age and sex, according to the formula:Z-score = [(X/M)L − 1]/(LS) were L is the Box-Cox transformation, M is the median, and S is the generalized coefficient of variation [20]. Univariate analyses to compare the characteristics of the children at baseline and at follow-up were conducted through the t-test for paired data and by the McNemar test for categorical variables. The relationship between the variables under the baseline condition was investigated through the linear regression coefficient and graphically by scatter plot with regression line. To investigate the effect of the time between the baseline time and the follow-up the deltas of each variable of interest have been calculated: all the deltas used in the analysis were obtained as the difference between the baseline value and the follow-up value of each variable. At the follow-up time, univariate linear regression models explored the association between the outcome, i.e., delta SBP z-score, and delta of explanatory variables. On the basis of the significance of the regression coefficients at baseline, a mediation analysis was performed to deeply understand the role of the HOMA-index as possible mediator between BMI z-score or WtHr on the SBP z-score. The mediation analysis was performed to evaluate the role of HOMA-index in mediating the relationship between BMI z-score, WtHr z-score, and BP z-score values under baseline conditions and the effect of changes in BMI z-score and WtHr on changes in BP z-scores after the follow-up period. In the mediation analysis, a third variable (called the mediator) is added to the analysis of the relationship between the independent and dependent variables in order to improve understanding of this relation. A mediator is a variable that transmits the effect of the exposure on an outcome. The goal of this analysis is to partition the total treatment effect into two components: the indirect effect that occurs due to the mediator and the direct effect that captures the treatment effect, net of the mediator. Perfect mediation occurs when the relationship between a treatment and the outcome has been completely explained by the mediator. In addition to the mediation analysis at baseline, another mediation model was applied to verify the mediation role of the delta HOMA-index between the delta BMI z-score or delta WtHr on the delta SBP z-score. ## 3. Results Table 1 describes the characteristics of the study population under baseline conditions and at the end of follow-up [18.6 (standard deviation, SD 9.3) months]. At recruitment, the mean age was 10.7 (SD 2.5) years. Fifty-seven percent of the children were male and $45.4\%$ had begun pubertal development. The percentage of NW individuals was $17.9\%$, OW were $34.4\%$ and OB were $47.7\%$. The percentages of study participants with normal BP, HN and HT were $56.6\%$, $14.5\%$ and $28.9\%$, respectively. HOMA-index values were significantly higher in children with SBP and/or DBP values ≥ 90th percentile (HN + HT) compared with those with values < 90th percentile [2.61 (1.9) vs. 3.03 (2.0), $$p \leq 0.030$$]. Similar results were observed regarding subjects with SBP z-score values ≥ 90th percentile in comparison with those with values < 90th percentile [2.63 (1.9) vs. 3.04 (2.7), $$p \leq 0.038$$]. In contrast, there was no difference between the HOMA-index values of children with DBP z-score ≥ 90th percentile compared with those with DBP z-score <90th percentile [2.73 (1.9) vs. 3.18 (2.0) $$p \leq 0.092$$]. HOMA-index values were significantly associated with both BMI z-score (β = 0.151, $p \leq 0.001$) and WtHr value (β = 1.250, $p \leq 0.001$). There was also a significant association between HOMA-index and SBP z-score (β = 0.081, $$p \leq 0.003$$), but not between HOMA-index and DBP z-score (β = 0.030, $$p \leq 0.123$$). The association between HOMA-index and SBP z-score was significant both in children with excess weight (β = 0.066, $$p \leq 0.019$$) and in those with normal weight (β = 0.391, $$p \leq 0.012$$) (Figure 1). BMI z-score was not significantly related to either SBP (β = 0.088, $$p \leq 0.128$$) or DBP (β = 0.050, $$p \leq 0.163$$) z-score. Waist to height ratio correlated significantly with DBP z-score (β = 0.0100.021, $$p \leq 0.020$$), but not with SBP z-score (β = 0.010, $$p \leq 0.196$$). The red lines delineate the distribution of variables in NW individuals and the blue lines delineate the distribution of variables in excess weight (OW + OB) individuals. The red dots in the scatter plots represent NW individuals and the blue dots represent individuals with excess weight (OW + OB). Multivariable regression models showed that HOMA-index remained significantly associated with SBP z-score after adjusting for BMI z-score or WtHr, while both BMI z-score and WtHr were not significantly related with SBP z-score. There was no significant association between HOMA-index and DBP z-score (Table 2). Mediation analysis showed that, under baseline conditions, a significant indirect effect was present between BMI z-score and SBP z-score, and that this association was completely mediated by HOMA-index values (indirect effect = 0.050, $$p \leq 0.015$$) (Figure 2a). No significant indirect effect was present between BMI z-score and DBP z-score (indirect effect = 0.020, $$p \leq 0.257$$) (Figure S1a). Similar results were observed regarding WtHr: an indirect effect was present between WtHr and SBP z-score, mediated by HOMA-index (indirect effect = 0.008, $$p \leq 0.008$$) (Figure 2b), but not between WtHr and DBP z-score (indirect effect = 0.002, $$p \leq 0.254$$) (Figure S1b). The effect of BMI z-score and WtHr on SBP z-score net of mediator (direct effect) was not significant. At the end of the follow-up, the percentage of children with excess weight (OW + OB) decreased from $82.1\%$ to $69.7\%$ and that of subjects with elevated BP values (HN + HT) from 43.4 to $23.9\%$ ($p \leq 0.001$, Table 1). At follow-up, the percentage of study participants with normal weight was $30.3\%$, that of OW $42.7\%$ and that of OB $27.0\%$. Children with normal BP values were $71.1\%$, those HN $11.2\%$ and those HT $17.7\%$. HOMA-index mean went from 2.80 to 2.72 ($$p \leq 0.654$$). There was a significant association between delta BMI z-score and delta SBP z-score (β = 0.26, $$p \leq 0.025$$), between delta WtHr and delta SBP z-score (β = 0.03, $$p \leq 0.002$$) and between delta HOMA-index and delta SBP z-score (β = 0.08, $$p \leq 0.001$$) from baseline to follow-up (Table 3). The association between delta of HOMA-index and delta of SBP z-score was still present after adjustment for BMI z-score, WtHr and transition from pre-puberty to puberty. While the significant relationship between delta BMI z-score and delta SBP z-score disappeared in the multivariable model, delta WtHr remained an independent predictor of delta SBP z-score from baseline to follow-up (Table 4). To test the robustness of HOMA-index mediation in the relationship between BMI, WtHr, and SBP observed under basal conditions, we analyzed whether this mediation was confirmed when we assessed the association between deltas of BMI and WtHr from baseline to follow-up and deltas of SPB z-score. Figure 3 displays this analysis. Mediation analysis showed that the effect of BMI z-score changes on SBP z-score changes was totally mediated by HOMA-index modifications (indirect effect = 0.11, $$p \leq 0.008$$, Figure 3a). Changes in HOMA-index only partially mediated the effect of modifications of WtHr on changes in SBP z-score (indirect effect = 0.005, $$p \leq 0.060$$, Figure 3b). A significant direct effect of delta WtHr on delta SBP z-score was present ($$p \leq 0.011$$). The results of the mediation analysis were similar after adjustment for the transition from pre-puberty to puberty, although the mediating role of the HOMA-index was slightly weakened (Figure S2a,b). Mediation analysis showed that the effect of deltas BMI z-score and deltas WtHr on deltas DBP z-score from baseline to follow-up was not mediated by HOMA-index (Figure S3a,b). ## 4. Discussion Our study in addition to confirming the association between insulin resistance and blood pressure in children, strongly suggests that, in a pediatric population at cardiovascular risk, the HOMA-index is an important mediator of the relationship between BMI and waist circumference with systolic blood pressure. This evidence is proved not only under baseline conditions, but also after a period when children were undergoing an intervention based on a non-pharmacological approach. Changes in systolic blood pressure following changes in BMI and waist circumference at the end of follow-up were significantly mediated by simultaneous modifications of HOMA-index. Several studies demonstrated an association between arterial hypertension and insulin resistance and this is also a problem for pediatric patients [21]. Insulin resistance is involved in the development of hypertension through various mechanisms. Hyperinsulinemia contributes directly to the development of endothelial dysfunction by inhibiting the production of nitric oxide, a potent vasodilator, thereby promoting increased vascular resistances [22] and an association between increased HOMA-index and impaired endothelial function since childhood has been proven [23]. Furthermore, insulin resistance leads to stimulation of the sympathetic nervous system resulting in vasoconstriction and increased blood pressure [24]. Activation of the sympathetic nervous system also results in an activation of the renin-angiotensin system with a consequent increase in angiotensin II levels and an increment not only in peripheral resistances, but also in renal sodium reabsorption [25]. The HOMA index is generally accepted as a measure of insulin resistance, however, a number of other indices have recently been suggested as an expression of this clinical condition [26]. It has been suggested that one of these, the triglyceride-glucose (TyG) index was superior to the HOMA index in the prediction of hypertension in adults [27]. Data on the pediatric population are very few, however, a Mexican study showed that the elevated TyG index is significantly associated with the presence of prehypertension and hypertension in children and adolescents. Future studies confirming this finding may certainly be useful in understanding the role of insulin resistance and the genesis of hypertension in children [28]. It is well known that obesity is a major cause of insulin resistance [17,29] and it has been shown that an association between insulin resistance and hypertension is already present in both overweight and obese children and adolescents [30,31]. Interestingly, this relationship is also evident in normal weight children: in a pediatric population, in which all weight classes were represented, the HOMA-index was shown to be an independent predictor of hypertension, after adjustment for body weight and fat distribution. In addition, HOMA-index was independently associated with absolute z-scores of blood pressure [8]. Despite the known association between excess weight and high blood pressure, not all overweight and obese children and adolescents are hypertensive. In a large cohort ($$n = 1201$$) of obese children and teenagers, only $25.9\%$ had systolic blood pressure values ≥ 90th percentile. However, individuals with the “metabolically unhealthy obesity” phenotype, in addition to having higher blood pressure than their metabolically healthy peers, also had significantly higher levels of HOMA-index [32]. These results were later confirmed in a smaller study [33]. In our study population, under basal conditions, there was a significant association between HOMA-index and SBP z-score values, whereas this finding was not present when we consider the relationship between BMI and waist circumference with blood pressure. Moreover, HOMA-index was higher in the sub-group of children with SBP greater than 90th percentile and the association between HOMA-index and systolic blood pressure was strongly significant even after adjustment for indexed values of BMI and waist circumference. Children with higher blood pressure values were those with higher level of insulin resistance. It is interesting that this applied to children with excess weight as well as those with normal weight. As expected, HOMA-index values were higher in children with higher BMI z-scores, however, the relationship between HOMA-index and SBP z-score was present regardless of weight class. These observations suggested to us that the degree of insulin resistance might be a mediator between body weight, waist circumference, and blood pressure values. The mediation analysis confirmed that that HOMA-index plays an important role in mediating the effect of both BMI z-score and WtHr on systolic blood pressure. To validate the results derived from the cross-sectional analysis, we wanted to test whether the association between HOMA-index and systolic blood pressure was maintained when blood pressure values changed. Since in our population the changes in blood pressure at follow-up were accompanied by consensual changes in weight and waist circumference, we also assessed whether the HOMA-index maintained a mediating role in this relationship. Changes in weight and waist circumference at follow-up, were significantly associated with changes in systolic blood pressure, as were changes in HOMA-index. Again, deltas of HOMA-index were associated with blood pressure deltas independently of deltas of BMI and waist circumference. HOMA-index was the main mediator of the effect of BMI on blood pressure modifications. On average, the intervention was associated with a reduction in BMI in our sample. A randomized study showed beneficial effects on body weight associated with insulin resistance improvement in overweight children undergoing dietary treatment [34]. Moreover, in our previous study, we observed that the factor most strongly associated with improved insulin resistance values in a pediatric population undergoing a dietary-behavioral intervention was the decrease in BMI z-score [11]. In the present study, the greater the weight reduction during follow-up, the greater the decrease in systolic blood pressure, and this effect appeared totally mediated by the amount of reduction in insulin resistance. While mean BMI z-score values decreased significantly from baseline to follow-up, the same was not true for HOMA-index values. Since we observed a significant association between changes in HOMA-index and changes in SBP z-score, this means that children in whom insulin resistance increases may experience an increase in systolic blood pressure. Taken together, these findings support the hypothesis that HOMA-index is an important mediator of the effect of BMI on systolic blood pressure. More complex seems to be the relationship between central adiposity, HOMA-index and systolic blood pressure. As with BMI, reductions in waist circumference were also significantly associated with reductions in systolic blood pressure at the end of follow-up, however, this effect seemed to be only partly mediated by insulin resistance modifications. It is reasonable to think that visceral fat has close linkages with other factors and mechanisms associated with blood pressure values. For example, reduced adiponectin levels are associated with both elevated waist circumference and high blood pressure values in adults and children [35,36]. An increase in adiponectin levels associated with waist circumference reduction could have a direct effect on blood pressure values in our population. It has been suggested that a number of novel adipokines may be involved in the pathogenesis of cardiovascular disease related to excess weight [37]. In addition, it was shown that serum uric acid increases as the waist circumference to height ratio increases [38], and a relationship between hyperuricemia and elevated blood pressure values has been shown as early as in pediatric age [38,39]. Recently it has been suggested that insulin resistance may be a mediator of the effect of serum uric acid in leading to increased vascular stiffness [40,41]. A Chinese study performed in adults, showed that elevated serum uric acid levels were associated with an increased risk of incident hypertension, and insulin resistance played a mediating role in the relationship between serum uric acid and hypertension [42]. It is possible to speculate that visceral fat, of which waist circumference is an expression, may exert its effects on blood pressure independently of insulin resistance or that newly discovered cytokines may in turn mediate between insulin resistance and its effects on blood pressure. It is therefore possible to think that there is a complex interaction between central adiposity, hyperuricemia, insulin resistance, and blood pressure even in our population. Various indices of relative weight have been proposed and applied to indicate obesity or body fatness [43]. A child’s body is a growing organism, for this reason it’s incorrect to use the raw data of BMI as an indicator of body mass. Among the indices proposed to evaluate adiposity in children, we have chosen the BMI z-score, because it is the most widely used index in all available pediatric studies. In this way our data can be compared with those of other authors. Furthermore, the most recent Clinical Practice Guideline for obesity evaluation and treatment of the American Academy of Pediatrics recommends using the BMI z-score for studies in children and adolescents, in particular for assessing longitudinal change in adiposity over time, as in the case of our study [44]. Several limitations of this study need to be acknowledged. First, the study is not a randomized trial, so a control group is missing. A control group was not included because in our opinion (as well as in the opinion of the Ethics Committee) it would have been unethical not to offer any kind of treatment to hypertensive and/or overweight children referred to our center by their family pediatricians because of a clinical problem. Second, we have no evidence of patients’ compliance with the intervention, particularly with regard to the low-sodium diet adherence. The effect of dietary salt restriction as a tool to reduce blood pressure is known and documented in adults [45] and suggested for pediatric populations [46]. However, performing a 24-h urine sodium collection before and after our intervention period would not have been enough to give a measure of the actual dietary sodium intake of our patients, but would only have given information on the intake of salt the day preceding the test. To have consistent data on the level of adherence to the low sodium diet, we would need to measure urine sodium repeatedly and frequently throughout the follow-up period. Unfortunately, this was not possible, given the size of the sample and the need not to ask too much of the children’s families. It should also be emphasized that sodium intake estimation formulas derived from urinary sodium are not validated in children. Sodium intake is significantly associated with insulin resistance [47]. It has recently been shown that in excess weight insulin-resistant subjects reduction of dietary sodium improves cardiac function, and this effect may be associated with improvement in insulin resistance [48]. We can only speculate, without having any evidence that hypertensive children with the greatest reductions in blood pressure at follow-up were those in whom the low-sodium diet was most closely followed. Third, mediation analysis only allows to say that a factor acts as a mediator on the effect of one variable on another variable, but it does not prove a causal relationship. Furthermore, as to our knowledge no studies on this topic in pediatric populations using this statistical approach are available, our data need confirmation. In conclusion, our study shows that insulin resistance plays an important role in determining systolic blood pressure values in children. For the same BMI values and waist circumference, individuals with higher HOMA-index levels would therefore be at higher risk of hypertension. Furthermore, the reduction in blood pressure values that comes with a decrease in body weight is closely associated with a concomitant decrease in insulin resistance. Children in whom improved dietary and lifestyle habits lead to blood pressure values improvement would be those in whom the degree of insulin resistance is most reduced. Our data suggest the importance of insulin dosing in children and adolescents with excess weight and/or high blood pressure and that reducing insulin resistance may be a potentially valuable strategy in lowering high blood pressure in this population. Children represent a unique model for studying the pathophysiology of essential hypertension, as confounding factors such as aging, comorbidities, medications and smoking are absent. Thus, our data could represent a noteworthy piece in the complex puzzle of the etiopathogenesis of essential hypertension. ## References 1. Sorof J.M., Lai D., Turner J., Poffenbarger T., Portman R.J.. Overweight, Ethnicity, and the Prevalence of Hypertension in School-Aged Children. Pediatrics* (2004) 113 475-482. DOI: 10.1542/peds.113.3.475 2. 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--- title: The Effects of 12 Weeks of Concurrent and Combined Training on Inflammatory Markers, Muscular Performance, and Body Composition in Middle-Aged Overweight and Obese Males authors: - Reza Bagheri - Mehdi Kargarfard - Khosro Jalali - Damoon Ashtary-Larky - Neda Cheraghloo - Hamid Ghobadi - Babak Hooshmand Moghadam - Alexei Wong - Michael Nordvall - Frédéric Dutheil journal: Nutrients year: 2023 pmcid: PMC10056532 doi: 10.3390/nu15061482 license: CC BY 4.0 --- # The Effects of 12 Weeks of Concurrent and Combined Training on Inflammatory Markers, Muscular Performance, and Body Composition in Middle-Aged Overweight and Obese Males ## Abstract Aim: Previous studies have focused on the order of endurance and resistance training when performing concurrent training (CT). However, no study has compared the effects of combined training with CT orders on inflammatory markers, muscular performance, and body composition in overweight and obese males. Therefore, the purpose of the current study was to compare the effects of 12 weeks of CT and combined training on the aforementioned markers in overweight and obese males. Methods: Sixty middle-aged overweight and obese males (age 51 ± 4 years) were randomly assigned into one of four groups: endurance followed by resistance training (ER; $$n = 15$$), resistance followed by endurance training (RE; $$n = 15$$), combined resistance and endurance training (COM), or control (CON; $$n = 15$$). Anthropometric, body composition, inflammatory marker, and muscular performance measurements were collected at baseline and after 12 weeks. Results: FFM remained unchanged in all three intervention groups ($p \leq 0.05$). Reductions in FM in the RE group were significantly greater than in CON ($$p \leq 0.038$$). The increases in serum concentrations of adiponectin in the RE group were significantly greater than in all other groups ($p \leq 0.05$). Increased serum concentrations of CTRP3 in all intervention groups were significantly greater than the CON group ($p \leq 0.05$); moreover, the increases in the RE group were significantly greater than CON ($p \leq 0.001$). Regarding CTRP5, the increase in RE was significantly greater than COM ($$p \leq 0.014$$). The RE group experienced significantly greater increases in CTRP9 than all other groups ($p \leq 0.05$), and the decreases in serum concentrations of CRP and TNF-α were significantly greater in the RE group compared to CON and ER ($p \leq 0.05$). Vo2max in the ER group was significantly greater than COM ($$p \leq 0.009$$), and all interventions resulted in higher gains compared to CON ($p \leq 0.05$). The increases in leg press strength, chest press strength, lower-body power, and upper-body power in the RE group were significantly greater than in the COM group ($p \leq 0.05$). In addition, the increases in chest press strength in the ER group were significantly greater than COM ($$p \leq 0.023$$). Conclusions: Regardless of training order, CT improved inflammatory markers, body composition, power, and VO2max. Notably, our analysis indicated significantly greater improvements in adiponectin, CTRP5, CTRP9, CRP, and TNF-α levels when RT preceded ET in CT sessions compared to other exercise training sequences. These findings suggested that the order of exercise training may have a significant impact on the effectiveness of CT on inflammatory markers, which has potential implications for exercise prescription and optimization of health-related training outcomes. ## 1. Introduction Obesity is linked to chronic low-grade inflammation, which can lead to metabolic dysfunction and related diseases such as coronary artery disease (CAD) [1,2]. Adipose tissue produces adipokines, which can have pro-inflammatory or anti-inflammatory effects [3]. Dysregulation of adipokine synthesis or release may contribute to the development of obesity-related disorders [1,4]. Adiponectin and members of the C1q/tumor necrosis factor-related protein (CTRP) family are peptides secreted by adipocytes that regulate metabolism and inflammation [5]. CTRP family members, which have structural similarities with adiponectin [6], might play a significant role in the development and progression of CAD. The primary CTRP family members connected to the pathophysiology of CAD are CTRP3, CTRP5, and CTRP9. These proteins primarily operate in white adipose tissue around the heart and to a lesser extent in visceral tissue such as the liver, governing endothelial function, inflammatory response, and metabolic dysfunction [7,8]. CTRP3 is a cardioprotective and anti-inflammatory cytokine found in human plasma [9]. By modulating inhibitory toll-like receptors (TLRs) and nuclear factor kappa B (NF-κB) signaling, CTRP3 reduces insulin resistance and obesity-related chronic inflammation [10]. After coronary stent insertion, elevated levels of CTRP5 may cause immediate restenosis by promoting the growth, migration, and inflammation of vascular smooth muscle cells through activating the Notch1, transforming growth factor (TGF)-β, and hedgehog pathways [11]. CTRP9, the closest paralog of adiponectin, plays a role in controlling lipid metabolism by demonstrating anti-inflammatory and anti-atherosclerotic properties and providing cardioprotective benefits in CAD. Additionally, CTRP9 activates the Akt, AMPK, and p$\frac{42}{44}$ MAPK pathways to enhance glucose uptake, thereby regulating glucose metabolism [12]. Compelling epidemiological evidence supports the hypothesis that regular exercise training, including endurance training (ET) and resistance training (RT), confer therapeutic and preventive benefits by counteracting degenerative processes associated with obesity. Such benefits encompass a reduction in systemic inflammatory markers as well as weight loss and salubrious effects for individuals with a variety of cancer types [13,14]. Accordingly, exercise, particularly RT, has become increasingly popular. These advantages align with the latest guidelines from both the World Health Organization and the American College of Sports Medicine, which endorse physical activity as an intervention for enhancing overall health. The anti-inflammatory benefits of regular exercise may be mediated by both a decrease in visceral fat mass (by lowering the production of adipokines) and the development of an anti-inflammatory milieu after exercise bouts [15]. ET and RT are frequently prescribed to reduce inflammatory-related disease risk [13]; however, incorporating RT and ET simultaneously in the same training period—also known as combined training or concurrent training (CT) [16,17]—may further enhance the anti-inflammatory benefits of regular exercise [18]. To our knowledge, a single study has measured biomarkers of the CTRP family after exercise. Hae Yoon Choi et al. demonstrated that a 3-month combined exercise program (45 min of aerobic exercise at an intensity of 60–$75\%$ of maximum heart rate and 20 min of resistance training performed five times per week) significantly decreased CTRP3 levels and modestly increased CTRP5 levels in obese Korean women [19], which contradicted evidence showing increased CTRP3 following physical activity. In multiple regression models, CTRP3 concentrations were favorably linked with adiponectin but adversely associated with retinol-binding protein 4 (RBP4) levels. RBP4 induces insulin resistance in adipocytes indirectly by increasing proinflammatory cytokine secretion from macrophages [19]. Despite the fact that CT may result in a number of advantageous adaptations, there are possible issues regarding whether some of the targeted adaptations to RT or ET that occur (depending on the order) could be compromised due to an “interference effect” [20]. The interference effect simply states that ET signaling stunts muscle growth or other muscular adaptations (e.g., muscular strength, power, etc.) [ 21]. There are insufficient data about the impact of exercise sequence within a session on interference effects between acute bouts of RT and ET on human performance and/or chronic health-related adaptations. Nevertheless, a rationale for RT and ET training orders during CT may be dependent on molecular interference effects, particularly when applied to special populations such as those with obesity. Due to the molecular signaling mechanisms involved in RT and ET, the sequence of RT and ET in a CT session (i.e., endurance followed by resistance exercise vs. resistance followed by endurance exercise) may yield distinct effects. Performing RT prior to ET, cycling instead of running, separating exercise bouts by 6–24 h, and adopting strategies that minimize overall exercise volume (i.e., utilizing high-intensity intervals, 2–3 days of ET, increasing protein intake, etc.) may potentially minimize reductions in muscular adaptations when concurrently performing ET and RT [21]. Other crucial factors associated with adaptation to exercise training, such as inflammatory markers, have not been investigated in the context of CT order. In the present study, we included a combined training group, which sometimes is used interchangeably with CT. In combined training, in contrast to CT, for instance, participants do not finish RT before transitioning to perform ET. However, participants perform six sets of RT and then move to perform 10 min of ET (combined training is outlined in Materials and Methods section). The main reason for the inclusion of this training protocol is that the anabolic environment of RT may reduce the interference effect of ET on muscular adaptations. Therefore, the primary this study aimed to investigate the effects of 12 weeks of CT order and combined training on inflammatory markers (adiponectin, tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), CTRP3, CTRP5, and CTRP9) in overweight and obese males. We hypothesized that various orders of RT and ET would stimulate secretions of adiponectin, TNF-α, CRP, CTRP3, CTRP5, and CTRP9. The second objective was to investigate the impact of training procedures on body composition, muscular performance, and cardiorespiratory fitness. ## 2.1. Participants Sixty overweight and obese middle-aged men (age: 51 ± 4 years) took part in this study. The inclusion criteria were: age ˃ 40 years, body mass index (BMI > 27 kg·m−2), sedentary (less than 1 h of activity per week in the previous year), 7–8 h of sleep per 24 h, and no chronic use of nutritional supplements or pharmaceuticals, particularly nonsteroidal anti-inflammatory drugs. In addition, participants were otherwise healthy and devoid of health complications including but not limited to Parkinson’s disease, heart disease, and diabetes. Further participant inclusion criteria requirements were non-smoking status and no hormonal or mental health therapies, no regular moderate-to-heavy aerobic or resistance training within the past year, and alcohol consumption. Supplementation and the usage of drugs that alter the metabolism of muscle mass or fat mass (FM) were also regarded as exclusion criteria. A physician examined all possible participants based on these criteria using the Physical Activity Readiness Questionnaire (PAR-Q) and medical health/history questionnaires. In order to mitigate attrition, study participants were informed that successful completion of their 12-week intervention with high adherence would entitle them to an additional 6 months of complimentary training and nutrition services following the conclusion of the study. The protocol for the research was approved by the Institutional Human Subjects Committee. The research procedures were conducted at the Azad University of Najadfabad in Isfahan, Iran (RECNAJAFABADIAUIR.1399.08), and all experiments adhered to the Declaration of Helsinki. ## 2.2. Study Design A participant eligibility and allocation flowchart is depicted in Figure 1. Before the initial measurements, all tests and methods were thoroughly explained to the participants, who were randomly assigned into one of four groups: endurance followed by resistance training (ER; $$n = 15$$), resistance followed by endurance training (RE; $$n = 15$$), combined resistance and endurance training (COM), or control (CON; $$n = 15$$). Anthropometric, body composition, inflammatory marker, and strength and aerobic performance measurements were collected at baseline and 12 weeks post-intervention (48–72 h after the last training session). Every measurement was taken at the same time of day (within 1 h) and under identical environmental conditions. For the course of the trial, participants were asked not to modify their typical lifestyle and food choices. ## 2.3. Anthropometry and Body Composition Participants were instructed upon entering the laboratory to urinate (void) completely within 30 min of the test. Each participant’s body mass was measured using a digital scale (lumbar, Hong Kong, China) to the nearest 0.1 kg, and height was measured with a stadiometer (Race industrialization, Shanghai, China) to the nearest 0.1 cm. Bioelectrical impedance equipment (Inbody 720, Seoul, Republic of Korea) was used to conduct the BMI, waist–hip ratio (WHR), fat mass (FM), and fat-free mass (FFM) measurements. Before the test, the participants were told to abstain from physical activity for 48 h and fast for 12 h (overnight with at least 8 h of sleep). ## 2.4. Blood Sampling and Analysis After a 12 h overnight fast, samples (5 mL) were collected from the cubital vein using standard techniques. Blood samples were collected at baseline and 48 h after the last training session. Following completion of blood collection, samples were centrifuged at 3000 rpm for 20 min, and the serum was stored at −70 °C for future analysis of adiponectin, CTRP3, CTRP5, CTRP9, TNF-α, and CRP (ZellBio GmbH, Lonsee, Germany). ## 2.5. Strength Testing After a body composition evaluation and blood test, a strength test was conducted 24 h later. In order to calculate customized training intensity for the RT protocol, a one-repetition maximum (1RM) was calculated. Before commencing testing, research personnel discussed each test’s objective, associated risks, potential discomforts, and participant obligations. Prior to the testing phase, all participants were advised to abstain from consuming alcohol for 48 h, caffeinated beverages for 12 h, and meals for 2 h. Nevertheless, ad libitum water consumption was permitted. After a short general and specific warm-up, all exercises included in the RT program were evaluated for strength testing (i.e., leg extension, leg curl, bench press, lat pulldown, lateral raise, and abdominal crunch) using variable-resistance machines. The participants performed two attempts separated by a 5 min rest period, and their highest lifted weight and number of repetitions [22] were recorded. The number of repetitions to fatigue did not exceed 10. Maximal strength was estimated from these assessments using a previously published equation: 1RM = weight/(1.0278 − 0.0278 × reps) [21]. ## 2.6. Power Testing Upper- and lower-body anaerobic power was assessed via Monark Wingate cycle ergometry (Monark model 894e, Vansbro, Sweden) as previously described. Briefly, participants were acquainted with the test and instructed to stay seated in the saddle for the test duration. Participants cycled or cranked against a pre-determined resistance ($7.5\%$ of body mass for the lower body test and $5.5\%$ for the upper body test) as fast as possible for 30 s. Participants were verbally encouraged to pedal as hard and fast as possible throughout the whole 30 s test. Peak power output was documented in real time during the test using Monark Anaerobic test software (3.3.0.0). ## 2.7. Aerobic Power VO2max was estimated using the Modified Bruce procedure. The treadmill began at 2.74 km per hour (1.7 miles per hour) and $0\%$ grade (or incline). During three-minute intervals (stages), the treadmill’s elevation and/or speed increased. When participants were unable to continue due to exhaustion, discomfort, or other medical indicators as previously indicated, the test was terminated [23]. The following formula was utilized for VO2max prediction: VO2max = 14.8 − (1.379 × T) + (0.451 × T²) − (0.012 × T³), where T is the time in minutes to complete the test. ## 2.8. Concurrent Training Participants in all training groups completed the supervised training 3 times a week, with at least 48 h between each session, for 12 weeks. The ER group performed ET first followed immediately by RT, while the RE group performed RT first followed immediately by ET. Participants in the COM group also performed both RT and ET protocols with a combination block of resistance and endurance exercises repeated twice in weeks 1–6 and three times in weeks 7–12 to achieve three sets per resistance exercise and 30 min of ET. The order of RT and ET in the COM group is reported in Figure 2. ## 2.9. Preparatory Phase Prior to the intervention data collection, all participants completed one week of CT consisting of three exercise sessions to familiarize themselves with ET and RT. This phase was intended to provide education on proper lifting methods, familiarization with all exercises and equipment, and confirmation that all participants began the research with equivalent levels of expertise [24]. ## 2.10. Resistance Training Protocol Following the preparatory phase, RT was initiated at $50\%$ of the 1RM and gradually increased to $80\%$ of 1RM during the final week of the intervention. A total of 8 to 14 repetitions were performed over two sets during weeks 1 through 6 and three sets during weeks 7 through 12. Furthermore, rest intervals between sets ranged from 30 to 75 s and were progressively increased in correspondence with the intensity of the RT. The exercises utilized in the study included leg extension, leg curl, bench press, lat pulldown, lateral raise, and abdominal crunch performed on variable-resistance machines as noted above. ## 2.11. Endurance Training Protocol The exercise regimen consisted of 20 min at $55\%$ of maximum heart rate on a fixed-speed bicycle ergometer (11900 Community Rd, Poway, CA, USA) in the first week, which progressed to 30 min at $70\%$ of maximal heart rate in the final week of the intervention. A polar heart rate monitor was used to measure the intensity of the workout (Polar S810, Polar Electro, Kempele, Finland). ## 2.12. Cool Down Irrespective of group assignment, all participants completed a 10 min active cool-down consisting of low-intensity exercise on a cycle ergometer (4 min), slow walking (4 min), and light lower extremity static stretching with an emphasis on the quadriceps, hamstrings, gastrocnemius, and erector spinae. ## 2.13. Nutrient Intake and Dietary Analysis Prior to training, participants were encouraged to record as precisely as possible, using 24 h food log recalls, every calorie and nutrient consumption ingested over the course of 6 days (4 non-consecutive weekdays and 2 non-consecutive weekend days). Dietary record information was deemed the participant’s usual diet, and participants were required to maintain this diet throughout the duration of the study. Food records were kept daily by participants throughout the study using the mobile phone applications Easy Diet Diary (Xyris Software Pty Ltd., Brisbane City, Australia) for those with iPhones (Apple Inc., Cupertino, CA, USA; $$n = 28$$) and My fitness pal (MyFitnessPal Inc., San Francisco, CA, USA), Iran, for those with Android-based devices ($$n = 32$$). After the entry of each food item into Diet Analysis Plus version 10 (Cengage, Boston, MA, USA), total energy consumption and the amount of energy generated from macronutrients (proteins, fats, and carbohydrates) were determined. ## 2.14. Statistical Analysis An a priori sample size calculation was conducted using G-power 3.1.9.2 software. The rationale for the sample size was based on a previous work, which documented significant improvements in CRP concentrations in overweight and obese individuals. By utilizing the equation for effect size (ES) {(mean before − mean after CT)/the pooled standard deviation}, this study revealed an ES of 0.53 {(4.90 − 3.54)/5.07}. In the present study and based on α = 0.05, a power (1 − β) of 0.80 and an ES = 0.53 (highest approximate effect size), a total sample size of at least 44 participants ($$n = 11$$ per group) was needed for sufficient power to detect significant changes in CRP concentrations. We recruited 15 participants per group due to potential dropouts. All data and values are presented as means ± standard deviations (SDs). An analysis of variance (ANOVA) was used to compare the mean values for each variable between groups. Tukey’s honestly significant difference (HSD) test was used to determine if the difference in variables between two sets of groups was statistically significant. The Bonferroni test was used to compare the mean values between each pair of groups. The paired t-test was applied to compare the means of two variables for the same subject. An analysis of covariance (ANCOVA) was used to evaluate whether the means of variables in the post-test were equal across levels of each participant group, thus controlling the effects of variables in the pre-test. Pearson’s linear regressions were performed with a $95\%$ confidence interval (CI). Training volume was analyzed using ANOVA with repeated measures. All analyses were performed using SPSS (version 26). p-values less than 0.05 were regarded as statistically significant. Figures were generated using GraphPad Prism (version 8.4.3). ## 3.1. Body Composition All pre- and post-intervention anthropometric and body composition data are shown in Table 1. All three intervention groups showed a significantly decreased body mass (RE = −5.6 kg ($95\%$ confidence interval = −3.3 to −7.9; $p \leq 0.001$; ES = 1.28), ER = −4.3 kg ($95\%$ CI = −2.1 to −6.5; $$p \leq 0.001$$; ES = 0.57), and COM = −3.3 kg ($95\%$ CI = −1.3 to −5.2; $$p \leq 0.003$$; ES = 0.78)) and BMI (RE = −1.9 kg·m−2 ($95\%$ CI = −1.1 to −2.7; $p \leq 0.001$; ES = 1.51), ER = −1.4 kg·m−2 ($95\%$ CI = −0.7 to −2.1; $$p \leq 0.001$$; ES = 0.99), and COM = −1.1 kg·m−2 ($95\%$ CI = −0.4 to −1.7; $$p \leq 0.003$$; ES = 0.28)), while FM was decreased only in the RE and ER groups (RE = −5.9 kg ($95\%$ CI = −1.3 to −10.5; $$p \leq 0.014$$; ES = 0.82) and ER = −4 kg ($95\%$ CI = −0.1 to −8; $$p \leq 0.044$$; ES = 0.62)). In addition, WHR significantly decreased only in the RE and COM groups (RE = −0.037 m ($95\%$ CI = −0.018 to −0.05; $$p \leq 0.001$$; ES = 0.23) and COM = −0.034 m ($95\%$ CI = −0.015 to −0.053; $$p \leq 0.044$$; ES = 1.47)). FFM remained unchanged in all three intervention groups ($p \leq 0.05$). The ANCOVA results showed that the changes in BM and BMI in RE were significantly greater than in the CON group. Changes in FM for participants in the RE group were significantly greater than in CON. The reductions in WHR were significantly greater in RE than in ER and CON; also, the decreases in COM were significantly greater than in ER and CON. ## 3.2. Inflammatory Markers All pre- and post-intervention inflammatory marker results are shown in Figure 3. All three interventions showed significantly increased serum concentrations of adiponectin (RE = 1.9 ng/mL ($95\%$ CI = 2.3 to 1.5; $p \leq 0.001$; ES = 1.3), ER = 0.9 ng/mL ($95\%$ CI = 1 to 0.8; $p \leq 0.001$; ES = 0.75), and COM = 1 ng/mL ($95\%$ CI = 1.2 to 0.9; $p \leq 0.001$; ES = 0.91)); CTRP5 (RE = 5 pg/mL ($95\%$ CI = 6.4 to 3.5; $p \leq 0.001$; ES = 1.70), ER = 2.9 pg/mL ($95\%$ CI = 4.5 to 1.3; $$p \leq 0.001$$; ES = 1.21), and COM = 2.1 pg/mL ($95\%$ CI = 3.5 to 0.6; $$p \leq 0.008$$; ES = 1.17)); and CTRP9 (RE = 11.6 pg/mL ($95\%$ CI = 15.6 to 7.6; $p \leq 0.001$; ES = 1.02), ER = 4.1 pg/mL ($95\%$ CI = 6.4 to 1.9; $$p \leq 0.001$$; ES = 0.36), and COM = 9.6 pg/mL ($95\%$ CI = 12.7 to 6.4; $p \leq 0.001$; ES = 0.78)), while CTRP3 was significantly increased only in the RE and COM groups (RE = 10.9 pg/mL ($95\%$ CI = 15.8 to 6; $p \leq 0.001$; ES = 0.34) and COM = 6.3 pg/mL ($95\%$ CI = 10 to 2.5; $$p \leq 0.003$$; ES = 0.18)). However, all three intervention groups showed significantly decreased serum concentrations of TNF-α (RE = −2.6 ng/mL ($95\%$ CI = −1.8 to −3.4; $p \leq 0.001$; ES = 0.39), ER = −0.7 ng/mL ($95\%$ CI = −0.3 to −1.1; $$p \leq 0.002$$; ES = 0.11), and COM = −1.9 ng/mL ($95\%$ CI = −1 to −2.7; $p \leq 0.001$; ES = 0.31)) and CRP (RE = −1.1 ng/mL ($95\%$ CI = −0.8 to −1.3; $p \leq 0.001$; ES = 2.2), ER = −0.5 ng/mL ($95\%$ CI = −0.2 to −0.7; $p \leq 0.001$; ES = 0.78), and COM = −0.8 ng/mL ($95\%$ CI = −0.3 to −1.3; $$p \leq 0.002$$; ES = 1.15)). The ANCOVA results showed that the increases in serum concentrations of adiponectin in the RE group were significantly greater than in all other groups ($p \leq 0.05$). In addition, the increased adiponectin in all intervention groups was significantly greater than in the CON group ($p \leq 0.05$). Increased serum concentrations of CTRP3 in all intervention groups were significantly greater than in the CON group ($p \leq 0.05$); moreover, the increases in the RE group were significantly greater than in the ER and CON groups ($p \leq 0.05$). Increased serum concentrations of CTRP5 in all intervention groups were significantly greater than in the CON group ($p \leq 0.05$). In addition, the increases in RE were significantly greater than in COM ($$p \leq 0.002$$). Lastly, The increases in RE and ER were significantly greater than in CON ($p \leq 0.05$). The RE group experienced significantly greater increases in CTRP9 than all other groups ($p \leq 0.05$), and the decreases in serum concentrations of CRP and TNF-α were significantly greater in the RE group compared to CON and ER ($p \leq 0.05$). ## 3.3. Muscular Performance and Dietary Intakes All pre- and post-intervention aerobic and strength assessment results are shown in Table 1. All three interventions showed a significantly increased VO2max (RE = 6.5 mL·kg·min ($95\%$ CI = 5.4 to 7.6; $p \leq 0.001$; ES = 2.22), ER = 8.3 mL·kg·min ($95\%$ CI = 6.7 to 10; $p \leq 0.001$; ES = 2.52)), and COM = 5.2 mL·kg·min ($95\%$ CI = 3.6 to 6.8; $p \leq 0.001$; ES = 1.31) leg press strength (RE = 9.2 kg ($95\%$ CI = 7.7 to 10.7; $p \leq 0.001$; ES = 1.81), ER = 7 kg ($95\%$ CI = 5 to 8.7; $p \leq 0.001$; ES = 1.93), and COM = 5.4 kg ($95\%$ CI = 3.9 to 6.8; $p \leq 0.001$; ES = 1.5)); chest press strength (RE = 8.1 kg ($95\%$ CI = 6.5 to 9.7; $p \leq 0.001$; ES = 2.71), ER = 6.8 kg ($95\%$ CI = 5.5 to 8.2; $p \leq 0.001$; ES = 2.64), and COM = 4.5 kg ($95\%$ CI = 4 to 5; $p \leq 0.001$; ES = 1.98)); lower-body power (RE = 40.3 w ($95\%$ CI = 29 to 51.6; $p \leq 0.001$; ES = 2.14), ER = 20.8 w ($95\%$ CI = 5.4 to 36.1; $$p \leq 0.011$$; ES = 0.88), and COM = 14.2 w ($95\%$ CI = 6.6 to 21.7; $$p \leq 0.001$$; ES = 0.91)); and upper-body power (RE = 24.8 w ($95\%$ CI = 18 to 31.6; $p \leq 0.001$; ES = 1.92), ER = 13.3 w ($95\%$ CI = 5 to 21.6; $$p \leq 0.004$$; ES = 0.91), and COM = 16 w ($95\%$ CI = 6 to 26.1; $$p \leq 0.004$$; ES = 1.31)). The ANCOVA results indicated that the VO2max values in the ER group were significantly greater than in COM ($$p \leq 0.009$$) and that all interventions resulted in higher gains compared to CON ($p \leq 0.05$). The increases in leg press strength, chest press strength, lower-body power, and upper-body power in the RE group were significantly greater than in the COM group, and the gains in all intervention groups were significantly greater than in the CON group ($p \leq 0.05$). In addition, the increases in chest press strength in the ER group were significantly greater than in COM ($$p \leq 0.023$$). The dietary intakes of participants are shown in Table 2. There were no significant changes between groups for energy (kcal/day) or macronutrient (g/day) intake from pre- to post-intervention ($p \leq 0.05$). ## 3.4. Training Volume The results shown in Table 3 depict that the main, groups, and interaction effects were significant for the training volume of RT. Additionally, there were significant differences over time in each of the groups during 12 weeks of training intervention. On the other hand, the only between-group difference was observed for the RE vs. COM groups. Regarding the training volume of ET, the data demonstrated that there was no significant difference for the groups and interaction effects, but there was for the main effect. ## 3.5. Correlations To assess the potential relationships between training-induced changes in FM (Δ FM) and inflammatory markers (Δ (marker)) independently of RE, ER, COM, or CON, a correlation matrix is presented in Figure 4. Serum concentrations of TNF-α and CRP showed a moderate positive relationship with Δ FM, while serum concentrations of adiponectin, CTRP3, CTRP5, and CTRP9 showed a moderate negative relationship. For linear regression of individual Δ (marker) as a function of Δ FM, the data were examined using the extra sum-of-squares F-test to first consider if pooled data could be considered as a single model. No data were considered a single group. Only Δ CTRP5 showed a significant direct linear relationship with training-induced changes in FM. ## 4. Discussion This study sought to determine the impacts of CT order on serum concentrations of inflammatory markers (C1q/TNF-related proteins), muscle strength and power, VO2max, and body composition in men with obesity. Our findings indicated that CT, regardless of training order, improved inflammatory markers, body composition, muscular strength and power, and VO2max; however, performing RT prior to ET and combined training may prove more effective in improving CTRP3 and WHR. The CTRPs display differential effects on regulating metabolic homeostasis and cardiovascular function [25]. Previous studies suggested that the dysregulation of CTRPs may play an important role in the pathogenesis of obesity [26,27]. Accordingly, all three CT interventions in the present study resulted in increased CTRPs; however, it should be noted that CTRP improvement in the RE group was greater compared to other intervention groups. The positive effects of various exercise strategies on CTRP have been reported in prior investigations. For example, Sadeghi et al. assessed the effects of different training protocols (RT, ET, and CT) on serum levels of CTRP5 in patients with T2DM [28]. It was determined that, similar to our findings, serum CTRP5 levels increased in all three intervention groups compared to the control. In another study, Hasegawa et al. showed that 8 w of aerobic exercise training (60–$70\%$ peak oxygen uptake for 45 min, 3 days/w) increased serum CTRP3 and CTRP5 in middle-aged and older adults [29]. Moreover, Choi et al. reported that a 3-month combined exercise program modestly increased CTRP-5 levels in obese Korean women [19]. Interestingly, they showed that exercise training significantly decreased CTRP-3 levels. Our findings showed positive effects of all training interventions on CTRP-3, CTRP-5, and CTRP-9, except for the effects of ER on CTRP-3. As a result, CTRPs may help explain a possible mechanism for the mediation of anti-inflammatory and metabolic-improving effects of exercise. It has been shown that CTRP-3 decreases IL-6 and TNF-α secretion in LPS-treated monocytic cells and suppresses nuclear factor kappa B (NF-κB) signaling [30]. Further, Wölfing et al. showed that CTRP-3 stimulates the secretion of adiponectin and resistin in adipocytes; the latter of which exert important anti-inflammatory and obesity-regulating effects, respectively [31]. In another study, CTRP-3 concentrations were positively associated with adiponectin levels following a combined aerobic and resistance training program [19]. CTRP-5 on the other hand reportedly increases glucose uptake and fatty acid oxidation in myocytes by enhancing glucose transporter GLUT-4 translocation and stimulating adenosine monophosphate-activated protein kinase (AMPK) phosphorylation, respectively [32]. Lastly, CTRP9, an adipocytokine with the highest amino acid identity to adiponectin [33], has multiple functions including regulation of glucose and lipid metabolism [33,34], acting as an anti-inflammatory agent by reducing proinflammatory cytokine expression [35,36], and prevention of cellular oxidative damage [34]. It has been claimed that adiponectin, a hormone mostly released by adipocytes, mimics many of the metabolic benefits of exercise training [37], and our results supported the findings of previous research that indicated exercise training enhances serum levels of adiponectin [14,38]. While much of this research has focused on adiponectin-elevating properties of CT [39,40], the effects of CT order on serum adiponectin are virtually unknown; only a single investigation reported on the effects of eight weeks of varied order CT, which showed no change in adiponectin in overweight women [41]. In contrast, our results showed a significant elevation in adiponectin in all exercise intervention groups (with the greatest pre- to post-change noted in the RE group), perhaps due to resistance exercise measurably stimulating adiponectin release earlier than aerobic exercise. Certainly when compared to the results of Hosseynzade et al., differences in the study population, training volume, and/or exercise intervention duration may explain such discrepancies between results [41]. It is well documented that exercise training decreases CRP and TNF-α in various populations [42,43] by inhibiting the activation of NF-κB and leading to decreased circulating concentrations of pro-inflammatory cytokines, including TNF-α and CRP [33]. Moreover, the reductions in inflammatory cytokines are reported to be directly associated with a loss in FM, particularly when exceeding $5\%$ of overall FM [44]. Although several studies have reported the positive effects of CT on inflammation, the effects of CT order on inflammatory markers, as with adiponectin, are virtually unknown. Banitalebi et al. failed to find any differences between varied types of CT training on inflammatory markers; however, similar to adiponectin, our findings indicated that resistance exercise performed prior to aerobic exercise (RE group) may prove more effective in regulating markers of inflammation status; including CRP and TNF-α [45]. While there were no group differences between measures of upper- and lower-body strength or aerobic power and all exercise interventions (likely the result of similar training volumes), all participants in these groups showed improved chest and leg press strength, upper- and lower-body power, and VO2max. Similarly, our findings for body composition and anthropometrics revealed no significant between-group differences in FFM, a measure associated with strength and power; this again was likely due to similar training volumes between exercise interventions [46,47]. One of the key strengths of this study was the utilization of novel adipokines related to the CTRP family, which have not been previously evaluated following CT. Additionally, the observed correlations (mainly colorogram) between changes in FM and adipokines may yield practical insights for mitigating FM as part of health promotion efforts. The current research comes with limitations such as the fact that body composition was measured using bioelectrical impedance, a common technique that determines BFP based on methods developed for normal-weight subjects and that frequently assumes that body hydration is constant and unaffected by obesity and overweight [48]. Moreover, the gene expression of CTRPs using biopsy samples in order to more accurately characterize the upregulation of inflammatory markers was not determined but certainly provides grounds for further investigation. In conclusion, regardless of training order, CT improved inflammatory markers, body composition, power, and VO2max in middle-aged overweight and obese males. Our analysis also indicated significantly greater improvements in adiponectin, CTRP5, CTRP9, CRP, and TNF-α levels when RT preceded ET in CT sessions compared to other exercise training sequences. These findings suggested that the order of exercise training may have a significant impact on the effectiveness of CT on inflammatory markers and have potential implications for exercise prescription and optimization of health-related training outcomes. Future studies should evaluate the use of nutritional strategies (particularly protein intake) in combination with different exercise training sequences within CT along with their impact on body composition and adipokines in diverse populations and have a specific focus on females. ## References 1. Ellulu M.S., Patimah I., KhazáAi H., Rahmat A., Abed Y.. 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--- title: Lipidomic Analysis Reveals Differences in Bacteroides Species Driven Largely by Plasmalogens, Glycerophosphoinositols and Certain Sphingolipids authors: - Eileen Ryan - Belén Gonzalez Pastor - Lee A. Gethings - David J. Clarke - Susan A. Joyce journal: Metabolites year: 2023 pmcid: PMC10056535 doi: 10.3390/metabo13030360 license: CC BY 4.0 --- # Lipidomic Analysis Reveals Differences in Bacteroides Species Driven Largely by Plasmalogens, Glycerophosphoinositols and Certain Sphingolipids ## Abstract There has been increasing interest in bacterial lipids in recent years due, in part, to their emerging role as molecular signalling molecules. Bacteroides thetaiotaomicron is an important member of the mammalian gut microbiota that has been shown to produce sphingolipids (SP) that pass through the gut epithelial barrier to impact host SP metabolism and signal into host inflammation pathways. B. thetaiotaomicron also produces a novel family of N-acyl amines (called glycine lipids) that are potent ligands of host Toll-like receptor 2 (TLR2). Here, we specifically examine the lipid signatures of four species of gut-associated Bacteroides. In total we identify 170 different lipids, and we report that the range and diversity of Bacteroides lipids is species specific. Multivariate analysis reveals that the differences in the lipid signatures are largely driven by the presence and absence of plasmalogens, glycerophosphoinositols and certain SP. Moreover, we show that, in B. thetaiotaomicron, mutations altering either SP or glycine lipid biosynthesis result in significant changes in the levels of other lipids, suggesting the existence of a compensatory mechanisms required to maintain the functionality of the bacterial membrane. ## 1. Introduction Bacterial lipids have recently emerged as influential contributors to the microbe–host molecular dialogue [1,2,3]. Lipids are hydrophobic or amphipathic small molecules found in all living cells, including bacteria, with important functions in membrane structure, energy storage and cell signalling [4]. Based on their chemical structures and biosynthetic origins, lipids have been grouped into eight categories; fatty acyls (FA), glycerolipids (GL), glycerophospholipids (GP), sphingolipids (SP), sterol lipids (ST), prenol lipids (PR), saccharolipids (SR), and polyketides (PK), and within each category, there are distinct classes and subclasses [5,6]. To date, over 40,000 biologically relevant lipids (mostly mammalian) have been listed to the LIPID MAPS Structural Database [7]. However, there is growing interest in cataloguing non-mammalian lipids, including those produced by gut microbes, prompted, in part, by the identification of some bacterial lipids as molecular signals [1,3,8]. Bacteroides are early colonisers of the mammalian gut, establishing stable, long-term, and generally beneficial interactions with their human host [9]. Bacteroides have been shown to produce a variety of important bioactive lipids, including sphingolipids (SP) and N-acyl amines called glycine lipids [10,11,12,13,14,15,16,17,18,19]. It is now well recognised that Bacteroides are one of only a few bacterial genera that produce SP [13,20,21]. B. fragilis generates a bioactive SP (α-galactosylceramide, α-GalCer), which binds to the antigen-presenting protein, CD1d, thus influencing the number and function of natural killer T cells (NKT-cells) in the intestine, with consequences for the progression of colitis in a murine model [10,11]. In another study, the lack of bacterial SP production was shown to promote intestinal inflammation, along with concurrent changes in the host SP pool [13]. Indeed, SP produced by Bacteroides have been shown to cross the epithelial barrier and impact hepatic SP pools [21]. Glycine lipids (GlyL) are a family of lipids derived from the initial N-acylation of glycine, which results in the production of a mono-acylated glycine molecule called commendamide [16,22]. Commendamide is further modified by an O-acylation, resulting in a diacylated glycine with additional modifications, to generate a family of glycine lipids that includes a serine-glycine dipeptido-lipid (flavolipin, FL) and a large complex glycine lipid called Lipid 1256 [18,23,24,25]. Interestingly, commendamide was originally identified in a screen for agonists of GPCR G2A/132 that result in increased levels of NF-kB expression [16]. GlyL, FL and Lipid 1256 have been reported to signal to eukaryotic cells by engaging TLR2, promoting the production of pro-inflammatory cytokines [14,15,17,18,19]. It is clear, therefore, that Bacteroides may produce unique lipids with the potential to signal to the mammalian host. However, a comprehensive examination of the Bacteroides lipid signature has not been conducted, and this limits a full appreciation of their potential in the host–microbe dialogue. In the present study we describe the pathways and compare the lipid signatures of four important species of Bacteroides: namely B. thetaiotaomicron, B. fragilis, B. ovatus and B. vulgatus (recently elevated to Phocaeicola vulgatus), with a particular focus on the bioactive SP and glycine lipids. We identify 170 different lipids and show that the lipid signatures vary in a species-dependent manner. In addition, we show that mutations in SP or glycine lipid biosynthesis significantly change the lipid signature of B. thetaiotaomicron, and these compensatory changes need to be considered when studying the role of these lipids in Bacteroides and in the microbe-host dialogue. We describe, for the first time, a comprehensive and qualitative comparison of the lipid signatures of four important Bacteroides species. We identify a group of Bacteroides core lipids and uncover species-specific differences in plasmalogen, glycerophospholipid and sphingolipid metabolism, with more subtle differences observed in glycine lipid production. This data will provide a useful platform for the further characterisation of the lipid-based host–microbe dialogue and the influence of microbial lipids on host health and disease states. ## 2.1. Materials Organic solvents (Supelco 2-propanol and acetonitrile) used for the extractions or precipitations and mobile phase preparation were hypergrade for LC-MS LiChroslv® and obtained from Merck (Darmstadt, Germany). Buffers used for mobile phase preparation were from Fisher Chemical Optima™, LC-MS grade Formic Acid from Fisher (Leicestershire, UK) and LC-MS grade LiChropur™ ammonium formate from Merck (Darmstadt, Germany). Internal standard, applied for normalization to each sample, N-palmitoyl-D-erythro-sphingosylphosphorylcholine (16:0 SM, Avanti Polar Lipids, powder) was purchased from Merck (Darmstadt, Germany). ## 2.2. Bacterial Strains and Growth Conditions B. thetaiotaomicron VPI-5482 ∆tdk (gift from Eric Martens), B. fragilis 638R (gift from Eduardo Rocha), B. ovatus ATCC8483 ∆tdk (gift from Eric Martens and Nicole Koropatkin) and B. vulgatus ATCC8482 (gift from Eric Martens) were anaerobically cultured at 37 °C in brain heart infusion (BHI) medium (Sigma) supplemented with hemin (5 μg mL−1), $0.1\%$ (w/v) cysteine and $0.2\%$ (w/v) sodium bicarbonate. The B. thetaiotaomicron ∆SPT mutant was a gift from Eric Brown [13], whilst the B. thetaiotaomicron ∆glsB mutant, disrupted for N acyl transferase, was constructed as previously described [25]. ## 2.3. Lipid Extraction Bacteria were inoculated in BHIS broth, and after 24 h, cultures were normalised to 1OD600, then pelleted by centrifugation at 8000 rpm × 10 min. Pellets were washed twice with phosphate bufferered saline (1xPBS). Bacterial pellets were subject to a single phase isopropanol lipid extraction, and the protein percipitation procedure was as described by Sarafian et al. [ 26]. This methodology represents a one-step, one phasic extraction method that is shown to precipitate proteins and to extract a broad range of lipids of different polarity [27]. Briefly, washed pellets were resuspended in isopropanol (to a final density of 1OD600 mL−1) with added internal standard (16:0 SM, 1 µg/mL) for normalization purposes. Samples underwent vortexing for 30 s, then they were incubated at room temperature for 10 min, with occassional mixing, before overnight storage at −20 °C. The supernatent was collected following centrifugation for 20 min at 14,000× g and stored at −80 °C for LC-MS analysis. ## 2.4. LC-MS Conditions Bacterial isopropanol lipid extracts were analysed using Waters Xevo™ G2-XS QTOF Mass Spectrometer coupled to a Waters ACQUITYTM UPLCTM system. Extracted samples were injected (5 μL injection volume) onto an ACQUITY CSH™ column (100 mm × 2.1 mm, 1.7 μm; Waters) at 55 °C with a flow rate of 400 μL/min. The mobile phases consisted of phase A (acetonitrile/water (60:40, v:v) with 10 mM ammonium formate and $0.1\%$ formic acid), which was subject to gradient mixing with mobile phase B (isopropanol/acetonitrile (90:10, v:v) containing 10 mM ammonium formate and $0.1\%$ formic acid) (Table S1 in the Supporting Information details the gradient parameters). Mass spectrometry was performed under both positive and negative ESI modes using the following paramaters: Acquistion mode: MSE; acquistion range: from m/z 100 to 2000; acquisition time: 1 s/scan; source temperature: 120 °C; desolvation temperature: 550 °C; nitrogen gas flow: 900 L/h; capillary voltage: 2.0 kV (positive mode) or 1.5 kV (negative mode); cone voltage: 30 V. For both ionization modes, leucine enkephalin (m/z 556.2771 in ESI+, m/z 554.2615 in ESI−) was continuously infused at 30 μL/min and sampled every 30 s for lock mass correction. ## 2.5. Characterisation of Plasmalogens Acid hydrolysis was applied to confirm the presence of plasmalogens in B. thetaiotamicron extracts according to Murphy et al. [ 27]. Briefly, isopropanol was removed from lipid extracts under nitrogen, and eppendorfs were inverted over five drops of concentrated HCl in a test tube cap for 5 min. This caused the complete hydrolysis of the vinyl ether bond of the plasmalogens, while the diacyl ester bonds remained intact, a confirmatory feature of plasmalogens. The samples were re-extracted with isopropanol then reanalysed by LC-MS, as described above. ## 2.6. MS Data Processing and Statistical Analyses For targeted analysis, MS data was processed using the open-source, Skyline-daily (beta) freeware (MacCoss Lab Software, University of Washington, Washington, DC, USA). An in-house Bacteroides lipid library was constructed from mining the existing literature, to include classes of sphingolipids, N-acyl amines, fatty acids, and glycerophospholipids with cross checking of raw data in both ionisation modes for accurate mass (where delta mass < 5 ppm), retention time, and MS/MS fragmentation, where possible. This database, together with LC-MS raw data files, was uploaded into Skyline for processing following lock mass correction. Both positive and negative mode data were processed, following application of a correction factor (based on peak intensity of several common lipids detected in both modes), then normalised to the internal standard. Data was log-transformed using MetaboAnalyst 5.0 web-based platform [28,29], prior to multivariate (principle component analysis (PCA), heatplot representation, ANOVA statistical scrutiny) and univariate (volcano plots) analysis. Customised python scripts were applied to produce volcano plots. GraphPad Prism 5.0 was used to produce bar charts and for regression analysis. For untargeted analysis, raw LC-MS data files were first processed (peak alignment) using Progenesis QI (WatersTM, UK) for both positive mode and negative mode data and then imported into MetaboAnalyst 5.0 for median normalisation, log transformation and multivariate analyses. ## 3.1. The Lipid Signatures of Bacteroides Are Species Specific Mass spectrometry applications and analyses reveal that a diverse range of lipids are associated with gut resident representatives of Bacteroides (see Table S2 for a full list of the lipids identified in this study). We fully acknowledge that different lipids may be uncovered using other lipid extraction procedures. Multivariate analysis indicates that B. vulgatus and B. fragilis lipid signatures co-cluster, whilst B. thetaiotaomicron and B. ovatus lipid signatures occupy distinct positions (Figure 1A,B). This analysis suggests that B. vulgatus and B. fragilis may have similar lipidomes, which are distinct from both B. thetaiotaomicron and B. ovatus. Targeted LC-MS–based lipidomics reveal the diversity of lipids present in Bacteroides. In total, we identified 170 individual lipids distributed across 4 lipid categories or 26 lipid ‘subgroups’ (according to convention defined in LIPID MAPS) (Figure 1C). The Bacteroides lipidome is dominated by glycerophospholipids (GP) and sphingolipids (SP), with smaller contributions from fatty acyls (FA) and glycerolipids (GL) (see Figure 1C). Targeted analysis also reveals that the observed differences in the lipid profile between Bacteroides species are largely driven by species-dependent signatures related to the presence of plasmalogens (P), diacyl glycerophosphoinositols (PI) and certain sphingolipids (SP), including dihydroceramidephosphoinositol (Cer PI) and α-galactosyl dihydroceramide (GalCer) (Figure 1D). In contrast, fatty acids, hydroxy fatty acids, N-acyl amines, diacylglycerol (DG), glycerophosphoethanolamine (both diacyl PE and lyso PE (LPE)), diacyl glycerophosphoserine (PS), dihydroceramide (dihydroCer) and dihydroCer phosphoethanolamines (Cer PE) represent relatively stable core lipids found in all species of Bacteroides examined in this study (Figure 1D). ## 3.2. N-Acyl Amines Comprise a Signifcant Proportion of the Fatty Acyl (FA) Component of Bacteroides Lipids The FA category of LIPID MAPS includes fatty acids and N-acyl amines. B. thetaiotaomicron and B. fragilis have the highest representation in the FA category, which includes hydroxy fatty acids (C15 to C17) and N-acyl amines (Figure 1C). N-acyl amines comprising GlyL and FL with varying acyl chain lengths and their respective mono-acylated derivatives (mono-GlyL, mono-FL) were detected in all four Bacteroides species (see Figure 2). GlyL represented between $4\%$ of B. fragils and $32\%$ of B. ovatus total N-acyl amines detected (Figure 2C). For the most part, the major GlyL was the previously reported GlyL at m/z 568 in the positive ion mode; however, B. ovatus contained similar amounts of a different glycine lipids at m/z 554 in the positive ion mode, presumably with a shorter carbon chain length (Figure S1B). For the most part, lyso-GlyL (also known as commendamide) was a minor component of the lipid signatures detected for all Bacteroides species examined (Figure 2C). In general, FL accounted for most of the N-acyl amines detected (Figure 2D), and this lipid was abundantly represented amongst all Bacteroides species, with lyso-FL approximately 20 times more abundant in the B. fragilis lipid signature (Figure 2D). The best characterised FL, at m/z 655 in the positive ion mode, is also known as Lipid 654 (FL-654), owing to its molecular weight in the negative ion mode, and this was the most abundant N-acyl amine in extracts from B. fragilis and B. vulgatus, whilst the shorter carbon chain length FL, at m/z 641 in the positive ion mode, was the most abundant in B. thetaiotaomicron and B. ovatus (Figure S1A). These different chain lengths may indicate important strain-specific differential signalling potential [30]. In addition, a series of ‘unknown’ but predicted N-acyl amines with m/z values (in positive ion mode) of 1230.9207 (Unknown_1231), 1244.9363 (Unknown 1245), 1258.9520 (Unknown 1259) and 1272.9676 (Unknown_1273) were also detected (see Table S2). B. vulgatus contained a relatively higher proportion of these ‘unknown’ lipids (Figure 2E), with Unknown_1259 and Unknown_1273 as most abundant (Figure S1C). Therefore, the profile of N-acyl amines is qualitatively similar across all of the examined Bacteroides, although there are some quantitative differences that may be physiologically important, given the important signaling role of the glycine lipid family. ## 3.3. Dihydroceramide Phophoethanolamine (Cer PE) Is the Most Abundant Sphingolipid (SP) Detected in All Four Bacteroides In this study, SP was found to represent between $19\%$ (B. ovatus) and $29\%$ (B. vulgatus) of the total lipids detected (Figure 2A). Figure 3 shows the levels of SP subgroups extracted from each examined Bacteroides species with respect to the steps in the SP biosynthesis pathway. Briefly, the biosynthesis of bacterial SP is initiated by serine palmitoyltransferase (SPT), which catalyses a reaction between a fatty acyl-CoA and serine, or alternatively alanine, to form keto-sphinganine (sph) or deoxy-keto-sph, respectively. The pathway to dihydroCer synthesis may proceed similarly to that observed in eukaryotes, harnessing keto reductase activity [31] to form sph. Alternatively bacterial Cer synthase (CerS) could directly add an acyl chain to 3-keto-sph to form an oxidised dihydroCer intermediate (ox-dihydroCer), which could then be reduced to dihydroCer by bacterial Cer reductase (CerR) [32]. DihydroCer represents the central hub of SP metabolism, and it can undergo modification with different head groups (Figure 3D). The biosynthesis of B. fragilis α-GalCer was recently reported via a ceramide UDP-GalCer synthase [33], whilst the biosynthesis of Cer PI was recently proposed through either Cer PI synthase or haloalkanoate dehalogenase (HAD) hydrolase activity [34]. In our study, dihydroCer was shown to account for approx. $20\%$ of the SP fraction in B. ovatus, B. vulgatus and B. thetaiotaomicron (Figure 3C). However, in B. fragilis, dihydroCer accounted for only $2\%$ of the SP fraction (Figure 3C). Instead, B. fragilis appears to accumulate higher levels of keto-sphinganines (keto-sph), which are upstream intermediates in the SP biosynthetic pathway (Figure 3A). Moreover, there were detectible levels of sphinganine (sph) and deoxy-sph in both B. thetaiotamicron and B. ovatus (Figure 3A). These intermediates may be generated by the reduction of keto-sph by a keto-reductase and/or the hydrolysis of dihydroCer by ceramidases (Figure 3A,C). Recent studies have suggested that there may be multiple pathways for SP biosynthesis in Bacteroides, and the varying levels of intermediates detected across the four species examined in this study support this observation [31,32]. Cer PE was identified as the most abundant SP in all four species, with levels between $35\%$ for B. ovatus and $81\%$ for B. fragilis of the total SP detected (Figure 3D). In the current study, Cer PI represented $3\%$, $6\%$ and $1\%$ of the total SP fraction in B. thetaiotaomicron, B. ovatus and B. vulgatus, respectively, and this SP was not detected in B. fragilis (Figure 3D). Moreover, α-GalCer was detected in only B. fragilis and B. vulgatus (Figure 3D). Therefore, there is a wide range (both qualitatively and quantitatively) of SP and their intermediates produced across the Bacteroides species examined in this study. ## 3.4. Plasmalogens and Phosphoinositol (PI) Lipids Are Not Found in All Bacteroides Species Glycerophospholipids (GP) are the primary buiding blocks of bacterial cell membranes. Their synthesis, biochemical diversity and relative levels amongst Bacteroides species were examined and are depicted in Figure 4. Amongst other bacteria representatives, the biosynthesis of most bacterial GP primarily begins from the central metabolite cytidine diphosphate-DG (CDP-DG), leading to the production of phosphoinositol (PI) in one direction or glycerophosphoserine (PS) in the other. PI is formed either via PI phosphate (PIP) intermediates [34,35] or directly via diacylglycerols (DG) and CDP-alcohol-phosphotransferase, the latter pathway is predicted in silico and it remains to be validated [34]. PS acts as a metabolic intermediate; it can undergo decarboxylation to form glycerophosphoethanolamines (PE) via PS decarboxylase [35]. In bacterial membranes, Lyso GP such as LPE are generated as metabolic intermediates in phospholipid synthesis or from membrane degradation via the action of phospholipases [36], whilst glycerophosphoethanolamines plasmalogen (PE-P) can be formed from PE via plasmalogen synthase (P synthase) in anaerobes, specifically Clostridium perfringins [37]. Bringing these systems and representations together for Bacteroides species (Figure 4), DG, the simplest glycerol based membrane lipid and a metabolic intermediate to GP, accounts for between $3\%$ (B. fragilis) and $13\%$ (B. ovatus) of the total lipid content of Bacteroides (Figure 4A) under anaerobic conditions. Thereafter, the GP lipid fraction was dominated by diacyl PE ($97\%$ of total GP) in all four Bacteroides species tested (Figure 4B), with minor amounts of lyso-PE (LPE) and diacyl PS also detected (Figure 4B). On the other hand, diacyl PI was detected in lipid extracts of B. thetaiotaomicron and B. ovatus but not in B. fragilis and B. vulgatus (Figure 4B). Interestingly, we detected a number of plasmalogens (both diacylated (PE-P) and lyso-derivatives (LPE-P)) in B. thetaiotaomicron only (Figure 4B) at m/z [M-H]- of 576.4035, 590.4191, 604.4348, 618.4504, 632.4661, 646.4817, 660.4974 (PE-P) and 394.2364, 408.2521, 422.2677, 436.2834 (LPE-P) (Table S2). Fragments at m/z 196.0380 and 140.0118, corresponding to the loss of the plasmenyl group and the ethanolamine phosphate ion, respectively, were detected in negative mode MS2 spectra, confirming that these plasmalogens are derived from PE. The presence of plasmalogens in B. thetaiotaomicron was confirmed by specific acid hydrolysis assay (see Figure S2). ## 3.5. A Mutation in Sphingolipid (SP) Biosynthesis Results in Global Changes in the Lipid Signature, including Reductions in the Levels of GlyL Given that the synthesis of all lipids requires similar building blocks, we reasoned that disrupting the production of any class of lipids could result in major compensatory lipid signature alterations. To qualify and measure these changes, we compared the lipid signatures of B. thetaiotaomicron ∆SPT, mutated for the gene encoding serine palmitoyltransferase, the enzyme required for the first step in SP biosynthesis, with the wild-type parent strain, post anaerobic growth and lipid extraction at 1OD600 biomass. Following extraction, mass spectrometry and analysis, as described above, multivariate analyses and comparison of lipid signatures showed that deletion of SPT results in dramatic changes to the B. thetaiotaomicron lipid profile (Figure 5A and Figure S3C). Moreover, these changes were not limited to SP lipids, indeed, 15 of the 26 lipid ‘subgroups’ (Figure 5A) or 86 of 170 individual lipids (Figure S3C) were significantly decreased in the ∆SPT mutant compared to the wild-type parent strain (>2 fold, p value (<0.05)). Furthermore, in the ∆SPT mutant strain, all SPs were depleted, whilst GlyL and FL were also significantly decreased relative to the WT parent (Figure 5B). Moreover, all PE (mono- and diacyl) and PE plasmalogens (mono- and diacyl) were significantly reduced in the ∆SPT mutant (Figure 5B). Indeed, the total identifiable lipids detected in the ∆SPT mutant decreased by $60\%$ overall relative to the WT (Figure S3B). Diacyl PI was the only lipid subgoup that increased in the ∆SPT mutant relative to WT (Figure 5C). Therefore, as predicted, a mutation in SP production results in global changes in the lipid profile of the membranes of B. thetaiotaomicron. ## 3.6. A Mutation in Glycine Lipid Biosynthesis Results in Changes in the Sphingolipid Pool In B. thetaiotaomicron, the glsB gene encodes the enzyme responsible for the first step in glycine lipid biosythesis (Figure 2B) [25]. We reasoned that disrupting the enzyme activity may ilicit global changes in its lipid signature. To qualify and measure these changes, we compared the lipid signatures of B. thetaiotaomicron ∆glsB with the wild-type parent strain, post anaerobic growth and post lipid extraction of 1OD600 biomass. Following extraction, mass spectrometry and analysis, as described above, multivariate analyses and comparison of lipid signatures showed that 8 of the 26 lipid ‘subgroups’ or 36 of the 170 individual lipids identified in this study were decreased in the ∆glsB mutant compared to the WT strain (Figure 6A and Figure S4A–C). As expected, GlyL and the related FL and complex ‘unknown’ lipids were depleted in the ∆glsB mutant extracts compared to the WT strain (Figure 6B). On the other hand, 6 of the 26 lipid ‘subgroups’ (or 37 of the 170 individual lipids) proved significantly increased in the ∆glsB mutant compared to the WT parent strain (Figure 6C). These lipids include SP subgroups Cer PE, dihydroCer and Cer PI (Figure 6C) and GP subgroups DG, diacyl PI and diacyl PS (Figure 6C). Therefore, it appears that depletion of the glycine lipids is compensated for by increasing other lipid groups, particularily SP and GP. ## 4. Discussion Microbial lipids are becoming recognised as interkingdom signalling molecules, and as such, they represent an interesting avenue for the potential development of novel biomarkers and/or therapeutics. In this study, we set out to construct a lipid map for a range of mammalian gut resident Bacteroides species, namely B. thetaiotaomicron, B. fragillis, B.ovatus and B. vulgatus. Individually, these selected Bacteroides may account for up to $6\%$ of intestinal bacteria in healthy humans, with B. vulgatus reported as enriched to represent up to $40\%$ in patients with Crohn’s Disease [38]. This work points to the presence of core lipid species, common to all Bacteroides species examined, but also to species-specific lipid signatures, which could be disrupted and redistributed through targeted pathway mutation. We acknowledge that results may have differed if other lipid extraction procedures would have been used. Those identified represented four LIPID MAPS categories and 26 lipid ‘subgroups’ (Table S2) across the four selected species. PE (diacylated) of various fatty acyl chain lengths (Table S2) was noted as the major core lipid subgroup in all four Bacteroides species examined (Figure 4). Very recently, Bae et al. [ 30] identified a diacyl PE, with two branched fatty acyl chains (PE 15:0a/15:0i), from the cell membrane of Akkermansia muciniphila, as an agonist of TLR2-TLR1, which leads to the release of certain cytokines. This immunomodulatory activity is dependent on the presence of methyl branches on both fatty acyl chains; also, there is a requirement for one fatty acyl chain as antesio-branched while the other is iso-branched. It is possible that one of the three PE (30:0) isomers, detected in the current study (Table S2), may contain a similar molecular structure. Our data suggest that PE is likely formed via the decarboxylation of PS, given that PS (diacylated) was also detected, albeit in relatively minor amounts, in all four species. Different lyso-PE (LPE) species were also noted as minor core lipids in all four Bacteroides. These lipids are likely generated as metabolic intermediates in PE synthesis or from the degradation of PE [36]. The role of LPE in bacteria remains poorly characterised, although it is believed that LPE may mitigate membrane stress induced by non-bilayer lipids such as cardiolipin. Whilst CL is an important GP in some Gram-negative bacteria, such as E. coli [35], and CL has been reported in some Bacteroides [39,40], we did not detect CL in any of the Bacteroides species examined in this study. Plasmalogens, or vinyl ether lipids, are produced by the modification of the fatty acid at the sn-1 position of GP such that it is linked via an alkenyl or plasmenyl bond rather than an ester bond (Figure 3 shows the structure of PE Plasmalogen). Plasmalogens have a broad phylogenetic distribution; they are present in biological membranes of bacteria, protozoa, invertebrates and mammals [41]. Amongst bacteria, plasmalogens are rarely detected in aerobes [42]; they are sparse in facultative anaerobes [43] but appear to be common in anaerobes including certain gut-associated Bifidobacteria and Clostridia species [43,44,45,46,47,48]. Interestingly, in our study, plasmalogens were detected in one species of Bacteroides only, B. thetaiotaomicron (Figure 4), suggesting that these lipids are not ubiquitous in this genus. In 1969, Kamio et al. [ 48] reported the occurence of plasmalogens in strict anaerobe Bacteroides ruminicola, isolated from the rumen of a sheep; the strain has since been reclassified as Prevotella ruminicola species, genus Fibrobacter. Hence, our study is the first to confirm the presence of PE plasmalogens, both mono and diacyl, in a species of Bacteroides that is a normal component of the human gut microbiota. In mammals, plasmalogens play unique roles in membrane structure; in membrane trafficking and in cell signalling, reduced levels of circulating plasmalogens are linked to metabolic and neurological diseases including diabetes and Alzheimer’s disease [49,50,51]. The exact role(s) of plasmalogens in and from bacteria are not yet known; however, by virtue of their vinyl ether bond, they are likely to be involved in modulating membrane morphology, with the potential to provide protection from oxidative stress [52]. We previously described a family of N-acyl amines in B. thetaiotaomicron, called glycine lipids [25]. In this study we confirmed the presence of glycine lipids in all species of Bacteroides tested, suggesting that these lipids are widespread in this genus (Figure 2). Indeed, bioinformatic analysis indicates that the genetic potential for the production of glycine lipids is restricted to genera in the phylum Bacteroidota (our unpublished data). Nonetheless, there were species differences in the relative quantities of specific glycine lipids, despite identical and controlled growth and lipid isolation conditions. The physiological relevance of these differences is not clear. FL-654 was the most abundant glycine lipid detected in all Bacteroides tested. FL-654 has been reported to act as a TLR2 agonist in several studies, suggesting a possible role in inflammation in the host [14,17,18]. More recently, low-density lipoprotein receptor deficient (Ldlr−/−) mice fed a high fat diet (HFD), but who received chronic, 7-week, intraperitoneal administration of FL-654, were attenuated for atherosclerosis progression, and they displayed decreased markers of liver injury compared with vehicle control-injected mice [53], suggesting that these lipids may be beneficial. The glycine lipid family also includes high-molecular lipid molecules such as Lipid 1256, characterized from Porphyromonas, a relative of Bacteroides, consisting of a diacyl glycerophosphoglycerol (PG) linked to the FL [19]. Lipid 1256 was reported to be even more potent as a TLR2 ligand than the related GlyL and FL [19]. In the current study, we initially assumed that Unknown_1259 (Table S2) was the same as Lipid 1256; however, given the absence of PG in Bacteroides lipid extracts and the difference of a proton in the observed m/z, Unknown_1259 is more likely to be FL linked to PE. Work is ongoing to confirm the structure of this potentially novel lipid. Compared to well-studied bacteria such as E. coli, Bacteroides do have an unusual membrane lipid composition, in that approximately $50\%$ of the lipids, extractable with chloroform-methanol, are SP or free ceramides [54]. In the present study, using isopropanol extraction, SP represented between $19\%$ and $29\%$ of the total lipids detected in B. ovatus and B. vulgatus, respectively. For the most part, dihydroCer and Cer PE (dihydroCerPE in bacteria) represent the core SP detected (Figure 3), and this is typical of bacteria in the phylum Bacteroidota [55]. Interestingly, both dihydroCer and Cer PE are shown to be negatively correlated with inflammation and with Inflammatory Bowel Disese (IBD) in humans [13]. The same authors also reported that deoxy-dihydroCer in B. thetaiotaomicron is formed via the utilisation of alanine, rather than serine, by SPT. In the present study, putative deoxy-dihydroCer was detected in all four Bacteorides species tested, and it was notably higher in representation in B. vulgatus (Figure 3). In addition, a putative oxidised dihydroCer (ox-dihydroCer) lipid was detected as a minor core lipid, supporting the notion that dihydroCer synthesis may proceed via bacterial ceramide synthase (CerS), which could directly add an acyl chain to keto-sph producing ox-dihydroCer, which may then be reduced to dihydroCer by bacterial ceramide reductase (CerR) (Figure 3). The upstream SP, keto-sph, was particularily abundant in B. fragilis, suggesting a potentially slower conversion to dihydroCer and/or faster rate of sythesis via SPT (Figure 3A). This highlights potentially important species-specific differences in enzyme kinetics and flux, through the SP biosynthetic pathway. The presence of sph and deoxy-sph in B. thetaiotaomicron and B. ovatus may be indicative of increased keto-reductase activity [30] and/or a slower rate of conversion to dihydroCer in these Bacteroides species. It may also be interpreted to indicate the presence of a ceramidase, which hydrolyses dihydroCer to sph [32], an activity that appears absent in both the B. fragilis and B. vulgatus species examined. Depending on the species, two other complex SP were detected in the present study. Cer PI, which consist of a inositol phosphate on a sphingoid backbone, were detected in B. thetaiotaomicron and B.ovatus as reported for B. thetaiotaomicron previously by Brown et al. [ 13]. Here, we also show that Cer PI are present in B. vulgatus (Figure 3). The gene clusters reponsible for inositol lipid synthesis in some Bacteroides species have recently been described [13,34,56] and involve either PI Cer synthase, typical of B. thetaiotaomicron and B.ovatus, or HAD hydrolase activity, typical of B. vulgatus (Figure 3). Heaver et al. [ 34] report that inositol and inositol lipids, both membrane and capsule lipids, are likely implicated in resistance to host immune defences and therefore may influence their fitness and maintenance in the mammalian gut. The ‘non-phosphate’ containing complex glycosphingolipids, α-GalCer, were detected in B. fragilis and, to a lesser extent, in B. vulgatus. This is consistent with previous reports on the generation of α-GalCer by B. fragilis [10,56] and lower levels produced by B. vulgatus [57]. B. fragilis α-GalCer was recently reported as dependant on ceramide UDP-galactosylceramide synthase activity [33]. α-GalCer has been shown to be a potent stimulator for invariant NKT cells [10], whereby the sphinganine chain branching is a critical determinant of NKT activation [58]. SP and glycine lipids are found in all Bacteroides tested and are likely to have a structural role in the membrane of these bacteria. Therefore, we examined the changes in lipid signatures following mutations to these two major bioactive lipiid pathways in B. thetaiotaomicron. Using a mutation in the glsB gene, we showed that B. thetaiotaomicron compensate for the absence of glycine lipids by increasing some SP, DG, diacyl PI and diacyl PS. In contrast, we show an overall decrease in lipid diversity associated to the B. thetaiotaomicron ∆SPT mutant, including a significant decrease in many glycine lipids, PE (both diacyl and lyso) and PE plasmalogens (both diacyl and lyso). The only attempt at compensation appears to be through a signifcant increase in PI (Figure 5C). Given that Cer PI are not formed in the ∆SPT mutant, the increase in PI may simply be due to their reduced coupling to ceramide (Figure 3D). Thus, perturbations in glycine lipid or SP biosynthesis results in significant and distinct changes in the levels of other lipids, suggesting the existence of compensatory mechanisms required to maintain the functionality of the bacterial membrane. The relatively dramatic global lipid decreases in the ∆SPT mutant may suggest that SP have a key structural role in the membranes of Bacteroides; without SP, other key membrane lipids may be impeded from organising or inserting into the membrane such that they are therefore depleted from the lipidome. In summary, we show that Bacteroides produce diverse lipids, some of which are core lipids while others are species specific. We point to the biochemical processes and to the gaps in our knowledge in understanding their production under anaerobic conditions. We further demonstrate that plasmalogen production is unique to B. thetaiotaomicron among the species examined. The exact role of these plasmalogens, in or between bacteria, in reacting to oxygen or as molecular signalling molecules to the host, remains to be elucidated. Given plasmalogen representation in key gut resident bacteria, their modulation may represent untapped therapeutic targets for different disease states [59]. For B. fragilis, the most notable difference in the lipid profile was their relatively higher abundance of lyso-FL keto-sph and α-GalCer. There is a sparsity of knowledge on the bio-activity of bacterially derived keto-sph. The bioactivity of α-GalCer, however, has received considerable attention, since certain lyso-FL species can act as agonists of TLR2 [10,56,58], a bacterial recognition Toll-like receptor that mediates macrophage release. B. ovatus was notable—relative to the other species—in its accumulation of DG, PI and Cer PI. Indeed, B. ovatus ATCC8483 has been shown to reduce mucosal inflammation by up-regulating IL-22 secretion [60,61]. Interestingly, B. vulgatus presented relatively more ‘unknown’ lipids, specifically LPE and PS and deoxy-dihydroCer. Given the association of B.vulgatus with IBD [15], these observations may prove important. The ‘unknown’ lipids are structurally related to Lipid 1256, a potent TLR2 ligand that also promotes the production of pro-inflammatory cytokines. In addition, deoxy-dihydroCer are considered ‘dead-end’ toxic lipids [62], and they are implicated in the progression of Type 2 diabetes [63,64]. In short, this study aimed to elucidate, understand, characterize and examine the relative synthesis and lipid signatures associated with important gut resident bacteria, Bacteroides species. It unearthed key and unique lipid species representation. It will provide a useful platform for further studies to elucidate lipid-based host–microbe and microbe–microbe dialogues and may prove important in the context of addressing host health and disease states. ## References 1. 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--- title: Effect of Free Cross-Linking Rate on the Molding of Bulk SiOC Ceramics authors: - Lei Zheng - Weilian Sun - Zhijian Ma - Hongchao Ji - Bo Sun journal: Materials year: 2023 pmcid: PMC10056544 doi: 10.3390/ma16062446 license: CC BY 4.0 --- # Effect of Free Cross-Linking Rate on the Molding of Bulk SiOC Ceramics ## Abstract Polymer-derived ceramics (PDCs) have many advantages in ceramic molding and ceramic properties, but because of the obvious volume shrinkage in the process of precursor transformation into ceramics, it is easy for defects to appear in the forming process of bulk PDCs. Herein, theoretical analyses and experimental studies were carried out to improve the quality of sintered samples and realize the parametric design of raw materials. Firstly, based on the HPSO/D4Vi cross-linking system, the mathematical model of the free cross-linking ratio was established, and the theoretical value was calculated. After that, the samples with different free cross-linking rates were heated at 450 °C and 650 °C for different holding times. It was found that the free cross-linking ratio (α) had a significant impact on the weight loss of the samples. When the difference of the α value was $10\%$, the difference of the samples’ weight loss ratio could reach $30\%$. Finally, the morphology of sintered products with different α values was analyzed, and it was found that obvious defects will occur when the free cross-linking ratio is too high or low; when this value is $40.8\%$, dense and crack-free bulk ceramics can be obtained. According to analysis of the chemical reaction and cross-linking network density during sintering, the appropriate value of the free cross-linking ratio and reasonable control of the cross-linking network are beneficial for reducing the loss of the main chain element and C element, alleviating the sintering stress, and thus obtaining qualified pressureless sintered bulk ceramic samples. ## 1. Introduction Compared with traditional ceramic technology, polymer-derived ceramics (PDC) have obvious advantages in composition design and processing technology [1]. PDCs can regulate ceramic products through the design of precursor molecular structures and ceramic transformation conditions. Through the organic–inorganic amorphous–crystalline transformation, the preparation of ceramic materials with special composition and phase structure can be realized, which is difficult to achieve in traditional ceramics [2,3,4]. Moreover, the precursor polymer has obvious physical properties of organic polymers, which can be formed by the molding technology usually applicable to organic polymers [5], such as film coating [6], impregnation [7], vapor deposition [8], electrostatic silk imitation [9], 3D printing [10], etc., and then ceramic products can be obtained by high-temperature pyrolysis. Therefore, PDCs play an important role in the production of high-performance ceramics with complex structures [11], and have important applications in aerospace [12], new energy [13], optical metamaterials [14], military [15], and other fields. Due to the high density difference between the polymer precursor and ceramic phase, there will be a high volume shrinkage when the polymer precursor is converted to the ceramic phase, which is easy to produce defects and cracks during pyrolysis, affecting the performance of ceramic components [10]. Therefore, PDCs have more advantages in one-dimensional and two-dimensional structure forming, such as ceramic film, ceramic coating, ceramic fiber, porous structure, etc., while in the forming of a three-dimensional block structure, the volume effects of filler or pressure forming are usually used to obtain a higher-strength compact ceramic block structure. For example, Zhang et al. [ 16] prepared three-dimensional SiC/SiC ceramic components by adding SiC whiskers and SiC particles to polycarbosilane. The research results showed that the mechanical properties of the ceramic materials were significantly improved after adding whiskers and particles to the polymer precursor, and the ceramic sample with a tensile strength of 21.3 MPa and a fracture toughness of 1.89 MPa·m$\frac{1}{2}$ was obtained. With the quantity of added SiC whiskers unchanged, the linear shrinkage of the ceramic materials decreased from $18.32\%$ to $8.3\%$ with the increase in SiC particles. The weight loss decreased from $17.5\%$ to $10.6\%$, which was mainly due to the low porosity of ceramic materials prepared by the combination of ceramic particles and whiskers, and the ceramic particles enhanced the strength of the samples. Sun et al. [ 17] used solid ammonium carbamate as the ammonia source, methyl vinyl dichlorosilane and vinyl trichlorosilane as the mixed silicon source to synthesize the SiCNO ceramic precursor, and prepared ceramic blocks by the hot-pressing sintering method. When T < 1500 °C, the product is amorphous SiCNO ceramics. The ceramic density (2.15 g·cm−3), bending strength (138.1 MPa), and fracture toughness (2.32 MPa·m$\frac{1}{2}$) were obtained when $T = 1500$ °C. When T > 1500 °C, the SiCNO material was decomposed and Si2N2O was precipitated from the matrix. Because of the sp2 hybridization of C-N, nitrogen atoms play the role of “bridging nitrogen”, reducing the tendency of carbon precipitation in the materials as free carbon. Although the pressure condition is effective at improving the performance of ceramic samples, it also limits the forming process of precursor ceramics and restricts the complexity of ceramic parts. Therefore, researchers started from more fundamental factors to study the influence of the precursor composition, proportion, and sintering process on ceramic yield and stress, so as to obtain precursor ceramic blocks with better performance and fewer defects under pressureless conditions [18]. Based on DLP technology, Schmidt et al. [ 19] mixed high-yield polysiloxane and photosensitive polysiloxane containing acrylate groups into photocuring materials. By adjusting the ratio of the two components, they realized the control of ceramic yield, shrinkage, and other parameters. When the proportion of photosensitive polysiloxane was $50\%$, the ceramic yield was $40\%$ and the shrinkage was $42\%$; with the proportion of photosensitive polysiloxane reduced to $33\%$, the ceramic yield was $60\%$ and the shrinkage was $30\%$. Yu et al. [ 20] found that hyperbranched polycarbosilane synthesized from chloromethyl-dichlorosilane, chloromethyl-trichlorosilane, and allyl chloride, with cyclohexanone oxide cobalt naphthenate as catalyst, could realize the cross-linking reaction at room temperature, so the ceramic yield could reach $70\%$. With the introduction of the alkynyl group, the cross-linking density could be further improved, and the ceramic yield could reach $82.5\%$. The designability of precursor molecules is one of the most important characteristics that distinguish precursor ceramics from traditional ceramics [21]. Herein, to better understand the effects of the precursor molecular structure, raw material ratio, and sintering process on sintering stress and ceramic yield, the concept of the free cross-linking rate and its theoretical calculation method were proposed for the two-component precursor system of polymethylhydrosiloxane (HPSO) and polytetramethyltetravinyllic cyclic tetrasililoxane (D4Vi). The ceramic yield and macro- and micromorphology differences of ceramic samples under different free cross-linking ratios were compared through experiments, which provided a theoretical basis for the molecular design of precursors for the pressureless formation of high-yield compact ceramic blocks. ## 2. Calculation of Free Cross-Linking Rate In the process of thermal curing, the severity of the cross-linking reaction is not completely consistent with the curing time, and the yield of pyrolytic ceramics is not consistent with the content of active groups. This is caused by the difference in the molecular weight of the precursor polymer. The silicone polymer with lower molecular weight needs to be cross-linked to generate a silicone polymer with higher molecular weight, and then it can be cross-linked into a network structure. This shows that the speed of the cross-linking reaction and the network density of cross-linking products cannot be determined by the content of active groups alone. For this reason, we put forward the concept of the free cross-linking ratio and deduced the calculation method of its theoretical value. The degree of cross-linking reaction can be evaluated by easily measured or known raw material parameters, such as active group content and molecular weight, and parametric design of raw material parameters can be realized. ## 2.1. Modeling of Free Cross-Linking In order to better characterize the relationship between curing time and the number of cross-linking points in the system, the free cross-linking ratio (α) was defined as the average mass ratio of organics that are occupied by non-cross-linking groups (such as methyl) and cannot be cross-linked. Ideally, it is considered that no self-cross-linking reaction of the vinyl group or dehydrogenation reaction of the hydrosilyl group occur under the conditions of thermal cross-linking reaction. It is assumed that there are three types of cross-linking points: the effective cross-linking point which formed by C=C and Si-H; the cross-linked point which formed on the Si atom and adjacent chained atom (O atom); and the invalid cross-linking point, that the cross-linked point is occupied by nonreactive atoms (such as methyl, redundant vinyl, redundant hydrogen, etc.), which cannot undergo a cross-linking reaction, and it is only a virtual cross-linking point. The total cross-linking point is the Summation of the effective cross-linking point, the cross-linked point, and the invalid cross-linking point. Hence, the parameters are set as follows:nSiV: The number of silicon atoms on a molecular chain (or ring) of vinyl silicone oil;nC=CV: The amount of vinyl on a molecular chain (or ring) of vinyl silicone oil;nCH3V: The number of methyl groups on a molecular chain (or ring) of vinyl silicone oil;nOV: The number of O atoms on a molecular chain (or ring) of vinyl silicone oil;nSiH: The number of silicon atoms on a molecular chain (or ring) of hydrogen silicone oil;nHH: The amount of hydrogen on a molecular chain (or ring) of hydrogen silicone oil;nCH3H: The number of methyl groups on a molecular chain (or ring) of hydrogen silicone oil;nOH: The number of oxygen atoms on a molecular chain (or ring) of hydrogen silicone oil.φC=C: Mass content of vinylφH: Mass content of hydrosilyl group The flow of model and calculation is shown in Figure 1. Since the oxygen atom on the chain can only extend the chain and cannot provide additional cross-linking points, the total number of cross-linking points of a molecular chain (or ring) of vinyl silicone oil is twice the total number of silicon atoms: 2nSiV. Similarly, the total number of cross-linking points of a molecular chain (or ring) of hydrogen silicone oil is twice the total number of silicon atoms: 2nSiH. Therefore, the number of cross-linked points in cyclovinyl silicone oil is nSiV. [1]nSiV=nCH3V+nC=CV2, Likewise, the number of cross-linked points in chain hydrogen-containing silicone oil is (nSiH−1):[2]nSiH=nCH3H+nC=CH2−1, The cross-linked point and effective cross-linked point can improve the cross-linking degree of the polymer system, while the ineffective cross-linked point cannot improve the cross-linking degree of the cross-linked system. Accordingly, when the number of invalid cross-linking points increases, the number of effective cross-linking points and cross-linked points will decrease, and the cross-linking difficulty of the polymer system or the density of the cross-linking network will decrease. When there are four invalid cross-linking points on the Si atom, it is impossible to cross-link; when there are three invalid cross-linking points on the Si atom, it can only be cross-linked with adjacent molecules to form larger molecules; when there are two invalid cross-linking points on the silicon atom, theoretically, the molecular chain can only grow infinitely to form a linear structure, and cannot be cross-linked into a network. Only when there are fewer than two invalid cross-linking points on the silicon atom can the cross-linking become a network. Moreover, it is possible to cross-link into a more dense network structure when the average number of invalid cross-link points on each Si atom is less. ## 2.2. Calculation of Invalid Cross-Linking Point For simple polymers, there is a certain relationship between the content of active groups and the molar weight of each group in the molecule. For annular vinyl silicone oil:[3]φC=$C = 27$nC=CV28nSiV+16nSiV+15nCH3V+27nC=CV, For a hydrogen-containing silicone oil long-chain structure:[4]φH=nHH28nSiH+16(nSiH−1)+15nCH3H+27nC=CH, Combined with Formulas [1] and [3], the number of methyl groups on each silicon atom of vinyl silicone oil with an annular structure can be calculated:[5]CV=nCH3VnSiV=54−98φC=C27−12φC=C, Similarly, Formulas [2] and [4] can be used to calculate the number of methyl groups on each silicon atom of hydrogen-containing silicone oil with a chain structure:[6]CH=nCH3HnSiH=2−64φH14φH+1+14φH+2nSiH(14φH+1), ## 2.3. Calculation of Free Cross-Linking Rate When the vinyl content and hydrogen content in the solution are 1:1, and it is considered that polymerization reaction continues between vinyl groups or dehydrogenation reaction continues between silyl groups, the active groups of the system are effective cross-linking points. Since every two chemical bonds on the silicon atom are combined into a cross-linked chemical bond, and there are two cross-linked bonds on each silicon atom, the cross-linking vacancy rate αV and αH satisfies the following relations: Vinyl silicone oil:[7]αV=CV22=CV4, Hydrogen silicone oil:[8]αH=αH22=αH4, Then, the proportion α of the total equivalent void cross-linking points in the polymer solution is α:[9]α=αVmV+αHmHmV+mH, where mV is the weight of vinyl silicone oil, and mH is the weight of hydrogen silicone oil. Because the molar weight of vinyl silicone oil and hydrogen-containing silicone oil is the same, their weight also has a corresponding relationship: mV=27mH; then, the molecular weight ratio is substituted:[10]α=27αVφH+αHφC=C27φH+φC=C, In the formula, φC=C, φH, nSiV, and nSiH can be obtained through raw material parameters, spectrum detection, chemical titration, viscosity test, etc.; αV and αH can be obtained by Formulas [7] and [8], all of which are known parameters. When α < 0.5, the average number of invalid cross-linking points on each silicon atom is less than 2, meaning that the polymer can cross-link the network polymer; when α > 0.5, the number of invalid cross-linking points on each silicon atom is more than 2 on average, then the polymer can only be cross-linked into linear or local network cross-linking, which will lead to serious weight loss during sintering, and a dense ceramic structure cannot be obtained. ## 3.1. Main Raw Materials Tetramethyltetravinyl cyclotetrasiloxane (D4vi, molecular weight 344, active group content φC=$C = 31$%) was provided by Guangzhou Shuangtao Fine Chemical Co., Ltd. Polymethylhydrosiloxane (HPSO, Guangzhou Shuangtao Fine Chemical Co., Ltd., Guangzhou, China): Sample A, viscosity is 22 mPas, active H group content is φH=$0.2\%$, molecular weight is 1000; Sample B, viscosity is 200 mPas, active H group content is φH=$0.2\%$, molecular weight is 15,000; Sample C: viscosity is 200 mPas, active H group content is φH=$0.3\%$, molecular weight is 15,000; Sample D: viscosity is 200 mPas, active H group content is φH=$0.5\%$, molecular weight is 15,000. Catalyst: Karstedt catalyst, Shenzhen Aokai Organosilicon Co., Ltd. (Shenzhen, China), with an effective ingredient concentration of 3000 ppm. Calcined kaolin, 8000 mesh (particle diameter less than 1.6 μm), with a two-dimensional lamellar structure was purchased from Hebei Jijiang Technology Co., Ltd. (Shijiazhuang, China). ## 3.2. Experimental Methods Hydrogen-containing silicone oil and vinyl silicone oil were prepared according to the active group ratio of 1:1. After stirring for 10 min with magnetic agitators, Karstedt catalyst was added at the dosage of 20 ppm. After stirring for 30 min, samples A, B, C, and D were obtained: A (α = $46.7\%$), B (α = $43.3\%$), C (α = $40.8\%$), D (α = $36.6\%$). The specific experimental steps are as follows: [1] The four samples A, B, C, and D were taken, respectively, each of which was 20 g. The samples were solidified in an incubator at 80 °C and kept for 1 h. After that, the samples were heated to 150 °C at 2 °C/min in a tubular-atmosphere furnace for 1 h, and then heated to 450 °C at 2 °C/min and kept for 0.5 h, 1 h, 2 h, and 4 h, respectively. The temperature was lowered below 100 °C at the rate of 2 °C/min and cooled naturally to obtain samples E11~E14 (holding for 0.5 h), F11~F14 (holding for 1 h), G11~G14 (holding for 2 h), and H11~H14 (holding for 4 h). [2] The four samples A, B, C, and D were taken, respectively, weighing 20 g. The samples were heated and solidified for 1 h in a constant-temperature oven at 80 °C, then heated to 150 °C for 1 h in a tubular-atmosphere furnace at a rate of 2 °C/min. Subsequently, they were heated to 700 °C at the rate of 2 °C/min for 0.5 h, 1 h, 2 h, and 4 h, respectively. The samples were cooled down below 100 °C at the rate of 2 °C/min, and were naturally cooled to obtain E21~E24 (holding for 0.5 h), F21~F24 (holding for 1 h), G21~G24 (holding for 2 h), and H21~H24 (holding for 4 h). [3] The 8000-mesh calcined kaolin was dried in a constant-temperature drying oven for 12 h, and then the ball mill was used for 2 h to obtain the white and evenly dried kaolin powder. A total of 61 g of calcined kaolin was put into four beakers and 50 g of samples A, B, C, and D were added, respectively. After full stirring, a white slurry with good fluidity was obtained by standing at 0 °C for 4 h. The samples were stirred by a rotary mixer at 1000 rpm for 15 min, placed at 0 °C for 4 h, and stirred for 5 min in a vacuum vibrating agitator. The samples were placed in an incubator at 80 °C for 1 h after curing, and heated to 1000 °C in a tubular-atmosphere furnace at a heating rate of 2 °C/min. The samples were kept for 1 h at 150 °C, 400 °C, 620 °C, and 1000 °C respectively, then dropped below 100 °C at the rate of 2 °C/min and cooled naturally. The samples were cut into 10 mm × 10 mm × 40 mm to obtain E3, F3, G3, and H3. ## 3.3. Experimental Characterization The weight of samples E11~E14, F11~F14, G11~G14, and H11~H14 were recorded before and after sintering, and the weight loss of the samples was calculated. The same procedure also applied to samples E21~E24, F21~F24, G21~G24, and H21~H24. The sintered macroscopic morphologies of the samples E3, F3, G3, and H3 were observed, and the samples were fixed on the sample table with conductive adhesive for gold-spraying treatment, and then the micromorphologies of the cracking products were observed under S-4800 scanning electron microscope, which is made by Hitachi, LTD. ( Tokyo, Japan). ## 4.1. Weight Loss of Samples with Different Free Cross-Linking Rates under 450 °C Table 1 shows the weight loss ratio of samples (E11~H14) at 450 °C under different holding periods. As a whole, the weight loss ratio decreased from the bottom left to the top right. The data in longitudinal comparison of the table showed that the holding time at 450 °C had a significant impact on the weight loss ratio. According to the thermal weight loss characteristics of siloxane precursor system [22,23], the weight loss before 450 °C was mainly divided into two stages: first, the weight loss of the system caused by the escape of non-cross-linked small molecules, and then the separation of small molecules generated in the cracking process. However, the weight loss difference of sample E1 group at 0.5 h and 4 h was more than $25\%$, indicating that not only did CH4 small-molecule pyrolysis gas escape, but also the loss of Si atoms during pyrolysis. According to the spectral characteristics of precursors of SiOC system [24,25], the remaining chemical bonds in the system after 350 °C were mainly Si-C bonds, Si-O bonds, C-H bonds, C-C bonds, and a small amount of unstable Si-Si bonds. At 450 °C, the activation energy was above 200 kJ/mol, close to the activation energy required for Si-C bond and C-C bond, which was 263 kJ/mol and 234 kJ/mol. The C-H bond and Si-O bond with stronger bond energy did not reach the breaking temperature [26], while the Si-C bond and C-C bond were destroyed, generating a large number of free radicals, and the following reactions occurred:[11]≡Si−CH3 → ≡Si·+·CH3 [12]≡Si−CH2−CH2−Si≡ → ≡Si−CH2·+·CH2−Si≡ [13]≡Si−CH2−→ ≡Si·+·CH2− The free radicals formed in the above reaction could seize other groups or H atoms from the main chain, and could also combine with each other, thus forming a variety of possible results, such as improving the density of the cross-linked network, reducing the density of the cross-linked network, or escape in small molecular form. It could be expressed as the following reaction. Reactions that increased the density of cross-linked networks:[14]≡Si·+·CH2−Si≡ → ≡Si−CH2−Si≡ [15]≡Si−CH2·+ ·CH2−Si≡ → ≡Si−CH2−CH2−Si≡ [16]≡Si−CH2·+ CH3−Si≡ → ≡Si−CH2−CH2−Si≡+ H [17]≡Si·+ CH3−Si≡ → ≡Si−CH2−Si≡+ H Reactions that decreased the density of cross-linked networks:[18]≡Si−CH2−Si≡ → ≡Si−CH2·+·Si≡ [19]≡Si−CH2·+·CH3 → ≡Si−CH2−CH3 [20]≡Si·+·CH3 → ≡Si−CH3 Reaction to produced small-molecule gas:[21]CH3·+·CH3 → CH3−CH3↑ [22]CH3·+·H → CH4↑ [23]H·+·H → H2↑ Therefore, the reaction at this stage became very complex, with multiple possibilities and high repeatability. The local cross-linking network density changed dynamically; however, it could be confirmed that the escape of small molecules is the combination of two or more free radicals, meaning that when small molecules escape, free radicals that could be connected with each other are left on the main chain, which could be recombined into cross-linked bonds in the dynamic equilibrium, such as:[24]≡Si−CH2−R1 +R2−Si≡ → ≡Si−CH2−Si≡ +R1−R2↑ At this time, the Si-C-Si chain was formed between Si atoms originally separated on two molecular chains and cross-linked together, that is, under high-temperature conditions, methyl that had no cross-linking activity provided cross-linking. By removing two methyl groups or one methyl group and one hydrogen atom, a cross-linking structure was established, increasing the number of cross-linking bonds in the system and forming a denser cross-linking network structure, thus effectively inhibiting the loss of Si-O elements, which was shown in a nonlinear relationship between the weight loss ratio and the insulation time. As the reaction proceeds, the density of the cross-linking network increased, reducing the reaction conditions that can lose weight and slowing down the weight loss speed. Comparing the data in Table 1 horizontally, it was found that the weight loss decreased significantly with the decrease in α. With the same content of active H, the precursor polymer with low residual cross-linking ratio had less weight loss, while the weight loss of the precursor polymer with high residual cross-linking ratio increased significantly. The active group promotion of the sample E1 and F1 groups was completely consistent, but their weight loss ratio was obviously different, which was caused by two reasons. On the one hand, there was a significant difference in the molecular weight of hydrogen-containing silicone oil in the sample E1 and F1 groups. According to the principle of the α value calculation, $50\%$ of the chemical bonds of Si atoms were used to make the system cross-linked into linear macromolecules. On this theoretical basis, $46.7\%$ of the Si-O backbone in the sample E1 group was connected with inactive groups, only $3.3\%$ could make the Si-O backbone cross-linked into a network structure, and $6.7\%$ of the sample F1 group could be used for the Si-O backbone cross-linked into a network structure. For the density of the cross-linking network, the sample E1 group was significantly lower than the sample F1 group. In the cross-linking process, when the uniformity and completeness could not be absolutely provided, there would be a large number of components that were not cross-linked or only cross-linked into macromolecules, which would vaporize and escape during the heating process, resulting in an obvious weight loss. However, a denser network structure of the sample F1 group was formed during the cross-linking process, the requirements for uniformity and completeness in cross-linking reaction were reduced, and the weight loss caused by volatilization was relatively reduced. For the sample G1 and H1 groups, the density of the cross-linking network was much higher than that of the sample E1 and F1 groups, hence only a small amount of non-cross-linked gas escaped during the heating process. On the other hand, the molecular weight of the sample E1 group was significantly lower than that of the sample F1 group, indicating that the number of Si-C bonds formed on each hydrogen-containing silicone oil molecule was significantly less than that of the sample F1 group. It was known that the Si-C bond energy was lower than the Si-O bond energy, and it was more likely to be break at high temperatures. When the Si-C bond on the hydrogen-containing silicone oil was completely broken, it would escape in the form of gas. For the sample F1 group, since there were more Si-O bonds on each molecule on average, the loss of the Si element was lower than that of the sample E1 group. For the sample G1 and H1 groups, the molecular weight was larger and the content of active groups was higher, making the molecular chains less likely to be separated, and their weight loss was mainly small-molecule cracked gases such as methane, ethane and ethylene; consequently, their weightlessness was lower. According to the comparison of horizontal data and vertical data in Table 1, the weight loss near 450 °C mainly included the escape of unconnected small-molecule gas, the loss of main chain caused by the breakage of cross-linked bond, and the escape of small-molecule pyrolysis gas. The loss of elements on the main chain of polymer was the main reason for the difference in weight loss. The lower free cross-linking ratio and higher density of the cross-linking network were important factors to inhibit the weight loss of the main chain in the system. At the same time, the loss of the main chain would be suppressed with the increase in the density of the cross-linking network. However, due to the large number of methyl groups in the system, the weight loss of small-molecule pyrolysis gas would be sustained under this reaction condition. ## 4.2. Weight Loss of Samples with Different Free Cross-Linking Rates under 700 °C Table 2 shows the weight loss of samples (E21~H24) at 700 °C for different holding duration. The horizontal comparison table shows that the weight loss ratio of the sample was affected evidently by the α value, which was due to the weight loss caused by 400~600 °C during the heating process. However, compared with the data in Table 2, it was found that the weight loss ratio increased slowly with the temperature holding time, indicating that the system gradually turned to the thermal stability stage, but there were still some differences between samples with different free cross-linking rates. According to the calculation of reaction kinetics [26], the activation energy at this stage was more than 350 kJ/mol, close to the C-H bond breaking condition (387 kJ/mol), and a large number of H radicals were released from the system:[25]≡C−H → ≡C·+·H The reaction activity of the system was further enhanced by H free radicals, which could combine with each other to synthesize H2 and escape, such as:[26]H·+·H→ H2↑ Moreover, H radicals could attack the radicals connected to C to form H2, such as:[27]≡CH +·H → ≡C·+ H2↑ In addition, H radicals could replace the—CH3 group, such as:[28]≡C−CH3+·H → ≡C·+ CH4↑ [29]≡Si−CH3+·H → ≡Si·+ CH4↑ Furthermore, they might also attack other covalent bonds, such as the C-C bond and Si-C bond, then combine into small-molecule gas and escape, leaving a large number of free radicals, such as:[30]≡C−CH3+·H → ≡CH·+·CH3 [31]CH3·+·CH3→ CH3−CH3↑ This made the reaction of the system very complex. Although there was a certain mass loss due to the separation of the main chain and the C element in the early stage, and the density of the cross-linking network of the system increased significantly, a large number of H atoms were still preserved in the system in the form of C-H bonds. The weight loss at this stage was mainly caused by the loss of the H atoms, which was due to the stronger bond energy of the H-H bond and the steric hindrance effect. H atoms had fewer chances to attack the Si-C chain and C-C chain; they mainly combined with H on the C atom and produced H2 to escape, resulting in a small amount of weight loss and a dense cross-linking network. With the addition of the C-H bond to the main reaction, each C-H bond provided an additional cross-linking point, greatly changing the number of cross-linkable points of the original cross-linking system. When the reaction was completed and the H atom was completely separated, only Si, O, and C elements remained in the system, forming a highly dense network system, that is, the amorphous [SiOC] structure. In this process, excessive C atoms can combine into graphite form with other excessive C atoms, while more excessive C atoms can combine with Si, O, and C atoms to form the amorphous [SiOC] structure. This is also the reason why the excessive C in the system exists in more than one forms. At this time, excessive C in the system might form a stable graphite structure during rearrangement, but it was easier to combine the amorphous structure in the [SiOC] system in the form of the C-C bond, Si-C bond, etc. This is highly consistent with the views in a large amount of the literature. Therefore, the weight loss stage near 700 °C was an important stage in which the system was transformed from organic matter to inorganic matter. The physical properties at this stage also changed significantly, with a large increase in hardness and density and a significant shrinkage in volume, reflecting obvious characteristics of ceramic materials. On the other hand, it was also a process of volume shrinkage and hardness improvement. When the free cross-linking ratio was too low, it was easy to have large stress and structural defects. Combined with the weight loss performance and the chemical reaction process of the two stages, it could be inferred that the process and significance of the chemical reaction at the 450 °C and 700 °C stage were obviously different: The 450 °C stage was dominated by the fracture and rearrangement of the Si-C bond and C-C bond, which caused obvious weight loss. When the α value was low, it was mainly the loss of the C element; on the contrary, it was the loss of the Si and C elements. When the value was determined, the weight loss ratio had a certain relationship with the heating time, which was manifested as the characteristics of organic matter. The main significance of the reaction process was to establish a high-density Si-O-C-H network to ensure a more stable structure of the system during the continuous heating. For block structures, the high free cross-linking rate and the slow heating rate would reduce the density of the cross-linking network in the system, which would easily lead to an obvious increase in weight loss, a decrease in density, shrinkage deformation, and crack defects. In the 700 °C stage, the C-H bond fracture and rearrangement were the main causes of weight loss, which was mainly caused by the loss of H atoms. The weight loss ratio had a high correlation with the content of H atoms in the system, but a low correlation with the heating time, and the materials showed inorganic properties. The significance of this stage was to establish a Si-O-C network and form an amorphous structure of [Si-O-C]. For block structures, the low free cross-linking rate and the fast heating rate would lead to strong system shrinkage stress and crack defects. ## 4.3. Effect of Free Cross-Linking Rate on Block Structure Molding Figure 2 shows the macromorphology of the sintered samples E3, F3, G3, and H3. From the conclusion of the previous section, the α value and the weight loss ratio of the sample had the relationship of E3 > F3 > G3 > H3. From the macromorphology, the samples were quite different. The samples with higher α values, such as E3 and F3, had obvious cracks. Sample E3 had obvious morphological deformation; even though sample F3 had no obvious deformation, there were three obvious large cracks and a large number of tiny cracks. Meanwhile, samples G3 and H3 with lower α values retained a relatively complete appearance. No cracks were found in sample G3, and only two obvious cracks appeared in sample H3 at the concentrated stress at the boundary of the sample. From the microstructure perspective, as shown in Figure 3, there were large differences in sample morphology. The section flatness of samples E3 and F3 with higher α values was poor, indicating that the sample structure was loose and there was a significant difference between the strength of the sintered precursor and the reinforcement particles, and a large number of microcracks were obviously found in the microstructure of sample E3. The defect degree of sample F3 was reduced, but a certain number of microcracks could still be found. Samples G3 and H3 with lower α values had smoother cross sections and more uniform microstructures, with smaller pore sizes and no microcracks. The macro- and micromorphology studies showed that the α value plays an important role in the properties of sintered samples. During the heating process of the precursor polymer, due to the enhanced molecular vibration under high-temperature conditions, the phenomenon of molecular chain slip was intensified, and the polymer showed strong creep performance and high elastic modulus, which was the basis for the precursor ceramic slurry to remain dense during the shrinkage process. The creep properties and elastic modulus of polymers were affected by heating temperature and the density of the cross-linking networks. When the α value was high, it caused obvious weight loss, which reduced the strength of the precursor and increased the shrinkage rate, leaving a large number of pores and cracks in the system and seriously affecting the macrostructure of the sample. When the α value was low, the temperature required for creep increased. When the precursor polymer shrunk significantly and a large amount of gas escaped, the sample did not have better deformation capacity and elastic modulus, and brittle fracture cracks would occur. In the process of converting the precursor polymer into a ceramic, the stress on the object is complex, including the stress caused by volume shrinkage due to weight loss, the stress released due to high-temperature creep, and so on. These factors influence each other complexly. For the HPSO/D4Vi system, the free cross-linking rate of $40\%$ is a balance point. In this condition, according to the regular of crosslinking network density, dense precursor ceramic blocks can be obtained by keeping the heating preservation at 400 °C and 600 °C. ## 5. Conclusions In this paper, a model of a free cross-linking rate was established. Aiming at the HPSO/D4Vi system, a theoretical method was proposed to calculate the free cross-linking rate by molecular weight and active group content. In addition, the weightlessness behavior at different pyrolysis temperatures was studied. Under a condition of 450 °C, the weight loss behavior of the cracking was dominated by C weight loss. It was found that the high free cross-linking rate would lead to the insufficient density of the cross-linking network of the system, resulting in an increase in the weight loss proportion of C, obvious weight loss, and the easy generation of large deformation and stress, eventually inducing structural defects. When cracking at 650 °C, the weight loss behavior dominated by H loss found that the value of the free cross-linking ratio and the holding time would not have a significant impact on the weight loss of the H element, but would significantly increase the density of the cross-linking network. 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--- title: Design of a Nutritional Survey to Detect High Dietary Salt Intakes and Its Usefulness in Primary Care Compared to 24-Hour Urine Sodium Determination authors: - Amelia Jiménez Rodríguez - Luis Palomo Cobos - Amelia Rodríguez-Martín - Patricia Fernández del Valle - José P. Novalbos-Ruíz journal: Nutrients year: 2023 pmcid: PMC10056562 doi: 10.3390/nu15061542 license: CC BY 4.0 --- # Design of a Nutritional Survey to Detect High Dietary Salt Intakes and Its Usefulness in Primary Care Compared to 24-Hour Urine Sodium Determination ## Abstract Many population studies report salt intakes that exceed the WHO recommendation (2 g/day of Na+ or 5 g/day of salt). We do not have tools for detecting high salt intakes that are easy to apply in primary health care (PHC). We propose the development of a survey to screen for high salt intake in PHC patients. A cross-sectional study of 176 patients determines the responsible foods, and a study of 61 patients studies the optimal cut-off point and discriminant ability (ROC curve). We assessed the salt intake using a food frequency questionnaire and a 24 h dietary recall and used a factor analysis to identify the foods with the highest contribution to be included in a high intake screening questionnaire. We used 24 h urinary sodium as a gold standard. We identified 38 foods and 14 factors representing a high intake, explaining a significant proportion of the total variance ($50.3\%$). Significant correlations (r > 0.4) were obtained between nutritional survey scores and urinary sodium excretion, allowing us to detect patients who exceed salt intake recommendations. For sodium excretion ≥ 2.4 g/day, the survey has a sensitivity of $91.4\%$, a specificity of $96.2\%$ and an area under the curve of 0.94. For a prevalence of high consumption of $57.4\%$, the positive predictive value (PPV) was $96.9\%$ and the negative predictive value (NPV) was $89.2\%$. We developed a screening survey for subjects with a high probability of high salt intake in primary health care, which could contribute to the reduction in diseases associated with this consumption. ## 1. Introduction According to the INTERSALT study, high salt intakes are closely related to increased blood pressure, and low intakes lead to lower blood pressure levels [1]. The scientific evidence for the role of salt in the pathogenesis of hypertension has been confirmed by numerous studies [2,3]; after the age of 50, almost $50\%$ of the population suffers from hypertension, and between $13\%$ and $16\%$ of all deaths are attributed to it. According to the WHO, hypertension is responsible for at least $45\%$ of deaths due to heart disease, and $51\%$ of deaths due to stroke [4]. Meta-analyses of randomized trials have shown that reducing salt intake by 6 g/day would reduce the incidences of stroke by $24\%$ and coronary heart disease by $18\%$, preventing more than 2.5 million deaths worldwide from stroke and cardiovascular events [5]. However, the recommendation to reduce the salt intake in patients with hypertensive or cardiovascular disease, which is so common in primary care, is rarely supported by the prior use of food frequency questionnaires to identify the consumption of high salt foods. Although an excessive dietary salt intake is clearly related to different causes of morbidity, such as hypertension, cardiovascular disease (CVD), overweight, osteoporosis, or gastric cancer [6], it is not common for primary care, hospital medical, or nursing practices to use methods to reliably measure the degree of the salt intake of patients. Enquiries about a high salt intake dietary pattern do not go beyond generic questions, and as a result, recommendations to reduce salt intake in hypertensive or cardiovascular patients are rarely based on knowledge of the patient’s diet and are not accompanied by documentation or other tools to identify foods with a high salt content. The WHO recommends reducing one’s dietary salt intake as a cost-effective strategy to reduce blood pressure and the risk of CVD, stroke, and coronary heart disease [7]. This recommendation is strong when the daily salt intake in adults is higher than 5 g/day [8] according to the WHO, or 5.8 g of salt in the US dietary recommendations [9]. The problem for clinicians is how to identify patients who exceed these dietary salt limits. The standard method for assessing salt intake is to measure 24 h of urinary sodium excretion, which is rarely used in primary health care due to its cumbersome nature. To simplify the measurement, equations such as those of Tanaka [10] and Kawasaki [11] or the INTERSALT equation [12] are used, which have been used to quantify sodium intake from sodium excretion in fractional or spot urine samples at certain times of the day [10]. Another way to measure salt intake is through the use of food questionnaires, such as food frequency questionnaires of the most and least salt-rich foods, or 24 h food diaries that are spread over several days. However, these consumption surveys often include all types of foods, without differentiating according to their Na+ contribution, making it risky to make dietary recommendations that discriminate between foods with the highest salt intake [13]. The aim of this study is to develop a nutritional survey that will allow us to easily identify individuals with high salt intakes in primary care consultations and to determine an optimal cut-off point from which to detect patients who exceed the recommended limits, based on a broad list of foods present in the usual diet of Spanish adults that have been shown to correlate with 24 h urinary salt excretion. We analyze the predictive value of high intakes for the different cut-off points proposed in international recommendations. ## 2.1. Design of Nutritional Survey The food items included in the survey were obtained from a cross-sectional observational study of a normotensive and hypertensive population sample, in which total daily salt intake, estimated from 24 h urinary sodium excretion (gold standard), and behaviors related to salt addition were determined. The origin, method of recruitment, and characteristics of the sample of 176 participants are described in a previous study [14]. In summary, participants belonged to an urban health center (Cáceres, Spain), were aged 46–75 years, were consecutively invited to participate, and underwent an interview and a physical examination including measurement of blood pressure, abdominal circumference, BMI, blood, and 24 h urine collection. Blood parameters included the following, among others: Na+ (sodium), K+ (potassium), glucose, cholesterol, urea, creatinine, and GFR (glomerular filtration rate). In urine we also determined albuminuria, albumin/creatinine ratio, creatinine, and glucose. The research protocol was favorably evaluated and approved by the Biomedical Research Ethics Committee of the Health Council. All participants signed the informed consent form. All participants were asked to complete two food surveys: a food consumption frequency questionnaire (FFQ) validated in the adult population [15], and a 24 h recall nutritional survey, which included questions of typical foods of the Extremadura region with high Na+ content, as well as behaviors related to the addition of salt in food preparation and the use of salt shakers at the table. The sodium content of the food was extracted from the food composition table (BEDCA network) [16]; the nutritional assessment was performed based on the intake of the recall of food ingested in the last 24 h assessed with the EvalFINUT program of the Ibero-American Nutrition Foundation [17]. Study participants collected a 24 h urine sample on the same day as the nutritional assessments, so we studied the correlations between estimated salt intake from the surveys and that determined from 24 h urinary sodium excretion. We consider the presence of Na+ in 24 h urine between 2.07 and 5.05 g/L/day (equivalent to 90–220 mEq/L/day) as reference values. To assess the completeness of 24 h urine collections, we included self-report and a 24 h urine volume and assessment of 24 h creatinine excretion based on calculations using age, sex, and weight. The same study [14] described the characteristics of the average salt intake found in the sample (6.6 g in men and 7.5 g in women) and identified the foods with the highest salt contribution to the participants’ diets based on correlations between reported intake in surveys and 24 h urinary sodium excretion. With these foods identified, using an exploratory factor analysis, we developed a specific nutritional survey to screen for high salt intakes. The factor analysis allows for data reduction and finding homogeneous groups of foods rich in Na+ with higher intakes, which represent the dietary profile of patients with high salt intakes. Reliability (internal consistency) was assessed using Cronbach’s alpha test, with values ranging from 0 to 1; values α > 0.70 were considered acceptable, and α > 0.80 good. Construct validity was determined by exploratory factor analysis; sample adequacy was assessed by applying the Kaiser-Meyer-Olkin (KMO) test, with values greater than 0.5, and Bartlett’s test of sphericity, with significant values. To determine unidimensionality, the following criteria were taken into account: [1] that all items had a Pearson’s r > 0.30 in the first factor during extraction; [2] that the first factor explained a significant proportion of variance with respect to the other factors; and [3] that the total variance explained by the main extracted factors was greater than $50\%$. Varimax rotation was used to minimize the number of foods and determine which foods had high loadings for each factor. To include an item in the orthogonal factors, values with a Pearson’s r > +0.40 were considered relevant. ## 2.2. Survey Validity, Optimal Cut-Off, and Discriminant Ability A frequency of consumption survey with the selected foods was created from the results of the factor analysis, using an odd 7-point Likert-type scale, with 1 representing never and 7 representing more than once a day (see Supplementary Materials File). The scoring method was the sum of the individual items. This new survey was applied to a new sample of 61 participants with the same selection criteria and in the same primary care centers, and we collected variables such as age, sex, weight, height, diseases and comorbidities, and whether or not they were on a diet for any reason. At the same time, 24 h of urine was collected from the participants to estimate their actual salt intake. To determine an optimal cut-off point in the questionnaire score to help identify subjects whose salt intake can be considered high, ROC curves were generated, using as possible cut-off points those that maximize sensitivity and specificity (maximum Youden index) for the recommended high salt intake endpoints of 3 g/day and 2.4 g/day. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated for each of these cut-off points, and the area under the curve (AUC) was calculated as an index of accuracy. We then used logistic regression to analyze the probability of high sodium intake detected by the questionnaire according to the different recommended values (2.4 and 3 g/day Na+) adjusting the results for age, sex, and BMI. ## 3. Results A total of 176 patients participated in the study, with a mean age of 62 (SD 8) years and a slight female predominance ($53.4\%$). A total of $32.4\%$ have experienced higher education; $43.7\%$ are employed; and $69.9\%$ perform physical exercise, of which $67.9\%$ reach the recommendations of 150 min a week. A total of $78.4\%$ are overweight and obese (BMI ≥ 25), and $61.15\%$ report eating out at least once a week. A total of $61.4\%$ of patients reported drinking alcoholic beverages and only $16.5\%$ were smokers. With regard to the main clinical characteristics of interest for this study, almost $60\%$ of the study participants had hypertension, $42\%$ had dyslipidemia, and $17\%$ had diabetes mellitus; most of the patients were treated for these chronic conditions. The average Na+ intake estimated using the food frequency questionnaire (FFQ) was around 2.5 g; the Na+ intake determined by the FFQ typical of the local Extremaduran diet provided an approximate value of 3 g in both men and women. From the 24 h dietary recall nutritional survey, we obtained a total dietary Na+ intake of 6.6 g in men and 7.5 g in women. We found a good correlation between the Na+ estimated in the generic food consumption frequency questionnaire and the questionnaire assessing the subset of foods in the local diet. The perception of salt consumption and its use at the table was also asked; $56.3\%$ think they have an “adequate” salt consumption while $39.8\%$ think their salt consumption is “low”. A total of $32.4\%$ say that they add salt to food once it is at the table and $56.9\%$ of patients add more salt while preparing food in the kitchen. The total 24 h urinary Na+ excretion in the population is 3.7 (SD 1.4) g and is significantly higher in men (4.2 g/day vs. 3.2 g/day in women). Urine Na+ levels are higher than the recommended 2 g in $92\%$ of the subjects; if we use 2.5 g/day as a cut-off point, this percentage would decrease to $75\%$. If we classify sodium intake on the basis of the Na+/K+ ratio and consider high sodium intake when its absolute value is above 1, the percentage of subjects in the sample with high sodium intake is $79.54\%$. Considering the levels of salt intake as determined by 24 h urine and the perception of consumption expressed by the patients, almost $84\%$ of patients with elevated Na+ excretion levels are not aware that they are consuming more salt than recommended. When we analyzed the correlation between Na+ intake estimated using the different questionnaires and the values determined in 24 h urine (gold standard), Na+ estimated using both instruments had a very weak significant correlation with urinary Na+ excretion. On the basis of the correlations between the intakes obtained from the food consumption frequency questionnaires and the 24 h urinary sodium excretion, we identified the foods that, due to their frequency of consumption and salt content, could represent a higher contribution of salt to the diet of the participants. The questionnaire was designed using the 48 foods that were highly correlated with salt intake (Supplementary Table S1). Cronbach’s alpha showed an overall internal consistency of 0.221 and after exploratory factor analysis, 10 foods were removed. The final version had a Cronbach’s alpha of 0.926. The sample adequacy study showed a KMO of 0.636 and Bartlett’s test was statistically significant. The factor analysis grouped the 38 foods into 14 factors with an eigenvalue greater than or equal to 1. This result explained a significant percentage of the total variance ($50.30\%$) (Table 1). The resulting questionnaire (Supplementary Table S3) was administered to a sample of 61 patients, aged 46–75 years, who signed and accepted the informed consent form, with Spanish nationality or with more than 25 years of residence in Spain (assuming they followed a similar Mediterranean dietary pattern), without eating disorders or severe chronic kidney disease, and selected consecutively in several primary care consultations in the Cáceres health area. These participants were asked about the frequency of consumption of these foods in the previous month, using 7-point Likert scale. The subjects had a mean age of 58.6 years (SD 7.8); $47.5\%$ were women and the mean score obtained was 128.8 points (SD 7.82) (Table 2). In Table 2, we can observe the average Na+ intake of the subjects by 24h urine excretion, showing levels above the recommended levels. The sample of subjects had a mean excretion of 3.14 g/day, being able to distinguish two main cuts; a total of 35 subjects with ≥2.4 and about 27 subjects with equivalent levels ≥ 3 g. The points on the ROC curves (Figure 1 and Figure 2) providing the highest Youden index for classifying subjects with sodium excretion greater than 3 and 2.4 g/day are a score of 126 and 124 points, respectively. The optimal cut-off point for detecting patients with salt intake above 3 g/day, set at 126 points, estimated a sensitivity of $92.59\%$ ($95\%$ CI: 80.86–100), specificity of $79.41\%$ ($95\%$ CI: 64.35–100), PPV of $78.13\%$ ($95\%$ CI: 62.24–100), and NPV of $93.10\%$ ($95\%$ CI: 82.16–100); while for the cut-off point corresponding to ≥2.4 g/day, 124 points or more, the sensitivity was $91.43\%$ ($95\%$ CI: 80.73–100), the specificity was $96.15\%$ ($95\%$ CI: 86.84–100), the PPV was $96.97\%$ ($95\%$ CI: 89.61–100), and the NPV was $89.29\%$ ($95\%$ CI: 76.04–100) (Table 3). Table 3 shows the logistic regression analysis, where it is observed that the cut-off point of 124 points is significantly associated with a higher probability of having a urinary sodium excretion ≥ 2.4 g/day (OR: 269.9; $95\%$ CI 21.4–3402.2), whereas with the cut-off point of 126 points the probability of having a urinary sodium excretion ≥ 3 g/day, is somewhat lower (OR: 66.4; $95\%$ CI 9.1–484.5). ## 4. Discussion Increased urinary sodium excretion, representing dietary sodium intake, is associated with hypertension. In a dose–response meta-analysis assessing the relationship between sodium intake (estimated from dietary intake or urinary excretion) and risk of hypertension in cohort studies, an almost linear relationship between sodium intake/excretion and hypertension risk was found, with an excess risk starting at 3 g/day [18]. Other studies have demonstrated that due to the J-shaped association of sodium intake with plasma renin activity and systolic blood pressure, the risk of mortality and cardiovascular events increases when intake exceeds 5 g/day [19,20]. Meta-analyses demonstrate that a reduction in dietary sodium intake according to public recommendations is associated with an average reduction in systolic/diastolic blood pressure of $\frac{5.7}{2.9}$ mm Hg in hypertensive subjects [21]. In people with normal blood pressure, the effects of sodium reduction were more consistent on potential side effects (hormones and lipids) than the effect on blood pressure. This review reinforces the validity of recommendations to prevent cardiovascular disease by reducing sodium intake in hypertensive adults. Worldwide, less than 5–$10\%$ of people consume less than 2.3 g/day of sodium, but it is difficult to estimate this intake accurately because the salt content of meals is uncertain. We have found that patients underestimate the amount of salt in food and salt added in cooking or at the table; hence, the best method to determine salt consumption is by quantifying the amount excreted in 24 h urine. However, the determination of Na+ in 24 h urine as a gold standard for the detection of patients with high intakes is rarely used because of its complicated collection and cost. Instead, many studies propose the use of fractionated urine samples or standardized questionnaires to quantify the frequency of consumption of foods with a higher salt content [22,23,24]. Considering urinary Na+ excretion as a reference value, when we estimate total dietary Na+ intake from the analysis of those collected in the 24 h reminder nutritional survey, we tend to overestimate intake (we obtained 7.25 g/day, which almost doubles the urine values), while the generic food frequency questionnaire underestimates it [22]. Malavolti et al. [ 25] estimated dietary Na+ and K+ intakes in 719 Italian adults using the FFQ; the mean sodium intake was estimated at 2.15 g/day while the mean potassium intake was 3.37 g/day; these values are very similar to those obtained in our participants using the generic food frequency questionnaire, which underestimates intake (compared with 24 h urine collections). The foods that contributed most to sodium intake were cereals, meat products (especially processed meat), and dairy products, and for potassium, they were red and white meats, fresh fruit, and vegetables; these are many convenience and processed foods (industrialized countries) and few related to local dishes (pickles, sausages, cheese, and salted fish in Mediterranean countries) [24,25,26,27]. Following the recommendations of similar studies [26,27,28], we contrasted Na+ intake using two food frequency questionnaires and nutritional assessment with a 24 h recall nutritional survey. We found greater validity in the quantification of Na+ intake using specific salt-rich foods questionnaires, such as the one we propose. At present, there is no specific questionnaire for the detection of subjects with high salt intake adapted to our setting that can be easily applied in primary care to hypertensive patients and/or those with associated comorbidities. Most of the studies we reviewed on food questionnaires did not categorize foods according to the amount of salt they contain according to the food composition table [11,15,29] or their contribution to total salt intake. Several authors [27,30] agree on the need for an adapted dietary questionnaire that would allow us to assess the salt intake of patients who are eligible for intervention in the short or medium term, as this would facilitate the restriction of salt in the diet of our patients and the implementation of preventive educational and dietary interventions [22]. In our study, we designed a food consumption frequency questionnaire in which we basically assessed the intake of a set of foods that represent a significant contribution of salt to the diet, either because of their sodium content or because of the combination of their sodium content and the amount of their intake, not with the aim of obtaining a precise estimate of the amount of salt, but rather to detect subjects with a high intake. Dietary variability is described according to the community and/or city in Spain where each participant resides, but no great variations are obtained. For example, in a study carried out in seven regions in cities such as La Coruña, Barcelona, Burgos, Palma de Mallorca, Pamplona, Valencia, and Zaragoza, the diet was collected by means of a food frequency questionnaire validated for the Spanish population [31]. All those interviewed had a high intake of dairy products and pulses, and fruit and vegetables had a high intake in Mallorca and Valencia, whereas it was low in La Coruña. Olive oil consumption was high in all places except Burgos, where $74.3\%$ of the women studied were below the recommended three servings per day. As a result of this study, an insufficient intake of vitamin E was found in La Coruña and Burgos. Therefore, we can observe that dietary peculiarities were only found in areas far from the coast with a higher consumption of dairy products. There are regional variations in the consumption of certain foods; for example, in Spain, more fish is generally eaten in southern areas than in central areas, more meat in the inland, and more vegetables in the eastern regions [31]. As a proposed screening tool, the questionnaire we have developed is not intended to quantify the salt intake precisely, but to detect subjects with a high probability of high salt intake. The sample from which the initial list of foods with a significant contribution to salt intake was drawn [14] and the sample in which the survey was tested are similar in terms of the most common epidemiological variables such as age, sex, comorbidities, height, and weight. The foods included in the questionnaire are routine foods in our Mediterranean diet, in any Spanish region. However, we have included in the questionnaire some foods that are not so commonly consumed outside the region of Extremadura (such as some Extremaduran cheeses or paprika as an additive), but that are useful to give more strength to the questionnaire. The survey elaborated on the frequency of consumption of foods with a high Na+ intake in the diet, which leads to determining a modifiable risk in patients. It a novel survey that is easy to apply (no more than 10 min, taking into account that we also ask about pathology and diet and record anthropometric measurements) and very useful and essential for primary care teams. In this way, we would only have to interview our patients for a few minutes in the consulting room, and according to the score obtained in the nutritional survey and the determination of anthropometric parameters, we could act accordingly on the estimated risk associated with high Na+/salt intake. Once this questionnaire has been completed and verified, its usefulness for the follow-up of patients in whom we are going to intervene with dietary salt restriction would need to be tested. In these patients, the questionnaire would help to focus on which foods to intervene with, and the sensitivity to change the questionnaire could help in assessing compliance with our recommendations in the primary care office. These results suggest that a nutritional survey consisting of a combined food consumption and food frequency questionnaire could be valid for the identification of populations with high salt intakes. Food frequency questionnaires allow us to obtain information on the pattern of usual consumption in different populations. The use of food frequency questionnaires is an applicable methodology that is easy, quick, and less costly. In addition, they involve less effort for the interviewed subjects than invasive/non-invasive tests, such as 24 h blood or urine samples [28]. Although there are doubts about the beneficial effects of reducing salt intake in populations with moderate intake levels [19], a linear relationship between all levels of salt intake and the incidence of hypertension, cardiovascular disease [18], and all-cause mortality [32] seems to have been demonstrated. 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--- title: How Do Nursing Students Perceive Moral Distress? An Interpretative Phenomenological Study authors: - Chiara Gandossi - Elvira Luana De Brasi - Debora Rosa - Sara Maffioli - Sara Zappa - Giulia Villa - Duilio Fiorenzo Manara journal: Nursing Reports year: 2023 pmcid: PMC10056563 doi: 10.3390/nursrep13010049 license: CC BY 4.0 --- # How Do Nursing Students Perceive Moral Distress? An Interpretative Phenomenological Study ## Abstract Background: Research shows that the longer nurses care for terminally ill patients, the greater they experience moral distress. The same applies to nursing students. This study aims to analyze episodes of moral distress experienced by nursing students during end-of-life care of onco-hematologic patients in hospital settings. Methods: This study was conducted in the interpretative paradigm using a hermeneutic phenomenological approach and data were analyzed following the principles of the Interpretative Phenomenological Analysis. Results: Seventeen participants were included in the study. The research team identified eight themes: causes of moral distress; factors that worsen or influence the experience of moral distress; feelings and emotions in morally distressing events; morally distressing events and consultation; strategies to cope with moral distress; recovering from morally distressing events; end-of-life accompaniment; internship clinical training, and nursing curriculum. Conclusions: Moral distress is often related to poor communication or lack of communication between health care professionals and patients or relatives and to the inability to satisfy patients’ last needs and wants. Further studies are necessary to examine the quantitative dimension of moral distress in nursing students. Students frequently experience moral distress in the onco-hematological setting. ## 1. Introduction Nurses experience moral distress (M.D.) when they feel that the ethically correct action to take is different from what they are tasked with doing [1]. Several studies are focused on moral distress among the nursing population [2,3,4,5]. Research shows that the longer nurses care for terminally ill patients, the greater they experience M.D. [6,7,8]. In 1984, Jameton defined M.D. as “a condition that occurs when the nurse makes a judgement by virtue of a given situation and is faced with obstacles that prevent the next action to be taken” [9]. Subsequently, the same author distinguished M.D. into initial and reactive. In the former, the nurse experiences frustration, anger and anxiety in the face of obstacles to instructions and interpersonal conflicts over professional values [1]. In the second, the nurse experiences the consequences of having difficulty in processing feelings arising from the initial MD. Other authors have expanded the concept of MD and introduced the concept of moral residue [10]. These authors define moral residue as persistent feelings and personal disagreements resulting from the MD that continue even after the event has ended. It can cause much damage over time, especially when a person is repeatedly exposed to morally distressing events. Nurses may be exposed to a ‘crescendo effect’ phenomenon [11]. Indeed, repeated exposure that accumulates over time can affect the moral conscience of professionals, causing great distress and putting future actions at risk. The same applies to nursing students [12,13,14]. Nursing students in clinical settings improve their own learning, performance, and autonomy [15]. Despite this, internship clinical training can also lead to ethical conflict and dilemmas that could deprive students of their capability in nursing care. Research shows that morally distressing events are first experienced by nursing students in nursing care offered to terminally ill patients [16,17]. Only a few papers highlight the impact of nursing care on nursing students in onco-hematological settings. In the nursing curriculum, a goal of end-of-life nursing education is to train nurses who are comfortable with death and dying [16]. Likewise, in internship clinical training, the nursing assistant plays an important role [18]. Nursing students’ experiences of morally distressing events can have important implications; some of them reported experiences of a sense of failure, disappointment, anger, guilt, nervousness, confusion, and frustration [19]. Moreover, M.D. can cause anxiety and physical symptoms such as gastrointestinal issues, insomnia, and headaches [18,20]. This study aims to analyze the episodes of M.D. experienced by nursing students during the end-of-life care of onco-hematologic patients in hospital settings, to describe their involvement in decision-making, their coping strategies, consequent reflections, and the effects on their profession and future career. ## 2.1. Study Design This study was conducted in the interpretative paradigm using a hermeneutic phenomenological approach [21]. ## 2.2. Recruitment and Sampling Purposeful sampling was used to select meaningful information about participants for a detailed study [22,23]. Researchers located second and third-year nursing students with experience in onco-hematological settings. No deliberate selection based on demographic characteristics was made. The snowballing sampling method was then applied [24]. The researchers contacted each student by telephone, explaining the aim of the study and gave an appointment to the students who decided to participate in the study. Each participant was assigned a numeric code to ensure anonymity and to allow researchers to identify and contact the student easily whenever they needed clarifications or explanations. ## 2.3. Data Collection The study was conducted following the consolidated criteria for reporting a qualitative research checklist (COREQ) [25]. The interviews were conducted between March 2017 and August 2018. The interviews were conducted by a female PhD nurse researcher who is an expert in qualitative research and who was not involved in nursing training. They had an informal talk with the students to facilitate the interaction and let participants tell their own stories, in their own words [24,26]. At the end of each interview, the researcher took notes of the patient’s or interviewer’s emotions, body language, and interjections to have a complete picture of what had happened. The research team consisted of four undergraduate students from the Bachelor of Science in Nursing course, one PhD in Nursing and Public Health, and an associate professor in Nursing. The interviews took place in a quiet and secluded location, for example, in the place where the phenomenon under investigation routinely happens [27]. Data were collected through in-depth face-to-face interviews [24]. The interviews were made up of open-ended questions about morally distressing events experienced by students and probing questions when needed (Table 1) [28]. Sample size and domain size were estimated at the point of saturation [24]. ## 2.4. Data Analysis For the data analysis, the researcher did not use any software. Data were analyzed following the principles of the Interpretative Phenomenological Analysis (IPA) [24]. The first step of the IPA provides the immersion of each researcher in the original data. Two researchers independently started the data analysis, investigating one transcript after another in-depth. At this stage, it is important to read and re-read the transcripts, make margin notes, create a summary list of the margin notes, develop the emergent themes, and look for connections among them. In cases of disagreement, the two researchers returned to the original texts of the interviews and their notes and reformulated the shared themes. Once each interview is individually analyzed, the process is then extended to scan the entire set of transcripts for a full listing of themed summaries, a grouping of the themed summaries, recoding transcripts with overall themes, and finalizing the list of themes with extracts [24]. ## 2.5. Study Rigor The criteria to promote trustworthiness referred to credibility, transferability, dependability, and confirmability [29,30]. Strategies have been adopted to ensure trustworthiness: the prolonged engagement of the researchers in the study, multiple interviews and notes, member checking, and triangulation [29,30]. Verbatim quotes were included throughout the analysis to give the participants a voice, so that readers could trace back the research team’s interpretations, demonstrating sensitivity toward the context [31]. ## 2.6. Ethical Consideration The study was developed per the Helsinki Declaration and the national ethical principles for scientific research and obtained the approval of the centers involved. All participants were informed of the purpose and methodology of the study by signing an informed consent form. Given that the study involved nursing students alone, non-formal ethics committee approval was needed. A synopsis of the study protocol was, however, submitted to the local committee, which gave us the clearance to proceed. ## 3. Results From a pool of 21 nursing students, 18 females and three males, three nursing students have been excluded because they had not experienced a morally distressing situation during their internship, and one nursing student because he did not give his consent (Table 2). Seventeen nursing students agreed to participate, fifteen women, and two men with a mean age of 22.1 years. Sociodemographic data were shown in Table 2. These interviews were audio-recorded and transcribed verbatim. The interviews lasted between 22′54″ and 107′24″ with a total mean recorded time of 53′13″. There was consensus among the participants in the in-depth interviews that M.D. was a common experience in their internship clinical training. 953 units were identified from transcribed interviews and clustered into 368 labels, 47 categories, and eight themes (Figure 1). ## 3.1. Causes of M.D. M.D. is often related to poor communication or a lack of communication between health care professionals and patients and relatives or both, and to the inability to satisfy patients’ last needs and wants. Family members’ attitudes often influenced patients’ end-of-life treatment decisions. In fact, in many situations relatives exerted considerable pressure on patients in various ways, sometimes more than they intended to. However, these situations are recognized as a source of M.D. by nursing students. Several nursing students experienced M.D. in situations in which they felt that medication was given only to satisfy a medical order or relatives’ wants. Moreover, the participants described situations in which they felt that starting a new therapy was totally inappropriate for the patient’s health status and not necessary to alleviate their suffering. Differences in the assessment of the patient’s condition between students and their nursing assistant or physician were also mentioned as morally distressing. Therefore, nursing students experience M.D. when a patient or family member or both, are unaware of the terminal clinical condition due to the lack of clear communication by physicians. ## 3.2. Factors That Worsen or Influence the Experience of M.D. Some clinical conditions (like frequent relapses of the disease, severe co-morbidities, or the young age of the terminally ill onco-hematological patients) are factors that have negatively influenced the nursing students’ experience of M.D. Besides nursing students’ lack of experience due to their status and the difficult relationship with their training assistants and the health care professionals, were factors that made the experience of M.D. even worse. In some interviews, students pointed out that their religious faith and personal experiences (such as the death of a relative because of cancer) had sometimes influenced their perception of M.D. However, the M.D. of the students was relieved when their point of view on a situation coincided with that of the care team, making them feel supported and safe. Often, students cope positively with morally distressing events when the training assistant and the whole care team share their thoughts and caring actions. ## 3.3. Feelings and Emotions in Morally Distressing Events Nursing students have contrasting feelings and emotions when caring for terminally ill patients. Only a few nursing students experienced positive emotions (related to unexpected healing). Morally distressing events led most nursing students to experience feelings of powerlessness, anger, anguish, and frustration in assisting terminally ill patients and, for this reason, many of them realized they needed to talk to other people and think about their experience, wondering how they could have acted differently. ## 3.4. Morally Distressing Events and Consultation Many nursing students highlighted that the consultation after a morally distressing event was useful. After the consultation with an internship assistant, they felt supported and understood and it helped them to better deal with their malaise. On the other hand, other participants reported that the consultation did not allow them to resolve their ethical dilemma because neither their colleagues nor the training assistants showed sympathy. Some nursing students finally preferred not to consult anyone due to a lack of courage or the fear of being judged. ## 3.5. Strategies to Cope with M.D. Several strategies to cope with M.D. were reported by nursing students who realized that students, registered nurses, and patients’ relatives had different ways to deal with it. Nursing students sometimes prefer to remain silent and just listen to the patient to avoid saying something inappropriate. In other cases, they distracted the patient by trying not to talk about the illness. Nursing students observed that some registered nurses who were accustomed to dealing with death seemed cold and a little standoffish even though they realized that their attitude was not due to a lack of sensitivity but to the attempt to avoid being involved emotionally. ## 3.6. Recovering from Morally Distressing Events Many nursing students re-thought and re-evaluated morally distressing events. They wondered if they had done their best and made the right choice. This process allowed them to re-evaluate their actions and professionals’ actions, wondering how they would have acted if they had been in their place. The experience of an M.D. had sometimes led to students’ professional and personal growth. ## 3.7. End-of-Life Accompaniment Some nursing students discussed the concept of “good death” and reflected on what a “good” end-of-life accompaniment should be like. According to students, a good end-of-life accompaniment should guarantee pain management and the satisfaction of patients’ last needs and wants. Moreover, the closeness of relatives could be useful to terminally ill patients. ## 3.8. Internship Clinical Training and Nursing Curriculum According to the participants in this research, university lectures on end-of-life care ensure a good theoretical preparation to deal with situations of M.D. even if, only during clinical training, students can apply the theory, deepen it, and sometimes even better understand it. Some nursing students also highlighted that they have deepened the issue of end-of-life care within conferences or educational opportunities outside the university context. ## 4. Discussion The aim of this study was to analyze the episodes of M.D. experienced by nursing students during the end-of-life care of onco-hematology patients in a hospital setting, to describe their involvement in decision-making, coping strategies, consequent reflections, and the effects on their profession and future career. Onco-hematology, as well as other care settings investigated in the literature [32,33,34], is a care setting in which nursing students often experience M.D. due to its ethical, care, relational, and organizational complexity. The interviewed students pointed out that M.D.s are often generated by reduced or absent communication between caregivers and patients and relatives or both, and the inability to meet patients’ ultimate needs and wishes. Furthermore, the respondents highlighted the presence of conflicting demands from relatives [2,16,18,35,36]. Communication is a very important soft skill for nursing professionals and university education should help students to cope with the daily challenges of nursing. Developing the “soft skills” of communication through education would help students, and thus future professionals, to make the best use of the technical skills for the patient in front of them [36,37,38]. The students’ M.D. experiences were also related to decision-making problems concerning the difference in thinking between students and training assistants and between students and the multidisciplinary team or both, with particular emphasis on the difficulty of relating to professionals who were not always willing to exchange views. Furthermore, regarding the support received from university and clinical tutors, students often stressed the difficulty of expressing their experiences for fear of not being heard or of being judged and evaluated negatively. Previous studies, however, suggest that sharing daily frustrations with peers as well as with leaders are protective factors against M.D. [39], both for students and professionals. Furthermore, positive social relationships among peers have been a widely used strategy for students, who have found comfort in peers [36]. They allow the student an open and honest confrontation, which helps them to reflect and reframe their experiences, even individually, understanding their nuances and facets and thinking about how to deal with similar situations in the future. The present study showed that many of the situations that generate M.D. and the strategies implemented by students are the same as those described by nurses [33,36]. The feelings and emotions related to the students’ experience of M.D. were negative: feelings of helplessness, anger, anxiety, confusion, and frustration accompanied the participants even after some time, underlining the long-term impact of their experiences of discomfort. This could mean that students could become nurses who will manifest an early M.D. residual [1], resulting in a moral residue [11]. This could generate the crescendo effect, which would lead to young nurses being very dissatisfied with their work and even leaving the profession a few years after starting it [40]. Therefore, training can help to reduce, manage, and prevent M.D. also in students. Trainers, who are the facilitators of learning, should help the student to identify situations that generate M.D. and to implement strategies and interventions to manage it, and prevent negative effects on students, and thus on future professionals. ## Limitations In analyzing the data, the research study did not consider certain factors that could influence an individual’s perception of M.D. such as gender, religious beliefs, and any previous personal experiences of participant’s contact with the end of life. Furthermore, as this is a qualitative study, the results are not replicable as the subjects, experiences, and context are unique [41]. ## 5. Conclusions Further studies are necessary to examine the quantitative dimension of M.D. in nursing students, and also in different clinical settings, to identify strategies that might help students manage their lived experience during their nursing education. ## References 1. Jameton A.. Nursing practice, the ethical issues. Int. J. Nurs. 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--- title: Asprosin Enhances Cytokine Production by a Co-Culture of Fully Differentiated Mature Adipocytes and Macrophages Leading to the Exacerbation of the Condition Typical of Obesity-Related Inflammation authors: - Agnieszka I. Mazur-Bialy journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10056564 doi: 10.3390/ijms24065745 license: CC BY 4.0 --- # Asprosin Enhances Cytokine Production by a Co-Culture of Fully Differentiated Mature Adipocytes and Macrophages Leading to the Exacerbation of the Condition Typical of Obesity-Related Inflammation ## Abstract Asprosin, a fasting-induced, glucogenic, and orexigenic adipokine, has gained popularity in recent years as a potential target in the fight against obesity and its complications. However, the contribution of asprosin to the development of moderate obesity-related inflammation remains still unknown. The present study aimed to evaluate the effect of asprosin on the inflammatory activation of adipocyte–macrophage co-cultures at various stages of differentiation. The study was performed on co-cultures of the murine 3T3L1 adipocyte and the RAW264.7 macrophage cell lines treated with asprosin before, during, and after 3T3L1 cell differentiation, with or without lipopolysaccharide (LPS) stimulation. Cell viability, overall cell activity, and the expression and release of key inflammatory cytokines were analyzed. In the concentration range of 50–100 nM, asprosin increased the pro-inflammatory activity in the mature co-culture and enhanced the expression and release of tumor necrosis factor α (TNF-α), high-mobility group box protein 1 (HMGB1), and interleukin 6 (IL-6). Macrophage migration was also increased, which could be related to the upregulated expression and release of monocyte chemoattractant protein-1 (MCP-1) by the adipocytes. In summary, asprosin exerted a pro-inflammatory effect on the mature adipocyte–macrophage co-culture and may contribute to the spread of moderate obesity-associated inflammation. Nevertheless, further research is needed to fully elucidate this process. ## 1. Introduction Asprosin, discovered in 2016 by Romere et al., is an adipokine released mainly by adipocytes in the white adipose tissue during fasting conditions [1]. Its primary function is the regulation of glucose production and its release from the liver, and hence, it is considered a glucogenic peptide [1,2]. Moreover, Li et al. [ 3] reported that glucose production stimulated by asprosin is also observed in diet-induced obesity. This recent study also showed that asprosin is involved directly in stimulating food intake. After crossing the blood–brain barrier, it activates agouti-related protein (AgRP) neurons in the hypothalamus as an orexigenic peptide [2]. Undoubtedly, under normal conditions, this molecule activity contributes to maintaining the proper energy homeostasis by the regulation of appetite and glucose secretion. On the other hand, some studies have shown that its pathologically elevated level is observed in obese individuals [4,5] as well as in patients with insulin resistance [6,7] and diabetes mellitus type 1 (DM1) [8] or type 2 (DM2) [9,10,11]. The use of anti-asprosin antibodies reduced food consumption by attenuation of AgRP neurons activation in obese individuals and potentially may contribute to combat obesity and related diseases [1,12]. Although asprosin is a newly discovered molecule, the effects of its action are relatively well known due to the high interest in research on this protein; for a review, see [13]. Nevertheless, still little is known about its mechanism of action, especially in terms of mechanisms related to the induction of inflammation in crucial insulin-sensitive tissues (skeletal muscle and pancreatic beta cells) and the enhancement of tissue damage by activating pro-inflammatory mechanisms [14]. Asprosin has been shown to impair insulin secretion from pancreatic beta cells under hyperglycemic condition by activating pro-inflammatory mechanisms related to the TLR4 pathway [15]. It was also found that asprosin reduces skeletal muscle insulin sensitivity by generating ER stress and inducing pro-inflammatory factors [16], as well as potentiates hyperlipidemia-induced endothelial inflammation in obese individuals by activating the IKKβ-NF-κBp65 pathway [17]. However, to date, there is no evidence that asprosin is directly or indirectly responsible for modulating adipocyte function resulting in obesity-related inflammation. Nevertheless, its significantly elevated level in obesity and the related enhancement of tissue damage, as well as the activation of a proinflammatory response in the pancreas and skeletal muscle suggest that it is, at least in part, involved in the regulation of moderate obesity-related inflammation. Therefore, the present study aimed to determine whether asprosin has a modulating effect on the course of moderate inflammation in an adipocyte–macrophage co-culture system which mimics the state of obesity related-inflammation. It is also important to determine whether the stage of adipocyte maturity, i.e., the hypertrophy of the adipocytes, affects the nature of asprosin action. ## 2.1. Overall Activity and Viability of the Cell Co-Culture The first stage in this research was the evaluation of the effect of asprosin on the overall viability and activity of cell co-cultures at different stages of differentiation. The evaluation was performed on the 7th, 14th, and 21st day of differentiation, wherein on day 21, a fully differentiated and hypertrophic adipocyte culture was obtained, corresponding to the hypertrophied adipocytes observed in obese individuals. The studies showed no effect of asprosin (0–100 nM) on the viability and overall activity of the cells on the 7th, 14th, and 21st day of differentiation (Figure 1). Additional analysis, not reported here, indicated that the highest dose of asprosin (150 nM) reduced the viability of the fully differentiated adipocytes (day 21). Due to the cytotoxic effect of the high dose of asprosin (150 nM), this dose was omitted in further studies. Studies on the mechanism of the cytotoxic action of high doses of asprosin are in progress. ## 2.2. Asprosin Enhances Cytokine Production by the Co-Cultured Cells In the next stage of the study, the effect of asprosin on the intensity of the expression and release of key proinflammatory cytokines such as TNF-α (tumor necrosis factor α), IL-1β, IL-6 (interleukin 1 and 6), and HMGB1 (high-mobility group box protein 1) was evaluated in co-cultures with and without lipopolysaccharide (LPS) stimulation. Studies have shown no effect of asprosin alone on the expression and release of the analyzed cytokines. The effect was observed only when LPS stimulation was carried out. In incompletely mature cell (LPS-stimulated) co-cultures (day 14), which are characterized by a lower saturation of lipid droplets [18], asprosin did not modulate the expression and release of key inflammatory cytokines. However, it was noted that at higher concentrations of asprosin, in low-maturity co-cultures (day 7), the intensity of expression and release of the main pro-inflammatory cytokines, i.e., TNF-α, IL-6, and HMGB1, were reduced as compared to those in the LPS-stimulated co-culture ($p \leq 0.05$). A different effect of asprosin was observed when it acted on an LPS-stimulated co-culture containing fully differentiated and mature adipocytes (day 21). The highest concentration of asprosin (100 nM) resulted in the increased release of pro-inflammatory TNF-α, IL-6, IL-1β (Figure 2; $p \leq 0.05$), and HMGB1cytokines (Figure 3; $p \leq 0.05$). The analysis of these cytokines expression at the mRNA level showed that the increase in cytokine release was dependent on the enhancement of the expression of these cytokines in both macrophages and adipocytes (Figure 2, respectively). It should also be noted that the effect of asprosin seemed to be more pronounced in macrophages than in adipocytes. Moreover, the level of cytokine expression in macrophages was much higher than that in adipocytes. It was also noted that the expression of pro-inflammatory factors was significantly higher in adipocyte–macrophage co-cultures than in monocultures of adipocytes and macrophages, which suggests a significant additive effect of the cooperation of these cells in co-culture. These observations confirmed that both cell populations that build the adipose tissue and participate in the development of obesity-related inflammation are sensitive to the effects of asprosin, which acts on them as a pro-inflammatory activator. ## 2.3. Asprosin Influences the Release of the Key Adipokines Leptin and Adiponectin The effect of asprosin on the release of leptin and adiponectin, the key adipokines, depends on the degree of adipocyte maturity. There was no evidence of an effect of asprosin on the release of both adipokines in co-cultures on days 7 and 14, both at the mRNA and at the protein level, regardless of LPS stimulation. However, it was noted that the high concentration of asprosin acting on both the mature LPS-stimulated co-culture and adipocyte monoculture decreased the release of anti-inflammatory adiponectin with a simultaneous slight increase in the release of leptin. This effect was apparent at both mRNA and protein levels for adiponectin (Figure 4). As in the case of the release of key pro-inflammatory cytokines, the level of the analyzed adipokines was lower in the monoculture of adipocytes than in the co-culture, which may indicate the additive interaction of macrophages. The effect of asprosin itself on the level of the released adipokines by non-LPS-treated cells was not observed; the levels of both leptin and adiponectin were similar to those in control cells. ## 2.4. Asprosin Increases the Migration of Macrophages towards Cultured Mature Hypertrophic Adipocytes The last stage of the study was the assessment of the influence of asprosin on the influx of macrophages into adipose tissue, resulting from the activity of chemotactic factors released by cells present in the adipose tissue. It is well known that macrophages are responsible for the development of moderate inflammation associated with obesity. For this purpose, the migration of macrophages into the supernatants of the adipocyte culture was investigated. Studies have shown that asprosin influences the profile of released chemotactic factors and the migration of macrophages by acting on mature adipocyte cultures. A high dose of asprosin significantly increased the degree of macrophage migration toward both the co-culture and the adipocyte supernatant from cultures on day 21, but not from cultures on day 7 or day 14 (Figure 5A). This increase in migratory activity corresponded to an increase in both expression and release of the main macrophage chemotactic factor MCP-1 (Figure 5B). This effect was noted in cultures grown for 21 days but not in cultures grown for 7 or 14 days. In the case of cultures on day 7, a slight decrease in MCP-1 expression was noted, which, however, was not confirmed by a decrease in the release of the chemokine itself or a decrease in macrophage influx. Moreover, as noted for the previously mentioned cytokines, the expression of MCP-1 was significantly higher in macrophages than in adipocytes (Figure 5B). Thus, asprosin had a stronger effect on macrophages than on adipocytes. Nevertheless, the effect of asprosin itself on both macrophages influx and the level of released MCP-1 by non LPS-treated cells was not observed. It is likely that in the obesity state, its elevated level may increase the influx of immunocompetent cells into the adipose tissue, thereby increasing the pro-inflammatory activation of the tissue. ## 3. Discussion The adipose tissue plays an important role both as an energy reservoir and as an endocrine organ that releases numerous biologically active factors with a broad spectrum of activity [19]. Nevertheless, in the case of excessive energy supply, which we observe during obesity, its activity changes significantly. An excessive accumulation of lipid droplets in obese individuals leads to structural and functional changes in the adipose tissue, which manifest as adipocyte hypertrophy, increased recruitment of immunocompetent cells such as macrophages or lymphocytes, increase in the pro-inflammatory activity and release of cytokines such as TNF-α, MCP-1, and IL-6, imbalance between the release of the anti-inflammatory proteins adiponectin and leptin, and increase in hypoxia and fibrosis [20]. All these changes consequently lead to the development of moderate inflammation accompanying obesity and increased necrosis of adipose tissue cells, which underlie the development of insulin resistance or metabolic syndrome [21,22]. Keeping this in mind, it becomes important to explore the mechanisms of the above phenomenon to find appropriate therapeutic strategies. Therefore, the present study analyzed the role of asprosin in the development and regulation of inflammation by using a cellular model of the co-culture of the main components of the adipose tissue, i.e., adipocytes and macrophages. The results of the present study showed that asprosin, which is mainly a fasting-induced, glucogenic, and orexigenic adipokine that regulates the body’s energy homeostasis, is also involved in modulating the inflammatory response in hypertrophic adipose tissue. The mechanisms of the development of moderate obesity-associated inflammation, although well described, have not yet been fully explained, for example, from the perspective of new endogenous factors that could act as potential immunomodulators. According to the current research, asprosin seems to be such a modulator, whose activity depends on the degree of maturity and hypertrophy of the co-culture it affects. According to previous scientific reports, in physiological conditions, the concentration of asprosin ranges from 5 to 10 nM [23]; however, in the state of obesity, its value may increase even 5–10 times. In the current work, the effects of both physiological (10 nM) and pathological concentrations of asprosin accompanying obesity (50 and 100 nM) were investigated. Our study used three time points of adipocyte differentiation and maturation, wherein after 21 days of full differentiation, hypertrophic adipocytes corresponding to those observed in obese individuals were obtained. It was observed that by acting on co-cultures on the 21st day of incubation, asprosin in high concentration significantly increased both the expression and the release of crucial pro-inflammatory cytokines such as TNF-α, IL-1β, IL-6. and HMGB1, both from adipocytes and from macrophages. The increase in the concentration of these cytokines is observed in the state of moderate obesity-related inflammation, and their activity is associated with the development of, for example, insulin resistance [21,22]. At the end of last year, Shabir et al. [ 24] showed that asprosin activates THP-1 macrophages to release TNF-a, IL-12, IL-8, and IL-1b, which may be at least partly related to the TLR4 pathway and NF-kB activation. In turn, the protective effect of asprosin on macrophages was noticed by Zou et al. [ 25] in an artheriosclerosis model. They observed a reduction in both the accumulation of lipid droplets by macrophages and the formation of macrophage foam cells that was associated with the p38/Elk-1 pathway activation. In our study, it was also observed that high concentration of asprosin increased the migration of macrophages to the supernatant from adipocyte cultures, which was related to the increase in the release of the chemokine MCP-1, the main chemotactic factor for macrophages. This condition may potentially predispose to an increased influx of macrophages to the adipose tissue and, consequently, to an enhanced pro-inflammatory activity. Attention is also drawn to the increase in the release of the highly pro-inflammatory alarmin HMGB1, which in clinical conditions is associated with the development of septic shock [26], and whose elevated level is observed in obese individuals [27]. This alarmin secondarily enhances pro-inflammatory activation through the TLR4/NF-kB downstream pathway activation and the excessive release of pro-inflammatory TNF-α, IL-1β, IL-6 from adipose tissue cells [28]. As reported by Zhang et al. [ 29], HMGB1 plays a key role in the development of obesity-related inflammation and insulin resistance [30]. It should be noted that HMGB1 can be released passively from damaged, necrotic adipocyte cells [31] and can also be induced, for example, by an increase in the release of pro-inflammatory TNF-α [29], which was observed in the current study. The increase in necrosis is a state typical of the hypertrophic adipose tissue observed in the course of obesity. Our present research showed that asprosin appears to exacerbate the pathological state of moderate inflammation in highly hypertrophic adipose tissue. It also indicates that asprosin may be a target of obesity therapy, not only because of its stimulating effect on food intake but also because of its potentially pro-inflammatory effect. An interesting question seems to be whether the use of asprosin antibodies, apart from the effect of reducing food intake as a result of lowering the concentration of asprosin (as shown by [1]), could have the effect of reducing the severity of obesity-related inflammation. This aspect needs to be explored in future studies. The effect mentioned above was not observed when the co-cultures were incubated with asprosin at earlier time points, i.e., on the 7th and 14th day of cell maturation. This finding may suggest that the activity of asprosin as a factor promoting inflammatory activation is related to hypertrophic adipocytes typically observed in obesity as a pathophysiological state. Nevertheless, it cannot be ignored that asprosin, acting at earlier time points, reduced the pro-inflammatory activation, thereby exerting a protective effect. This aspect should be investigated in more detail in future studies. The protective effect of asprosin was already demonstrated in mesenchymal stromal cells used in the treatment of myocardial infarction (MI) [32], in cardiomyocytes in hypoxia state [33], and in cardiac microvascular endothelial cells [34], and, hence, asprosin is considered a potential cardioprotective agent. The protective effect of asprosin was found to be associated with a reduction in free radicals resulting from the increase in the expression of antioxidant enzymes, namely, SOD-2, and a reduction in apoptosis by activating the ERK$\frac{1}{2}$ and PI3K/AKT pathways [32]. Keeping this in mind, it can be seen that the action of asprosin is not universal and depends on the type of cells as well as on the metabolic and physiological state. This is especially apparent in the case of increased damage and apoptosis of pancreatic cells in obese individuals. As asprosin is a recently discovered adipokine, our knowledge of its mechanisms of action is limited, and further research is needed to determine its significance in both physiological and pathophysiological states. ## 4.1. Culture of Murine Cell Lines The study was conducted on the mouse monocyte–macrophage RAW 264.7 cells and 3T3 L1 preadipocytes differentiated to adipocytes. The 3T3 L1 cell line was kindly provided by Professor Alicja Jozkowicz from the Department of Medical Biology, Jagiellonian University, while the RAW 264.7 cell line was purchased from the European Type Culture Collection (ECACC, Sigma-Aldrich, St. Louis, MO, USA). Both cell lines were tested to be mycoplasma-free. The preadipocytes were differentiated to mature adipocyte using a standard protocol described previously [35]. Briefly, the cells were grown until confluence under standard conditions (37 °C, $5\%$ CO2) in DMEM medium supplemented with a $1\%$ antibiotic solution and $10\%$ calf serum. Two days after reaching confluence, the growth medium was replaced with a differentiation medium for the next 2 days (DMEM with $10\%$ fetal bovine serum [FBS], 0.5 mM isobutylmethylxanthine [IBMX], and 0.25 µM dexamethasone). Subsequently, the cells were incubated in DMEM containing $10\%$ FBS and 1 µg/mLof insulin until the end of the 3 week differentiation process. Adipocyte differentiation was evaluated by Oil-Red-O staining. To investigate the effects of asprosin on the development of moderate obesity-related inflammation, a co-culture of adipocytes and macrophages, two major adipose tissue populations, was used. To consider the influence of the differentiation stage and maturity of the adipocytes as well as the deposition of lipid droplets, the assessment was performed after 7, 14, and 21 days of differentiation. For the co-culture model, the adipocytes at the various stages of differentiation were kept in the lower parts of 12-well plates, while RAW 264.7 cells were cultured in plate inserts (0.4 µm pore size). The cells were treated with different concentrations of asprosin (0–100 nM) for 24 h. Then, to induce a state of mild inflammation, the co-cultures were stimulated with lipopolysaccharide (LPS; 100 ng/mL; Escherichia coli, serotype 0111: B4; Sigma-Aldrich, St. Louis, MO, USA) for the next 6 h (gene expression studies) or 24 h (protein release studies). During the incubation, 3T3 L1 and RAW 264.7 cells were cultured under standard conditions (37 °C, $5\%$ CO2). Fresh cells were used for cytometric assessment and RNA isolation. The supernatants and cell pellets were frozen at −60 °C for quantification of the protein levels. ## 4.2. Cell Activity and Viability Overall cell viability and activity were measured using a commercial CellTiter kit (Promega, Madison, WI, USA) by assaying the reduction of [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium by active mitochondria in the tested cells. The tests were performed according to the manufacturer’s instructions by using a spectrophotometer (Expert Plus, ASYS/Hitech). ## 4.3. Real-Time PCR Analysis of Gene Expression Total RNA was purified from 3T3 L1 adipocytes and RAW264.7 macrophages cultured in four separate repetitions for each batch of the experiment. The RNeasy Plus Mini Kit with elimination of genomic DNA was used for RNA extraction. The RNA concentration and quality were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Rockford, IL, USA). The High-Capacity cDNA Reverse Transcription kit was used for the reverse transcription of mRNA to cDNA. Real-time PCR gene expression analysis of TNF-α, IL-1β, IL-6, MCP-1, HMGB1, adiponectin, and leptin was performed using the StepOne system from Applied Biosystems and TaqMan primers. Glucose-6-phosphate dehydrogenase (GAPDH) was used as a housekeeping gene for normalizing the amounts of cDNA. Changes in the gene expression level were calculated based on the 2−∆∆Ct algorithm. ## 4.4. Macrophage Migration To evaluate the effect of asprosin on the release of chemotactic factors responsible for the recruitment of immunocompetent cells, a 48-well microchemotaxis chamber (Neuro Probe) and the RAW 264.7 macrophage cell line were used. The test was performed according to the manufacturer’s protocol. N-formyl-L-methionyl-L-leucyl-phenylalanine (fMLP) was used as a chemoattractant agent in the positive control sample, and DMEM medium was used as the negative control. The chamber was incubated for 45 min under standard culture conditions. The membrane was subsequently washed in PBS and stained. The macrophages that had migrated to the lower side of the membrane were counted in four microscopic fields in each well. The results are expressed as the mean numbers of migratory macrophages per well. ## 4.5. Determination of Cytokine/Chemokine Release The cytokine levels in the supernatants collected after 24 h of incubation with or without LPS were quantified using a commercial Elisa kit. The concentrations of TNF-α, IL-1β, IL-6, MCP-1, HMGB1, adiponectin, and leptin were determined according to the manufacturer’s instructions and analyzed using an Expert Plus spectrophotometer (ASYS/Hitech, Eugendorf, Austria). ## 4.6. Statistical Analysis The data were tested for normality of distribution, and the differences among the groups were determined using the Duncan’s new multiple range test 3.1. All data are expressed as mean ± standard deviation (S.D.) with the level of statistical significance (p) set at 0.05. ## 5. Conclusions This study for the first time assessed the effect of asprosin on the inflammatory activation typical of an obesity-related condition using a co-culture of adipocytes and macrophages. This study allowed us to determine whether the inflammation associated with obesity may be at least in part related to the activity of asprosin, the concentration of which increases in people with obesity. On the basis of the obtained results, it can be concluded that asprosin, as a biomolecule released from the adipose tissue of obese individuals, can be considered an immunomodulatory factor with a potential pro-inflammatory action. Asprosin might not only regulate food intake but also be associated with the pathomechanism of obesity-related diseases through the development of moderate obesity-related inflammation. ## References 1. Romere C., Duerrschmid C., Bournat J., Constable P., Jain M., Xia F., Saha P.K., Del Solar M., Zhu B., York B.. 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--- title: First Comparative Evaluation of Short-Chain Fatty Acids and Vitamin-K-Dependent Proteins Levels in Mother–Newborn Pairs at Birth authors: - Tamás Ilyés - Marius Pop - Mihai Surcel - Daria M. Pop - Răzvan Rusu - Ciprian N. Silaghi - Gabriela C. Zaharie - Alexandra M. Crăciun journal: Life year: 2023 pmcid: PMC10056575 doi: 10.3390/life13030847 license: CC BY 4.0 --- # First Comparative Evaluation of Short-Chain Fatty Acids and Vitamin-K-Dependent Proteins Levels in Mother–Newborn Pairs at Birth ## Abstract Background: The interplay between vitamin K (vitK) (as carboxylation cofactor, partially produced by the gut microbiota) and short-chain fatty acids (SCFAs), the end-product of fiber fermentation in the gut, has never been assessed in mother–newborn pairs, although newborns are considered vitK deficient and with sterile gut. Methods: We collected venous blood from 45 healthy mothers with uncomplicated term pregnancies and umbilical cord blood from their newborns at birth. The concentrations of total SCFAs and hepatic/extra-hepatic vitK-dependent proteins (VKDPs), as proxies of vitK status were assayed: undercarboxylated and total matrix Gla protein (ucMGP and tMGP), undercarboxylated osteocalcin (ucOC), undercarboxylated Gla-rich protein (ucGRP), and protein induced by vitK absence II (PIVKA-II). Results: We found significantly higher ucOC (18.6-fold), ucMGP (9.2-fold), and PIVKA-II (5.6-fold) levels in newborns, while tMGP (5.1-fold) and SCFAs (2.4-fold) were higher in mothers, and ucGRP was insignificantly modified. In mother–newborn pairs, only ucGRP ($r = 0.746$, $p \leq 0.01$) and SCFAs ($r = 0.428$, $$p \leq 0.01$$) levels were correlated. Conclusions: We report for the first time the presence of SCFAs in humans at birth, probably transferred through the placenta to the fetus. The increased circulating undercarboxylated VKDPSs in newborns revealed a higher vitamin K deficiency at the extrahepatic level compared to liver VKDPs. ## 1.1. The Placenta The placenta is a crucial but transient organ in pregnancy, developed from fetal tissue, as an interface between the circulation of the mother and the fetus. The placenta has two sides, a fetal and a maternal side. The circulation of the mother and the fetus do not come in direct contact with each other, as there is a thin layer of cells forming a barrier between the two. This thin layer is initially a double layer made up of cylindrical cells that merge to form a single layer called syncytiotrophoblast later during the pregnancy. These cells form chorionic villi that contain fetal capillaries and facilitate the exchanges between the maternal and fetal circulation [1]. In addition to its organization into a villous structure, the maternal side of the cells of the syncytiotrophoblast also present an array of microvilli that further increase the surface area available for transfer [2]. The syncytiotrophoblast is a continuous cell layer with no intracellular junctions. Thus, all transport must take place through the apical and basal membranes of the syncytiotrophoblastic cells. The exact nature of the transplacental transport remains poorly understood, although there are numerous postulations regarding the mechanisms by which it takes place [1]. It is accepted that trans-placental transport falls into one of three categories: passive diffusion, transporter protein mediated transfer, or endo-/exocytosis [1]. Diffusion is the main pathway by which blood–gas exchange takes place through the placenta. Even though the placental barrier thickness decreases as the pregnancy progresses, the rate of oxygen transfer remains relatively constant [3]. The diffusion of charged molecules is dependent on the electrical potential difference between the two circulations. There is a small potential difference across the human placenta, and as the pregnancy advances, it decreases even further [4,5]. While the diffusion of small liposoluble molecules occurs rapidly and is mainly dependent on blood-flow rate, the passive diffusion of hydro-soluble molecules is limited and is more dependent on transporter proteins [1]. The protein-mediated transport of molecules is of two types: facilitated diffusion (according to a concentration gradient) and active transportation (against a concentration gradient). An example of a molecule transported through facilitated diffusion through the placenta is glucose, via the GLUT family of transporter proteins [6]. The main advantage of protein-mediated transport compared to passive diffusion is the ability to maintain a relatively constant rate of transfer (through increased or decreased protein expression) in the case of physiological or pathological modifications to the transport surface [1]. Endocytosis vesicles may fuse with lysosomes with the content being digested, or they may pass undisturbed through the cell [7,8]. These processes are summarized in Figure 1. ## 1.2. Vitamin-K-Dependent Proteins Vitamin K has a number of structurally different vitamers, generally divided into two large groups: phylloquinones (vitamin K1) and menaquinones (MK) [9]. Both vitamin K1 and MK serve as cofactors in the carboxylation reaction of glutamic acid residues from proteins rich in them, also known as vitamin-K-dependent proteins (VKDPs) [10]. Carboxylation of these glutamic acid residues produces gamma carboxy glutamic acid (Gla), which enables VKDPs to bind calcium [11]. Human and animal requirements for vitamin K are difficult to establish because part of vitamin K is provided by food, and part is produced through biosynthesis by microorganisms in the digestive tract. Vitamin K1 are present mainly in leafy green vegetables, the main vitamin K1 source of the human body being plant-based foodstuffs [12]. On the other hand, MK are found in meat and fermented dairy and cereal foodstuffs (curd cheese, natto, sauerkraut) but are also produced by the gut microbiota metabolism [13]. Fecal MK concentration has been found to vary according to microbiota composition [14]. The placenta also acts as a barrier for vitamin K, which leads to a ratio of vitamin K levels of about 30:1 between the maternal blood and cord blood of the fetus [15]. The most well-known hepatic VKDPs are the coagulation factors such as protein induced by vitamin K absence II (PIVKA-II), the undercarboxylated form of prothrombin, which is used to establish the vitamin K deficiency at the hepatic level. In comparison with hepatic VKDPs, there are many other VKDPs that are not produced in the liver (known as extra-hepatic VKDPs), the majority of them being involved in bone health and in preventing vascular calcification, which require higher amounts of vitamin K for their carboxylation [16]. Matrix Gla protein (MGP), osteocalcin (OC), growth arrest specific protein 6 (Gas6), and Gla rich protein (GRP) are some of the most studied extra-hepatic VKDPs, their carboxylation status depending on extra-hepatic vitamin K levels [17]. ## 1.3. Short-Chain Fatty Acids Short-chain fatty acids (SCFA) are carboxylic acids with up to five carbon atoms [17]. Methanolate, acetate, propionate, butyrate, and valerate are all considered SCFAs, but the majority of studies generally focus on acetate, propionate, and butyrate, as these are produced in the highest quantities in the gut. The main source of SCFAs in the body is through the fermentation of indigestible dietary fibers by the gut microbiota. Short-chain fatty acids are absorbed from the large intestine in variable amounts and have multiple roles in the human body such as increasing leptin production, either directly or through two G-protein coupled receptors, GPR-41 and GPR-43, for which they are ligands [18,19,20,21,22]. Even if SCFAs have been detected in the meconium of newborns, only acetate and propionate were identified in significant quantities, while butyrate and valerate were absent [23]. Due to only acetate and propionate being present in meconium, this is either the result of inoculation of the newborn through contact outside the womb, or is the result of a yet unidentified production mechanism other than the gut microbiota. The presence of SCFAs in the fetal circulation has only been identified in pigs [24] but not in humans. Because newborns at birth are considered sterile, the presence of SCFAs in their circulation could be the result of transfer from the mother. ## 1.4. Microbiota Even if the fetus, up to the moment of birth, has long been considered as being completely sterile [25], recent studies have shown the presence of small quantities of bacteria in the amniotic fluid, the umbilical cord, as well as the placenta [23,26]. Furthermore, species as Enterococcus and Staphylococcus have been identified in the meconium [27]. The inoculation of these structures appears to be the result of ascension of microbes from the vaginal microbiota and/or of hematogenous dissemination. A concrete source of these microbes has yet to be identified [26]. Studies that have presented evidence of microbial presence in structures previously considered sterile, due to using very sensitive quantification techniques such as polymerase-chain reaction to identify bacterial DNA, could neither exclude accidental contamination nor prove unequivocal colonization in these structures [23]. Consequently, the question still remains whether the fetus is actually completely sterile, but if there is an existing fetal microbiota, it is not present in a high enough concentration to influence the presence of bacterial metabolites in the fetus. This suggests that any substances produced by bacteria probably originate from the mother and pass through the placenta to the fetus. Considering that the placenta is a barrier between the mother and fetus for vitamin K originating from either the gut microbiota or from dietary intake, we evaluated the hepatic and extra-hepatic levels of undercarboxylated VKDPs as a result of vitamin K deficiency in newborns. Since the colon of the newborn is considered sterile, one of our goals was to assess the total SCFA concentration for the first time in mothers and umbilical cord blood of their newborns, hypothesizing the possibility of placental transportation of SCFAs. ## 2.1. Study Design We included 45 mothers and their newborns from the No. 1 and No. 2 obstetrics and gynecology clinics of County Emergency Clinical Hospital of Cluj-Napoca, Romania. All included mothers gave their informed consent to participate in the study. All births took place between October and December of 2020, and all newborns were born at term. Mothers admitted for childbirth, either via caesarian section or natural birth, were included in this study. Both smoking and non-smoking mothers were included. Only normally evolved pregnancies were taken into consideration. Mothers who did not give their informed consent for participation in the study and cases in which either the mother or the child suffered severe complications related to childbirth were excluded. Newborns with an APGAR score less than 9 as well as their mothers were also excluded. Cases in which the mothers received peri-partum antibiotic or anticoagulant treatment were not included in the study. ## 2.2. Sample Collection and Storage Venous blood samples were obtained from mothers immediately post-partum and simultaneously with the one collected from the umbilical cord of their newborns after cord clamping. The blood samples were collected into plain, additive-free vacuum tubes. After 30 min at room temperature, the samples were centrifuged at 3000× g for 10 min. The separated serum was aliquoted into sterile 1.5 mL microcentrifuge tubes and subsequently stored at −80 °C until analysis. Anthropometric data were collected from patient observation files. ## 2.3. Sample Analysis Total SCFAs, undercarboxylated osteocalcin (ucOC), undercarboxylated Gla-rich protein (ucGRP), PIVKA-II, total matrix Gla protein (tMGP), undercarboxylated matrix Gla protein (ucMGP), and vitamin D (vitD) concentrations were assayed using commercial enzyme-linked immunosorbent assay (ELISA) kits (MyBioSource®, San Diego, CA, USA) following the manufacturer’s protocols. After processing, the ELISA plates were read by an automated ELISA plate reader (Organon Teknika 230 s, Oss, The Netherlands). Total calcium (Ca), glucose (Glu), triglycerides (TG), total cholesterol (TC), iron (Fe), magnesium (Mg), and phosphorous (P) levels were measured by conventional methods using an automated biochemistry analyzer (Mindray BS-480, Shenzhen Mindray Bio-Medical Electronics Co., Shenzhen, China). For all analyses, intra- and inter-assay coefficients of variation were under $10\%$. ## 2.4. Statistical Analysis Statistical analysis of the gathered data was carried out using RStudio Desktop (RStudio© PBC v1.4.1106, Boston, MA, USA). A value of p ≤ 0.05 was considered statistically significant. Analysis for the normality of quantitative data distribution was conducted using the Shapiro–Wilk test. Normally distributed data were presented as mean ± standard deviation, while non-Gaussian distributed data were presented as median (minimum–maximum). Qualitative data were expressed as frequencies. The correlation between quantitative data was analyzed using Pearson’s R coefficient or Spearman’s coefficient according to the normality of the data. Comparison of means and medians was accordingly carried out using either Student’s t-test or the Mann–Whitney U test. ## 3. Results The total number of subjects included in this study was 90 (45 mothers and their newborns). The characteristics of the study participants are presented in Table 1 and Table 2. Table 2 also contains the comparison of the means and medians between mothers and newborns for parameters that were measured in both. Parameters with significant positive correlations between mothers and newborns are presented in Table 3. Total maternal cholesterol was significantly negatively correlated with newborn ucOC (r = −0.378, $p \leq 0.05$), maternal Ca with newborn PIVKA-II (r = −0.361, $p \leq 0.05$), maternal PIVKA-II with newborn Fe (r = −0.5, $p \leq 0.05$) and maternal BMI with newborn Mg (r = −0.350, $p \leq 0.05$). Other parameters did not present any statistically significant correlations between mothers and newborns. ## 4. Discussion The results obtained in this study in part are sustained by previous research and raise a number of hypotheses regarding the nature of the mother–fetus placental transfer of different compounds. There was a marked difference between the levels of certain analytes between mothers and newborns. The levels of PIVKA-II were over five times higher in the newborns compared to the mothers. The levels of tMGP were, on the other hand, over five times higher in the maternal serum, while ucMGP had an opposite trend, being over nine times as high in newborns as in their mothers. Importantly, the placenta is considered a barrier for vitamin K between the mother and fetus [15]. This suggests the possibility that newborns present a constant, physiological deficit of vitamin K, and for certain VKDPs, the carboxylation reaction happens in mothers, but the carboxylated form of the VKDPs cannot traverse the placental barrier or does so only in minute quantities. Another possible explanation is that the various tissue carboxylases are not yet sufficiently mature in newborns [28]. It is known that vitamin K, as all liposoluble vitamins, is transported via facilitated diffusion through the syncytiotrophoblast and that this transport is relatively poor [15,29]. This confirms the fact that PIVKA-II is much higher in the serum of the newborns [30]. It would also explain why ucMGP is higher in the newborns, while tMGP (which takes into account the carboxylated conformations as well) is higher in the maternal circulation. The newborns simply have much less vitamin K than the mothers; thus, vitamin K deficiency bleeding (VKDB) in the newborn could not be ascribed to the immaturity of the liver, which forms insufficient coagulation factors, but is instead caused primarily by a vitamin K deficiency. Human milk is also a poor source of vitamin K. Breastfed infants are more susceptible to VKDB than those fed otherwise [28]. An interesting finding that provides a counter argument to the above is a strong positive correlation of vitamin D (another liposoluble vitamin) levels between mothers and newborns, along with the fact that vitamin D levels in the newborns were double that of the mothers, echoing the results of previous studies [31]. At first glance, this would suggest that the transport of vitamin D through the placenta is actually a very effective one. However, this is countered by the fact that, if these levels were the result of trans-placental transport, the levels should not be higher in the newborn than the mothers. More plausible explanations for this are either that vitamin D is actually transported as one of its precursors and then converted to the active form by the fetus, or that vitamin D is actually transported via active transportation against the gradient and is present in higher concentration in the fetus, because it is required for sustained growth. The latter is supported by the fact that ucOC levels were also higher in newborns, suggesting that ucOC is the result of vitamin D stimulatory effects on osteoblasts [28]. Regarding lipid metabolism and transfer, cholesterol levels were four times lower and triglycerides were eight times lower in newborns than their mothers, suggesting a poor transfer of lipids through placenta or poor endogenous cholesterol and triglycerides synthesis in newborns. Moreover, this large difference in lipid levels may result from food intake of mothers within 5–12 h before labor. On the other hand, vitamin D levels were two-fold higher in newborns than in their mothers, which was associated with significantly higher levels of calcium, phosphorus, and magnesium in newborns. Iron was also almost double in newborns compared to their mothers, suggesting that for oligoelements, vitamin D may play a role in the trans-placental transport by stimulating the fetus’ uptake of ions (see Table 2). While previous studies reported PIVKA-II and OC levels in newborns [32,33] other extrahepatic undercarboxylated VKDPs were not taken into account, and neither was the degree of their change in newborns. We can hypothesize that vitamin K deficiency is reflected in the undercarboxylation of extrahepatic Gla proteins more than hepatic proteins. An alternative hypothesis for the above-mentioned discrepancy in undercaboxylated VKDPs is that trans-placental differences in observed concentrations are influenced by the number of glutamic acid residues. This is sustained by the fact that the differences in concentrations between mothers and newborns coincide with the number of glutamic acid residues to be carboxylated in each VKDP: GRP contains 16 residues [34], followed by PIVKA-II with 10 [35], MGP with 5 [36], and OC with 3 [37]. This would suggest that the trans-placental transport of VKDPs is inversely proportional to the number of these residues present in each molecule. Confirmation of this hypothesis, however, will require studies that investigate the transport proteins responsible for trans-placental VKDPs transfer, as well as assaying the carboxylated and total VKDP concentrations for each protein. While PIVKA-II and ucMGP both had differences between mothers and newborns, the same does not apply to GRP as well. In the case of ucGRP, there was no statistically significant difference between the concentrations in mothers compared to newborns. Moreover, there was a very strong positive correlation between the maternal and newborn levels of ucGRP. A possible explanation is that compared to the other VKDPs, GRP is actually transported in its undercarboxylated conformation through the placenta. This hypothesis is also sustained by the very strong positive correlation found in ucGRP between the maternal and newborn levels. One of the weaknesses of this study was that only the undercarboxylated conformations of VKDPs were measured. This study assessed the undercarboxylated VKDPs, because the aim was to evaluate the extent of vitamin K deficiency in newborns. Further studies should measure the carboxylated VKDPs as well, in order to provide more insight into the carboxylation status in the newborn, as well as the ratio between the carboxylated and undercaboxylated conformations. Because the fetus is considered sterile or near-sterile [23,25,26], any SCFA present in the fetal circulation would have a high probability of originating from the mother. The present study reports for the first time total SCFAs in newborn umbilical cord blood, which were less than half of their mothers. Because SCFAs can, theoretically, only originate in significant quantities in the mother’s gut microbiota, and because maternal and fetal SCFAs levels presented a strong positive correlation, it can be postulated that the newborn’s SCFAs are transferred from the mother through the placenta. This was also postulated in a previous study carried out in pigs [24]. Because the levels of SCFAs are higher in the mother’s circulation compared to the newborns, we assume that the transport takes place according to the concentration gradient. However, the SCFAs of the mothers are double those of the newborns, so even if this transfer appears to take place passively, it is still not very efficient. The human placenta was shown to be readily permeable to molecules between 1350–5200 Daltons [38,39]. Because SCFAs are much smaller and are hydro-soluble molecules, one possible mechanism is facilitated diffusion. The trans-placental transfer of SCFAs has yet to be studied and validated by further studies, especially by selectively comparing the amounts of different SCFAs. This study assayed only total SCFAs in serum. In order to gain more insight into circulating SCFAs in newborns, future studies should determine each SCFA separately via gas chromatography coupled with mass spectrometry (GC-MS) or high-performance liquid chromatography coupled with MS (HPLC-MS). It would also be beneficial for future research to concomitantly determine the concentration of each individual SCFA from meconium, urine, umbilical cord blood, and maternal blood [40]. Such an approach would clarify the exact source of the SCFAs present in the newborn and the mechanism of the trans-placental trasport. ## 5. Conclusions This study analyzed for the first time the trans-placental transfer of SCFAs, as well as the relationship between VKDPs and total SCFAs in serum as proxies for the maternal gut microbiota. Newborns were found to have a deficit of vitamin K, proven by the presence in higher concentrations of all undercarboxylated VKDP with higher concentrations of extra-hepatic compared to hepatic VKDPs. Therefore, in newborns at term compared with their mothers, ucOC and ucMGP, as extrahepatic VKDPs, are the most sensitive to vitamin K deficiency, in comparison with ucGRP or the hepatic PIVKA-II. 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--- title: High Homocysteine Levels Are Associated with Cognitive Impairment in Patients Who Recovered from COVID-19 in the Long Term authors: - Pinar Oner - Seda Yilmaz - Serpil Doğan journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10056581 doi: 10.3390/jpm13030503 license: CC BY 4.0 --- # High Homocysteine Levels Are Associated with Cognitive Impairment in Patients Who Recovered from COVID-19 in the Long Term ## Abstract In this study, we measured the levels of depression and cognition in people recovering from COVID-19. Moreover, we aimed to investigate the relationship between depression and cognition levels by measuring homocysteine concentrations. It included 62 people recovering from COVID-19 (at least 3 months after positive RT-PCR) and 64 people without COVID-19 (control group). At first, the homocysteine levels of participants were measured. Beck Depression Inventory (BDI) and Montreal Cognitive Assessment (MoCA) were performed to collect data. Homocysteine levels of the group recovering from COVID-19 (x− = 19.065 µmol/L) were higher than the control group (x− = 11.313 µmol/L). There was no significant difference between the groups for BDI scores. The MoCA scores of the group recovering from COVID-19 (x− = 20.774) were lower than the control group (x− = 24.297). There was a negative high (r = –0.705, $p \leq 0.001$) correlation between homocysteine levels and MoCA scores. Linear regression analysis is shown to be significant, and the MoCA explanatory value of the variables in the model is $58.6\%$ ($p \leq 0.0001$). A 1 µmol/L observed increase in homocysteine level constituted a risk for a 0.765-point decrease in MOCA scores. In patients recovering from COVID-19, early interventions to high homocysteine levels may prevent cognitive impairments that may persist in the long term. ## 1. Introduction The COVID-19 pandemic is one of the biggest health problems in recent history. This pandemic, which first appeared in China, quickly affected the world, and millions of cases and deaths were reported [1]. The clinical picture can be seen with many symptoms such as fever, sore throat, weakness, muscle aches, loss of smell, and cough in patients [2]. Many different clinical manifestations have been discovered so far, but we know very little about the long-term effects of this disease. The long-term effects of COVID-19, called ‘Long COVID’, have attracted attention recently [3]. In a recent longitudinal study, some morphological changes were observed in the brains of those with COVID-19 [4]. This result is exciting to understand the psychiatric and neurological consequences that may occur in patients with this infection and to plan new studies. COVID-19 infection can affect the central nervous system in different ways. The developing immune response to SARS CoV-2, which settles in the respiratory system, may lead to an increase in cytokines, chemokines, and immune cells that increase neuroinflammation in the brain. Even if SARS-CoV-2 is rare, it can reach the nervous system directly. SARS-CoV-2 may generate an autoimmune response against the nervous system. Activation of latent herpesviruses such as Epstein–Barr virus during COVID-19 infection can trigger neuronal damage. SARS-CoV-2 can disrupt blood flow in nerve cells by triggering the formation of cerebrovascular and thrombotic diseases. This may also disrupt the blood–brain barrier and increase the severity of neuroinflammation and ischemia. In addition, pulmonary and multi-organ dysfunction disorders caused by severe COVID-19 can lead to conditions that can negatively affect neural cells by leading to nerve cells hypoxemia, hypotension, and metabolic disorders [5]. Homocysteine is not found in the diet; it is an amino acid produced from methionine. Methionine and homocysteine are both precursors of each other. The ubiquitous methionine cycle is an essential part of body metabolism [6]. Homocysteine can be involved in two separate metabolic pathways: transsulfuration and methylation pathway. A high level of homocysteine in the blood is called hyperhomocysteinemia, and this condition has been associated with some diseases [7]. Hyperhomocysteinemia is associated with stroke, heart attack, and cardiovascular disease. Hyperhomocysteinemia causes endothelial damage in vessels and causes deterioration of existing hemostasis. It also contributes to the development of inflammation [8]. Other proinflammatory factors, such as homocysteine and interleukin-6 (IL-6), C-reactive protein (CRP), and alpha-1-antimotrypsin, have been associated with neuroinflammation and cognitive decline. Moreover, homocysteine has been reported as an independent factor in the impairment of information processing, general cognitive function, and fluent intelligence. The combination of high homocysteine and increased inflammation can be used as an indicator of cognitive impairment [9]. There is increasing evidence of an association between high homocysteine levels and depression [10]. Hyperhomocysteinemia is closely associated with neurodegenerative diseases as well as poor cognitive performance [11]. It has been observed that there is an increase in psychiatric diseases such as depression in COVID-19 patients [12]. COVID-19 infection can cause cognitive decline [4]. Additionally, among COVID-19 patients, those with worse outcomes have been found to have higher homocysteine levels [7]. Studies on homocysteine in COVID-19 have focused on the cardiovascular system. This study, however, includes a neuropsychiatric approach. In addition, it is new research for ‘Long COVID’. In this study, we measured the levels of depression and cognition in people who had previously recovered from COVID-19. Furthermore, we aimed to investigate the relationship between depression and cognition levels and homocysteine by measuring homocysteine concentrations. ## 2.1. Inclusion and Exclusion Criteria One hundred twenty-six people who were admitted to the outpatient neurology clinics of Elazig Fethi Sekin City Hospital were included in the study. Previous data of 62 people in our hospital’s microbiology laboratory had at least 1 positive reverse transcription polymerase chain reaction (RT-PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Previous data of 64 people in the control group had no history of COVID-19 and no positive RT-PCR test for SARS-CoV-2. For the recovered COVID-19 group, only those who had recovered from COVID-19 infection once were included in the study; those who had recovered from repeated COVID-19 infection were not included in the study. Homocysteine levels of all participants were measured at the time of admission. Although there are different definitions for ‘Long COVID’ or ‘post-COVID-19 syndrome’, the most common definition deals with showing symptoms lasting more than 3 months from the first symptom onset [3]. In this study, only those patients who had received a positive RT-PCR test for COVID-19 at least 3 months earlier were included in the group of those who had recovered from COVID-19. For both groups, those between the ages of 18 and 65 were included in the study. Since some psychiatric and cognitive disorders may occur in the patients in the intensive care unit in the long term [13], those who had been hospitalized in the intensive care unit and who had had severe COVID-19 infection were excluded from the study. Since homocysteine levels may be high in obese patients [14], only those patients with BMI < 30 were included in the study. Again, exclusion criteria for both groups were a history of cardiovascular system disease or thromboembolism, a history of chronic disease, mental retardation, a neurological disorder that may cause cognitive impairment, a history of psychiatric disease, alcohol-substance use, smoking, folic acid use, and continuous drug use. At the same time, the Beck Depression Inventory (BDI) and Montreal Cognitive Assessment (MoCA) results, which were evaluated at the time of the patient’s admission, were included in the study. All participants were provided with detailed information about the study, and then a written consent form was obtained. The principles of the Declaration of Helsinki were followed throughout the entire study. Our study was approved by the Elazig Firat University Clinical Research Ethics Committee (No: $\frac{2022}{02}$-31). ## Homocysteine Blood samples were taken from the antecubital vein (6 mL). Samples were collected and analyzed in vacuum tubes, including $15\%$ K3 ethylene diamine tetraacetic acid-anticoagulation tubes (Sarstedt, Essen, Belgium). The levels of homocysteine were tested using the spectrophotometric method on an Abbott Architect c8000 (Abbott, Abbott Park, IL, USA) Chemistry System. ## 2.3.1. Beck Depression Inventory (BDI) Beck et al. [ 15] developed this scale. Hisli [16] reported the Turkish validity and reliability study of the scale. It consists of 21 items in total. The participant is asked to give a score between 0 and 3 for each item. The total score obtained from all items reveals the severity of depression, and an increase in this score indicates an increase in the severity of depression. ## 2.3.2. Montreal Cognitive Assessment (MoCA) This is a scale used to assess mild cognitive impairment. Cognitive functions such as visuospatial and executive functions, naming, memory, attention, language, abstraction, and orientation are evaluated. A maximum of 30 points can be obtained from this scale. A score of less than 21 indicates cognitive impairment, while higher scores indicate better cognition. Nasreddine et al. [ 17] developed this scale, and Selekler et al. [ 18] performed Turkish validity and reliability study. ## 2.4. Statistical Analysis The data obtained in the research were analyzed using the SPSS 22.0 (Statistical Package for Social Sciences) for Windows program. Kurtosis and skewness values were examined to determine whether the research variables showed a normal distribution, and it was observed that they showed a normal distribution. Parametric methods were used in the analysis of the data. The difference of continuous variables according to the groups was analyzed with the t-test. Differences between the ratios of categorical variables in independent groups were analyzed with Chi-square and Fisher’s exact tests. Pearson’s correlation analysis was performed between the continuous variables of the study. Linear regression analysis was performed to determine the predictive parameters of MoCA scores. Receiver-operating characteristic (ROC) analysis was used to estimate the optimal cut-off values of homocysteine in patients who had recovered from COVID-19. $p \leq 0.05$ values were considered to indicate statistical significance. ## 3. Results There was no significant gender difference between the groups (χ2 = 0.031; $$p \leq 0.501$$). It is seen that there are 31 ($50.0\%$) men and 31 ($50.0\%$) women in the group who had recovered from COVID-19, and 33 ($51.6\%$) men and 31 ($48.4\%$) women in the control group. There was no significant difference in terms of age between the group who had recovered from COVID-19 (39.742 ± 9.426) and the control group (40.516 ± 9.188). There was no significant difference in terms of the years of formal education between the group who had recovered from COVID-19 (7.048 ± 5.787) and the control group (6.063 ± 5.345). The mean time that passed after those who had recovered from COVID-19 received their positive RT-PCR test results was found to be 10.435 ± 5.180 (Table 1). Homocysteine levels differed significantly between the groups (t[124] = 10.306; $p \leq 0.001$). Homocysteine levels (x− = 19.065 µmol/L) of the group who had recovered from COVID-19 were found to be higher than the homocysteine levels (x− = 11.313 µmol/L) of the control group (Table 1) (Figure 1). There was no significant difference between the BDI scores of the group who had recovered from COVID-19 (x− = 4.274) and the BDI scores (x− = 3.515) of the control group (Table 1). MoCA scores differed significantly between the groups (t[124] = −5.137; $p \leq 0.001$). The MoCA scores of the group who had recovered from COVID-19 (x− = 20.774) were lower than the MoCA scores (x− = 24.297) of the control group (Table 1) (Figure 1). Comparison of homocysteine, MoCA, BDI levels between the two groups is shown in Figure S1. The analyses of the correlation coefficients indicated there was a high negative correlation with a value of between the homocysteine levels and the MoCA scores in patients who recovered from COVID-19 (r = −0.705, $p \leq 0.001$) (Table 2) (Figure 2). There was a positive correlation between the MoCA scores and the age of the participants in patients who recovered from COVID-19 ($r = 0.252$, $$p \leq 0.048$$). There was a positive correlation between the MoCA scores and the years of formal education in patients who recovered from COVID-19 ($r = 0.253$, $$p \leq 0.048$$). Correlation relationships between other variables were not statistically significant (Table 2). There was a positive correlation between the MoCA scores and the years of formal education in the control group ($r = 0.303$, $$p \leq 0.015$$). There was a negative correlation between the homocysteine levels and the MoCA scores in the control group (r = –0.260, $$p \leq 0.038$$) (Table 3). Linear regression analysis was performed on the patients who recovered from COVID-19. It is seen that this model is significant (F:15.836; df:5; $p \leq 0.0001$), and the MoCA explanatory value of the variables in the model is $58.6\%$ (R2 = 0.586; $p \leq 0.0001$). It was observed that a 1 µmol/L increase in homocysteine level constituted a risk for a 0.765-point decrease in MOCA scores (Table 4). Linear regression analysis was performed in the control group. It is seen that this model is significant (F:16.527; df:4; $p \leq 0.001$), and the MoCA explanatory value of the variables in the model is $35.3\%$ (R2 = 0.353; $p \leq 0.001$). It was observed that a 1 µmol/L increase in homocysteine level constituted a risk for a 0.594-point decrease in MOCA scores (Table 5). Linear regression analysis was performed in all participants. It is seen that this model is significant (F:28.021; df:4; $p \leq 0.0001$), and the MoCA explanatory value of the variables in the model is $48.1\%$ (R2 = 0.481; $p \leq 0.0001$). It was observed that a 1 µmol/L increase in homocysteine level constituted a risk for a 0.693-point decrease in MOCA scores (Table 6). The ROC analysis demonstrated that homocysteine > 14 had $83.87\%$ sensitivity and $82.81\%$ specificity for predicting recovery from COVID-19 (AUC: 0.906, % 95 CI:0.841, 0.951; $p \leq 0.0001$; cut-off > 14) (Figure 3). ## 4. Discussions In line with the results of our study, we found that homocysteine levels were higher in those who had recovered from COVID-19 than the control group. There was no significant difference in BDI scores between the groups, but those who had recovered from COVID-19 had lower MoCA scores. We found a highly negative correlation between homocysteine levels and MoCA scores in those who had recovered from COVID-19. In the regression analysis, a 1 µmol/L increase in homocysteine level constituted a risk for a 0.652-point decrease in MOCA scores. In recent studies, it has been observed that the level of homocysteine is high in COVID-19 patients. In a retrospective study, homocysteine levels were found to be significantly higher in COVID-19 patients who did not survive [19]. Homocysteine has been proposed as a potential biomarker for cardiovascular risk in COVID-19 patients [20]. As it is known, there is a positive relationship between thromboembolism and homocysteine levels [21]. In our study, homocysteine levels were found to be significantly higher in patients who had recovered from COVID-19. For its viral RNA, SARS-CoV-2 transfers a methyl group from the host cell’s S-adenosylmethionine (SAM) to S-adenosylhomocysteine (SAH) and produces homocysteine through a series of metabolic processes [19]. This mechanism of SARS-CoV-2 that causes homocysteine production may be the reason for the high homocysteine levels that we found in our study in patients who had recovered from COVID-19. In addition, it is known that disruption of enzymes involved in the metabolism of B vitamins increases homocysteine concentration [22]. The reason for the high homocysteine concentration we found in our study may be due to a direct viral effect or a secondary mechanism of action causing these enzymes to deteriorate in those who had recovered from COVID-19. In addition, in our study, there was no relationship between the time that passed after being tested positive for COVID-19 and homocysteine levels. In other words, homocysteine levels remained high in those who had recovered from COVID-19 a long time ago. High homocysteine levels are associated with the MTHFR mutation. The most common single nucleotide polymorphism for MTHFR is the MTHFR C677T polymorphism. This is a common genetic cause of hyperhomocysteinemia [21]. Together with the more severe course of COVID-19 disease, MTHFR C677T polymorphism and hyperhomocysteinemia were thought to be associated [23,24]. MTHFR C677T polymorphism may be the reason why homocysteine levels of patients who had recovered from COVID-19 remained high despite the time that passed in our study. Depressive symptoms have been reported in COVID-19 patients [10]. In a study, it was observed that patients hospitalized due to COVID-19 had increased depression rates in the next one-month follow-up [25]. A review of the post-COVID-19 syndrome concluded that there might be clinically significant depressive symptoms, but this does not mean that people with post-COVID-19 syndrome will have a higher depression rate than the general population [26]. In our study, there was no significant difference between the depression levels of patients who had recovered from COVID-19 and the control group. This result may be related to the time that passed between the active infection period of those who had recovered from COVID-19 in the sample of our study and the time when BDI was applied, which made us think that COVID-19 may not cause depression in the long term. Cognitive impairment can be described as a common symptom of COVID-19 infection. Brain fog with cognitive decline has been found to be significantly associated with ‘Long COVID’ [27]. Many factors that have not yet been clarified have been proposed as the cause of cognitive decline in those who have recovered from COVID-19. These factors include hypoxia, respiratory failure, drugs used, a direct viral infection of the central nervous system, inflammation, endothelial damage, and cerebrovascular events [28]. In a previous study, cognitive impairments were seen in cases of those aged 64 years on average recovering from severe COVID-19 [29]. In another study, cognitive impairments were observed in cognitive function assessments of patients hospitalized for COVID-19 infection before discharge [30]. In our study, which included patients with an average age of 39 years and who had recovered from COVID-19 infection long before those patients in the previous studies, the MoCA scores of those who had recovered from COVID-19 were considerably lower than the control group. This result supported the negative effect of COVID-19 on cognitive functions. Those who were studied longer and those who were younger had higher MoCA scores. This is also an expected result because better cognitive performance is observed in those with younger ages and higher education levels [31]. In addition, MoCA scores were not associated with the time that passed after being tested positive for COVID-19. Those who recovered from COVID-19 longer ago also continued to have low MoCA scores. This result showed that cognitive impairment might continue even after a long period since the onset of the COVID-19 infection. Hyperhomocysteinemia is a risk factor for neurodegenerative diseases. Many studies show that homocysteine plays a role in cognitive impairment, memory decline, and brain damage [32]. In our study, in which we think that prolonged cognitive impairment may be related to homocysteine, there was a highly negative correlation between homocysteine levels and MoCA scores in patients who had recovered from COVID-19. Those with higher homocysteine levels had lower MoCA scores, meaning they had more cognitive impairment. In addition, in the regression analysis, we found that the increase in homocysteine level poses a risk for the decrease in MOCA scores. There are some possible reasons for these results. Homocysteine is neurotoxic and may compromise the integrity of the blood–brain barrier [33]. Apart from that, homocysteine initiates a proinflammatory process and causes neurological dysfunction through oxidative stress. Oxidative stress caused by homocysteine can be caused by an increase in reactive oxygen species, inactivation of the nitric oxide synthase pathway, and lipid peroxidation, which forms in the brain by blocking the NMDA receptor [32]. Hyperhomocysteinemia is associated with thromboembolism and vascular damage [21]. Therefore, homocysteine may cause cognitive impairment by causing cerebrovascular events. Based on the results of our study, homocysteine, which was previously seen as a risk factor for neurodegenerative diseases and cognitive impairment, can be considered as one of the causes of cognitive impairment in the long term in patients who recovered from COVID-19. Treatment of hyperhomocysteinemia varies according to the underlying cause. Despite different underlying causes, treatments with vitamin supplements containing B6, B12, and folic acid can be effective in reducing homocysteine levels [22]. Although the cause of high homocysteine levels in patients with COVID-19 is not fully known, it may be beneficial to use B vitamins in the treatment to prevent cognitive functions by reducing homocysteine levels in these patients in the long term. There were some limitations in our study. The number of participants in the study was small. In order to generalize our results to society, new studies with a larger number of participants should be carried out. There were no detailed data in our study on the severity of infection of those who had recovered from COVID-19. New studies can be planned in which patients who have experienced mild, moderate, and severe COVID-19 infection are examined separately. In addition, while cognitive impairments are measured, impairments in different cognitive areas can be examined in more detail in new studies. It is not known which of the factors that may cause high homocysteine concentration, such as direct viral effect, gene mutation, and impaired vitamin metabolism, have an effect on patients with COVID-19. We recommend that further studies be conducted to elucidate the underlying causes. Thus, in patients with COVID-19, early interventions to high homocysteine levels will prevent cognitive disorders that may persist in the long term. The MoCA scale has some limitations. In addition to showing the significant and moderate correlations of MoCA subtest scores with the cognitive areas that are intended to be evaluated, the accuracy rate is poor in predicting cognitive impairment in their specific areas. Unlike neuropsychological test scores, the fact that MoCA subtest scores are not adjusted for age or education level may cause poor validity estimates. Though the MoCA subtest scores show cognitive impairment with a slightly above-average accuracy, they remain below the expected level for diagnostic screening in terms of health care. Physicians who want to minimize false positives or negatives may prefer to interpret the performance of a particular screening test based on its high specificity or high sensitivity in a cognitive area. However, in this case, it will not guarantee an accurate identification of cognitively impaired individuals and will not completely reflect the general cognitive ability model. Therefore, it is recommended to dispatch this information to a neuropsychological evaluation in cases where it is necessary for diagnostic purposes [34]. Many modern methods are available for detecting and diagnosing diseases. Combining the homocysteine level we have detected in the blood with modern methods in new studies may be an important scientific development [35,36,37,38]. ## 5. Conclusions In conclusion, homocysteine levels were higher and MoCA scores were lower in our study for those who had recovered from COVID-19. However, there was no significant difference in terms of BDI. As a very important finding, there was a high negative correlation between homocysteine and MoCA scores, and an increase in homocysteine level poses a risk for a decrease in MOCA scores. In our study, homocysteine levels remained high, and cognitive impairment continued despite the time that passed after COVID-19 positivity. We think that our study provides important data for ‘Long COVID’. 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--- title: Green Synthesis, Characterization and Bioactivity of Mangifera indica Seed-Wrapped Zinc Oxide Nanoparticles authors: - Shanmugam Rajeshkumar - Royapuram Parthasarathy Parameswari - Dayalan Sandhiya - Khalid A. Al-Ghanim - Marcello Nicoletti - Marimuthu Govindarajan journal: Molecules year: 2023 pmcid: PMC10056584 doi: 10.3390/molecules28062818 license: CC BY 4.0 --- # Green Synthesis, Characterization and Bioactivity of Mangifera indica Seed-Wrapped Zinc Oxide Nanoparticles ## Abstract In the realm of nanoparticles, metal-based nanoparticles have traditionally been regarded as the pioneering category. Compared to other nanoparticles, zinc oxide nanoparticles have several advantages, including optical and biological properties, which provide them a significant competitive advantage in clinical and biological applications. In the current investigation, we used an aqueous Mangifera indica seed extract to synthesize nanoparticles of zinc oxide (ZnO NPs). UV-Vis spectroscopy, Fourier transform infrared spectroscopy analysis, atomic force spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy were used to characterize the synthesized ZnO NPs. The nanoparticles were assessed for their potential to inhibit bacterial growth and protect cells from free radical damage. According to the current study’s findings, zinc oxide nanoparticles that had been modified with the aid of mango seeds were very efficient in preventing the development of the tested bacteria and were also powerful antioxidants. ## 1. Introduction Nanotechnology has been gaining attention recently as a potential platform for future growth in several fields. Nanotechnology has drawn significant attention in the healthcare, engineering and food industries by offering novel prospects in the respective fields. In particular, theranostics, a cutting-edge combination system of therapeutics and diagnostics, utilize nanotechnology principles for target-specific drug delivery and enhanced bioavailability of active pharmaceutical ingredients [1,2]. The field of nanotechnology deals with various synthesis methods, particle size reformations and structural variations of nanoparticles. Nanoparticles are nanosized materials ranging in size <100 nm with high thermal stability, high surface-to-volume ratio, high electrical, mechanical, optical as well as magnetic properties [3]. In the last decade, the use of nanoparticles has been the most significant archetype advancement in engineering, medicine and technology [4]. Nanoparticles may be classified as organic and inorganic nanoparticles. While metals and metal-derived oxide nanoparticles come under the inorganic nanoparticles classification, organic nanoparticles include solid lipid nanoparticles, polymeric nanoparticles, lipid-based nanocarriers, liposomes and carbon-based nanomaterials [5]. Metal nanoparticles are promise for site-specific drug administration, clinical diagnostics, bio-imaging, dental implants, and biomedicine due to their selectivity, sensitivity, optical, and electrical capabilities [6,7]. The method used to synthesize metal nanoparticles is an important key factor. Synthesis of metal nanoparticles may be accomplished using a wide variety of physical and chemical methods, including sol-gel, chemical reduction, hydrothermal, laser ablation, ion sputtering, etc., [ 8,9]. However, these methods encounter many downsides, including cost-expensive, instrumentations, skilled labour and environmental toxicity. Therefore, the green synthesis method has become an optimal method of choice for nanoparticles, wherein plants and microorganisms are used [9,10]. Furthermore, the green synthesis method of nanoparticle preparation has been considered eco-friendly and safer due to its stabilizing and reducing potentials [10]. Many different metals, including silver (Ag) [10], gold (Au) [11], copper (Cu) [12] and trace elements [13], have been used as reduction and coating agents for the fabrication of nanoparticles. Nevertheless, Ag, Au and Cu nanoparticles have been reported for their toxicity and consequent limit in clinical applications [10,11,12]. Zinc oxide (ZnO) is a rare, inorganic metallic oxide that has received significant attention as a safe, biocompatible and economical material. US FDA has approved ZnO as the safest metal oxide [14]. Zinc is best recognized for maintaining protein and nucleic acid interactions in cells and tissues. As compared to other physiologic metals like iron, cobalt, and manganese, ZnO’s chemical stability is far superior [15]. Zinc oxide nanoparticles (ZnO NPs) possess a wide range of engineering applications, such as catalysis, a piezoelectric device, pigments, chemical sensors, bio-molecular detection, diagnostic, cosmetic material and especially for UV protection [16,17,18,19,20,21,22]. Additionally, ZnO NPs have been demonstrated to possess anticancer, antimicrobial, anti-inflammatory and antidiabetic properties [23,24,25,26]. As ZnO is considered safe and exhibits significant antimicrobial properties, it has the potential against infectious diseases [26]. On exposure to metal nanoparticles, the bacterial cell membrane undergoes depolarization due to adsorption, resulting in permeability changes in the bacterial cell wall. Further, the changes lead to the free radical formation and thus cause membrane damage resulting in antibacterial or bactericidal activity [27,28]. Green nanotechnology has originated from green chemistry, which seeks to synthesize metal nanoparticles using medicinal plants. Medicinal plants are reliable sources of several chemical components needed to synthesize metallic nanoparticles [29], viz. polyphenols, flavonoids, alkaloids, terpenes, etc., which act as potent agents to reduce the metal from ionic state into its respective oxide forms during the process [8,30]. Phytochemicals found in medicinal plants have replaced the reducing agents’ sodium citrate, ascorbate and sodium borohydride in the chemical manufacturing technique of nanoparticles. As a result, the potentially harmful effects of chemical-reducing agents on the environment will be mitigated [29,31]. As a result, scientists are increasingly interested in developing methods for creating metal-based nanoparticles from plant extracts that are benign to the surrounding ecosystem. The biosynthesis of ZnO NPs has been shown utilizing several plant extracts, including Lobelia leschenaultiana [32], Agathos mabetulina [33], Laurus nobilis [34], Moringa olifera [35], Acalypha indica [36], Aspalathus linearis [37], Carica papaya [38], green tea leaves [39], Euphorbia jatropa latex [40], Andrographis paniculata [41], Chamaecostus cuspidatus [42]. Compared to the above sources, the advantage of preparing ZnO NPs using mango seed extracts has been cost-effectiveness, as rinds and seeds are considered waste materials. However, the mango seed powder contains protein, oil, crude fiber and carbohydrate and is also enriched in potassium, magnesium, phosphorus, calcium and sodium. Non-essential amino acids such as arginine and glutamic acid and essential amino acids such as valine and phenylalanine are also present. In addition, it has other vitamins, including different Vitamin B (1, 2, 6 and 12), Vitamin C and Vitamin K. The nutritious value of mango seed powder is much higher [43]. Mangoes were used in the synthesis of zinc oxide nanoparticles, an economically and ecologically sound breakthrough [44,45]. Hence, our work synthesized ZnO NPs using mango seed extract. The structure and shape of synthesized ZnO NPs were investigated using a variety of techniques, ultraviolet-visible spectroscopy (UV-vis.), Fourier transform infrared (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and including atomic force microscopy (AFM). The antibacterial and antioxidant activities of ZnO NPs that were produced through green synthesis have also been investigated. ## 2.1. UV-Visible Spectroscopy As shown in Figure 1, ZnO NPs prepared with mango seeds powder were recorded using UV-Visible spectroscopy. UV-Visible spectra measurements were carried out at various time intervals of 4, 12 and 24 h. The absorbance of ZnO NPs rose gradually over 23 h, as evidenced by a linear relationship between the first value and subsequent readings performed at 1 h 13 min, 3 h 35 min and 19 h 12 min. The formation of ZnO NPs was confirmed by the observation of maximal absorbance at 480 nm, which is the typical wavelength for this formation. The observed UV spectrum around 450 nm indicates that in the reduction process, the semiconducting property of ZnO has not been lost [46]. ## 2.2. FT-IR Spectroscopy Characterization and an explanation of the functional groups involved in the synthesis, reduction, and stabilization of ZnO NPs were accomplished with the assistance of FT-IR spectroscopy. As shown in Figure 2, ZnO NPs may be mediated by extracts from mango seeds. Specifically, a prominent peak was detected at 3336.85 cm−1, analogous to the stretching vibration of −OH and –NH2 groups. These functional groups could be produced from the water and mango seed extract [8]. Further, another broad peak was observed at 2924.09 cm−1, corresponding to the carboxylic group’s O-H stretch. This peak can be seen in the spectrum. The peak at 1658.78 cm−1, which coincides with the C = O stretch that the ketone group has. The N = O bend of nitro groups is related to a peak located at 1352.10 cm−1, and peaks at 1203.58 cm−1 and 1020.34 cm−1, are associated with the C-O stretch of ester and ether groups, respectively. The peaks associated with aromatic groups are located at 825.53 cm−1, 754.17 cm−1, and 416.62 cm−1. Although numerous studies have reported using plant extracts for metal nanoparticle synthesis, the mechanism of bio-reduction is still elusive. On the other hand, the phytochemicals that are present in the plant source are the ones that are thought to be responsible for the nanoparticles’ reduction and stabilization [14,46]. In agreement, we speculate in the present study that the phytochemicals, such as polyphenols, flavonoids, and carotenes found in the seed extract, might be deeply involved in the bio-reduction of the nanoparticles [42,43]. ## 2.3. Scanning and Transmission Electron Microscope (SEM) The SEM was used in order to investigate the ZnO NPs’ morphology. Mango seeds mediated ZnO NPs surface morphology is seen in Figure 3. ZnO NPs powder was kept in a carbon-coated copper grid. The SEM image (Figure 3a) shows the presence of spherical, cylindrical, rectangle and triangle shape clustered nanoparticles with narrow size distribution. The images were captured at different magnifications of 27,000×, 44,000×, 49,000×, and 66,000×. Nanoparticles agglomerated, perhaps owing to mango seed extract phytoconstituents. The size of the nanoparticle was found to be approximately between 40 to 70 nm. Figure 3b shows TEM analysis of green synthesized ZnO NPs to confirm particle size. TEM confirmed that M. indica seed extract-mediated ZnO NPs were 40–60 nm. ## 2.4. EDAX Analysis Figure 4 shows the ZnO NPs’ EDAX elemental composition. Here, the seed-mediated ZnO NPs shows signals of Zinc, Oxygen and Carbon, as shown in the tabular column (Table 1). EDAX analysis also helps to identify the presence of any other compounds. It displays the energy in KeV. Furthermore, the EDAX spectra also help in confirming the pureness of the ZnO NPs synthesized from mango seed extract. ## 2.5. XRD Study of Generated ZnO NPs The XRD pattern of the synthesized ZnO NPs is shown in Figure 5. ZnO NPs were synthesized from mango seed extract, and their structure was characterized by XRD analysis. Analysis of ZnO NPs by XRD showed six different peaks between 10 and 90° in the 2θ value. The obtained ZnO NPs pattern was consistent with the XRD pattern published by the Joint Committee on Powdered Diffraction Standards (JCPDS file no: 85-1355). Meanwhile, peaks were shown with 2θ values of 31.4°, 34.0°, 35.9°, 47.1°, 56.2°, 62.5° and 68.7° which corresponds to [100], [002], [101], [102], [110], [103] and [200] respectively. ZnO NPs synthesized from Andrographis paniculate, Chamaecostus cuspidatus and Agathosma betulina showed an identical XRD pattern [33,41,42]. ## 2.6. Atomic Force Microscopy (AFM) In Figure 6, the 3D images obtained by the AFM are reported. The outcome provided 2D and 3D imaging of biosynthesized ZnO NPs, revealing their average size to be 55 nm and their spherical form (Figure 6). ## 2.7. Antibacterial Activity The antibacterial activity of ZnO NPs that had been aided by mango seed extract against Bacillus subtilis and E. coli was studied (Figure 7). Previous research indicated that ZnO NPs demonstrated strong antibacterial action against a wide variety of microorganisms [14,45,46,47]. In agreement, the present study results also exhibited a significant inhibitory effect against the tested pathogens, compared with standard antibiotics. Earlier studies have also shown the ZnO NPs bactericidal activity, indicating that the nanoparticle can completely kill the bacteria [48]. Nevertheless, our study showed that ZnO NPs prepared from mango seed extract possess bacteriostatic properties. Both the tested organisms exhibited a minimum inhibitory concentration (MIC) at 10 μL. The inhibitory effect is indirectly proportional to the size of the nanoparticle. The smaller size of the nanoparticles the greater will be the inhibitory effect [49]. ## 2.8. Antioxidant Activity The DPPH experiment assessed ZnO NPs’ antioxidant capabilities. The experimental methodology used 0.1 mM DPPH solution to examine ZnO NPs at 10, 20, 30, 40, and 50 µg (Figure 8). After mixing the solutions together, they were left to sit for 30 min at room temperature and out of the light. After that, absorbance and optical density were both obtained at a wavelength of 517 nm. The reference standard used in this assay was Ascorbic acid. According to the results, the level of DPPH inhibition dramatically increased whenever there was a higher concentration of ZnO NPs. The results that were acquired revealed that the ZnO NPs that were produced had powerful antioxidant capabilities. ## 3.1. Mango Seed Extract Preparation Raw mangoes were purchased and used as a source for harvesting mango seeds. The mango’s endocarp part (seed part) was cut into smaller pieces and shade dried for 5–7 days in Nanobiomedicine Lab, Saveetha Dental College and Hospitals, India. The shade-dried mango seed was ground into coarsely powdered form. A conical flask containing 100 mL of distilled water and 1 g of powdered mango seed. It was continuously stirred using a magnetic stirrer up to 600 rpm. After that, it was placed in the heating mantle at 70 °C and heated for 15–20 min until the hard powder became soft and mushy. Whatman No.1 filter paper was used to filter the fluid. Figure 9 depicts the steps required to make mango seed aqueous extract. ## 3.2. Zinc Oxide Nanoparticle Synthesis A 10 mM Zinc nitrate solution was added to 75 mL of distilled water. To that, 25 mL of the ready-to-use seed extract was added, and everything was mixed well. At 30 °C, the solution combination was maintained in an orbital shaker for 15 h. ZnO NPs formed when the solution’s colour changed from white to dark brown after 15 h (Figure 10). The solution was then centrifuged at 8000 rpm for 20 min. After the centrifugation process, the supernatant was discarded, and the pellet was washed twice with deionized water in order to eliminate any remaining residual contaminants. After that, the pellet was extracted from the centrifuge and kept in an oven with heated air at a temperature of 80 °C. Upon drying, the pellet was ground into a powder. This ZnO NPs powder was used for further SEM, EDX, AFM, XRD and FT-IR investigation. ## 3.3. ZnO NPs Characterization In order to evaluate the UV absorption spectra of the ZnO NPs that were created, a Shimadzu UV spectrophotometer was used to measure the spectrum from 300 to 800 nm. This was done so that the spectra could be used to calculate the UV absorption spectra. An FT-IR spectrum was recorded with a BRUKER alpha 2. In order to determine the nanoparticles’ X-ray diffraction (XRD) pattern, XPERT PRO, PANalytical XRD was used. Using a JEOL-JSM IT 800 model, we performed the SEM-EDAX analysis. Atomic force microscopy (AFM) was used in order to investigate the synthesized ZnO NPs for their size and surface roughness (Nanosuf AGG Switzerland). ## 3.4. Antioxidant Activity According to the findings of Koleva et al. [ 50], the 2,2-diphenyl-1-picrylhydrazyl (DPPH) test was used to evaluate the ZnO NPs’ capacity to scavenge free radicals. A DPPH solution of 150 µM in 100 mL of methanol was made. For this experiment, 190 µL of DPPH solution was combined with 10 µL of the synthesized ZnO NPs and varying doses of standard ascorbic acid (10–50 µg/mL). The liquid was let to rest at room temperature and in the dark for 30 min. Instead of a sample or standard, 200 µL of methanol was used in the control blank. The maximum absorbance was found to be at 517 nm. ZnO NPs’ ability to scavenge DPPH radicals was calculated using the following formula. % free radical scavenging effect = [(Control absorbance − Test absorbance)/Control absorbance] × 100 ## 3.5. Antibacterial Activity Gram-positive B. subtilis and Gram-negative E. coli strains were used in an experiment to test the antibacterial activity of synthesized ZnO NPs. The experiment was conducted using the agar well diffusion method [41]. The nutritional broth was contaminated with the clinical pathogens and cultivated for a whole day at 37 °C. Cotton swabs made from sterile material were used to apply a suspension of the organisms to be tested on Mueller-Hinton agar (MHA) plates that had been prepared using aseptic methods. With a clean borer, four holes were drilled in each MHA plate. Then, 50 μL of precursor, 50 μL of ZnO NPs and 15 μL of ampicillin were introduced into the bored wells. Afterward, plates were incubated at 37 °C for 12 h. A distinct zone of inhibition surrounded each well after incubation, which could be measured with the ruler. ## 3.6. Statistical Analysis The value of antioxidant activity was expressed in terms of Mean ± SE for three independent experiments. One-Way ANOVA followed by Tukey post hoc multiple comparison tests were performed to compare and evaluate the data with p ≤ 0.05 considered to be significant. ## 4. Discussion Green nanotechnology employs medicinal plants for the synthesis of metal as well as other nanomaterials that have the potential to be used in the identification and treatment of various diseases/disorders. Metal nanoparticles synthesized from medicinal plants, microbes and other food sources have been shown to be safe and economical. However, environmental sustainability is a concern due to the load on global food security and the scarcity of natural resources [51]. In this context, researchers have initiated to utilize the biowaste materials from various plant and fruit sources to synthesize metal-based and metallic oxide nanoparticles. Several ways for generating ZnO NPs utilizing various plant extracts have been established by researchers [32,33,34,35,36,37,38,39,40,41,42]. We report here on the large - scale production of ZnO NPs by using an aqueous extract of mango seed powder as the raw material. The ZnO NPs were produced by combining a zinc nitrate solution combination with an aqueous extract of mango seeds as the phytoconstituents throughout the synthesis process. The solution’s colour dramatically altered after being incubated for a certain amount of time. The shift in hue is attributed to the production of ZnO NPs and the resulting surface plasmon resonance from the collective excitation of free electrons in the NPs. The qualitative phytoconstituent analysis of aqueous mango seed extract showed the presence of polyphenols, tannins, flavonoids and terpenoids, which might be attributed as responsible for biological properties. These phytochemicals found in the mango seed aqueous extract may also be liable for reducing the Zinc ions to zinc oxide and, thus, nanoparticle preparation. In this work, we used green synthesis, an environmentally friendly approach to create ZnO NPs from fruit biowaste. A zinc nitrate solution was combined with an aqueous extract of mango seeds in order to produce ZnO NPs. This method was determined to be cheap, quick and safe for the environment. In a previous investigation, ZnO NPs were synthesized using the seeds of the longan fruit. The seed was found to be enriched in catechin and flavonoids [52]. The current investigation also employed Mango seed extract to produce ZnO NPs. The findings demonstrated the emergence of ZnO NPs. ZnO NPs formation was originally seen as a colour shift in the metal solution upon addition of mango seed extract; this observation was subsequently confirmed using further physicochemical techniques. Absorption spectra at 480 nm, typical of ZnO NPs, were observed through UV-Vis spectroscopy. Previous research also reported the UV-Visible absorption peak up to 381 nm using Pomegranate extract [53]. The FT-IR analysis demonstrates the presence of carboxylic, ketone, nitro, ester and ether groups. The XRD analysis predicts seven prominent peaks. The SEM data indicate the presence of spherical and cylindrical nanoparticles between the size range of 40–70 nm and a few rectangular and triangular particles. Recently, a study by Rini et al. [ 2021] reported that ZnO NPs synthesized using Ananas comosus peel extract had a spherical shape [54]. The findings of EDAX indicate the existence of carbon, oxygen and zinc. The morphology of the nanoparticle’s surface is studied using an AFM. In addition, data on the size and surface roughness of the produced nanoparticles are provided. The TEM analysis identified the nanoparticle’s shape as spherical and the size ranging from 40–60 nm. It was reported in a prior research that the average size of the ZnO NPs that were synthesized using Azadirachta indica extract was 9.6–25.5 nm, and that their form was spherical [55]. The AFM may be used to take 3D images without causing any damage. The AFM data indicated that the nanoparticles were present in the spherical form [56]. Furthermore, the mango seed extract-assisted ZnO NPs have been demonstrated to possess potent antibacterial and antioxidant properties, as observed from agar well diffusion assay and DPPH free radical scavenging assay. Even while research has shown that ZnO NPs has a powerful antioxidant capacity, the method by which this occurs is still a mystery. During the green synthesis of nanoparticles, the functional groups of the phytoconstituents have been shown to form a linkage with the ZnO. This linkage may improve the potential of ZnO NPs in free radical scavenging effect [57]. The high redox potential of the ZnO breaks the water molecules into hydroxyl and hydrogen radicals, stabilizing the DPPH free radicals and inhibiting the DPPH effect [58]. According to the findings, there was a considerable increase in the inhibitory capacity of ZnO NPs against the DPPH free radicals that was dose-dependent. It may be deduced from this that the ZnO NPs that were manufactured using the mango seed extract exhibited a substantial amount of antioxidant activity. Furthermore, as was previously indicated, the phytoconstituents’ functional groups adsorbing onto the nanoparticles’ surfaces might be a contributing cause to their inhibitory action on free radicals [59]. One of the most significant issues in the medical field is the incorrect and excessive use of antibiotics, which results in antimicrobial resistance. The persistent emergence of bacterial resistance has raised the need for novel antibiotics. Metal nanoparticles, which have been reported to have potent antibacterial action in a majority of investigations, are considered among the promising as well as novel antibiotic agents [27,28]. Production of new biomedical implants involves the use of metallic nanoparticles in order to prevent any bacterial infections [59]. As a result, one of our goals was to determine whether or not the ZnO NPs that we had manufactured using mango seed extract have any antibacterial properties. In the present experiment, it was discovered that the ZnO NPs that were artificially manufactured have a substantial antibacterial activity. ZnO NPs have been shown to inhibit bacterial growth via a variety of distinct methods, which has led to its use in antibacterial applications. According to a number of studies, metal nanoparticles have the ability to physically interact with the cell wall of bacteria, as well as with sub-cellular components [60]. On exposure to metal nanoparticles, the bacteria’s cell wall undergoes membrane damage due to the adsorption of metal oxide on the cell wall. The negatively charged surface of the bacteria stimulates electrostatic interactions between strong positive charges such as ZnO (>9) with high isoelectric points [58]. This results in the membrane depolarization effect, which alters the cell wall’s permeability, allowing an easier penetration of the nanoparticles and producing reactive oxygen species (ROS) inside the bacterial cell. The increased ROS production eventually causes oxidative stress and elevates lipid peroxides, thereby degradation of macromolecules and resulting in cell death [61,62]. Ion leaching is another proposed mechanism for the antibacterial effect of metal nanoparticles. The pH and rate of dissolution of ZnO NPs have also been shown to cause inhibition in bacterial cell growth [28,63]. Further, it was also reported that spherical-shaped nanoparticles could easily penetrate the cell wall of the bacteria, thus resulting in cellular membrane damage [48]. ## 5. Conclusions In conclusion, our findings have provided new insights into the biomedical applications of biowastes, such as seeds or peels of medicinal plants, in the preparation of metal nanoparticles. This work showed that ZnO NPs synthesized with the aqueous mango seed were efficient against tested clinical pathogens and exhibited considerable antioxidant activity. The seed-mediated nanoparticles show very good antimicrobial potential against Gram-negative and Gram-positive bacterial isolates, viz., B. subtilis and E. coli. Therefore, it is possible to propose that this technique of synthesizing nanoparticles by employing plant extracts may assist in discovering new unique active pharmaceutical components and heal the sickness, considering the high nutritional content and cost-effectiveness. However, before clinical investigations can be carried out, it is necessary to conduct in vitro toxicity tests using human cells as well as in vitro and vivo models. ## References 1. Gomes Marin J.F., Nunes R.F., Coutinho A.M., Zaniboni E.C., Costa L.B., Barbosa F.G., Queiroz M.A., Cerri G.G., Buchpiguel C.A.. 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--- title: 'Susceptibility of Diabetic Patients to COVID-19 Infections: Clinico-Hematological and Complications Analysis' authors: - Banan Atwah - Mohammad Shahid Iqbal - Saeed Kabrah - Ahmed Kabrah - Saad Alghamdi - Aisha Tabassum - Mohammed A. Baghdadi - Hissah Alzahrani journal: Vaccines year: 2023 pmcid: PMC10056589 doi: 10.3390/vaccines11030561 license: CC BY 4.0 --- # Susceptibility of Diabetic Patients to COVID-19 Infections: Clinico-Hematological and Complications Analysis ## Abstract Background: Coronavirus disease 2019 has become a global health threat resulting in a catastrophic spread and more than 3.8 million deaths worldwide. It has been suggested that there is a negative influence of diabetes mellites (DM), which is a complex chronic disease, on COVID-19 severe outcomes. Other factors in diabetic patients may also contribute to COVID-19 disease outcomes, such as older age, obesity, hyperglycaemia, hypertension, and other chronic conditions. Methods: A cohort study was conducted on the demographics, clinical information, and laboratory findings of the hospitalised COVID-19 with DM and non-DM patients were obtained from the medical records in King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Results: Among the study population, 108 patients had DM, and 433 were non-DM patients. Patients with DM were more likely to present symptoms such as fever ($50.48\%$), anorexia ($19.51\%$), dry cough ($47.96\%$), shortness of breath ($35.29\%$), chest pain ($16.49\%$), and other symptoms. There was a significant decrease in the mean of haematological and biochemical parameters, such as haemoglobin, calcium, and alkaline phosphate in people with diabetes compared to non-diabetics and a considerable increase in other parameters, such as glucose, potassium, and cardiac troponin. Conclusions: According to the findings of this study, patients who have diabetes have a greater risk of developing more severe symptoms associated with COVID-19 disease. This could result in more patients being admitted to the intensive care unit as well as higher mortality rates. ## 1. Introduction The first severe acute respiratory syndrome (SARS)-associated coronavirus was discovered in China in February 2003, during the SARS outbreak, which then spread to four other countries [1]. SARS can be transmitted from an infected person via aerosols or droplets, which can be inhaled directly or indirectly through contact with affected surfaces [2]. At the end of December 2019, there was evidence of pneumonia cases caused by an unknown causative agent in China (Wuhan city) [3]. It was confirmed as the 2019 novel Coronavirus on 7 January 2020, and is now known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) [4]. On 6 February 2020, the Saudi government implemented early measures and restrictions to prevent the introduction and spread of SARS-CoV-2 in the country. The World Health Organization (WHO) declared coronavirus disease 2019 on 11 February 2020 as the name of the epidemic disease caused by SARS-CoV-2 (COVID-19) [4]. On 2 March 2020, the first case of COVID-19 was reported in the Kingdom of Saudi Arabia (KSA) [5]. Since then, despite the early restrictions, more cases have been reported, with 8200 cases and 92 deaths recorded by 18 April 2020. Although SARS-CoV-2 mostly affects the lungs, it can also impact the brain, heart, and digestive tract. It has been noted that $75\%$ of COVID-19 patients who are hospitalised have at least one COVID-19-linked comorbidity. Additionally, SARS-CoV-2 causes consequences related to hypercoagulability such as gangrene, stroke, pulmonary embolism, and other related concerns [6] In extremely or critically ill individuals with comorbidities, neurological problems are common [7]. The most common cardiovascular complications described in COVID-19 patients are increased risk for myocardial infarction, fulminant myocarditis rapidly evolving with depressed systolic left ventricle function, arrhythmias, venous thromboembolism, and cardiomyopathies mimicking STEMI presentations [8]. The risk factors associated with COVID-19 infection prognosis and mortality have been documented. They include older age and underlying medical conditions such as hypertension, diabetes mellitus (DM), and coronary heart disease [9,10,11]. Impairments in insulin secretion/function and glucose metabolism are at the heart of type II diabetes mellitus (T2DM), a chronic metabolic disorder with a worldwide impact [12]. Alguwaihes A et al. suggested that the number of hospitalised COVID-19 positive diabetic patients is greater than the number of non-diabetic patients by 2:1 [13]. Similarly, the mortality rates are higher in COVID-19 patients with DM than in patients without DM, which may be due to severe clinical symptoms in the diabetic patients [13,14]. However, it has been demonstrated that DM is not the only factor associated with hospital deaths in COVID-19-infected patients [15]. Although DM is more frequent in COVID-19 patients, a study in the United States demonstrated a lack of evidence of a direct link between diabetes in these patients and the mortality [16]. Therefore, other potential factors, such as other chronic diseases and cardiometabolic dysfunctions in diabetic patients, may contribute to a worse COVID-19 outcome [17,18]. It was also suggested that severe vitamin D deficiency, elevated creatinine, smoking, and the use of β-blockers might also influence COVID-19 severity and mortality rates [13]. The haematological parameters in COVID-19-infected patients with and without type 2 DM were investigated [19]. It was demonstrated that white blood cells (WBCs) and neutrophil counts in the diabetic patient group were more significant than in the non-diabetic group. In contrast, there was no significant difference in the red blood cell (RBC) count between the two groups [19]. In this present study, we focused on exploring the association of DM and COVID-19 infection in terms of clinical presentation, laboratory parameters, and its effect on the disease outcome. ## 2.1. Study Design and Study Population A retrospective cohort analysis was conducted on hospitalised confirmed COVID-19 patients admitted to King Faisal Specialist Hospital and Research Centre (KFSH&RC) Riyadh and Jeddah, Saudi Arabia, between 1 March 2020 and 31 March 2021. All confirmed COVID-19 patients with complete laboratory investigation are included in the current study. ## 2.2. Data Collection Patients’ data were obtained from the information system for patients diagnosed according to the WHO COVID-19 guidelines with a positive nasal and pharyngeal swab real-time reverse transcriptase-polymerase chain reaction. Data contained demographic information, clinical information, and laboratory findings. ## 2.3. Statistical Analysis The data analysis was conducted using SAS programming language. Descriptive analysis of frequency, percentage, and mean with standard deviation were performed for categorical and continuous variables. Following tests for normality (Shapiro–Wilk test), variables that did not fulfil the normality assumption were compared using the Wilcoxon test. Chi-squared and Fisher tests were performed to evaluate the differences in the prevalence of categorical variables. A p-value of 0.05 was used to indicate a statistically significant result. ## 3.1. Demographic and Clinical Data The current study included 541 patients, out of which, 108 ($19.96\%$) were diabetic and 433 ($80.04\%$) were non-diabetic. There were 252 ($47.01\%$) female and 289 ($53.41\%$) male patients. The mean ages ± SD for the DM and non-DM groups were 60.3 ± 14.23 and 37.4 ± 18.8 years respectively. The age variation was significant between the two groups as diabetics were much older than the non-diabetics (p = <0.0001). The body mass index was significantly higher in diabetics in contrast to non-diabetics ($$p \leq 0.0002$$). As this was a hospital-based study, the patients included corresponded to categories 3 to 7 of the WHO ordinal scale. Based on the available data, we could not differentiate them further separately under each scale of the WHO. On admission, when compared with the non-diabetics, the diabetics were more likely to present with symptoms including fever ($50.48\%$, p = <0.000), anorexia ($19.51\%$, $$p \leq 0.002$$), dry cough ($47.96\%$, $$p \leq 0.024$$), shortness of breath ($35.29\%$, $$p \leq 0.002$$), chest pain ($16.49\%$, $$p \leq 0.010$$), haemoptysis ($4.60\%$, $$p \leq 0.023$$), confusion ($6.98\%$, $$p \leq 0.000$$), loss of consciousness ($4.30\%$, $$p \leq 0.024$$), vomiting ($16.67\%$, $$p \leq 0.035$$), and diarrhoea ($25.77\%$, $$p \leq 0.012$$) as shown in Table 1. ## 3.2. Haematological and Biochemical Findings Table 2 describes the laboratory data from DM and non-DM cohorts. In diabetic patients, when compared with the non-diabetics, there was a statistical significant decreases in the mean haemoglobin (p = <0.0001), haematocrit (p = <0.0001), serum sodium (p = <0.0001), bicarbonate ($$p \leq 0.02$$), calcium (p = <0.0001), and alkaline phosphate ($$p \leq 0.06$$) values, whereas there was a significant increase in prothrombin time ($$p \leq 0.09$$), partial thromboplastin time ($$p \leq 0.088$$), serum urea (p = <0.0001), creatinine (p = <0.0001), glucose (p = <0.0001), alanine aminotransferase ($$p \leq 0.0023$$), aspartate amino transferase (p = <0.0001), PO2 ($$p \leq 0.0031$$), PO2/FiO2 ($$p \leq 0.0587$$), lactate ($$p \leq 0.034$$), ferritin (p = <0.0001), potassium ($$p \leq 0.0061$$), total bilirubin ($$p \leq 0.0014$$), creatine kinase ($$p \leq 0.0169$$), and cardiac troponin (p = <0.0001). There was no significant variation for other lab variables between the two groups. Out of the 108 diabetics, 81 ($75\%$) developed severe disease and complications, whereas, among the non-diabetics, 208 ($48\%$) developed severe disease. A significant finding was increased ICU admissions and the necessity for mechanical ventilation among the DM group compared to the non-DM group. ( Table 3). Furthermore, among the diabetics, 10 ($3.5\%$) developed renal failure and 11 ($3.9\%$) developed sepsis. In contrast, only 6 ($2.1\%$) and 6 ($2.1\%$) for non-diabetics, respectively, developed these symptoms, indicating that the clinical outcome in diabetic COVID-19 patients is worse in contrast to that of non-diabetic COVID-19 patients. Mortality was significantly high among the diabetic cohort when compared to non-diabetics ($35.18\%$ vs. $20.6\%$). ## 4. Discussion COVID-19 and type 2 diabetes mellitus (T2DM) are inversely correlated. Diabetes with poor control makes COVID-19 more severe and is linked to higher morbidity and mortality. Additionally, the COVID-19 pandemic has led to poor diabetes control, the advancement of prediabetes to diabetes, an increase in the number of newly diagnosed cases of the disease, and a surge in corticosteroid-induced diabetes [20]. In this retrospective study, we analysed the clinical and laboratory data from 541 COVID-19 patients, including 108 cases of type 2 diabetes and 433 non-diabetic cases. The clinical and laboratory characteristics of COVID-19 in diabetic patients have been reported in numerous other studies as well [13,19,21,22]. In our study, there was a statistically significant age difference between the two groups. Patients in the diabetic group made up a disproportionately high percentage of the elderly, whereas those in the non-diabetic group were generally younger. A similar result was also reported by a Saudi Arabian study [13]. A study from Iran reported that the median age of COVID-19 patients with diabetes was 59 years, while the median age of non-diabetic patients was 37 years, similar to our findings [23]. Diabetes is a chronic disease of the elderly, which explains why the average age of COVID-19 patients with diabetes is higher. Other studies have demonstrated that old age is a major risk factor influencing the prognosis of COVID-19 [9,24]. The prevalence of confirmed diabetes among hospitalised COVID-19 patients was $19.96\%$ in this study. The pooled prevalence of diabetes among hospitalised COVID-19 patients was $14.34\%$, according to a meta-analysis of 83 observational studies [21]. Multiple reports have shown that people with diabetes are more likely than those without diabetes to develop a life-threatening case of COVID-19 infection [25]. Previous reports indicated an increased morbidity and mortality in diabetes patients infected with Streptococcus pneumonia and influenza virus. [ 26]. In our study, there was a significant difference between the two groups in terms of the presenting symptoms. Compared to non-diabetic patients, diabetic patients were more likely to present with fever, dry cough, shortness of breath, chest pain, anorexia, haemoptysis, confusion, loss of consciousness, vomiting, and diarrhoea. Similar to what we found, a study from Iran found that people with diabetes most often complain of fever, shortness of breath, and cough. On the other hand, people without diabetes were more likely to have chest pain and a sore throat [23]. Many other studies have also found that the groups have similar clinical signs [27,28]. In contrast to the non-DM group, we observed a significantly higher incidence of diarrhoea in the DM patients. It is well known that diabetic complications such as neurodegeneration, which can cause sympathetic and vagal nerve dysfunction and increase gastrointestinal peristalsis, resulting in diarrhoea or constipation, are not uncommon [19]. High blood glucose levels can disrupt normal intestinal bacterial flora and promote the growth of harmful bacterial flora, both of which can lead to diarrhoea [29]. Therefore, people with diabetes caused by COVID-19 should focus on preserving a healthy balance of bacteria in their intestinal tract. We found a significant difference in the basal metabolic index (BMI) between groups, with people with diabetes being more likely to be obese. Obese people were more likely to develop serious disease and complications [30]. Anaemia is a common complication of diabetes, which is a chronic condition. In our research, we found that both diabetics and non-diabetics had significantly lower haemoglobin and haematocrit levels than healthy controls. People with diabetes had significantly lower levels of sodium, bicarbonate, and calcium, and higher serum potassium, compared to the control group in our study. Prothrombin and activated partial thromboplastin times were significantly elevated in the diabetes group, as were liver enzymes and cardiac-specific markers such as troponin and creatine kinase. In a separate study, individuals with diabetes and COVID-19 were found to have low serum sodium and elevated aPTT [24]. Some biochemical and haematological alterations in type 2 diabetes mellitus patients differ from those in healthy persons. These metrics should be closely followed up on and supervised in diabetic individuals. A comparison of non-COVID-19 diabetics and non-diabetics revealed that the T2DM group had higher mean values of SGPT, alkaline phosphatase, urea, serum creatinine, total cholesterol, triglycerides, and LDL than the control group did. When compared to the control group, the mean values of haemoglobin, RBC, MCV, MCHC, and MCH in the T2DM group were significantly lower. The T2DM group had a substantially greater mean white blood cell count and differential white blood cell count than the control group. The mean neutrophil/lymphocyte ratio and platelet/lymphocyte ratio in the T2DM group were not statistically different from the control group [31]. The rapid and severe decline in metabolic activity in diabetic patients is explained by a direct effect of SARS-CoV-2 on the function and survival of beta cells [18]. Diabetics had impaired innate and adaptive immunity, which was characterised by a persistent, low-grade inflammatory state that abruptly changed their systemic metabolic state [22,30]. Many tissues contained ACE2, the COVID-19 binding receptor in host cells. The respiratory system, particularly the lung, cardiac tissue, and the gastrointestinal and renal systems, particularly proximal tubular cells, have the highest levels of transcripts [32,33]. Diabetes patients have higher levels of ACE2, which facilitates virus uptake and increases the risk of disease severity [34,35]. SARS-CoV-2 binds to ACE2 receptors, which are expressed in pancreatic tissue, and beta cells in particular, leading to a decrease in insulin secretory capacity of beta cells. Thrombosis is a well-known complication of severe COVID-19, and it may be made worse by stress and cytokine storm, which may cause diabetic ketoacidosis or hyperglycaemic hyperosmolar syndrome [18]. Increased coagulation and thrombotic and inflammatory events in COVID-19 and increased coagulability in diabetes may be a mechanism linking the severity of COVID-19 to diabetes [32]. There have also been reports of endothelial cell dysfunction, compromised platelet function, and coagulation disturbance leading to atherosclerosis and cardiovascular complications [36]. With type 2 diabetes, mildly elevated liver enzymes are frequently observed. Along with insulin resistance, metabolic syndrome, and type 2 diabetes, increased activity of liver enzymes such as AST, ALT, and GGT that indicate liver injury was also linked to these conditions [37,38]. Abnormalities in insulin-sensitive tissues, such as the liver, in terms of triglyceride storage and lipolysis were early indicators of insulin resistance. A high concentration of free fatty acids is a direct toxicant to hepatocytes, which is why insulin-resistant patients tend to have high levels of this compound. Hepatocyte injury is associated with elevated levels of proinflammatory cytokines, a feature shared by diabetes mellitus and COVID-19 [38]. The activation of CD4+ T cell differentiation through Th1 and Th2 cells, as well as the dysfunction of Th17 cells and T regulatory cells, which affect the balance of pro- and anti-inflammation, are all potential pathways that diabetes may follow. Immune system imbalance may also lead to the secretion of inflammatory cytokines [39]. In our study, diabetic patients outnumbered non-diabetics in the severe illness cohort by a ratio of 2:1 ($76\%$ vs. $48\%$). According to other studies, hyperglycaemia patients with COVID-19 had a higher incidence of severe COVID-19 than normoglycemic individuals [40,41]. A previous Saudi Arabian study found comparable results [13]. According to the same survey, COVID-19 patients with diabetes have a higher mortality rate than patients without diabetes. However, other factors such as advanced age, congestive heart failure, smoking, beta blocker use, prevalence of bilateral lung infiltrates, elevated creatinine, and serum vitamin D deficiency, are more accurate predictors of fatal outcome [13]. According to another study, $26.8\%$ of the elderly COVID-19 patients with an increased risk of death were diabetic [42]. In a recent investigation, the function of circulating monocyte subsets and NK cells in the genesis and severity of COVID-19 in diabetics was reported. NK cells exhibit anti-SARS-CoV-2 activity but are diminished functionally in severe COVID-19. Furthermore, since COVID-19 sufferers’ NK cells showed poor anti-fibrotic activity, NK cell dysfunction may play a role in the progression of the disease to fibrotic lung disease. Long-lasting NK cell dysfunction was brought on by a heightened IFN-α response and associated with an unfavourable disease trajectory, suggesting NK cells’ involvement in the immunopathogenesis of COVID-19 [43]. Pulmonary fibrosis, one of the aftereffects of SARS-CoV-2 infection that can cause chronic dyspnoea and necessitate oxygen supplementation after COVID-19, is more prevalent in persons with poorly controlled diabetes. It is also realistic to say that post-COVID-19, an already-existing low-grade inflammatory condition, such as that found in T2DM, may become worse and continue to be at a high level, which may result in a number of symptoms [20]. In our study, there was a big difference between the DM group and the non-DM group when it came to being admitted to the ICU and needing mechanical ventilation. In the DM group it was $17.9\%$, compared to $12.3\%$ for the non-DM group. Others have reported finding the same results [44]. According to a study from China, diabetic patients with COVID-19 were more likely to die if they were elderly males with hypertension and cardiovascular disease who also presented with shortness of breath. Patients with diabetes were more susceptible to complications, had a greater proportion of ICU admissions, and had a high mortality rate [15]. Previous studies indicate that during the most recent H1N1 influenza pandemic, there was a significant increase in ICU admissions and mortality among diabetes patients [45,46]. According to reports, pulmonary dysfunction and intensified inflammation are the mechanisms that relate diabetes to increased mortality and shorter survival times in diabetic individuals. Aggressive glycosylation, which causes an excess of advanced glycation end product formation has been linked to the hyperglycaemia brought on by diabetes. It was thought that abnormal glycosylation was connected to immunoglobulin malfunction [39]. Another study showed that prolonged NK cell dysfunction caused by an increased IFN-α response is linked to an unfavorable disease course, supporting NK cells’ role in the immunopathogenesis of COVID-19. [ 43] Another study found that decreased cell proliferation induces an adaptive-like NK cell phenotype, which has an early prognostic value for higher TGF and IFN levels in COVID-19 infection, which is related with disease severity. Increased TGF and IFN levels, as well as disease severity, were associated with the accumulation of adaptive-like FcR/low NK cells in COVID-19 patients. One of the key future initiatives will be to analyse these NK phenotypes in the DM and non-DM population to study the association between these unique phenotypes and protections in people with diabetes and COVID-19 [47]. Among patients who developed severe disease, there was a substantial difference between the two groups; diabetics were more likely to develop renal failure and sepsis than non-diabetics. Diabetes patients are predisposed to developing infections of varying severity due to underlying metabolic changes, chronic inflammation, and impaired immunity [48,49]. A more recent study using multiomics showed that in contrast to the expected distinction between mild and moderate infections, the plasma multiomic profiles revealed striking similarities between moderate and severe cases of COVID-19. Changes in lipid, amino acid, and xenobiotic metabolism, as well as a noticeable increase in inflammatory cytokines, characterise this dramatic transformation [50]. The loss of particular classes of metabolites and metabolic processes coincides with an increase in inflammatory signalling, which also showed the significant transition between mild and moderate illness. Many atypical immune cell morphologies occur in this stressed plasma environment during mild disease and become more pronounced with worsening disease. This immune response axis independently coincides with substantial plasma composition alterations, clinical blood coagulation measures, and the abrupt switch from mild to moderate illness [50]. In another report, single cell multiomic analysis revealed a strong interaction between plasma metabolites and metabolic reprogramming networks specific to different cell types that are related to illness severity and may predict survival. It also revealed that a small, metabolically hyperactive fraction of CD8+ T cells, possibly exclusive to the SARS-CoV-2 virus, shows increasing metabolic activity as the disease’s severity increases [51]. The metabolic profile of people with more severe disease was generally more activated, but the level of activation varied by cell type. The significant antiviral activities of CD8+ T cells and B cells were compatible with their elevated metabolic activity, whereas CD4+ T cells, natural killer (NK) cells, and monocytes each showed a somewhat lower elevation. Two distinct monocyte subpopulations have been identified, with inflammatory monocytes increasing in quantity and metabolic activity per cell, while non-classical monocytes act in the other direction [51]. These data suggest that COVID-19 is accompanied with metabolic reprogramming of immune cells and shows that each major type of immune cell has a unique metabolic profile [51]. Examining metabolic activity at the resolution of specific cell types in diabetics with COVID-19 may give insights regarding disease severity and outcome and aid in future research directions. Understanding immunological responses in COVID-19 patients is crucial for determining the efficacy of therapies, predicting illness prognosis, and comprehending the reported variation in disease severity [49]. A recent study found protective and harmful gene modules that defined unique trajectories associated with moderate versus severe results. The authors argue that despite heterogeneity and regardless of the infecting virus, it is important to identify host response modules since doing so could lead to new intervention options, including diagnostics for identifying patients who are more likely to experience severe consequences [52]. Microvascular complications in diabetes patients include neuropathies and end-stage renal disease [43]. In our study also there was an increase in the risk of acute renal failure in diabetics when compared to non-diabetics, which is a significant finding. Between the diabetic cohort and the non-diabetic cohort, mortality was significantly higher ($35.18\%$ vs. $20.6\%$). Similar to our report, another study found that diabetic COVID-19 patients had a mortality rate of $35.4\%$ while non-diabetic COVID-19 patients had a mortality rate of $20.3\%$ [22]. Post-COVID-19 acute complications are a developing worldwide health issue; type 2 diabetes is one of the potential risk factors. A multiomic analysis of 309 COVID-19 patients suggested that patients prone to post-acute sequelae of COVID-19 may be predicted early in the course of the illness. Immunological connections between post-acute sequelae of COVID-19 infection weaken over time, resulting in different immunological states throughout convalescence. Hence identification of indicators that may indicate long-term disease by comparing patient symptoms with in-depth profiling of blood cells and plasma components throughout COVID-19 infection is needed. [ 53]. Patients with type 1 and type 2 diabetes mellitus have a high risk of a bad prognosis from COVID-19, and immunisation should be prioritised in this population. However, future studies must address numerous outstanding concerns pertaining to COVID-19 immunisation [54,55]. Clinicians can minimise the burden by advising, reassuring, and supporting older persons with diabetes during the time of epidemics. Providing adequate nourishment through frequent meals and preventing weight loss are more important than optimising the diet at such times [56]. Hyperglycaemic disorders and other coexisting illnesses are made worse by inappropriate medication therapy, which in turn increases morbidity and death. It has been noted that corticosteroids, antivirals, and immunisation raise blood glucose. Alternately, certain biologics, anti-infectives, and antiparasitic medications may decrease blood glucose. In patients with uncontrolled blood glucose who are at risk for diabetes complications, this information may offer recommendations for alternate medications. The healthcare team’s risk/benefit analysis should be applied to this patient population for COVID-19 treatment and prevention recommendations [57]. In our study, we did not fully evaluate the confounding factors and their impact on the disease outcomes which is a limitation in this study. ## 5. Conclusions Diabetes mellitus enhances the COVID-19 mortality risk and is associated with a particularly severe illness course. In addition, people with diabetes frequently suffer from comorbidities that affect clinical outcomes. COVID-19 influences the pathophysiology of diabetes significantly. In the present study, people with diabetes are more likely than patients without diabetes to develop severe symptoms and problems due to COVID-19. Patients with diabetes were considerably more likely to require intensive care unit (ICU) admission and mechanical breathing, both of which contributed to an increased risk of death. Diabetes is one of the high risk factors for COVID-19, which is associated with a high rate of morbidity and mortality, and hence the need for prompt and adequate care. ## References 1. Severe Acute Respiratory Syndrome 2. SARS 3. Riou J., Althaus C.. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. 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--- title: 'Bladder Cancer and Risk Factors: Data from a Multi-Institutional Long-Term Analysis on Cardiovascular Disease and Cancer Incidence' authors: - Biagio Barone - Marco Finati - Francesco Cinelli - Antonio Fanelli - Francesco Del Giudice - Ettore De Berardinis - Alessandro Sciarra - Gianluca Russo - Vito Mancini - Nicola D’Altilia - Matteo Ferro - Angelo Porreca - Benjamin I. Chung - Satvir Basran - Carlo Bettocchi - Luigi Cormio - Ciro Imbimbo - Giuseppe Carrieri - Felice Crocetto - Gian Maria Busetto journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10056598 doi: 10.3390/jpm13030512 license: CC BY 4.0 --- # Bladder Cancer and Risk Factors: Data from a Multi-Institutional Long-Term Analysis on Cardiovascular Disease and Cancer Incidence ## Abstract Background: Bladder cancer (BCa) is a heterogeneous disease with a variable prognosis and natural history. Cardiovascular disease (CVD), although completely different, has several similarities and possible interactions with cancer. The association between them is still unknown, but common risk factors between the two suggest a shared biology. Materials and Methods: This was a retrospective study that included patients who underwent transurethral resection of bladder tumor at two high-volume institutions. Depending on the presence of a previous history of CVD or not, patients were divided into two groups. Results: A total of 2050 patients were included, and 1638 ($81.3\%$) were diagnosed with bladder cancer. Regarding comorbidities, the most common were hypertension ($59.9\%$), cardiovascular disease ($23.4\%$) and diabetes ($22.4\%$). At univariate analysis, independent risk factors for bladder cancer were age and male sex, while protective factors were cessation of smoking and presence of CVD. All these results, except for ex-smoker status, were confirmed at the multivariate analysis. Another analysis was performed for patients with high-risk bladder cancer and, in this case, the role of CVD was not statistically significant. Conclusions: Our study pointed out a positive association between CVD and BCa incidence; CVD was an independent protective factor for BCa. This effect was not confirmed for high-risk tumors. Several biological and genomics mechanisms clearly contribute to the onset of both diseases, suggesting a possible shared disease pathway and highlighting the complex interplay of cancer and CVD. CVD treatment can involve different drugs with a possible effect on cancer incidence, but, to date, findings are still inconclusive. ## 1. Introduction Bladder cancer (BCa) is a heterogeneous disease with a variable prognosis and natural history [1]. It comprises non-muscle invasive disease (NMIBC), i.e., bladder cancers that do not involve the muscular layer of the bladder, and muscle-invasive disease (MIBC) which instead is characterized by the invasion of the muscle layer of the bladder. More than $70\%$ of bladder cancers are diagnosed as NMIBCs. For MIBC, it is important to identify those at higher risk to better control future disease recurrence and progression [2,3,4]. In a recent meta-analysis, tobacco smoking and occupational exposure remain the most important risk factors for bladder carcinogenesis [5,6], but unfortunately, no protective factors have yet been identified. Cardiovascular disease (CVD) is a leading cause of death worldwide, and although seemingly unrelated, CVD and cancer have several similarities and possible interactions [7]. The association between cardiovascular disease and cancer is still unknown, but risk factors, such as hypertension, obesity and diabetes, can be common, suggesting a shared biology, currently under evaluation [8]. Inflammation is an important mechanism as it is reported to be a possible trigger for both carcinogenesis and CVD. If we consider, once again, that common risk factors such as obesity, hyperglycemia, hypertension, and hypertriglyceridemia induce inflammation, then this could connect cancer and CVD [9,10]. Vincent et al., in a recent review, highlighted shared disease pathways which overlap in risk factors between cancer and CVD. The authors offered a framework for a system-based approach to reduce overall risk burden, providing opportunities for joint risk-factor modification [10]. Another potential etiologic factor is medications and, in our aging population, medications prescribed for cardiovascular indications are becoming more and more common. Anti-hypertensives, anticoagulants, and statins, used alone or in combination, are potential factors. However, their action, particularly on bladder cancer carcinogenesis, is still not fully understood [11]. There are no conclusive correlations between cardiovascular disease and bladder cancer in the literature and no data are reported to analyze if BCa incidence is increased or decreased in patients suffering from CVD. Furthermore, no relationship between high- or low-risk BCa and CVD has been reported. The only related evidence has been reported by Kok et al. who performed a population-based cohort study on 39,618 adults. They observed that patients suffering from hypertension had a $32\%$ increased risk of developing bladder cancer, but when examined by gender, this result was statistically significant only for females [12]. Therefore, our aim was to further evaluate the relationship between bladder cancer and cardiovascular disease in a multi-institutional study, including more than 2000 BCa patients. ## 2. Materials and Methods This was a retrospective study approved by the institutional review board of “Policlinico Riuniti of Foggia” with protocol # 31/CE/2022 (DCS #7) on 28th of February 2022 and conducted in accordance with the World Medical Association Declaration of Helsinki. All patients included provided written informed consent for the procedures as well as for the participation and publication of the study. Patients involved underwent transurethral resection of bladder tumors (TURBT) at two large volume institutions—University of Naples “Federico II” and University of Foggia—between 1st of January 2008 and 31st of December 2021. Eligibility criteria were age >18 years and a previous ultrasound, cytology, or cystoscopy suggested a potential malignancy. Every patient underwent a cystoscopy prior to TURBT, in accordance with European Association of Urology (EAU) guidelines. All clinical and laboratory data, as well as patient’s history, such as comorbidities and therapies, were retrieved and analyzed from medical records. All surgical specimens were processed, described, and reviewed by dedicated uropathologists. For urothelial cancer, grade was classified according to the WHO/ISUP 2016 grading system [13]. Pathological stage was assigned following the current American Joint Committee on Cancer 2017 TNM staging system (VIII edition) [14]. Patients who exhibited high-grade (HG) NMIBC or MIBC underwent staging with abdominal-pelvis computed tomography (CT) scan with contrast, chest CT scan or X-ray, and bone scan. Second-level examinations, such as magnetic resonance imaging (MRI) or total body positron emission tomography (PET)-CT were performed only in case of clinical suspicion or symptoms. For a previous history of CVD, patients were divided into two groups: Group A, which included all the patients who had a history of confirmed CVD (such as stroke, heart failure, valvular heart disease) or had undergone related surgery (such as percutaneous transluminal coronary angioplasty); Group B included all the patients who did not report similar conditions. Another sub-classification was carried out, dividing high-risk tumors from the others. The majority of patients involved were of Caucasian ethnicity. All data are reported in Table 1. ## Statistical Analysis Descriptive statistics were reported as means and standard deviations for continuous variables while frequency and percentages were reported for categorical variables. According to the normality of data, assessed via the Kolmogorov–Smirnov test, T-test and Mann–Whitney U test were used for group comparisons for continuous variables. Similarly, categorical variables were compared between the two groups via the Chi-square test. Finally, univariate and multivariate logistic regression were used in order to obtain the odds ratio (OR), and the corresponding $95\%$ confidence interval (CI), for protective/risk factors for any bladder cancer and HG NMIBC. All statistical analyses were performed using IBM SPSS software (version 25, IBM Corp, Armonk, NY, USA) and considering $p \leq 0.05$ as statistically significant. ## 3. Results A total of 2050 patients were included in the study. The descriptive statistics of the patients involved in the study are reported in Table 1. Of the patients, 577 ($28.1\%$) were non-smokers, 865 ($42.2\%$) ex-smokers, and 578 ($28.2\%$) active smokers. Regarding other comorbidities, the most common were hypertension ($59.9\%$), followed by cardiovascular disease ($$n = 479$$, $23.4\%$), and diabetes ($22.4\%$). A total of 1638 ($81.3\%$) patients reported bladder cancer. In particular, 333 ($16.2\%$) had grade 1 disease, 425 ($20.7\%$) grade 2, and 880 ($42.9\%$) grade 3. Multifocality was reported in 544 ($26.5\%$) of cancers, while concomitant carcinoma in situ was reported in 64 ($3.1\%$). Median follow-up was 2.11 years. When patients were compared regarding their CVD status, both groups were comparable in terms of years of smoking, years without smoking, cigarettes per day, and a history of previous cancers. Conversely, patients with CVD (Group A: 479 patients) were older than those without CVD (Group B: 1569 pts) with a mean of 73.88 ± 8.87 vs. 69.79 ± 12.11 ($p \leq 0.0001$), as well as a slightly higher prevalence among males ($89.8\%$ vs. $75.1\%$, $p \leq 0.0001$). Group A had a higher prevalence of comorbidities compared to Group B, and reported a higher rate of ex-smokers. Interestingly, the CVD group reported a lower rate of bladder cancer with 368 ($78.3\%$) vs. 1269 ($82.2\%$) in Group B ($$p \leq 0.048$$), despite a higher presence of grade 3 cancer ($46.4\%$ vs. $42.8\%$, $$p \leq 0.024$$) and slightly higher pathological stages, with 119 ($26.4\%$) vs. 306 ($20.1\%$) for pT1, 56 ($12.4\%$) vs. 186 ($12.2\%$) for pT2 and 2 ($0.4\%$9 vs. 2 ($0.1\%$) for pT4 ($$p \leq 0.005$$). Data are reported in Table 2, Figure 1 and Figure 2. According to these results, in order to clarify the role of other statistically significant comorbidities in the two groups as potential risk factors for bladder cancer, we performed a univariate and multivariate logistic regression, with regard to the presence or absence of any bladder cancer, in the entire cohort. As reported in Table 3, independent risk factors for bladder cancer were age (OR = 1.037, $95\%$ CI 1.027–1.047, $p \leq 0.0001$) and male sex (OR = 1.675, $95\%$ CI 1.263–2.222, $p \leq 0.0001$) while protective factors were the cessation of smoking (OR = 0.743, $95\%$ CI 0.569–0.970, $$p \leq 0.029$$) and the presence of CVD (OR = 0.781, $95\%$ CI 0.605–1.008, $$p \leq 0.047$$). All these results, except for ex-smoker status, were confirmed with multivariate analysis, reporting, in particular, an even lower odds ratio for CVD (OR = 0.659, $95\%$ CI 0.496–0.875, $$p \leq 0.004$$). At this point, to further clarify the protective role of CVD in BCa, we performed a univariate and multivariate logistic regression for both groups (Table 4). Apart from age and male sex which reasonably influenced the odds ratio for BCa, only COPD, in the group of patients with CVD, was statistically significant among the other evaluated risk factors, reporting OR = 2.447 ($95\%$ CI 1.250–4.788, $p \leq 0.0001$) and OR = 2.863 ($95\%$ CI, 1.359–6.028, $$p \leq 0.006$$) on univariate and multivariate analysis, respectively. A similar analysis was performed for patients with high-risk BCa. As reported in Table 5, in this case, the role of CVD was not statistically significant, while age and male sex continued to be independent predictors of HG BCa on multivariate analysis with an OR = 1.056 ($95\%$ CI 1.040–1.072, $p \leq 0.0001$) and OR = 2.088 ($95\%$ CI 1.343–3.248, $$p \leq 0.001$$). Similar results were reported for the univariate and multivariate logistic regression analyses stratified for the two groups (Table 6); among patients with CVD (Group A), age and COPD were associated with high-risk BCa, reporting an OR = 1.047 ($95\%$ CI 1.009–1.087, $$p \leq 0.016$$) and OR = 2.993 ($95\%$ CI 1.264–7.086, $$p \leq 0.013$$), respectively; for patients without CVD, the only predictors for HG BCa were, similarly, age and male sex, reporting in the multivariate analysis an OR = 1.058 ($95\%$ CI 1.040–1.076, $p \leq 0.0001$) and OR = 2.029 ($95\%$ CI 1.239–3.322, $$p \leq 0.005$$), respectively. ## 4. Discussion This multi-institutional retrospective analysis was conducted on a large population of more than 2000 patients that underwent trans-urethral resection of the bladder and all procedures were performed at two academic high-volume urologic oncologic centers. In our study, more than $81\%$ were diagnosed with bladder cancer, confirmed at pathological evaluation. Our study pointed out a positive association between CVD and BCa incidence; univariate and multivariate analysis confirmed that CVD was an independent protective factor for BCa. This effect, reported for the entire population, was not confirmed for the high-risk tumors. Patients with CVD also exhibited a higher prevalence of other comorbidities, such as diabetes and COPD, as well as a history of smoking. Nevertheless, the presence of CVD independently predicted the incidence of BCa, but not its aggressiveness. Another interesting point was the differential impact of comorbidities among BCa patients, depending on tumor staging. Specifically, a reduced risk of harboring BCa was observed in patients with CVD, but this statement was not valid for high-risk tumors. The evolution of BCa genomics could possibly explain the relationship between different tumor histotypes and risk factors. BCa is characterized by a high mutational heterogeneity, with frequent mutations in multiple different signaling pathways, including cell-cycle genes, tyrosine kinase receptors, PI3K/AKT/mTOR, and chromatin regulatory gene mutations [15]. Some of these pathways, such as fibroblast growth factor signaling, contribute also to atherosclerosis by enhancing an inflammatory response in vascular smooth muscle [16]. FGFR3 mutations are more common in non-invasive low grade papillary tumors and could possibly explain the different impact of CVD on this heterogeneous and complex disease [17]. It is well known that low-risk cancers and high-risk cancers follow two completely different pathways. On one side, altered cells follow hyperplasia that evolve toward low-grade tumors, on the other, they become dysplastic (tp53 mutation) and follow the CIS pathway that could evolve toward invasive carcinoma [18]. Probably, even this mechanism should be taken into consideration to better understand why CVD is not able to perform its protective effect over high-risk tumors. A possible link between CVD and BCa has been proposed in the past few years, although no conclusive evidence has been established yet [7,19,20,21]. A significant overlap in epidemiology and modifiable risk factors would prove how both the screening and management of certain chronic conditions can reduce the downstream risk of both cardiovascular disease and incident BCa [9,22]. Further studies are required to evaluate, in addition to the role of CVD, the influence of other variables as age, gender and body mass index [23]. Moreover, several biological and genomics mechanisms clearly contribute to the onset of both diseases, suggesting a possible shared disease pathway and highlighting the complex interplay of cancer and CVD [16]. Chronic inflammation and oxidative stress, for example, could promote the expression of the adhesion molecules and growth factors necessary to not only foster atherosclerosis, but also malignancies [24]. The inflammatory intersection is further reinforced by recent findings in the CANTOS trial (canakinumab anti-inflammatory thrombosis outcomes study), where the specific targeting of interleukin 1β (a key mediator of inflammation) conferred cardiovascular benefit and, unexpectedly, a significant decrease in cancer incidence [25]. CVD treatment and control generally involve a multitude of drugs and related drug-interactions, with a possible effect on cancer incidence. Considering BCa risk, several studies have considered the relationship with aspirin but have demonstrated inconsistent results and overall null associations [26,27,28]. Similar results were assessed for any antihypertensive agents or statin use in a population-based case-control study, highlighting the need for additional long-term follow-up prospective research [18,29,30]. A possible protective role related to certain CVD medications could explain the differential impact in our cohort. A second-level analysis evaluating the association of BCa and drug prescription in our multicenter series will be the object of further studies. To the best of our knowledge, this paper is the first to highlight a possible link between CVD and BCa carcinogenesis and differentiation. Less than $20\%$ of our population who underwent TURBT in our multicenter series did not exhibit BCa, while chronic inflammation was the most common finding at pathologic examination. Patients with CVD are usually at higher risk of exhibiting hematuria due to antiplatelet/antithrombotic drugs, which often requires second-level examination such as ultrasound, cystoscopy or a computed tomography scan [31]. Performing such diagnostic procedures could also increase the detection of benign pseudocarcinomatous lesions. Bladder epithelial proliferations such as chronic cystitis, are most typically seen in association with radiation or chemotherapy, but a recent study found that in almost $90\%$ of cases there was still a possible etiology for proliferation in the form of localized ischemia and peripheral vascular disease [32]. This relationship could possibly justify the lower incidence of BCa in the CVD group, although the higher incidence of high-risk tumors seems to underline a different oncologic pattern between the two groups. Further prospective studies are required to truly assess the impact of CVD on bladder carcinogenesis. 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--- title: Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis authors: - Shaodan Hu - Yiming Sun - Jinhao Li - Peifang Xu - Mingyu Xu - Yifan Zhou - Yaqi Wang - Shuai Wang - Juan Ye journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10056612 doi: 10.3390/jpm13030519 license: CC BY 4.0 --- # Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis ## Abstract Infectious keratitis (IK) is a common ophthalmic emergency that requires prompt and accurate treatment. This study aimed to propose a deep learning (DL) system based on slit lamp images to automatically screen and diagnose infectious keratitis. This study established a dataset of 2757 slit lamp images from 744 patients, including normal cornea, viral keratitis (VK), fungal keratitis (FK), and bacterial keratitis (BK). Six different DL algorithms were developed and evaluated for the classification of infectious keratitis. Among all the models, the EffecientNetV2-M showed the best classification ability, with an accuracy of 0.735, a recall of 0.680, and a specificity of 0.904, which was also superior to two ophthalmologists. The area under the receiver operating characteristics curve (AUC) of the EffecientNetV2-M was 0.85; correspondingly, 1.00 for normal cornea, 0.87 for VK, 0.87 for FK, and 0.64 for BK. The findings suggested that the proposed DL system could perform well in the classification of normal corneas and different types of infectious keratitis, based on slit lamp images. This study proves the potential of the DL model to help ophthalmologists to identify infectious keratitis and improve the accuracy and efficiency of diagnosis. ## 1. Introduction Corneal opacity is the fifth-leading cause of blindness worldwide [1], and infectious keratitis (IK) is the leading cause of corneal blindness in both developed and developing countries [2]. The most prominent feature of IK is that the growth of pathogens in the cornea leads to local opacity and roughness, and each pathogen shows its unique characteristics in the growth [3]. According to the types of pathogens, infectious keratitis can be divided into bacterial keratitis (BK) [4], fungal keratitis (FK) [5], and viral keratitis (VK) [6]. Once corneal infection occurs, it may progress rapidly, leading to irreversible visual impairments such as corneal scars, endophthalmitis, and corneal perforation [7]. Therefore, early detection and timely medical intervention are critical to stop or slow the progression of the infection. Corneal scrape culture is currently the gold standard for the diagnosis of IK, but there are drawbacks, such as the risk of corneal injury, the low positive rate of culture, and long diagnostic cycles [8,9]. New techniques such as polymerase chain reaction (PCR) [10,11,12] and confocal microscopy [13] have also been used clinically to assist in diagnosis, but these methods require sophisticated equipment, complex procedures and experienced technicians. At present, the initial diagnosis of IK is highly dependent on ophthalmologists, who need to combine personal experience to distinguish the visual features of corneal lesions. Slit lamp microscopy is the most common ophthalmologic examination used to evaluate the appearance of IK. Slit lamp photographs are commonly used to record and monitor the progression of IK [14]. However, because of the diversity of pathogens and the similarity of lesion manifestations, it is difficult to identify different IK, even for experienced corneal specialists. Overall, there is still a lack of an efficient and accurate diagnostic tool to help guide the treatment of infectious keratitis in clinical practice. In recent years, artificial intelligence (AI) technology has developed rapidly, and medicine has become the frontier area of AI applications. Recent studies of deep learning (DL) have shown great promise in the use of clinical images to detect common diseases [15]. The convolutional neural network (CNN), as its representative algorithm, has been proven to be very effective in medical image recognition and classification [16]. The automatic classification of medical images can not only reduce the workload of doctors and improve the efficiency and repeatability of screening procedures, but also improve patient outcomes through early detection and treatment. Although there are more than 200,000 ophthalmologists worldwide, there is a current and anticipated future shortage in the number of ophthalmologists in both developing and developed countries [17]. The widening gap between demand and supply can affect the timely detection of infectious keratitis, especially in remote or medically underserved areas [18]. In situations where ophthalmologists are in short supply or medical resources are limited, artificial intelligence is expected to become a practical tool for front-line medical care. In the field of ophthalmology, a large number of studies have developed high-precision AI diagnostic systems based on rich examinations and image data for diseases of the posterior segment of the eye, such as diabetic retinopathy, glaucomatous optic neuropathy, and retinal detachment [19,20,21]. The application progress of deep learning in different fundus diseases has extended to early screening, grading and stage diagnosis and even to the prediction of treatment effects [22,23,24]. In contrast, the application of deep learning in anterior segment diseases is limited and has great research potential. Recent studies have attempted to apply deep learning to slit lamp images for the diagnosis of corneal diseases. Gu et al. [ 25] developed a DL model to identify infectious keratitis, non-infectious keratitis, corneal dystrophy or degeneration, and corneal neoplasm, with results similar to their ophthalmologists. However, to our knowledge, the application of DL to the classification of pathogens in infectious keratitis remains limited. Thus, we decided to construct a deep learning system for the automatic classification of slit lamp images to achieve an intelligent diagnosis of infectious keratitis, which could offer assistance to ophthalmologists in screening and diagnosing infectious keratitis. We hope that it will help reduce the rate of clinical misdiagnoses, save patients’ vision, and further alleviate the burden on medical resources and society’s economy. ## 2.1. Image Dataset This study retrospectively established a dataset that included 2757 slit lamp images collected from 744 patients between August 2016 and September 2021 in the Eye Center at the Second Affiliated Hospital of the Zhejiang University School of Medicine. The images were acquired by experienced ophthalmic technicians using two Topcon SL-D701 slit lamp biomicroscopes affixed with DC-4 digital cameras in the diffuse illumination mode. In the slit lamp image dataset, 2165 images were taken from IK patients at the active stage, including bacterial keratitis (BK), fungal keratitis (FK) and viral keratitis (VK). In addition, 592 images taken from healthy eyes with negative fluorescence staining were classified into the category of normal cornea. The representative images for each category are shown in Figure 1. All cases of IK were diagnosed by cornea specialists from our center, based on medical histories, clinical manifestations, corneal examinations, laboratory methods, and follow-up outcomes. The diagnostic labels based on the medical records’ information were considered to be the true labels of this study. The diagnostic criteria were [1] BK: positive corneal scraping results for bacteria (microscopic staining or tissue culture); [2] FK: positive corneal scraping results for fungi (microscopic staining or tissue culture) or fungal hyphae found under confocal microscopy; [3] VK: positive PCR test results for viruses on corneal scraping; [4] the corresponding typical clinical history; [5] the corresponding typical manifestations of corneal lesions; [6] the corresponding anti-pathogenic drugs were effective. The first three are laboratory gold standards, wherein one of which must be met; the last three are auxiliary indicators, at least one of which must be met. The exclusion criteria were [1] patients with not enough evidence for a definite diagnosis or with mixed infections; [2] patients with corneal perforation, corneal scarring, a history of corneal surgery, or other corneal diseases; [3] images with poor quality, including poor-field, defocused, and poor-location images. Images that met any of the above criteria were excluded. Our two researchers independently reviewed all data in detail before any analysis and ensured that each image was correctly matched to the specific individual. ## 2.2. Data Preparation The slit lamp images were initially preprocessed, including with shuffle and normalization. For each category of images, the dataset was divided into a training set, validation set and test set. For reducing the impact of the dataset imbalance, the ratio of the BK was adjusted to 0.65:0.2:0.15 and the other three groups were 7:1:2. All images collected from the same patient were only divided into the same dataset to avoid data information leakage from the test set and incorrect evaluation of the model performance. The distribution of the slit lamp image datasets is shown in Table 1. Data augmentation is an essential approach to automatically generate new training samples and improve the generalization of the DL models [26]. We obtained samples using several strategies: [1] random cropping images with ranges of ($0\%$, $30\%$); [2] random rotation with ranges of (−10°, +10°); [3] color jitter with ranges of (−$10\%$, + $10\%$); [4] random horizontal flip; [5] adding Gaussian noise with mean = 0, variance = 1, and amplitude = 8. In addition, we tripled the sample size of the BK to reduce the effect of dataset imbalance. Finally, each image was resized to 224 × 224 pixels to be compatible with the original dimensions of the experiment networks. ## 2.3. Deep Learning Model Figure 2A shows the flowchart of the DL system for the automatic diagnosis of IK based on slit lamp images. For the development of the DL diagnosis models, we applied various classical and efficient algorithms, including VGG16 [27], ResNet34 [28], InceptionV4 [29], DenseNet121 [30], ViT-Base [31] and EffecientNetV2-M [32]. The above networks are all CNN structures, except for ViT, which uses the Transformer structure. As the latest algorithm proposed in 2021, EffecientNetV2 has achieved state-of-the-art performance in many major image classification tasks, and its structure is shown in Figure 2B. The training set was used to train the DL models for differentiating infectious keratitis, whereas the validation set was used to verify their performance. All models were implemented using the Pytorch platform with an Nvidia RTX 2080TI GPU. For each model, 150 epochs were set for the training, and the batch size was set at 15. During model training, RMSPromp optimizer and cosine annealing were applied to help the model converge quickly. Moreover, hyperparameter tuning was used to optimize all models according to the validation results. The test set was used to evaluate the performance of the optimal model. The heatmaps were plotted by the Gradient-weighted Class Activation Mapping (Grad-CAM) technique [33]. It can generate visual interpretations for CNN-based deep learning models to build trust in the predicted results and provide references for doctors. ## 2.4. Performance Assessment Six different DL models in this study were evaluated in an independent test set, and the images obtained from the same patient were not scattered to different datasets. The performance of the DL models for the classification of IK was evaluated by calculating the accuracy, recall and specificity. Statistical analyses were conducted using Python 3.8.10 (Wilmington, DE, USA) and Pycharm 2021.1.3 (Professional edition). To compare the classification ability of the models, the receiver operating characteristics (ROC) curves were created using the packages Scikit-learn (version 0.24.2) and Matplotlib (version 3.2.2). The horizontal axis of the ROC curve is the false positive rate (FPR), which is 1-specificity, and the vertical axis is the true positive rate (TPR), which is the recall. The area under the ROC curve (AUC) can measure classification performance. The closer the value of AUC is to 1, the better the performance is. We also recruited two ophthalmologists to independently classify the same test set and evaluate their classification results using the same metrics. Then, the classification performance of the best-performing model was compared by two ophthalmologists (i.e., Doctor 1 and Doctor 2). The confusion matrices were plotted by Matplotlib (version 3.2.2), which is helpful in analyzing the misclassification of each category by the model and ophthalmologists. ## 3.1. Performance of the DL Models Six DL algorithms were used in this study to train the models for the classification of BK, VK, FK and normal cornea. The performance of these DL models in the test set was evaluated by accuracy, recall, and specificity, as shown in Table 2. The best classification algorithm was EffecientNetV2-M, with an accuracy of 0.735, a recall of 0.680, and a specificity of 0.904. Doctor 1 and Doctor 2 reached an accuracy of 0.661 and 0.685, a recall of 0.636 and 0.648, and a specificity of 0.884 and 0.891, respectively. The macro-average ROC curves of the six DL models are shown in Figure 3. InceptionV4 reached the highest AUC value of 0.86, while EffectNetV2-M reached 0.85, higher than other models (VGG16 AUC = 0.83, ResNet34 AUC = 0.82, ViT-Base AUC = 0.82, DenseNet121 AUC = 0.81). ## 3.2. Comparison with the Ophthalmologists Figure 4A compares the macro-average ROC curves between the EffecientNetV2-M model and the ophthalmologists. The EffecientNetV2-M model achieved an AUC of 0.85, higher than two ophthalmologists, which were 0.76 and 0.77, respectively. The ROC curves of the EffecientNetV2-M model for each category are shown in Figure 4B, with Doctor 1 corresponding to Figure 4C and Doctor 2 corresponding to Figure 4D. For the AUCs of each category, the EffecientNetV2-M model, Doctor 1 and Doctor 2 reached a normal cornea AUC of 1.00, 0.89 and 0.97, respectively; VK of 0.87, 0.75 and 0.78, respectively; FK of 0.87, 0.74 and 0.72, respectively; and BK of 0.64, 0.66 and 0.61, respectively. Figure 5A,B shows the confusion matrices of the EffecientNetV2-M model and the average results of the two ophthalmologists. The horizontal axis represents the true category labels, and the vertical axis represents the predicted category labels. As the green color of the matrix deepens, it means that the value increases. The distribution of the last line shows the difficulty of classifying BK, where the real BK is easily misjudged as FK, either by the DL model or by the ophthalmologists. In addition, it can be seen that the model is more likely to recognize the real VK as the other groups, whereas the ophthalmologist is more likely to recognize the other groups as VK. The confusion matrix reveals the similarities and differences between the algorithm and the ophthalmologists in misclassification. ## 3.3. Heatmaps Figure 6 presents examples of heatmaps generated by the EffecientNetV2-M model, accompanied by the corresponding original image. The redder regions represent the areas that are of greater concern during the model classification process, and the bluer regions represent those of relatively less concern. The heatmaps highlighted the areas with corneal lesions, which are highly correlated with the identification of infectious keratitis. ## 3.4. Discussion As a common emergency in ophthalmology, timely and accurate treatment is essential for the prognosis of infectious keratitis. However, the diagnosis of infectious keratitis is a huge challenge clinically due to the diversity of pathogens and the similarity of clinical manifestations. In this study, we constructed a DL diagnosis system for IK to automatically classify BK, FK, VK, and normal corneas by analyzing slit lamp images. Six DL models were developed using the same dataset and compared with the performance of two ophthalmologists. In the end, the EffecientNetV2-M model performed better than the other models and the ophthalmologists, with an accuracy of 0.735, a recall of 0.680, and a specificity of 0.904. Our results suggested that the DL model could be useful for ophthalmologists to screen and diagnose infectious keratitis, thereby reducing the rate of misdiagnosis clinically, saving patients’ vision, and further alleviating the burden of medical resources and socioeconomic issues. In addition, DL technology makes it possible to provide telemedicine services for the diagnosis of IK in places where timely ophthalmologic assessment cannot be performed, such as in rural areas. Recently, some studies have attempted to apply deep learning to slit lamp images for the diagnosis of keratitis. Kuo et al. [ 34] identified fungal and nonfungal keratitis using the DenseNet network based on 288 corneal photographs, with an accuracy of $69.4\%$ and an AUC of 0.65. Ghosh et al. [ 35] adopted the ensemble technique for identifying BK and FK based on 194 cases, obtaining a recall rate of 0.77 and an F1 score of 0.83. Li et al. [ 36] developed a DL system that can automatically classify keratitis, other cornea abnormalities, and normal corneas with slit lamp and smartphone images, with an AUC of more than 0.96. Xu et al. [ 37] compared three image-level algorithms for the classification of BK, FK, HSK, and other corneal disorders, with the DenseNet model achieving optimal accuracy ($64.17\%$), which was superior to 421 ophthalmologists (49.27 ± $11.5\%$). In addition, a large international study [38] quantified the performance of 66 cornea specialists in the image-based differentiation of BK and FK, with AUCs of 0.39–0.82 (mean of 0.61). Compared with the previous studies, the advantages of this study are as follows. First, we collected and built a relatively large and diverse image dataset, including 2757 slit lamp images from 744 patients. Furthermore, the classification task of this study was relatively more complex—to distinguish BK, FK, VK, and normal cornea. Next, we evaluated and compared the classification performance of six different models and two ophthalmologists. The EffecientNetV2 model outperformed other classical models and ophthalmologists in this study, achieving an AUC of 0.85 and an accuracy of 0.735. The ophthalmologists reached an AUC of 0.76 and 0.77, with an accuracy of 0.661 and 0.685, respectively. Our results were better than most previous studies. It is significant that the EffecientNetV2 algorithm used in this study is currently the latest and strongest CNN, which improves the index by comprehensively optimizing the network width, network depth and resolution. Lastly, visual heatmaps were generated to make our DL system interpretable. The heatmaps of the EffecientNetV2-M model highlighted areas that were highly correlated with the lesions of IK. The interpretability of this model can be useful in real-world applications, as it can help ophthalmologists understand how the DL system produces the final output. The EffecientNetV2-M model performed best in this study, but its ability to classify different types of IK varied. The AUCs of the normal cornea, VK, FK and BK were 1.00, 0.87, 0.87 and 0.64, respectively. This means that the model has a strong classification ability to distinguish normal cornea images from infectious keratitis and has a good classification effect for VK and FK, but not so good for BK. The possible reasons are as follows: [1] each species of BK may have different lesion characteristics, and the same species may even have different manifestations in different stages of infection, which makes the classification difficult; [2] different types of IK (especially BK and FK) may have similar lesion characteristics, which may lead to their misclassification; [3] the number of images in each group is relatively limited (especially BK), which leads to the failure of the DL models to learn the unique features of various types of keratitis comprehensively. According to the confusion matrices, about half of the real BK images were misjudged as FK by both the DL algorithm and the ophthalmologists. This confirmed that the lesion characteristics of BK and FK are similar, which makes visual diagnosis challenging. In addition, comparing the misclassification between the algorithm and the ophthalmologists may help improve the diagnostic model. However, it must be noted that few clinicians use only a single slit lamp image to diagnose infectious keratitis. The optical quality of the image may affect their judgment, such as in the presence of reflection and artifacts. Therefore, these factors may lead to inconsistency and error when ophthalmologists perform this classification task. There are also some limitations in this study. First, the dataset was still relatively inadequate compared with some large studies, and cases were not well balanced across groups. In particular, the number of BK images is relatively small, which may also be the main reason for the poor classification effect of BK. The relatively limited number of BK patients may be due to the empirical clinical use of antibiotic eye drops, resulting in a low probability of the laboratory detection of the bacteria. In contrast, bacteria with stronger antibiotic resistance are easier to be detected but are usually accompanied by serious clinical manifestations such as hypopyon. Fortunately, recent studies may have found effective treatments for drug-resistant bacterial corneal infection [39,40]. For us, an early collection of untreated keratitis images may be a solution to increasing the number of BK images. Second, we only evaluated the performance of the developed model on an internal dataset. Various data enhancement processes were used to increase the diversity of the datasets to prevent overfitting and improve the reliability of the training model. However, it is currently difficult to conclude whether the model can be used to screen real patients. In the future, we will collect more cases for model development and validation, including data from multiple centers. Third, we excluded mixed infections with different types of keratitis in this study, although this condition is very common clinically. We believe that identifying mixed infections is a much larger and more difficult deep-learning task. The DL models must fully learn the characterization of each keratitis before it is possible to identify two or more types of keratitis infections. This may require a good understanding of the unique lesion characteristics of different IK, such as corneal infiltration, bacterial or fungal moss, hypopyon and so on. By combining the manual labeling of corneal lesions, it may be possible to improve the focus of the model on the lesion and learn more comprehensive information. In addition, the combination of the model with relevant clinical history information (e.g., trauma, underlying disease, medication history, etc.) may also be a method to enhance model recognition ability. Lastly, our model could not identify the degree or stage of the keratitis infection. We believe that this recognition task needs corneal multi-level information, and we are working on this by combining other corneal examinations, such as fluorescent staining, anterior segment optical coherence tomography (AS-OCT), corneal topography and so on. In any case, we believe that the DL diagnostic system can be further improved to better assist the identification of IK, which can help improve diagnostic accuracy and efficiency. ## 4. Conclusions In this study, we proposed an intelligent diagnosis system for IK using DL technology to analyze slit lamp images. Compared with the other models and ophthalmologists, the EffecientNetV2-M model achieved higher accuracy and AUC in this study. 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--- title: Relationship between Total Antioxidant Capacity, Cannabinoids and Terpenoids in Hops and Cannabis authors: - Philip Wiredu Addo - Zohreh Poudineh - Michelle Shearer - Nichole Taylor - Sarah MacPherson - Vijaya Raghavan - Valérie Orsat - Mark Lefsrud journal: Plants year: 2023 pmcid: PMC10056619 doi: 10.3390/plants12061225 license: CC BY 4.0 --- # Relationship between Total Antioxidant Capacity, Cannabinoids and Terpenoids in Hops and Cannabis ## Abstract Efficient determination of antioxidant activity in medicinal plants may provide added value to extracts. The effects of postharvest pre-freezing and drying [microwave-assisted hot air (MAHD) and freeze drying] on hops and cannabis were evaluated to determine the relationship between antioxidant activity and secondary metabolites. The 2,2-diphenyl-1-picrylhydrazine (DPPH) reduction and ferric reducing ability of power (FRAP) assays were assessed for suitability in estimating the antioxidant activity of extracted hops and cannabis inflorescences and correlation with cannabinoid and terpene content. Antioxidant activity in extracts obtained from fresh, undried samples amounted to 3.6 Trolox equivalent antioxidant activity (TEAC) (M) dry matter−1 and 2.32 FRAP (M) dry matter−1 for hops, in addition to 2.29 TEAC (M) dry matter−1 and 0.25 FRAP (M) dry matter−1 for cannabis. Pre-freezing significantly increased antioxidant values by $13\%$ (DPPH) and $29.9\%$ (FRAP) for hops, and by $7.7\%$ (DPPH) and $19.4\%$ (FRAP) for cannabis. ANOVA analyses showed a significant ($p \leq 0.05$) increase in total THC (24.2) and THCA (27.2) concentrations (g 100 g dry matter−1) in pre-frozen, undried samples compared to fresh, undried samples. Freeze-drying and MAHD significantly ($p \leq 0.05$) reduced antioxidant activity in hops by $79\%$ and $80.2\%$ [DPPH], respectively and $70.1\%$ and $70.4\%$ [FRAP], respectively, when compared to antioxidant activity in extracts obtained from pre-frozen, undried hops. DPPH assay showed that both freeze-drying and MAHD significantly ($p \leq 0.05$) reduced the antioxidant activity of cannabis by $60.5\%$ compared to the pre-frozen samples although, there was no significant ($p \leq 0.05$) reduction in the antioxidant activity using the FRAP method. Greater THC content was measured in MAHD-samples when compared to fresh, undried ($64.7\%$) and pre-frozen, undried ($57\%$), likely because of decarboxylation. Both drying systems showed a significant loss in total terpene concentration, yet freeze-drying has a higher metabolite retention compared to MAHD. These results may prove useful for future experiments investigating antioxidant activity and added value to cannabis and hops. ## 1. Introduction Hops (Humulus lupulus) possess unique chemical compounds that contribute greatly to the bitterness, flavor, and aroma of beer [1]. Cannabis (Cannabis sativa), is a close relative of hops and is predominately cultivated for its medicinal and psychotropic properties [2]. Hops and cannabis both belong to the taxonomy family Cannabaceae and thus have related physiological traits and contain similar secondary metabolites, some of which exhibit antioxidant capacity [3]. Plant antioxidants play important roles in the acclimation or adaptation of plants to a variety of environmental stressors and are beneficial for human health [4]. As part of a balanced nutritional diet, these antioxidants provide protection against damage caused by free radicals involved in the development of many chronic illnesses such as cancer and cardiovascular diseases [5]. Antioxidants are bioactive compounds that, even in small amounts, slow or stop oxidation processes influenced by reactive oxygen species (ROS) or ambient oxygen enzymes [6,7]. Various studies have reported that diverse naturally occurring antioxidants are found in medicinal plants at different concentrations and with varied physical and chemical properties [8,9,10,11,12,13,14,15,16]. Although antioxidants are classified as either lipid-soluble (hydrophobic) and water-soluble (hydrophilic), plant-based antioxidants such as phenolic compounds and vitamin C are mostly hydrophilic [17,18]. Phenolic compounds (terpenes, flavonoids, and carotenoids) in plants act as structural polymers, attractants for insects, ultraviolet protectors, signal compounds, and defense response chemicals [19]. Hydrophobic antioxidants such as carotenoids and vitamin E protect cell membranes from lipid peroxidation [20]. Antioxidants may alternately be classified as enzymatic or non-enzymatic based on their catalytic action [18,21]. Enzymatic antioxidants convert harmful oxidative products via a multi-step enzymatic process, in the presence of cofactors such as copper, zinc, manganese, and iron to stable hydrogen peroxide (H2O2), converting it to water [22]. Non-enzymatic antioxidants prevent the spread of free radicals [18]. Based on their direct or indirect antioxidant defense mechanism, plant antioxidants can be classified as primary or secondary [15,23]. Primary antioxidants such as catalase act as chain-breaking antioxidants by reacting directly with free radicals [24]. Secondary antioxidants, including glutathione-s-transferase, work indirectly as singlet oxygen quenchers, peroxide decomposers, metal chelators, oxidative enzyme inhibitors and UV radiation absorbers [23]. Hops contain α-acids (cohumulene, humulone, and adhumulone), β-acids (colupulene, n-lupulone and adlupulone), and xanthohumol, which are the precursors of bittering agents in beer [25]. Xanthohumol is the major prenylated flavonoid in hops and it is synthesized in glandular trichomes of hop inflorescences [26]. Bitter acids in hops are formed from the acylation of one molecule of acyl-CoA and three molecules of malonyl-CoA to form phlorisovalerophenone [27,28]. Hops may be considered a natural antioxidant since the α-acids, β-acids, and xanthohumol present in this plant have significant hydroxyl radical scavenging and antioxidant activities [26,29,30]. A comparative study using three hop accessions (Calypso, Cascade, and Cluster) demonstrated the presence of high antioxidant activity with the DPPH assay, measuring 342.3, 211.8, and 196.8 µg mL−1, respectively [16]. Major active secondary compounds found in the cannabis plant are the cannabinoids [31], a group of chemical compounds that alter neurotransmission activity of the brain by acting on the cannabinoid receptors [32,33,34,35,36]. Research studies have shown that cannabinoids exhibit antioxidant properties [37,38,39]. Cannabinoids, like other antioxidants, interrupt free radical chain reactions, chelating free radicals by donating their electrons or hydrogen atom and transforming them into less active forms [18]. Dawidowicz et al. [ 2021] showed that the degree of antioxidant activity by acidic and neutral cannabinoids can be attributed to the number of phenolic hydroxyl groups in individual cannabinoids. Cannabinolic acid (CBDA) ($13.3\%$) and cannabidiol (CBD) ($53.3\%$) showed significantly ($p \leq 0.05$) greater scavenging power compared to tetrahydrocannabinolic acid (THCA) and tetrahydrocannabinol (THC), respectively. Hops cannot synthesize cannabinoids as they lack the oxidocyclase enzymes needed to convert cannabigerolic acid (CBGA) to various cannabinoids [40]. Other antioxidant compounds of interest produced by hops and cannabis are terpenes and phenols [9]. Terpenes, or isoprenoids, are one of the largest and most diverse groups in plants [41]. Although terpenes and volatile phenols are mostly responsible for their characteristic aroma, they possess beneficial health benefits such as anticancer, antimicrobial, antifungal, antiviral, analgesic, anti-inflammatory, and antiparasitic activities [42,43,44]. In vitro studies by Rufino et al. [ 2015] showed that myrcene, one of the most abundant terpenes in hops and cannabis, has significant anti-inflammatory and anti-catabolic properties, and is useful for halting or slowing down cartilage destruction and osteoarthritis progression. Phenolic compounds, including terpenes, are reportedly powerful antioxidants with high scavenging properties [8]. Plant secondary metabolite biosynthesis and antioxidant activity can be disrupted and altered during postharvest storage and drying [45,46,47]. Storage studies by Grafström et al. [ 2019] over four years showed that CBD is not prone to oxidative degradation and is stable over time, while decarboxylation of THCA to THC which occurs in stored plant material is increased by the presence of oxygen and higher temperatures [48,49]. Specifically, THC concentrations markedly increase from $1.5\%$ to $2.1\%$, $12.3\%$ and $12.8\%$ when stored at 50 °C, 100 °C, and 150 °C, respectively, due to THCA decarboxylation [45]. Hop buds stored at 20 °C in a dark room showed decreased α-acid concentrations from 186.9 μmol g−1 to 37.0 μmol g−1 and β-acids from 107.7 μmol g−1 to 50.9 μmol g−1. Both α-acids and β-acids are oxidized rapidly during hop storage [50]. Decreases in α-acids and β-acids can decrease the antioxidant capacity of hops. The effects of pre-freezing and drying on hop terpene content has been reported and the optimal conditions for freeze-drying and microwave assisted hot air drying (MAHD) were explored [51]. This previous study showed that the low temperature used during freeze-drying preserved $16.6\%$ to $68.3\%$ of the major terpenes present in hops compared to hot air and MAHD systems, respectively. Pre-freezing caused significant structural damage to hops and this was similarly observed for cannabis in a related trial [51]. The main objective of this follow-up study was to investigate the effects of pre-freezing, prior to drying hops and cannabis, on antioxidant capacity using pre-optimal drying conditions. The suitability and efficiency of 2,2-diphenyl-1-picrylhydrazine (DPPH) and ferric reducing antioxidant power (FRAP) assays were examined and compared for estimating total antioxidant activity (TAC) in hops and cannabis extracts from biomass subjected to these postharvest methods. Given the legislative focus on documenting scientific literature that scrutinizes the therapeutical potential of cannabis for medical use, the relationship between antioxidant capacity and valued secondary metabolites in these two crops was examined. ## 2.1. Ferric Reducing Antioxidant Power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) Calibration Curves This study aimed to compare the suitability and efficiency of the DPPH and FRAP assays when measuring total antioxidant activity in hops and cannabis extracts procured from differently processed biomass, including a pre-freezing step followed by freeze-drying or MAHD. The DPPH and FRAP colorimetric assays are universal tools that are currently used for assessing nonenzymatic antioxidants present in plants [15,52]. The DPPH assay measures the radical scavenging activity of most phenolic compounds such as flavonoids and tannins [37,53]. The FRAP assay is a measure of the transition metal ion chelating activity of antioxidants such as ascorbic acid, uric acid and polyphenolic compounds such as catechins under acidic conditions [37,54]. Bleaching of the DPPH solution from violet to pale yellow increases with an increase of antioxidant activity in each sample (Figure 1A). This assay is based on the reduction of the free radical DPPH to DPPH-H. The FRAP assay uses the reduction of ferric ions (Fe3+) to ferrous ions (Fe2+) as the signal and measures the change in absorbance at 593 nm owing to the formation of a blue colored Fe2+-tripyridyltriazine compound from the colorless oxidized Fe3+ form by the action of electron-donating antioxidants (Figure 1B). The percentage of radical scavenging capacity for different Trolox concentrations used for DPPH and FRAP assay calibration curves is shown in Figure 2. Figure 2 shows inhibition values using 0.005 to 2.5 mM Trolox concentrations. Preliminary data exhibited a flattening of the graph between 2.5 and 10 mM Trolox concentration. This can be attributed to the almost complete quenching of DPPH and FRAP by Trolox, which does not affect the absorbance values. As such, sample dilution is necessary to dilute samples to within the measurable range (0.005 to 2.5 mM Trolox concentrations). A similar curve flattening observation was made by Sochor et al. [ 2010] and Pisoschi et al. [ 2009], where the absorbance of Trolox did not change at concentrations of 200–1000 μmol L−1 and 0.15–0.2 mM, respectively. Calibration graphs (Figure 2) used to quantify the antioxidant capacities of hops and cannabis in this study are linear, in the range 0.005 to 2.5 mM for Trolox, with strong correlation coefficients (R2) of 0.996 and 0.982 for DPPH and FRAP, respectively. ## 2.2. Antioxidant Activity of Hops and Cannabis The observed TEAC and FRAP values determined in hops and cannabis are presented in Figure 3. The antioxidant activity of extracts derived from fresh, untreated hops was 3.6 TEAC (M) dry matter−1 and 2.32 FRAP (M) dry matter−1. Extracts from fresh, untreated cannabis samples had 2.29 TEAC (M) dry matter−1 and 0.25 FRAP (M) dry matter−1 antioxidant values. The lower antioxidant activity observed in cannabis relative to hops can be attributed to the presence of α-acids and β-acids in hops [25]. Analysis of variance tests showed that pre-freezing, freeze-drying and MAHD significantly affected ($p \leq 0.05$) the antioxidant activity of hops and cannabis when evaluated with the DPPH and FRAP assays. Pre-freezing the hops and cannabis samples before drying increased the antioxidant values by $13\%$ (DPPH assay) and $29.9\%$ (FRAP assay) for hops, and by $7.7\%$ (DPPH assay) and $19.4\%$ (FRAP assay) for cannabis (Figure 3). DPPH assays used for this study show that freeze-drying and MAHD significantly ($p \leq 0.05$) reduced the antioxidant activity in hops by $79\%$ and $80.2\%$, respectively, compared to pre-frozen, undried samples. A similar observation was made for hops using the FRAP assay, as antioxidant activity was reduced by $70.1\%$ and $70.4\%$ under freeze-drying and microwave-assisted hot air drying, respectively, when compared to pre-frozen, undried hops. Both freeze-drying and MAHD significantly ($p \leq 0.05$) reduced the antioxidant activity of cannabis by $60.5\%$ using the DPPH assay. However, there was no significant ($p \leq 0.05$) difference between the antioxidant activity values for pre-frozen, freeze-dried and microwave-assisted hot air dried cannabis samples using the FRAP method. ## 2.3. Cannabinoid and Terpenes in Hops and Cannabis For a comparison of different postharvest treatments and valued phytochemicals in extracted hops and cannabis extra inflorescences, total THC content and major cannabinoid concentrations (tetrahydrocannabinolic acid [THCA], tetrahydrocannabinol [Δ9-THC], tetrahydrocannabivarin [THCV], cannabigerolic acid [CBGA], and cannabigerol [CBG]) in C. sativa were determined (Figure 4). In the same figure, the cannabinoid and terpene content in extracts obtained from fresh, undried cannabis samples were compared to cannabinoids and terpene content in extracts obtained in this study. CBDA, CBD, and total CBD content are not presented, as the concentration of CBDA and CBD was below the limit of detection of the instrumentation and methodology. Extracts from fresh, undried Cannabis sativa had total THC, THCA, THC, and CBG concentrations of 20.5 g 100 g dry matter−1, 23.1 g 100 g dry matter−1, 0.27 g 100 g dry matter−1, and 0.16 g 100 g dry matter−1, respectively. ANOVA analyses showed a significant ($p \leq 0.05$) increase in the total THC (24.2 g 100 g dry matter−1) and THCA (27.2 g 100 g dry matter−1) concentrations in extracts obtained from pre-frozen, undried samples compared to fresh, undried samples. However, there was no significant ($p \leq 0.05$) increase in THC (0.32 g 100 g dry matter−1) and CBG (0.22 g 100 g dry matter−1) concentrations in extracts from the pre-frozen, undried samples compared to the fresh, undried samples. The concentration of CBGA measured herein was below the limit of detection of the instrumentation and methodology in the extracts of fresh and pre-frozen, undried samples, likely because CBGA serves as the precursory molecule to the other cannabinoids [55]. Various bioengineering studies have demonstrated that the prenylation of olivetolic acid (OA) by geranyl diphosphate (GPP) to form a CBGA is an anabolic process [35,56,57]. Hence, the observed increase in the average concentration of CBGA to 0.63 g 100 g dry matter−1 (MAHD-dried samples) and 0.6 g 100 g dry matter−1 (freeze-dried samples) can be attributed to the high drying temperatures used. Recent published reviews of the cannabis post-harvest processing methods [49,58] indicate that with the application of heat, THCA and THCVA change into their active forms of THC and THCV, respectively. Compared to the fresh and pre-frozen, undried samples, extracts from MAHD biomass had significantly ($p \leq 0.05$) greater THC content by $64.7\%$ and $57\%$, respectively. ANOVA analyses show that the change in THCA and THC in freeze-dried samples compared to the fresh and pre-frozen, undried samples was not significant ($p \leq 0.05$). A total of 16 and 7 terpene compounds were identified in the cannabis and hop samples, respectively. All seven terpene compounds identified in hops were present in cannabis at different concentrations. Despite the major differences in secondary compounds in cannabis and hops used for the study, the main terpenes were myrcene, caryophyllene, and humulene. These provide the inflorescence with a peppery, citrus, and hoppy mixed aroma [41,59]. The caryophyllene concentration in cannabis was $71.2\%$ greater than that of hops. However, humulene had a higher concentration ($54.8\%$) in hops compared to cannabis. Data represented in Figure 5 and Figure 6 indicate that the concentration of myrcene in fresh, undried hops was reduced from 1.9 to 0.3 g 100 g dry matter−1 (MAHD) and to 0.7 g 100 g dry matter−1 (freeze-dried) and for fresh, undried cannabis, the concentration reduced from 0.3 to 0.1 g 100 g dry matter−1 (MAHD) and to 0.2 g 100 g dry matter−1 (freeze-dried). Rajkumar et al. [ 2017] showed that compared to fresh, undried carrots, myrcene was reduced from 2.3 to 0.4 g 100 g dry matter−1 (MAHD) and to 1.6 g 100 g dry matter−1 (FD). This shows that freeze-drying resulted in a higher terpene retention compared to MAHD for these crops. Major terpene content was similarly determined and compared for cannabis and hops subjected to the same postharvest drying conditions (Figure 5 and Figure 6). The average total terpene content from fresh, undried cannabis and hop samples was 4.3 g 100 g dry matter−1 and 3.3 g 100 g dry matter−1, respectively. ANOVA analyses showed that the increase in the total terpene content to 4.4 g 100 g dry matter−1 and 3.6 g 100 g dry matter−1 for cannabis and hops, respectively, by pre-freezing was not significant ($p \leq 0.05$). For freeze-dried and microwave-assisted hot air dried hop samples (Figure 6), the average total terpene significantly ($p \leq 0.05$) reduced to 1.5 g 100 g dry matter−1 and 1.2 g 100 g dry matter−1, respectively. However, freeze drying preserved the total terpenes (3.9 g 100 g dry matter−1) in cannabis samples compared to microwave-assisted hot air drying (2.8 g 100 g dry matter−1) (Figure 5). The high temperature used during MHAD significantly ($p \leq 0.05$) reduced total terpene content in the fresh, undried samples from 4.3 to 2.8 g 100 g dry matter−1. Terpenes evaporate easily in MAHD since the cannabis and hop structures and dimensions permit its evaporation even at 35 °C, while freeze-drying uses a relatively very low temperature which limits the evaporation of terpenes [60]. Hence, freeze-drying, rather than hot-air drying, is recommended to help preserve terpenes in hops and cannabis during postharvest processing. ## 3. Discussion Antioxidants may be hydrophobic (lipid-soluble) and hydrophilic (water-soluble) substances, yet plant-based antioxidants are mostly hydrophilic [18,61]. Results obtained in this study showed an increased antioxidant activity in the pre-frozen samples, which can be attributed to the structural damage caused by the ice crystal formation reported previously in a preceding study; scanning electron microscopy analyses of cannabis samples showed that the cold temperature used during pre-freezing and consequent ice crystal formation caused wrinkling of cannabis trichome stalks and cannabis trichome heads to fall off [51]. Other research has shown that pre-freezing exerts positive effects on the quality and functional properties of plant material since a frozen state allows the release of bioactive compounds as bound phenolic acids and anthocyanins, resulting in increased antioxidant activity [13,62]. Leong and Oey [2012] showed that pre-freezing apricots (Prunus armeniaca) at −20 °C increased the concentration of vitamin C and β-carotene by $55.5\%$ and $10.7\%$, respectively. The high temperature used during MAHD and freeze-drying caused a significant ($p \leq 0.05$) reduction in the antioxidant activity in both hops and cannabis using the DPPH assay compared to the pre-frozen samples. This can be attributed to a reduction in free phenolic compounds present in the samples, as DPPH measures the scavenging activity of phenolic compounds [11,30]. Lang et al. [ 2019] observed a significant ($p \leq 0.05$) reduction ($5.7\%$) in the total free phenolic compounds in rice (Oryza sativa) when the drying temperature was increased from 20 °C to 80 °C. Significant ($p \leq 0.05$) differences were not observed between the antioxidant activity values for pre-frozen, freeze-dried and MAHD-dried cannabis samples using the FRAP method, likely due to the presence of iron-chelating compounds such as cannabinoids in the cannabis extract samples (Figure 3). Cannabinoids can interfere with the FRAP assay by chelating the Fe3+ irons in the FRAP reagent mixture; Dawidowicz et al. [ 2021] showed that cannabinoids are antioxidant agents as they can scavenge free radicals, and THC’s antioxidant activity was greater by $35.3\%$ with the FRAP assay when compared to DPPH assay. Given these data, the FRAP assay is recommended for determining antioxidant activity in cannabis and hop inflorescences. Further studies using other antioxidant activity assays such as oxygen radical absorbance capacity (ORAC) and determining the presence of antioxidants in different cannabis and hop plant organs could be explored. The significant ($p \leq 0.05$) increase in total THC and THCA concentrations can be attributed to the pre-freezing step. Pre-freezing causes structural damage to trichome structures and can be considered as an abiotic stressor [51,63]. Ahmed et al., [ 2013] reported that abiotic stresses increased total phenolic compounds (TPC) by $62.5\%$ in barley (Hordeum vulgare) compared to the control upon harvest. Taking this into account, it is plausible that the structural damage incurred by trichomes during pre-freezing step helps release trapped secondary metabolites. Cannabinoid analyses showed a significant ($p \leq 0.05$) increase in THC for MAHD-dried samples compared to fresh, undried ($64.7\%$) and pre-frozen, undried samples ($57\%$). This can be explained by the non-enzymatic decarboxylation process [49]. However, freeze-drying did not cause a significant change in the concentration of THC and THCA in the samples. Hence, freeze-drying can be used to preserve the secondary metabolites present in cannabis and these data support previous findings [51]. These findings are comparable to other crops preserved in this manner [64,65]. Moreno et al. [ 2020] showed that the non-enzymatic decarboxylation of acidic cannabinoids to neutral cannabinoids increases with the increase in temperature. Using a decarboxylation time of 60 min, the concentration of THC increased from 0.02 g 100 g dry matter−1 (80 °C) to 0.03 mg g dry matter−1 (120 °C). Similar observations were made for the terpenes present in hops and cannabis. MAHD caused significant ($p \leq 0.05$) thermal degradation of terpenes in the studied samples. ## 4.1. Sample Preparation Hops (Brewer’s gold) were cultivated outdoors at McGill University’s Macdonald Campus farm in Sainte-Anne-de-Bellevue, QC, Canada. Hops were planted on 3 May 2022, and harvested from mid-September to the end of October 2022. Preliminary tests were conducted using a split plot design to limit the differences between the hops harvested from the different plots. The cannabis inflorescence was harvested from an indoor-grown accession (Qrazy Train). Harvested hops and cannabis biomass was pre-frozen at −20 °C for a minimum of 24 h prior to drying and analysis as described previously [51]. The initial moisture content of the hops and cannabis inflorescence was determined using a hot air oven (Fisher Scientific 6903 Isotemp oven, Waltham, MA, USA). Each sample was dried at 50 °C for 24 h. ## 4.2. Freeze Drying of Hops and Cannabis Optimal freeze-drying conditions for cannabis and hop biomass identified previously were applied to this experiment [51]. For each condition, approximately 100 g pre-frozen cannabis and hop inflorescence samples were placed in plastic trays and transferred to a laboratory-scale vacuum freeze-dryer (Martin Christ Gefriertrocknungsanlagen GmbH Gamma 1–16 LSCplus, Osterode, Lower Saxony, Germany) with a condenser temperature of −55 °C. Freeze-drying was carried out at 20 °C for 24 h at 0.85 mbar until the sample reached a dry basis moisture content of $12\%$. Dried samples were transferred into a food-grade plastic bag and stored in a refrigerator at 5 °C before analyses. Each experiment was performed in triplicate using three different biomass samples. ## 4.3. Microwave-Assisted Hot Air Drying of Hops and Cannabis (MAHD) Optimal MAHD conditions for cannabis and hop biomass identified previously were applied to this experiment [51]. MAHD was conducted in an automated laboratory-scale microwave oven with several modifications. Briefly, the main components were a 2450 MHz microwave generator (Gold Star 2M214, Seoul, South Korea) with adjustable power (0 to 750 W), waveguides, a three-port circulator, a manual three-stub tuner to match the load impedance, microwave couplers to measure forward and reflected power, a carbon load to absorb reflected power, and a microwave cavity made of brass (0.47 × 0.47 × 0.27 m) in which the samples were processed. In each experiment, approximately 100 g pre-frozen hops and cannabis inflorescence were placed in a nylon mesh sample holder tray (diameter = 0.21 m). The plant material was spread in one layer and placed inside the microwave cavity. Drying was performed until the sample reached a dry basis moisture content of $12\%$. Dried samples were transferred into a plastic bag and stored in a refrigerator at 5 °C before analyses. Drying was performed in triplicate under each condition. ## 4.4. Extraction of Secondary Metabolites Representative samples for each of the drying conditions and fresh samples were immersed in liquid nitrogen before grinding with a coffee grinder (Hamilton Beach, Belleville, ON, Canada). Ground samples were allowed to equilibrate to room temperature before 0.75 g was weighed in a 50 mL Falcon tube and recorded. Each sample was allowed to sit for 10 min on the scale (Mettler AE50 analytical balance, Columbus, Ohio, United States of America) until there was <1 mg change in mass. This is done to ensure that most of the liquid nitrogen had evaporated from the sample and the proper sample mass was obtained. For the extraction of secondary metabolites, 20 mL high-pressure liquid chromatography (HPLC)-grade methanol (Thermo Fisher Scientific, Waltham, MA, USA) was added to each Falcon tube and vortexed (Thermo Scientific vortex, Waltham, MA, USA) for 20 min at 500 rpm. Each sample was filtered using Whatman™ filter paper (Thermo Fisher Scientific, Waltham, MA, USA) and allowed to filter for 20 min. Residual cannabis biomass was placed into a new 50 mL Falcon tubes and subjected to a second extraction process to ensure $99.5\%$ of the secondary metabolites were extracted. The second extract was added to the corresponding first extract, resulting in a 40× dilution total extract. ## 4.5. Measuring Antioxidant Activity with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) Assay Antioxidant activities of hops and cannabis were determined using the DPPH assay introduced by Brand-Williams et al. [ 1995] and used by Dawidowicz et al. [ 2021] for cannabis, with some modifications. A calibration curve was generated using different serial dilutions of a 10 mM Trolox® standard (Sigma-Aldrich, Saint Louis, MI, USA) in HPLC-grade methanol (Thermo Fisher Scientific, Waltham, MA, USA). A stock solution of 0.1 mM DPPH ion (Sigma-Aldrich, Saint Louis, MA, USA) in HPLC-grade methanol was prepared fresh daily. Aliquots (100 μL) of extracted samples or standards were placed in 15 mL Falcon tubes and 2900 μL of DPPH ion stock solution was added. The mixture was subjected to vigorous vortexing (Thermo Scientific vortex, Waltham, MA, USA) for 30 sec then incubated for 30 min at room temperature in the dark. Absorbances were measured at 517 nm using the Ultropec 2100 pro ultraviolet/visible spectrophotometer (Biochrom Limited, Cambridge, England). A DPPH ion solution was used as a control and HPLC-grade methanol was used to zero the spectrophotometer. The average radical scavenging activity of the samples was calculated and the DPPH inhibition (%) was calculated using Equation [1]. Concentration (M) of Trolox equivalent antioxidant activity (TEAC) using the calibration curve was calculated using Equation [2]. Results are reported as the concentration (M) of Trolox equivalent antioxidant activity (TEAC) per gram dry matter sample using Equation [3]. The experiment was carried out in triplicate. [ 1]% DPPH inhibition=Absorbancecontrol−AbsorbancesampleAbsorbancecontrol [2]TEAC M=% DPPH inhibition−8.700936.3611000 [3]TEAC M dry matter g−1=Extraction volume 0.04 L× TEAC MAnalysis volume 0.0001 L×sample mass −% mc×sample mass ## 4.6. Measuring Antioxidant Activity with the Ferric Reducing Antioxidant Power (FRAP) Assay The antioxidant capacity of hops and cannabis was additionally determined using the ferric reducing antioxidant power (FRAP) assay based on methods developed by Benzie and Strain [1996] and Dawidowicz et al. [ 2021] for cannabis, with some modifications. The standard curve was prepared using different serial dilution concentrations (10–0.004 mM) of Trolox (Sigma-Aldrich, Saint Louis, MI, USA). The FRAP reagent was prepared from 300 mM sodium acetate buffer (pH 3.6), 20 mM 2,4,6-tri(2-pyridyl)-s-triazine (TPTZ) (Sigma-Aldrich, Saint Louis, MI, USA) solution in 40 M hydrochloric acid (Thermo Fisher Scientific, Waltham, MA, USA) and 20 mM ferric chloride (FeCl3) (Sigma-Aldrich, Saint Louis, MI, USA) solution in proportions of 10:1:1 (v/v), respectively. The FRAP solution was prepared fresh daily and warmed to 37 °C in a water bath for 10 min prior to use. An aliquot (100 μL) of extracted samples or standards was placed in 15 mL Falcon tubes and 2900 μL FRAP stock solution was added. After vigorous vortex (Thermo Scientific vortex, Waltham, MA, USA) for 30 sec, the mixture was incubated for 60 min at room temperature and in darkness. Absorbances were measured at 593 nm using the Ultropec 2100 pro ultraviolet/visible spectrophotometer (Biochrom Limited, Cambridge, England). The FRAP solution was used as a control and HPLC-grade methanol was used to zero the spectrophotometer. The experiment was carried out in triplicate. FRAP inhibition was calculated using Equation [4]. The FRAP value (antioxidant activity) was calculated using the calibration curve and Equation [5]. Results are reported as FRAP value (M) per gram dry matter sample using Equation [6]. [ 4]FRAP inhibition AU= Absorbancesample− Absorbancecontrol [5]FRAP value M=FRAP inhibition−0.12631.22281000 [6]FRAP value M dry matter g−1=Extraction volume 0.04 L× FRAP value MAnalysis volume 0.0001 L×sample mass −% mc×sample mass ## 4.7. Cannabinoid Analyses Waters Acquity Ultra High-Performance Liquid Chromatography (UPLC) with a tunable ultraviolet (TUV) detector (Waters™, Mississauga, ON, Canada) was used for cannabinoid analyses. Each extract was further diluted 50× (for analysis of major cannabinoids) and 4× (for analysis of minor cannabinoids and terpenes) using HPLC-grade methanol (Thermo Fisher Scientific, Waltham, MA, USA). One-milliliter samples of each extract were pipetted into HPLC vials for cannabinoid analysis. The Waters cortex column was used to separate cannabinoids with a sample injection volume of 2 μL and a column temperature of 30 °C, equipped with an isocratic gradient pump. Mobile phase A consisted of $22\%$ reverse osmosis water and $0.1\%$ formic acid (Sigma-Aldrich, Saint Louis, MI, USA). HPLC-grade acetonitrile ($78\%$) (Thermo Fisher Scientific, Waltham, MA, USA) was used for mobile phase B. Quantification of the cannabinoids was performed using an external calibration curve developed using 7 standard cannabinoids (LGC standards, Manchester, NH, USA and Sigma Aldrich, Saint Louis, MI, USA). ## 4.8. Terpene Analysis Terpene analysis assay previously described by Addo et al. [ 2022] was used for this study. Gas chromatography-tandem mass spectrometer was used for terpene analyses. One-milliliter samples of each extract were pipetted into gas chromatograph (GC) vials for terpene analysis. Separation of the terpenes was performed with an Agilent 7820A GC coupled to an Agilent 7693 autosampler and a flame ionization detector (FID) (Agilent Technologies, Mississauga, Ontario, Canada). The system was equipped with an injector containing a capillary column (30 m × 250 μm × 0.25 μm nominal Agilent Technologies DB-5 Model) using split injection (ratio 50:1) with a hydrogen carrier gas (40 mL min−1). An injection volume of 5 μL each sample with a 10 μL syringe size was used. The oven temperature of the mass spectrometer was initially programmed at 35 °C and held for 4 min. The temperature was increased at a rate of 10 °C min−1 up to 105 °C held for 0 min, increased at a rate of 15 °C min−1 up to 205 °C held for 0 min, and lastly increased at a rate of 35 °C min−1 up to 270 °C held for 5 min. The inlet temperature into the FID detector was set at 340 °C. Spectra were recorded at three scans from 50 m z−1 to 400 m z−1. The ionization mode was used with an electronic impact at 70 eV. Quantification of the terpenes was performed using an external calibration of 37 terpenes mostly found in cannabis (LGC standards, Manchester, NH, USA and Sigma-Aldrich, Saint Louis, MI, USA). ## 4.9. Statistical Analysis All experimental determinations were performed in triplicate. Results from chemical analysis were expressed as average ± standard deviation and were calculated by MS Excel. Statistical analyses were conducted using the JMP software (JMP 4.3 SAS Institute Inc., Cary, NC, USA) with a confidence level ($p \leq 0.05$) of $95\%$. Pairwise comparisons of means were carried out using the Student’s statistical t-test. The analysis of the independent variables’ effect (pre-freezing, drying systems, and antioxidant assays) on the dependent variables (antioxidants activity, cannabinoids, and terpenes) was assessed using JMP software. The least-square multiple regression method was used to evaluate the relationship between the independent and dependent variables. Analyses of variance (ANOVA) was carried out to evaluate whether there were significant differences ($p \leq 0.05$) amongst the samples. ## 5. Conclusions The effects of postharvest processing on hops and cannabis were evaluated to determine the relationship between antioxidant capacity and secondary metabolites. The study compared the efficiency of DPPH and FRAP assays to estimate total antioxidant activity in hops and cannabis extracts. The antioxidant activity of extracts derived from fresh, untreated samples were 3.6 TEAC (M) dry matter−1 and 2.32 FRAP (M) dry matter−1 for hops, and 2.29 TEAC (M) dry matter−1 and 0.25 FRAP (M) dry matter−1 for cannabis. The results showed that although freezing of inflorescences is a preservation technique, pre-freezing the hops and cannabis samples before drying increased the antioxidant values by $13\%$ (DPPH assay) and $29.9\%$ (FRAP assay) for hops, and by $7.7\%$ (DPPH assay) and $19.4\%$ (FRAP assay) for cannabis. Data showed that freeze-drying and MAHD significantly ($p \leq 0.05$) reduced the antioxidant activity in hops by $79\%$ and $80.2\%$ [DPPH], respectively, and $70.1\%$ and $70.4\%$ [FRAP], respectively, compared to pre-frozen, undried hops. For cannabis, the DPPH assay showed that both freeze-drying and MAHD significantly ($p \leq 0.05$) reduced the antioxidant activity of cannabis. However, there was no significant ($p \leq 0.05$) difference between the antioxidant activity values for pre-frozen, freeze-dried, and MAHD cannabis samples using the FRAP method because of the presence of iron-chelating cannabinoids in the cannabis. Results showed that the FRAP assay accurately determines the antioxidant activities of cannabinoids compared to the DPPH assay and is a valuable assay for the cannabis industry. ANOVA analyses showed a significant ($p \leq 0.05$) increase in the total THC (24.2 g 100 g dry matter−1) and THCA (27.2 g 100 g dry matter−1) concentrations in pre-frozen, undried samples compared to fresh, undried samples. Non-enzymatic decarboxylation was observed by the significant ($p \leq 0.05$) increase in the THC in MAHD-dried samples compared to fresh, undried ($64.7\%$) and pre-frozen, undried ($57\%$). Although both drying systems showed a significant loss in the total terpene concentration, freeze-drying has higher terpene retention compared to MAHD. Freeze drying should be used as the drying system for medicinal plants to reduce the postharvest losses of secondary metabolites and decarboxylation of cannabinoids. ## References 1. 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--- title: 'Insulin Resistance and Bone Metabolism Markers in Women with Polycystic Ovary Syndrome: A Cross-Sectional Study on Females from the Islamic University Medical Center' authors: - Fahad Khalid Aldhafiri - Fathy Elsayed Abdelgawad - Gihan Mohamed Mohamed Bakri - Tamer Saber journal: Medicina year: 2023 pmcid: PMC10056621 doi: 10.3390/medicina59030593 license: CC BY 4.0 --- # Insulin Resistance and Bone Metabolism Markers in Women with Polycystic Ovary Syndrome: A Cross-Sectional Study on Females from the Islamic University Medical Center ## Abstract Background and Objectives: polycystic ovarian syndrome (PCOS) prevails in females in the 18–40-year-old age group and varies from 5–$20\%$ depending on the demographic and diagnostic standards. It is unknown how long passes between the onset of a specific symptom and the appearance of the disease. The three most significant characteristics of PCOS include irregular menstruation, a polycystic ovarian shape found by pelvic ultrasound, and hyperandrogenism, which could possibly delay menarche. This study’s objective was to assess insulin resistance and bone bio-markers’ metabolism-involved characteristics of females with PCOS. Materials and Methods: We present a cross-sectional study carried out on 100 female patients suffering from PCOS and 100 healthy female subjects as a control living in Saudi Arabia in the Al-Madinah Al-Munawara Region between May 2021 and March 2022. The age of the studied groups ranges from 20–40 years, and patients were categorized into three groups; group I (control, $$n = 100$$), group IIa (overweight or obese females with PCOS, $$n = 70$$), and group IIb (non-obese females with PCOS, $$n = 30$$). The diagnosis of PCOS was carried out as per Rotterdam criteria as recommended for adolescent and adult subjects. All the groups were subjected to physical examination, and anthropometric measures, biochemical parameters, endocrine activity, and clinical parameters were determined. The data obtained were computerized and analyzed statistically using the SPSS program for range, mean, and standard deviation. ANOVA test with post hoc Tukey test was applied to assess the pattern and variation among the test and control groups. Results: In the present study, age, waist circumstances, systolic blood pressure, and diastolic blood pressure were reported enhanced in the PCOS over the control group. Additionally, anthropometric measures were reported slightly upregulated in group IIa over group IIb ($p \leq 0.001$). Biochemical parameters including glucose, insulin incidence, and lipids were reported higher in the PCOS over the control group, where group IIa showed slightly increased values compared to group IIb ($p \leq 0.001$). On the contrary, PTH, Ca+2, and 25(OH)D levels were reported lower in the PCOS over the control group. However, in the control groups, a slight variation was reported as higher in group IIa compared to group II. In the study, PTH and 25(OH)D were found associated with bone metabolism; a lower level of PTH and 25 (OH) D is linked with a decline in bone density. Conclusions: Lower serum levels of PINP and osteocalcin along with the 25(OH)D were associated with the PCOS compared to the control group, imposing a higher risk of the syndrome. On the contrary, an elevated level of NTx in groups IIa and IIb over the control group was associated with insulin resistance and bone metabolism. ## 1. Introduction A complicated endocrine and metabolic disorder called polycystic ovarian syndrome (PCOS) is associated with chronic anovulation/oligomenorrhea, hyperandrogenism, and insulin resistance [1]. The European Society for Human Reproduction and Embryology and the American Society for Reproductive Medicine (ESHRE/ASRM) established the Rotterdam guidelines for PCOS in 2003 [2,3]. The diagnosis of PCOS is a challenging procedure that necessitates ruling out other probable reasons, notably hyperandrogenism and menstrual irregularities (hyperprolactinemia, non-classical congenital adrenal 21-hydroxylase deficiency, thyroid disorders, androgen-secreting malignancy, and Cushing’s disorder). When at least two of the following factors for polycystic ovary syndrome are present, namely oligomenorrhea or anovulatory cycles with anomalous menstrual cycle, increased levels of circulating androgens or clinical manifestations of androgen excess, and ultrasound evidence of polycystic ovary syndrome, the disorder can be scientifically diagnosed and defined. Nearly 5–$10\%$ of females of reproductive age develop PCOS, which is accompanied with endocrine abnormalities. PCOS varies in the population globally [4,5,6]. Moreover, the incidence of PCOS differs among populations based on the testing parameters used, with the incidence rate as per the Rotterdam criteria being approximately 2–3 times higher than as per those based on the National Institutes of Health (NIH) criteria [7,8,9,10,11,12]. Insulin resistance (IR) and PCOS are closely linked in female populations across the world. Because insulin’s physiological functions such as carbohydrate intake and metabolism, glucose synthesis, and lipid metabolism are no longer as effective, increased insulin levels are essential to achieve adequate metabolism function in individuals with insulin resistance. When the pancreatic beta cells are physiologically normal, there is an increased level of circulating insulin whenever IR is present [13]. Insulin promotes tyrosine phosphorylation on tyrosine residues and stimulates a cell’s intrinsic kinase after interacting with a receptor on the cell surface [14,15]. As per studies, diminished receptor adhesion in insulin signaling promotes insulin sensitivity, decreasing in PCOS females. The primary contributor to a reduction in insulin sensitivity is the serine phosphorylation of the insulin receptor and IRS-1 by intracellular serine kinase. Consequently, PCOS females exhibit decreased insulin mediated PI3K activation and resistance to insulin’s metabolic changes [16]. Obesity is still a crucial variable in PCOS women’s condition, although the post-receptor mechanism disruption is associated with insulin-resistant and lean/normal-weight in such females [17,18]. PCOS in females is connected to hyperinsulinemia and hyperandrogenemia, in which the ovary preserves its sensitivity to insulin activity and, as a response, produces androgen in addition to systemic insulin resistance [19,20]. Furthermore, research has shown that females with PCOS as per predefined criteria for the multiple sclerosis (MS) often have an increased prevalence of hypertension, dyslipidemia, and abdominal obesity [21]. For a clinical diagnosis of the metabolic syndrome, central adiposity should be linked to at least two of the following pathophysiological symptoms: hypertension, elevated triglyceride levels, decreased high-density lipoprotein cholesterol (HDL-C) levels, or a rise in fasting blood sugar levels [22]. As per studies, several PCOS females are more resistant to insulin than control-group females despite meeting the same age and body mass index (BMI) criteria. Additionally, compensatory hyperinsulinemia remains involved prominently with IR in PCOS females [23]. Obesity remains one of key causes for insulin resistance in PCOS females; however, recent research has demonstrated that this condition is independent of body weight [24]. Overall, $3\%$ of the human genome is subject to the control of the vitamin D receptor gene that also controls blood pressure and genes involved in lipid and glucose metabolism [25,26]. Females having PCOS who do have metabolic syndrome also depend on vitamin D, and it has been demonstrated that serum vitamin D levels in the blood enhance the risk of MS [27]. The hormones in IR, hyperinsulinemia, and obesity have an impact on calcium homeostasis. Vitamin D serum levels in obese people and PCOS women are low, whereas parathyroid hormone (PTH) concentrations are higher [28,29,30]. Earlier studies have demonstrated a relationship between PCOS as well as certain bone health factors, most notably a decrease in osteocalcin, which is a marker of bone development, as well as decrease in spinal and femoral bone mass in PCOS individuals with BMIs ≤ 27 kg/m2 [31,32]. An impact of androgens on females is not completely explored, even though estrogen is essential for growth and maintaining bone density in females. The ovary as well as other glandular tissue is believed to be where androgens transform into estrogens, which then attach to estrogen receptors in the target tissues. Androgens have an influence on bone metabolism in this manner [33]. Therefore, the hyperandrogenism brought on by PCOS in women’s ovarian and adrenal glands could have an influence on bone turnover and bone mineral density (BMD). On the other hand, menstruation disruption at this critical period may have a similar effect since maximal bone mass is achieved between the late teenage years to the mid-thirties [34]. The prospective risk for osteoporosis among young women with PCOS because of their irregular periods of menstruation and amenorrhea is not yet established. BMD alterations, BMD increases, or BMD losses have not really been observed in PCOS affected women either [35,36]; hence, it is unknown whether BMD changes occur with PCOS [37]. ## 2. Aim of the Study The rationale of present investigation is to explore and characterize IR, biochemical features of bone metabolism, and association with metabolic characteristics in females with PCOS. The study also examines and describes IR, biochemical markers of bone metabolism, and association with metabolic parameters in females with PCOS. ## 3. Subjects and Methods A maximum of 200 participants, namely 100 clinically diagnosed PCOS individuals and 100 control participants, were enrolled in the cross-sectional study. The number of individuals participating in this study was calculated according to the equation for calculating sample size, which is as follows: =n/(1 + (n − 1)/P) where A is the adjusted sample size, n is the sample size, and P is the population size. Between May 2021 and March 2022, the study was conducted at Al-Madinah Al-Munawara, Saudi Arabia. The participants who signed consent were between the ages of 20 and 40. During follow-up at the obstetrics and gynecology clinic at the Islamic University Medical Center, subjects were chosen from outpatient clinics. According to the diagnosis, the included participants were separated into two groups, with group II being further divided into group IIa and group IIb. Group I included 100 healthy female patients as controls;Group IIa included 70 overweight or obese female patients diagnosed as having polycystic ovary syndrome and with BMI above 25 kg/m2;Group IIb included 30 non-obese female patients diagnosed as having polycystic ovary syndrome and with BMI less than 25 kg/m2. All study subjects were subjected to physical, anthropometric, and laboratory examinations. ## 3.1. Exclusion Criteria All 200 enrolled individuals in this instance met the predetermined inclusion criteria. Subjects in the study group, who were all from twenty to forty years of age, demonstrated PCOS symptoms in compliance with the Rotterdam diagnoses. The additional diagnoses of oligo-anovulation, such as hyperprolactinemia, Cushing’s disease, untreated hypothyroidism, congenital adrenal hyperplasia, and adrenal tumors, were also excluded. Participants who reported using androgens, valproic acid, cyclosporine, diazoxide, or minoxidil; having taken oral contraceptives, metformin, thiazolidinediones, or spironolactone for longer than the previous three months; or who were pregnant were also excluded from the study. Before a clinical diagnosis of PCOS, participants who had diabetes mellitus (pre-existing) were excluded from the research. ## 3.2. Physical Examination and Anthropometric Measures The physical examination included a chest, abdominal, and neurological examination with stress on blood pressure measurement. The blood pressure was measured and recorded in the enrolled participants using standard laboratory conditions. Both systolic and diastolic pressures were checked and recorded twice using an automated blood pressure measurement instrument and the appropriate-sized cuff (bladder within the cuff must encircle $80\%$ of the arm), with measurements separated by two minutes. Anthropometric parameters were determined as per the standard protocols. The body weight of all the participants in both the study and control groups was determined to the nearest 200 gm. BMI was determined with a ratio of weight (kg) over height squared (m2). WC was determined to the nearest 0.5 cm at the end of expiration at the midpoint between the top of the iliac crest and the lowest rib in an upward orientation. Patients with PCOS were diagnosed according to the Rotterdam criteria [2]. The main clinical symptoms and symptoms of PCOS are mentioned below, although not every PCOS patient will encounter these: regular missing periods, obesity increase particularly around the waist, excessive facial hair development (hirsutism), persistent acne (generally resistant to conventional therapy), and greasy skin are the chief clinical signs linked to PCOS. The diagnosis of PCOS also points to loss of hair on the scalp, difficulties conceiving or having children, significant dark skin spots (acanthosis nigricans) and skin tags, moderate to severe pelvic pain, mood swings, anxiety, and depression. Here, in present study, for all the registered participants, the Rotterdam criteria were applied for diagnosing PCOS, including two out of the three features in the revised criteria:Oligomenorrhea (irregular menstrual periods) or amenorrhea (absence of menstrual periods);Hyperandrogenism (based on clinical signs in the body) and/or biochemical signs (hormone levels in the blood);Polycystic ovaries (on the ultrasound): Polycystic ovaries are described on an ultrasound scan as the “presence of 12 or more follicles in one or both ovaries measuring 2–9 mm in diameter, and/or increased ovarian volume (>10 mL)” [38]. ## 3.3. Laboratory Measurements After a 10–12 h overnight fasting, fresh blood samples were collected from the study and control groups in the morning. The blood was drawn from the antecubital vein between the second and the fifth days of the cycle, during the early follicular phase. Obtained blood specimens were centrifuged to collect the serum after 20 min of clotting time. Using a standard biochemical laboratory diagnostic protocol, the serum concentrations of calcium, phosphorus, total alkaline phosphatase (ALP), fasting glucose (FG), total cholesterol (TC), total triglycerides (TG), high-density lipoprotein cholesterol (HDL-cholesterol) and low-density lipoprotein cholesterol (LDL-cholesterol), calcium, phosphorus, and total alkaline phosphatase (ALP) were calculated with an automated Siemens Dimension Clinical Chemistry System [39,40,41,42]. The Vitros 3600 (Ortho–clinical Diagnostic, Johnson, and Johnson Co., Colorado Springs, CO, USA) immunodiagnostic system’s Reagent Pack and Vitros calibrators were made to produce the result of estrogen, follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin (PRL), total testosterone, 25-hydroxyvitamin D (25(OH)D), and intact parathyroid hormone (iPTH) [43,44,45,46,47,48,49,50,51]. Chemiluminescent micro particle immunoassay (CMIA) was used for the quantitative determination of architect dehydroepiandrosterone sulfate (DHEA-S), fasting insulin (FI), and sex hormone-binding globulin (SHBG) [52,53,54]. Further, intact human osteocalcin was determined using ELISA, 96-well plates, and a micro plate reader [55]. Patients were instructed to submit a second-morning void urine sample for quantitative NTx assessment using the Vitros NTx Reagent Pack and Vitros NTx calibrators on the Vitros immunodiagnostic instrument. Bone collagen was quantified in the recent research as nanomoles of bone collagen equivalent per liter (nM BCE) [56,57]. Using the continuity formula, urine dilution was adjusted utilizing urine creatinine analysis and nanomoles of bone collagen equivalent per liters per millimole creatinine [58]. NTx nMBCE/mMcreatinine= NTx nMBCE/Urinary creatinine mM Similarly, non-HDL cholesterol was calculated using following equation [59]. Non−HDL = TC –HDL cholesterol Additionally, the Vermeulen formula was used for free testosterone calculation [60]. The studies also included dual-energy X-ray absorptiometry (DXA scan), the qualitative insulin sensitivity check index (QUICKI), and the homeostasis model evaluation of insulin resistance (HOMA-IR). Using fasting insulin and fasting glucose levels, HOMA-IR determined insulin resistance using the following formula: [insulin (uU/mL)] [fasting glucose (mg/dL)]. HOMA-IR higher than 2.5 was employed to define insulin resistance [61]. Here, in the present study, both fasting insulin (uU/mL) and glucose (mg/dL or mmol/L) levels from blood samples were determined using QUICKI formula. The QUICKI formula for insulin and glucose level used for insulin resistance/sensitivity is shown below [62]. 1/logFasting Insulin+ log Fasting Glucose Bone densitometry, also known as scanning dual-energy X-ray absorptiometry (DXA), was employed to examine and estimate bone-loss measurement. DXA provides a consistent method for determining bone mineral density (BMD) [63,64]. ## 3.4. Statistical Analysis Here, in present study, Statistical Package for Social Science (SPPS) version 27.0 was used for the analysis of collected data, anthropometric measures, and laboratory measurements. The analyzed data from the present study are summarized in tables as range, mean, and standard deviations (SD). Further, analysis of variance (ANOVA) along with post hoc Tukey test was performed to examine the difference between the study and control groups. Pearson correlation coefficient was used to analyze two quantitative variables. Statistical significance was detected when the p-value was equal to or less than 0.05, and high significance was detected when the p-value was less than 0.001. ## 4. Results Table 1, Table 2, Table 3, Table 4 and Table 5 demonstrate both the biochemical and clinical parameters of the study groups categorized in accordance with the polycystic ovary syndrome criteria. There was no statistically significant difference in age, SBP, DBP, TG, FSH, PRL, PTH, ALP, NTx, or BMI Z or T score between the groups in investigation. Nevertheless, there was a statistically significant difference in the WC, BMI, FG, and FI between groups I and IIa. The biochemical parameters showed a statistically significant difference in HOMA, QICKI, cholesterol, HDL, non-HDL, LH, estrogen, SHBG, female total testosterone, female free testosterone, female testosterone percent, DHEAS, total Ca, pH, 25(OH) D, osteocalcin, and PINP. HOMA, QICKI, cholesterol, HDL, LDL, LH, estrogen, SHBG, female total testosterone, female free testosterone, female testosterone percent, DHEAS, albumin-corrected total Ca, P, 25(OH)D, osteocalcin, and PINP were significantly different between the groups I and IIb. In terms of cholesterol, LDL, and non-HDL, there was no statistically significant distinction between group I and group IIb. Between groups IIa and IIb, there was a statistically significant difference in WC, BMI, cholesterol, LDL, non-HDL, SHBG, DHEAS, 25(OH)D, osteocalcin, and PINP. In terms of FG, FI, HOMA, QICKI, HDL, LH, estrogen, female total testosterone, female free testosterone, female testosterone percent, Ca, P, osteocalcin, and PINP, there was a lack of significant difference between groups IIa and IIb. In Table 6, there is a statistically significance positive correlation between NTx and QICKI and a statistically significance negative correlation between NTx and BMI, insulin, HOMA IR, cholesterol, and female testosterone. There was a statistically significance positive correlation between osteocalcin and parathyroid, ALP, and 25(OH)D and a statistically significance negative correlation between osteocalcin and prolactin. Finally, there was a statistically significance positive correlation between PINP and WC and testosterone. ## 5. Discussion In this cross-sectional study, 200 females attending the Islamic University Medical Centre were grouped into group I, including 100 healthy females; group IIa 70, including overweight or obese females with PCOS; and group IIb, including 30 non-obese females with PCOS. Figure 1 demonstrates the comparison of mean 25(OH)D levels among the three groups of participants. As the results show in Figure 1, group II (obese females with PCOS) reported a minimum level of serum means of 25(OH)D ng/mL compared to the other two groups i.e., IIb (non-obese with PCOS) and control (group I). A higher level of mean 25(OH)D ng/mL was reported in the control group (group I). Previously, Lin and Wu [2015] demonstrated the role of vitamin D in polycystic ovary syndrome [65]. Thomson et al. [ 2012] determined the level of 25(OH)D in healthy and PCOS patients. A reduced level, i.e., less than 20 ng/mL of 25(OH)D triggers a higher prevalence of PCOS cases (67–$85\%$) [66]. Additionally, a low level of 25(OH)D is also involved with IR in females with PCOS [67,68]. Bone turnover is directly associated with the rate of osteoporosis, and the role of 25(OH)D is pivotal in bone density. In the present study, serum levels of NTx were examined in all three groups and were reported higher in PCOS patients compared to the control. Interestingly, as the results show in Figure 2, group IIb, i.e., non-obese females with PCOS, reported higher serum levels of NTx than group IIa, i.e., obese females with PCOS. Previously, Iba et al. [ 2008] demonstrated a higher risk of bone turnover in patients with an elevated level of NTx. NTx is a potential biomarker for bone density and bone re-absorption. The serum NTx level changes significantly after menopause, and a declining level of 25(OH)D poses further risk of PCOS [69]. The serum NTx level is not directly associated with PCOS; however, elevated plasma levels of (25(OH)D play a pivotal role in minimizing the risk of PCOS. Procollagen type I N propeptide (PINP) serum levels were compared among the three groups and are demonstrated in Figure 3. As the results show in Figure 3, both groups IIa and IIb reported a declining level of PINP. In the study, the PINP level was higher in the control group than the PCOS groups, while group IIa (obese females with PCOS) showed significantly higher levels than group IIb (non-obese females with PCOS). PINP is the marker for bone formation, and as the results show in Figure 3, both groups IIa and IIb reported a sharp decline in serum level; hence, bone formation remains hampered. Lingaiah et al. [ 2017], in a multicenter study, demonstrated that a decreased level of PINP is associated with PCOS [70]. Additionally, the PINP level is also associated with 25(OH)D level and with risk factors of hyperandrogenism, hyperinsulinemia, and obesity [71]. PCOS poses a negative impact on bone health, where obesity is a crucial risk factor associated with 25(OH)D [72]. PTH indirectly enhances vitamin D via an increased level of Ca+2. Additionally, an increase in PTH level also enhances the PINP level [73]. In the present study, a declining level of PINP reported in the two groups, i.e., IIa and IIb, indicates a higher risk of PCOS. Similarly, comparison of osteocalcin among the three groups is shown in Figure 4, where groups IIa and IIb demonstrate a declining level of osteocalcin compared to group I (control). Additionally, group IIa reported the lowest level of osteocalcin, which demonstrates that obesity is a key cause of PCOS. Serum osteocalcin is a key biomarker for osteoporosis. On the contrary, Singh et al. [ 2015] reported a higher level of serum osteocalcin in a case-control study where patients showed a sharp rise in osteocalcin level over the control [74]. Vs et al. [ 2013] report a negative correlation between bone mineral density and osteocalcin level [75]. In the present study, physical examination, and anthropometric measures among all the three groups are summarized in Table 1. As the data show in Table 1, five variables including age, waist circumference, body mass index, systolic blood pressure, and diastolic blood pressure were examined. Among these variables in different groups, the BMI findings of group IIa were significant, while the other two groups were highly significant. Similarly, WC findings demonstrated significant outcomes in the post hoc test. Chitme et al. [ 2017] investigated an association between physical examination and anthropometric measures and the risk of PCOS. A hospital-based case-controlled study with 132 patients along with control reported a mean age with a higher incidence of 29.74 ± 3.32 years. Additionally, BMI and WC in cases and controls were reported at 28.2 ± 6.08, 97.44 ± 15.11 cm, and 109.22 ± 17.39 cm, respectively [76]. Lim et al. [ 2012] and Borruel et al. [ 2013] in their studies also reported a similar pattern of physical examination and anthropometric measures [77,78]. The comparison of other variables remains non-significant. In Table 2, biochemical parameters, glucose, and insulin indices are summarized. As the results show in Table 2, fasting glucose, fasting insulin, HOMA IR, and QICKI were determined for group I (control), IIa (obese females with PCOS), and IIb (non-obese females with PCOS). In the present findings, except for the non-obese females with PCOS, the other two groups, namely the control and obese females with PCOS, reported significance in all the variables, while group IIb’s reports were non-significant. Najem et al. [ 2008] demonstrated clinical and biochemical profiles in PCOS cases in a retrospective study. Here, in the present study, $10\%$ were reported diabetic, with an elevated level of serum testosterone and serum prolactin at $26\%$ and $31\%$, respectively. PCOS prevalence depends on multiple risk factors, while obesity and insulin resistance remain critical [79]. According to Mario et al. [ 2012], a higher risk of PCOS was reported in cases with central obesity and insulin resistance [80]. The lipid profile in the study group showed a similar pattern, as shown in Table 3. Here, in the lipid profile analysis, group IIa reported non-significance ($p \leq 0.05$), while the other two groups were significant ($p \leq 0.05$). Table 4 summarizes the association between hormones and PCOS in the study groups. Here, in the present study, the serum level of FSH and PRL showed a non-significant difference between the control group and PCOS groups ($p \leq 0.05$), while LH, estrogen, SHGB, female total and free testosterone, and DHEAS showed a significant difference and a highly significant difference between the control group and PCOS groups ($p \leq 0.05$) and ($p \leq 0.001$). Vitamin D level is crucial in bone density and health in the present study, namely parameters PTH, Ca, PH, ALP and 25(OH)D, NTs, osteocalcin, and PINP. The serum level of PTH was reported as NS ($$p \leq 0.17$$), and the Ca level was highly significant ($p \leq 0.008$) for the control group and significant ($p \leq 0.03$) for group IIa while significant ($p \leq 0.05$) for group IIb. A similar pattern was reported for the Ph level, where it was highly significant ($p \leq 0.001$) for the control group and significant ($p \leq 0.02$) for group IIa while non-significant ($p \leq 0.92$) for group IIb. For the ALP, all the study groups reported serum levels that were non-significant ($p \leq 0.11$). In the study, levels of 25(OH)D were reported as highly significant among the three study groups ($p \leq 0.001$). On the contrary, NTx serum level in the study groups was reported as non-significant ($$p \leq 0.09$$). Additionally, serum levels of osteocalcin and PINP were reported as highly significant ($p \leq 0.001$) for the control group (group I) and group IIa and non-significant ($$p \leq 0.95$$ and 0.43) for group IIb, respectively. Table 6 summarizes the Pearson correlation coefficient where female testosterone and 25(OH)D are associated with a lower level of osteocalcin. Sam et al. [ 2015] examined the level of hormones, namely 25(OH)D, PTH, and testosterone, in the increase of PCOS cases [81]. Previously, Glintborg et al. [ 2006] evaluated the risk factors associated with the increase in the cases of PCOS [82]. On the contrary, a positive correlation was reported between NTx level and the biochemical parameters of glucose, insulin indices, and lipids ($p \leq 0.001$). Additionally, for PINP serum level, the Pearson coefficient was reported as significant in the case of total testosterone and female testosterone ($p \leq 0.05$). Metabolic dysfunction, which is associated with obesity, impaired 25(OH)D, and PTH, is critical as it confers a higher risk of PCOS [83]. ## 6. Conclusions Depending on age and clinical diagnosis, PCOS is a multifactorial endocrine and metabolic condition with a higher incidence (up to $20\%$). Chronic anovulation/oligomenorrhea, hyperandrogenism, and insulin resistance are all clinical signs of PCOS. The biochemical profiles of glucose, lipids, and hormones (PTH, LH, FSH, and 25(OH)D) are among the risk factors linked to PCOS. The present investigation strengthens and expands the study group’s comprehension of the biochemical and hormonal mechanisms underlying PCOS. According to the study, the average age of patients with PCOS among obese females was 32 years (20–42 years), and they displayed WCs of 93 cm, BMIs of 29 kg/m2, SBPs of 118 mmHg, and DBPs of 82 mmHg. The study also indicates that serum 25 (OH) D levels were considerably decreased in women with PCOS comparison to the control group. The serum NTx levels were found to be greater in women with PCOS in comparison to the control group, indicating the increased impact of PCOS on bone metabolism. The study demonstrated significantly decreased PINP and osteocalcin levels in women with PCOS versus the control group. As bone markers, the Pearson coefficient showed a strong correlation with the biochemical and hormonal variables of NTx, osteocalcin, and PINP. It is evident that the endocrine activity in PCOS females was higher compared to the controls. In the PCOS cases compared to the controls, 25(OH)D and Ca+2 levels were decreased. In addition, the serum concentrations of PTH, NTx, and PINP were higher in the PCOS cases compared to the controls, but osteocalcin levels decreased. In context of the above, the ongoing study arrived at the conclusion that bone formation markers are significantly lower in females with PCOS than in healthy females, perhaps having a long-term effect on these women’s bone mass. 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--- title: Synergistic Detrimental Effects of Cigarette Smoke, Alcohol, and SARS-CoV-2 in COPD Bronchial Epithelial Cells authors: - Abenaya Muralidharan - Christopher D. Bauer - Dawn M. Katafiasz - Heather M. Strah - Aleem Siddique - St Patrick Reid - Kristina L. Bailey - Todd A. Wyatt journal: Pathogens year: 2023 pmcid: PMC10056639 doi: 10.3390/pathogens12030498 license: CC BY 4.0 --- # Synergistic Detrimental Effects of Cigarette Smoke, Alcohol, and SARS-CoV-2 in COPD Bronchial Epithelial Cells ## Abstract Lung conditions such as COPD, as well as risk factors such as alcohol misuse and cigarette smoking, can exacerbate COVID-19 disease severity. Synergistically, these risk factors can have a significant impact on immunity against pathogens. Here, we studied the effect of a short exposure to alcohol and/or cigarette smoke extract (CSE) in vitro on acute SARS-CoV-2 infection of ciliated human bronchial epithelial cells (HBECs) collected from healthy and COPD donors. We observed an increase in viral titer in CSE- or alcohol-treated COPD HBECs compared to untreated COPD HBECs. Furthermore, we treated healthy HBECs accompanied by enhanced lactate dehydrogenase activity, indicating exacerbated injury. Finally, IL-8 secretion was elevated due to the synergistic damage mediated by alcohol, CSE, and SARS-CoV-2 in COPD HBECs. Together, our data suggest that, with pre-existing COPD, short exposure to alcohol or CSE is sufficient to exacerbate SARS-CoV-2 infection and associated injury, impairing lung defences. ## 1. Introduction More than 16 million Americans are affected by a disease associated with cigarette smoking, including heart disease, cancer, chronic bronchitis, and chronic obstructive pulmonary disease (COPD), with millions more undiagnosed cases estimated to exist [1]. Alcohol overconsumption is linked to cigarette smoking, with more than $80\%$ of people who abuse alcohol also smoking cigarettes [2]. Furthermore, alcohol abuse has been associated with lung diseases such as pneumococcal pneumonia [3] and COPD [4]. Alcohol metabolism can cause severe injury to the respiratory system, including acute respiratory distress syndrome, profoundly impacting the innate and adaptive immunity in the lungs [5]. This can increase the likelihood of pulmonary infections and exacerbate disease severity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a virus that causes coronavirus disease 2019 (COVID-19), has given rise to one of the largest pandemics in history. Spreading to more than 216 countries and territories in less than 8 months, the number of cases continues to increase worldwide, with more than 600 million confirmed cases and over 6.8 million deaths reported to the World Health Organization (WHO) [6] (https://covid19.who.int/ accessed on 21 February 2023). One of the reasons SARS-CoV-2 has such a high transmission rate is that many infected individuals are asymptomatic. However, when symptomatic, the infected individuals present with a wide range of symptoms from fever to pneumonia to acute respiratory distress to multiorgan failure and death [7]. About $15\%$ of patients develop pneumonia. Compared to other common viral infections, SARS-CoV-2 results in a higher number of hospitalizations with a substantial increase in the need for oxygen therapy and ventilatory support [8,9,10,11]. The severity of the symptoms and resulting prognosis most often depend on various risk factors and underlying conditions. Some of the major risk factors that can increase infection-related morbidity and mortality are older age [12], obesity [13], cigarette smoking [14], alcohol use disorders [15], and pre-existing lung disease [15,16]. Smoking is the leading cause of COPD. Along with using harmful substances such as cigarette smoke, alcohol, or any agent that can affect the lung environment, infections with respiratory pathogens can further exacerbate pre-existing COPD. Alcohol abuse, one of the top three lifestyle-related causes of death in the United States, increased during the COVID-19 pandemic, potentially significantly impairing lung immunity [17,18,19]. Together, cigarette smoke, alcohol, and SARS-CoV-2 can harm mucosal immunity in a healthy individual. Compounded with COPD, the outcomes can be catastrophic. Indeed, SARS-CoV-2 infection of COPD patients results in more severe disease leading to worse outcomes than non-COPD patients [20]. COPD and smoking are also associated with poor prognosis in COVID-19 patients [16]. Risk factors, such as cigarette smoking, alcohol misuse, and pre-existing lung diseases like COPD, have been shown to exacerbate SARS-CoV-2-mediated mortality and COVID-19 severity. Therefore, it is important to understand the individual effects and synergistic effects of these agents/diseases on immunity and pathogen clearance. Here, we studied the impact of a short exposure to cigarette smoke and/or alcohol on acute SARS-CoV-2 infection of ciliated primary human bronchial epithelial cells isolated from patients with COPD. ## 2.1. Isolation and Culture of Human Bronchial Epithelial Cells (HBEC) Deidentified human lungs were accepted from LiveOn Nebraska when they could not be utilized for transplantation in accordance with our IRB-approved protocol (IRB # 318-09-NH). The ‘Healthy’ donors of the lungs used in these experiments were free from chronic lung diseases such as COPD or asthma. None of the donors was a current smoker. They had less than a 20-pack-year history of smoking and did not have a history of alcohol use disorder. Ages ranged from 20–64. The ‘COPD’ donors were collected with the University of Nebraska Medical Center (UNMC) lung transplant registry and biorepository (IRB # 122-16-FB). All COPD donors had end-stage COPD requiring transplantation. Their ages ranged from 54–65. All of them had quit smoking for longer than one year but had a greater than 50-pack per year history of smoking. HBECs were isolated from 3 healthy donors and 3 COPD donors using an established protocol [21]. First, the large airways from the lungs were dissected and placed in a collagenase solution for 36–48 h. Next, the lumens of the large airways were scraped, and the resulting HBECs were plated in collagen-coated plates in Bronchial Epithelial Grow Media (BEGM) (Lonza, Basel, Switzerland). Subsequently, the HBECs were grown at Air-Liquid Interface (ALI) on inserts using Pneumacult-ALI media (StemCell Technologies, Cambridge, MA, USA) for 28 days until cilia were formed and could be validated for normal beating via Sisson Ammons Video Analysis (Ammons Engineering, Clio, MI, USA) [22]. ## 2.2. Virus SARS-CoV-2 wild-type strain USA-WA$\frac{1}{2020}$ (BEI Resources, Manassas, VA, USA) was propagated in Calu-3 cells (ATCC: HTB-55). All experiments involving viral infections were conducted at the University of Nebraska Medical Center (UNMC) Biosafety Level 3 (BSL3) facility with Institutional Biosafety Committee approval. ## 2.3. HBEC Treatments and Infection Primary HBECs were grown at the air-liquid interface, as described above. Once HBECs developed cilia, they were treated with 50 mM ethanol (Decon Labs, King of Prussia, PA, USA) and/or $5\%$ cigarette smoke extract (CSE; [23]) diluted in the basilar ALI media for 1 h at 37 °C and $5\%$ CO2. Following incubation, the basal media containing ethanol and/or CSE was removed, and fresh ALI media was added. The cells were then infected with 500 plaque-forming units (PFU) of the SARS-CoV-2 virus diluted in ALI media through the apical side. Following an 18-h infection at 37 °C, the viral inoculum was removed, and the cells were scraped and collected in Buffer AVL with carrier RNA (Qiagen, Hilden, Germany). The basal media was collected and stored at −80 °C for LDH assay and IL-8 ELISA. ## 2.4. RNA Extraction and Quantitative Polymerase Chain Reaction (qPCR) RNA was isolated from the cells using QIAamp Viral RNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. UltraPlex 1-Step ToughMix (QuantaBio, Beverly, MA, USA) was used along with 2019-nCoV CDC Probe and Primer Kit for SARS-CoV-2 (Catalog: KIT-nCoV-PP1-1000) for the CoV-2 qPCR reactions. Human ACE-2 primer/probe and 18S Ribosomal RNA control (Applied Biosystems, Waltham, MA, USA) were used for the hACE-2 qPCR reactions. QuantStudio 3 Real-Time PCR machine (Applied Biosystems, Waltham, MA, USA) was used with QuantStudio Design and Analysis software version 1.5.1 (Applied Biosystems, Waltham, MA, USA) for analysis. Results are expressed as CoV-2 or ACE-2 expression determined using 2−(ΔCt) method with 18S ribosome as the endogenous control. ## 2.5. Lactate Dehydrogenase (LDH) Activity Assay LDH activity was determined in the treated/infected HBEC basal media using an LDH Activity Assay Kit (Sigma-Aldrich, St. Louis, MO, USA) according to the manufacturer’s instructions. LDH activity is reported as milliunit/mL. ## 2.6. Interleukin-8 (IL-8) ELISA Secreted IL-8 in the treated/infected HBEC basal media was determined using Human IL-8/CXCL8 DuoSet ELISA kit (R&D Systems, Minneapolis, MN, USA) according to the manufacturer’s instructions. IL-8 is reported as ng/mL. ## 2.7. Statistical Analysis Statistical analysis was conducted using a two-way analysis of variance (ANOVA). Tukey’s post hoc test was used to adjust for multiple comparisons between different test groups. Tests were performed at a $5\%$ significance level. All statistical analyses were performed using GraphPad Prism 8 (San Diego, CA, USA) software. ## 3.1. Short Exposure to Cigarette Smoke or Alcohol Augments SARS-CoV-2 Infection in HBECs Isolated from COPD Patients We aimed to determine the effect of ethanol and cigarette smoke on SARS-CoV-2 infection of HBECs isolated from healthy and COPD patients. We exposed fully ciliated healthy and COPD HBECs to ethanol, CSE, or ethanol and CSE combined for 1 h before infecting the cells with SARS-CoV-2. Following infection, the cells were collected using qPCR to determine viral titer and ACE-2 expression. Because HBECs are grown for 3–4 weeks on ALI for proper cilia development, and the number of cells may vary from well to well, the viral titer and ACE-2 levels were normalized to 18S ribosomal endogenous control. In the healthy HBECs, neither ethanol nor CSE increased viral load, while single treatment of COPD HBECs with ethanol or CSE significantly augmented SARS-CoV-2 titers in the cells compared to the respective ‘no treatment’ controls (Figure 1A). Importantly, single, and combined treatment with ethanol and CSE significantly increased viral load in COPD HBECs compared to the healthy group, whereas viral titer was comparable in the ‘no treatment controls between normal and COPD HBECs. Together, short exposure to alcohol or cigarette smoke compounded with COPD substantially exacerbated SARS-CoV-2 infection in vitro. We then sought to identify if the changes in viral titer were due to alterations in ACE-2 expression, a SARS-CoV-2 entry receptor. Similar to the pattern observed with infection (Figure 1A), there were no differences in ACE-2 expression within the healthy HBECs, while single treatment with ethanol or CSE significantly increased ACE-2 levels in COPD HBECs compared to the ‘no treatment’ control (Figure 1B). However, with the dual treatment of ethanol and CSE, although ACE-2 levels increased in the COPD group, only a slight (not statistically significant) rise in infection was observed compared to the COPD control group. Interestingly, ACE-2 expression in untreated COPD controls was lower than in untreated healthy controls (Figure 1B), but this did not translate to decreased viral load (Figure 1A). This could be due to alternate mechanisms of entry used by the virus. ## 3.2. SARS-CoV-2 Infection following Short Exposure to Cigarette Smoke and Alcohol Exacerbates Injury in HBECs from COPD Patients but Not in Healthy Patients Following SARS-CoV-2 infection of ethanol/CSE-treated HBECs, we collected the basal media to quantify LDH activity to help determine levels of injury induced by the combination of treatments and viruses in healthy and COPD HBECs (Figure 2). Notably, the trends in LDH activity were similar to that of infection in Figure 1A, with increasing infection levels correlating to higher injury. In the absence of COPD, neither ethanol nor CSE affected LDH activity, while COPD significantly exacerbated ethanol and/or CSE-mediated injury (Figure 2). Moreover, when the only virus was present (‘no treatment’ control), levels of cellular damage in healthy and COPD HBECs were comparable. Therefore, acute SARS-CoV-2 infection in HBECs from donors with COPD following short exposure to alcohol and/or cigarettes results in substantially higher cellular injury compared to HBECs from normal donors. It is important to note other studies have shown that HBECs exposed to $20\%$ CSE for 48 h or $5\%$ CSE for 24 h do not have an increase in LDH [24,25,26,27,28]. In addition, we have shown in many of our previous studies that 100 mM ethanol does not increase LDH [29,30,31,32,33]. Therefore, since we exposed the cells to $5\%$ CSE and/or 50 mM ethanol for 1 h in this study, we do not expect the treatments alone in the absence of the virus to have any effect on LDH activity in HBECs. ## 3.3. Elevated IL-8 Secretion Correlated to the Synergistic Damage Induced by SARS-CoV-2, Cigarette Smoke, and Alcohol in COPD HBECs IL-8 is a cytokine that is elevated in the airway epithelium of patients with COPD [34]. Indeed, airway epithelial cells isolated from COPD patients produce more IL-8 at baseline than cells derived from healthy controls [35,36,37,38,39,40,41]. Similarly, it is well-established that CSE increases IL-8 production in transformed airway epithelial cell lines [42,43,44,45] and primary airway epithelial cells [46,47,48,49,50]. As such, we aimed to determine IL-8 release in ethanol/CSE-treated and SARS-CoV-2-infected healthy and COPD HBECs. Therefore, we collected the basal media from treated/infected HBECs and quantified IL-8 secretion using ELISA. In agreement with infection (Figure 1A) and injury (Figure 2), infection of ethanol and/or CSE-treated healthy HBECs had no effect on IL-8 secretion (Figure 3). Furthermore, as expected, IL-8 levels in the ‘no treatment controls of COPD HBECs were significantly higher than that of healthy HBECs. This IL-8 secretion was further augmented with the treatment of CSE alone and a combination of ethanol and CSE in COPD HBECs (Figure 3). Overall, IL-8 levels correlated to the synergistic injury caused by SARS-CoV-2 infection and ethanol/CSE treatment of COPD HBECs. ## 4. Discussion Individuals who regularly smoke cigarettes are more likely to overconsume alcohol [51], while most individuals with alcohol use disorders also smoke cigarettes [52]. To make matters worse, alcohol use rose during the COVID-19 pandemic [53,54]. Importantly, both cigarette smoke and alcohol can severely compromise the innate immunity of the respiratory system. Consequently, cigarette smoking, alcohol use disorders, and pre-existing lung diseases such as COPD are major risk factors augmenting SARS-CoV-2-related morbidity and mortality [14,15,16,55]. Patients hospitalized for COVID-19 between March and May 2020 with COPD had more severe disease and worse prognoses than non-COPD patients [20]. COPD increased mortality by 1.5-fold in patients with pneumonia, where SARS-CoV-2 was the etiological agent [56]. Furthermore, patients with COPD and COVID-19 pneumonia had more cardiovascular events, longer hospital stays, and a 7-fold increase in mortality compared to non-COVID-19 pneumonia [56]. Alcohol can suppress cough and impair mucociliary clearance from the lung, abrogating innate immunity [57,58]. Furthermore, alcohol abuse has been shown to induce ineffective pathogen clearance and alter inflammatory responses in the lungs [5]. Chronic alcohol use can also increase oxidative injury in the lungs, resulting in elevated inflammatory cytokine levels [59]. For instance, higher levels of the chemokine RANTES and cytokines IL-6 and IL-8 were observed in the bronchoalveolar lavage fluid (BALF) of individuals with alcohol use disorders, predisposing heavy drinkers to more severe COVID-19 [60,61]. Alcohol use disorders increase the likelihood and severity of lung infections in patients with chronic lung diseases such as bronchitis, pneumonia, and COPD [62,63]. COPD and alcohol use together can cause considerable damage to lung immunity, specifically against viral infections. In our study, we showed that an increase in IL-8 secretion (Figure 3) accompanied the augmented infection (Figure 1A) and injury (Figure 2) seen in COPD HBECs following short exposure to alcohol/CSE and acute SARS-CoV-2 infection. Although some studies at the beginning of the COVID-19 pandemic suggested that nicotine had a protective role against SARS-CoV-2 [64,65], this has been disproven by other recent studies where smoking has been strongly associated with COVID-19 disease severity [66,67,68]. A recent study found increased soluble ACE-2 activity in the BALF of smokers and vapers compared to non-smokers [69]. ACE-2, an entry receptor for SARS-CoV-2, is expressed in membrane-bound and soluble forms in the lungs. Soluble ACE-2 can also bind the spike protein of SARS-CoV-2 and allow viral entry through receptor-mediated endocytosis [70]. Similarly, ciliated HBECs acutely exposed to cigarette smoke (14 puffs of smoke for 1 day) also had elevated membrane-bound and soluble ACE-2 activity compared to air-exposed controls. This translated to increased infection by SARS-CoV-2 pseudovirus in HBECs exposed to cigarette smoke [69]. Here, we used a 1-h ethanol/CSE exposure HBEC model, which was insufficient to see changes in live wild-type SARS-CoV-2 infection in healthy HBECs but enough to increase infection in treated COPD HBECs (Figure 1A). In contrast to our study, where the cells were infected for one day, single-cell RNA sequencing of differentiated HBECs from healthy and COPD patients 7 days after SARS-CoV-2 infection showed substantially higher infection in COPD HBECs than healthy cells [71]. This correlated with an increase in transmembrane serine protease 2 (TMPRSS2) and cathepsin B (CTSB), proteases involved in SARS-CoV-2 infection, and a decrease in protease inhibitors in COPD HBECs. In addition, inflammatory cytokines such as IL-6, commonly associated with COPD exacerbations and severe COVID-19, were upregulated in COPD HBECs from day 3 post-infection, while interferon responses, specifically IFN-β, were almost completely absent from day 4 post-infection in COPD compared to healthy cells [71]. Inhibition of proteases and therapeutic correction of inflammatory imbalances effectively restored viral clearance in COPD HBECs, highlighting the predisposition of COPD patients to severe COVID-19 [71]. One of the limitations of our study is the lack of multi-cell or in vivo systems where the increase in IL-8 secretion would have resulted in further enhancement of lung injury due to the recruitment of inflammatory cells such as neutrophils. However, primary ciliated HBECs, used in this study, are an excellent, translatable model for human SARS-CoV-2 infection. Indeed, SARS-CoV-2 was detected within multi-ciliated epithelial cells during the early stages of infection in COVID-19 patients, with ACE-2 predominantly localizing to the motile cilia of airway epithelial cells [72,73]. Other studies have established the serious implications of cigarette smoking, alcohol abuse, and lung conditions like COPD on the outcomes of SARS-CoV-2 infection. 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--- title: High Fructose Causes More Prominent Liver Steatohepatitis with Leaky Gut Similar to High Glucose Administration in Mice and Attenuation by Lactiplantibacillus plantarum dfa1 authors: - Thunnicha Ondee - Krit Pongpirul - Kanyarat Udompornpitak - Warumphon Sukkummee - Thanapat Lertmongkolaksorn - Sayamon Senaprom - Asada Leelahavanichkul journal: Nutrients year: 2023 pmcid: PMC10056651 doi: 10.3390/nu15061462 license: CC BY 4.0 --- # High Fructose Causes More Prominent Liver Steatohepatitis with Leaky Gut Similar to High Glucose Administration in Mice and Attenuation by Lactiplantibacillus plantarum dfa1 ## Abstract High-sugar diet-induced prediabetes and obesity are a global current problem that can be the result of glucose or fructose. However, a head-to-head comparison between both sugars on health impact is still lacking, and Lactiplantibacillus plantarum dfa1 has never been tested, and has recently been isolated from healthy volunteers. The mice were administered with the high glucose or fructose preparation in standard mouse chaw with or without L. plantarum dfa1 gavage, on alternate days, and in vitro experiments were performed using enterocyte cell lines (Caco2) and hepatocytes (HepG2). After 12 weeks of experiments, both glucose and fructose induced a similar severity of obesity (weight gain, lipid profiles, and fat deposition at several sites) and prediabetes condition (fasting glucose, insulin, oral glucose tolerance test, and Homeostatic Model Assessment for Insulin Resistance (HOMA score)). However, fructose administration induced more severe liver damage (serum alanine transaminase, liver weight, histology score, fat components, and oxidative stress) than the glucose group, while glucose caused more prominent intestinal permeability damage (FITC-dextran assay) and serum cytokines (TNF-α, IL-6, and IL-10) compared to the fructose group. Interestingly, all of these parameters were attenuated by L. plantarum dfa1 administration. Because there was a subtle change in the analysis of the fecal microbiome of mice with glucose or fructose administration compared to control mice, the probiotics altered only some microbiome parameters (Chao1 and Lactobacilli abundance). For in vitro experiments, glucose induced more damage to high-dose lipopolysaccharide (LPS) (1 µg/mL) to enterocytes (Caco2 cell) than fructose, as indicated by transepithelial electrical resistance (TEER), supernatant cytokines (TNF-α and IL-8), and glycolysis capacity (by extracellular flux analysis). Meanwhile, both glucose and fructose similarly facilitated LPS injury in hepatocytes (HepG2 cell) as evaluated by supernatant cytokines (TNF-α, IL-6, and IL-10) and extracellular flux analysis. In conclusion, glucose possibly induced a more severe intestinal injury (perhaps due to LPS-glucose synergy) and fructose caused a more prominent liver injury (possibly due to liver fructose metabolism), despite a similar effect on obesity and prediabetes. Prevention of obesity and prediabetes with probiotics was encouraged. ## 1. Introduction One of the main global health problems is obesity-induced diabetes mellitus, which is linked to other problems, including dyslipidemia and cardiovascular disease, with devastating consequences, especially in critically ill patients [1]. High carbohydrate intake is one of the important causes of metabolic syndrome that induces obesity and is diagnosed by three of the following five criteria; impaired fasting blood glucose (FBG), high fasting blood triglyceride, low high-density lipoprotein (HDL), high blood pressure, and abnormal waist circumference (apple-shaped body) [2]. Interestingly, metabolic syndrome is found in approximately 20–$25\%$ of the world’s population, and impaired fasting blood glucose can progress to a prediabetes condition (higher plasma glucose than normal, but not high enough for a diabetes diagnosis) or open type 2 diabetes mellites that are associated in part with an unhealthy diet [3]. Among high-carbohydrate diets, glucose is well known for its adverse effects, while fructose appears to have fewer health effects, as fructose syrup, but not glucose or sucrose, is currently used for most beverages and soft drinks [4]. There are inconsistent data on the impacts of fructose on body weight, as some publications do not demonstrate obesity after fructose administration in rodents [5,6,7], while other groups report weight gain from fructose [8]. On the one hand, some publications indicate that fructose does not exert specific metabolic effects on increased body weight [9]. However, fructose may be a carbohydrate with greater obesogenic potential than other sugars, in part due to the increased accumulation of triacylglycerol in hepatocytes [10]. Fructose and glucose seem to have some differences in liver lipogenesis and insulin signaling [11]; however, again with some controversial data [12]. Likewise, glucose has been mentioned to have more adverse effects on enterocytes and gut microbiota than fructose [13], but the data on this topic are still very limited. In fact, obesity-induced inflammation through several mechanisms, such as hypoxic hypertrophic adipocyte, adipocyte apoptosis [14,15], reduced adiponectin with elevation of leptin [16], mitochondrial dysfunction [17], and metabolic endotoxemia due to intestinal barrier defect [18] leads to atherosclerosis, a major vascular consequence of obesity [19]. In fact, endotoxin (lipopolysaccharide; LPS) has a molecular weight of 10–100 kDa and is found in the cell walls of Gram-negative bacteria, which are the most abundant organisms in the gut microbiota [20]. Although molecules with molecular weight (MW) greater than 0.6 kDa are generally unable to cross the tight junction barrier of the intestinal tract under normal circumstances [20], intestinal damages severe enough to allow translocation of pathogen molecules from the intestinal tract into the bloodstream are often called ‘leaky intestinal or intestinal leakage’ in several conditions, including obesity and diabetes [21,22,23,24]. Immune responses against endotoxins during obesity can be very potent because the activation by pathogen-associated molecular patterns (PAMP) of the organism is naturally more severe than the response toward damage-associated molecular patterns (DAMP) of the host cell [25]. Perhaps, glucose consumption could directly induce intestinal inflammation leading to a defect of the gut barrier and metabolic endotoxemia with systemic inflammation [26]. However, fructose could induce more potent steatohepatitis, known as nonalcoholic fatty liver disease (NAFLD) or obesity-induced steatohepatitis, which is more profoundly exacerbated by the presence of LPS in the blood circulation [27]. Therefore, the comparison between glucose and fructose consumption in terms of obesity, prediabetes, leaky gut, and systemic inflammation is interesting. The high abundance of carbohydrates in the diet produces gut dysbiosis [28] (an alteration of organisms in the intestinal tract [29]), in part due to the different abilities in carbohydrate metabolism between different groups of bacteria [30,31]. The increase in gut mucosal damage means that high MW molecules, such as LPS, can be directly translocated into the liver and circulatory system [32,33] leading to more severe steatohepatitis and systemic inflammation, respectively. Because intestinal leakage in several causes [32] is mainly attenuated by host-beneficial probiotics [34,35,36], in part through improved intestinal integrity by some anti-inflammatory substances [37,38,39,40,41]. Among several strains of probiotics, Lactiplantibacillus plantarum species (previously known as Lactobacillus plantarum) are lactic acid producing bacteria that are often used [42], in part due to (i) the tolerance to acid in the stomach and bile in the intestine [43], (ii) the well-known synergy with other combinations of probiotics [44], and (iii) the relatively easy preparation procedure. Furthermore, the adverse effect of Lactobacilli probiotics is not prominent and is reported primarily in immune-compromised hosts [45]. Recently, L. plantarum dfa1 isolated from the Thai population demonstrated probiotic properties in vitro [46]. Furthermore, Thai isolated probiotics may have some properties different from Caucasians isolated probiotics due to the possible influence of some specific characteristics (ethnics, diets, climate, and co-evolution impact) in the population [47,48,49]. Then we hypothesized that (i) there could be differences between high glucose and high-fructose diets and (ii) L. plantarum dfa1 could attenuate the conditions of mice with a high-carbohydrate diet. Therefore, the administration of a high-sugar diet in mice with or without L. plantarum dfa1 was compared with in vitro experiments. ## 2.1. Animals and Animal Model The animal care and use procedure was authorized by the Faculty of Medicine of Chulalongkorn University, Bangkok, Thailand (SST $\frac{025}{2563}$) according to the standards of the US National Institutes of Health. Then, 8-week-old male C57BL/6 mice were purchased from Nomura Siam (Pathumwan, Thailand). Mice with regular diet (RD) using standard laboratory food (Mouse Feed Food No.082, C.P. Company, Bangkok, Thailand) consisting of $55.5\%$ carbohydrates (no sugar), 31.3 % protein, and $13.2\%$ fat with an energy content calculated at 3.04 kcal/g. The high-carbohydrate diet was modified regular mouse food ($23.5\%$ protein and $10.0\%$ fat) with $66.5\%$ carbohydrates using $24.8\%$ glucose or $24.8\%$ fructose for the high-glucose diet (HGD) and the high-fructose diet (HFrD), respectively, with an energy content calculated at 3.04 kcal/g that is equal to the energy of the regular diet. Lactiplantibacillus plantarum dfa1 was isolated from the feces of Thai volunteers from the Thailand Science Research and Innovation Research Institute (TSRI: RDG6150124) at the Chulalongkorn University Faculty of Medicine [26]. The bacteria stock culture was stored in deMan Rogosa Sharpe broth (MRS) (Oxoid, Hampshire, UK) containing $20\%$ (vol/vol) glycerol at −80 °C and cultured on MRS agar under anaerobic conditions using gas generation sachets (Anaero Pack-Anaero, Mitsubishi Gas Chemical, Tokyo, Japan) at 37 °C for 48 h before use. The spectrophotometer (Bio-Rad, Smart Spec 3000; Bio-Rad, Hercules, CA, USA) at optical density using a 600 nm wavelength (OD600) of 0.15 (approximately 1 × 109 CFU) in 0.5 mL of phosphate buffer solution (PBS) or PBS alone was administered orally every other day for 12 weeks before sacrifice with cardiac puncture under isoflurane anesthesia. At 3 days before sacrifice, mice were tested for fasting blood glucose (FBG), fasting blood fructose, fasting insulin, homeostatic model evaluation of insulin resistance (HOMA-IR or HOMA index), lipid profile (cholesterol and triglyceride), oral glucose tolerance test (OGTT), insulin tolerance test (ITT), and gut leakage. At sacrifice, the liver and skin were snap frozen in liquid nitrogen and kept at −80 °C before use. Feces from all parts of the colon were combined and collected for microbiome analysis. In particular, the separation of mice was performed here because the microbiome analysis of the same cage could be similar to that of coprophagy (the consumption of feces from other mice). ## 2.2. Mouse Sample Analysis and Gut Leakage Measurement After fasting for 12 h with free access to drinking water, fasting blood glucose, fructose, and insulin (FI) were determined by colorimetric assay (glucose and fructose) (Cayman Chemical, Ann Arbor, MI, USA) and mouse Ins1/insulin ELISA kit (Sigma, MO, USA). Total cholesterol and triglycerides were evaluated using a cholesterol and triglyceride quantification kit (Sigma-Aldrich, St. Louis, MO, USA), while low- and high-density lipoprotein cholesterol (LDL and HDL) was quantified by lipid profile assays (Crystal Chem Inc., Downers Grove, IL, USA). Liver damage (serum alanine transaminase) and serum cytokines were determined by the EnzyChrom™ Alanine Transaminase Assay Kit (EALT-100; BioAssay Systems, Hayward, CA, USA) and enzyme-linked immunosorbent assays (ELISA) for mouse cytokines (Invitrogen, Carlsbad, CA, USA), respectively. The HOMA index was calculated followed by the following formula; HOMA index = [fasting insulin in µU/mL × fasting blood glucose in mmol/L]/22.5. For the oral glucose tolerance test (OGTT), mice fasted overnight (16–18 h) were administered orally with glucose solution (2 mg/kg body weight) before blood glucose measurement. For the insulin tolerance test (ITT), fasting mice for 6 h were administered intraperitoneally with human insulin (Humulin R, 0.75 units/kg body weight) before measuring blood glucose at 0, 15, 30, 60, 90, and 120 min afterward. The area under the curve (AUC) of OGTT and ITT was calculated by a trapezoidal rule. Gut permeability was determined by the fluorescein isothiocyanate dextran (FITC-dextran) assay and endotoxemia following previous publications [50,51,52]. As such, FITC-dextran, a non-absorbable molecule with 4.4 kDa molecular mass (Sigma-Aldrich, St. Louis, MO, USA) at 12.5 mg per mouse, was administered orally 3 h before detecting FITC-dextran in serum by fluorospectrometer (NanoDrop 3300; ThermoFisher Scientific, Wilmington, DE, USA). Serum endotoxin (LPS) was measured by detection of HEK-Blue LPS (InvivoGen, San Diego, CA, USA) and data were recorded as 0 when LPS values were less than 0.01 EU/ml due to the limited lower range of the standard curve. ## 2.3. Liver Analysis For histology, paraffin embedded sections (4 µm thick) stained with hematoxylin and eosin (H & E) of $10\%$ formalin-fixed samples were evaluated. The obesity-induced liver damage scoring system was used as follows; steatosis (0–3), lobular inflammation (0–3), and hepatocellular balloon degeneration (0–2) [53]. The thickness of subcutaneous fat was determined following a previous publication [54]. For the detection of lipids in the liver, the livers were sonicated (High-Intensity Ultrasonic Processor, Newtown, CT, USA) in 500 µL of ice-cold PBS containing the protease inhibitor cocktail (I3786) (Sigma-Aldrich, St. Louis, MO, USA) and lipids were measured from the supernatant by quantification assays as mentioned above. Furthermore, oxidative stress and an antioxidant molecule in the homogenized liver were evaluated after a previous study [55] using malondialdehyde (MDA) and glutathione (GSH) assays (Cayman Chemical Company, Ann Arbor, MI, USA). Furthermore, for cytokine detection in colon tissue, samples were weighed, cut, and thoroughly sonicated (High-Intensity Ultrasonic Processor, Newtown, CT, USA) in 500 mL of ice-cold PBS containing the protease inhibitor Cocktail (I3786; Sigma-Aldrich, St. Louis, MO, USA) and cytokines of the supernatant measured by ELISA (Invitrogen; ThermoFisher Scientific, Wilmington, DE, USA). ## 2.4. Fecal Microbiome Analysis Feces (0.25 g per mouse) of different cages were used in each experimental group for microbiota analysis following a previous protocol [56]. In summary, metagenomic DNA was extracted from 0.25 g of feces using the DNeasy PowerSoil Kit (Qiagen, MD, USA) using the Miseq300 platform (Illumina, San Diego, CA, USA) at the Omic Sciences and Bioinformatics Centre and Microbiome Research Unit for Probiotics in Food and Cosmetics, Chulalongkorn University. The raw sequences were quality processed and classified into operational taxonomic units (OTU) following Mothur’s standard operating platform procedures [57,58]. Bioinformatic analyses included good coverage, alpha diversity (e.g., Chao), and beta diversity. Linear discriminant effect size analysis (LEfSe) and meta-stats were also performed to determine the species marker and the unique representative species of the interested group, respectively [57,59]. ## 2.5. Responses of Enterocytes and Hepatocytes Caco-2 (HTB-37) or HepG2 (HB-8065) from the American Type Culture Collection (ATCC) (Manassas, VA, USA) was maintained in supplemented Dulbecco’s modified Eagle medium (DMEM) consisting of 5.5 mM glucose at 37 °C under $5\%$ CO2 and subcultured before use in the experiments. Cells at 1 × 106 cells/well were then incubated with an additional 25 mM/well of glucose or fructose with or without 100 µg/mL of lipopolysaccharide (LPS) from E. coli O26: B6 (Sigma-Aldrich, St. Louis, MO, USA) before determining supernatant cytokines (Quantikine Immunoassay; R & D Systems, Minneapolis, MN, USA). For enterocytes, the expression of occludin (intestinal tight junction) and the nuclear factor kappa B (NF-κB) in relative to β-actin (a housekeeping gene) was carried out according to the 2−ΔΔCp method [60]. The primers were as follows; occludin, forward 5′-CCAATGTCGAGGAGTGGG-3′, reverse 5′-CGCTGCTGTAACGAGGCT-3′; NF-κB, forward 5′-AGCACAGATACCACCAAGACC-3′, reverse 3′-GGGCACGATTGTCAAAGAT-5′; β-actin forward 5′-CCTGGCACCCAGCACAAT-3′, reverse 5′-GCCGATCCACACGGAGTACT-3′. The monolayer enterocytes were determined by transepithelial electrical resistance (TEER) using Caco-2 cells (HTB-37) at 5 × 104 cells per in the upper compartment of a Tran swell plate from a 24 well chamber for 15 days to establish the confluent monolayer. Subsequently, glucose or fructose (25 mM) with or without 1 µg/mL of LPS LPS) from E. coli O26: B6 (Sigma-Aldrich) was incubated at 37 °C at $5\%$ CO2. Subsequently, TEER was measured with an epithelial volt ohm meter (EVOM-2, World Precision Instruments, Sarasota, FL, USA) by placing the electrodes in the supernatant at the basolateral and apical chambers. The TEER value in the cell-free medium culture was used as a blank and was subtracted from all measurements. The unit of TEER was ohm (Ω) × cm2. The lower TEER represents the higher severity of the Caco-2 cell permeability defect. ## 2.6. Extracellular Flux Analysis Extracellular flux analysis was performed using Seahorse XFp analyzers (Agilent, Santa Clara, CA, USA) with the oxygen consumption rate (OCR) and the extracellular acidification rate (ECAR) representing mitochondrial function (respiration) and glycolysis activity, respectively, as previously described [23,24,61]. Briefly, stimulated Caco2 or HepG2 cells at 1 × 104 cells/well were incubated with Seahorse medium (DMEM complemented with glucose, pyruvate, and L-glutamine) (Agilent, 103575-100) for 1 h before activation by different metabolic interference compounds, including oligomycin, carbonyl cyanide-4-(trifluoromethoxy)-phenylhydrazone (FCCP) and rotenone/antimycin A, for evaluation of OCR. Parallelly, glycolysis stress tests were performed using glucose, oligomycin, and 2-Deoxy-d-glucose (2-DG) for ECAR measurement. Data were analyzed using Seahorse Wave 2.6 software. ## 2.7. Statistical Analysis Mean ± standard error (SE) was used for the presentation of the data. The differences between groups were examined for statistical significance by one-way analysis of variance (ANOVA) followed by Tukey’s analysis or Student’s t test for comparisons of multiple groups or two groups, respectively. All statistical analyses were performed with SPSS 11.5 software (SPSS, Chicago, IL, USA) and Graph Pad Prism version 7.0 software (La Jolla, CA, USA). A p-value of < 0.05 was considered statistically significant. ## 3.1. Both Glucose and Fructose Caused Obesity-Induced Prediabetes in Mice Attenuated by Lactiplantibacillus plantarum dfa1 Administration of glucose and fructose induced a similar severity of obesity compared to the use of the regular diet, as indicated by body weight (Figure 1A), despite an equal energy content between the high carbohydrate and the regular diet. Significant weight gain was demonstrated at as early as 12 weeks of the experiments (Figure 1A). Similarly, similar obesity parameters, including serum lipid profile (total cholesterol, triglycerides, LDL, and HCL), visceral fat deposition at several sites (mesentery, perirenal, and retro-peritoneum), and subcutaneous fat, were also demonstrated between a diet containing glucose and fructose (Figure 1B–J). Due to well-known obesity-induced prediabetes and insulin resistance [62], several diabetic parameters were determined. As such, a similar severity of prediabetes was demonstrated between glucose and fructose obesity by fasting blood glucose, fructose, and insulin, together with an oral glucose tolerance test and the HOMA index (see method) (Figure 2A–F). In particular, an elevation of fasting blood glucose after fructose administration and vice versa supported the interchangeability between glucose and fructose [63]. Glucose appears to induce more damage to the intestinal tract than fructose, as demonstrated by increased intestinal permeability (FITC-dextran assay); however, with similar endotoxemia (Figure 2G,H). Furthermore, intestinal integrity damage resulted in part in higher serum cytokines (TNF-α, IL-6, and IL-10) in mice administered glucose than fructose administration (Figure 2I–K). On the other hand, the impacts of fructose on the liver appear to be more prominent than those of glucose, at least in part, due to the more rapid intestinal absorption and liver delivery of fructose than glucose [64]. As such, liver damage, as indicated by liver enzyme (alanine transaminase), liver weight, liver injury score from the histology of fructose-administered mice, was higher than glucose administration (Figure 3A–D). The delivery of fructose from the intestinal tract to the liver was possibly better than glucose, as the carbohydrate content (glucose and fructose) in mouse livers after fructose administration was more prominent than in the glucose group (Figure 3E,F), which also supports the interchangeability of glucose and fructose. For liver lipid content, triglycerides, but not cholesterol, alone with oxidative stress (malondialdehyde; MDA) in mouse livers after fructose administration was more prominent than in glucose consumption groups without a difference in the reducing molecule (glutathione; GSH) in livers (Figure 3G–J). These data supported the well-known fructose-induced steatohepatitis and liver injury [65]. With L. plantarum dfa1 on a high-carbohydrate diet, the body weight of the mouse was not different from that of regular diet control mice that supported the impacts of the antiobesity effects of lactobacilli as previously published [46]. Although fasting blood triglyceride and fat deposition in the mesentery and subcutaneous tissue of probiotic-administered mice were higher than those of the control group, other obesity parameters, including lipid profiles in blood, perirenal fat, and retroperitoneal adipose tissue, were similar to those of control mice (Figure 1B–J), which implies the efficacy of probiotics against obesity. Due to obesity-induced prediabetes [46], the attenuation of weight gain by probiotics reduced the severity of glucose intolerance and insulin resistance, as indicated by several markers (fasting blood glucose, fructose, insulin with OGTT, and HOMA score) (Figure 2A–F). Parallelly, L. plantarum dfa1 also strengthened intestinal integrity in obese mice, as evaluated by the FITC-dextran assay, resulting in less severe endotoxemia in conjunction with reduced systemic inflammatory cytokines (Figure 2G–K). Although prediabetic condition, as indicated by higher fasting blood biomarkers, OGTT and HOMA score, and systemic inflammation (serum cytokines) in obese mice administered probiotics with probiotics was still higher than in non-obese control mice, all these parameters improved significantly (Figure 2A–K). Furthermore, L. plantarum dfa1 also attenuated liver injury induced by the high-carbohydrate diet [66] as all parameters of liver damage, including liver enzyme, liver histology, liver carbohydrate (glucose and fructose), lipid content, and oxidative stress (MDA) were reduced by probiotics (Figure 3A–I). Despite the more prominent severity of liver injury in fructose administration compared to glucose, probiotics similarly attenuated liver injury in the model (Figure 3A–J). Because (i) Gram-negative bacteria in the gut is a source of endotoxin (LPS) [20] that could enter the bloodstream (obesity-induced endotoxemia) [24] and (ii) intestinal dysbiosis causes a defect of the intestinal barrier [56,67,68], the effect of L. plantarum dfa1 on strengthening the intestinal barrier (serum FITC-dextran assay) could be, in part, due to the impact on intestinal dysbiosis. ## 3.2. A Similarity of the Intestinal Microbiota in Mice with Glucose Versus Fructose Administration and a Subtle Impact of Lactiplantibacillus plantarum There was no difference in the analysis of the fecal microbiome between control non-obese mice and mice with carbohydrate-induced obesity, as indicated by the abundance of bacteria in phylum, class, order, family, genus, and alpha diversity (Chao1 and Shannon score) (Figure 4A–I). However, some differences between mice with high-carbohydrate (glucose or fructose) and regular diet were demonstrated. As such, Clostridium bacteria (obligate Gram-positive anaerobe) in the Firmicutes phylum [69] in glucose and fructose group and lower Allobaculum spp. ( strictly anaerobic Gram-positive bacteria in the Firmicutes phylum [70]) in the fructose group compared to the control were demonstrated (Figure 5A). With the Linear discriminant effect size analysis (LEfSe), the unique bacteria in the control and high-glucose were Bacteroides (mostly Gram-negative anaerobes) and Prevotellaceae (a group of bacteria in the Bacteroidota phylum), respectively, while fructose diet induced Clostridial bacteria (Figure 5B). Likewise, the Cladogram (a phylo-genetic tree diagram showing the relationships among a group of clades) were different (Figure 5C). Despite similar Gram-negative bacterial burdens in feces and the fecal microbiota (Figure 4A–I) of mice with a high-carbohydrate diet compared to the regular diet, endotoxemia induced by high carbohydrate diet-induced endotoxemia (Figure 2H) implies an impact of carbohydrate on intestinal permeability [13]. With the administration of probiotics, there was only a subtle change in the analysis of the fecal microbiome. Although L. plantarum dfa1 did not alter the fecal abundance of the main group of fecal microbiota, including Firmicutes (Bacillota), Bacteroides, and Proteobacteria, probiotic administration increased the estimation of Lactobacilli and Chao1 abundance in mice administered glucose, but not in the fructose group, compared to control mice from control mice of regular diet (Figure 4G,H). With the administration of L. plantarum dfa1, the representative bacteria in the high glycemic and fructose diet were Lactobacilli spp. ( the beneficial bacteria) with Oscillospiraceae (the normal microbiota in the Firmicutes group) and Duboseilla spp. ( Firmicutes group) with Lachnospiraceae (Firmicutes group), respectively (Figure 5B). Here, the impacts of the high-carbohydrate-diet on the gut microbiota were subtle and resulted in the undetectable beneficial effect of probiotics on gut bacteria alteration. ## 3.3. More Prominent Impact on Glucose Lipopolysaccharide-Induced Enterocyte Damage Than Fructose, with Similar Glucose-Fructose Impacts on Hepatocytes Although the high abundance of lipopolysaccharide (LPS) from Gram-negative bacteria, the most abundant organisms in the intestinal tract, leads to natural resistance of enterocytic responses to LPS [71], a high concentration of LPS in the presence of elevated levels of carbohydrates could have some impact on enterocytes. In measuring enterocyte integrity, a high concentration of glucose and fructose alone did not alter enterocyte permeability measured by transepithelial electrical resistance (TEER), while a high abundance of LPS alone induced enterocyte damage, as indicated by the reduced TEER value (Figure 6A). Furthermore, TEER was lower in LPS plus carbohydrate (glucose or fructose) when compared to LPS alone and LPS with glucose produced more prominent damage than LPS with fructose (Figure 6A), supporting the well-known glucose-induced enterocyte integrity defect [13]. In fact, occludin gene expression (an enterocyte tight junction molecule) was negatively regulated by glucose plus LPS, but not under other conditions (Figure 6B). Parallelly, high concentrations of glucose or fructose alone did not induce inflammatory responses, as indicated by the expression of NF-κB and supernatant cytokines (TNF-α and IL-8) (Figure 6C–E). However, LPS with glucose, but not fructose, elevated supernatant cytokines (Figure 6C–E), implying a possible higher toxicity of glucose than fructose as a synergy to LPS-induced enterocyte injury. In cell energy status, glucose and fructose alone did not alter mitochondrial and glycolysis activities in Caco-2 cells (Figure 6F–I). However, LPS alone at high doses decreased maximal respiration without alteration of glycolysis activity compared to the DMEM control (Figure 6H,I). However, LPS plus high glucose concentration induced a higher maximal glycolysis than other groups (Figure 6H), possibly correlated with the most prominent enterocyte damage (TEER, occludin expression, and inflammatory markers) in this group (Figure 6A–E). Meanwhile, LPS plus glucose or fructose similarly reduced mitochondrial activity (maximal respiration) compared to the DMEM control (Figure 6I). For hepatocytes, carbohydrate alone (without LPS) did not induce inflammatory responses (Figure 7A–C), similar to the neutral effect of carbohydrate alone in Caco2 cells (Figure 6A–E). However, 72 h of glucose incubation alone, but not fructose alone, elevated basal respiration and maximal respiration without alteration of glycolysis (Figure 7D–H). In hepatocytes with LPS plus carbohydrate, there was an additive pro-inflammatory effect compared to LPS alone, as demonstrated by supernatant cytokines, without the difference between LPS plus glucose and LPS plus fructose (Figure 7A–C). For cell energy status, LPS alone did not change mitochondrial and glycolysis activities compared to the control with elevated maximal respiration and glycolysis capacity in glucose plus LPS compared to LPS alone (Figure 7D–H). These data suggested various impacts of glucose and fructose on different cell types. ## 4. Discussion Although both glucose and fructose induced similar obesity severity, glucose caused more severe leaky gut-induced systemic inflammation, while fructose generated more severe steatohepatitis, and L. plantarum dfa1 attenuated all mouse parameters. ## 4.1. Prediabetes with Prominent Steatohepatitis or Systemic Inflammation in Obese Mice after Glucose or Fructose Administration, Respectively, and the Various Effects of Different Sugars Despite the same calculated energy content at 3.04 kcal/g between high-sugar diets ($66.5\%$ carbohydrates with $24.8\%$ glucose or fructose) versus regular diet with $55.5\%$ carbohydrates without sugar component, both diets containing glucose and fructose generated similar obesity in mice. Because (i) the high-sugar diets (both glucose and fructose) consisted of a lower fat component ($10\%$ fat) than the regular diet ($13.2\%$ fat), and (ii) the daily weight reduction in mouse food was similar in all groups, obesity in experimental mice was the result of sugars but not of the fat component or different amounts of diet consumption. Interestingly, both fructose and glucose could induce indistinguishable prediabetes in mice, as indicated by fasting plasma glucose and OGTT, possibly due to the similar severity of obesity of both forms of carbohydrates. The interchangeability between glucose and fructose is possible in mice, as indicated by increased fasting blood glucose in mice administered fructose and vice versa. Fructose is absorbed from the intestine through glucose transporters 5 (GLUT 5) and diffuses into the bloodstream primarily through GLUT 2 (and also GLUT 5) independently of sodium absorption and ATP hydrolysis, resulting in massive fructose uptake by the liver [64,72]. Meanwhile, glucose in the intestine is mainly absorbed through the Na+/glucose cotransporter 1 (SGLT1), followed by GLUT2 [73]. For increased blood glucose after fructose ingestion, absorbed fructose is usually altered into glucose in the small intestines and livers as a well-known fructose-induced gluconeogenesis in the liver [63,74] that could be responsible for increased blood glucose after fructose administration in our mice. On the other hand, conversion from glucose to fructose in the body is also possible, as endogenous fructose production from absorbed glucose is demonstrated by activating the polyol pathway, which is demonstrated in multiple tissues in the pathogenesis of metabolic syndrome and renal disease [75,76]. The interchangeable glucose-fructose in both fasting blood and liver tissue in our mouse model supports the importance of both sugars in the pathogenesis of metabolic syndrome [77,78,79]. Despite the similarity in the severity of prediabetes and obesity, fructose induced steatohepatitis more prominently than glucose in our model. In fact, fructose is metabolized exclusively in the liver by fructokinase, whereas glucose is metabolized anywhere in the body, including the enterocyte before liver transport and is metabolized by liver glucokinase into glucose-6 phosphate and later to fructose-6 phosphate and pyruvate by the rate-limiting enzyme phosphofructokinase [80]. In particular, fructokinase activity is more rapid than glucokinase, as the Michaelis constant (Km), the substrate concentration at which the reaction rate is $50\%$ of the maximal rate (Vmax), of fructokinase (Km 0.5 mM) is much lower than glucokinase (Km 10 mM) indicating a more rapid activation of fructokinase [81]. Furthermore, liver conversion of glucose to fructose-6 phosphate and then to pyruvate is regulated by insulin, while fructose is rapidly transformed directly into triose-phosphate independent of insulin and continuously enters the glycolytic pathway with low Km of fructokinase for fructose, and the absence of negative feedback from ATP or citrate [82]. The largest portion of fructose triose-phosphate is mainly converted to glucose and glycogen through gluconeogenesis, and some parts are converted to lactate [83]. Although the impact of glucose on hepatocytes is controlled by insulin, fructose is converted to fatty acids and improves reesterification of fatty acids and the synthesis of very low-density lipoproteins (VLDL) -triglycerides (TG) without any control systems [84], resulting in higher amounts of lipid in the liver of mice with fructose administration than in the glucose group. In the liver, fructose can also be converted to glucose and glycogen, while glucose is stored as glycogen, and high glucose in hepatocytes (from the ingestion of glucose or fructose) increases the formation of glycerol-3 phosphate and accelerates liver TG production [85]. Therefore, the head-to-head comparison between glucose and fructose ingestion in our model clearly demonstrated the most prominent liver adverse effect of fructose over glucose, especially in terms of the initiation of a nonalcoholic fatty liver and the abundance of lipids in the liver [78]. On the other hand, glucose caused more severe leaky intestinal systemic inflammation (FICT-dextran and serum cytokines) than fructose, despite a similar level of endotoxemia, supporting a possible higher enterocyte toxicity of glucose as mentioned in a previous publication [13]. Despite the well-known intestinal dysbiosis due to the high carbohydrate content of the gut [86], there was only a subtle change between control and carbohydrate-administered mice in our model, perhaps due to differences in fecal collection between different publications. Here, the fecal collection from metabolic cages is a selection of feces from the large intestine, while high carbohydrate could affect the small intestine (the main site of intestinal carbohydrate absorption) [87]. Then the intestinal and liver injury in our model could be mainly due to high carbohydrates themselves, but not to carbohydrate-induced gut dysbiosis. ## 4.2. Cellular Toxicity of High-Carbohydrate Concentration Although it is the main carbohydrate absorption site in the small intestine, hyperglycemia can induce enterocyte injury in all parts of the intestines, as it is one of the leading causes of intestinal integrity damage [88]. Due to the high abundance of LPS in the intestinal contents of the Gram-negative microbiota, the ingestion of carbohydrates in large amounts possibly overcomes the natural resistance to LPS of enterocytes, causing leaky intestinal and systemic inflammation. Similarly, absorbed carbohydrate and pathogen molecules from a leaky intestine are transported early to the liver through the portal vein [20] and the presence of a high abundance of carbohydrate with LPS is also possibly toxic to hepatocytes. Then both enterocytes and hepatocytes were tested with glucose or fructose with or without LPS in vitro. In enterocytes, high concentrations of glucose or fructose alone did not alter enterocyte integrity (TEER) and a high dose of LPS (1 µg/mL) was necessary to reduce TEER (indicating enterocyte tight junction damage) supporting the natural strength of enterocytes against several insults [89]. Due to the need for cell energy for inflammatory responses [90], the LPS-carbohydrate synergy in improving enterocyte inflammation could be due to the increased cell energy of the high carbohydrate content that is ready to be used for LPS-induced cytokine production. In fact, there was an increase in enterocyte maximum glycolysis activity in LPS plus carbohydrate (more prominent in glucose than in fructose). However, the maximum glycolysis activity was not increased by carbohydrate alone without LPS and LPS alone without carbohydrate, implying the synergy of both factors, including LPS with TLR-4 signaling and enterocyte carbohydrate absorption molecules (SGLT1 and GLUT2) in the increase in glycolysis. Furthermore, the differences in enterocyte impacts of glucose and fructose (more toxicity and glycolysis activity by glucose activation than fructose incubation) might be due in part to differences in cell absorption pathways (GLUT2 for fructose versus SGLT1 and GLUT2 for glucose). More studies on these topics are interesting. For hepatocytes, incubation with glucose alone, but not fructose, increased mitochondrial function (basal and maximal respiration) and glycolysis activity (glycolysis capacity), despite non-cytokine production. Although both glucose and fructose enter hepatocytes through GLUT (GLUT2 for glucose versus GLUT2, GLUT5, and GLUT8 for fructose) [91], the additional intracellular metabolism of fructose and glucose by fructokinase and glucokinase, respectively, could be different, which, at least in part, leads to a different impact of both carbohydrates on hepatocytes. In particular, there were also different impacts of carbohydrate on enterocytes and hepatocytes according to the extracellular flux analysis. With LPS, there was an increase in inflammatory responses (supernatant cytokines) and extracellular flux analysis (mitochondria and glycolysis) in LPS plus carbohydrate (similar between glucose and fructose) compared to LPS alone, implying LPS-carbohydrate synergy on hepatocyte inflammation. Despite the similar inflammatory synergy between LPS-glucose versus LPS-fructose in hepatocytes, the impacts of glucose-induced cell energy status in liver cells were more prominent than those of fructose, suggesting the possibly non-cell energy medicated mechanisms of inflammatory synergy between LPS and fructose that could be different from LPS-glucose. Although more mechanistic studies are needed, our data demonstrate a synergy between LPS and high carbohydrate doses on injury in both enterocytes and hepatocytes, partly through an alteration in cell energy status. ## 4.3. Lactiplantibacillus Plantarum Attenuated the Severity of Mice with High-Sugar Diets In our model, both glucose and fructose induced similar obesity-induced prediabetes and intestinal barrier defect (FITC-dextran assay and increased serum LPS) with only a subtle change in the intestinal microbiota compared to control mice. Although high sugar diet-induced gut dysbiosis is a well-known characteristic [28], the slightest changes in the microbiota in our study could be due to the limited sample size in each group. Despite this limitation, the alteration of the gut microbiota in both glucose and fructose-administered mice can be demonstrated here through differences in possibly unique bacteria in each group by linear discriminant effect size analysis (LEfSe). Furthermore, the administration of probiotics in mice with a high glucose diet significantly elevated the estimation of Chao-1 richness (the total number of species observed in the community) highlighting the beneficial effect of probiotics, despite the limited number of samples. More studies with an adequate number of mice in each group are needed for a solid conclusion on the alteration of the microbiome induced by a high-sugar diet. However, probiotics are known to attenuate obesity through various mechanisms [24], including more effective energy use [68], promoted intestinal hormones [92], and reduced lipid absorption in the host [93] with a previously known efficacy of L. plantarum dfa1 against lipid-induced intestinal damage [46]. Our current data here supported the impact of probiotics on high sugar diet-induced prediabetes; however, indirectly through the reduced weight gain that was possibly more prominent than the influence on gut dysbiosis. As such, probiotics attenuate the severity of the model in nearly all aspects, despite a subtle change in the microbiota of the model. L. plantarum dfa1 growth in the feces of mice with high glucose appears to be better than in the feces with fructose, since there was a higher abundance of Lactobacillus spp. by microbiome analysis (Figure 4G) that possibly correlated with the estimate of increased fecal microbiota (Figure 4H) only in the glucose group but not in mice administered fructose. However, a similar attenuation effect of probiotics was observed between mice administered fructose and glucose, despite the different abundances of Lactobacilli from microbiome analysis, also indicating that the probiotic effect of our high-sugar diet model should be due to an anti-prediabetes or an anti-obesity effect. In fact, Lactobacillus, Bifidobacterium, Clostridium, and Akkermansia are indicated as a bacterial group with beneficial changes in insulin resistance through several possible mechanisms (reduced carbohydrate absorption, improved energy utilization, facilitation of some intestinal enzymes and anti-inflammation) [94,95]. There were a number of limitations due to the study’s “proof-of-concept” characteristics, particularly with regard to the mechanical interpretation of the observed data. It would be interesting to see further research on metagenomic, metabolomic, and functional microbiota studies. Although our data support the use of probiotics for the prevention of high-carbohydrate-induced prediabetes, more research on these subjects is needed for the upcoming clinical translation. ## 5. Conclusions Although both the high-glycemic and high-fructose diet are harmful, there are possible different health impacts between these sugars. Indeed, glucose caused more prominent damage to intestinal integrity (leaky gut), while fructose caused more profound steatohepatitis. However, both sugars induced a similar severity of prediabetes and obesity. Because LPS is an overwhelming pathogen molecule in the intestine that can be transferred to the liver during leaky intestine, simultaneous stimulation by sugars and LPS in enterocytes and hepatocytes is regularly possible. Interestingly, glucose plus LPS induced a more prominent injury in enterocytes and hepatocytes than fructose with LPS, as indicated by TEER and supernatant cytokines, respectively, partly through a more prominent glycolysis activity. L. plantarum dfa1 effectively attenuated prediabetes and obesity despite only a subtle impact on the gut microbiota, which implies a possible impact on insulin resistance. The use of probiotics is encouraged to prevent carbohydrate-induced prediabetes. ## References 1. 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--- title: 'The Development of Appetite: Tracking and Age-Related Differences in Appetitive Traits in Childhood' authors: - Elena Jansen - Gita Thapaliya - Jennifer Beauchemin - Viren D’Sa - Sean Deoni - Susan Carnell journal: Nutrients year: 2023 pmcid: PMC10056659 doi: 10.3390/nu15061377 license: CC BY 4.0 --- # The Development of Appetite: Tracking and Age-Related Differences in Appetitive Traits in Childhood ## Abstract Appetitive traits are associated with body weight. Increased understanding of how appetitive traits evolve from early life could advance research on obesity risk and inform intervention development. We report on tracking and age-related differences in appetitive traits in childhood within the RESONANCE cohort. Parents of RESONANCE children aged 6.02 ± 2.99 years completed the Child Eating Behavior Questionnaire (CEBQ). Pearson correlations of appetitive traits and age were tested for all participants contributing at least one observation, using each participant’s first observation ($$n = 335$$). Children’s first and second observations of the CEBQ ($$n = 127$$) were used to test tracking (paired correlations) and age-related differences (paired t-tests) within individuals. CEBQ correlations with age suggested that satiety responsiveness, slowness in eating, emotional undereating, and desire to drink decreased with age (r = −0.111 to r = −0.269, all $p \leq 0.05$), while emotional overeating increased with age ($r = 0.207$, $p \leq 0.001$). Food fussiness demonstrated a quadratic relationship with age. Paired t-tests further supported an increase in emotional overeating with age (M: 1.55 vs. 1.69, $$p \leq 0.005$$). All CEBQ subscales demonstrated moderate to high tracking ($r = 0.533$ to $r = 0.760$, $p \leq 0.001$). Our initial findings within the RESONANCE cohort suggest that food avoidant traits are negatively related with age, while emotional overeating increases with age, and that appetitive traits track through childhood. ## 1. Introduction Appetitive traits are dispositions toward food that differ between individuals [1] and show associations with body weight in childhood [2]. A key proposition of the behavioral susceptibility theory of child obesity [3] is that even as children age and start to consume different types of food (e.g., milk to table foods) and eat in different contexts (e.g., with peers, at school), appetitive traits persist. That is, if a child shows a more avid appetite and a greater interest in food in infancy, similar food approach tendencies are expected as the child grows up. However, although tracking of body weight through childhood is well-established [4], relatively few studies have directly examined the question of whether child appetite tracks, i.e., whether children maintain their relative levels of appetite in relation to their peers across development. Moreover, few studies, including those examining tracking, have described developmental changes in appetitive traits, i.e., the ways in which appetitive traits change with age across the population. Answering these questions is important because an understanding of normative appetite development is essential to interpret the meaning of observed individual differences. For example, if a child or group of children has different appetitive trait scores to a comparison group or the same individual/s at a different age, are these levels consistent or inconsistent with what one might expect based on extant data? Clarity on normative developmental trajectories of child appetite could also inform interventions to prevent or treat obesity by addressing eating behavior. For example, a deeper understanding of tracking and developmental trends could aid in the identification of critical windows for population intervention. In addition, personalized information for parents about their child’s appetite and expected trajectories could be used to develop tailored advice and support for responsive feeding practices. Starting these interventions early is essential, since a recent study found that eating behaviors in childhood have long-lasting influences on not only diet and weight status, but also eating behaviors in adults [5]. Appetitive traits in adults, in turn, have been found to be associated with body weight [6,7]. For example, one recent study of a UK cohort using the Adult Eating Behavior Questionnaire (AEBQ) demonstrated that food responsiveness, enjoyment of food, and emotional overeating were positively associated with BMI, while satiety responsiveness, emotional undereating, and slowness in eating were negatively associated with BMI [6]. Similar findings were obtained in a study of an Australian sample, which found that emotional overeating was positively associated with BMI, while satiety responsiveness and slowness in eating were negatively associated with BMI [7]. Two early studies from the UK including 322 children at ages 4 and 11 years [8] and 31 children at ages 2 and 5 years, respectively [9], used the Child Eating Behavior Questionnaire (CEBQ) [10] to investigate the stability (i.e., tracking within individuals; same rank order over time) and discontinuity (i.e., age-related differences within individuals) of appetitive traits during childhood. Both studies found evidence for stability across all subscales (with the exception of enjoyment of food in the latter study) but differed in their findings relating to (dis-)continuity. Ashcroft et al. [ 8] found that all measured scales changed from age 4 to 11 years (i.e., decrease in satiety responsiveness, slowness in eating, food fussiness, and emotional undereating and increase in food responsiveness, enjoyment of food, and emotional overeating; desire to drink was not assessed), while Farrow et al. [ 9] found a decrease in desire to drink from 2 to 5 years but no changes in food responsiveness, emotional overeating, enjoyment of food, satiety responsiveness, slowness in eating, emotional undereating, or food fussiness. Similar findings have more recently emerged from prospective or cross-lag studies with 800 to 3800 children from Europe, such that tracking has been observed for all scales across the different time lags (e.g., 7 to 10 years [11], 4 to 10 years [12], and 6 to 8 to 10 years [13]). We are aware of fewer studies that both report and statistically compare paired mean scores across different child ages. Costa et al. [ 11], similar to Ashcroft, found that most traits changed between 7 and 10 years of age (i.e., increase in food responsiveness, enjoyment of food, and emotional overeating; decrease in satiety responsiveness, slowness in eating, and emotional undereating, and also in desire to drink), with no change in food fussiness. The aim of the current study was to build on the above findings by investigating how appetitive characteristics evolve during childhood via the RESONANCE study, a US cohort including children aged 2 to 15 years. Due to the wide age range of the cohort, we were able to explore cross-sectional relationships of all Child Eating Behavior Questionnaire scales with age through early to later development, in addition to conducting analyses of change and tracking within subjects over a relatively narrow timespan. ## 2.1. Study Sample The data used in the present study came from RESONANCE, a large ongoing cohort study of socioeconomically diverse mother–child dyads beginning in infancy with a focus on early brain development [14,15]. RESONANCE is part of the NIH-funded ECHO program (http://echochildren.org (accessed on 24 January 2023)). Children (983 currently active) are followed longitudinally, with study visits every 6 (biannually up to age 24 months) or 12 months (annually after age 24 months). Participants were recruited either during pregnancy or when children were between the ages of birth and 5 years old. A variety of methods was used, including flyers; Facebook and social media; radio advertisements; community events; and in-person information sessions at school, daycares, hospitals, and community centers. Infants and children with known risk factors for learning and/or psychiatric disorders were excluded (e.g., birth prior to 32 weeks’ gestation or birthweight < 1500 g, non-singleton or complicated pregnancy, neurological trauma in child, psychiatric history in a parent or sibling) [16]. For the current study, all participants with at least one assessment of appetitive characteristics in early childhood were included. For the longitudinal data analysis, participants with two or more observations were selected. For the present paper, only the first and second observations (i.e., assessments of appetitive traits) for the Child Eating Behavior Questionnaire were evaluated. Written consent was obtained from parents or legal guardians in accordance with ethics approval from the host institution’s Institutional Review Board (IRB no.: 1500991). ## 2.2.1. Child Eating Behavior To assess appetitive traits in children consuming solid foods, parents of children aged 2 years and older completed the Child Eating Behavior Questionnaire (CEBQ) [10]. The Child Eating Behavior Questionnaire consists of 35 items measured on a Likert-type scale (1 = “never” to 5 = “always”) and comprises eight subscales assessing food approach behaviors, including food responsiveness (FR, example item 1: “Even if my child is full s/he finds room to eat his/her favorite food”, example item 2: “If given the chance, my child would always have food in his/her mouth”), enjoyment of food (EF, example item 1: “My child looks forward to mealtimes”, example item 2: “My child is interested in food”), emotional overeating (EOE, example item 1: “My child eats more when worried”, example item 2: “My child eats more when s/he has nothing else to do”), and desire to drink (DD, example item 1: “If given the chance, my child would drink continuously throughout the day”, example item 2: “If given the chance, my child would always be having a drink”), and food avoidant behaviors, including satiety responsiveness (SR, example item 1: “My child leaves food on his/her plate at the end of a meal”, example item 2: “My child cannot eat a meal if s/he has had a snack just before”), slowness in eating (SE, example item 1: “My child takes more than 30 min to finish a meal”, example item 2: “My child eats more and more slowly during the course of a meal), food fussiness (FUS, example item 1: “My child is difficult to please with meals”, example item 2: “My child decides that s/he doesn’t like a food, even without tasking it”), and emotional undereating (EUE, example item 1: “My child eats less when angry”, example item 2: “My child eats less when s/he is tired”). Scores were averaged for each subscale, and mean subscale scores were used in the analyses. ## 2.2.2. Sample Characteristics Mothers reported demographic characteristics for themselves and their children, including maternal age, education (grouped as (partial) high school, partial college or specialized training, college graduate, or graduate training (masters, PhD)), pre-pregnancy BMI, and subjective social status (MacArthur Scale of Subjective Social Status, range from 1 to 10, with higher scores indicating higher subjective social status) [17], as well as child sex, race, and ethnicity. Children’s weights and heights were measured during the lab visits. Child BMI z-scores were calculated using the WHO Anthro version 3.2.2. [ 18], WHO Anthro Plus, and macros [19]. ## 2.3. Data Analysis Analyses were conducted using cross-sectional as well as longitudinal data. For the cross-sectional analysis, all mothers who had at least one observation on the Child Eating Behavior Questionnaire were included, using the first observation available. Cross-sectional Pearson correlations between appetitive trait scores and child age were examined first. For those Child Eating Behavior Questionnaire subscales where no significant Pearson (linear) correlation was seen with age, in a second step models were fit that tested for quadratic relationships. Given the wide age range in the current sample, non-linear relationships between age and child development might have been expected. For the longitudinal analysis, participants’ first and second observations on the Child Eating Behavior Questionnaire were selected. First, paired t-tests were run to determine age-related differences within individuals. Cohen’s d was used to determine effect sizes [20]. Next, Pearson correlation coefficients were used to examine tracking within individuals, i.e., to test whether children kept their relative rankings amongst the group from first to second observations (i.e., rank-order stability across assessments). To adjust for the child’s age at the first observation and also for the time lag between observations 1 and 2, two additional partial Pearson correlations were conducted: one adjusting for child age, the other adjusting for child age and time lag. Analyses were conducted using SPSS 27 (IBM Corp., Armonk, NY, USA). ## 3. Results In total, 335 parents completed the Child Eating Behavior Questionnaire at least once while their children were between the ages of 2.0 and 15.2 years. Of these, 127 participants provided two observations, with an average time lag between measurements of 13.29 ± 3.80 months. Sample characteristics of the 335 participants are shown in Table 1. ## 3.1. Cross-Sectional Results Cross-sectional Pearson correlations between appetitive trait scores and child ages are presented in Table 2. Correlations for Child Eating Behavior Questionnaire subscales indicated decreases in satiety responsiveness (r = −0.160, $$p \leq 0.004$$), slowness in eating (r = −0.266, $p \leq 0.001$), and desire to drink (r = −0.227, $p \leq 0.001$) with age and increases in emotional overeating ($r = 0.203$, $p \leq 0.001$) with age. Quadratic relationships were examined for food responsiveness, enjoyment of food, and food fussiness, since these showed non-significant linear relationships with child age. The former two were not significant ($$p \leq 0.213$$ and $$p \leq 0.660$$), while food fussiness showed a quadratic relationship (upside-down U-shape) with age, with a peak of fussiness around the age of 6 years (beta = −0.568, $$p \leq 0.017$$). ## 3.2. Longitudinal Results Table 3 shows the mean scores of the Child Eating Behavior Questionnaire subscales at observation 1 and observation 2, as well as the results of the paired t-tests for age-related changes within individuals. Paired t-tests for Child Eating Behavior Questionnaire subscales indicated an increase in emotional overeating with age from the first to the second observation (M1st = 1.55 vs. M2nd = 1.69, $$p \leq 0.005$$). No significant mean change in appetitive traits between the first and second observations was seen for food responsiveness, enjoyment of food, desire to drink, satiety responsiveness, slowness in eating, food fussiness, or emotional undereating. Table 4 outlines the results of paired correlations for tracking within individuals. All eight Child Eating Behavior Questionnaire subscales showed significant positive correlations across observations: food responsiveness ($r = 0.644$, $p \leq 0.001$), enjoyment of food ($r = 0.708$, $p \leq 0.001$), emotional overeating ($r = 0.562$, $p \leq 0.001$), desire to drink ($r = 0.533$, $p \leq 0.001$), satiety responsiveness ($r = 0.726$, $p \leq 0.001$), slowness in eating ($r = 0.685$, $p \leq 0.001$), food fussiness ($r = 0.760$, $p \leq 0.001$), and emotional undereating ($r = 0.576$, $p \leq 0.001$). The same pattern of positive correlations was seen when adjusting for child age at the first Child Eating Behavior Questionnaire observation or adjusting for child age at the first Child Eating Behavior Questionnaire observation and the time lag between the first and second Child Eating Behavior Questionnaire observations. ## 4. Discussion The aim of this study was to investigate tracking and age-related differences in appetitive traits as assessed more narrowly in early to middle childhood, extending some of the previous studies conducted in Europe to children in the US. Cross-sectionally, we found that six out of the eight subscales assessed during early to middle childhood, were correlated with child age (i.e., emotional overeating, emotional undereating, desire to drink, satiety responsiveness, and slowness in eating), including one (food fussiness) showing a quadratic relationship and highlighting the importance of examining non-linear relationships. Longitudinally, we saw little evidence for age-related change within individuals, but we found that appetitive traits tracked within individuals. On the whole, our results demonstrated a decrease in food avoidant behaviors with age and an increase in emotional overeating with age, which is consistent with population survey and cohort data suggesting increasing likelihood of obesity development as children grow older [21]. Our cross-sectional observation that food avoidant traits were negatively associated with age was dependent on the appetitive dimension. The strongest age difference was for slowness in eating, which showed a negative correlation with age. However, significant negative correlations were also seen for satiety responsiveness and emotional undereating. A potential parsimonious explanation for these combined findings is that, while very young children are able to regulate their energy intake [22,23], this ability erodes over time due to environmental influences, including controlling parent feeding practices (such as parents’ restriction of children’s food intake) [24]. However, it is also possible, particularly within the infancy period, that eating speed increases along with motor dexterity and that as children get older they are increasingly involved in structured activities, such as childcare and school, that constrain schedules and effectively dictate how much time they spend eating, while exposure to social influences may lessen food avoidant behaviors. The decrease in emotional undereating could potentially have an additional set of determinants. For example, as children age, their emotion regulation may improve [25], making them less vulnerable to acute stress from extreme negative emotions, which may be more likely to decrease appetite than less severe emotions [26]. For food fussiness, a quadratic relationship with age was found, such that levels of food fussiness increased to about age six years before they started to decline. This finding is in line with those previously reported in a life-course analysis of pooled data from Ireland ($$n = 3246$$), where food neophobia (i.e., reluctance to eat novel foods) increased with age from 1 to 6 years before decreasing until early adulthood [27]. Our observation that emotional overeating increased with age was apparent in both our cross-sectional correlations with age and our within-subject paired t-tests using longitudinal data. While many appetitive characteristics have previously demonstrated strong genetic influence, emotional overeating has been found to show relatively high levels of environmental influence [28,29]. It is therefore possible that emotional overeating increases along with increases in potential environmental triggers. For example, as they progress through development, children are increasingly exposed to diverse palatable energy-dense foods as well as social situations that could promote emotional overeating. Although Farrow et al. [ 9] did not find a significant difference in emotional overeating between the ages of 2 and 5 years, our results are in line with several other previous studies that found that emotional overeating increases with age [8,30,31,32]. The continuity in appetitive characteristics that we observed in the majority of our within-subject tests (i.e., no significant mean differences in seven of the eight Child Eating Behavior subscales from the first to the second observation) was consistent with the results of Farrow and colleagues [9]. While Ashcroft et al. [ 8] and da Costa et al. [ 11] found discontinuity in all subscales they assessed, both our group’s and Farrow et al. ’s results were consistent with continuity across observations (with one exception each: desire to drink (Farrow) and emotional overeating (present study)). Our study extends the latter’s findings by observing continuity for appetitive traits through to middle childhood. The different results between studies are likely due to there being less developmental change occurring from 2 to 5 years [9] or across 13 months (present study) than across a 7-year timespan from early childhood (age 4) to middle childhood (age 11) [8]. All eight appetitive characteristics showed tracking through childhood. That is, children who scored relatively high for these traits at their first observation in childhood (mean age: 6 years) maintained high scores relative to their peers at their second observation (approximately 13 months later). The Child Eating Behavior Questionnaire findings are largely in line with previous studies indicating strong positive associations across observations, even with varying time lags [8,9,33,34,35,36,37]. The stronger correlations observed in our study compared with the study by Ashcroft et al. [ 8] are likely due to the smaller time lag between our observations. Together with previous findings, our current results suggest that, like temperament [38], appetitive characteristics track through childhood. ## Strengths and Limitations The findings of our study need to be considered in the light of its strengths and weaknesses. A feature of our study was that ages and time lags between observations varied across participants. While this variability could be viewed as a disadvantage, we note that our results hold even when controlling for this variation, promoting the generalizability of our results across ages and time lags. As shown in Table 1, more than half of the children had their first Child Eating Behavior Questionnaire observation before the age of 6 years. Another potential limitation was that children’s appetitive traits were reported by mothers using questionnaires, potentially leading to shared observer and shared method bias. However, any bias due to maternal report was likely consistent across both observations, and the Child Eating Behavior Questionnaire has been validated against behavioral tests [39]. Nevertheless, replication using repeated behavioral measures of appetitive characteristics obtained across a variety of settings would increase confidence in our results. It should also be noted that generalizability to other US families might be limited by the homogenous characteristics of the study sample, since the majority of the participants in our study (nearly $75\%$) identified as White. Finally, correlation coefficients between appetitive traits and age were small in this study, explaining only a limited amount of variance. Additionally, the Cohen’s d value for the mean change in emotional overeating was < 0.3, and the clinical significance of these results is unclear. ## 5. Conclusions To conclude, we found in the current study that appetitive characteristics measured in both early and middle childhood are largely stable and continuous. Future research examining the stability and continuity of appetitive characteristics across different developmental stages, starting in infancy and incorporating several assessment timepoints, will be essential to more fully understand the development of appetite and how it is temporarily or more chronically impacted by environmental (e.g., parent feeding) or other influences (for a relevant conceptual model, see [38]). For example, individual differences in child characteristics at any one observation might be the result of individual variation in the rate of brain maturation or in the timing of genetic influences. Since personality traits are more stable in older adults than in children or young adults [38], it would also be of interest to examine development across the whole life course. Nonetheless, our existing results have implications. In particular, our robust finding of increased emotional overeating with age may suggest a target behavior that could be addressed in early life to limit obesity risk. For example, a recent study demonstrated that a responsive parenting intervention in early life decreased emotional overeating in children, and the effect of the intervention on emotional overeating was mediated by parents’ use of food to soothe the child, suggesting a potential parental behavior that could be targeted [40]. An increased understanding of appetite development could also provide foundations for further investigation of biological contributions to appetite development and inform the development of interventions for parents that aim to promote parent–child food-related interactions that represent the best “fit” between parental behaviors and the child’s unfolding appetitive tendencies. ## References 1. Carnell S., Wardle J.. 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--- title: A Wearable Insole System to Measure Plantar Pressure and Shear for People with Diabetes authors: - Jinghua Tang - Dan L. Bader - David Moser - Daniel J. Parker - Saeed Forghany - Christopher J. Nester - Liudi Jiang journal: Sensors (Basel, Switzerland) year: 2023 pmcid: PMC10056665 doi: 10.3390/s23063126 license: CC BY 4.0 --- # A Wearable Insole System to Measure Plantar Pressure and Shear for People with Diabetes ## Abstract Pressure coupled with shear stresses are the critical external factors for diabetic foot ulceration assessment and prevention. To date, a wearable system capable of measuring in-shoe multi-directional stresses for out-of-lab analysis has been elusive. The lack of an insole system capable of measuring plantar pressure and shear hinders the development of an effective foot ulcer prevention solution that could be potentially used in a daily living environment. This study reports the development of a first-of-its-kind sensorised insole system and its evaluation in laboratory settings and on human participants, indicating its potential as a wearable technology to be used in real-world applications. Laboratory evaluation revealed that the linearity error and accuracy error of the sensorised insole system were up to $3\%$ and $5\%$, respectively. When evaluated on a healthy participant, change in footwear resulted in approximately $20\%$, $75\%$ and $82\%$ change in pressure, medial–lateral and anterior–posterior shear stress, respectively. When evaluated on diabetic participants, no notable difference in peak plantar pressure, as a result of wearing the sensorised insole, was measured. The preliminary results showed that the performance of the sensorised insole system is comparable to previously reported research devices. The system has adequate sensitivity to assist footwear assessment relevant to foot ulcer prevention and is safe to use for people with diabetes. The reported insole system presents the potential to help assess diabetic foot ulceration risk in a daily living environment underpinned by wearable pressure and shear sensing technologies. ## 1. Introduction Approximately one in three people with diabetes develop a diabetic foot ulcer (DFU), and among them, one in four of them will progress to lower limb amputation [1,2]. The management of DFU is challenging as the risk of re-ulceration is $40\%$ within the first year and $65\%$ over five years [1]. The five-year survival rate after diabetes-related amputation is up to $50\%$, which is worse than breast and prostate cancers [3]. This evidence suggests that the current DFU prevention strategy, involving education, screening and foot care, in the UK National Health Service (NHS) is not fully effective and remains elusive. It is also well-recognised that a research-led solution is one of the key solutions to help address this issue [1,4,5]. Wearable devices adopting a user-centered design and using IoT technologies to monitor health conditions may offer a way to improve outcomes [6]. The development of DFU is a complex process, especially for people with combinations of peripheral neuropathy, peripheral arterial disease, and foot deformity. Neuropathy results in the loss of protective sensation, which in combination with a foot deformity or insufficient blood flow, leads to localised tissue injury and tissue death [7]. The load acting upon the foot includes pressure acting perpendicular and shear acting parallel to the surface of plantar tissue. Pressure is known to be one of the key external causes of DFU, and a threshold of 200 kPa has been advised as a target for pressure-relieving footwear and orthotic interventions for those who have previously ulcerated (measured under clinical conditions) [8]. Long-term and daily monitoring of pressure and providing alerts to patients when excessive pressure is identified have been shown to reduce ulceration risk [9]. However, The National Pressure Ulcer Advisory Panel et al. [ 10] reported that the combination of pressure and shear is responsible for ulceration. Bader et al. [ 11] reported that both pressure and shear exerted on the skin could cause internal shear stresses in the underlying tissues, which act to distort tissues, pinch and occlude capillaries crossing tissue planes, reduce blood and lymphatic flow and cause physical disruption of tissues and contribute to diabetic foot ulceration. Plantar tissue for people with diabetes also tends to have a reduced tolerance to external loading and, when coupled with bony prominences such as heel, metatarsal heads and hallux, further exacerbates ulceration risk. The IWGDF [12] has also long recognised that pressure is coupled with shear stress, and both have an impact on cell and tissue integrity. Both shear and pressure are therefore important for DFU risk assessment, and indeed, elevated shear stress has been reported at key sites at risk of plantar ulceration during walking under controlled laboratory conditions [13] but never in real-world conditions. Insole systems that are sensitive to pressure but not shear have previously been developed for laboratory research purposes [14,15,16] as well as for the purpose of monitoring foot pressure in real-world living conditions. This includes the F-Scan System (Tekscan, Inc., Norwood, MA, USA), pedar (novel GmbH, München, Germany), XSENSOR (XSENSOR® Technology Corporation, Calgary, AB, Canada) and Orpyx SI (Orpyx Medical Technologies Inc., Calgary, AB, Canada). However, none of these can measure shear forces at the same time when pressure is measured. To provide comprehensive assessment of plantar loading, tools were reported to measure multi-directional plantar forces but only in laboratory settings [13,17,18]. These include a strain gauge-based pressure and shear sensing platform which was designed only for barefoot conditions [13] and thus is not a wearable solution. Wang et al. [ 17] developed an inductive-based insole sensing system, which requires specific footwear modification and strapping electronic devices on the shank, limiting its adaptation to common footwear. Takano et al. [ 19] developed a system consisting of a combined shear force sensor and F-Scan pressure sensor; however, it requires a specialised insole, an electronic box to be worn and a wired connection to a computer, which again is not wearable in everyday living. Amemiya et al. [ 18] directly attached piezoelectric-based sensors to the metatarsal heads, and it is not a wearable system that could be worn by patients outside the lab. The motivation of this study is to develop a sensorised insole system that is capable of measuring both pressure and shear stress but also can be adapted to a range of footwear without modification. Such a wearable system could underpin a diabetic foot ulcer prevention solution based on comprehensive plantar pressure and shear monitoring during daily living activities. Based on a previously reported tri-axial pressure and shear (TRIPS) sensing system [20], a sensorised insole system capable of measuring both pressure and shear simultaneously has been developed. The TRIPS sensors are thin and flexible and have previously been applied at the residuum/socket interface of lower limb amputees to measure real-time kinetic residuum and socket interactions [20,21]. In this work, we focus on reporting the design, development and evaluation of the sensorised insole system which incorporates TRIPS sensing technology. The insole with sensor integration was evaluated using both laboratory-based and human participants tests. The potential of using this wearable insole system for future DFU prevention is discussed. ## 2. Development of the Sensorised Insole System The TRIPS sensors’ working mechanism, design and development have been detailed in our previous publications [22]. In brief, a capacitive sensing mechanism was adopted to measure pressure and shear stresses (in two orthogonal directions) simultaneously as a function of time. Each sensor had an approximate dimension of 20 mm by 20 mm by 1 mm and was flexible. In this work, we focus on reporting the novel development of the sensorised insole system, which integrates these sensors ready for measuring pressure and shear across different plantar sites in real-time. Building upon a previously reported [20] single-sensor system, a bespoke electronic system was designed to incorporate multiple sensors, which requires additional power management, data storage and a system status indication module with a view to improving its usability in the daily living environment. ## 2.1. Sensor Locations The sensorised insole contains four TRIPS sensors, with the same dimensions (20 mm × 20 mm × 1 mm) and design, positioned at the heel, 5th metatarsal head (5MH), 1st metatarsal head (1MH) and hallux (Figure 1a). These locations were chosen as they represent the locations of the high occurrence of DFU and enable key gait events to be detected, for example, start and end of stance, heel-only and forefoot-only loading periods [23]. In the anterior–posterior direction, heel, 5MH, 1MH and hallux sensors were located at approximately $10\%$, $63\%$, $72\%$ and $92\%$ of the foot length measured from the posterior-most point. These percentages, in the anterior–posterior direction, were determined based on a foot morphological study [24] and a plantar pressure study [25]. In the medial–lateral direction of the heel, 5MH, 1MH and hallux sensors were located at approximately $0\%$, $15\%$, $14\%$ and $15\%$ of the foot width, measured from the long axis of the foot. These percentages, in the medial–lateral direction, were determined using plantar pressure distribution reported in previous studies [26,27]. ## 2.2. Insole Construction The sensorised insole (Figure 1b) consists of three layers of material, i.e., Ethylene-vinyl acetate or EVA (nora® Lunacell, nora systems GmbH, Weinheim, Germany), synthetic leather (Yampi, A. Algeo Ltd., Liverpool, UK) and Lycra. These are the typical materials used for constructing a layered orthotic insole, as they demonstrate suitability for appropriate biocompatibility, durability and shock absorption against industry standards [28,29]. Sensors were embedded in the middle EVA layer. Four square cut-outs were made to the middle layer such that the sensor could be placed at the corresponding anatomical locations without protrusion. Subsequently, a layer of synthetic leather and a layer of Lycra material were adhered to the top and bottom surfaces of the middle layer, respectively. This was to ensure there no direct contact between the skin and the sensor to avoid elevated stress introduced by the sensors. The overall thickness of the insole was less than 3 mm and, therefore, could be used as a standalone insole or adhered to a prescribed insole to ensure its wider clinical application. The sensorised insole was connected to a signal processing and data collection hub via a thin and flexible cable, exiting from the posterior–lateral side of the insole, as shown in Figure 2a. The posterior–lateral exit was chosen for the flexible cable to avoid contact at the navicular region where the tissue is prone to injury. The hub can be attached to the lateral collar of the footwear with no modification required on users’ footwear to ensure the device is wearable in a daily living environment, which is critical for monitoring the risk of DFU. ## 2.3. Sensorised Insole System Figure 2b illustrates the functional diagrams of the electronic system within the hub, formed by key sub-modules. The sensorised insole system consists of a sensorised insole and a hub containing an electronic system for data acquisition and processing. Four sensors were incorporated within an insole, forming a sensorised insole. The operating mechanism of the hub is detailed in a previous publication [20]. In brief, the main functionalities of the electronic hub system are controlled by a 32-bit microcontroller loaded with a real-time operating system which runs multi-threaded applications to manage tasks for each module, as shown in Figure 2b. Signals from the sensorised insole are processed by the digital signal processing module, containing capacitance-to-digital converters, at 100 Hz operating frequency. The digitised sensor signals are then communicated with the sensor system controller via the serial–peripheral interface. The sensor system controller subsequently sends both plantar stress data and real-time clock data to an onboard data storage module via the secure-digital input–output interface for data storage purposes. This provides the capability that plantar stress can be studied as a function of real-time in a year–month–day–hour–minutes format. The hub also provides a wireless data transfer function, so the data can be communicated wirelessly with an external device, such as a mobile phone. From a user perspective, a USB type-C connector is available on the hub for charging purposes, and a simple LED light, controlled by the system status indication module, is provided to the user for hub system status indication. ## 3.1. Experimental Setup and Test Method A uniaxial mechanical test machine (E1000, Instron, High Wycombe, UK) with a load cell capacity of ±1 kN was used to evaluate the performance of the insole system. Aluminium platens were designed, manufactured and attached to the test machine with a view of applying known pressure (Figure 3a) and shear stresses (Figure 3b) to the specified sensor location of the sensorised insole. Static and dynamic loading profiles were designed, and the test machine was programmed to convert the design loading profile to actuator movements. The known applied load from the test machine was then compared with the outputs of our sensorised insole system. ## 3.2. Pressure A step loading profile (Figure 4a), incorporating 20 loading and unloading steps with 10 kPa pressure per step, was designed to characterise static pressure measurement from the insole system. In static conditions, a linearity error of $2\%$ was estimated in a measurement range between 0 kPa and 300 kPa (Figure 4b). The cyclic loading profile was designed to evaluate the insole system performance in a controlled laboratory environment by applying representative load experienced during walking. The profile consists of a half sinusoidal wave with a loading amplitude of 250 kPa and a frequency of 1 Hz, followed by an unloading period of approximately 0.5 s. Accuracy error, the estimated percentage of the peak value, is approximately $4\%$ of the full scale in both static and dynamic test conditions. ## 3.3. Shear Stress Similar step-loading profiles were designed to evaluate shear stress measurement from the insole system in a static condition. The step profile consists of 10 loading and unloading steps in both positive and negative directions (Figure 5a). Each loading step corresponds to 9 kPa of shear stress increment. In static conditions, a linearity error of up to $3\%$ was estimated in a measurement range between −90 kPa and 90 kPa. A dynamic shear stress profile was designed such that a half-sinusoidal loading profile was applied with an amplitude of 50 kPa in both positive and negative directions at 1 Hz loading frequency. Followed by the dynamic load phase, an unloading phase of up to 0.5 s was also incorporated. In dynamic conditions, the accuracy error is estimated to be $5\%$ of the full scale. Stress measurements from the insole system were evaluated in this study. Low linearity errors of up to $3\%$ were revealed in both pressure and shear measurement. The accuracy error (up to $5\%$ of full scale in both pressure and shear) of the insole system reported in this study is equivalent to a recently reported SLIPS system [17], as well as a commercial pressure-only system [30]. ## 4.1. Test Protocol One healthy male participant (age 32 years, body mass 97 kg, height 177 cm, UK shoe size 8) with no lower limb injury, or known walking dysfunctions, was recruited for walking tests. The participant was asked to change into a pair of standard socks and trainers (React Miler 3, Nike Inc., Beaverton, OR, USA). The original insole in the trainer was removed and replaced with the sensorised insole. The participant walked for at least five minutes to ensure comfort at the start. Subsequently, he was asked to perform level walking along a 28 m corridor (Figure 6) at a self-selected speed. Walking cadence was recorded by counting the number of steps covered in 30 s and used to define self-selected walking cadence. The level walking test was repeated with two additional types of footwear (Figure 7). Plimsolls (Figure 7a) and therapeutic footwear (Figure 7c). The plimsoll has a flat outsole, representing typical retail footwear that would not be advised for people with diabetes due to the lack of sole thickness and inadequate upper support. The therapeutic footwear (Omar 11, fisio duna) was designed for people with diabetes [31] and had a forefoot rocker angle of 20°. The self-selected walking cadence was controlled by a digital metronome to minimise the effect of walking speed on plantar pressure and shear measurement. ## 4.2. Temporal Pressure and Shear Stress Profile during Level Walking Figure 8 shows the typical pressure, medial–lateral and anterior–posterior shear stress obtained from a healthy participant as a function of time when wearing a pair of everyday trainers. Peak pressure of up to 200 kPa was obtained across the four locations (Figure 8a). Within the stance phase, four distinctive peaks were revealed, with peak pressure at the heel revealed first in the initial contact phase of the gait and peak pressure at the hallux revealed at last at the hallux location, representing the push-off phase of the gait. These sequence-related peak events, as well as the timing between each of the two peaks, could be metrics of the roll-over characteristics of the foot, important as people with diabetes can experience loss of ankle range of motion and impaired gait as a result [32]. It is also important to note that in-shoe pressure of 200 kPa has been previously recommended by IWGDF as an indicative threshold to help prevent recurrent foot ulceration risk for people with diabetes. The real-time pressure and corresponding plantar sites reported here could also be potentially explored to facilitate the assessment. Figure 8b,c illustrates the shear stress in the medial–lateral direction and anterior–posterior direction, respectively. Up to 18 kPa and 16 kPa of peak shear stress were measured in the medial–lateral and anterior–posterior directions across the four locations, respectively. The peak shear stress reported in this study is lower than that measured barefoot, highlighting the difference between in-shoe and barefoot results [33]. It is also worth noting that the peak shear stress was significantly lower than peak pressure, which is consistent with previous studies [13,17]. To our best knowledge, this is the first study that reports in-shoe real-time shear stress in two orthogonal directions, which could be potentially used to study balance in the medial–lateral direction as well as braking and propulsive impulses during gait [34]. These are critical parameters as understanding balance may help better manage the risks of loading asymmetry due to loss of movement control and localised stress distributions, all of which may lead to ulceration [35]. ## 4.3. Effect of Footwear on Plantar Pressure and Shear Stresses Figure 9a illustrates the mean peak pressure (MPP) obtained at the four locations when wearing three types of footwear. Regardless of the footwear, higher pressures were obtained at the heel (up to 215 kPa) and hallux (up to 243 kPa) compared to the other two metatarsal locations. At all locations, the lowest pressures were obtained when wearing trainers compared to the value obtained with therapeutic and flat-sole footwear. The reduction in peak pressure of up to $20\%$ in all four locations, when wearing trainers may be attributed to the mechanical property, e.g., Young’s Modulus, as well as the microstructure of the material used for the footwear construction to achieve shock absorptions. The plimsoll and therapeutic footwear featured thin and rigid outsoles, respectively, which may have reduced the shock absorption capability. Among the four locations, the highest shear stress of up to 28 kPa and 33 kPa was revealed at the hallux location when wearing plimsolls, in medial–lateral and anterior–posterior directions, respectively. At all four locations, reductions of up to $75\%$ medial–lateral shear and $82\%$ anterior–posterior shear were evident when wearing therapeutic footwear compared to the plimsolls. This may be explained by the rocker sole (Figure 7c) incorporated in the therapeutic footwear design. In the early stance phase, the heel rocker assists the foot lowering to achieve foot flat in the midstance phase. In the terminal stance phase, the forefoot rocker helps transfer the load from the hindfoot to the forefoot and thereby achieve foot ‘roll-over’. Both these footwear features were absent in the plimsolls, requiring the activation of muscle forces to assist load transfer under the foot, generating different shear stresses at the plantar interface. In addition, up to $40\%$ and $61\%$ reduction in medial–lateral shear was revealed when wearing the therapeutic footwear compared to that obtained for the trainer at the heel and hallux, respectively. Similar shear stress reduction was also revealed in the anterior–posterior direction, where reductions of up to $71\%$ and $21\%$ were measured at the heel and hallux, respectively. This indicates that the reported insole system has adequate sensitivity and could detect expected differences in the effects of the trainer and therapeutic footwear, which have similar footwear construction features. The combined pressure and shear assessment may be used to offer insights to understand the effect of the design of footwear on loading characteristics at critical anatomical locations. This preliminary case study shows that pressure alone is not adequate to provide a comprehensive assessment of loading characteristics as a function of footwear design and choice. The significant difference in shear stress revealed when wearing therapeutic footwear may be potentially used as quantitative evidence to assist the design of footwear for DFU prevention. ## 5.1. Test Protocol Five participants, including three males and two females with diabetes at risk of ulceration, were recruited to participate in a walking evaluation. The primary aim was to detect whether the usage of the sensorised insole would induce notable changes in pressure for people with diabetes. Participants had a mean age of 67.2 years (range: 40–85 years) and UK shoe size between 8 and 9 with known diabetes duration 10.8 years (range: 2–22 years). The risk of foot ulceration was assessed on all participants based on IWGDF guidelines, resulting in four participants with moderate and one with a high risk of DFU. Participants completed walking at a self-selected pace along a 50 m walkway whilst wearing standardised therapeutic footwear (Omar 11, fisio duna) with and without the sensorised insole. Plantar pressure data were collected using the XSENSOR system (Foot and Gait v4, XSENSOR® Technology Corporation, Calgary, AB, Canada) at 50 Hz. To evaluate the safety of wearing the new insole system, the difference in MPP over 10 mid-gait steps was calculated [36] (Table 1); this represents a known marker for risk in the diabetic foot [12]. This was evaluated for regions of interest defined based on sensor locations stated in Figure 1a, with an additional boundary of $10\%$ in each direction to accommodate for misalignment (Figure 10). The group mean differences were then calculated. ## 5.2. Safety Evaluation on People with Diabetes Figure 10 illustrates the comparison of regions of interest for the peak pressure distribution map with and without the sensorised insole. Table 1 presents the MPP outcomes for each participant. The incorporation of the sensor within the insole resulted in −$9\%$, −$41\%$, −$16\%$ and −$11\%$ group mean percentage difference in peak pressure during walking at the heel, 5MH, 1MH and hallux, respectively. The 5MH region may also be affected by the raised lateral border of the XSENSOR measurement insole [30]. Due to the slight padding of the sensorised insole’s middle EVA layer, some reduction in pressure was observed across regions. The effect within individuals and at individual regions varied, with changes in pressure affected by proximity to other loaded sites and variation within the gait. The use of small and fixed pressure masking associated with sensor locations may have influenced the step-to-step variability. For sites which demonstrated increased pressure, the resulting change in pressure magnitude was less than or similar to the between-step standard deviation suggesting this may be underpinned by step-to-step variation. These changes are, therefore, beneficial or negligible and show that the sensorised insole introduced almost no risk to user comfort and tissue injury. ## 6. Discussion This paper presents an insole system that can measure real-time pressure and shear stresses under the foot. The design included all the elements required for a practical at-home solution, including a data storage interface, battery charging and mounting to footwear. The system is suitable for the assessment of the complex loading characteristics of people with diabetes and may inform guidance and management to underpin DFU prevention. In addition, the two-directional shear stresses, coupled with pressure, can be exploited to study balance in both sagittal and coronal planes, braking and propulsive impulses in people with diabetes and others affected by difficulties of movement control. Further work should seek to understand these kinetic parameters coupled with lower limb kinematics to provide a comprehensive biomechanical assessment of the foot in real-world settings of people’s daily lives and activities. The sensorised insole can be used in footwear with no modification or customisation required, assuming suitable footwear is chosen. This supports its use in daily living environments as a monitoring tool to provide warning to patients and health professionals when pressure and shear-related elevated DFU risks are detected. The insole presented in this study offers a significant advantage compared to other devices [17,18], where footwear modification is required, or over-sized device electronics are required to be attached to other parts of the lower limb, which may affect normal walking and also impact adherence and usage. These factors were subjected to further study as part of this project. The footwear used in this study represents the range of footwear available, including those offered for patients who have diabetes and are classified as at-risk of ulceration [37]. While therapeutic footwear is the recommended footwear for patients at high risk of ulceration [12], this is not standard provision across patients of lower risk. So, understanding the use of the insole system in a range of footwear and what changes to pressure and shear might occur due to different footwear is an important next step in research. Pressure values do not demonstrate large changes even across this known range of footwear; however, shear data presented in Figure 9 show potential for modification by footwear intervention and warrants further investigation. While initial work has highlighted the importance of activity type in plantar pressure assessment [38], it is unknown how these varied activities of daily living generate potential risk from shear loading for people with diabetes. Further, the sensorised insole presented here will enable measurements relevant to individual patients’ activity profiles, allowing for a more personalised monitoring and risk evaluation in a real-world setting. To facilitate these future studies, further work in assessing the performance of the sensorised insole in real-world conditions such as weather, different ground surfaces and terrains will be conducted. ## 7. Conclusions A first-of-its-kind sensorised insole system was reported, which is capable of measuring real-time plantar pressure and shear stress that could be potentially used by people with diabetes to help monitor and assess the risk of DFU. The technical performance of the system was validated through a combination of lab testing and initial walking trials. The insole and the wireless electronic hub were designed to be used with a range of existing footwear without the need for modifications. This is a significant improvement over any other existing devices reported in this field. These important wearability features and the comprehensive in-shoe pressure and shear measurement capability are essential for DFU prevention in the daily living environment. Preliminary results involving a healthy participant revealed such a wearable system is also sensitive to investigating the effect of different footwear on plantar loading. The safety of the device was further evaluated in diabetic participants. The result suggests that the inclusion of the sensorised insole itself does not elevate the plantar pressure and thus introduces no risk to user comfort and plantar tissue injury. Overall, our initial results reported here demonstrated the significant potential for the use of the sensorised insole in everyday living for DFU risk monitoring and prevention. ## 8. Future Work Future work should involve recruiting people with diabetes with different levels of DFU risks to investigate the association between the plantar loading profile and the formation of DFU. Data from one participant (UK shoe size 8) were reported here to underpin the technological development and potential suitability for people with diabetes. Sensorised insoles of different sizes should be designed to accommodate the need of an expanded population, and subsequently, device durability tests must be conducted. 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--- title: Integrating Multiple Single-Cell RNA Sequencing Datasets Using Adversarial Autoencoders authors: - Xun Wang - Chaogang Zhang - Lulu Wang - Pan Zheng journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10056671 doi: 10.3390/ijms24065502 license: CC BY 4.0 --- # Integrating Multiple Single-Cell RNA Sequencing Datasets Using Adversarial Autoencoders ## Abstract Single-cell RNA sequencing (RNA-seq) has been demonstrated to be a proven method for quantifying gene-expression heterogeneity and providing insight into the transcriptome at the single-cell level. When combining multiple single-cell transcriptome datasets for analysis, it is common to first correct the batch effect. Most of the state-of-the-art processing methods are unsupervised, i.e., they do not utilize single-cell cluster labeling information, which could improve the performance of batch correction methods, especially in the case of multiple cell types. To better utilize known labels for complex dataset scenarios, we propose a novel deep learning model named IMAAE (i.e., integrating multiple single-cell datasets via an adversarial autoencoder) to correct the batch effects. After conducting experiments with various dataset scenarios, the results show that IMAAE outperforms existing methods for both qualitative measures and quantitative evaluation. In addition, IMAAE is able to retain both corrected dimension reduction data and corrected gene expression data. These features make it a potential new option for large-scale single-cell gene expression data analysis. ## 1. Introduction The rapid development of high-throughput single-cell RNA sequencing (scRNA-seq) technologies has facilitated the study of the transcriptomic characterization of cell heterogeneity and dynamics [1,2,3,4]. In recent years, researchers have collected a large amount of single-cell gene expression data from different experiments at different times and on different sequencing platforms [5,6]. Inevitably, these data will have unexpected batch effects due to differences in time and experimental protocols, which may lead to spurious findings [7]. Therefore, correcting the batch effect should be an essential part of the analysis of multi-batch scRNA-seq data. At present, researchers have proposed a number of methods for batch effect correction [8]. However, almost all of these methods are unsupervised, i.e., they do not use cell-type information, including cell similarity-based methods such as MNN [9], BBKNN [10], Scanorama [11], clustering-based methods such as Harmony [12], DESC [13], a low-rank subspace ensemble framework [14], SCCLRR [15], and a novel strategy based on Autoencoders [16]. Although all of these methods have achieved some results, their effectiveness may vary depending on the complexity of the dataset. Therefore, it is important to carefully consider which method to use for a particular dataset, taking into account its unique characteristics and limitations. After an in-depth analysis of the scRNA-seq datasets, we identified three different scenarios (Figure 1): Closed set, where each batch contains exactly the same cell type; partial set, where the set of cell types in one batch is a subset of those in another batch; and open set, where each batch contains both the same and different types of cells, making it the most complex and realistic situation that current unsupervised methods cannot effectively resolve. Fortunately, the development of single-cell study and annotation methods is rapidly advancing [17,18,19]. As a result, an increasing number of publicly available annotated single-cell datasets [8] are now available, making it easier to capture information on cell types. Building on this progress, we developed a supervised method IMAAE to correct the batch effect in the above three scenarios. IMAAE utilizes cell type information to establish associations of the same type of cells between different batches. The method can either build a new batch or select one of the existing batches as an anchor and then use an adversarial autoencoder to convert the remaining batches to the anchor batch, effectively correcting the batch effect. We compare IMAAE with a variety of advanced batch correction methods, including the most widely used MNN, the more recent iMAP [20], and SCALEX [21] based on deep learning techniques and a supervised method, scGEN [22]. Experimentally, our method proved to be better than other methods in the standard set of evaluation metrics. IMAAE can obtain both corrected low-dimensional data and corrected gene expression data, providing strong support for downstream analysis. ## 2. Materials and Methods Our IMAAE framework workflow includes 3 major phases. There are two tasks in the first phase, data annotation, and processing. It aims to produce normalized data and neighbor connectivity maps after initial denoising (Figure 2a,b). The second phase is the anchor selection phase, where a certain batch is selected as the anchor batch, or an intermediate batch is established as the anchor batch (Figure 2c). The third phase is the batch effect correction phase (Figure 2d,e), where, based on the established mapping relationships, all batches of cells as the input data of the antagonistic autoencoder, and the anchor batch cells as the ideal output data for training, and then the batch effect can be corrected by the trained network. IMAAE is available at https://github.com/dongzuoyk/IMAAE (last access date: 9 March 2023). ## 2.1. Data Preparation and Data Preprocessing The data used in this study are publicly available annotated datasets. Human peripheral blood mononuclear cell dataset (PBMC) [23]. The data included two batches of human peripheral blood mononuclear cells from two healthy donors, which were generated by the 3′and 5′Genomics protocols, respectively. Each batch contained 12 different cell types, including 8098 cells in the 3′ batch and 7378 cells in the 5′ batch, with 33,694 genes per cell. Human pancreas dataset (Pancreas) [24,25,26,27,28]. This dataset was constructed using human pancreatic data from five different sources. Each batch contained 15 different cell types, for a total of 14,890 cells with 34,363 genes per cell. The data preprocessing step includes (Figure 2b): [1] Filtering, i.e., removing unwanted cells and genes according to user-defined rules. Cells expressing less than 600 genes and genes expressed in less than 3 cells were excluded from this study. [ 2] Selecting highly variable genes. Since the original gene dimension is very high and contains a large number of zero values, the study should focus on those high-variable genes. In this study, 2000 high-variable genes were selected for the study. [ 3] Normalization. Each cell was normalized by the total counts of all genes so that each cell had the same total count after normalization. The target total count for this study was 20,000. [ 4] Logarithmization. To logarithmize the gene expression data, in this paper, we used X = log(X + 1). [ 5] Principal component analysis (PCA). PCA was performed on the logarithmically scaled data to obtain the reduced dimensional data for constructing similar cell connectivity graphs. [ 6] Building connected graphs. Constructing connectivity graphs between cells of identical types across distinct batches. The cells on the connected graph are highly similar, and the spatial distance is smaller than other cells of the same type across batches. Note that steps [5] and [6] are the procedures for constructing the cross-batch similarity cell connected graph in the IMGG model [29], which are optional in the IMAAE model. The difference is that if these steps are not used, random selection will be adopted when establishing mappings between different batches of cells of the same type in subsequent steps. This method is fast and convenient, and the corrected data distribution will be more uniform, but the ability to identify cell subtypes will be lost. In contrast, if steps [5] and [6] are used, cells on the connected graph will be selected when establishing mappings between different batches of cells of the same type in subsequent steps, and the corrected data will still retain some batch-specific features that can theoretically be used for subtype analysis. We further elaborate on this in Additional Experiment 2. ## 2.2. Determining the Anchor Batch IMAAE is a flexible anchor-based method. Unlike other anchor-based methods, IMAAE can not only choose a certain batch as the anchor batch but likewise choose to construct an intermediate batch as the anchor batch, such as IMGG. In this work, we provide three ways to select an anchor batch:[1]Similar to IMMG, an intermediate batch is established as an anchor batch using the balanced mode.[2]A batch with a larger standard deviation is selected as the anchor batch. A larger standard deviation means that there is greater variability in the cells within the batch, which may cover more cell types.[3]The user can choose a batch as the anchor batch themselves. Different methods of establishing the anchor batch result in slightly different outcomes, but experimental results show that regardless of the method used, the IMAAE correction effect is always excellent (Additional Experiment 3). Unless otherwise specified, in this paper, the intermediate batch established using the balanced mode is used as the anchor batch for all other experiments. ## 2.3. Correcting the Batch Effect Using an Adversarial Autoencoder Our purpose is to transform all batches of cells to the anchor batch to correct the batch effect. Converting one batch to another is similar to style migration in the image domain, and the methods generally used are autoencoder and generative adversarial networks. We chose to design an adversarial autoencoder because autoencoder and generative adversarial networks have their own limitations in dealing with complex dataset scenarios (Additional Experiment 1). ## 2.3.1. Adversarial Autoencoder Network To address the limitations of the autoencoder and generative adversarial networks, we decided to design an adversarial autoencoder network by fusing the structures of the autoencoder and generative adversarial networks (Figure 2d). Our model contains three parts: encoder, decoder, and discriminator. The encoder and decoder can form a self-encoder network, and the encoder and discriminator can form a generative adversarial network. First, we input all batches of gene expression data x into the encoder to obtain the latent code z, which is distributed as q(z). Next, z is fed to the decoder for training to obtain data matching the features of the anchor batch, while the encoder and discriminator form a generative adversarial network to match the distribution q(z) of z with the true prior distribution p(z). Eventually, IMAAE can learn the parameters for converting all batches into anchor batches. In our work, we used the normal Gaussian distribution N(0, I) for the prior distribution p(z). ## 2.3.2. Loss Functions The reconstruction loss function of the autoencoder:[1]LR=1n∑$i = 1$nxi−x˜i2 where n denotes the number of cells in the dataset, xi denotes the original gene expression of the i-th cell, and x˜i denotes the gene expression generated by the autoencoder for cell i. The discriminant loss function for generative adversarial networks:[2]LD=−Ez∼pdatazDz+Ez′∼pz′Dz′+λEz^∼pz^z^‖∇z^Dz^‖2−12] where z is the true sample, pdataz is the true data distribution, z^ is the interpolated sample obtained by sampling uniformly between the true sample z and the noisy sample z′, λ is the weight of the gradient penalty term, ‖⋅‖2 is the L2 parametrization, and ∇z^Dz^ is the gradient of the discriminator at z^. p z^ z^ is the distribution of the interpolated samples, defined as:[3]pz^z^=Eα∼U0,1δz^−αz+1−αz′ where U0,1 is a uniform distribution over the interval 0,1 and δ is a Dirac delta function. The generator loss function for generative adversarial networks:[4]LG=−Ez′∼pz′Dz′ where z′ is the noise sampled from the prior distribution pz′, D⋅ is the discriminator function, and E is the expectation operator. ## 2.3.3. Hyperparameters We used the Adam optimizer [30] with parameters β1 = 0.5 and β2 = 0.999, a learning rate of 0.0002, a batch size of 1024, and an epoch number of 100. The encoder, decoder, and discriminator all consisted of a fully connected neural network. By default, the number of encoder nodes is 2000, 1000, 500, and 250, the number of decoder nodes is 250, 500, 1000, and 2000, and the number of discriminator nodes is 250, 125, 64, 8, and 1. It should be noted that the ReLU activation function [31] must be used at the end of the decoder, which ensures that the output will not have negative numbers. We also use the reparameterization technique at the end of the encoder. In addition, we have a hyperparameter “n_critic” to adjust the training ratio between the generative adversarial network and the self-encoder, e.g., when set to 2, the generative adversarial network will be trained twice and the self-encoder once. We recommend keeping the size of “n_critic” the same as the number of batches. ## 2.4. Comparison Methods This study compared the performance of batch correction using scGen, MNN, iMAP, SCALEX, and Harmony methods, respectively. The first three methods can only obtain corrected gene expression data, Harmony can only obtain corrected low-dimensional data, and SCALEX, similar to our IMAAE, can obtain both corrected low-dimensional data and corrected gene expression data. We used the same data preprocessing method for all methods (except SCALEX, which has an integrated preprocessing module). For each method, we run with default parameters. To assess the performance of each method, including IMAAE, the top 50 PC vectors extracted from the batch-corrected expression matrix were used for the calculation of evaluation metrics and visualization. ## 2.5. Evaluation Metrics To evaluate the batch correction performance of IMAAE and the other methods described above, we used three quantitative assessment metrics, average silhouette width (ASW) score [32], adjusted rand index (ARI) score [33], and normalized mutual information (NMI) score [34], and two qualitative assessment metric, uniform manifold approximation and projection (UMAP) visualization [35] and t-distributed stochastic neighbor embedding (t-SNE) visualization [36]. The UMAP plot and t-SNE plot can visualize the change before and after correcting the batch effect. ASW, ARI, and NMI are metrics used to evaluate clustering quality. ASW measures the similarity of data points within a cluster to those in other clusters and evaluates cluster separation and compactness. ARI measures clustering similarity while accounting for chance agreement, often used for comparing ground truth and clustering results. NMI measures mutual information between two clustering results and is normalized by entropy. Lower ASW, ARI, and NMI scores indicate better results when using batch as a label, while higher scores indicate better results when using cell type as a label. For comparison purposes, we calculated the F1 score for each metric (e.g., F1ASW=2×1−ASWbatch×ASWcelltype1−ASWbatch+ASWcelltype), so that higher values indicate better performance. All scoring metrics were calculated only for the cell types that were co-occurring in each batch. ## 3. Results We simulated three different scenarios using the PBMC dataset, the Pancreas dataset, and their subsets, i.e., closed set, partial set, and open set, respectively. The closed set scenario has the PBMC dataset (Figure 3a), Pancreas dataset (Figure 3e); the partial set scenario has the PBMC-subset2 dataset (Figure 3c, compared to the PBMC dataset, we removed B cells, CD4 T cells, and monocyte-CD14 cells in 3p batch), PBMC-subset3 dataset (Figure 3d, compared to PBMC dataset, we removed B cells and CD4 cells in B cells in the 3p batch); the open set scenario has the PBMC-subset1 dataset (Figure 3b, which consists of B cells and CD4 cells in the 3p batch and CD4 cells and CD4 naïve T cells in the 5p batch of the PBMC dataset) and the Pancreas-subset dataset (Figure 3f, compared to the Pancreas dataset, we removed ‘ductal’ and ‘beta’ cells in the indrop batch, acinar and beta cells in the smartseq2 batch, acinar and delta cells in the celseq2 batch, acinar and delta cells in the celseq batch, and acinar and delta cells in the fluidigmc1 batch). ## 3.1. IMAAE Performance for the Closed Set Scenarios We first conducted experiments on the Pancreas and PBMC datasets to evaluate the performance of the batch correction method in closed-set scenarios. On the Pancreas dataset and PBMC dataset, the first qualitative assessment was performed, and both UMAP visualization (Figure 4) and t-SNE visualization (Figure 5) showed that the original data had a large batch effect, while after correction by IMAAE, scGen, iMAP, and SCALEX, the batch effect has largely disappeared. On the Pancreas dataset UMAP plots show that IMAAE can discriminate some cell clusters with low numbers, and on the PBMC dataset t-SNE plots show clearer boundaries of different types of cell clusters compared to other methods IMAAE. The least effective method is MNN, which can only reduce the batch effect but cannot mix different batches of the same type of cells. Then quantitative assessment was performed (Table 1), and IMAAE had the highest F1 scores for all three assessment metrics and also obtained the highest scores in terms of cell types and substantially outperformed the other methods, with no significant difference between IMAAE and the other methods in terms of mixing different batches (the difference in scores was less than 0.1). The above two experiments show that all methods except the MNN method can effectively deal with the closed set problem. At the same time, our IMAAE performs optimally on each evaluation metric. We also note that these three quantitative evaluation methods cannot effectively assess the degree of batch mixing (the difference in scores for each method is less than 0.1 and does not match the UMAP visualization plot). ## 3.2. IMAAE Performance for the Partial Set Scenarios We carried out experiments on the PBMC-subset2 dataset to evaluate the performance of the batch correction method in partial set scenarios. On the PBMC-subset2 dataset, the qualitative evaluation was first performed, and the UMAP visualization plot (Figure 6a) showed that the original data had a large batch effect. Meanwhile, after correction by IMAAE, scGen, and iMAP, the batch effect had largely disappeared, and IMAAE was better than other methods in distinguishing different cell types, while SCALEX did not achieve the desired effect. We speculated that SCALEX was not applicable to the scenario, and the MNN method was similarly unsatisfactory. Then the quantitative assessment was performed (Table 1), and IMAAE had the highest F1 scores for all three assessment metrics. At the same time, we identified an often-overlooked problem, where if a certain type of cell appears in only one batch, improper processing will result in mapping to a nonexistent space, and the corrected data will be questioned. We used the PBMC-subset3 dataset for illustration, where B cells only appear in the pbmc_5p batch. We compared only the iMAP and IMAAE methods because the MNN effect was too poor, and the corrected data from SCALEX and scGen did not match the true gene expression data. Since B cells are present in only one batch, the usual practice is to correct B cells with the help of corrected parameters obtained using other cells using the same parameters. We expect to correct the batch effect of other cells while making B cells corrected as well. However, by converting pbmc_5p to pbmc_3p and then comparing the corrected pbmc_5p data with the uncensored pbmc_3p data, we found that the pbmc_5p batch of B cells did not overlap with the pbmc_3p batch of B cells (Figure 6b) so that whether the corrected B cells are still biologically meaningful would be questioned. Therefore, in IMAAE, we did not convert the cells that appeared in only one batch but kept the status quo. ## 3.3. IMAAE Performance for the Open Set Scenarios Experiments were also implemented on the PBMC-subset1 dataset to evaluate the performance of the batch correction method in an open-set scenario. On the PBMC-subset1 dataset, the first qualitative assessment was performed. The UMAP visualization plot (Figure 7) showed that the raw data had a large batch effect. IMAAE could mix different batches and keep different cell types separated, iMAP and SCALEX incorrectly mixed CD4 T cells with CD4 naive T cells, and scGen and MNN were less effective. Since we can already judge the performance of the method by UMAP plots alone, no quantitative analysis was performed in this experiment. It is worth mentioning that this dataset was carefully designed by us, CD4T cells and CD4 naive T cells have large similarities, and the similarity is greater than a certain degree. Almost all existing unsupervised methods failed, and only supervised methods can be used to deal with it. ## 3.4. IMAAE Performance on Low-Dimensional Data and Gene Expression Data Another advantage of IMAAE over other methods is that it is easy to obtain corrected data in low-dimensional space and gene expression data because of the adversarial autoencoder structure. Harmony and SCALEX can also obtain corrected low-dimensional data, but they have limitations in terms of dimensionality setting. Harmony can only obtain 50-dimensional data, and SCALEX can only obtain 10-dimensional data. In contrast, IMAAE is more flexible in this aspect, allowing researchers to select low-dimensional data based on different needs and preserve maximum original data information for downstream analysis tasks. We conducted experiments on the Pancreas dataset, and the UMAP plot shows (Figure 8) that IMAAE performs well at dimension 50, and the batch mixing effect is better than Harmony. At dimension 10, the delta cells of IMAAE overlap with beta cells and alpha cells, and the effect is worse than SCALEX. Therefore, we suggest that the hidden space of the IMAAE dimension should not be set too small. IMAAE, MNN, scGen, iMAP, and SCALEX can all obtain corrected gene expression data. The difference is that the data obtained by MNN, SCALEX, and scGen contain negative numbers and cannot be directly used for differential expression analysis. In contrast, the output data of the IMAAE model conform to the real gene expression distribution, so it is convenient for differential expression analysis. We utilized the PBMC dataset to show the top four differentially expressed genes for each cell type before and after correction for batch effects (Figure 9). By comparison, we can find that the top four differentially expressed genes for each cell type changed before and after correction. The observed changes in the number and identification of the differentially expressed genes after correction are expected since the correction algorithm adjusts the data. This means that IMAAE, similar to the MNN algorithm, can uncover new findings for differential expression analysis. ## 3.5. Running Time Comparison At the same time, we also compare the time performance of each method on different-size simulation datasets (Figure 10). IMAAE adopts a downsampling strategy when constructing anchor batches, with a maximum of 1000 samples for each cell type, thus keeping the time overhead well under control. Compared with other methods, IMAAE achieves leading performance on large data sets. ## 3.6. Additional Experiment 1 At the beginning of the IMAAE model design, we found that autoencoder and generative adversarial networks have limitations in complex dataset scenarios. We use the Pancreas-subset for illustration. We first trained an autoencoder model, and the implementation results showed that it performs well on closed-set problems, while when dealing with partial-set problems, a lot of noise appears on the UMAP visualization graph if batches with fewer cell types are selected as anchors (Figure 11a), which means that the noise data is lack of constraints. We then trained a generative adversarial network model. The experimental results show that it works well on the two-batch problem, while when dealing with three batches and more, it produces undercorrection (Figure 11b), which indicates that it is difficult to fit the distribution of multiple high-dimensional data at the same time for the generative adversarial network [28]. Based on these results, we decided to use generative adversarial networks to constrain the hidden space of the self-encoder and created the IMAAE model. Based on the experimental results, we expect that the IMAAE model can be further applied in the field of multi-style data reduction and style migration. ## 3.7. Additional Experiment 2 As depicted in Figure 12a, when applying steps 5 and 6, the corrected data of each batch still exhibit some batch-specific characteristics and show limited performance in batch mixing assessment. However, this data still retains the ability to further distinguish cell subtypes. In contrast, Figure 12b shows that without applying steps 5 and 6, the corrected data of each batch exhibit a more homogeneous distribution with no evident batch-specific characteristics, resulting in better batch mixing performance. Nevertheless, the data lose their ability to further subdivide cell subtypes. Researchers have the flexibility to decide whether to use steps 5 and 6 based on their task requirements. ## 3.8. Additional Experiment 3 On the Pancreas dataset, we adopted three different approaches to establishing anchor batches. Among them, the anchor batch determined by the maximum standard deviation pattern is the “celseq” batch, and the anchor batch selected by the custom pattern is the “indrop” batch. Using IMAAE to correct batch effects, the UMAP visualization (Figure 13) shows that all three patterns can correct batch effects well, and it is difficult to distinguish which one is better. Quantitative evaluation (Table 2) shows that the balanced pattern performs better than the other two patterns, which may be because the anchor batches established by the balanced pattern are closer in spatial distance to each batch, while the anchor batches determined by the other two patterns are farther away from other batches. In practical applications, the pattern for establishing anchor batches should be selected flexibly according to the task requirements. ## 4. Conclusions The batch effect poses a great challenge to scRNA-seq data analysis. In this study, we deeply analyze common dataset scenarios and propose the concepts of closed set, partial set, and open set, for which we design IMAAE, a deep learning-based supervised batch correction method. IMAAE is constructed by adversarial autoencoders to eliminate batch effects in scRNA-seq data by converting all batch cells to anchor batches. One of the advantages of IMAAE over other methods is the flexibility to choose an anchor batch. Most of the current anchor-based methods select a batch with many cell types as the anchor and convert other batches to the anchor batch. At the same time, IMGG creates an intermediate batch and converts other batches to the intermediate batch. Our IMAAE combines the features of these two types of methods, allowing both the selection of a particular batch as an anchor batch and the creation of intermediate batches as anchor batches, providing more perspectives for downstream analysis. Another advantage of IMAAE is that both corrected low-dimensional data and gene expression data can be obtained simultaneously. Thus, we can use the corrected gene expression data for differential expression analysis and the corrected low-dimensional data for some other tasks. We must note that the reason why IMAAE achieves excellent performance is that it relies heavily on well-annotated datasets, so IMAAE is not a substitute for traditional unsupervised methods but compensates for the fact that labels cannot be fully utilized when cell types are known. In conclusion, we believe that IMAAE can be useful for single-cell analysis. ## References 1. Consortium T.M.. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. 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--- title: Peptidome and Transcriptome Analysis of Plant Peptides Involved in Bipolaris maydis Infection of Maize authors: - Pijie Sheng - Minyan Xu - Zhenzhen Zheng - Xiaojing Liu - Wanlu Ma - Ting Ding - Chenchen Zhang - Meng Chen - Mengting Zhang - Beijiu Cheng - Xin Zhang journal: Plants year: 2023 pmcid: PMC10056677 doi: 10.3390/plants12061307 license: CC BY 4.0 --- # Peptidome and Transcriptome Analysis of Plant Peptides Involved in Bipolaris maydis Infection of Maize ## Abstract Southern corn leaf blight (SCLB) caused by Bipolaris maydis threatens maize growth and yield worldwide. In this study, TMT-labeled comparative peptidomic analysis was established between infected and uninfected maize leaf samples using liquid-chromatography-coupled tandem mass spectrometry. The results were further compared and integrated with transcriptome data under the same experimental conditions. Plant peptidomic analysis identified 455 and 502 differentially expressed peptides (DEPs) in infected maize leaves on day 1 and day 5, respectively. A total of 262 common DEPs were identified in both cases. Bioinformatic analysis indicated that the precursor proteins of DEPs are associated with many pathways generated by SCLB-induced pathological changes. The expression profiles of plant peptides and genes in maize plants were considerably altered after B. maydis infection. These findings provide new insights into the molecular mechanisms of SCLB pathogenesis and offer a basis for the development of maize genotypes with SCLB resistance. ## 1. Introduction Maize (Zea mays L.) is the most productive food and energy crop worldwide. Maize is susceptible to biotic and abiotic stresses that result in growth changes, as well as reductions in quality and yield [1,2]. Common maize diseases caused by various parasitic and semiparasitic pathogens include maize stalk rot, corn rust, corn smut, northern corn leaf blight, southern corn leaf blight, etc. [ 3,4]. The incidence and prevalence of these diseases varies with maize germplasm, pathogen type, planting region, and season [5]. Foliar diseases caused by fungal pathogens are a major threat to maize growth [3,4]. Pathogenesis is mainly associated with a reduction in photosynthetic area, chlorosis, and premature leaf senescence [6]. Southern corn leaf blight (SCLB) caused by Bipolaris maydis (B. maydis) is a typical wind-borne foliar fungal disease that is difficult to control and spreads widely worldwide, especially in warm and humid regions, such as the Southeastern United States or summer corn-growing areas along the Yangtze River and Huai River basins of China. This pathogen has two races (race O and race T) [7] and was initially described as a mild pathogen in 1925; however, in 1970 and 1971, the wide spread of the race T pathogen in the U.S. Corn Belt, especially in male sterile cytoplasmic corn in Texas [8], caused a massive SCLB epidemic, resulting in $15\%$ yield reduction and an estimated USD one-billion loss nationwide. Since then, B. maydis has been recognized as an important pathogen and SCLB has become one of the most destructive foliar diseases. Studies have shown that HmT toxin molecule produced by this pathogen promotes the expansion of mitochondria, resulting in abnormal oxidative phosphorylation and mitochondrial respiration and ultimately leading to cell death and maize pathogenesis, suggesting that the reaction between HmT toxin and mitochondria is crucial for the development of SCLB disease [9]. Most studies on SCLB focus on quantitative trait loci (QTL) for resistance to this disease and dozens of QTLs have been identified to be significantly associated with the disease [10,11,12,13]. In recent years, some biological control methods and extracts have shown antagonistic activity against B. maydis, including a cyclic lipopeptide antibiotic (iturin A2) purified from Bacillus subtilis B47 [14], the metabolites of Bacillus cereus C1L [15], and B. subtilis dzsy21 and its lipopeptides [16]. Peptides differ from proteins by the amount of amino acid residues the molecule contains. Traditionally, peptides are defined as molecules that consist of between 2 and 50 amino acids, whereas proteins are made up of 50 or more amino acids. In addition, peptides tend to be less well defined in terms of structure than proteins. Peptides are found in humans, animals, plants, and microorganisms. Many plant peptides have been studied and shown to play diverse and important roles and functions in plant growth and development [17,18,19,20], intercellular signaling [21], stress-signaling molecules [22,23,24], innate immune responses [25,26], and regulation of nutrient transport and utilization [27]. Peptidomics is a branch of proteomics that can be employed to identify and verify all endogenous peptides in biological samples, as well as to compare expression levels of target peptides in specific biochemical processes to provide sufficient data to study the structure and function of peptides. So far, peptidomics studies have mostly focused on neuropeptides and hormones in human diseases [28,29,30,31,32,33,34], and only a few studies have been conducted on plant peptidomics, including a database of unannotated secreted peptides in Arabidopsis [35], the role of wound-induced peptides in tomato [36], diversity analysis of cyclotides from Viola tricolor [37], and the role of an apoplastic peptide activates salicylic acid signaling in maize [38]. In the present study, a comparative peptidomics profile was generated using liquid chromatography–tandem mass spectrometry and integrated with the transcriptome data previously obtained under identical experimental conditions to investigate the role of plant peptides in response to B. maydis infection in maize. ## 2.1. Identification of Phenotypes and Characteristics of SCLB-Infected Maize Compared to the uninfected control group, disease symptoms, especially lesions, gradually appeared on maize leaves inoculated with B. maydis spores. After infection, symptom manifestation started with the appearance of light-gray translucent water stains on the leaves, accompanied by chlorosis on day 1 (Figure 1a). On day 5, the spots spread rapidly and fused into larger yellowish-brown irregular lesions on the leaves (Figure 1b). Microscopy observation of the infected leaves after trypan blue staining revealed that, on day 1 after infection, conidia of the pathogen began to germinate on the leaves and growing hyphae began to spread. On day 5, dense hyphae proliferated, and oval sporangia gradually appeared on the top of some hyphae (as shown by the arrows). Pathogen infection induces the accumulation of reactive oxygen species (ROS) in plants. As signaling molecules, ROS trigger a series of resistance cascade responses in plants. To understand the relationship between plant disease resistance and ROS burst, in this study, we performed diaminobenzidine tetrahydrochloride (DAB) and nitrotetrazolium blue chloride (NBT) staining to access the levels of H2O2 and superoxide ions (O2−) in the leaves of the infected plants. The distribution and severity of reddish-brown precipitates on the leaves indicate that the accumulation of H2O2 in the treatment group increased with the aggravation of the disease (Figure 2a). NBT staining showed that the infected maize leaves exhibit dark blue spots that increase in number and intensity as the disease progresses (Figure 2b), indicating the presence of superoxide ions. Studies on ROS accumulation in plants showed that lower concentrations of hydrogen peroxide act as signaling molecules in stress-signal transduction pathways to induce plant defense responses, and a higher concentration of hydrogen peroxide could directly kill invading pathogens. However, excessive hydrogen peroxide can over-oxidize membrane lipids and damage membrane systems and biological macromolecules, such as proteins, lipids, and nucleic acids. Therefore, there should be a balance between the production and scavenging of H2O2 during plant defense responses. The activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and the content of malondialdehyde (MDA) in the infected and uninfected maize treatment groups were measured to determine their physiological changes during plant response to the infection (Figure 2c–f). SOD, POD, and CAT are antioxidant enzymes in plants. In this study, the activities of the three enzymes were higher in the infected maize plants than in the control plants seven days post-infection. The enzyme level in the infected plants increased in the first five days but decreased on the seventh day. This can be attributed to the high consumption of the three enzymes during the elimination of excess free radicals as the disease progressed. Additionally, it may be due to the compensatory effect between different enzymes during disease progression. MDA is the product of membrane lipid peroxidation. The MDA content in infected plants was higher than that in control plants at seven days after infection and increased with time, indicating a positive correlation between MDA content of maize leaves and degree of plant stress. ## 2.2. Illumina Sequencing and DEGs Analysis Four cDNA libraries were constructed from total RNA extracted from infected maize leaves to identify the genes linked to B. maydis infection in maize. Table 1 provides an overview of the RNA-Seq reads generated from four libraries. A higher Q20 value ($98.17\%$ and $98.30\%$ in CK and B. maydis treatments, respectively) and Q30 value ($94.65\%$ and $94.99\%$ in CK and B. maydis treatments, respectively) indicated higher transcriptome sequencing quality. Fragments per kilobase of transcript per million mapped reads (FPKM) was used to determine the gene expression level in the samples and compare the mRNA level of maize in response to SCLB disease. Differentially expressed genes (DEGs) were significantly screened across the different samples based on the criteria of \|log2 fold change\| ≥2 and p ≤ 0.01. Compared with the control group, 2146 DEGs were identified (Table S1), of which 1291 were up-regulated and 856 were down-regulated (Figure 3). ## 2.3. Functional Classification of DEGs To understand the main biological functions of the DEGs in SCLB-infected maize, we performed GO enrichment analysis to establish the primary metabolic processes and signal transduction pathways involved in DEGs. Sulfur compound metabolic process (GO:0006790) and photosynthesis (GO:0015979); plastid (GO:0009536) and chloroplast (GO:0009507); and cation binding (GO:0043169) and metal ion binding (GO:0046872) were the most abundant GO terms in the biological process, cellular component, and molecular function, respectively (Figure 4a). The number of DEGs involved in each KEGG pathway was also investigated to understand the biological and signal transduction pathways in SCLB-infected maize (Figure 4b). Out of 2146 DEGs, 90 significant KEGG pathways (FDR < $5\%$) were enriched, including carbon metabolism, plant hormone signal transduction, phenylpropanoid biosynthesis, carbon fixation in photosynthetic organisms, photosynthesis, and plant circadian rhythm. Carbon metabolism was the pathway category with the largest number of genes [359], followed by the plant hormone signal transduction pathway. ## 2.4. Identification, Comparison, and Characterization of DEPs Proteome Discover (ThermoFisher version 2.1) was used for peptide data analyses. A total of 768 peptides were detected in Sample S1, of which 724 were unique peptides belonging to 293 precursor proteins (Table S2). Correspondingly, 712 peptides were detected in Sample S2, of which 672 were unique peptides belonging to 282 precursor proteins (Table S3); meanwhile, 351 peptides were detected in Sample S3, of which 337 were unique peptides belonging to 182 precursor proteins (Table S4). Most peptides ($94.5\%$, $94.7\%$, and $96.7\%$) corresponded to the individual precursor protein, revealing that the identified peptides had good sequence coverage and specificity. By filtering and screening differentially expressed peptides (DEPs) according to the threshold value set by fold change > 1.25 (up-regulated) or < 0.8(down-regulated), 455 and 502 significant DEPs were identified in SCLB-1d and SCLB-5d, respectively, while 262 were common in both cases (Tables S5–S7). A Venn diagram showed that 10 DEPs were common in SCLB-1d-1, SCLB-1d-2, and SCLB-1d-3 (Table 2; Figure 5a), while 20 were common in SCLB-5d-1, SCLB-5d-2, and SCLB-5d-3 (Table 3; Figure 5b). The 262 DEPs were also analyzed based on other parameters, including molecular weight (Mw) and isoelectric point (pI). The results showed that Mw of DEPs mainly varied between 800 and 2500 Da, whereas pI was mainly distributed in the ranges of 3–6 and 8–11. There was a pattern in the relative distribution of Mw versus pI of these peptides, and Mw was mainly gathered into four groups and located around pI 4, 6, 9, and 10 (Figure 6a). Furthermore, we counted the C-terminal and N-terminal amino acids of the preceding peptides to explore the cleavage sites of protease during peptide formation (Figure 6b). It was found that amino acids of the identified peptides occurred in the following sequence: N-terminal sequence A, D, G, and K; C-terminal sequence A, T, and L. The dominant C-terminal amino acids of the preceding peptides were A and G, and most frequent cleavage sites of N-terminal amino acids of the subsequent peptides were K, L, G, and D. ## 2.5. Functional Classification of DEPs’ Precursor Proteins Analysis of the 262 DEPs’ precursor proteins showed that they belong to 147 non-repetitive proteins. GO and pathway enrichment analysis of their precursor proteins suggested that 236 DEPs might have biological events (Figure 7a). GO analysis showed that the main biological process categories were hydrolase activity, peptidase activity, and peptidase activity acting on L-amino acid peptides. The main cellular components involved were thylakoid, thylakoid part, photosynthetic membrane, and plastid. The molecular function levels were mainly related to the metabolic process, photosynthesis, and carbohydrate metabolic process. Enrichment of the KEGG pathway involved two major pathways, namely the photosynthesis and metabolic pathways, accompanied by other pathways, including carbon fixation in photosynthetic organisms, carbon metabolism, phenylpropanoid biosynthesis, pyruvate metabolism, glycosphingolipid biosynthesis in ganglio series and in globo and isoglobo series (Figure 7b). ## 3. Discussion In natural ecosystems, plants fall victims to biotic and abiotic stresses, which affect their growth, development, yield, and quality [39,40,41,42]. As a result, plants have evolved complex mechanisms to perceive external signals and to respond promptly and adequately to potential phytopathogens [43,44,45,46]. Peptidomics has been extensively applied in the discovery and functional research of neuropeptides and hormones [29,30]. However, there are only a few descriptions of plant biotic stress responses from the perspective of plant peptides and plant peptidomics [47]. In this exemplary study, we searched for and identified many peptide changes in maize peptidome following B. maydis infection. Subsequently, we summarized the adaptive or defensive peptides produced by maize in response to SCLB stress. The knowledge gathered from the characteristics of the DEPs in infected maize helps us further understand the pathophysiology of SCLB development and provides an effective technique for improving disease prevention and treatment. To our knowledge, this is the first study that combines transcriptome and peptidome data to elucidate responses triggered by biostimuli at the plant molecular level. Our results suggest that SCLB disease alters the transcriptome and peptidome of maize, as demonstrated by several DEGs and DEPs. ROS are key signaling molecules that enable cells to respond rapidly to different stimuli. In plants, ROS play a crucial role in sensing abiotic and biotic stresses, integrating different environmental signals, and activating stress-response networks, which in turn contribute to the establishment of defense mechanisms and plant resilience [48]. In this study, we identified that peroxidase C0PKS1 and cysteine protease 2 B4F9B5 are commonly found in DEGs and DEPs. Cysteine proteases have previously been shown to modulate immunity in maize [49]. In addition to these precursor proteins that were found to be annotated, we identified a number of proteins that are not annotated but frequently appear in peptidomic data (B6TA80, B4FTI5, B6TEI9, etc.), and we suggest that these proteins might perform certain functions through their degradation peptides in response to SCLB stress. Studies postulate that many biotic defense responses in multicellular organisms are mediated by proteins that act as signal molecules or antimicrobial agents [50,51]. As small molecule proteins, bioactive peptides are also shown to be involved in the defense stress response of organisms [36,38]. In the present study, a comparative peptidome analysis was performed using the TMT-labeling approach to characterize the peptidome of maize infected with B. maydis infection, and the results show that B. maydis infection significantly alters the peptidome of the stressed plants. A total of 262 DEPs derived from 147 precursor proteins were identified from the peptidome of the infected maize group. Among the DEPs, 10 and 20 common DEPs found in maize leaves on day 1 and day 5 after infection, respectively. Thus, we hypothesized that as the disease progress, maize plants employ various mechanisms to increase their resistance or adaptability, and these mechanisms might involve many peptides associated with defense or growth maintenance. Up- or down-regulated peptides might be related to the tolerance or resistance of maize to SCLB. Of course, further studies on gene silencing or peptide processing are needed to validate our hypothesis. The characteristics of DEPs in infected maize showed specificity in protease cleavage sites and isoelectric point range. Most of the DEPs were internally from precursor proteins, whereas some were from the C-terminal, and approximately $1\%$ were from N-terminal. The functions of the precursor proteins mostly involved metabolic processes, photosynthesis, and carbohydrate metabolic processes. Thus, we speculate that the functions of these DEPs could be the same or different from those of their prerequisite proteins. Further studies on the functions of the DEPs identified in this study will provide new insights into understanding the mechanisms of maize response to SCLB. In the present study, we found that a peptide SRINPLVRLK was present in both groups simultaneously. The peptide is located at 143-152 of maize extracellular ribonuclease LE (ID: B6SSH9). B6SSH9 was the most common precursor protein in both cases, but it has not been manually annotated in Swiss-Port database. We predicted the secondary structure of B6SSH9 (Figure 6c) using PSIPRED software [52]. The results show that another peptide EKDYFETALSFR formed a helix in B6SSH9. These findings indicate that these peptides play a significant role in maize response to SCLB infection, further suggesting that precursor proteins may have similar functions in maize SCLB resistance. Further bioinformatic analysis of the 262 common DEPs demonstrated that the main cellular components of precursor proteins and DEGs were photosynthesis and chloroplast thylakoid membrane, consistent with RNA-Seq results, indicating that these GO terms are crucial in SCLB resistance. KEGG enrichment of precursor proteins and DEGs showed that carbon fixation in photosynthetic organisms/biological pathway was enriched in both cases. Based on their functions, these peptides may play the same role as the precursor proteins in resisting SCLB. A peptide AYGEAANVFGKTKKNTD from the oxygen-evolving enhancer protein 2-1 chloroplast increased in RNA-Seq (NM_001323898.1) but decreased in peptidome results. Therefore, more studies are needed to determine whether these peptides play the same role as their precursor proteins in responding to B. maydis infection. ## 4.1. Plant Materials and B. maydis Inoculation Maize (Chang 7-2 variety) seeds were obtained from the National Engineering Laboratory of Crop Resistance Breeding, Anhui Agricultural University, China (31.5° N 117.3° E). The seeds were surface-sterilized by soaking in $70\%$ ethanol for 3 min, then soaking in $7\%$ NaClO for 20 min. The seeds were washed with sterilized distilled water three times after ethanol treatment and at least five times after NaClO treatment. Afterward, the seeds were germinated in a growth chamber at 22 ± 0.5 °C and $60\%$ humidity under $\frac{16}{8}$ h light/dark, respectively. After germination, the seedlings were transplanted into nutrient soil and cultured in a greenhouse at 28 ± 1 °C under a cycle of $\frac{16}{8}$ h day/night, respectively, for 21 days. Two culture media were used to culture B. maydis. The first was potato dextrose agar (PDA) medium; 200 g of sliced peeled potato was boiled in 1 L of distilled water for about 1 h and filtered through 2-4 layers of gauze to save effluent (potato infusion). Then, 20 g/L of dextrose and 10-20 g/L of gar were added to the filtrate, and the volume was adjusted to 1 L with distilled water. The medium was sterilized using an autoclave at 121 °C for 20 min. The second was corn kernel medium; corn kernel was soaked in water for 24 h, the surface water was absorbed with filter paper, and then sub-packed into tissue culture bottles ($\frac{1}{2}$ volume), sterilized at 121 °C for 1 h, and left to cool at room temperature until use. Pure virulent strain of B. maydis was provided by Prof. Ding (School of Plant Protection, Anhui Agricultural University, Hefei, China). The fungus was activated and cultured in PDA medium in a Petri dish. Afterward, mycelia were transferred from the Petri dishes to corn kernel medium and cultured in the dark at 28 °C for 10 days. Then, the spores were induced by ultraviolet light and washed with $0.05\%$ Tween-20 aqueous solution. Spore suspension (1 × 105 U/mL) of B. maydis was evenly sprayed on corn leaves at the 6-7 leaf stage in the evening. After inoculation, the plants were bagged for 24 h to keep moisture. Leaves were collected at day 1 and day 5, respectively, and those without infection were considered the control group and those with infection were considered the infected group. ## 4.2. Measurement of ROS Accumulation, MDA Content, and Antioxidant Enzyme Activities Superoxide anion radical (O2−) accumulation in the leaves was determined by DAB and NBT staining as described previously [53,54]. MDA content and antioxidant enzyme activities were measured before and after pathogen infection, as described in a previous study with minor modification [55,56]. Briefly, the leaf samples were homogenized in phosphate-buffered solution (pH 7.8), then the supernatant was incubated with $5\%$ TBA at 100 °C for 10 min, and the absorbance was measured at 600 and 532 nm. A decrease in NBT absorbance at 560 nm was used to assess SOD activity, whereas the disappearance of H2O2 at 240 nm was used to determine CAT activity. The oxidation of guaiacol was used to determine POD activity. ## 4.3. Total RNA Isolation, mRNA Library Construction, and Sequencing Two biological replicates were performed for RNA-seq. We named the uninfected maize leaves at day 5 CK-5d-1 and CK-5d-2, while the infected maize leaves at day 5 were named SCLB-5d-1 and SCLB-5d-2. RNA-seq libraries were prepared and sequenced by Applied Protein Technology Co., Ltd. (Shanghai, China). RNA-seq analysis of the constructed library was performed using the Illumina HiSeq 2000 sequencing system with an Agilent 2100 Bioanalyzer and 2100 RNA Nano 6000 Assay Kit (Agilent Technologies, Santa Clara, CA, USA). The quality of raw readings was evaluated using FastQC [57] software (v0.11.3) (Babraham Bioinformatics, Cambridge, UK). Trimmomatic [58] was used to eliminate reads containing adapter or poly-N from the original data to obtain clean reads. The clean reads were aligned with the B73 maize genome (https://maizegdb.org/B73_RefGen_v4, accessed on 15 April 2021) using Hisat2 [59]. StringTie software (v1.0.4) [60] was used to assemble novel transcripts and multiple transcripts produced by alternative splicing. Gene expression was assessed by measuring fragments per kilobase of transcript per million mapped reads (FPKM). Differentially expressed genes (DEGs) were identified using DESeq2 [61]. The false discovery rate (FDR) was used to determine the threshold of the p-value in multiple tests. The FDR-adjusted p-value ≤ 0.01 and the \|log2 fold change\| ≥ 2 were taken as the threshold to judge the significance of gene expression difference. The transcriptome data were uploaded to NCBI database (SRR16934612-SRR16934615). ## 4.4. Peptide Extraction and Tandem Mass Tag (TMT) Labeling The uninfected, as well as the infected, maize leaves at day 1 and day 5 were named CK-1d, CK-5d, SCLB-1d and SCLB-5d, respectively. Infected and uninfected maize leaves were irradiated in a conventional microwave oven set at high power for 20 s to rapidly increase the leaf temperature to 80 °C. Afterward, the leaves were ground in liquid nitrogen and suspended in 1.5 times volume of precooled acetone (20 °C), containing $10\%$ (v/v) TCA and $0.07\%$ (v/v) 2-mercaptoethanol. After precipitation at −20 °C for 1 h, the collected proteins were centrifuged at 10,000 rpm at 4 °C for 10 min. The pellets were washed twice with cold acetone containing $0.07\%$ (v/v) 2-mercaptoethanol. The protein pellets were lyophilized and kept at −80 °C or immediately extracted with protein extraction buffer containing 8 M urea. The protein extracts were centrifuged at 12,000 rpm for 30 min at 4 °C. The protein content in the supernatant was quantified using a protein assay BCA kit (Solarbio, Beijing, China) according to the manufacturer’s instructions. The supernatant was filtered through a Microcon® YM-10 centrifugal filter unit (Millipore, Billerica, MA, USA) to remove the proteins with a molecular weight greater than 10 kDa. The peptide filtrate was desalted and concentrated on a PierceTM C18 spin column (ThermoFisher Scientific, Waltham, MA, USA) according to the instructions of the manufacturer. TMTsixplexTM Label Reagent Set was purchased from ThermoFisher Scientific (Waltham, MA, USA). Each TMT labeling reagent was dissolved in 42 μL of ACN, then 2.5 μL was added to each sample and incubated at room temperature for 1 h. Three sets of biological replicate peptide samples were marked with TMT 6-plex labeling reagents according to the manufacturer’s instructions and named S1, S2, and S3. After the reaction, $5\%$ hydroxylamine of the same volume was added to remove the TEAB nonspecifically bound to Tyr residues and quench the reaction. The labeled samples were combined, desalted, dried, and stored at −80 °C. Three biological replicates were performed. ## 4.5. Chromatography and MS/MS Analysis Labeled peptide samples of infected and uninfected maize leaves were quantified on an EASY-nLC 1200, coupled with a Thermo Q Exactive Orbitrap Mass Spectrometer (ThermoFisher Scientific, Waltham, MA, USA). First, the peptide samples were dissolved in $0.1\%$ formic acid and concentrated on an Acclaim Pep-Map100 C18 trap column (2 cm × 100 μm i.d., 5 μm, 300 Å, ThemoFisher Scientific, Waltham, MA, USA). Then, on an Acclaim Pep-Map RSLC C18 analytical column (150 mm × 50 μm i.d., 2 μm, 100 Å, ThemoFisher Scientific, Waltham, MA, USA), the peptides were continuously separated with a gradient elution profile. The gradient profile started from $3\%$ to $8\%$ solvent B within 2 min, then increased to $8\%$ to $28\%$ solvent B within 90 min, followed by $28\%$ to $44\%$ solvent B from 92-110 min, $44\%$ to $99\%$ solvent B from 110–112 min, and kept for 8 min at a flow rate of 3.0 μL/min. Solvent A contained $0.1\%$ formic acid, whereas solvent B was acetonitrile with $0.1\%$ formic acid. The orbitrap fusion hybrid mass spectrometer was operated in high-energy collision dissociation (HCD) under the data-dependent acquisition mode. The mass spectrometer was obtained in the positive ionization mode with a spray voltage of 2.2 kV, capillary temperature of 275 °C, m/z range of 300 to 1800, and resolution of 240,000. The duration of MS survey and MS/MS accumulation were 1 and 2 s, respectively. The first six signals with the highest intense ion (>5 × 104) in the collected MS spectra were fragmented to create the subsequent MS/MS spectra. All MS/MS images were collected using high-energy collision cracking set at 38 eV with a resolution of 30,000. The MS/MS resolution was set to 30 k, the automatic gain control was set to 5e5, and the maximum ion accumulation time was set to 60 ms. ## 4.6. Database Search and Peptide Identification Multiple strategies were employed to identify peptides. Proteome Discover (ThermoFisher version 2.1) was used to identify peptides from LC-MS/MS data. Enzyme specificity was set to unspecific. The mass tolerance for fragments and peptides were 20 ppm and 0.02 Da, respectively. The sequences were searched against the Zea mays sequences in the UniProt database (http://www.uniprot.org/, accessed on 2 March 2021, UPID: UP000007305) and concatenated to the database of proteomics research. The fixed modifications were carbamidomethyl-Cys, 6-plex TMT at the N-termini and Lys-6-plex TMT, but Met oxidation was variable. Each peptide quantification value was exported to an Excel output file, and the average peptide ratios or fold change (FC = treatment/control) were determined by dividing the quantification value of each peptide in the treated samples by the quantification value of control samples. Differentially expressed peptides (DEPs) represented peptides with FC ≥ 1.25 (up-regulated) or ≤0.8 (down-regulated), along with significantly different abundances (p-value < 0.05). The peptidomics data were uploaded to iProX database (iProX ID: IPX0005992000). ## 4.7. Functional Enrichment Analysis To establish the cellular components, molecular functions, and biological processes involved in DEGs, we utilized the web-based Omicsbean program [62] (http://www.omicsbean.cn/ accessed on 2 March 2021) to classify the function of Gene Ontology (GO) [63] and the Kyoto Encyclopedia of Genes and Genomes (KEGG) [64]. The GO database (http://www.geneontology.org/ accessed on 2 March 2021) was utilized to annotate DEGs and DEPs. KEGG is a route database for systematic analysis of gene functions (http://www.genome.jp/kegg/ accessed on 2 March 2021). KEGG pathways are classified as follows: A is for Metabolism; B is for Genetic Information Processing; C is for Environmental Information Processing; D is for Cellular Processes; and E is for Organismal Systems. KEGG pathway enrichment analysis adopts the same hypergeometric method as GO enrichment analysis. During analysis, the rowttest function performed a t-test for each gene using genefilter, p-value was set to <0.05 and calculated using Fisher’s exact test with a hypergeometric algorithm, and the ‘Benjamini–Hochberg’ method for multiple test correction were used for p-value adjustment analysis. The adjusted p-value < 0.05 was considered statistically significant. Proteins and genes with apparent expression changes were enriched for GO and KEGG pathways, and their functions were identified. ## 5. Conclusions This study identified DEPs and DEGs between B. maydis-infected maize and the corresponding uninfected maize, enabling systematic analysis of genes and peptides associated with SCLB resistance in maize. These findings provide new perspectives toward understanding the molecular mechanisms underlying SCLB resistance. Further studies are needed to discover the functions of each peptide in response to B. maydis infection in maize. ## References 1. 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--- title: 'Distribution of OGTT-Related Variables in Patients with Cystic Fibrosis from Puberty to Adulthood: An Italian Multicenter Study' authors: - Andrea Foppiani - Fabiana Ciciriello - Arianna Bisogno - Silvia Bricchi - Carla Colombo - Federico Alghisi - Vincenzina Lucidi - Maria Ausilia Catena - Mariacristina Lucanto - Andrea Mari - Giorgio Bedogni - Alberto Battezzati journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10056682 doi: 10.3390/jpm13030469 license: CC BY 4.0 --- # Distribution of OGTT-Related Variables in Patients with Cystic Fibrosis from Puberty to Adulthood: An Italian Multicenter Study ## Abstract Background: Insulin secretion and glucose tolerance is annually assessed in patients with cystic fibrosis (PwCF) through oral glucose tolerance tests (OGTTs) as a screening measure for cystic fibrosis-related diabetes. We aimed to describe the distribution and provide reference quartiles of OGTT-related variables in the Italian cystic fibrosis population. Methods: Cross-sectional study of PwCF receiving care in three Italian cystic fibrosis centers of excellence, from 2016 to 2020. We performed a modified 2-h OGTT protocol (1.75 g/kg, maximum 75 g), sampling at baseline and at 30-min intervals, analyzing plasma glucose, serum insulin, and C-peptide. The modified OGTT allowed for the modeling of β cell function. For all variables, multivariable quantile regression was performed to estimate the median, the 25th, and 75th percentiles, with age, sex, and pancreatic insufficiency as predictors. Results: We have quantified the deterioration of glucose tolerance and insulin secretion with age according to sex and pancreatic insufficiency, highlighting a deviation from linearity both for patients <10 years and >35 years of age. Conclusions: References of OGTT variables for PwCF provide a necessary tool to not only identify patients at risk for CFRD or other cystic fibrosis-related complications, but also to evaluate the effects of promising pharmacological therapies. ## 1. Introduction Cystic fibrosis-related diabetes (CFRD) is the most common comorbidity in patients with cystic fibrosis (PwCF), with a reported prevalence increasing with age, and affecting approximately $40\%$ of adult PwCF [1,2]. Abnormalities of insulin secretion, compromised nutritional status, and more severe lung inflammation are all associated in patients with CFRD [3]. These conditions accelerate the decline in lung function, ultimately leading to lower survival. In recent years, the introduction of modulator therapies directly targeting the underlying defect of cystic fibrosis changed the natural history of the disease, improving both the nutritional status and pulmonary function of PwCF [4]. While CFTR modulator therapy of any kind does not constitute a cure for CFRD to this day, only limited data are available on long-term effects and early (pre-puberal) administration. Of available CFTR modulator therapies, the ivacaftor monotherapy showed the most promising results [5,6], while the lumacaftor/ivacaftor studies targeting delta F508 homozygous patients showed no significant effects [7,8,9,10,11]. Even the new elexacaftor/tezacaftor/ivacaftor therapy is showing, at best, modest improvement on glucose tolerance in preliminary studies [12,13,14,15,16,17]. Initiating modulator therapies at a young age has the potential to preserve β cells functionality, but the detection of these seemingly small effects needs reference data to compare longitudinal data to the natural history of glucose tolerance and insulin secretory parameters. CFRD may remain clinically silent for years, with insulin secretion defects beginning earlier than glucose intolerance [18]. The presence of this defect in early ages has severe clinical implication: they are related to the future worsening of glucose tolerance and CFRD [19,20]; they are associated with lung disease in young PwCF with mild to normal pulmonary function [21] independently from hyperglycemia [22]; and insulin is also an important anabolic hormone, and the catabolic effect of insulin insufficiency has important implication on growth [23,24], and is specifically associated with reduced adult height [25]. PwCF undergo annual screening for CFRD with an oral glucose tolerance test (OGTT), starting at ten or even six years of age [26]. Considering that standard OGTT cannot detect insulin secretion defects, we implemented a modified OGTT protocol in three separate and geographically distributed cystic fibrosis Italian centers, expanding the previously studied Milan cohort of PwCF [27]. The modified OGTT protocol included insulin and C-peptide measurement in addition to glucose, allowing for the modeling of parameters describing β cell function, insulin secretion, insulin clearance, and OGTT insulin sensitivity [28,29]. The aim of this study is to describe cross-sectionally at the Italian level the progression of OGTT parameters, β cell function, insulin clearance, and insulin sensitivity up to adulthood, while also providing national references for these parameters. ## 2.1. Study Design, Setting, and Participants This was a cross-sectional study of the Italian cystic fibrosis population that consecutively enrolled patients between 2016 and 2020. Patients were recruited from three Italian cystic fibrosis centers of excellence, selected to represent the northern, central, and southern geographical areas of Italy: the Cystic Fibrosis Centre of the Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy; the Cystic Fibrosis Unit of the Bambino Gesù Children’s Hospital, Rome, Italy; and the Cystic Fibrosis Referral Center of the University Hospital G. Martino, Messina, Italy. To be eligible, patients had to be clinically stable in the previous 3 weeks (absence of major clinical events including pulmonary exacerbations, no change in their habitual treatment regimen including introduction of antibiotics or steroids). Exclusion criteria were diagnosis of CFRD, or treatment with insulin or oral hypoglycemic agents in the previous 6 months. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the University of Milan (protocol code $\frac{53}{19}$, 26 November 2019). Informed consent was obtained from all subjects involved in the study. ## 2.2. Variables and Measurements Outcomes for the main analysis were:OGTT parameters: glucose, insulin, and C-peptide (sampled before and at 30, 60, 90, and 120 min of the OGTT), and their area under the curve (AUC).β cell function: β cell glucose sensitivity, basal insulin secretion, insulin secretion at a fixed glucose concentration, total insulin secretion.insulin clearance: basal and OGTT insulin clearance.insulin sensitivity: quantitative insulin-sensitivity check index (QUICKI, for basal insulin sensitivity), and a 2-h oral glucose insulin sensitivity index (2-h OGIS for OGTT insulin sensitivity). Predictors of the outcomes were age (continuous; in years), sex (categorical; 0 = Female, 1 = Male), and pancreatic sufficiency status (categorical; 0 = pancreatic sufficient, 1 = pancreatic insufficient). ## 2.2.1. CFTR Gene Mutation, Clinical Evaluation, Anthropometric and Pulmonary Assessment CFTR mutations were classified by epidemiological prevalence (F508del homozygous, F508del heterozygous, other) and combining the type of CFTR defect with clinical severity (class I to VI, with decreasing level of severity) [30]. All patients were evaluated before enrollment in the study. The clinician informed the patient of the study procedures and collected informed consent. Clinical records of the following variables were collected: pancreatic insufficiency, intermittent and chronic infections, history of lung transplant, and CFTR modulator therapy. Anthropometric assessment consisted of measuring weight and height following standard procedures [31]. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. For patients < 20 years, standard deviation scores were calculated based on the Centers for Disease Control and Prevention growth charts for weight, height, and BMI [32]. Patients were classified according to BMI in underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25–29.9 kg/m2), or obese (BMI ≥ 30 kg/m2), and for patients ≤20 years old according to BMI z-score [33]. Patients were also classified following the Cystic Fibrosis Foundation recommendations: for women BMI ≥ 22, for men BMI ≥ 23, and for people younger than 21 years old ≥ 50th percentile [34]. Spirometry was performed according to the American Thoracic Society and European Respiratory Society guidelines [35]. The forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) were expressed as percentage of the reference values [36]. ## 2.2.2. Oral Glucose Tolerance Test and Laboratory Exams All subjects received a 2-h OGTT (1.75 g/kg, maximum 75 g) sampling at baseline, and at 30-min intervals, the subjects had determinations of plasma glucose, serum insulin, and C-peptide concentrations. Based on plasma glucose concentrations, patients were classified in glucose tolerance categories according to [18]. On the same day, C-reactive protein, and glycated haemoglobin (HbA1C) were also measured. Plasma glucose was measured on fluoride plasma samples (Gluco-quant; Roche/Hitachi analyser; Roche Diagnostics), and the other analytes were measured by commercial assays (ECLIA-Cobas C6000; Roche Diagnostics). Based on the plasma glucose concentrations, patients were assigned to one of the following categories of glucose tolerance [18]: normal glucose tolerance, normal glucose tolerance with impaired fasting glucose, indeterminate glucose tolerance, impaired glucose tolerance, cystic fibrosis-related diabetes without fasting hyperglycaemia, and cystic fibrosis-related diabetes with fasting hyperglycaemia. ## 2.2.3. Modeling of β Cell Function and Other OGTT-Derived Indices Beta-cell function was assessed by modeling from OGTT glucose and C-peptide, as previously described [27,29,37], using a model that describes the relationship between insulin secretion and glucose concentration. The model expresses insulin secretion as the sum of two components. The first component represents the dependence of insulin secretion on absolute glucose concentration at any time point during the OGTT through a dose-response function relating the two variables. Characteristic parameter of the dose-response is the mean slope over the observed glucose range, denoted as β-cell glucose sensitivity. The dose-response is modulated by a potentiation factor, which accounts for the fact that during an acute stimulation, insulin secretion is higher in the descending phase of hyperglycemia than in the ascending phase at the same glucose concentration. As such, the potentiation factor encompasses several potentiating mechanisms, including prolonged exposure to hyperglycemia, non-glucose substrates, gastrointestinal hormones, and neural modulation. It is set to be a positive function of time and is constrained to average unity during the experiment. In normal subjects, the potentiation factor typically increases from baseline to the end of a 2-h OGTT [29]. To quantify this excursion, we calculated the ratio between the 2-h and the baseline value. This ratio is denoted as potentiation ratio. The second insulin secretion component represents the dependence of insulin secretion on the rate of change of glucose concentration. This component is termed the derivative component and is determined by a single parameter, denoted as rate sensitivity. Rate sensitivity is related to early insulin release [29]. The β cell function parameters derived from the model were β cell glucose sensitivity, i.e., the slope of the relationship between insulin secretion and glucose concentration, and basal and total OGTT insulin secretion. Basal insulin clearance was calculated as the ratio between basal insulin secretion and concentration, and OGTT insulin clearance as the ratio of total insulin secretion and insulin AUC. Fasting insulin sensitivity was calculated as QUICKI index [38] and insulin sensitivity during the OGTT as the OGIS index [28]. The total glucose, insulin, and C-peptide excursions during the OGTT were calculated as the glucose AUC using the trapezoidal rule. ## 2.3. Bias and Study Size Information bias was mitigated in this study as every PwCF should undergo an annual OGTT screening to early detect glucose intolerance. While it is possible that more severe patients may have been screened more intensively, we specifically excluded OGTT performed in acutely ill patients, according to the inclusion criteria. On the other hand, it is not advisable to conduct an OGTT on diabetic patients, so patients that resulted diabetic after measuring fasting glycemia were not allowed to continue the test. Moreover, the test is less tolerated by young patients, and they may have not completed the test at a higher rate than older patients. Survival bias was a possible source of selection bias that was tested, analyzing the raw trend lines of each outcome variable. The number of patients tested in the recruiting centers during the study period determined the sample size. ## 2.4. Quantitative Variables and Statistical Methods Age was binned with 5-year bins from 10 to 35 years, and patients outside this range were grouped in two extreme groups due to small group sizes. Most continuous variables were not Gaussian-distributed, and all are reported as median (50th percentile) and interquartile range (IQR; 25th and 75th percentiles). Discrete variables are reported as the number and proportion of subjects with the characteristic of interest. The relationship between age groups and outcomes was described through quantile regression to estimate within each age group the median, the lower, and the upper quartile of each outcome, adjusting both for sex and pancreatic insufficiency status. ## 2.5. Italian Reference Values To limit the influence of outliers, all continuous variables besides age were winsorized using a tail of 0.01, meaning that values under the 1st percentile were put equal to the 1st percentiles, and values above the 99th percentile were put equal to the 99th percentile [39]. The 25th, 50th and 75th percentiles of each OGTT-related variable were estimated from a quantile regression model employing the variable as outcome and age (continuous, years), sex (discrete, 0 = female; 1 = male) and pancreatic insufficiency (0 = no; 1 = yes) as predictors [40]. Because of missing data for most variables, we fitted quantile regression using multiple imputation by chained equation (MICE), as detailed in the Supplementary Materials. ## 3.1. Participants Characteristics A total of 369 patients were included in statistical analysis, their characteristics are presented in Table 1. Median (IQR) patient age was 19 [15, 24] years), ranging from 6 to 56 years, and $56\%$ of patients were females. The most frequent CFTR mutation was the F508del ($23\%$ were F508del homozygous, and $43\%$ were F508del heterozygous), and $79\%$ of the patients were pancreatic insufficient. While $90\%$ of patients were of normal weight, only $42\%$ were above the BMI target set by the Cystic Fibrosis Foundation. Glucose tolerance was normal in $65\%$ of the patients, while $8.1\%$ resulted affected by CFRD without fasting hyperglycaemia (patients affected by CFRD at basal measurement were not allowed to continue the OGTT). ## 3.2. Main Results Table 2 shows patient age distribution and count by age group, stratified by sex and pancreatic insufficiency, while the relationships between outcomes and age groups are shown in Figure 1, Figure 2 and Figure 3. Visual inspection of Figure 1 shows increasing glucose values in the second hour of the OGTT, and decreasing insulin and C-peptide values, going from younger to older ages (see Panel A and B). This was paired with decreasing β cell glucose sensitivity and insulin secretion (see Panel C), increasing insulin clearance (see panel D) and greater fasting insulin sensitivity (see Panel E). Despite these overall trends, most variables showed a deviation from linearity at one or both extreme age groups (≤10 and >35 years). Indeed, contrary to the overall trend highlighted in Figure 1, secretory parameters (C-peptide at all time points, β cell glucose sensitivity, basal and total insulin secretion) of patients ≤10 were significantly lower than in patients (10,15] years old (see Table 3). On the other hand, patients >35 years of age showed similar, if not improved (see C-peptide and β cell glucose sensitivity in Table 3) than younger patients, instead of continuing the deteriorating trend. Outcomes stratified by pancreatic status showed the greatest differences between groups when compared to differences between sexes (see Figure 2), both in glucose tolerance and insulin secretory parameters (see Figure 3). ## 3.3. Italian Reference Values To produce reference values of the OGTT parameter, patient selection was performed to obtain a sample that was of uniform composition and sufficiently sized on the age scale. We did not have a sufficiently sized sample to flexibly model the deviation occurring at extreme age groups (≤10 and >35) and thus, the analysis was limited to central age groups as they showed a more linear trend. We chose to limit the sample size to post-puberal patients (≥13 years old for females and ≥15 years for males) for three reasons: [1] exploratory analysis identified a peak in secretory parameters in the age group that includes puberty; [2] an increase in secretory parameters occurring with puberty seems compatible with other changes occurring during puberty (i.e. greater insulin resistance [41]); and [3] the sample was not sufficiently sized before puberty to model a slope change and the sample generally exhibited a linear trend for all variables, excluding patient before puberty (Figure S3 in the Supplementary Materials show linear trends of OGTT-related parameters stratified by sex and puberty status). Patients >35 years old were also excluded from the analysis for similar reasons: [1] visual inspection, and Table 3 showed at least no deterioration, and in the case of β cell glucose sensitivity, an improvement of insulin secretory parameters after the 30–35 years age group; [2] these changes are compatible with a survival bias; and [3] the sample was not sufficiently sized to model age groups >35 years. Figure 4 shows point estimates from quantile regression of all OGTT-related variables, stratified by sex. Most variables show an either increasing or decreasing trend with age: glucose tolerance (see raw glucose values in the second hour of the OGTT and glucose AUC) degrades with age, but it does not seem related to increasing insulin resistance, as insulin sensitivity (both fasting as QUICKI or during OGTT as OGIS) is stable throughout the age range; insulin secretion (expressed by raw insulin and C-peptide values, and modeled basal and total insulin secretion) generally degrades with age, accordingly with decreasing β cell glucose sensitivity and increasing insulin clearance. We deem all changes going from puberty to 35 years of age clinically significant, except for fasting glucose, glucose values in the first hour of OGTT, and insulin sensitivity parameters, that have shown minimal changes. Figures S4–S8 in the Supplementary Materials show point estimates and $95\%$ confidence intervals from quantile regression of all OGTT-related variables stratified by sex, while Figure S9 shows β-cell glucose sensitivity stratified by age groups and glucose tolerance categories. ## 4. Discussion In this study we present a description of the distribution and Italian reference quartiles for OGTT parameters and their AUCs, β cell function, insulin clearance, and insulin sensitivity. For all variables, we described the distribution from pre-puberal age to old adults accounting for differences in sex and pancreatic insufficiency between age groups, while we produced reference values for post-puberal patients and young adults that are age- and sex-specifically adjusted for pancreatic insufficiency. Our data confirmed an approximately linear degradation of glucose tolerance and insulin secretion during adulthood of PwCF and up until 35 years of age, with trend deviations occurring both in younger and older patients (these age groups were therefore excluded from the modeling of reference quartiles). We provide suggestive findings in the two extreme age groups: our data and the comparison with fasting insulin reference data for the general population (see Table S1, Figures S1 and S2 in the Supplementary Materials) suggest that a peak in insulin secretion occurs approximately across puberty, both in PwCF and in the general population. On the other hand, patients >35 years of age show better glucose tolerance and insulin secretion than younger peers, seemingly identifying survivors with preserved pancreatic function. Puberty. Puberty seems associated with an increase in insulin secretory parameters. Our exploratory analysis highlighted a peak in C-peptide values, and modeled β cell glucose sensitivity, basal and total insulin secretion. The dynamics of insulin secretion across puberty are poorly studied even in the general population, although available evidence seems to confirm our data. Insulin secretion in puberty was studied cross-sectionally by [42] in 23 subjects using the hyperglycemic clamp technique; they found that adolescents display greater insulin and C-peptide responses when compared to both pre-adolescents and adults, even though glucose responses were similar for all groups. The authors of [43] also studied insulin secretory capacity using the hyperglycemic clamp in 133 subjects cross-sectionally, and 24–27 subjects longitudinally, both analyses showing an increase of insulin secretory capacity across puberty and a decrease later in adulthood. Moreover, combined general-population reference data for fasting insulin available in the Supplementary Materials [44,45,46,47] show an increasing trend before puberty and a decreasing trend thereafter. Superimposing cystic fibrosis quartiles of fasting insulin produced in this study to combine reference quartiles for the general population shows that at least fasting insulin, that is reflection of insulin secretion, seems generally preserved in young PwCF, although a faster degeneration with age is evident in PwCF, as expected. Older patients. PwCF >35 years old show a better glucose tolerance and insulin secretion than younger patients. We provide two possible explanations for this phenomenon: [1] we only included patients without CFRD, as OGTT is not feasible in those patients. As CFRD prevalence increases with age [26], and it represents the final stage of progressive glucose tolerance and insulin secretion degradation, patients with lower glucose tolerance and lower insulin secretion are progressively excluded in our analysis (selection bias); and [2] as CFRD is associated with lower survival, PwCF that live longer tend to show better pancreatic function (survival bias). In this cross-sectional analysis, patients with better pancreatic function are present in all age groups, but the combination of the aforementioned factors cause a deviation from linearity in the extreme age group of patients >35 years old; therefore, they were excluded from the analysis. Older CFRD-free PwCF may represent a model to better understand the mechanism behind CFRD occurrence. References values in post-puberal patients. We previously published OGTT-related variable quartiles from a smaller sample recruited at a single center [27]. In this study, we can confirm the relationship between OGTT-variables and age while introducing novel findings: [1] glucose tolerance deteriorates with age seemingly starting after puberty, in particular in the second hour of the OGTT; [2] glucose tolerance derangements are seemingly due to reduced insulin secretion, as highlighted by reduced raw insulin values, both fasting and particularly during OGTT, but perhaps more importantly as highlighted by a reduction of the β cell response to an increase in glucose concentration that delay the insulin response, causing glucose elevation in the second half of the OGTT; [3] insulin sensitivity does not deteriorate with age, reinforcing the hypothesis that glucose derangements recorded in PwCF are caused by an insulin secretory defect and not by an increased insulin resistance; [4] contributing to insulin secretory defects, both basal and OGTT insulin clearance appear to increase with age, although this may be in fact due to lower circulating and secreted insulin levels that do not saturate the systemic (mostly hepatic and renal) abilities to clear insulin; and [5] β cell dysfunction highlighted by reduced β cell glucose sensitivity is closely related to pancreatic dysfunction. Sex differences. In comparison with [27], sex-related differences shown in this study seem reduced. Here we accounted for differences in pancreatic insufficiency, and, perhaps more importantly, we included only post-puberal patients, as younger patients displayed a puberty-related increment in insulin secretion that we could not model reliably. It is plausible that the inclusion of pre-puberal patients in our previous work displaced the overall linear trends of studied parameters. In contrast, when accounting for age differences in puberty incidence, the two sexes show similar trends. Pancreatic insufficiency. Pancreatic sufficiency status was the greatest determinant of glucose tolerance and insulin secretion. There is a known correlation and plausible biological mechanism linking pancreatic insufficiency and β cell function, with pancreatic insufficient patients showing lower glucose tolerance and lower insulin secretion [48,49]. In PwCF with pancreatic insufficiency, exocrine pancreas is replaced in large amounts by fibrotic and/or fatty tissue, while islet mass is relatively conserved [50]. Emerging theories have suggested that a crosstalk between pancreatic ductal epithelial cells and beta cells may be a main contributor to beta cell dysfunction in PwCF, as CFTR is expressed in ducts and diseased ducts may influence β cell function through exocrine-derived proinflammatory factors [51]. In this study, we included pancreatic sufficiency status as a covariate to adjust for difference in pancreatic insufficiency prevalence in calculated quartiles, but we do not provide separate references for pancreatic sufficient and insufficient patients, as outcomes related to glucose tolerance and insulin secretion are likely to occur independently from pancreatic insufficiency when considering the endocrine pancreatic function. The study has some limitations. As has been noted, the sample size and age distribution prevented to acceptably model patients in the extreme age groups, and so they were excluded from the analysis. No gold-standard methods were used to measure insulin secretion and sensitivity, although we consider these reasonable constraints of a relatively large study. A potential limitation of the modeling of β cell function employed in this study is the use of the C-peptide kinetic model by [52], as previously reported [27]. On the other hand, this study improved on the previous work [27] by avoiding the trajectories bias produced by including puberal and pre-puberal patients in the analysis, by improving sample size with greater recruitment of patients from multiple sites, and finally, by including the contribution of pancreatic sufficiency status in the developed quantiles. In conclusion, we provide a description of the distribution and Italian reference quartiles for OGTT-related variables. We have already shown how these parameters are likely to predict overt CFRD [19], how the deterioration of these parameters is linked with lung function [22] and reduced adult stature [25], and finally, how these parameters are seemingly unaffected by the lumacaftor/ivacaftor combination therapy when administered in post-puberal PwCF [11]. These references provide a necessary tool to not only identify PwCF at risk for CFRD or other cystic fibrosis-related complications, but also to evaluate the effects of promising pharmacological therapies. As administration in early ages of new therapies has the greatest potential to provide significant improvements in glucose tolerance and insulin secretion, a better characterization of the natural history of these parameters during puberty is strongly needed. ## References 1. Lanng S., Hansen A., Thorsteinsson B., Koch C.. Glucose tolerance in patients with cystic fibrosis: Five year prospective study. BMJ (1995.0) 311 655-659. 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--- title: Prognostic Value of Soluble AXL in Serum from Heart Failure Patients with Preserved and Reduced Left Ventricular Ejection Fraction authors: - Helena Cristóbal - Cristina Enjuanes - Montserrat Batlle - Marta Tajes - Begoña Campos - Josep Francesch - Pedro Moliner - Marta Farrero - Rut Andrea - José Tomás Ortiz-Pérez - Albert Morales - Manel Sabaté - Josep Comin-Colet - Pablo García de Frutos journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10056687 doi: 10.3390/jpm13030446 license: CC BY 4.0 --- # Prognostic Value of Soluble AXL in Serum from Heart Failure Patients with Preserved and Reduced Left Ventricular Ejection Fraction ## Abstract Heart failure (HF) is classified according to the degree of reduction in left ventricular ejection fraction (EF) in HF with reduced, mildly reduced, and preserved EF. Biomarkers could behave differently depending on EF type. Here, we analyze the soluble form of the AXL receptor tyrosine kinase (sAXL) in HF patients with reduced and preserved EF. Two groups of HF patients with reduced (HFrEF; $$n = 134$$) and preserved ejection fraction (HFpEF; $$n = 134$$) were included in this prospective observational study, with measurements of candidate biomarkers and functional, clinical, and echocardiographic variables. A Cox regression model was used to determine predictors for clinical events: cardiovascular mortality and all-cause mortality. sAXL circulating values predicted outcome in HF: for a 1.0 ng/mL increase in serum sAXL, the mortality hazard ratio (HR) was 1.019 for HFrEF ($95\%$ CI 1.000 to 1.038) and 1.032 for HFpEF ($95\%$ CI 1.013 to 1.052). In a multivariable Cox regression analysis, sAXL and NT-proBNP were independent markers for all-cause and cardiovascular mortality in HFpEF. In contrast, only NT-proBNP remained significant in the HFrEF group. When analyzing the event-free survival at a mean follow-up of 3.6 years, HFrEF and HFpEF patients in the higher quartile of sAXL had a reduced survival time. Interestingly, sAXL is a reliable predictor for all-cause and cardiovascular mortality only in the HFpEF cohort. The results suggest an important role for AXL in HFpEF, supporting sAXL evaluation in larger clinical studies and pointing to AXL as a potential target for HF therapy. ## 1. Introduction Heart failure (HF) is a clinical syndrome caused by a deterioration of the heart’s function [1]. HF is a growing health concern linked to an aging population and the increasing prevalence of cardiovascular risk factors [2,3]. The most common HF causes include myocardial dysfunction due to coronary artery disease (CAD), hypertension, and valve disease, among other less prevalent causes. Its initial diagnosis is based on the presence of clinical symptoms and signs associated with cardiac dysfunction [1]. However, this lacks sufficient accuracy or specificity. Therefore, most clinical practice guidelines recommend the measurement of blood biomarkers such as natriuretic peptides to confirm HF diagnosis [4]. This area of research has been very active in recent years, as indicated by a large number of studies and reviews on this topic [5,6,7]. HF is classified into three main subtypes depending on left ventricular ejection fraction (EF). HF with reduced EF (HFrEF) is characterized by an EF ≤ $40\%$. However, roughly $50\%$ of HF patients present an EF > $40\%$. This group of patients has been recently subdivided into two, those with preserved EF (≥$50\%$; HFpEF) and an intermediate group named HF with mildly reduced EF (EF 41–$49\%$; HFmrEF). The definition has important implications for the characteristics, prognosis, and treatment of HF. HFmrEF and HFrEF have a higher frequency of underlying CAD compared to those with HFpEF [8]. HFpEF is more frequently associated with hypertension and is more prevalent in women and older patients [9,10]. However, one should consider that EF is a continuous variable with a normal distribution in the population [11]. The use of cardiac markers with high prognostic value in HF evaluation is crucial for patient triage. Brain natriuretic peptide (BNP) and NT-proBNP, the products of the cleavage of pre-proBNP, have been the gold standard biomarkers in HF [12]. Elevated natriuretic peptide concentration associates with abnormal hemodynamics at the heart ventricle and cardiac dysfunction in HF. Employing the level of different natriuretic peptides in the diagnosis of acute HF is well established and included by guidelines in the clinical practice [1,13,14]. However, these guidelines mention that the use of other biomarkers apart from BNP or NT-proBNP should be considered for risk stratification in the management of HF [14]. The use of blood biomarkers is especially relevant in HFpEF, as natriuretic peptides are less elevated. In this context, we have proposed sAXL as a candidate to take on this role [15]. AXL is a receptor tyrosine kinase with functions in immune regulation and tissue homeostasis [16]. AXL is processed in the extracellular membrane of cells by ADAM$\frac{10}{17}$ proteolysis, releasing the extracellular portion of the molecule, known as soluble AXL (sAXL) [17]. Previous studies have shown that sAXL is increased in HF patients with reduced ejection fraction (HFrEF), correlating with an increased AXL abundance in cardiac tissue [15]. High sAXL levels are associated with a worse prognosis in HFrEF [15,18,19]. Furthermore, in patients suffering myocardial infarction with ST-segment elevation, sAXL levels are increased in patients undergoing adverse left ventricular remodeling [20]. These clinical studies suggested that AXL could influence multiple aspects of cardiovascular physiology via its diverse effects on vascular and immune cells [21]. Multiple studies suggest that after engaging its ligand GAS6, AXL drives vascular remodeling by regulating the biology of leukocytes, VSMCs, ECs, and pericytes, thereby facilitating pathological processes such as neointima proliferation in the vasculature induced by redox stress [22,23], flow [23] or mechanical injury [24,25,26]. More recently, several studies using animal models have shown that AXL influences on the response of the heart to damage. In rats subjected to thoracic transverse aortic constriction, axl expression increased, correlating with left ventricular hypertrophy. This rise was matched by the appearance of the soluble form of AXL in blood, which only increased in the initial hypertrophy group [27]. In a mouse strain where axl is depleted in myeloid cells, there is a reduction in proinflammatory cytokines after reperfusion in a myocardial infarction model [28], similar to what has been observed in livers subjected to profibrotic stimuli [29]. Inhibition of AXL while maintaining MerTK, a second GAS6 receptor, improves cardiac healing in those models [28]. AXL has also a prominent role in cardiac allograft vasculopathy. AXL-deficient recipient mice displayed fewer immune cells and reduced neointima formation in grafted vessels. This function was linked to AXL expression in myeloid cells [30]. Interestingly, gas6 knockout animals also show an improved allograft heart survival, suggesting that GAS6 interaction with AXL is mediating this effect [31]. Indeed, the lack of GAS6 reduces leukocyte extravasation in different models of local inflammation [31]. All these studies employing different preclinical models suggest that AXL is a substantial player in heart physiology, especially in response to a chronic damage, as those involved in the development of HF. An aspect that has not been evaluated yet in the literature is the specific role of AXL in HF with preserved ejection fraction. Therefore, here, we intend to study the prospect of sAXL as a prognostic biomarker in the context of HF. The specific objectives of the present study were [1] to define the value of sAXL as a biomarker in patients with chronic HFpEF patients compared to a similar cohort of HFrEF patients, [2] to compare its predictive performance of clinical outcomes with NT-proBNP measurements, and [3] to validate the prognostic sAXL value in HFrEF patients with a different cohort from our previous studies. First, we determined the serum sAXL concentration in a cohort of patients with HFpEF and compared the sAXL serum concentration with their concentrations in similar patients with HFrEF. Clinical outcomes were studied, including the endpoint of all-cause death (as the primary endpoint) and cardiovascular death or re-admission due to HF (as the secondary endpoint). ## 2.1. Study Design, Study Population and Ethics Samples and data from patients included in this study were handled and provided by the Biobank HUB-ICO-IDIBELL (PT$\frac{20}{00171}$), integrated into the ISCIII Biobanks and Biomodels Platform. They were processed following standard operating procedures with the appropriate approval of the Ethics and Scientific Committees. The study population derives from DAMOCLES (Definition of the neuro-hormonal Activation, Myocardial function, genomic expression, and clinical OutComes in heart faiLurE patientS), an observational, prospective cohort study of 1236 consecutive chronic HF patients. The cohort was recruited between January 2004 and January 2013 at a single center. The methodology of the DAMOCLES study has been published previously [32,33]. Briefly, the inclusion criteria for the patients included consisted of a diagnosis of chronic HF following the European Society of Cardiology (ESC) criteria, to have had at least one recent acute decompensation of HF requiring intravenous diuretic therapy (either hospitalized or in the day-care hospital), and to be in stable condition at the time of inclusion in the study. Exclusion criteria were: significant primary valvular disease, clinical signs of fluid overload, pericardial disease, restrictive cardiomyopathy, hypertrophic cardiomyopathy, hemoglobin (Hb) concentration below 8.5 g/dL, chronic liver disease or active malignancy. The patients were recruited regardless of their percentage of left ventricular EF. The study was approved by the local ethics committee for clinical research and was conducted following the principles of the Declaration of Helsinki. All patients gave written informed consent before their inclusion in the DAMOCLES study. ## 2.2. Definition of Study Cohorts and Selection Criteria Using samples from the DAMOCLES study, we selected two different nested cohorts of patients with HF for the purpose of the present investigation. The two cohorts consisted of 134 HF patients from the DAMOCLES cohort study matching the criteria of HFrEF and HFpEF, respectively. A collection of 20 samples of unrelated, healthy blood donors from the same geographical area were used as a reference group. ## 2.3. Clinical Assessment at the Time of Inclusion A baseline assessment was performed for all DAMOCLES participants at the study entry. This detailed evaluation included the collection of information about demographic characteristics, exhaustive medical history to gather clinical and disease-related factors: New York Heart Association (NYHA) functional class was recorded at enrollment in DAMOCLES based on patient symptoms, comorbidities, laboratory information, medical treatment, and the most recent determination of left ventricular EF (Table 1). The sources of information employed in order to generate the database of the study consisted in the patient’s medical history and standardized questionnaires. ## 2.4. Blood Sample Management Laboratory data and blood sample management methods have been previously reported by our group [32]. Blood samples were collected in serum tubes, immersed in ice, and immediately processed in aliquots of 250–500 µL. The resulting serum samples were frozen and stored at −80 °C using the Micronics® (High Wycombe, UK) system. Samples and data from patients included in this study were provided by the Biobank HUB-ICO-IDIBELL (PT$\frac{20}{00171}$), integrated with the Spanish Biobank Network. Samples and data were processed following standard operating procedures with the appropriate approval of the Ethics and Scientific Committees. ## 2.5. Clinical Laboratory Determinations Serum N-terminal pro b-type natriuretic peptide (NT-proBNP) concentration was measured in pg/mL using an immunoassay based on chemiluminescence with the Elecsys System (Roche®, Basel, Switzerland). This determination employs a two-step sandwich assay and was performed in Cobas® analyzers. sAXL was measured in serum using a commercial sandwich ELISA, consisting of a capture monoclonal antibody recognizing the extracellular domain of AXL and a biotinylated polyclonal antibody linked to biotin for the detection step as previously described [34]. The ELISA was purchased from R and D systems and has been validated in [35]. Samples were diluted 1:50 in a solution containing $1\%$ bovine serum albumin in phosphate-buffered saline (pH = 7.4). Hemoglobin levels in g/dL were obtained by laser-based impedance colorimetry. The glomerular filtration rate (GFR) was calculated from the determination of serum creatinine using the Modification of Diet in Renal Disease Study Group (MDRD) equation, a widely used parameter for measuring excretory kidney function [36]. Weight was recorded upon inclusion in order to estimate the body mass index (BMI) using the formula: BMI = weight (kg)/height (m2). ## 2.6. Follow-Up and Major Heart Failure Events Ascertainment DAMOCLES study participants were followed for a median of 2.93 years (mean 3.3 years). Follow-up was conducted by trained study personnel and lasted until November 2015. The data on mortality and the cause of death were obtained from hospital and primary care electronic medical records, and/or by direct interview with the patients’ relatives. ## 2.7. Statistical Methods All analyses were performed using the SPSS software (version 28.0; IBM, New York, NY, USA). Cross-sectional and longitudinal descriptive analyses were performed using the baseline and follow-up data from the DAMOCLES cohort [32,33]. Demographic characteristics, results from clinical laboratory tests and clinical characteristics, as well as laboratory tests results were summarized using basic descriptive statistics according to HF with reduced or preserved EF. For categorical variables, number and percentage were reported, and for continuous variables median and interquartile range was used. χ2, Student’s T, and non-parametric tests were used to compare characteristics across strata. The log-rank test was used to assess the association of each individual variable with survival. Survival curves were obtained using the Kaplan–Meier product limit estimator. The adjusted effect of important factors on patient survival was then determined with Cox proportional hazards regression using the forward stepwise method based on the likelihood ratio. Cox’s regression is a semi-parametric model widely used to establish association between predictors and time-to-event, as it makes fewer assumptions than parametric models. Two multivariable models were employed using age and NYHA class (model 1) and including NTproBNP and eGFR (model 2). The parameters included had clinical relevance in the etiology of HF (age; NYHA class; NTproBNP). eGFR was included in the multivariable model 2 as it has shown association with AXL in previous studies [21]. sAXL distribution values in quartiles were analyzed, and the 3rd quartile value was used as a cut-off point for stratification in the Kaplan–Meier survival curves. Subdivision in quartiles is useful analytical tool, as quantiles are less susceptible than means to long-tailed distributions and outliers. Patients were divided in two groups with sAXL below or equal the 3rd quartile (sAXL ≤ Q3) or above (>Q3). All statistical tests were solved fixing the probability of type I error (alpha) at $5\%$, and confidence intervals (CI) were obtained for a $95\%$ likelihood. Values of p below 0.05 were considered statistically significant. ## 3.1. Characteristics of HF cohorts The baseline characteristics of the HFrEF and HFpEF cohorts are shown in Table 1. Their demographic and clinical characteristics are consistent with those expected for each cohort. Patients with HFpEF had a higher proportion of women, were older and had a higher body mass index (BMI). Systolic blood pressure (SBP) was higher in the HFpEF, while the glomerular filtration rate (eGFR) was lower, indicative of a higher frequency of renal dysfunction in the HFpEF group. No differences were observed in diagnostic criteria for diabetes. Interestingly, while the proportion of NYHA III-IV patients was higher in the HFpEF group, there were no differences in all-cause mortality or major cardiovascular events during the follow-up, which were similar in both groups. Additionally, HFrEF had a higher ischemic etiology percentage, and more patients were treated with ACE/ARB and /or β-blockers compared to HFpEF patients. ## 3.2. sAXL Values Are Higher than a Group of Healthy Individuals and Similar in Both HF Groups Next, we measured the concentration of sAXL in serum of these HF samples. As there is no reference range established for sAXL in the general population, the results of the two groups were compared with a group of unrelated healthy individuals ($$n = 20$$) of the same geographical area (female $40\%$; age 61 [43–80]). The serum concentration of sAXL was higher in HFrEF patients, 37.2 ng/mL (IQR: 28.4–46.8; $$p \leq 0.004$$) and HFpEF patients, 37.9 ng/mL (IQR: 30.0–44.5; $p \leq 0.001$) compared to the healthy group, 31.5 ng/mL (IQR: 27.9–34.4; Figure 1). Both HF cohorts had similar sAXL levels ($$p \leq 0.807$$). HFpEF patients with NYHA class III–IV ($$n = 64$$) had also higher serum concentration of sAXL than those with NYHA class I–II ($$n = 70$$; $$p \leq 0.025$$, Figure 1). ## 3.3. Patient’s Prognosis According to sAXL and NTproBNP Levels in Serum In an adjusted Cox proportional hazards model, serum sAXL concentration showed a significant association with all-cause mortality in both HF groups (Table 2). Interestingly, when cardiovascular death was considered, only the HFpEF group remained significant. No associations were observed for the end point of re-admission due to HF. Serum sAXL concentration was significantly associated with two combined end-points: major adverse event (re-admission due to HF or all-cause mortality), or major cardiovascular event (readmission or cardiovascular death) in the HFpEF group. In contrast, this was not the case in the HFrEF group, where sAXL was not significantly associated to re-admission due to HF or to cardiovascular mortality, nor with the combined end points. For comparison, we include in Table 2 the same analysis for NT-proBNP. The association with the different end points was much better for NT-proBNP compared to sAXL in the HFrEF group. However, in the HFpEF group, sAXL showed equal significance in the association with all-cause mortality and cardiovascular mortality than NT-proBNP. We performed a multivariable regression analysis (Table 3), considering as predictors sAXL (ng/mL), age and NYHA class (model 1). Then, we included in the analysis NT-proBNP, and eGFR (model 2). Interestingly, sAXL was a predictor of time to death (all-cause) only in the HFpEF group, together with NT-proBNP, but not in the HFrEF group. In HFrEF, only age and NT-proBNP in model 2 remained significant. When only cardiovascular death was considered, the same pattern was observed, although in this case sAXL remained a better predictor than NT-proBNP, which did not reach significance. The multivariable model showed a similar result when additional parameters were added, including diabetes, hemoglobin, BMI, ischemic etiology, sex and number of comorbidities (results not shown). ## 3.4. Patient’s Characteristics and Prognosis According to sAXL Levels in Serum Next, we analyzed the characteristics and prognosis of both cohorts according to the baseline sAXL serum concentration divided in quartiles. HFrEF patients with sAXL > Q3 displayed higher concentration of NT-proBNP and hemoglobin, and a marked decrease in eGFR (Table 4). In this group, there was no difference in all-cause or cardiovascular-related mortality. HFpEF patients in the highest sAXL quartile had increased all-cause and cardiovascular mortality, compared to those with lower sAXL. No differences were observed in age, SBP, eGFR, BMI. Interestingly, patients with sAXL > Q3 had only marginally higher NT-proBNP, and a very significant decrease in hemoglobin (Table 4). In a survival analysis, HFrEF patients with sAXL values in the highest quartile (>Q3) had a poorer outcome, with a median survival of 1019 days (IQR: 573–1464), compared to those with lower sAXL (2176 days, IQR: 1616-2735; log rank $$p \leq 0.024$$). This was also observed in HFpEF patients, where sAXL > Q3 had a mean survival of 1408 days (IQR: 626–2190) compared to 2506 days (IQR: 1741–3271) in those ≤Q3 (log rank $$p \leq 0.022$$). The survival curves are shown in Figure 2. Similarly, a Cox regression univariable model, using the lowest quartile as baseline, showed that HFpEF patients with sAXL in > Q3 had a significant association with all-cause mortality (HR of 3.6), while the association was not significant in the HFrEF group (Table 5). Similar results were obtained for cardiovascular mortality. In order to evaluate the performance of serum sAXL as a valuable HF biomarker in each group of patients, we analyzed the predictive value of NT-proBNP dividing the cohorts in quartiles and compared the result with the performance of sAXL (Figure 2). As expected, the natriuretic peptide was a very good marker of the severity of the disease in both groups (Figure 2), as reflected in the HFrEF by a median survival time in the >Q3 group of 716 days (IQR: 463–969), compared with 2541 days (IQR: 1711-3371) for the rest of patients in the HFrEF cohort (log rank $p \leq 0.0001$). In the HFrEF cohort, NT-proBNP >Q3 was equally a very good predictor of mean survival: days 1066 (IQR:906–1226) compared to 2718 days (IQR: 1825–3612) for those in ≤Q3 (log rank $p \leq 0.0001$). ## 4. Discussion Studies using preclinical models have shown that the AXL receptor tyrosine kinase could be considered a potential target in cardiac diseases [21]. In particular, several reports in the literature have suggested that modulating specifically the GAS6/AXL interaction would improve chronic heart failure [21]. Studies of patients suffering from this condition have also pointed to the role of GAS6/AXL in the pathological processes of a deteriorating heart. End-stage HF patients undergoing transplantation had increased AXL in the heart, while a group of chronic HFrEF patients had sAXL concentration in serum $25\%$ higher than healthy individuals [15]. Here, we observed a $23\%$ increase in the mean concentration of the HFrEF group from the DAMOCLES cohort (Table 1). Further, we extended this observation to an HFpEF group, finding a quantitatively similar elevation of the serum sAXL concentration ($26\%$) compared to a group of unrelated healthy individuals. In patients that develop HF after a myocardial infarction (MI) with Killip>1, this increase in sAXL seems to occur progressively. Interestingly, there was an association of sAXL at seven days post MI with left ventricular remodeling [20], indicative of an activation of AXL signaling or its processing in the initial stages of heart failure in connection with cardiac structural changes in the initial stages of heart failure. In the context of heart transplantation, increased sAXL after transplant could also reflect the fitness of the organ, as its levels are higher in patients having a cardiovascular event during a three-year follow-up [37]. The concentration of sAXL in blood seems to be a valuable biomarker of prognosis in patients with chronic HF. In this respect, the prognosis of HFrEF after one-year follow up was worse in the group of patients with sAXL concentration in the highest quartile [15]. This was confirmed in a prolonged follow-up (3.6 years) in the same group of patients [18]. Here we could confirm that the association of high sAXL concentration and worse prognosis was not restricted to reduced EF; HFpEF patients also displayed increased all-cause mortality if the sAXL was in the highest quartile. In a multivariable analysis, the difference was confirmed, and only the NT-proBNP and sAXL values were independent risk factors for all-cause mortality. Clearly, in both groups, NT-proBNP was a very good prognostic marker of survival, as has been already demonstrated in other studies [38,39,40]. As mentioned by Salah et al. [ 2019], comorbidities could contribute relatively more to prognosis in patients with HFpEF with lower NT-proBNP levels than in patients with HFrEF. In this context, the determination of sAXL when a person is diagnosed with HF could provide additional information to NT-proBNP measurements [39]. In a recent analysis of the DRAGON-HF trial, Liu and coworkers have confirmed that sAXL could predict clinical outcomes in a large population (>1000 symptomatic HF patients) independent of NT-proBNP [19]. Interestingly, when only cardiovascular mortality was considered, sAXL increased its association with survival [19]. In our study, this was found specifically in the HFpEF group. In HFpEF, NT-proBNP was a worse predictor than sAXL for cardiovascular mortality (Table 2). The fact that NT-proBNP and sAXL have different diagnostic and prognostic values for HFrEF and HFpEF emphasizes the divergent etiological causes of both HF entities. HFpEF is becoming the most common HF form due to the ageing of the population and the increase in the prevalence of obesity, metabolic syndrome, and diabetes mellitus [3,9]. Despite this, many clinical trials, including effective HFrEF medication, have not succeeded to identify treatments for HFpEF. A putative explanation for such failure could be that patients with HFpEF have different pathophysiological pathways activated and therefore, they should be treated differently [9]. Further research on whether sAXL could identify a subset of HFpEF patients with a common phenotype is warranted with larger patient cohorts. Our results indicate that sAXL is elevated in HFpEF patients and due to the implication of AXL in many cardiac pathologies, as described, it could be a determinant factor in HFpEF development. Comparing sAXL with other suggested biomarkers in HF would be informative, including ST2 and troponins. ST2 is a fragment of the interleukin 1 receptor-like 1 [41,42,43]. Therefore, this molecule shares similarities with sAXL, as both molecules originate by receptor shedding in the cellular membrane and are implicated in immunomodulation. Troponin concentration is prognostic in acute and chronic heart failure and has been suggested as a tool for a personalized approach to HF management [44,45]. Previously, we compared the performance of sAXL with troponin T in a chronic HFrEF cohort and in heart transplant patients, showing that both biomarkers are independent predictors of adverse outcomes [18,37]. Further studies using these biomarkers are necessary to establish their relative clinical utility in HF. There are limitations in our analysis, including that the comparison of HF groups was made to a reference group that was not related to the HF cohorts and had different demographic characteristics. Further, as the inclusion and follow-up finished in 2015, more recent groups of HF patients should be considered in order to include the most recent therapies used in HF. The effect of these therapeutic regimes on the plasma biomarkers should be studied. The inclusion of the patients in the preserved or reduced EF groups was performed in order to maximize the different characteristics of each group while avoiding patients that could be considered in the intermediate group. In this sense, our groups represent those patients with a clear diagnosis of HFpEF and HFrEF. This selection could explain the reduced association of sAXL with mortality in the HFrEF observed in the present study compared to previous ones with different inclusion criteria [15,18,19]. In addition, one should be cautious in considering the role of AXL in the context of HF. AXL activation might represent a restorative signal in the heart, with increased sAXL levels reflecting the activation of the pathway in the context of cardiac damage. At present, it is still necessary to acquire a better knowledge of the complex role of the GAS6/AXL system in cardiovascular biology. ## 5. Conclusions To sum up, in our study, serum concentration of sAXL is shown to be elevated in both HFpEF and HFrEF groups, providing a significant measure for determining the prognosis of the disease. The consistent increase in sAXL associated with HF could reflect an increased activation of the GAS6/AXL system, paralleling the evolution of the disease. AXL is already a valuable target in other pathologies [46,47], and several drugs have been developed to regulate its activity specifically. Our results reveal sAXL as a predictor of time to death (all-cause) in HFpEF patients. Of note, sAXL has a similar predictive association to NT-proBNP in the HFpEF cohort, justifying its future evaluation in larger studies and pointing to AXL as a promising target for HF therapy. ## References 1. McDonagh T.A., Metra M., Adamo M., Gardner R.S., Baumbach A., Böhm M., Burri H., Butler J., Čelutkienė J., Chioncel O.. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. 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--- title: The Relationship of CCL5 and CCR1 Variants with Response Rate and Survival Taking into Account Thalidomide/Bortezomib Treatment in Patients with Multiple Myeloma authors: - Sylwia Popek-Marciniec - Wojciech Styk - Magdalena Wojcierowska-Litwin - Aneta Szudy-Szczyrek - Paul Dudek - Grazyna Swiderska-Kolacz - Joanna Czerwik-Marcinkowska - Szymon Zmorzynski journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10056693 doi: 10.3390/jcm12062384 license: CC BY 4.0 --- # The Relationship of CCL5 and CCR1 Variants with Response Rate and Survival Taking into Account Thalidomide/Bortezomib Treatment in Patients with Multiple Myeloma ## Abstract [1] Background: Chemokines and chemokine receptors play an important role in tumor development. The aim of this study was to check the significance of CCL5 and CCR1 variants with response rate, survival, and the level of regulated on activation, normal T cells expressed and secreted (RANTES/CCL5) in multiple myeloma (MM) patients; [2] Methods: Genomic DNA from 101 newly diagnosed MM patients and 100 healthy blood donors were analyzed by Real-time PCR method (for CCL5 and CCR1 genotyping). In a subgroup of 70 MM patients, serum samples were collected to determine the level of RANTES; [3] Results: multivariate Cox regression showed increased risk of disease relapse or progression (HR = 4.77; $$p \leq 0.01$$) in MM patients with CG + CC genotypes of CCL5 rs2280788. In contrast, CT + TT genotypes of CCL5 rs2107538 were associated withdecreased risk of death (HR = 0.18; $$p \leq 0.028$$) and disease relapse or progression (HR = 0.26; $$p \leq 0.01$$). In MM patients with major genotypes of rs2280789, rs2280788, and rs2107538, higher survival rates were observed in response to treatment with thalidomide and bortezomib. Statistically significant lower RANTES levels were seen in minor genotypes and heterozygotes of CCL5 and CCR1 variants; [4] Conclusions: Major genotypes of CCL5 variants may be independent positive prognostic factors in MM. ## 1. Introduction Multiple myeloma (MM) is a hematologic malignancy, characterized by clonal expansion of plasma cells in the bone marrow (BM) [1]. MM cells proliferate and grow mainly within BM, where they create a specific environment [2]. The MM microenvironment in BM is composed of different cells, such as mesenchymal stromal cells (MSCs), dendritic cells (DCs), and T lymphocytes [3]. The immune components in the BM myeloma niche are related to MM progression and aggressiveness [4,5,6,7]. Cytokines and chemokines play an important role in regulating immune responses for cancer cells. The cytokine network is involved in the growth and progression of MM cells and also participates in the destruction of the bone marrow. The MM cells and BM microenvironment stimulate paracrine or autocrine secretion of several cytokines which may promote the growth, development, and progression of MM [8,9,10]. Chemokines are involved in the colonization and growth of myeloma cells in the bone marrow and the formation and activation of osteoclasts. The activity of osteoclasts is increased in areas close to myeloma cells, resulting in increased bone resorption and reduced bone formation [11]. Multiple myeloma cells secrete several chemokines and express a variety of chemokine receptors which participate in cell homing, tumor growth, and progression [12]. Several known chemokines show higher concentrations in MM patients’ plasma, namely: IL-1β, IL-4, IL-6, IL-8, CCL3, CCL4, and CCL5 [8,13]. The C-C chemokine ligand 5 (CCL5), also known as RANTES (regulated on activation, normal T cells expressed and secreted), belongs to the C-C chemokine family whose members also include CCL3 (MIP-1α) and CCL4 (MIP-1β) [14]. CCL5 is expressed by T lymphocytes, macrophages, platelets, synovial fibroblasts, tubular epithelium, and certain types of tumor cells [14]. Studies carried out so far have shown that increased levels of CCL5 in tissues or plasma are markers of an unfavorable prognosis in patients with colorectal, gastric, breast, and ovarian cancer [15,16,17,18,19]. Abnormal expression and activity of CCL5 and its receptor CCR5 have been found in hematological malignancies and solid tumors [20]. Elevated CCL5 levels have also been described in multiple myeloma [8]. CCL5 activity is mediated by binding to CCR5 and also to CCR1 and CCR3 receptors [14]. In multiple myeloma, blockage of the CCL5/receptor axis leads to inhibition of osteoclast formation and myeloma cell adhesion to stromal cells [21,22]. Cytokines and growth factors are produced and secreted by myeloma cells in the BM microenvironment and are regulated by autocrine and paracrine loops. Hence, the expressions of CCL5 can be regulated by the NF-κB factor after activation by other cells [23,24]. However, it has been proven that selected polymorphisms in the CCL5 gene affect the level of its transcription. The human CCL5 gene is located on chromosome 17 (locus 17q12) and consists of a promoter region, three exons, and two introns. The CCL5 gene is short but filled with many polymorphisms, three of which, may affect CCL5 expression: rs2107538 (g.−403G > A), rs2280788 (g.-28C > G), and rs2280789 (g.In1. + 1T > C) [25,26,27]. The CCL5 variants have been associated with an increased risk of several cancers including: gastric cancer [28], prostate cancer [29], and breast cancer [30]. Moreover, correlations were found between the progression of pancreatic adenocarcinoma and colon cancer diseases and particular genotypes of the CCL5 gene [19,31]. However, to date, there are no publications regarding the possible effect of the CCL5 polymorphisms on MM risk and outcome. Taking into account the above literature data, we hypothesize that CCL5 and CCR1 variants may be associated with the risk of MM development, and these variants may also affect patient response to treatment. The present study investigates the selected CCL5 and CCR1 variants, their association with selected clinical and laboratory disease parameters, and the response to bortezomib/thalidomide-based therapies. To our knowledge the presented results were not previously published by other authors. ## 2.1. Patients and Samples A total of 201 unrelated subjects with high quality of DNA, comprising 101 newly diagnosed patients with MM and 100 health controls, were included in this study. Controls and samples were selected from the same ethnic group living in south-western Poland (Caucasian population). All MM patients were hospitalized between 2013 and 2019 at the Chair and Department of Hematooncology and Bone Marrow Transplantation, Medical University of Lublin. The study obtained positive opinions (no. KE-$\frac{0254}{165}$/2013 and no. KE-$\frac{0254}{337}$/2016) from the Bioethics Committee at the Medical University of Lublin, according with the ethical standards established by the Helsinki Declaration. All methods were performed in accordance with the relevant guidelines and regulations. Detailed patient characteristics are shown in Table 1. The healthy blood donors (50 males and 50 females, with mean age 37.6 years) visited the Regional Blood Donation and Blood Treatment Center in Kielce, Poland. All participants of the study provided written informed consent. The inclusion and exclusion criteria for all individuals included in the study are described in Table 2. Therapeutic induction regimens consisted of thalidomide and/or bortezomib combined with steroids and/or cyclophosphamide. The group of 37 MM patients underwent autologous hematopoietic stem cell transplantation (auto-HSCT). Response to treatment was evaluated according to the International Myeloma Working Group guidelines, as described elsewhere [32,33]. Overall survival (OS) encompassed time from diagnosis until relapse, progression, death due to tumor effect or last follow-up, and time from diagnosis until death by any cause or last follow-up, respectively. The median follow-up time of MM patients enrolled in the study was 18.16 months. Progression-free survival (PFS) was estimated as the time elapsed between treatment initiation and tumor progression or death from any cause [34]. Peripheral blood from healthy blood donors and bone marrow aspirates from MM patients were used for DNA isolation and determination of CCL5 and CCR1 variants. The samples of serum ($$n = 70$$) were used to determine the level of regulated on activation, normal T-cell expressed and secreted (RANTES)/chemokine (C-C motif) ligand (CCL5) in MM patients. Cell cultures were established from bone marrow aspirates to carry out the research associated with bortezomib treatment ($$n = 50$$), as described by Zmorzynski et al. [ 35]. ## 2.2. DNA Isolation DNA isolation was performed using a commercial kit (Qiagen, Germany) according to the manufacturer’s procedure. The concentration and quality of DNA was checked using a NanoDrop device (Thermo Fisher Scientific, USA, Waltham, MA, USA). ## 2.3. Genotyping We selected the most analyzed genetic variants of CLL5 (rs2280789, rs2280788, and rs2107538) and CCR1 (rs3181077) genes. Genotyping was performed using TaqMan SNP genotyping assays on Applied Biosystems (USA, Waltham, MA, USA). For genotyping analysis, 7500 Fast Real-time PCR (Applied Biosystems, USA, Waltham, MA, USA) was used. ## 2.4. Enzyme-Linked Immunosorbent Assay (ELISA) A specific ELISA kit (MyBioSource, San Diego, CA, USA) was used (according to manufacturer’s protocol) to determine the level of RANTES/CCL5 in serum samples collected from 70 MM patients. The plate reader (TK Biotech, Poland, Warsaw) at wavelength of 450 nm for measurement of RANTES/CCL5 was used. The serum samples were diluted 20 times. The concentration read from the standard curve was multiplied by the dilution factor (2×). ## 2.5. Bortezomib In Vitro Treatment Bone marrow aspirates ($$n = 50$$) (mean number of plasma cells—$31.31\%$ ± 20.69) were used to establish cell cultures as described previously [35]. The number of apoptotic, necrotic, and viable cells was evaluated by means of the Annexin V-Cy3 Apoptosis Detection Kit according to manufacturer’s protocol (Sigma-Aldrich, USA, Saint Louis, MO, USA) (Figure 1). ## 2.6. Statistical Analysis Laboratory values of MM patients with studied variants were compared using an independent t-test for continuous variables and a Chi-square test for categorical variables. The association of studied variants with clinical data was evaluated using a Chi-square test or Fisher’s exact test (when one expected value was <5). The quantitative data was shown as frequency or percentage. Deviation of genotype frequencies in controls (healthy blood donors) and cases (MM patients) from Hardy–Weinberg equilibrium (HWE) was assessed by a Chi-square test with Yates’s correction for the groups with less than five patients [36]. For the $95\%$ confidence interval (CI), we assumed $$p \leq 0.05$$ and χ2 = 3.84; therefore, if the χ2 ≤ 3.84 and the corresponding p ≥ 0.05, then the population is in HWE, as described previously [37]. The Cox proportional hazard model was used for univariate and multivariate analysis of OS and PFS. The Kaplan–Meier method and the log-rank test were used for survival analysis. Pairwise linkage disequilibrium (LD) was measured using D’ and Dmax values, as well as the squared correlation coefficient r2. Pearson’s correlation analysis was used to evaluate the correlation between RANTES/CCL5 concentration and laboratory/clinical data (free light chain ratio, age, % of plasma cells in bone marrow, number of platelets, estimated glomerular filtration rate concentration of: hemoglobin, albumins, β2-microglobulin, calcium ions, creatinine, and C-reactive protein). We assumed a $5\%$ error of inference and the related level of significance $p \leq 0.05$, pointing to the existence of statistically significant differences. Statistical analyses were performed using the Statistica ver. 12.5 (StatSoft) software. ## 3. Results The presented study included 101 MM patients (53 males and 48 females). The variants of CCL5 and CCR1 genes, as well as the level of RANTES/CCL5 in serum of MM patients were analyzed. Moreover, we performed cell cultures in a subgroup of MM patients (from bone marrow samples) with bortezomib to check whether the genotypes of CCL5 and CCR1 genes may be related to the effects of this drug. ## 3.1. Frequencies of Alleles and Genotypes and Their Association with MM Risk Genotyping was successful in all the individuals investigated within the study. This was one of the inclusion criteria for MM patients and healthy blood donors. The CCL5 and CCR1 variants were in Hardy–Weinberg equilibrium (Table 3). Two variants—rs2280788 and rs2107538—were in the same haplotype block in MM patients and the control group—D’ = 0.90 and D’ = 0.91, respectively (Table 4). The correlation factor in all studied variants was low in MM patients and healthy blood donors—r2 range 0.18–0.31 and 0.09–0.25, respectively (Table 4). The CCL5 and CCR1 variants were balanced (Table 5). We did not observe statistically significant differences between allele and genotype frequencies among MM patients and healthy blood donors. In the case of CT and CT + TT genotypes of rs2107538, as well as T-allele of rs2107538, statistical tendency was observed with the risk of MM development (Table 5). ## 3.2. CCL5 and CCR1 Variants as a Risk Factors of Death and MM Progression Minor genotypes were analyzed together with heterozygotes due to their small sample size. The only exception is for CC genotype of rs318077 variant due to there being a sufficient number of this rare/minor genotype—$$n = 9$$ (of MM patients) and $$n = 12$$ (of healthy blood donors). A univariate Cox analysis revealed that patients at stage III according to ISS had a 2.80-fold ($$p \leq 0.004$$) increased risk of death (Table 6). In the case of MM patients with auto-HSCT, lower risk of death was observed. Similar findings were observed in the case of disease relapse or progression in MM patients at stage III according to ISS (HR = 2.79, $p \leq 0.001$) and with auto-HSCT (HR = 0.39, $$p \leq 0.03$$) (Table 6). The univariate Cox analysis did not show the impact of analyzed variants on the risk of death or disease relapse or progression in MM patients. The multivariate Cox regression analysis confirmed that patients with auto-HSCT had a decreased risk of death and disease relapse or progression (Table 7). In contrast, patients at stage III according to ISS had an increased risk of death and disease relapse or progression. Moreover, multivariate Cox regression showed increased risk of disease relapse or progression (HR = 4.77; $$p \leq 0.01$$) in MM patients with CG + CC genotypes of CCL5 rs2280788 variant. In the case of CT + TT genotypes of CCL5 rs2107538 variant, decreased risk of death (HR = 0.18; $$p \leq 0.028$$) and disease relapse or progression were observed (HR = 0.26; $$p \leq 0.01$$) (Table 7). The analysis of response rate showed that MM patients without auto-HSCT had an increased chance of progressive disease (PD) (Table 8). Similar results were observed in MM patients with genotypes AG + GG (rs2280789) of CCL5 gene (Table 8). ## 3.3. Association of Studied Variants with Clinical/Laboratory Values We analyzed potential relationships between clinical/laboratory results and selected appropriate genotypes. We found that AG + GG genotypes of CCL5 rs2280789 were associated with lower levels of C-reactive protein. At the level of tendency, the changes in the concentration of creatinine (in rs2280789 variant), C-reactive protein (in rs2107538 variant), albumins (in rs318077 variant), % of plasma cells (in rs318077 variant), and estimated glomerular filtration rate (in rs318077 variant) were observed (Table 9). The CC + CT genotypes (of rs318077 variant) and CT + TT genotypes (of 2107538 variant) were associated with higher (OR = 2.72, $$p \leq 0.028$$) and lower (OR = 0.32, $$p \leq 0.027$$) risk of chromosomal aberrations presence, respectively. Other variants were not associated with the presence of chromosomal aberrations. Moreover, we did not observe statistically significant associations between studied variants and specific types of chromosomal aberrations—del(17p13.1) or t(4;14). ## 3.4. Survival of MM Patients Taking into Account Type of Tratment and Studied Variants We analyzed the association between studied genotypes and survival of MM patients (by log rank test). Without taking into account the type of treatment, we did not observe statistically significant changes in OS and PFS. Furthermore, a log rank (Mantel–Cox) analysis taking into account studied variants and the type of treatment (thalidomide vs. bortezomib vs. both thalidomide and bortezomib) was performed. We found an association of AA genotype of rs2280789 with the type of treatment and OS ($$p \leq 0.026$$) (Figure 2). Similar results were observed in the case of GA + GG genotypes of rs2280789 variant, GG genotype of rs2280788 variant, and CC genotype of rs2107538 variant (Figure 2). In MM patients with AA genotype (of rs2280789), GG genotype (of rs2280788) or CC genotype (of rs2107538), a higher survival rate in treatment with thalidomide and bortezomib was observed (Figure 2). Moreover, in the analysis of treatment and PFS, we found statistically significant associations with AA genotype and GA + GG genotypes of rs2280789 variant, GG genotype of rs2280788 variant, CC genotype of rs2107538 variant and TT genotype of rs318077 variant (Figure 3). In MM patients with AA genotype (of rs2280789), GG genotype (of rs2280788), CC genotype (of 21107538) or TT genotype (of rs318077)—a higher PFS rate was found (Figure 3). In the next step, we checked if there were differences in OS or PFS between major genotypes (of studied variants) and one type of treatment. There were no statistically significant differences between major genotypes and OS/PFS in patients treated with bortezomib ($$p \leq 0.905$$/$$p \leq 0.54$$), thalidomide ($$p \leq 0.826$$/$$p \leq 0.924$$) or both of these drugs ($$p \leq 0.392$$/$$p \leq 0.692$$), respectively. ## 3.5. Levels of RANTES/CCL5 in Serum of MM Patients We investigated whether CCL5 and CCR1 variants have an impact on RANTES/CCL5 level. We found that their concentration depended on the type of studied genotype. Statistically significant lower RANTES/CCL5 levels were observed in minor genotypes and heterozygotes (Table 10). Moreover, we have analyzed the most frequent haplotypes and their association with RANTES/CCL5 concentration. Haplotypes with a frequency lower than $5\%$ were excluded from our analysis. We observed a statistically significant difference in RANTES/CCL5 concentration between the two most common haplotypes (Table 11). All results obtained in a Pearson’s correlation analysis (RANTES/CCL5 concentration vs. clinical/laboratory data) were statistically insignificant, including C-reactive protein concentration ($r = 0.038$, $$p \leq 0.76$$) and % of plasma cells ($r = 0.021$, $$p \leq 0.86$$). ## 3.6. Bortezomib In Vitro Treatment In in vitro studies, bortezomib increased the number of apoptotic and necrotic cells in all studied genotypes (Figure 4). However, in most cases the differences between the number of apoptotic, necrotic, or viable cells relative to bortezomib doses (for example, 1 nM vs. 2 nM) were statistically insignificant. A higher number of apoptotic cells was observed at 1 nM of bortezomib in patients with AA genotype (of rs2280789) in comparison to those with GA + GG genotypes ($16.79\%$ vs. $11.37\%$, $$p \leq 0.021$$). A higher number of viable cells was found at 1 nM of bortezomib in cells with GA + GG genotypes (of rs2280789) in comparison to AA genotype—$86.36\%$ vs. $77.31\%$, $$p \leq 0.02.$$ ## 4. Discussion Multiple myeloma cells proliferate and grow mainly within bone marrow, where they create an environment that promotes disease progression, drug resistance, bone destruction, and immune escape [2]. MM and BM cells communicate with each other through the secretion and binding of cytokines and chemokines. The concentration of released chemokines may have a predictive value for the course of the disease. Reports published so far on the role of RANTES/CCL5 in cancer have been controversial. Some studies suggest that RANTES/CCL5 production may lead to a more immune-suppressive activity in tumor microenvironment (TME) [19], while other evidence suggests that RANTES/CCL5 is in favor of tumor immunity [40,41,42,43]. The expression, and thus the concentration of the RANTES/CCL5 chemokine, may be conditioned by external factors (stimulation by other cells) or genetic polymorphisms. Three single nucleotide polymorphisms (SNPs) in CCL5, namely rs2107538, rs2280788, and rs2280789, are the most frequent variants associated with inflammatory diseases. A growing body of research confirms that cytokine gene variants are an important factor in predicting the outcomes of the development and treatment of solid cancer diseases and hemato-oncological diseases. However, knowledge of CCL5 gene polymorphisms implications with regard to MM susceptibility remains elusive. In our study, we analyzed the association of CCL5 and CCR1 variants with the risk and the outcome of MM as well as response to thalidomide and/or bortezomib treatment. In addition, we took into account the in vitro response to multiple doses of bortezomib. In our research, we described association of the T-allele of rs2107538 with the risk of MM development at the level of tendency. We observed an increased risk of disease relapse or progression in MM patients with CG + CC genotypes rs2280788. A similar finding was observed in patients with TC + CC genotypes rs2280789. Our work is the first to examine the relationship between CCL5 polymorphisms and the risk and course of MM disease. Several studies have found an association between CCL5 variants and an increased risk of cancer. In the study by Shan et al., rs2107538-T allele was significantly associated with triple negative breast cancer (TNBC) [30]. Both CCL5 rs2280788-GC and CCL5 rs2280789-CC genotypes in their study showed a slightly significant association with TNBC risk [30]. Additionally, they showed that CCL5 variants (rs2107538 and rs2280789) were linked to CCL5 serum and mRNA levels [30]. Eskandari-Nasab and colleagues demonstrated that CCL5 rs2107538 variants were associated with an increased risk of breast cancer [44]. Their results indicated that individuals carrying the CCL5 rs2107538-GA or GA + AA genotypes or A allele had higher risk of developing breast cancer compared to those carrying the CC genotype or C allele [44]. In Suenaga et al., they demonstrated that patients with colorectal cancer who possessed CCL5 rs2280789 G alleles had poorer outcomes with shortened PFS and OS rates, as well as poor tumor response compared to those with the AA variant. Interestingly, they also found that CCL5 was not only expressed in cancerous tissue, but also in non-neoplastic mucosal tissues, and the clinical impact of this CCL5 allele did not differ depending on primary tumor location in the colon [19]. There are no studies on the involvement of CCL5 polymorphisms in the development of hematological malignancies. However, several studies have been published on the association of CCL5 polymorphic variants with the development of graft-versus-host (GVHD) disease in hematological patients after allo-HSCT. The development of GVHD involves soluble and cellular components of both the adaptive and innate immune response. The migration of leukocytes to and from secondary lymphoid tissues is therefore an essential component of GVHD [45,46]. Data from Choi et al., suggest that CCR1/CCL5 receptor–ligand interactions play a role in allo-specific T-cell responses and demonstrate that CCR1 expression on donor cells contributes to the development of GVHD [47]. Kim and colleagues showed that the CG genotype of rs2280788 in recipients of allo-HSCT was significantly associated with a higher incidence of chronic GVHD, extensive chronic GVHD, and severe grade of chronic GVHD compared to CC genotype [48]. In contrast, a study by Shin et al. suggested that CCL5 variants may be associated with acute GVHD rather than chronic GVHD, as well as relapse-free survival in patients treated with allo-HSCT [49]. In the studies presented above, CCL5 polymorphisms have been rather negative prognostic factors of the risk of developing graft versus host disease. In our study, MM patients with auto-HSCT were included, and not those with allo-HSCT. Moreover, a lower risk of death and disease relapse or progression in MM patients with auto-HSCT was observed. Several studies have found an association between CCL5 polymorphisms and decreased risk of cancer and other diseases. Liou and colleagues indicated that women who inherit A allele of CCL5 rs2107538 may be at reduced risk of gastric cancer [28]. A study by Singh et al. suggests that TT genotype of the CCL5 rs2280789 variant plays an important role in increased CCL5 expression in T cells, which may enhance Th1 immunity and help in protection against tuberculosis [50]. This suggests that increased CCL5 expression strengthens the defensive properties of the immune system. Qiu et al. through their work showed that CCL5 can play an anti-tumor role in breast cancer [51]. CCL5/RANTES secretion was induced by IL-27 activity. In the presented materialthe presence of the T-allele of CCL5 rs2107538 variant was associated with higher risk of MM at the level of tendency. In our study we described some positive impacts of the studied variants on the course of the MM disease. We found higher PFS rates in individuals with CC-rs2107538, GG-rs2280788, and AA-rs2280789. Additionally, the same individuals were in the group with higher survival rate in treatment with thalidomide and bortezomib. In the case of GA + GG genotypes of rs2280789 variant poor response to bortezomib was observed. It should be noted that GA + GG genotypes were associated with statistically lower CCL5/RANTES level in comparison to AA genotype (of rs2280789). The best response to treatment should be observed in patients being treated with both—bortezomib and thalidomide. These drugs are characterized by different modes of action. Immune dysfunction including dendritic cells deficiencies is a hallmark of MM. These cells play an important role in MM pathophysiology. The lack of their proper immunological function results in drug resistance and the subsequent failure of immunotherapeutic approaches [52]. Dendritic cells that were exposed to bortezomib showed reduced secretion of chemoattractants involved in inflammation and lymphocyte recruitment such as CCL5/RANTES [53,54]. Kuwahara-Ota and colleagues found that secretion of CCL5/RANTES by myeloma cells is a prerequisite for induction of immunosuppressive myeloid-derived suppressor cells in MM [55]. An increase in immunosuppressive myeloid-derived suppressor cells is associated with MM progression and treatment resistance. Thalidomide has been shown to possess immunomodulatory attributes, including the inhibition of cytokine production including CCL5/RANTES [56]. A new class of thalidomide derivatives was developed. These immunomodulatory drugs (IMiDs) are structurally related and have their unique set of anti-inflammatory, immunomodulatory, antiproliferative, antiangiogenic and toxicity profiles [57]. ImiDs were identified as potent inhibitors of immunosuppressive myeloid-derived suppressor cells induction through independent downregulation of CCL5/RANTES in myeloma cells, and downregulation of a receptor for CCL5 chemokine [55]. The poor response to bortezomib in comarison to thalidomide exhibited in the GA-GG genotypes may be due to stronger effect of thalidomide (via inhibition of immunosuppressive myeloid-derived suppressor cells). We analyzed CCL5/RANTES concentration in the serum of MM patients and its correlation with clinical/laboratory data. No statistically significant correlations were found. Nevertheless, we found that CCL5/RANTES concentration depended on the type of studied genotype of CCL5 and CCR1 gene. Lower concentration of CCL5/RANTES were observed in a group of patients with minor genotypes and heterozygotes (analyzed together as one group) of all studied variants. Based on these results, it can be concluded that individual genotype and haplotype affect the expression and secretion of the CCL5/RANTES in MM patients. In addition to neoplastic diseases, CCL5 variants may be a positive factor in the course of other inflammatory and/or autoimmune diseases. Van Veen and colleagues found that the low-producer CCL5 allele rs2107538-G was associated with reduced risk of severe axonal loss, whereas the high-producer CCL5 allele rs2107538-A was associated with a worse clinical course of multiple sclerosis [58]. In a study by Zhernakova et al., CCL5 variants were significantly associated with serum concentration of chemokine and development of diabetes type 1 (T1D) [59]. The rs4251719A-rs2306630A-rs2107538A haplotype was associated with low CCL5/RANTES production and confers protection from T1D [59]. The number of studies related to the role of CCR1 variants in neoplastic diseases is very limited. CCR1 is involved in the recruitment of inflammatory immune cells including neutrophils, monocytes, and lymphocytes [60]. CCL5/RANTES activity is mediated by binding to CCR1 and also to CCR5 receptors [14]. In our studies, we included only one variant of the CCR1 gene—rs318077. We found that individuals with the TT genotype of rs318077 had higher PFS rates. Further CT + TT genotypes of rs318077 were associated with higher risk of chromosomal aberrations. There are some limitations of our study. The number of MM patients was relatively small, in part due to the low incidence of the disease. However, the group of MM patients included in the study was large enough for most analyses. Some analyses were not possible as a result of low frequency of alleles in the population. For evaluation of apoptosis in our in vitro study, fluorescent microscopy was applied instead of flow cytometry-based apoptosis detection (FACS). FACS analysis is more reliable than quantitative apoptosis evaluation. In addition, the FACS method differentiates between cells in early and late apoptosis. Unfortunately, retrospective analysis of apoptosis with the use of flow cytometry is not possible. The set used for apoptosis and necrosis detection was dedicated and validated to fluorescent microscopy. Analysis of CCL5/RANTES level was performed in serum. Using bone marrow plasma instead of serum would have been more informative. Despite this study’s limitations and the need for prospective studies with larger sample sizes, our findings suggest that major genotypes of CCL5 variants may be independent positive prognostic factors in MM. ## 5. 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--- title: Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression authors: - Fouad H. Awad - Murtadha M. Hamad - Laith Alzubaidi journal: Life year: 2023 pmcid: PMC10056696 doi: 10.3390/life13030691 license: CC BY 4.0 --- # Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression ## Abstract Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare. ## 1. Introduction The advancement of digital medical technology, coupled with the exponential growth of medical data, has led to biomedical research becoming a data-intensive science, resulting in the emergence of the “big-data” phenomenon, as reported in the literature, such as in [1]. Data have become a strategic resource and a key driver of innovation in the era of big data, transforming not only the way biomedical research has been conducted, but also the ways in which people live and think, which has been highlighted in studies such as [2]. To capitalize on this, the relevant departments in the medical industry should focus on collecting and managing medical health data and use this information as a foundation for later developments through the integration, analysis, and application requirements required to employ big data in the medical field [3]. Big medical data and image detection is an essential element of healthcare that plays a critical role in the storage, organization, and analysis of medical information [4]. the effective classification of medical data enables the efficient retrieval and examination of patient records, which can aid in the diagnosis and treatment of illnesses. It can also assist in identifying trends and patterns in patient health data, enabling healthcare professionals to recognize potential risk factors and take preventative measures. Furthermore, medical data classification has facilitated the advancement of new treatments and therapies by allowing researchers to analyze large datasets and uncover potential correlations and trends [5]. COVID-19 data classification has involved organizing and labeling data related to the coronavirus pandemic, such as information about confirmed cases, deaths, and vaccination rates. These types of data have often been used to track the spread of the virus and inform public health decisions. Image detection techniques have been used to identify COVID-19-related images, such as X-ray scans showing lung abnormalities associated with the virus. These techniques have assisted healthcare professionals and researchers better understand and track the spread of the virus. However, there have been several challenges and problems associated with COVID-19 data classification and image detection. One major challenge has been ensuring the accuracy and reliability of the data being used. There have been errors and biases in the data that affected the results. Additionally, there have been privacy concerns related to collecting and using personal health data. There have also been technical challenges in developing and implementing image detection algorithms, such as difficulties in obtaining a sufficiently large dataset for training. Overall, addressing these challenges is crucial in order to effectively use data and image detection techniques to understand and combat the COVID-19 pandemic. In this study, an efficient and high-performance solution to enhance the accuracy of medical data classification and image detection was proposed. Advanced k-means clustering was merged with both classification and detection techniques to elevate the performance and accuracy of these techniques [6]. To evaluate the performance of medical data classification, a large medical dataset was used. Furthermore, to evaluate the effectiveness of the detection technique, a dataset comprising X-ray COVID-19 and CT images was utilized. The results indicated that the proposed models significantly improved the performances of classification and detection. The proposed model’s contributions were the following:The successful application of advanced parallel k-means clustering as a pre-processing step for both the images and the data to improve the accuracy of image feature extraction and detection, as well as the accuracy of data classification. Both hardware and software improvements were employed to significantly accelerate the classification and detection processes. Hardware acceleration was achieved by utilizing the latest neural engine processor while the software optimization involved using parallel-processing mechanisms. This paper is divided into seven sections. The introduction addresses the significance of medical data classification and medical image detection. Section 2 discusses various data classification and image detection algorithms, including their advantages and limitations. Section 3 addresses the current challenges and features of solutions for processing large amounts of medical data and images. Section 4 presents the proposed solution. Section 5 outlines the methodology and performance metrics used to evaluate the proposed solution. The implementation and results of the proposed solution are presented in Section 6. Section 7 concludes the paper. ## 2. Data Classification Data classification is the process of organizing and categorizing data based on predetermined criteria [7]. It is a crucial aspect of many applications, including data management, data analysis, and information retrieval. ## Logistic Regression Algorithm Logistic regression is a type of binary classification algorithm that is used to predict the probability of an event occurring [8]. It has been commonly used in machine learning for applications such as spam detection, medical diagnosis, and sentiment analysis [9]. The logistic-regression model maps the input features x1,x2,...,xn to a predicted output variable y that has a value between 0 and 1, representing the probability of the event occurring [10]. The logistic function, also known as the sigmoid function, is used to model the relationship between the input features and the predicted output variable. The sigmoid function is defined as [10]:f(z)=11+e−z where z=w0+w1x1+w2x2+...+wnxn is the linear combination of the input features and their corresponding weights, with w0 as the bias term. The logistic regression algorithm aims to find the optimal values for the weights w0,w1,w2,...,wn that minimize the error between the predicted output variable and the true output variable [11]. This is achieved by maximizing the likelihood function, which is the probability of the observed data according to the model parameters [12]. The likelihood function for logistic regression is:L(w)=∏$i = 1$mf(zi)yi(1−f(zi))1−yi where m is the number of training examples, yi is the true output variable for the ith example, and zi is the linear combination of the input features and weights for the ith example [13]. The optimal values for the weights can be found using gradient descent, which involves iteratively updating the weights in the direction of the negative gradient of the likelihood function [14]. The updated rule for the weights is:wj:=wj−α∂L(w)∂wj where α is the learning rate, and ∂L(w)∂wj is the partial derivative of the likelihood function with respect to the jth weight. The logistic regression algorithm can be summarized in the following steps:Initialize the weights w0,w1,w2,...,wn to random values. Calculate the linear combination zi for each training example using the current weights. Calculate the predicted output variable yi for each training example using the logistic function. Calculate the error between the predicted output variable and the true output variable for each training example. Calculate the gradient of the likelihood function with respect to each weight. Update the weights using the gradient descent update rule. Repeat steps 2–6 until the error converges or a maximum number of iterations is reached. One of the main advantages of logistic regression is its simplicity and ease of implementation. It is a straightforward algorithm that can be easily implemented using standard statistical software [15]. Additionally, logistic regression is highly interpretable, allowing users to understand the contributions of each independent variable to the predicted probability. It is also robust regarding multicollinearity, meaning that it can handle correlated independent variables without producing biased estimates. However, logistic regression is not without its challenges. One of the main limitations is that it is only suitable for binary classification problems, meaning that it can only predict the likelihood of an event occurring or not occurring [16]. ## 3. Image Detection Technique Image detection is a technique used to identify and locate specific objects, features, or patterns within an image. It is a crucial aspect of many applications, including object recognition, facial recognition, and scene comprehension [17]. In the field of healthcare, image detection is used to analyze and interpret medical images, such as X-rays, CT scans, and MRIs. These images provide important diagnostic information that can be used to identify and treat diseases. ## YOLOv4 Algorithm The You Only Look Once version 4 (YOLOv4) algorithm is a state-of-the-art object detection algorithm that processes an entire image and directly predicts the bounding boxes and class probabilities for all objects in the image. It uses a convolutional neural network (CNN) to extract features from the input image and then apply them a series of convolutional and fully connected layers in order to predict the class probabilities and bounding box coordinates for each object [18]. The YOLOv4 algorithm predicts object classes and bounding box coordinates by dividing the input image into a grid of cells and predicting the class probabilities and bounding box offsets for each cell [19]. Specifically, for each cell in the grid, the algorithm predicts:The probability of an object being present in that cell (denoted pobj).The x and y coordinates of the center of the bounding box, relative to the coordinates of the cell (denoted by bx and by, respectively).The width and height of the bounding box relative to the size of the cell (denoted by bw and bh, respectively).The class probabilities for each object class (denoted by pc1, pc2,…, pcn, where n is the number of classes). These predictions are made using a series of convolutional and fully connected layers in the YOLOv4 network. The network architecture is based on a variant of the DarkNet architecture, which consists of multiple convolutional layers and followed by max-pooling layers, and ends with multiple fully connected layers [20]. The final layer of the network outputs a tensor that is the shape of (grid size) × (grid size) × (number of anchor boxes) × (5 + number of classes), where the 5 refers to the objectness score, bx, by, bw, and bh [21]. The YOLOv4 algorithm then uses non-maximum suppression to remove redundant bounding boxes for the same object [22]. Specifically, for each class, it applies non-maximum suppression to the set of predicted bounding boxes with objectness scores above a certain threshold. This threshold is usually set to a value between 0.5 and 0.7, depending on the desired balance between precision and recall [23]. The YOLOv4 algorithm can be trained using a loss function that measures the errors between the predicted and ground-truth bounding boxes and class probabilities [24]. The loss function consists of two components: a localization loss that penalizes errors in the predicted bounding box coordinates, and a classification loss that penalizes errors in the predicted class probabilities. The localization loss is typically computed using the mean squared error (MSE) between the predicted and ground-truth bounding box coordinates, while the classification loss is typically computed using the cross-entropy loss between the predicted and ground-truth class probabilities [25]. Algorithm 1 shows the main steps of the YOLOv4 algorithm. Algorithm 1 YOLOv4 object detection algorithmRequire: Input image IEnsure: Bounding boxes B and class probabilities C 1:Pre-process I to obtain an input tensor X2:Apply the backbone network to obtain feature maps F1,F2,...,Fn3:Apply the neck network to combine the feature maps and obtain a single feature map F4:Apply the detection head to F to obtain a set of candidate boxes Bc and class probabilities Cc5:Apply non-maximum suppression (NMS) to Bc and Cc to obtain the final set of bounding boxes B and class probabilities C, respectively6:return B and C Note that this algorithm assumes that the YOLOv4 architecture has already been trained on a large dataset of images with labeled objects and that the resulting model has been saved and can be loaded for inferences on new images. The backbone network, neck network, and detection head are all components of the YOLOv4 architecture, and their specific details are beyond the scope of this pseudo-coded algorithm [26]. One way that YOLOv4 has been used in medical image analysis has been in the detection of abnormalities and lesions in images [27]. For example, it was used to identify abnormalities in CT scans of the brain, which could then be used to diagnose and treat brain tumors. By analyzing CT scans with YOLOv4, healthcare professionals could more accurately identify abnormalities and determine the appropriate course of treatment. During the COVID-19 pandemic, YOLOv4 has also been used to analyze chest X-rays, which have often been used to diagnose the virus [28]. By detecting characteristic patterns associated with COVID-19, such as lung abnormalities, YOLOv4 assisted healthcare professionals in making accurate diagnoses and providing timely treatments for patients [29]. In addition to its use in detecting abnormalities within images, YOLOv4 has also been used to detect objects in images, such as medical instruments and organs. This was particularly useful for identifying and tracking objects during surgical procedures, such as in the detection of brain tumors [30]. ## 4. Medical Data Classification and Detection Medical data classification and image detection are two critical areas in healthcare that could benefit from the latest advancements in machine-learning and computer-vision technologies. In recent years, there has been a significant increase in the amount of medical data generated due to the availability of electronic health records and medical imaging technologies. This growth in medical data has provided new opportunities for developing more accurate and efficient methods for classification and image detection, which could lead to improved diagnoses, treatments, and patient outcomes. ## 4.1. Medical Data and Image Classification The classification of medical data refers to the process of assigning a label or category to a particular medical dataset. The classification of medical data could be used for various applications, such as disease diagnosis, drug discovery, and prognosis. The following are the state-of-the-art techniques used in medical data classification. However, the classification of medical data has not been without challenges. One of the main challenges has been the large volume of data that must be classified [41]. Medical data are typically generated at a rapid rate and can be difficult to manage due to size and complexity. Additionally, medical data classification often involves working with sensitive and personal information, which requires strict adherence to the privacy and security measures in place. Another challenge has been the lack of standardization in medical data classification, which has led to confusion and difficulties in data retrieval and analysis [42]. Finally, the constantly evolving nature of the healthcare field means that medical data classification systems must be regularly updated and adapted to meet changing needs. ## 4.2. Medical Image Detection Image detection in healthcare refers to the process of detecting and identifying medical conditions or abnormalities in medical images such as X-rays, CT scans, and MRI scans. Image detection plays a vital role in the diagnosis and treatment of various medical conditions [43]. The following are the state-of-the-art techniques used in image detection in healthcare. ## 5. Related Works A literature review was conducted to examine the most recent approaches and techniques for medical data classification in this field. The related works presented here were selected based on their technological similarity to the proposed solution and their focus on medical data. Furthermore, all papers were chosen based on their publication in high-quality journals. Furthermore, as COVID-19 has attracted the attention of researchers in the healthcare field, most of the papers selected in this review were related to the global COVID-19 pandemic. In [51], the aim was to evaluate the performance of parallel computing and advanced k-means clustering as a pre-processing step for data classification and image detection in medical applications. To achieve this, the researchers utilized a parallel logistic regression algorithm and a mobile neural engine processor. The k-means clustering technique was used to pre-process both images and data, resulting in improvements in feature extraction, the removal of noise and outlier pixels, and classification accuracy. The results of this study showed that their proposed approach outperformed traditional methods both in terms of both accuracy and efficiency, making it a promising approach for medical data analysis and processing. In 2021, the researchers in [52] proposed a new method for optimizing the performance of the k-means clustering algorithm on parallel and distributed computing systems. The study employed a hybrid approach that combined the traditional Lloyd’s algorithm with a new partitioning technique. The proposed approach was evaluated using various datasets, and the results showed that the hybrid approach outperformed both the traditional Lloyd’s algorithm and other state-of-the-art parallel k-means algorithms, in terms of both accuracy and efficiency. The study concluded that the proposed approach was a promising solution for large-scale clustering tasks on parallel and distributed computing systems. In [53], the authors proposed a new framework for automating the diagnosis of Alzheimer’s disease (AD) using a machine-learning approach. The proposed framework utilized a combination of several machine-learning algorithms, including principal component analysis (PCA), support vector machine (SVM), and k-nearest neighbors (KNN) to classify brain images as normal or AD. The study used two different datasets, and the results showed that the proposed framework achieved high accuracy and specificity when classifying brain images as AD. The study concluded that the proposed framework could be a valuable tool for the early diagnosis and monitoring of AD. In 2022, [54] investigated the potential use of deep learning algorithms for the detection of COVID-19 in chest X-ray images. The study proposed a deep-learning model based on convolutional neural networks (CNNs) that had been trained on a large dataset of chest X-ray images. The model was tested on a separate dataset of chest X-ray images, and the results showed that the proposed model achieved high accuracy, sensitivity, and specificity, in detecting COVID-19. The study concluded that the proposed deep-learning model could be a valuable tool for the rapid and accurate detection of COVID-19 in chest X-ray images, especially in regions with limited access to COVID-19 testing facilities. A literature review was conducted in order to review the most recent approaches and techniques for medical image detection. The authors of [55] developed a machine-learning algorithm that could accurately classify patients with severe COVID-19 and predict their risk of in-hospital mortality. The study collected data from electronic health records of patients with severe COVID-19, including demographics, vital signs, laboratory values, and comorbidities. A machine-learning algorithm based on a gradient-boosting machine (GBM) was developed and trained on the collected data. The results showed that the proposed GBM model achieved high accuracy in classifying patients with severe COVID-19 and predicting their risk of in-hospital mortality. The study concluded that the proposed machine-learning algorithm could be a valuable tool for clinicians to make more informed decisions about the management of patients with severe COVID-19. In 2021, the authors of [56] proposed a classification solution using transfer learning to assess the suitability of 3 pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) for mobile applications. These models were selected for their accuracy and efficiency with a relatively small number of parameters. The study used a dataset compiled from various publicly available sources and evaluated the models using performance measurements and deep-learning approaches, such as accuracy, recall, specificity, precision, and F1-scores. The results demonstrated that the proposed method produced a high-quality model with a COVID-19 sensitivity of $94.79\%$ and an overall accuracy of $92.93\%$. The study suggested that computer-vision techniques could be utilized to improve the efficiency of detection and screening processes. In 2021, the authors of [57] employed convolutional neural networks (ConvNets) to accurately identify COVID-19 in computed tomography (CT) images, enabling the early classification of chest CT images of COVID-19 by hospital staff. ConvNets automatically learned and extracted features from medical image datasets, including the COVID-CT dataset used in this study. The objective was to train the GoogleNet ConvNet architecture using 425 CT-coronavirus images from the COVID-CT dataset. The experimental results indicated that GoogleNet achieved a validation accuracy of $82.14\%$ on the dataset in 74 min and 37 s. This study demonstrated the potential of ConvNets in improving the accuracy and efficiency of COVID-19 detection in medical imaging. In 2022, the authors of [58] proposed a new method for improving the quality of CT scans using contrast limited histogram equalization (CLAHE) and developed a convolutional neural network (CNN) model to extract important features from a dataset of 2482 CT-scan images. These features were then used as input for machine-learning methods such as support vector machine (SVM), Gaussian naive Bayes (GNB), logistic regression (LR), random forest (RF), and decision tree (DT). The researchers recommended an ensemble method for classifying COVID-19 CT images and compared the performance of their model with other state-of-the-art methods. The proposed model outperformed existing models with an accuracy of $99.73\%$, a precision of $99.46\%$, and a recall of $100\%$. In 2022, the authors of [59] described an approach that used a generative adversarial network (GAN) to improve the accuracy of a deep-learning model for classifying COVID-19 infections in chest X-ray images. To generate additional training data, the COVID-19 positive chest X-ray images were fed into a styleGAN2 model, which produced new images for training the deep-learning model. The resulting dataset was used to train a CNN binary classifier model that achieved a classification accuracy of $99.78\%$. This method could aid in the rapid and accurate diagnosis of COVID-19 infections from chest X-ray images. ## 6. Proposed Solution The proposed solution was designed with two main objectives: medical data classification and medical image detection. Each model is described in detail in this section. ## 6.1. Advanced Parallel K-Means Clustering In order to implement the modified parallel k-means clustering on the mobile execution unit and the SoC, the algorithm had to be modified to take advantage of a multi-core general-purpose processor and a multi-core neural engine. Each operating system offered a unique set of utilities for parallel operation. The iOS environment, due to its use of Objective-C programming, has an additional tool called dispatch queues, in addition to standard tools, such as processes and threads. Although iOS is a multi-tasking operating system, it did not allow multiple processes for a single program, resulting in only one procedure being available. However, the Android OS had a limitation in its Java and Kotlin programming languages, which was the hardware-limited access and lack of pointer support, making it difficult to fully utilize the system hardware. A lightweight process is a thread of any type. Threads share memory with their parent process while processes themselves do not. This led to issues when two threads simultaneously modified the same resource, such as a variable, resulting in illogical outcomes. In the iOS environment, threads were a finite resource on any POSIX-compliant system. Only 64 threads could be active at once for a single process. While this is a large number, there were logical reasons to exceed this limit. The overall processing, as shown in Figure 1, of the on-device parallel clustering consisted of two jobs: managing the dataset and clustering execution, and performing the parallel k-means clustering itself. The general-purpose processor cores were responsible for managing the clustering in the neural engine cores. After executing the k-means clustering on a sub-block of the data, each core sent the centroid point-value to the general-purpose cores. The general-purpose cores then evaluated whether the centroid value was less than the centroid threshold. If it was less, a signal was sent to the execution mechanism to process the clustering again. Figure 2 shows a flowchart of advanced parallel k-means clustering on the neural engine and general-purpose cores. ## 6.2. Advanced Classification Solution Pre-processing medical data with advanced parallel k-means clustering was a useful technique to improve the classification performance of logistic regression algorithms. K-means clustering is a machine-learning algorithm that is used to partition a dataset into a specified number of clusters. By using advanced parallel techniques, it is possible to process data more efficiently and quickly. Pre-processing the medical data with k-means clustering improved the accuracy and precision of the logistic regression algorithms by ensuring the data were simpler to classify. The k-means algorithm divided the data into clusters based on similar characteristics, such as age or sex. This assisted in reducing the noise and the complexity of the data, making it simpler for the logistic regression algorithm to accurately classify the data. In addition to improving the accuracy and precision of the classification process, pre-processing the medical data with k-means clustering also reduced the computational resources required to operate the logistic regression algorithm. By reducing the size and complexity of the dataset, it was possible to operate the logistic regression algorithm more efficiently and quickly. After clustering the data using k-means clustering, the next step in the process was to perform the logistic-regression classification. The steps for performing parallel logistic-regression classification were the following:Pre-processing: As with non-parallel logistic regression, it was important to pre-process the data before applying the model. This included tasks such as missing-value imputation, scaling, and feature selection. Splitting the data: The data had to be split into training and testing sets in order to evaluate the model’s performance on unfamiliar data. Choosing a parallelization method: We had to decide whether to use data parallelism, model parallelism, or a hybrid parallelism. Partitioning the data: Depending on the chosen parallelization method, the data had to be partitioned into smaller chunks and distributed across multiple processors or devices. Training the model: Each processor or device was responsible for training a separate logistic-regression model on its chunk of the data. The models were then combined to form the final model. Evaluating the model: The trained model was then evaluated on the testing data. This involved calculating evaluation metrics, such as accuracy, precision, and recall. Assessing the model’s predictions: Once the model had been trained and evaluated, it was used to make predictions according to new data. To achieve this, the model’s parameters were used to calculate the probability of an instance belonging to each class. The class with the highest probability was then predicted as the output. In Algorithm 2, the input is the data D and the number of processors or devices n to be used for parallelization. The output is the trained logistic-regression model M. The data were pre-processed and split into training and testing sets. The parallelization method was chosen, and the training data were then partitioned into smaller chunks. A separate logistic-regression model was trained on each chunk of data, and the models were combined to form the final model. The model was then evaluated on the testing data and the returned results. Algorithm 2 Parallel Logistic-Regression Classification1:procedure ParallelLogisticRegressionClassification(D, n)2: Pre-process data D3: Split data into training and testing sets Dtrain and Dtest4: Partition data Dtrain into n smaller chunks Dtrain,1,Dtrain,2,⋯,Dtrain,n5: for i←1 to n do6: Train logistic-regression model Mi on chunk Dtrain,i7: end for8: Combine models M1,M2,⋯,Mn to form final model M9: Evaluate model M on testing data Dtest10: return Model M11:end procedure The algorithm had two input parameters. The first was the clustered dataset, which included a new feature extracted by the clustering process. The second input was the number of chunks into which the dataset would be partitioned. The number of partitions depended on the number of neural engine cores available, with each chunk trained on a single core. The standard CPU cores handled general tasks, such as data partitioning; reading and writing data for the neural engine cores; combining models (M1, M2,…, Mn); and evaluating models. ## Classification Pre-Processing Using k-means clustering as a pre-processing step could potentially improve the performance of the logistic-regression classification in several ways:Dimensionality reduction: K-means clustering was used to group similar data points together into clusters, which reduced the number of features in the dataset. By selecting the centroids of the clusters as the new features, we reduced the dimensionality of the data and removed the noise, which improved the performance of the logistic regression. Feature engineering: K-means clustering was used to create new features that captured the structure of the data. We added a new binary feature that indicated whether a data point belonged to a particular cluster or not. These new features enabled the logistic regression to capture complex relationships in the data that had not been apparent previously. Outlier detection: K-means clustering improved the identification and removal of outliers in the dataset. Outliers had a significant impact on the performance of the logistic regression, and removing them improved the accuracy of the model. Data normalization: K-means clustering was used to normalize the data by scaling it to a range from 0 to 1. Normalizing the data improved the performance of the logistic regression by reducing the impact of outliers and ensuring that all features were on a similar scale. In the proposed parallel logistic regression, the weighted-combination method assisted in forming the final logistic-regression model from individual models that had been trained by each processor or device. An overview of the process is provided:Train individual models: The dataset was divided into subsets, and each subset was used to train a logistic-regression model on a separate processor or device. Obtain model weights: Once the individual models had been trained, each model was assigned a weight based on its performance on a validation set. The weights were determined using a variety of methods, such as the accuracy or the area under the receiver-operating characteristic curve (AUC-ROC).Combine the models: The predicted probabilities or coefficients from each individual model were multiplied by their corresponding weights, and the weighted sum was used as the final output. For example, if there were three individual models with weights of 0.3, 0.5, and 0.2, the predicted probabilities of each model were multiplied by 0.3, 0.5, and 0.2, respectively, and then summed to obtain their final predicted probabilities. Model selection: The performance of the final model was evaluated on a validation set, and the weights assigned to the individual models were adjusted to improve the performance of the final model. This process was repeated until the desired level of performance was achieved. Apply the final model: Once the final model was selected, it was implemented to make predictions on new data. The weighted-combination method can be an effective way to leverage the power of multiple processors or devices to train logistic-regression models in parallel. By assigning weights to each individual model, the final model can benefit from the strengths of each model while mitigating their weaknesses. ## 6.3. Advanced Image Detection In this study, we proposed a novel approach for pre-processing images using advanced parallel k-means clustering and then applying image detection using YOLOv4. The k-means clustering algorithm was used to divide the images into segments, which were then processed in parallel by multiple processors. The parallel-processing of the image segments resulted in a significant reduction in the overall processing time. The k-means algorithm is a popular method for clustering data based on similarity. It groups similar data points together and forms clusters. In the proposed approach, k-means was used to divide the images into segments, where each segment represented a cluster of similar pixels. The parallel-processing of these segments was achieved by distributing the segments across multiple processors. This allowed for a more efficient use of resources and resulted in a significant reduction in the overall processing time. After the image had been segmented, the image detection algorithm YOLOv4 was applied to each segment. YOLOv4 is a state-of-the-art object detection algorithm that has been widely used for image-processing tasks. It can accurately detect and classify objects in an image, making it an ideal choice for this application. The proposed approach provided several advantages over traditional image-processing methods. The use of advanced parallel k-means clustering allowed for a more efficient use of resources, resulting in faster processing times. Additionally, the application of YOLOv4 to the image segments improved the accuracy of object detection. Overall, the proposed approach was a powerful tool for image processing on mobile devices. ## Stage 1: Image Clustering and Pre-Processing The intricate structure of the information in images makes the clustering of X-ray (radiographs) and CT-scan images challenging. A considerable visual resemblance exists between X-ray and CT images of the same class. Furthermore, because of the varied X-ray image types, orientation changes, alignments, and diseases, there was a significant variance within a class. The quality of the X-ray images also varied significantly, in addition to the contents. As illustrated in the accompanying diagram, the image clustering framework in this study was divided into two phases: image feature extraction and image clustering. Then, the clustering process was carried out using the machine-learning engine-specific processors in contemporary mobile devices. Maintaining dataset characteristics while improving clustering efficiency was recommended [6]. Algorithm 3 outlined the primary steps for clustering the pixels in the input image, using the modified k-means clustering algorithm, as described earlier in this section. Algorithm 3 K-Means Image ClusteringRequire: Image Dataset Input: Random Centroid Points Start: Clustering Pixels while pixels≠end do Select: Neural Engine Core Assign: Processing to Core Calculate: Mean Value Set: Pixel-to-Cluster end while Output: Clustered Pixels Initially, patient X-ray and CT-scan images of COVID-19 disease were segmented using the k-means clustering algorithm, which then split the image into a set of regions that could be processed and analyzed. Due to the high performance achieved through the modification of the aforementioned algorithm, this step resulted in a thorough scan of the images and the segmentation of their content at a high speed, in preparation for the next stage, which was the application of the YOLOv4 algorithm. Second, incoming images were resized to 640 by 640 px and normalized using a normalize procedure. The improved K-means clustering algorithm, based on mobile neural engine processors [6], was then used to further match the training data with the k-mean YOLOv4 model. A suitable anchor size setting facilitated model convergence and provided useful prior information, and this sped up the model training process and resulted in more accurate values. The full implementation flowchart of anchor sizes is provided. Figure 3 summarizes the main steps of the first stage of image clustering. By clustering pixels in an image, we simplified the image by reducing the number of colors and tones. This assisted in removing noise and unwanted details from the image, making it easier to extract relevant features. Once the pixels were clustered, a new image was created where each pixel was assigned to its corresponding cluster, based on the map image. This new image was called a clustered image. The clustered image contained fewer colors and tones than the original image and could be used to extract features that were more representative of the image content. For example, in the medical image analysis, k-means clustering was used to segment an X-ray or CT-scan image into regions based on the density of the tissue. By clustering the pixels in the image, we identified regions that corresponded to bones, organs, and other tissues, which were then evaluated for feature extraction. These features included the size, shape, and texture of the tissue, which was then used to detect abnormalities and other features that could be indicative of a disease or condition. ## 6.4. Stage 2: YOLOv4 Image Detection After processing the images, the second stage of scanning the images commenced using the YOLOv4 algorithm, which could handle and detect objects in images at high speeds. Objects were easier to identify and detect in the pre-processed images due to the image content being segmented into consistent data aggregates. As shown in Figure 4, every object detector began by compressing and processing the images using a convolutional neural network backbone, which could then be used to make predictions at the endpoint of the image classification. To detect objects, several bounding boxes had to be constructed around images, requiring the concatenation of the convolutional feature layers of the backbone and the convergence of all the layers of features in the backbone at the neck. The YOLOv4 system utilized image-resizing, non-maximal suppression, and a single convolutional neural network to identify objects. It generated multiple bounding boxes and class probabilities simultaneously. Although the system was efficient for detecting objects, it could have difficulty identifying the locations of smaller objects precisely. The input images were divided into an S × S grid, with each grid cell responsible for identifying an object if the centroid of the object was within that grid cell. Using information from the entire image, each grid cell predicted the bounding boxes (B) and the confidence ratings for those boxes. These confidence scores represented the likelihood that an object was present in the box, as well as the accuracy of the object class prediction. The confidence score was defined as:[1]conf=Pr(classi\|obj)×Pr(obj)×IoUpredtruth where [2]Pre(obj)∈[0,1] here, Pr(object) denotes the likelihood that there will be an object in the grid cell, and Pr(classic\|obj) denotes the likelihood that a particular object will appear based on the presence of an item in the cell. ## 6.5. Stage 3: K-Means–YOLOv4 Clustering YOLOv4 used Bag of Specials, which is a technique that adds minimal delays to inference times while significantly enhancing performance. The algorithm evaluated various activation functions. As features flowed through the network, the activation functions were altered, as depicted in Figure 5. Using conventional activation functions, such as ReLU, had not always been sufficient to push feature creation to its optimal limit, which has led to the development of novel techniques in the literature to slightly improve this method. To summarize Stage 3 as an algorithm, Algorithm 4 was written. As shown in the algorithm, the YOLOv4 detector received the clustered image before initializing the YOLOv4 layers on it. The clustered images had clustered pixels, which improved the performance of the layers in recognizing the objects, contents, and features of the images. Algorithm 4 K-Means–YOLOv4 ClassifierRequire: Image Dataset Input: Random Centroid Points Start: Clustering Pixels while pixels≠end do Select: Neural Engine Core Assign: Processing to Core Calculate: Mean Value Set: Pixel to Cluster end while Run: YOLO’s Backbone on Clustered Image if Image Contains (COVID) then Flag: Image as Affected else Flag: Image as non-Affected end if Output: Classified Image ## 7. Performance Evaluation and Datasets Performance metrics were crucial for evaluating both classification and detection techniques. In addition, the experiment environment, datasets, and data preparation used to assess these metrics were equally important. Therefore, this section provides a detailed explanation of the performance metrics, datasets, and environment, as they related to the obtained results and the implementation of the proposed solution. The dataset consisted of a diverse set of information, which was classified into four distinct categories. The dataset was split into a training set ($70\%$) and a testing set ($30\%$), with the training set being used to train the machine-learning algorithms and the testing set being used to evaluate their performance. The machine-learning algorithms were applied for classification, using features extracted through the feature-engineering process. The proposed algorithm was compared to various categorization approaches and was found to be highly effective on X-ray images in the experiments. The proposed solution was implemented using the Dart ARM-based programming language, which is suitable for resource-constrained mobile devices, along with specialized deep-learning code for machine-learning engines on mobile devices. For iOS devices, the Swift programming language was utilized, which is known for its ease-of-use and safety features, while Kotlin (the native Android language) was employed for Android devices. This approach allowed for the solution to be easily implemented on different mobile devices and platforms, providing a more versatile and widely accessible solution. The k-means-YOLOv4 approach was evaluated on mobile devices equipped with machine-learning engines, including an iPhone 11 Pro Max with a dedicated 16-core machine-learning processor and the Samsung S22 with a system-on-a-chip, featuring a 16-bit floating-point neural processing unit (NPU). The testing dataset was divided into two categories: X-ray images and CT-scan images. ## 7.1. Performance Metrics Recall (R), Precision (P), F1-score (F1), specificity (S), and accuracy were used as the performance criteria to examine deep-learning performance. ## 7.2. Dataset To validate the proposed solution in various scenarios and on varied dataset properties, experiments were conducted using a number of different datasets. The characteristics of all the datasets are summarized in Table 1. All datasets were downloaded from the Kaggle website. A range of dataset sizes was used in this paper to evaluate the performance of the proposed solution with different dataset sizes ranging from a few thousand rows to millions of rows. Therefore, a dataset with 54 MB was used. For image detection and to confirm the model’s robustness, two independent datasets were collected and tested. The dataset used in this paper was created using the analysis conducted by [60] and can be downloaded at https://github.com/muhammedtalo/COVID-19 (accessed on 20 February 2023). The dataset consisted of 500 pneumonia, 125 COVID-19, and 500 no-findings X-ray images. It was created using two separate resources: X-ray images obtained from multiple open-access sources of COVID-19 patients in the Cohen [61] database, and the chest X-ray database for normal and pneumonia X-ray images, provided by Wang et al. [ 62]. The COVID-19 dataset included 43 female patients and 82 male patients. Metadata were not available for all patients in this dataset. Positive COVID-19 patients were, on average, around 55 years old. This was a versatile dataset that could be used for multi-class and binary classification tasks. The dataset from Harvard Lab [55] was also used in this study. The dataset consisted of non-enhanced chest CT scans of more than 1000 individuals diagnosed with COVID-19. The average age of the CT-scan patients was 47.18 years, with a standard deviation of 16.32 years and a range from 6 to 89 years. The population was composed of $60.9\%$ males and $39.1\%$ females. The most common self-reported co-morbidities among patients were coronary artery or hypertension disease, interstitial pneumonia or emphysema, and diabetes. The positive PTPCR patient images were obtained from in-patient treatment sites for COVID-19 and accompanying clinical symptoms, between March 2020 and January 2021. The scans were taken during end-inspiration with the subjects in a supine position. The CT scans were conducted using a 16-slice helical mode on NeuViz equipment, without the use of intravenous contrast. The images were captured in DICOM format and were 16-bit gray-scale with 512 × 512 px. The slice thickness was determined by the operator and ranged from 1.5 to 3 mm, based on the clinical examination requirements. The CT scans were reviewed for the presence of COVID-19 infection by two board-certified radiologists. In cases where the first two radiologists were unable to reach a consensus, a third more-experienced radiologist provided the final judgment. The CT images showed a variety of patterns indicative of COVID-19-specific lung infections. In the third phase of our comparison, two datasets were used. The specifics of the two major subsections of the sourced image graphs were as follows. Radiography database for COVID-19 in [63]. The authors gathered chest X-ray images of COVID-19-positive individuals, along with healthy people and those with viral pneumonia, and made them accessible to the public on https://www.kaggle.com/ (accessed on 20 February 2023).Actualmed, Pau Agust Ballester, and Jose Antonio Heredia from Universitat Jaume I (UJI) created the Actualmed COVID-19 Chest X-ray Dataset for study (https://github.com/agchung/Figure1-COVID-chestxray-dataset/tree/master/image (accessed on 20 February 2023)). A total of 3106 images were utilized for model training, $16\%$ of which were used for model validation. A total of 806 non-augmented images from various categories were used to test the proposed solution and assess the performance. Furthermore, the large image datasets in Table 2 were used for the big-data evaluation. All the datasets were downloaded from the Kaggle website. ## Data Preparation The data clustering had to be prepared, and the primary parameters had to be selected before clustering, as follows:Noise Removal: The advanced parallel k-means clustering algorithm utilized the mean imputation as the method for handling missing data. In this approach, missing values were replaced with the mean value of the corresponding feature across all samples. This method is simple and computationally efficient, and it has been shown to be effective in practice. However, the mean imputation may introduce bias in the clustering results if the missing data were not missing completely-at-random (MCAR). If the missing data were missing-at-random (MAR) or missing not-at-random (MNAR), more sophisticated methods such as regression imputation and multiple imputation could be required to avoid bias. Number of Clusters: Selecting the optimal number of clusters in the advanced parallel k-means clustering was crucial for achieving effective cluster analysis. This is particularly true in the medical field, where the identification of meaningful clusters can lead to more accurate diagnoses and treatments. However, the traditional methods of finding k-value, such as the Elbow method or the Silhouette method, are not always sufficient in the medical field, where the data are often complex and high-dimensional. In such cases, expert knowledge could be required to identify clinically relevant subgroups, which could then be used to determine the optimal number of clusters. In this paper, the k-value set to 2 in the clustering of numeric and text data and set to 5 for image clustering, as there were 5 main gray-scale stages of colors in the X-ray and MRI images. ## 7.3. Operating System Implementation Dispatch queues are a feature of the Grand Central Dispatch (GCD) system, which is a part of the iOS and macOS operating systems. GCD provides a high-level, asynchronous programming interface for managing concurrent tasks. Dispatch queues are lightweight and provide a simple interface for executing tasks concurrently without consuming an excessive amount of system resources. Dispatch queues are managed by the operating system and can be used to process tasks on a first-in, first-out (FIFO) basis. This makes it easy to manage task dependencies and avoid competitive conditions, and tasks submitted to a dispatch queue can be executed in parallel with other tasks in the queue. Dispatch queues can be created with different priorities to manage the order of execution of tasks and ensure that high-priority tasks are executed first. Threads, in contrast, are a lower-level mechanism for achieving concurrency in a program. Threads achieve true parallelism, as multiple threads can execute simultaneously on different processor cores. Each thread has its own stack and program counter, and threads can share memory with other threads in the same process. Threads are managed by the operating system and can be used to process tasks concurrently in a more fine-grained way than dispatch queues. As compared to dispatch queues, threads have a higher overhead and require more system resources, making them less suitable for lightweight tasks. Threads can be used to implement more complex concurrency patterns, such as locking, synchronization, and message-passing. The proposed solution for implementing the modified parallel k-means clustering algorithm on iOS leveraged the advantages of dispatch queues to achieve concurrency. The GCD framework provided several types of queues, including serial and concurrent dispatch queues. A serial dispatch queue executed tasks one at a time, while a concurrent dispatch queue executed tasks concurrently. In the proposed solution, a concurrent dispatch queue was used to execute the k-means clustering algorithm on multiple cores simultaneously. Each task was scheduled on the dispatch queue, and the queue handled the scheduling of tasks across multiple cores. This allowed the algorithm to take advantage of the multi-core neural engine processor and general-purpose processor, leading to improved performance. Furthermore, GCD provided mechanisms to ensure thread safety and avoid competitive conditions through the use of synchronization techniques, such as semaphores and barriers. By utilizing these features, the implementation of the parallel k-means clustering algorithm on dispatch queues was more efficient and reliable. ## 8. Results and Discussion The proposed work was subjected to thorough testing and evaluation in multiple stages to ensure its effectiveness at various levels and within different contexts. The primary focus was on enhancing performance and leveraging the high speeds offered by the two integrated algorithms. ## 8.1. Operating System Performance The proposed solution was designed to be operating system independent and hardware accelerated. This meant that the advanced parallel k-means clustering could be executed on any operating system that had two processors: a neural engine processor and a general-purpose processor. However, both iOS and Android operating systems were designed to manage and take advantage of hardware allocation and management that included their neural engine processor. These dedicated operating systems were able to send specific tasks to a particular processor core, enabling the implementation and execution of the advanced parallel k-means clustering. Overall, this type hardware acceleration provides opportunities for future advancements of the operating systems, which is expected since the new M-family MacOS already supports dedicated neural-engine-core assignments. In order to evaluate the performance of the advanced k-means clustering across different operating systems, Table 3 presents two large datasets, each with over 9 million records. These were clustered using the advanced parallel k-means clustering algorithm on Windows OS, Android, and iOS systems. The performance results, as presented in Table 4, showed that the processing of 11 million records from the Google Play Store dataset doubled in speed with a dedicated ML processor. The next experiment was conducted using the education-sector dataset, and the mobile processor exhibited a performance up to 10-times faster than the desktop OS (Windows 11). Additionally, the performance of iOS was twice as fast as that of the Android OS. The performance differences observed between the iOS and Android operating systems, within the context of advanced parallel k-means clustering, could be due to several factors. It could be related to the differences in the underlying architectures of the two operating systems. Specifically, iOS was designed to take full advantage of its hardware resources, including the dedicated neural engine cores, which could explain the observed faster performance, as compared to Android. Additionally, the iOS architecture was based on the use of Objective-C and dispatch queues, which were designed to facilitate concurrent processing and task scheduling. These features provide a more efficient way to execute the parallel k-means clustering algorithm, potentially resulting in the observed faster performance. However, the performance differences observed could have also been influenced by other factors, such as the differences in the hardware configurations of the devices used to test the algorithms, as well as the specific implementation of the parallel k-means clustering algorithm on the different operating systems. ## 8.2. Data Classification Model The performance of logistic regression and naive Bayes algorithms could have been influenced by various factors, such as the size and complexity of the data, the hardware and software utilized, and the specific implementation of the algorithms. Typically, logistic regression has been faster than naive Bayes when working with large datasets, as the naive Bayes algorithm can become computationally demanding as the number of features increases. However, naive Bayes can be faster when working with smaller datasets or when the number of features is relatively limited. Table 5 illustrates the performance of both algorithms after classifying 10 million records of medical data. The table shows the speed and accuracy of both algorithms, which aided in determining which algorithm was more suitable for a specific application. While the speed of an algorithm was an important consideration, accuracy was also taken into account when a compromise between speed and accuracy may be necessary. Based on the datasets described in Section 7.2, the proposed solution was analyzed to evaluate its classification performance and accuracy. The results presented in Table 6 and Table 7 demonstrated the superior performance of the proposed solution, as compared to the logistic-regression and naive Bayes algorithms. The naive Bayes algorithm is known to be efficient for small datasets, but the proposed solution outperformed both algorithms, even when the dataset size increased. This highlighted the effectiveness of the proposed solution in handling larger datasets, which pose a significant challenge for traditional classification methods. Additionally, the strong performance of the proposed solution, as compared to the standard classification algorithms, such as logistic regression and naive Bayes, further emphasized its potential for practical applications. Overall, the results demonstrated the exceptional performance and potential of the proposed solution. One possible reason for the higher accuracy of the proposed solution was that it had been specifically designed to handle larger datasets, which may have been more challenging for traditional classification algorithms. For example, logistic regression and naive Bayes algorithms could have struggled to effectively classify data when the number of features increased significantly, as they can become computationally demanding as the number of features increases. In contrast, the proposed solution used more advanced techniques, including the advanced parallel k-means, parallel logistic regression, and the neural engine processor, to effectively classify the large datasets. Additionally, the proposed solution incorporated additional factors and features that were relevant to the classification task, which further improved its accuracy. Overall, the results demonstrated the effectiveness of the proposed solution in handling large datasets and achieving high accuracy in classification tasks. In order to examine the performance of the proposed solution with the recent advancements in medical data classification, the proposed solution was compared with the three most recent medical-data-classification approaches, which were: [65,66,67]. All solutions were compared with the proposed solution in terms of classification performance and classification accuracy, as shown in tables below. As shown in Table 8, the proposed solution significantly outperformed the three compared solutions, while the naive Bayes-based algorithm tended to be slower, the proposed solution was more effective than both the binary logistic regression and the logistic regression. This suggested that the proposed solution was particularly well suited for handling larger datasets, which could be more challenging for traditional classification algorithms. The results demonstrated the strong performance of the proposed solution, as compared to the conventional classification algorithms, indicating that it was an effective and reliable method for classification tasks. The accuracy of the proposed solution was compared with previous solutions, and the results, as shown in Table 9, demonstrated its high accuracy. Specifically, the proposed solution outperformed the comparable solutions, achieving an accuracy rate of $99.8\%$ while a novel binary-logistic-regression solution only achieved $98\%$ accuracy. The worst performance in terms of accuracy was observed in the logistic-regression solution designed for the prediction of myocardial infarction disease in [66]. These results suggested that the proposed solution was particularly effective at achieving high accuracy in classification tasks, and that it outperformed other approaches. ## 8.3. Training Proposed Model Feature extraction and classification are the two crucial components of the proposed image detection system. The quality of the extracted features was critical to the success of the classification process. Therefore, the extracted features were used to train the model in order to demonstrate its effectiveness in feature extraction. Figure 6 shows an image after applying the k-means clustering technique with feature selection. The resulting image was divided into two main clusters, black and white, in the first stage of learning. This clustered image was then used as a map for pixel-based feature extraction, where each pixel was assigned to its corresponding cluster based on the mapped image. In the next step, the pixel values were processed with their original values for the image detection process. This approach provided two benefits. Firstly, any outlier pixels due to the X-ray device or CT-scan process were removed. Secondly, a new feature was added to the image pixels, which was the pixel group. The cluster value associated with each pixel provided valuable information for image feature extraction and detection. By considering the cluster value, we could efficiently extract the relevant features from the image and ignore the noise and other irrelevant pixels. This approach significantly improved the accuracy of image detection and reduced false positives. When processing an X-ray image, the proposed solution began by extracting the lung features of the patient and then determined whether the lungs were normal or abnormal by classifying them as positive or negative, accordingly. ## 8.4. Object Detection Speed During the initial phase, the proposed work was compared with a range of standard algorithms frequently used for image classification. The proposed solution demonstrated exceptional performance, outperforming the other algorithms by up to 15-fold. It also outperformed the YOLOv4 algorithm by approximately $60\%$, as shown in the comparison presented in Table 10. To assess the performance of the proposed solution under various scenarios and with varied device specifications, it was tested using both a standard computer CPU (Intel Core i5-3.5 GHz) and GPU (AMD Radeon R9 M290X 2 GB). The results of the experiments showed that the proposed detection solution maintained its high performance, as compared to the YOLOv4 algorithm, as demonstrated in Table 11. The results showed that the proposed solution exhibited a high performance, which was up to 2.3 times faster on the GPU and up to 1.5 times faster on the CPU, as compared to the standard YOLOv4. Additionally, the proposed algorithm demonstrated a significant speed advantage, achieving speeds that were up to 7 times faster due to the high speed of the proposed algorithm and the efficient use of the artificial intelligence processors in modern mobile devices, as compared to recent solutions, such as (VGGCOV19-NET [70] and CAD-based YOLOv4 [71]). ## 8.5. Object Detection Performance In the second phase of the performance comparison, as shown in Table 12, the proposed solution was compared with two recent approaches that made adjustments to classification algorithms to handle X-ray images of COVID-19 patients. Due to the importance of the TN, TP, FN, and FP values [72], their values had been calculated first, as shown in Figure 7. In the last part of the comparison, the proposed work was compared to the benchmark examples, based on four performance measures, including recall, precision, F1-score, and accuracy. These represented the best testing factors for evaluating the performance of the classification algorithms and to ensure that the improvements achieved [73] by the proposed algorithm were accurate across all levels, which, in turn, would indicate its potential application in the medical field. The results, as shown in Table 13, illustrated the excellent performance of the proposed algorithm in the classification task of images when applied to the Fold 1–5 levels. When the algorithm treated images classified as infected images, it also showed superior accuracy, and the performance measures of the rest of the results are shown in Table 14 and Table 15. An advanced K-means clustering [6] combined with YOLOv4 solution enabled the rapid and accurate detection of COVID-19 within milliseconds, making it a useful tool in regions with a shortage of experienced doctors and radiologists. Additionally, the model could be utilized to identify patients in settings with limited healthcare facilities, even when only X-ray technology is available, and it could ensure more timely treatments for positive COVID-19 patients. One practical benefit of the concept was that it allowed for the identification of patients who did not require PCR testing, thereby reducing the overcrowding in medical facilities. In the second part of the performance comparison, as shown in Table 16, the proposed solution was compared with recent studies in which classification algorithms were modified to handle CT-scan images of COVID-19 patients. Due to the high speed of the suggested method and the extensive use of artificial intelligence processors prevalent in recent mobile devices, the proposed algorithm demonstrated superiority in its accuracy, recall, and other performance metrics. Figure 8 shows the learning curve accuracy of the proposed solution in both the training and testing stages. The accuracy of the proposed solution had consistent improvement. Furthermore, the learning curve began with an accuracy near $32\%$ and continued to improve, up to $99\%$. In the third part of the performance comparison, as shown in Table 17, the proposed solution was compared with recent studies that used a classification technique on brain MRI images to maximize the generalizability of the proposed solution. This comparison was conducted to show that the proposed solution could be adapted for various datasets and image types, as well as to classify other diseases, such as brain tumors. The results showed that the proposed solution had excellent performance across all four comparison parameters (recall, precision, F1-score, and accuracy). The dataset used in [75], which consisted of 280 samples of MRI images, was also used in this test. The dataset contained 100 images with normal tumors and 180 with abnormal tumors. Table 17 shows the proposed solution’s performance, as compared to a recent solution [75]. The results showed that the proposed solution outperformed the comparable solution, with an accuracy of up to $98\%$. The proposed solution was compared with a high-performance and highly accurate solution, which had been proposed in 2020 [76]. For datasets 1 and 2, the solution obtained $98.7\%$, $98.2\%$, $99.6\%$, and $99\%$ for classification accuracy and F1-Score, respectively. However, as shown in Table 18 and Table 19 with the best values across 7 folds, the proposed solution had excellent classification performance in terms of accuracy, recall, precision, and F1-score, as compared to the comparable 2020 approach. The excellent performance and accuracy of the proposed solution could be attributed to the optimization of the k-means clustering, which enhanced the recognition of the image characteristics by the classifier. Additionally, the optimization of the YOLOv4 algorithm through modified layers improved the ability to detect and recognize features, resulting in an overall improvement in performance. In order to evaluate the performance of the proposed solution on a vast amount of medical image detection, a set of big-medical-data was used, as described in Section 7.2 and (Table 3). Table 20 shows the performance of the proposed solution, as compared to recent approaches. The performance of the proposed solution in terms of recall, precision, F1-score and accuracy was up to $10\%$ better than the comparable solutions. The results of the proposed approach using advanced parallel k-means clustering, logistic regression, and YOLOv4 for medical data classification and image detection could have important implications for the field of healthcare. The accurate classification and detection of medical data could have a significant impact on patient outcomes by enabling earlier diagnoses and more effective treatment planning. The proposed approach has potential for improving the accuracy and efficiency of these tasks, which could ultimately lead to better patient outcomes and reduced healthcare costs. Furthermore, the proposed approach has the potential to contribute to the development of new solutions in these areas by providing a more efficient and effective means of pre-processing medical data. The use of advanced parallel k-means clustering for pre-processing reduced the dimensionality of the data, which made it easier to classify and detect patterns. This could lead to the development of new algorithms that are more effective for identifying specific medical conditions and abnormalities and could, ultimately, lead to new treatments and therapies. Additionally, the proposed approach could aid in the development of new medical imaging technologies. By improving the accuracy of image detection, the proposed approach could assist in identifying abnormalities that are difficult to detect using traditional imaging methods. This could lead to the development of new imaging technologies that are more accurate and effective and could, ultimately, improve patient outcomes. In terms of the overall medical-data field, the proposed approach using advanced parallel k-means clustering for pre-processing medical data, combined with logistic regression and YOLOv4 for classification and image detection, respectively, could contribute to the development of new solutions for medical data classification and image detection. Firstly, the use of advanced parallel k-means clustering for pre-processing medical data could significantly reduce the processing time and improve the accuracy of subsequent classification and detection tasks. This could be especially beneficial for large-scale medical datasets, where traditional clustering methods may not be feasible due to computational limitations. Secondly, the combination of logistic regression and YOLOv4 for classification and image detection, respectively, could improve the accuracy of these tasks in medical applications. Logistic regression is a simple and efficient algorithm that could be used for both binary and multi-class classification, while YOLOv4 is a state-of-the-art object detection algorithm that can detect multiple objects in an image with high accuracy. Thirdly, the proposed approach could potentially aid in the diagnosis, treatment planning, and disease monitoring in healthcare. The accurate classification and detection of medical data could provide clinicians with valuable insights into a patient’s condition and assist them in making informed decisions regarding treatments. Lastly, the proposed approach could also serve as a framework for the development of new solutions in medical data classification and image detection. The combination of advanced clustering methods, logistic regression, and object detection algorithms could be customized and optimized for specific medical applications and datasets. This could lead to the development of innovative solutions that address the unique challenges and complexities of medical data analysis. ## 9. Conclusions The proposed approach using advanced parallel k-means clustering for pre-processing medical data, combined with logistic regression and YOLOv4 for classification and image detection, respectively, effectively improved the performance of these algorithms, particularly when applied to large medical datasets. The results of the classification task showed that the approach was able to accurately classify the medical data, and the results of the image detection task using X-ray and CT scan images showed that the approach was able to effectively detect and classify the medical images. The use of advanced parallel k-means pre-processing and acceleration of the neural engine processor contributed to the improved accuracy and efficiency of the approach. This approach has the potential to significantly impact the field of healthcare, as it can aid in diagnostics, treatment planning, and disease monitoring. Further research and evaluation on larger and more diverse medical datasets could reveal additional benefits and potential applications. While the proposed solution has shown promise in improving the accuracy and efficiency of these tasks on large medical datasets, there were still limitations that should be considered. One limitation was the hardware dependency, as the acceleration of the k-means clustering was highly dependent on the neural engine processor, multi-core processor, and the operating system’s support for hardware management. Another limitation was the ability to improve 24-bit color images, which require a different number of k-values and could affect the clustering performance negatively. ## References 1. Lawonn K., Smit N.N., Buhler K., Preim B.. A survey on multimodal medical data visualization. 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--- title: The Mechanism of Kelulut Honey in Reversing Metabolic Changes in Rats Fed with High-Carbohydrate High-Fat Diet authors: - Khairun-Nisa Hashim - Kok-Yong Chin - Fairus Ahmad journal: Molecules year: 2023 pmcid: PMC10056699 doi: 10.3390/molecules28062790 license: CC BY 4.0 --- # The Mechanism of Kelulut Honey in Reversing Metabolic Changes in Rats Fed with High-Carbohydrate High-Fat Diet ## Abstract Metabolic syndrome (MetS) is composed of central obesity, hyperglycemia, dyslipidemia and hypertension that increase an individual’s tendency to develop type 2 diabetes mellitus and cardiovascular diseases. Kelulut honey (KH) produced by stingless bee species has a rich phenolic profile. Recent studies have demonstrated that KH could suppress components of MetS, but its mechanisms of action are unknown. A total of 18 male Wistar rats were randomly divided into control rats (C group) ($$n = 6$$), MetS rats fed with a high carbohydrate high fat (HCHF) diet (HCHF group) ($$n = 6$$), and MetS rats fed with HCHF diet and treated with KH (HCHF + KH group) ($$n = 6$$). The HCHF + KH group received 1.0 g/kg/day KH via oral gavage from week 9 to 16 after HCHF diet initiation. Compared to the C group, the MetS group experienced a significant increase in body weight, body mass index, systolic (SBP) and diastolic blood pressure (DBP), serum triglyceride (TG) and leptin, as well as the area and perimeter of adipocyte cells at the end of the study. The MetS group also experienced a significant decrease in serum HDL levels versus the C group. KH supplementation reversed the changes in serum TG, HDL, leptin, adiponectin and corticosterone levels, SBP, DBP, as well as adipose tissue 11β-hydroxysteroid dehydrogenase type 1 (11βHSD1) level, area and perimeter at the end of the study. In addition, histological observations also showed that KH administration reduced fat deposition within hepatocytes, and prevented deterioration of pancreatic islet and renal glomerulus. In conclusion, KH is effective in preventing MetS by suppressing leptin, corticosterone and 11βHSD1 levels while elevating adiponectin levels. ## 1. Introduction Metabolic syndrome (MetS) is composed of five clusters of risk factors which include central obesity, hyperglycemia, dyslipidemia and hypertension that increase an individual’s susceptibility to type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) [1]. According to the International Diabetes Federation (IDF) in 2006, an estimated 20–$25\%$ of the adult population worldwide suffers from MetS [2]. While in another study in 2017, around 12–$37\%$ of the Asian population and 12–$26\%$ of the European population suffered from MetS [3]. Increased consumption of high-calorie foods and reduced physical activity trigger the formation of adipose tissue in the internal or visceral organs [4]. The accumulation of adipose tissue causes tissue hypoxia, which leads to the release of free fatty acids (FFA) and adipokines by adipose tissue, contributing to the complications of MetS [5]. The release of FFA and cytokines, such as tumour necrosis factor-alpha (TNF-α) and interleukin-1-beta (IL-1β), can reduce the sensitivity of insulin function in insulin-sensitive tissues, leading to insulin resistance [6]. The inability of these tissues to correct insulin resistance causes hyperglycemia and increases the risk of developing T2DM [4]. In addition, the release of FFA into the blood through splanchnic circulation into the liver tissue stimulates the breakdown of FFA for the production of TG and VLDL [7]. The production of excessive VLDL particles is closely related to the renal clearance of HDL particles [8]. Meanwhile, increased leptin and reduced adiponectin secretions by adipose tissue also play a role in causing MetS risk factors [4]. Studies have shown that the hormone leptin is closely related to increased renal sympathetic activity and blood pressure [9,10]. Meanwhile, the secretion of adiponectin, which is an important hormone in increasing insulin sensitivity, is reduced in obese individuals [11]. Reduced adiponectin secretion is associated with insulin resistance, which can increase the risk of MetS [12,13]. Lifestyle and dietary modifications continue to be the major preventative approach for metabolic syndrome [14]. However, pharmacological therapies are commonly used to treat each of the MetS risk factors [15]. The use of functional food as an alternative approach for MetS prevention is actively being investigated nowadays. The presence of multiple bioactive compounds in functional food can target multiple pathways contributing to the development of MetS [16]. Bee-derived products or apitherapy rich in polyphenol content could be effective in managing MetS [17]. Several studies have found that bee honey can inhibit MetS by acting as an anti-inflammatory, anti-obesity and anti-hypertensive agent [18,19,20]. Kelulut honey (KH) is a type of stingless bee honey found in Malaysia [21]. KH is known to have a high content of antioxidants in comparison to other local honeys [22]. Moreover, studies show that KH can suppress each component of MetS through its antioxidant and anti-inflammatory effects [23,24]. However, the mechanisms of action of honey in managing metabolic syndrome remain elusive. Therefore, this study was conducted to study the mechanism of KH in inhibiting MetS in rats given a high-carbohydrate high-fat (HCHF) diet. ## 2.1. Liquid Chromatography-Mass Spectrometry (LC-MS) Analysis of Kelulut Honey Profiling of KH through LC-MS found that KH contains various phenolic compounds consisting of phenolic acid and flavonoid groups (Table 1). These phenolic compounds may contribute to KH’s ability to inhibit metabolic changes in rats receiving the HCHF diet. ## 2.2. Changes in Weight, Waist Circumference, BMI and Fat Percentage In this study, the HCHF diet caused an increase in obesity parameters such as weight ($p \leq 0.001$), fat percentage ($p \leq 0.001$), BMI ($p \leq 0.001$) and abdominal circumference ($$p \leq 0.006$$) in the HCHF group compared to the C group after 8 weeks. However, at week 16, only body weight ($p \leq 0.001$) and BMI ($p \leq 0.001$) were significantly increased compared to the C group. Meanwhile, KH supplementation for 8 weeks did not significantly reduce any of the obesity parameters in the HCHF + KH group significantly compared to the HCHF group (Figure 1a–d). ## 2.3. Serum TG and HDL In this study, the HCHF diet caused a significant increase in serum TG levels in the HCHF ($$p \leq 0.003$$) and HCHF + KH ($$p \leq 0.001$$) group at week 8 compared to baseline. In the HCHF group, the serum TG level continued to rise significantly at week 16 ($$p \leq 0.001$$) compared to the C group. KH supplementation decreased the serum TG level (0.79 ± 0.05 mmol/L, $$p \leq 0.002$$) compared to the C group at week 16. Meanwhile, serum HDL was not decreased in the HCHF and HCHF + KH group compared to the C group at week 8 ($$p \leq 0.174$$). However, at week 16, serum HDL level in the HCHF group was significantly decreased compared to the control group ($$p \leq 0.018$$). Similarly, KH supplementation for 8 weeks also saw a significant increase in serum HDL level for the HCHF + KH group (0.86 ± 0.06 mmol/L, $p \leq 0.001$) (Figure 2a,b). ## 2.4. Systolic and Diastolic Blood Pressure In this study, the HCHF diet caused a significant rise in SBP and DBP in the HCHF (SBP, $p \leq 0.001$; DBP, $p \leq 0.001$) and HCHF + KH (SBP, $p \leq 0.001$; DBP, $$p \leq 0.001$$) group at week 8 compared to their respective baseline values. In the HCHF group, the SBP ($$p \leq 0.002$$) and DBP ($$p \leq 0.001$$) continued to rise at week 16 in comparison to the C group. Meanwhile, KH supplementation improved both SBP ($p \leq 0.001$) and DBP ($p \leq 0.001$) at week 16 compared to the HCHF group (Figure 3a,b). ## 2.5. Fasting Blood Glucose and Oral Glucose Tolerance Test The FBG and OGTT results (area under the curve (AUC)) are shown in Figure 4a,b. In this study, the HCHF diet did not increase FBG and AUC of OGTT in the HCHF (FBG: $$p \leq 0.431$$, AUC OGTT: $$p \leq 0.759$$) and HCHF + KH (FBG: $$p \leq 0.968$$, AUC OGTT: $$p \leq 0.187$$) group after 8 weeks. Meanwhile, the HCHF + KH group showed a decreased AUC of OGTT in week 16 compared to week 8 ($$p \leq 0.001$$). However, the comparison between the groups was not significant (HCHF + KH vs. C, $$p \leq 0.569$$; HCHF + KH vs. HCHF, $$p \leq 0.176$$). ## 2.6. Serum Tumour Necrosis Factor Alpha (TNF-α), Interleukin-1-Beta (IL-1β) and Leptin At the end of the study, serum TNF-α ($$p \leq 0.531$$; Figure 5a) and IL-1β levels ($$p \leq 0.914$$; Figure 5b) did not show any significant difference among the three study groups. Serum leptin was elevated significantly in the HCHF group ($$p \leq 0.004$$ vs. the C group), but this change was prevented in the HCHF + KH group ($$p \leq 0.002$$ vs. the HCHF group; Figure 5c). ## 2.7. Serum Adiponectin and Corticosterone At week 16, serum adiponectin was higher in the HCHF + KH group compared to the HCHF group ($$p \leq 0.041$$; Figure 6a). Meanwhile, the serum corticosterone in the HCHF + KH group was significantly lower compared to both the C ($$p \leq 0.001$$) and the HCHF group ($$p \leq 0.019$$; Figure 6b). ## 2.8. 11-Beta-Hydroxysteroid Dehydrogenase Type-1 (11βHSD1) Enzyme and Fatty Acid Synthase Enzyme (FASN) At week 16, 11βHSD1 level in adipose tissue was significantly lower in the HCHF + KH group compared to the HCHF group ($$p \leq 0.017$$). No significant difference was observed in the 11βHSD1 level between the HCHF + KH and the C group ($$p \leq 0.268$$) (Figure 7a). Meanwhile, no significant differences were detected in the FASN level among the study groups ($$p \leq 0.301$$) (Figure 7b). ## 2.9.1. Adipose Tissue Histological examination of the adipose tissue showed that the HCHF diet caused adipocytes hypertrophy (Figure 8b), which was reflected by an increased area ($$p \leq 0.001$$) and perimeter of the adipocytes ($$p \leq 0.001$$) compared to the C group. In contrast, KH supplementation reduced these parameters significantly in the HCHF + KH group compared to the C group ($p \leq 0.05$) Figure 8c–e). ## 2.9.2. Liver Histological examination of the liver parenchyma showed that the hepatocytes of the HCHF group were larger compared to the C group (Figure 9a,b). The enlargement of hepatocytes was caused by the deposition of lipid droplets in their cytoplasm. Subsequent compression of the liver sinusoids was noted. Hepatocytes of the HCHF + KH group retained a normal morphology similar to the HCHF group (Figure 9c). ## 2.9.3. Pancreas Histological examination of the pancreas revealed that the pancreatic islets of the HCHF group were reduced in number compared to the C group (Figure 10a,b). There is also deposition of fat in the pancreatic parenchyma of the HCHF group. Meanwhile, in the HCHF + KH group the pancreatic islets were more numerous compared to the HCHF group (Figure 10c). This shows that the KH treatment for eight weeks can prevent the reduction in pancreatic islets caused by the HCHF diet. ## 2.9.4. Kidney Tissue Histological examination of the renal tissue revealed widening of Bowman’s space in the HCHF group compared to the C group (Figure 11(ai,bii)). Meanwhile, the glomeruli of the HCHF + KH group showed normal morphology without widening Bowman’s space compared to the HCHF group rats (Figure 11(ci,cii)). This shows that KH treatment for eight weeks might protect against glomerular changes compared to the HCHF group. ## 3.1. Liquid Chromatography-Mass Spectrometry (LC-MS) Analysis of Kelulut Honey Chemical analysis of KH was performed using ACQUITY Ultra High-Performance Liquid Chromatography (UHPLC) I-Class system (Waters, Milford, MA, USA) consisting of a binary pump, a vacuum degasser, an auto-sampler and a column oven. Phenolic compounds were chromatographically separated using a column ACQUITY UPLC HSS T3 (100 mm × 2.1 mm × 1.8 μm) (Waters, Milford, MA, USA) maintained at 40 °C. A linear binary gradient of water ($0.1\%$ formic acid) and acetonitrile (mobile phase B) (Merck, Darmstadt, Germany) was used as mobile phase A and B, respectively. The mobile phase composition was changed during the run as follows: 0 min, $1\%$ B; 0.5 min, $1\%$ B; 16.00 min, $35\%$ B; 18.00 min, $100\%$ B; 20.00 min, $1\%$ B. The flow rate was set to 0.6 mL/min and the injection volume was 1 μL. The UHPLC system was coupled to a Vion IMS QTOF hybrid mass spectrometer (Waters, Milford, MA, USA) equipped with a Lock Spray ion source. The ion source was operated in the negative electrospray ionisation (ESI) mode under the following specific conditions: capillary voltage, 1.50 kV; reference capillary voltage, 3.00 kV; source temperature, 120 °C; desolvation gas temperature, 550 °C; desolvation gas flow, 800 L/h, and cone gas flow, 50 L/h. Nitrogen (>$99.5\%$) was employed as desolvation and cone gas. Data were acquired in high-definition MSE (HDMSE) mode in the range m/z 50–1500 at 0.1 s/scan. ## 3.2. High-Carbohydrate, High-Fat Diet Preparation The high-carbohydrate and high-fat (HCHF) diet was prepared by mixing 175 g fructose (d-(−)-Fructose) (Chemiz, Shah Alam, Malaysia), 395 g sweetened condensed milk (Fraser & Neave Holdings Bhd., Kuala Lumpur, Malaysia), 200 g ghee (QBB Sdn. Bhd., Petaling Jaya, Malaysia), 25 g Hubble, Mendel, and Wakeman salt mixture (MP Biomedicals, California, CA, USA), 155 g powdered rat chow (Gold Coin Feedmills (M) Sdn. Bhd., Selangor, Malaysia) and 50 g water, in addition to $25\%$ fructose (Chemiz, Shah Alam, Malaysia) drinking water. Diet preparation was performed according to the method by Wong and colleagues [25]. ## 3.3. Kelulut Honey Preparation Raw KH was harvested from the stingless honeybee species, Heterotrigona itama, from a local honeybee farm (Gombak, Selangor, Malaysia). The sample was collected during October 2020. The honey was stored in a glass jar at 4 °C until further use. KH was diluted with distilled water at a 1:1 ratio upon administration. ## 3.4. Experimental Animals This study has been approved by the Animal Ethics Committee (UKMAEC), Laboratory Animal Resources Unit, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM) with the approval number ANAT/FP/2020/FAIRUS AHMAD/23-SEPT$\frac{.}{1126}$-OCT.-2020-SEPT-202. A total of 18 Wistar male rats ($$n = 6$$/group) weighing between 250–300 g were taken from the Laboratory Animal Resources Unit, UKM (Kuala Lumpur, Malaysia). During the study period, the rats were placed in the Animal Laboratory of the Department of Anatomy, Faculty of Medicine, UKM (Cheras, Malaysia) while the acclimatisation process was carried out during the first two weeks. The rats were placed in pairs in plastic cages. The laboratory environment was maintained at a temperature of 25 ± 3 °C, with good air ventilation with a 12 h light/dark cycle. ## 3.5. Study Design The rats were randomly divided into three groups, namely the control (C group) receiving normal rat chow, the HCHF group receiving an HCHF diet, and the HCHF + KH group receiving an HCHF diet and later supplemented with KH. Rats in the C group were given a normal chow (Gold Coin, Malaysia) and tap water ad libitum for 16 weeks. The other two groups received an HCHF diet and drinking water with $25\%$ fructose ad libitum for 16 weeks. The HCHF + KH group was supplemented with KH via oral gavage at a dose of 1.0 g/kg/day, which had been shown to reverse MetS in a study by Ramli et al. [ 2019] from the 8th week after HCHF diet initiation until the end of the study. Meanwhile, an equivolume of distilled water was given to both the C and HCHF group to mimic the gavage stress [23]. After 16 weeks of HCHF diet induction, the rats were sacrificed by decapitation and the organs were harvested for analysis. ## 3.6.1. Measurement of Body Weight, Abdominal Circumference and Body Mass Index The body weight of the rats was measured by using a digital weighing scale (Nimbus® Precision Balances, Adam Equipment, Buckinghamshire, UK). Abdominal circumference and body length were also measured using a standard measuring tape. Meanwhile, the body mass index (BMI) was calculated using the following equation: BMI = body weight (g)/length2 (cm2). Measurements were taken at baseline, 8th and 16th week of the study period. ## 3.6.2. Fat Percentage Measurement Dual-energy X-ray absorptiometry (DXA) scans were performed using Hologic Discovery Densitometer (Hologic QDR-1000 System, Hologic Inc., Massachusetts, MA, USA) with Small Animal Analysis Software. During the DXA scan procedure, rats were anaesthetized with ketamine, xylazine, tiletamine and zolazepam (KTX) mixture. A whole-body scan was performed at baseline, week 8, and 16 to provide measurements of fat percentage. ## 3.6.3. Serum Fasting Triglyceride and High-Density Lipoprotein Blood was obtained from the retroorbital vein of the anaesthetized rats. For serum extraction, the blood was left to clot at room temperature for 20 min and subsequently centrifuged at 4000 rpm for 30 min. Determination of fasting serum triglyceride (TG) and high-density lipoprotein (HDL) levels was done using the automated clinical chemistry system model Dimension® Xpand® Plus (Siemens AG, Munich, Germany). Serum TG and HDL levels are measured at baseline, week 8, and 16. ## 3.6.4. Blood Pressure Measurement Systolic (SBP) and diastolic blood pressure (DBP) were measured by using the CODA® tail-cuff blood pressure system (Kent Scientific Corporation, Torrington, CT, USA). Before measurement, rats were acclimatised to the CODA® tail-cuff blood pressure system for 10 min, while maintaining the rat’s tail temperature between 32 °C and 35 °C. Blood pressure readings were taken at baseline, week 8 and week 16. ## 3.6.5. Fasting Blood Glucose and Oral Glucose Tolerance Test All rats were fasted for 12 h before the fasting blood glucose (FBG) and oral glucose tolerance test (OGTT) was performed. For rats receiving the HCHF diet, the $25\%$ fructose drinking water was replaced with tap water. After 12 h of fasting, capillary blood glucose readings were taken and calculated as readings at the 0th minute through the capillary blood collection method. Immediately after the reading at the 0th minute was recorded, 2 g/kg of $40\%$ glucose solution [D (+)-glucose, ChemPur® Systerm®, Classic Chemicals Sdn. Bhd., Selangor, Malaysia] was given via oral gavage. Subsequently, glucose readings were retrieved at 30, 60 and 120 min. The OGTT test was performed at baseline, week 8 and week 16. ## 3.7.1. Serum Tumour Necrosis Factor Alpha (TNF-α), Interleukin-1-Beta (IL-1β) and Leptin Serum TNF-α, IL-1β and leptin levels were determined by a multiplex bead analysis, RADPCMAG-82K Rat Adipocyte Magnetic Bead Panel (Millipore, Watford, UK). Each sample (25 µL) was incubated with antibody-coated captured beads for 2 h under agitation at room temperature. After washing, the beads were further incubated with biotin-labelled anti-human cytokine and chemokine antibodies for 60 min, followed by streptavidin-phycoerythrin incubation for 30 min. Finally, the beads were washed and analysed with the Magpix reader (Luminex Corp., Austin, TX, USA). The standard curve of known concentrations of recombinant human cytokines was used to convert the fluorescent unit to the cytokine concentration. The detection limit of TNF-α, IL-1β and leptin were 0.1 pg/mL, 0.9 pg/mL and 2.3 pg/mL, respectively. Data were stored and analysed using Xponent 4.2 software (Luminex Corp., Austin, TX, USA). ## 3.7.2. Serum Adiponectin and Corticosterone Serum adiponectin and corticosterone levels were measured in week 16 using the enzyme-linked immunosorbent (ELISA) technique with RatADP/Acrp30 (Adiponectin) and Rat/Chicken CORT (Corticosterone) ELISA kit (Elabscience, Houston, TX, USA) following the manufacturer’s instructions. The optical density was measured via Multiskan™ GO Microplate Spectrophotometer (Thermo Scientific, MA, USA) at the wavelength of 450 nm. ## 3.7.3. Adipose Tissue Homogenization The adipose tissue samples collected (0.3 g) were placed in an Eppendorf tube containing four to five iron balls. A total of 1 mL of radioimmunoprecipitation assay (RIPA) lysis buffer, 10 µL of phenylmethylsulphonyl fluoride (PMSF) and 10 µL of sodium vanadate (Na3VO4) was added to the tube. Next, the tissue was homogenized using a Bioprep-24 homogenizer (Hangzhou Allsheng Instrument CO., Ltd., Hangzhou, China) with a setting of 6.0 m/s for 30 s. This process was repeated twice to ensure that adipose tissue was completely lysed. The sample was then cooled on ice for 30 min. The mixture was then centrifuged at 12,000 rpm for 15 min at 4 °C. Before each centrifugation, the fat layer (upper layer) was removed to obtain the supernatant. This process was repeated twice until no additional layer of fat remained. ## 3.7.4. 11-Beta-Hydroxysteroid Dehydrogenase Type-1 (11βHSD1) Enzyme and Fatty Acid Synthase Enzyme (FASN) Adipose tissue 11βHSD1 and FASN levels were measured in week 16 using enzyme-linked immunosorbent (ELISA) techniques with Rat 11βHSD1 (11 Beta Hydroxysteroid Dehydrogenase Type-1) and Rat FASN (Fatty Acid Synthase) ELISA kit (Elabscience, USA). The optical density was measured via Multiskan™ GO Microplate Spectrophotometer (Thermo Scientific, Massachusetts, MA, USA) at the wavelength of 450 nm. ## 3.8. Histomorphometry of Adipose Tissue, Liver, Pancreas and Renal Tissue Rats were sacrificed by decapitation following anaesthesia by the end of week 16. A mid-ventral abdominal incision was performed. Tissue excisions from visceral fat, liver, pancreas, and kidney were obtained, and preserved in a $10\%$ buffered formalin (Merck, Darmstadt, Germany). Subsequently, the tissue was processed, embedded in paraffin wax, and sectioned (6 µm thick) before staining with hematoxylin and eosin. The stained tissue section was mounted on a glass slide and examined under a light microscope with 200× total magnification. Photomicrographs of visceral adipose tissue, liver, pancreas, and kidney parenchyma were taken using a light microscope (Carl Zeis Primo Star, Zeiss, Oberkochen, Germany). The ImageJ software (National Health Institute, Bethesda, MD, USA) was used for histomorphometric analysis of the adipose tissue. ## 3.9. Statistical Analysis All parameters were analysed using SPSS® software version 26 (IBM, Armonk, NY, USA). The normality of the data was assessed using the Shapiro–Wilk test. Normally distributed data were analysed using mixed-design analysis of variance (ANOVA) with small effect analysis as the post hoc test and presented as mean ± standard error of the mean (SEM). Wilcoxon-rank sum test (for paired samples) was also performed to assess the difference between baseline versus week 8 and week 8 versus week 16 for the same group. A p-value < 0.05 indicates a statistically significant difference. ## 4. Discussion In this study, KH profiling found various phenolic compounds consisting of phenolic acids, such as cnidimonal, epigallocatechin(4β,8)-gallocatechin and E-p-coumaric acid, and flavonoid groups, such as naringin, kaempferol derivatives and isorhamnetin derivatives. These bioactive compounds could be responsible for the biological activities of KH observed in this study. For instance, kaempferol has been shown to inhibit adipocyte hypertrophy [26] and liver steatosis [27], as well as preventing the apoptosis of pancreatic islet cells [28]. Isorhamnetin is reported to ameliorate metabolic derangements, adipocytes hypertrophy and liver steatosis in obese mice [29]. Naringin has been found to suppress metabolic features and improve the number of functional β cells in rats [30,31]. Epigallocatechin gallate (EGCG) could inhibit 11βHSD1 activity and reduce cortisol levels [32,33], improve metabolic features and insulin sensitivity in animals [34], and renal histology [35,36,37]. p-coumaric acid could inhibit insulin resistance and increase adiponectin levels [38,39] Therefore, it is likely that phenolic compounds contained within KH contribute to the inhibition of MetS risk demonstrated in this study. In this study, HCHF diet feeding for 16 weeks resulted in MetS in rats. In week 8, the HCHF diet gave rise to MetS components, such as central obesity reflected by increased body weight, WC and body fat percentage; hypertriglyceridemia and hypertension (increased SBP and DBP). In week 16, MetS worsened with the emergence of additional components, such as increased BMI, and decreased HDL. Therefore, this study showed that HCHF diet feeding for 16 weeks promoted MetS development, whereby 3 out of 5 interim joint statement criteria have been achieved. The results of this study were similar to the study by Ramli et al. [ 2019], who reported that the HCHF diet after 16 weeks could induce MetS risk factors, such as central obesity, hypertriglyceridemia and high blood pressure [23]. Moreover, HCHF also resulted in degenerative changes in the liver, kidneys and pancreas. Adipocytes showed an increase in the size and parameters, indicating hypertrophy due to excessive calorie intake. Whilst dyslipidemia resulted in the accumulation of fat within the hepatocytes. Histopathological evaluation revealed widening of Bowman’s space in the renal corpuscles, which was probably due to an increased blood pressure. High caloric intake exceeding the energy requirement of an individual can cause weight gain [40]. In this study, high caloric intake through the HCHF diet for 16 weeks caused an increase in body weight and BMI as well as adipocyte hypertrophy. These observations were in line with previous studies which show that consumption of the HCHF diet for 16 weeks causes adipocyte hypertrophy and ectopic fat accumulation in rats [23]. During excessive calorie intake, adipocytes undergo hypertrophy to store their surplus energy. This growth continues until a critical diameter for visceral adipocytes is reached. Beyond this value, adipocytes stimulate the generation of new adipocytes from the precursor cells (adipogenesis) [41]. A study showed that dietary lipids stimulate the proliferation of adipose tissue progenitor cells in juvenile mice [42]. Excess calories systemically increased the mitogenic insulin-like growth factor 1 (IGF-1) levels, which is a systemic growth-promoting factor, whereas palmitoleic acid enhanced the sensitivity of progenitors to IGF-1, resulting in synergistic stimulation of proliferation. Furthermore, the high fructose content within the HCHF diet also influences the increase in TG levels. This is because fructose can be converted to glycerol-3-phosphate by bypassing the glycolysis pathway (phosphofructokinase) to produce a substrate for fatty acid synthesis [43]. Therefore, in this study, there was an increase in serum TG levels in HCHF-induced MetS rats. The increase in TG level gave rise to the accumulation of fat in hepatocytes as seen in histological observations. Under physiological conditions, FFA is taken up from plasma by the liver through specific fatty acid translocase (CD36) and fatty acid transport protein (FATP) family receptors undergoes β-oxidation, lipid droplet formation or VLDL formation [44]. However, fat accumulation due to excess calorie intake induces lipolysis, causing abundant FFAs release into the blood circulation [45]. This results in increased fatty acid uptake, to which the liver responds by increasing β-oxidation or esterification of fatty acid into TG [44]. Hepatic accumulation of TG is either utilised for VLDL formation or stored as lipid droplets in hepatocytes which contributes to the phenotypic hallmark of fatty liver disease. Similar findings were also observed by Rafie et al. [ 2018] whereby an increase in TG levels is reflected by fat accumulation within hepatocytes [24]. A study by Panchal et al. [ 2011] also demonstrated the accumulation of ectopic fat in liver tissue in rats after consumption of the HCHF diet for 16 weeks [46]. Increased TG levels promote TG-rich VLDL production [8]. Excess TG in VLDL will be transferred to HDL to form TG-rich HDL. This TG-rich HDL is then cleared from circulation via renal clearance, causing a decrease in HDL levels in the blood [47]. Therefore, this study found that there was a decrease in serum HDL levels of HCHF-induced MetS rats in week 16. The results of the study by Wong et al. [ 2018] also found lower HDL levels in rats given the HCHF diet after 16 weeks [25]. In this study, the HCHF diet increase SBP and DBP of the rats. According to Wong et al. [ 2018], the excessive sodium content in the HCHF diet may activate renin-angiotensin-aldosterone system (RAAS) [25]. RAAS activation causes vasoconstriction, which in turn increases blood pressure. Previous studies also found an increase in SBP and DBP readings after HCHF diet feeding [23,46]. In addition, central obesity is also known to cause glomerular hyperfiltration [48]. In this study, glomerular hyperfiltration resulted in glomerular changes with an increase in Bowman’s space. A study by Erejuwa et al. [ 2020] also showed that HFD feeding in rats caused glomerular changes with the presence of focal aggregate inflammatory cells, tubular necrosis, and glomerular atrophy [49]. Excessive visceral fat distribution is linked to hypertension by several possible mechanisms involved in hormonal, inflammatory and endothelial alteration [50]. In an insulin resistant state, the presence of excessive visceral fat can stimulate reabsorption of sodium and urates at the tubular level [51]. Some studies also consider the possibility of leptin to predict the onset of hypertension [52]. A recent study showed that leptin acts on adrenocortical cells to increase CYP11β2 expression and directly activates aldosterone secretion which modulates hypertension in female mice [53,54]. In addition, leptin also elicits symphato-exitatory effects which ensues the activation of RAAS [51]. A study showed that leptin acts as an autocrine mediator of Ang II-induced cardiomyocyte hypertrophy in hypertensive LVH rats, in which the treatment of telmisartan improves the myocardial remodeling in rats by inhibiting RAAS activity and leptin levels [55]. In this study, the HCHF diet did not increase FBG and AUC of glucose in rats receiving the diet. However, previous studies have found that the HCHF diet caused only mild impairment of glucose tolerance or insulin resistance [46]. Pre-diabetes is marked by an increase in plasma glucose concentration above the normal range but less than clinical diabetic values [56]. One of the characteristics of pre-diabetes is insulin resistance [57]. Insulin resistance can also be detected in individuals with a normal glucose tolerance [58]. A study by Townsend et al. [ 2018] found that $2.3\%$ of normoglycemic young adult subjects experienced insulin resistance [58]. Therefore, no significant impairment of glucose tolerance in FBG and OGTT readings in this study does not rule out the possibility of insulin resistance. However, a study conducted by Ramli et al. [ 2019] showed no difference in insulin activity in rats induced with a HCHF diet after 16 weeks [23]. The authors hypothesized that insulin resistance may still be in its early stages and may take more than 16 weeks to produce significant changes. In this study, histological observation found a diminishing number of pancreatic islet cells in MetS rats which reflects the deterioration of β cells. Previous studies have also found that the HFD diet causes the deterioration of pancreatic β cells in rats [59]. Diminishing β cells within the pancreatic islet results in insulin resistance [60]. In addition, in response to its deterioration, the β cells will further increase insulin secretion, which exacerbates insulin resistance [61]. Administration of 1.0 g/kg of KH for 8 weeks in HCHF diet-induced MetS rats successfully inhibited MetS-associated changes, such as dyslipidemia, high blood pressure and insulin resistance. The dose of KH given (1.0 g/kg/day) was based on the previous study by Ramli et al. [ 2019] which used a similar dose had shown positive result in reversing metabolic symptoms [23]. Furthermore, an in vivo study by Azam et al. [ 2022] demonstrated that KH supplementation at doses 0.5, 1.0, and 2.0 g/kg to rats for 4 weeks did not cause toxicity or mortality [62]. The author also implied that the medium lethal dose for daily consumption of KH was higher than 2.0 g/kg rats’ BW. Despite the lack of effects on body weight, WC, BMI and body fat percentage, KH significantly decreases the size and perimeter of adipocytes. Study by Ramli et al. [ 2019] also showed the inhibition of adipocyte hypertrophy after KH administration in rats [23]. The anti-adipogenic properties of KH may be contributed by the active components within. Isorhamnetin, which is a metabolite of the flavonoid group quercetin, is known for its anti-adipogenicity [63]. Studies carried out in vitro have found that incubation of preadipocyte 3T3-L1 with isorhamnetin saw inhibition in the adipogenesis at dose > 10µM [29,64]. These studies also demonstrated that isorhamnetin suppressed adipocyte differentiation by inhibition of peroxisome proliferator-activated receptor (PPARγ) which is a master regulator of adipogenesis. This will reduce fat deposition in ectopic tissues, such as liver, skeletal muscle and heart, as well as visceral adipose depots [65]. Meanwhile, KH supplementation also improved serum TG and HDL levels. This observation is similar to the study by Rafie et al. [ 2018], whereby KH supplementation for 6 weeks ameliorated serum TG and HDL levels in rats receiving a HFD diet [24]. Furthermore, the study also found reduced fat deposition within hepatocytes in KH-treated groups. This histological improvement may be due to the decrease in TG levels because it is the main source of fat deposition in liver tissue [66]. The presence of phenolic compounds in KH may play an important role in limiting TG levels and liver fat accumulation. Studies show that gallic acid, quercetin, and kaempferol are among the phenolic compounds that can reduce fat accumulation [67]. According to another study, kaempferol and quercetin compounds were found to be able to inhibit pancreatic lipase enzyme activity with subsequent reductions in TG, FFA and body weight [68]. Meanwhile, Yoon et al. [ 2021] have also found that $0.002\%$ p-coumaric acid in the HFD diet could reduce the accumulation of fat in the hepatocyte of HFD-induced obese rats [69]. In the study, p-coumaric acid suppressed the expression of lipogenic enzymes such as FASN and acetyl-CoA carboxylase (ACC) with consequent hepatic fatty acid oxidation. Therefore, this study shows that KH supplementation could prevent dyslipidemia and the progression of NAFLD. Similarly, there was also an improvement in blood pressure measurement in KH-treated groups. The results of this study are supported by Ramli et al. [ 2019], who also found a decrease in the SBP and DBP in rats after KH supplementation for 8 weeks [23]. The improvement in blood pressure measurement can be explained by the anti-inflammatory properties of the bioactive compound within honey. MetS is linked to an increase in reactive oxidative stress and oxidative markers which contributes to the impairment of vascular dilatation [46,70]. Meanwhile, KH supplementation in rats (4.6 g/kg) for 30 days saw a reduction in pro-inflammatory cytokine production such as TNF- α, IL-1β, IL-6 and IL-8 [71]. The presence of phenolic compounds, such as kaempferol and p-coumaric acid in honey, might contribute to its anti-inflammatory properties as both compounds are known to inhibit the expression of nuclear factor kappa-light-chain-enhancer of activated B cells protein which regulates the production of inflammatory markers [72,73]. In addition, this study also observed normal glomerular histology without an increase in Bowman’s space in the HCHF + KH group rats compared to the HCHF group. Previous studies have also found improvements in the renal histology of rats that received Nigerian honey supplementation for 16 weeks [49]. This indicates that honey supplementation could prevent glomerular damage, leading to the normalisation of blood pressure. However, KH treatment for 8 weeks did not alter FBG and OGTT of rats in the HCHF + KH groups. Interestingly, despite the sugar content within KH, this study found that KH did not increase FBG and OGTT AUC of glucose in rats receiving the HCHF diet. A study found that quercetin and isorhamnetin promotes glucose uptake by increasing glucose transporter type 4 (GLUT-4) translocation in skeletal muscle cells, thus demonstrating its advantage in preventing hyperglycemia [74]. In addition, histological observation showed that the administration of KH for 8 weeks prevented the diminishing of pancreatic islets. Previous studies have also found similar results in STZ-nicotinamide-induced diabetic rats treated with KH [18]. According to the study, the administration of KH for 28 days did not cause an increase in FBG in diabetic rats. Furthermore, the study found that the inhibition of apoptosis markers by KH prevented the reduction in pancreatic islet cells. Meanwhile, a study found that diabetic rats treated with naringin (0.1 g/kg) for 8 weeks saw an increase in β cells. According to the study, naringin stimulates the Forkhead box M1 (Fox M1) protein expression which is an important transcription factor for β-cell proliferation [31]. While an in vitro study found that kaempferol at 10 μM was able to prevent cellular apoptosis by downregulating caspase-3 activity of β cells in hyperglycemic pancreatic islet cells [28]. Thus, KH is beneficial in preventing the formation of insulin resistance. For inflammatory markers, this study found that the HCHF did not cause an increase in the serum levels of cytokines TNFα and IL-1β. A study by Stȩpień et al. [ 2014] found that the highly sensitive C-reactive protein (hs-CRP) is a more sensitive marker associated with obesity than IL-6 and TNF-α [75]. Similarly, a study conducted in Brazil on elderly cohorts found similar trend, whereby IL-1β, TNF-α and IL-12 were not statistically associated with MetS [76]. Instead, the presence of MetS was associated with higher IL-6 and CPR levels. However, the precise mechanism of these findings is not fully explained in these studies. Meanwhile, KH supplementation for 8 weeks did not alter serum cytokine levels. Similar result was also seen in other studies using polyphenol-rich food such as blueberries in insulin-resistant obese men and women as well as grapes in MetS men [77,78]. The lack of effects of KH on these inflammatory markers could be because these rats did not suffer from high levels of inflammation. 11βHSD1 is an enzyme that catalyses the conversion of cortisone to corticosterone [79]. Studies have shown that the expression of the 11βHSD1 enzyme is high in obese individuals [80]. Therefore, increased activity of this enzyme promotes the production of corticosterone hormone [79]. Excessive production of corticosterone hormone, in turn, causes disturbances in physiological metabolism leading to central obesity, insulin resistance, dyslipidemia and hypertension [81]. In this study, the HCHF diet induced a higher expression of the 11βHSD1 enzyme, which might be responsible for the metabolic changes in the rats. The HCHF + KH group showed lower 11βHSD1 expression, which indicates a reduction in corticosterone hormone secretion, and the amelioration of MetS risk factors. The molecular mechanism of KH in inhibiting 11βHSD1 may be contributed by the presence of phenolic compounds contained within it. An in vitro study demonstrated that pre-treatment of rat liver microsomal vesicles with EGCG caused oxidation of luminal NADPH to NADP+ [82]. The redox shift results in the indirect inhibition of 11βHSD1 mediated cortisol production at the substrate level. Meanwhile, Pathak et al. [ 2017] also found a reduction in circulating corticosterone level and 11βHSD1 pancreatic activity in HFD-fed rats [34]. Additionally, the study also saw improvements in rat weight reduction, fat mass, glycemic control and insulin concentration after EGCG supplementation. A previous study found that hypertrophy and hyperplasia of adipocytes in obese rats is linked to high levels of corticosterone [81]. The corticosterone hormone is important in regulating adipogenesis and the secretion of adipokines, such as leptin and adiponectin [83]. Therefore, in this study, the inhibition of the 11βHSD1 enzyme and corticosterone hormone production by KH could inhibit adipocyte hypertrophy, reduce leptin levels, and increase adiponectin levels in HCHF + KH rats. In turn, inhibition of adipocyte hypertrophy as well as regulation of hormone secretion prevents the formation of central obesity. The increase in adiponectin levels is closely related to the decrease in TG levels. Studies have found that adiponectin could activate 5’ adenosine monophosphate-activated protein kinase (AMPK) [84]. The activation of AMPK further causes phosphorylation of acetyl CoA carboxylase (ACC), which promotes fat burning in the tissue [85]. This, in turn, reduces the production of TG and VLDL, as well as increases the HDL levels in the blood. Concurrently, the reduction in hepatosteatosis as observed in the liver histology of this study also reflects suppression of TG production. In addition, increasing adiponectin levels can also increase insulin sensitivity through the phosphorylation of insulin receptor substrate 1 (IRS-1) and p3 MAPK [86]. Phosphorylation of these proteins leads to the membrane translocation of GLUT-4 [85]. This process in turn causes the uptake of glucose by body tissues. In addition, an in vitro study also found that adiponectin hormone protects pancreatic β cells from undergoing apoptosis [87]. Therefore, in this study, increasing adiponectin levels can prevent insulin resistance by increasing insulin sensitivity as well as protecting pancreatic islets from apoptosis due to MetS. Leptin is closely related to increased blood pressure activity by activating sympathetic activity [88]. Studies have found that the activation of renal sympathetic activity caused by leptin triggers RAAS activation, which leads to hypertension [89]. The RAAS activation subsequently causes vascular vasoconstriction [90]. Therefore, in this study, the reduction in leptin by KH was followed by the normalisation of blood pressure, which in turn restores glomerular deterioration due to hyperfiltration, as seen in histological observations. On the other hand, this study showed that there was no difference in FASN enzyme levels between all the study groups. In contrast, other studies have associated the increase in FASN expression with insulin resistance in metabolic syndrome rat model [91,92]. These studies suggested that the increase in FASN activity is prompted by hyperglycemia caused by insulin resistance which triggers the activation of a de novo lipogenic enzyme [92,93]. Suzuki et al. [ 2015] postulated that these changes are brought about by increased methylation and acetylation of histone in the promoter-enhancer region of FASN gene in the setting of hyperglycemia [92]. Therefore, the lack of increased FASN activity in this study might be due to the unelevated glycemic measurement observed in rats. Meanwhile, the bioactive component within KH did not modify the FASN parameter. An in vitro study conducted by Gómez-Zorita et al. [ 2017] observed a reduction of FASN expression in pre-adipocytes cultured with flavonoid compound apigenin and hesperidin at 25 µM for 8 days [94]. However, no significant reduction in FASN expression was observed with kaempferol at 25 µM. This suggests that phenolic compounds within KH do not have any effect on FASN enzyme levels. A summary of the mechanism of KH in reversing metabolic changes in MetS rats is depicted in Figure 12. The importance of this study is to examine the action of KH in treating MetS. Therefore, this study will form the basis for KH supplementation for MetS patients. However, the administration of the HCHF diet in the current study was only successful in influencing three out of five components of MetS, thus did not fully reflect the full potential of KH in reversing MetS. Therefore, some recommendations in further studies include increasing the duration of the HCHF diet beyond 16 weeks to increase the probability of influencing all components of MetS as contained in the JIS criteria. In addition, the physiochemical parameters of KH should also be incorporated, as they determine the quality of honey, which makes it beneficial. According to a study by Kek at al. [ 2017], the KH produced by Heterotrigona itama collected at different time demonstrated variations in physicochemical and antioxidant properties [95]. The KH that was used in this experiment was harvested in October 2020. Collecting KH samples at different times may yield different result. Furthermore, although the amelioration of MetS by KH supplementation that was seen in this study may be contributed by its rich phenolic compound; however, the molecular mechanism of these active compounds in reversing MetS is not fully understood. Therefore, a more in-depth study on the mechanism of these phenolic compounds may help in understanding the beneficial effect of KH on MetS. ## 5. Conclusions This study shows that the administration of KH for 8 weeks to rats with HCHF diet-induced MetS can improve central obesity, dyslipidemia, and hypertension. 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--- title: Contrasting Autoimmune Comorbidities in Microscopic Colitis and Inflammatory Bowel Diseases authors: - Istvan Fedor - Eva Zold - Zsolt Barta journal: Life year: 2023 pmcid: PMC10056705 doi: 10.3390/life13030652 license: CC BY 4.0 --- # Contrasting Autoimmune Comorbidities in Microscopic Colitis and Inflammatory Bowel Diseases ## Abstract Background: Inflammatory bowel diseases (Crohn’s disease and ulcerative colitis) and microscopic colitis (lymphocytic and collagenous colitis) are immune-mediated diseases of the gastrointestinal tract, with distinct pathophysiology. Objective: We sought to compare the prevalence of autoimmune diseases between microscopic colitis (MC) and inflammatory bowel diseases (IBDs) in our patient cohorts in their medical history. Methods: We collected data from 611 patients (508 with IBD, 103 with MC). We recorded cases of other autoimmune diseases. The screened documentation was written in the period between 2008 and 2022. We sought to determine whether colonic involvement had an impact on the prevalence of autoimmune diseases. Results: Ulcerative colitis patients and patients with colonic-predominant Crohn’s disease had a greater propensity for autoimmune conditions across the disease course than patients with ileal-predominant Crohn’s disease. Gluten-related disorders were more common in Crohn’s disease than in ulcerative colitis, and slightly more common than in microscopic colitis. In ulcerative colitis, 10 patients had non-differentiated collagenosis registered, which can later develop into a definite autoimmune disease. Conclusions: Predominantly colonic involvement can be a predisposing factor for developing additional autoimmune disorders in IBD. Ulcerative colitis patients may have laboratory markers of autoimmunity, without fulfilling the diagnostic criteria for definitive autoimmune disorders (non-differentiated collagenosis). ## 1. Introduction Albeit both microscopic colitis (MC) and inflammatory bowel diseases (IBDs) are immune-mediated disorders predominantly affecting the gastrointestinal tract, there are delicate differences in their presentation [1,2,3]. Inflammatory bowel diseases are known to display extraintestinal manifestations (EIMs) during the disease course [4]. These extraintestinal findings are usually not found in microscopic colitis. On the other hand, microscopic colitis is mostly recognized for the frequent accompanying autoimmune diseases [1,5]. Although IBD shows a correlation with an increased tendency for other immune–inflammatory diseases, the level of association does not reach the magnitude seen in MC. Neither of these conditions (MC and IBD) are regarded as classical autoimmune conditions, as they lack specific autoantibodies, and the pathogenesis of IBD is characterized by a pathologic reaction to the commensal intestinal microbiome. The role of microbes in the pathogenesis of MC is indicated via the observation that fecal stream diversion with ileostomy contributes to symptom resolution and intestinal healing [6]. To further indicate the possible role of intestinal microbes, ileostomy patients frequently relapse, once intestinal continuity is restored [7]. Moreover, in refractory cases, fecal microbiota transplantation (FMBT) may offer benefits [8,9], though the careful selection of the donor sample is crucial [10]. Traditionally, these diseases display increasing prevalence in rapidly developing countries, through the incidence plateaued in developed, welfare societies. Thus, it is very likely to be multifactorial in etiology, and the genetic background is not sufficient in itself for developing these conditions. As all of these immune-mediated diseases of the gastrointestinal system are distinct—though they overlap in certain characteristics—we attempted to highlight the distinguishing features through our patients. ## 2. Patients and Methods Our paper is an extension of two of our earlier datasets of patients with microscopic colitis (MC—103 patients) and inflammatory bowel disease (IBD—508 patients) [11,12]. In total, we included 611 patients in the current paper, within four groups. In the MC cohort, we had 103 patients, 67 females, 36 males with two different forms of MC (28 and 75 patients with lymphocytic and collagenous colitis, respectively). The IBD cohort consisted of 508 (303 with CD and 205 with UC) patients, 133 male and 170 female subjects with Crohn’s disease, and 89 male, and 116 female with ulcerative colitis. Apart from extending our registered data in the previous papers, we also aimed to compare different disease subgroups. For the basic characteristics of different subgroups, please refer to Table 1. To determine whether a patient had an immune-mediated disease, we read through the available previous medical records from the year 2008 until 2022. The reason behind opting for this method was that healthcare administration systems are optimized for administrative and financial purposes, thus the patient history does not always correspond to the entries below the “Diagnosis” tabs. To overcome this, we screened the whole text of each visit, and only registered those cases wherein the history indicated an autoimmune disease. Patient privacy was preserved by the use of anonymized data in this retrospective, non-interventional cohort study. The study protocol was approved by the local Ethics Committee which conformed to the provisions of the Declaration of Helsinki. As a retrospective observational study, we first recorded patient data in an Excel spreadsheet (Excel 2019, Microsoft Corporation, Redmond, Washington, DC, USA). The extracted data were analyzed with Medcalc Software (Medcalc Software Ltd. Version 20.014, Ostend, Belgium). We aimed to include as many patients whose health records were accessible for reviewing. We were particularly interested in the prevalence of diseases of autoimmune origin (AI diseases) in our IBD cohort, and also contrasting these data with our MC cohorts. Comparing the proportions with AI diseases was performed via chi-squared test [13]. We also compared the average ages at diagnosis, as well as the different entities that affected patients in different life stages. For contrasting age at diagnosis, an independent sample two-tailed t-test was used. ## 3.1. General Patient Characteristics of IBD and MC Cohorts For the general characteristics of our patients, please refer to Table 1. The findings reflect the commonly reported patterns, though remarkably, microscopic colitis patients were younger than expected—especially those with lymphocytic colitis. We would also like to highlight that the male-to-female ratio was more balanced in collagenous rather than lymphocytic colitis. The latter displayed a strong female predominance, whereas both IBD entities were more balanced. The difference between autoimmune comorbidities is marked between the MC and IBD cohorts. Moreover, we found that the difference within disease subgroups did not differ markedly (lymphocytic colitis vs. collagenous colitis, and CD vs. UC). The disease entities showed distinct pattern in the average age of diagnosis. For the proportions of patients in different subgroups organized according to age of diagnosis, please see Table 2. ## 3.2. Autoimmune Diseases in IBD and MC In our patient cohorts, we found a roughly two-fold difference between disease conditions. $38.8\%$ (40 patients out of 103) of all MC patients who had an AI-disease in their history; this ratio was $19.1\%$ in all IBD cases (97 patients from 508 total). This difference was marked, with a $19.7\%$ difference between IBD and MC cohorts, ($95\%$ CI: 10.1324–29.8622; $p \leq 0.0001$). As MC is confined to the colon (similar to ulcerative colitis—UC and the colonic subtype of Crohn’s disease: Montréal L2), we were interested in whether the “colonic predominant” phenotypes are more prone to develop other autoimmune diseases. Thereby, we compared the proportions of patients with accompanying autoimmune disorders in different Crohn’s disease subgroups (ileum and small bowel predominant, colonic predominant). For the results and possible implications, please refer to Table 3. ## 3.3. Colonic Involvement Table 3, note that CD with predominantly colonic involvement displayed a higher proportion of accompanying autoimmune diseases. Differences between small intestinal predominant and colonic dominant Crohn’s disease reached a statistically significant level, whereas 29 patients in the small intestinal predominant group developed AI disease (corresponding to $14.2\%$ of all patients with small-intestinal predominant Crohn’s), the ratio was $24.7\%$ in the colonic-predominant subgroup (22 patients with L2 Crohn’s disease). This $10.5\%$ difference between the groups was significant ($$p \leq 0.0297$$). By incorporating data from UC, $23.1\%$ of patients with pure colonic IBD had other immune-mediated inflammatory disorders in their history (68 patients out of 294—UC and CD L2 combined), whereas CD patients with predominantly small-intestinal involvement had autoimmune disorders in 29 cases out of 204 patients total ($14.2\%$). The difference reached a statistically significant level. Patients in the UC cohort had a similar tendency for developing AI disorders to colonic Crohn’s disease patients. A total of 22 patients with L2 Crohn and 46 patients with UC had one or more additional AI diseases in their histories ($24.7\%$ and $22.4\%$, respectively). This marginal difference ($2.3\%$) yielded a non-significant result ($$p \leq 0.6678$$). Therefore, purely colonic Crohn’s and UC patients had a similar risk for other autoimmune diseases. As MC shares the characteristic of pure colonic involvement with UC and L2 Crohn’s disease, we also assessed differences in comorbid AI diseases between these states. In MC (total patients: 103, patients with autoimmune diseases: 40 ($38.8\%$)) we saw a marked tendency for other autoimmune diseases in comparison with colonic IBD (difference: $15.7\%$, $95\%$ CI: 5.4351–26.2999 $$p \leq 0.0021$$—for the data, please refer to Table 3 and Table 5). Thus, autoimmune disorders are much more prevalent in MC. In Table 4 it is apparent that Crohn’s disease patients were less likely to develop other diseases of autoimmunity, though the difference between the groups did not reach a statistically significant level. While $16.8\%$ of all patients with Crohn’s disease had other autoimmune diseases in their medical history, this ratio was $22.4\%$ in UC (51 patients out of 303 in CD and 46 patients out of 205 in UC). The $5.6\%$ difference between the groups is not significant ($$p \leq 0.1153$$). This finding is contrary to previous reports, and we believe it may both reflect regional differences as well as a possible bias due to the small number of cases. ## 3.4. Undifferentiated Connective Tissue Disease (UCTD) in Ulcerative Colitis In our cohort, patients with UC were more prone to develop the condition of undifferentiated connective tissue disease (UCTD). While 10 patients (roughly $4.9\%$) with UC had UCTD in their disease histories, this was not as high in Crohn’s patients (four patients, approximately $1.3\%$). It is worth noting that the UC in this regard was comparable to microscopic colitis (please refer to Table 5). These patients had autoantibodies characteristic of manifest autoimmune diseases, but they did not meet the full diagnostic criteria for definite disease. The conversion of UCTD to overt autoimmune disease is variable. According to a 2009 publication by Bodolay et al., the conversion rate is roughly 30 to $40\%$ in a five-year course [14]. Most patients do not progress into AI diseases within five years of follow-up, and the prognosis of later autoimmune diseases is more favorable with preceding UCTD. All of the patients who had UCTD in their medical records were female. ## 3.5. Gluten-Related Disorders in IBD Gluten-related disorders were more prevalent in Crohn’s disease than in UC. In Crohn’s disease, there were 12 cases (approximately $4\%$ of total patients) with accompanying celiac disease (gluten-sensitive enteropathy—GSE), whereas this disease affected two ($1\%$) patients with UC. The ratio of patients with GSE was very similar in MC to that seen in CD: $3.9\%$ of all patients had GSE in their disease histories. A total of two patients with CD were registered with non-celiac gluten sensitivity—NCGS—while none of the UC patients had this in their medical records. Another gluten-related disorder, dermatitis herpetiformis—DH—was present in two of CD and one of UC cases (approximately $0.7\%$ and $0.5\%$, respectively). Remarkably, none of the patients in the MC cohort had dermatitis herpetiformis. In the CD cohort, patients with upper-intestinal involvement—L4 subtype—had a high prevalence of accompanying AI diseases. A total of four out of six patients with upper intestinal lesions had comorbid AI disease in their histories, although three of these cases ($75\%$) were celiac disease—GSE. The two disease entities have distinct histopathology and biopsy-confirmed alterations, characteristic of GSE in these cases. ## 3.6. Rheumatoid Arthritis (RA) in IBD IBD patients with RA pose a differential diagnostic challenge if they also have arthropathy. Peripheral arthritis may be an extraintestinal finding of their IBD. A total of 5 of CD patients (approximately $1.7\%$) and 11 of UC patients (approximately $5.4\%$) had RA in their medical records registered, whereas this ratio reached $6.9\%$ in cases with MC (seven patients, LC and CC combined). Moreover, RA presented the most marked difference between IBD subgroups: the difference between CD and UC proved to be significant ($$p \leq 0.0187$$). Thereby, one can conclude that RA seems to be more prevalent in UC than in CD. In cases of other immune-mediated diseases, the difference between Crohn’s disease and UC was not as marked. We believe our study was underpowered in revealing any additional differences. ## 3.7. Hepatobiliary Autoimmune Diseases It is noteworthy to add that we did not classify primary sclerosing cholangitis as a distinct autoimmune disease entity, but rather opted for recognizing the condition as an extra-intestinal manifestation of IBD [15]. PSC without underlying IBD is rare. Generally, PSC does not develop in MC, and it is mostly regarded as an EIM of IBD. There is scarce evidence in the literature for MC patients with PSC [16]. In our cohort, there were no patients with PSC in the MC cohort. In our IBD cohort, only three patients developed either primary biliary cholangitis or autoimmune hepatitis (PBC or AIH); thus, not even $1\%$ of all IBD patients had a hepatobiliary autoimmune disorder in their histories. In the microscopic colitis cohort—even though 104 patients’ data were screened, there was only one case of AIH and no cases of PBC. ## 3.8. Autoimmune Diseases of the Thyroid Hashimoto thyroiditis was prevalent in both CD and UC. Thyroid-related problems are not uncommon in the population, and we found Hashimoto thyroiditis to be the most common in our MC cohort as well. A total of 15 patients from our MC cohort had thyroid-related abnormality—14 ($37.5\%$) of them had Hashimoto thyroiditis in their medical records. The ratio in IBD was much less pronounced. 13 patients with CD ($4.3\%$) and 10 with UC ($4.9\%$) had thyroiditis of immune-mediated origin in their medical histories. ## 4. Discussion Our paper has its limitations, being a cross-sectional observational retrospective study from available patient data [17,18]. We also did not have a healthy control group; therefore, we were only able to compare disease subgroups with the data from other publications. Nonetheless, we believe the paper has interesting findings regarding autoimmune comorbidities in IBD and their relationship to colonic involvement. For a general comparison of microscopic colitis and inflammatory bowel diseases, we included Table 6 for a brief cardinal overview of these entities. Our findings highlight that IBD and MC patients greatly differ in their risks for developing autoimmune disorders during their disease course. MC patients had a two-fold increase in the prevalence of other autoimmune conditions compared to our IBD cohort. However, one should interpret this seemingly large gap between the groups with caution. While it is true that most autoimmune diseases manifest in the young adults or middle-aged population, our IBD cohort was relatively younger than patients with MC (for the age of diagnosis, please refer to Table 2 and Figure 1). As autoimmune disorders may develop later in life, it would be more plausible to compare lifetime risks. We should emphasize that we only assessed the prevalence of autoimmune disorders recorded in the history of patients. We did not assess—in a broader sense—all immune-mediated inflammatory disorders. Other authors (Convay et al. from Boston, US) reported total immune-mediated diseases; thus, they reported cases of asthma or skin disorders of atopic origin as well [19]. Remarkably, they found a much higher prevalence for psoriasis, whereas this was rare among our patients in IBD, comparable to otherwise healthy populations. Nonetheless, the prevalence of psoriasis in patients with IBD is generally higher than in the healthy population (please see the meta-analysis and systematic review by Alinaghi et al. [ 20]); therefore, it is possible that we had an underreport in this regard. We would also like to point out that not all authors reported an increased prevalence of psoriasis in IBD, though it seems that IBD predisposes the development of psoriatic arthritis [20,21]. Furthermore, certain biologic therapies (Secukinumab, an anti-IL17 monoclonal antibody) utilized in psoriasis may predispose patients to a subsequent development of IBD [22,23,24]. As this agent was not used in the management of any of our patients, we can safely rule out this possibility. Generally, the development of IBD in patients receiving Secukinumab is unlikely, and the agent was found to be safe (please refer to the meta-analysis by Schreiber et al. [ 22]). In connection with psoriasis, we found zero cases in our microscopic colitis cohort. We would like to emphasize the limited sample size as a possible explanation. We sincerely believe that, with the inclusion of larger samples, sporadic cases of psoriasis would have been registered. In the IBD cohort, patients with the colon-predominant disease were more prone to develop other immune-mediated diseases. Some authors regard ileum-predominant and colonic-predominant CD to be distinct entities [25]. Moreover, the colonic-predominant types may display inefficiency, and loss of response to anti-TNF therapies [26]. Autoimmune conditions that differed significantly between Crohn’s disease and ulcerative colitis were rheumatoid arthritis (RA) and undifferentiated connective tissue disease (UCTD). Contrary to our expectations, patients with ulcerative colitis had a greater prevalence of RA in their histories [27,28]. This conflicts with the data presented in the systematic review and meta-analysis, conducted by Chen and coworkers (BMC Gastroenterology 2020 [29]). As they included eight studies in their meta-analysis, and indeed, most of the studies reported RA to be more prevalent in Crohn’s disease, we believe that perhaps our sample was too small in size to observe this phenomenon. Another possible explanation for our findings is regional differences. One should bear in mind that the previously published data seems to favor the concept, of CD patients being more prone to develop additional immune-mediated diseases, but some authors described very similar representations. As our cohorts did not differ significantly in terms of autoimmune comorbidities, we would not like to draw any conclusions from our findings. It is possible that, regionally, there is no marked difference in the presence of autoimmune diseases between different IBD cohorts. We found that gluten-related comorbidities were more common in CD than in UC. As a side note, gluten (and casein)-free diets are gaining popularity as an ancillary lifestyle intervention in immune-mediated inflammatory disorders [30]. Nonetheless, the evidence is vague and weak; therefore, this approach cannot be recommended generally [31]. Please note that GSE (gluten-sensitive enteropathy, celiac disease) and dermatitis herpetiformis (DH) are indeed conditional autoimmune comorbidities with sensitive and specific autoantibodies, whereas non-celiac gluten sensitivity (NCGS) rarely displays any alterations in laboratory studies. Despite the non-celiac gluten sensitivity (NCGS) not being able to be classified as a classical autoimmune disorder, we included the registered cases, due to their relationship to gluten exposure [32,33]. Nonetheless, the pathophysiology behind NCGS is not well understood. The condition in itself is likely to be heterogeneous in origin. In certain patients, there could be alterations in immune regulation [33,34]. The low overall prevalence of hepatobiliary autoimmune diseases (AIH and PBC) poses the question of whether IBD or MC truly enhances the risk of developing these autoimmune conditions. We sincerely believe that this is the case: the overall prevalence of PBC and AIH is extremely low in the general population, and yet we were able to register four cases in the entire cohort (611 patients total, 508 patients with IBD, 103 patients with MC, four cases of AIH and PBC combined) [35,36]. IBD displayed showed a mildly increased prevalence of Hashimoto thyroiditis compared to the general population. The estimated prevalence of Hashimoto thyroiditis in the general population is 0.8–$1\%$. We would like to mention that thyroid-related abnormalities are not uncommon in the otherwise healthy population, and the incidence of these seems to be on the rise. Therefore, we cannot rule out that environmental and epigenetic factors also contribute to thyroid–gland disorders, as well as to diseases of immune-mediated origin. We would like to propose the concept of intestinal barrier dysfunction and “leaky gut” as a predisposing factor for developing immune-mediated inflammatory conditions. Even though it has been long known that these phenomena may be attenuated via additional glutamine supplementation, there is no recommendation for the use of this amino acid. According to our experiences in practice, patients do report increased energy and mood (thus, subjective quality of life) when administered glutamine supplementation. The literature nonetheless is controversial, on whether it truly offers benefits. Our view on the subject is that it is a plausible practice, with a sound physiologic background. Glutamine is not only a contributor to enterocyte proliferation (and thus the healing of intestinal lining) but it can also enhance tight-junction functions and reins pro-inflammatory signaling pathways [37]. While it is known that the incidence of newly diagnosed autoimmune disorders in IBD exceeds the numbers seen in healthy control populations, one may propose the idea that this risk can be decreased with adequate intestinal lining maintenance. Furthermore, prospective studies would provide greater insight into whether glutamine supplementation truly protects against immune-mediated inflammatory disorder development during a longer period. Even though a meta-analysis by Severo and colleagues in 2021 and a Cochrane review by Akobeng in 2016 found insufficient evidence that glutamine may have ameliorating effects in IBD, one cannot rule out the possible beneficial influences on disease course and life quality [38,39]. As there is evidence that intestinal barrier dysfunction and dysbiosis of microbiota may enhance the risk for autoimmune diseases, we sincerely believe that every factor contributing to healthy gut lining may be protective against the subsequent development of these conditions [40]. Certain factors that may disrupt the homeostasis of the gut microbiome are likely to contribute to both immune-mediated inflammatory diseases and leaky gut syndrome [41,42,43,44]. Even though the mechanisms are not entirely clear, excess, uncontrolled psychological stress was shown to predispose patients to a subsequent development of systemic immune diseases and IBD as well [45,46,47]. Moreover, the clinical course of IBD often reflects the psychological state of patients; therefore, it seems plausible that, in certain circumstances, psychological interventions (e.g., cognitive behavioral therapy—CBD) may provide benefits for patients with IBD [48]. Nonetheless, the consensus is not well established, and the evidence is vague, though these interventions have little risk. A Cochrane systematic review and meta-analysis found psychotherapies to be ineffective [49]. It is unclear why chronic stress exposure (thus, state of hypercortisolism) predisposes patients to immune-mediated inflammatory disorders, whereas the efficient therapies for these conditions generally contain (at least temporary) administration of exogenous glucocorticoids. Another observation regarding glucocorticoids according to which current steroid use may pose a somewhat increased risk for the development of autoimmune diseases also sounds counterintuitive but is described nonetheless [50]. On the other hand, aminosalicylates may protect against these conditions, and thus can be recommended for indefinite use in patients with inflammatory boswel diseases. ## 5. Conclusions Though there are overlaps in the presentation of microscopic colitis and inflammatory bowel diseases, they differ in fundamental characteristics. One of the key distinguishing features is the marked increase in autoimmune diseases in microscopic colitis. Moreover, patients with inflammatory bowel disease confined to the colonic segments displayed a higher prevalence of diseases of autoimmune origin. A substantial proportion of both ulcerative colitis and microscopic colitis patients had evidence of the presence of autoantibodies, without fulfilling the diagnostic criteria for overt autoimmune diseases (undifferentiated connective tissue disease). Crohn’s disease patients were more prone to develop gluten-related disorders, whereas it was not as prevalent in microscopic colitis as expected. 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--- title: 'Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes' authors: - Anish G. Potty - Ajish S. R. Potty - Nicola Maffulli - Lucas A. Blumenschein - Deepak Ganta - R. Justin Mistovich - Mario Fuentes - Patrick J. Denard - Paul M. Sethi - Anup A. Shah - Ashim Gupta journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10056706 doi: 10.3390/jcm12062369 license: CC BY 4.0 --- # Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes ## Abstract Machine learning (ML) has not yet been used to identify factors predictive for post-operative functional outcomes following arthroscopic rotator cuff repair (ARCR). We propose a novel algorithm to predict ARCR outcomes using machine learning. This is a retrospective cohort study from a prospectively collected database. Data were collected from the Surgical Outcome System Global Registry (Arthrex, Naples, FL, USA). Pre-operative and 3-month, 6-month, and 12-month post-operative American Shoulder and Elbow Surgeons (ASES) scores were collected and used to develop a ML model. Pre-operative factors including demography, comorbidities, cuff tear, tissue quality, and fixation implants were fed to the ML model. The algorithm then produced an expected post-operative ASES score for each patient. The ML-produced scores were compared to actual scores using standard test-train machine learning principles. Overall, 631 patients who underwent shoulder arthroscopy from January 2011 to March 2020 met inclusion criteria for final analysis. A substantial number of the test dataset predictions using the XGBoost algorithm were within the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) thresholds: $67\%$ of the 12-month post-operative predictions were within MCID, while $84\%$ were within SCB. Pre-operative ASES score, pre-operative pain score, body mass index (BMI), age, and tendon quality were the most important features in predicting patient recovery as identified using Shapley additive explanations (SHAP). In conclusion, the proposed novel machine learning algorithm can use pre-operative factors to predict post-operative ASES scores accurately. This can further supplement pre-operative counselling, planning, and resource allocation. Level of Evidence: III. ## 1. Introduction The optimal management of shoulder disorders depends on recognizing the natural history of disability caused by an injury and the anticipated outcomes after operative treatment when this is indicated. Clinical and patient-reported outcome measures are critical to truly understand post-operative function and monitor the progress of an effective care plan [1]. Using machine learning (ML), the goal of this study was to analyse such factors to determine the factors most predictive for successful outcomes. ML is a novel field of study that employs computer algorithms and statistical analysis to determine complex trends and patterns in the data that may not be easily discernible by humans. ML uses data to build empirical/statistical models to describe the behaviour of a system [2]. As such, there is a growing body of literature on machine learning to analyse data and answer clinical questions for both the diagnosis and prognostication of rotator cuff tears [3,4,5]. Recent reviews have demonstrated the wide range of potential applications, from the analysis of ultrasound to diagnose rotator cuff tears to the characterization of rotator cuff fatty degeneration on CT (computer tomography) scans [3,5]. Conversely, other studies have attempted to develop a clinical prediction tool to forecast the chance of complications versus clinical improvement following repair [4]. However, while exciting, there is much room for improvement regarding the application and accuracy of such ML models [5]. Focusing on patients with operative rotator cuff pathology, we developed a novel algorithm to predict arthroscopic rotator cuff repair (ARCR) outcomes. We examined pre-operative and post-operative American Shoulder and Elbow Surgeons (ASES) score. The ASES score is a widely reported and validated patient-reported outcome measure (PROM) applicable to all patients with shoulder pathologies and independent of their specific diagnosis [6,7].⁠ Through machine learning, we sought to understand whether specific pre-operative characteristics could ultimately predict recovery. Rotator cuff tears are an ideal musculoskeletal condition to study with machine learning. Many factors influence treatment and outcomes, complicating the ability of surgeons to predict outcomes reliably. Secondly, the burden of disease is substantial: each year, an estimated 250,000 people in the U.S. suffer rotator cuff tears, resulting in 3–4 billion dollars spent annually [8]. As a result, surgeons may be economically “punished” for taking on more complex cases with the potential for poorer outcomes, and such policies may act as a barrier to care for those who need it most. However, these questions cannot be adequately answered with conventional statistical methods such as simple linear regression, as large volumes of data can be scattered. Machine learning (ML) algorithms can overcome these limitations, improve prediction accuracy, and reduce the margin of error between actual and predicted data [2,9,10,11,12]. This approach was recently applied in orthopaedics to optimize the number of questions in the Knee Injury and Osteoarthritis Outcome Score (KOOS) activities of the daily living questionnaire following knee surgery [2]. The goal of this study is to use ML in prospectively collected pre- and post-operative data of patients who underwent ARCR to develop a novel algorithm to predict arthroscopic rotator cuff outcomes. We hypothesized that ML algorithms could be used to predict 3-, 6-, and 12-month post-operative ASES scores for patients who underwent rotator cuff repair. ## 2.1. Study Design We performed a retrospective review of prospectively collected data of patients who underwent ARCR performed by several surgeons between April 2011 and April 2019. The surgical technique was based on the surgeon’s preference. As the data were collected for a multi centric database, all surgeries were performed with single- or double-row suture bridge construct with a configuration of 3 medial 2 lateral, 2 medial 2 lateral, 1 medial 2 lateral, and 2 medial 1 lateral for the double-row fixation and 3 and 2 anchors for the single row construct. Data for analysis was extracted from the Surgical Outcome System (SOS) global registry, an international patient-reported outcome database maintained by Arthrex. No institutional review board (IRB) approval was required, as SOS global registry is IRB approved and adheres to Health Insurance Portability and Accountability Act (HIPAA) regulations. All SOS global registry users have access to the shared deidentified data. We included patients who had fully documented demographic and surgical data. Patients who did not complete pre-operative, 3-, 6-, and 12-month post-operative ASES surveys were excluded. Additional exclusion criteria included patients who underwent revision surgery, those who lacked complete follow-up at the specified time points, and patients with incomplete questionnaires. The dependent (or target) variables for this study were 3-, 6-, and 12-month post-operative ASES scores. Several patient- and surgery-related independent variables were considered for this multivariate analysis to better understand their impact on the target variables. Patient-related factors examined were gender, age, BMI, tobacco use, and past medical history of diabetes. Surgical-related factors included the number of tendons torn, tendon quality, Cofield tear size, retraction stage, tear shape, medial anchor type, number of medial knotless anchors, number of medial suture anchors, lateral anchor type, number of lateral knotless anchors, number of lateral suture anchors, pre-operative visual analogue pain score (VAPS), pre-operative ASES score, and the year of operation. The binary features in the dataset (gender, tobacco use, and history of diabetes) were encoded as “0” and “1”. The other categorical features in the dataset were converted to numerical values based on domain understanding to improve model predictions. The following encoding was used: tendon quality (poor: 1, fair: 2, good: 3, excellent: 4); Cofield tear size (small (<1 cm): 1, medium (1–3 cm): 2, large (3–5 cm): 3, massive (> 5 cm): 4); retraction stage (stage I: 1, stage II: 2, stage III: 3, stage IV: 4); tear shape (L-shaped posterior: 1, L-shaped anterior: 2, U-shaped: 3, avulsion/crescent: 4, massive contracted: 5, longitudinal: 6); anchor type (suture anchor: 1, knotless anchor: 0, tenodesis screw: 0). ## 2.2. Data Preparation and Model Building Data processing, analysis, and ML model building were performed using Python 3.7.4 (http://www.python.org accessed on 1 December 2022). Python packages such as matplotlib, NumPy, Pandas, and Scikit-learn were used for data wrangling, statistical analysis, visualization, and ML model building [13]. The surgical outcomes model is a multi-target regression problem, as the goal is to predict patient recovery at multiple time points (3, 6, and 12 months) after surgery using pre-operative information. To achieve this, the multioutputregressor function from the scikit-learn library in Python was used to fit multiple target variables. Cross-validation (CV) is a de facto standard to estimate model prediction errors and the most popular approach for model selection and hyperparameter tuning [14]. K-fold cross-validation involves partitioning the dataset into k equal-sized subsets (or folds), training the ML model on all but one subsets (i.e., k-1 subsets), and then evaluating the model on the held-out subset. This process is then repeated k times with a different subset held out each time. The data were randomly split into two sets: a training set with $80\%$ data and a test set with the remaining $20\%$ data. The $\frac{80}{20}$ data split is a commonly used ML method and was chosen accordingly [15]. The ML model was trained using the training data, and the model’s performance was confirmed on the test data. Each feature, prior to model building, was scaled (forcing the mean to 0 and scaling the variance to 1) to help to better interpret the model results. Several machine learning models (linear regression, ridge regression, lasso, support vector regression, k-nearest neighbour, random forest, and XGBoost) were evaluated in this study, and the best model was selected based on 10-fold CV error (Table 1). Although linear regression, ridge regression, and lasso had lower RMSE, XGBoost was chosen as the “best” model for further refinement based on acceptable root-mean-square error (RMSE) and normally distributed errors (less bias in model predictions). Moreover, linear regression, ridge regression, and lasso models highly weighted “gender” as the most important predictor of post-operative ASES, which medically seems highly unlikely. Therefore, XGBoost was chosen as the “best” model for this work. Hyperparameter tuning for the XGBoost model was performed to identify the “best” model based on minimizing the RMSE through 5-fold cross-validation [16]. This was accomplished by searching over a grid space of select key XGBoost hyperparameters (learning_rate: 0.001, 0.005, 0.01, 0.1; max_depth: 6, 8, 10; n_estimators: 200, 400, 500, 600, 700; min_child_weight: 0.5, 1, 2; colsample_bytree: 0.3, 0.5) using the “GridSearchCV” object in Scikit-learn. The set of XGBoost hyperparameters that resulted in the lowest cross-validation error were as follows: n_estimators = 400, learning_rate = 0.01, max_depth = 6, min_child_weight: 2, and colsample_bytree = 0.5. The performance of the “best” model was then evaluated on the test dataset to gauge its performance on this blind, held-out data. To better understand model predictions, the SHapley Additive exPlanations (SHAP) method was used to explain global feature importance and individual predictions [17]. SHAP values were first obtained using the TreeExplainer method to explain every prediction of the XGBoost model. The next step involved plotting the explanations using the “summary_plot” method. Explanation of individual predictions was performed using the “force_plot” method within the SHAP library. To summarize, the methods involved are shown in Figure 1 through a simple illustration. ## 2.3. Data Analysis Distributions of raw data were analysed for symmetry and skewness. The data were non-symmetric and left-skewed with a skewness of −0.45, −1.12, and −1.92 for the 3-, 6-, and 12-month post-operative ASES scores, respectively, meaning that the tail on the left side (or lower ASES scores) of the distribution is considerably drawn out compared to the right tail (or higher ASES scores) of the distribution (Figure 2). This is not surprising since the expectation is that most patients would experience an improvement in ASES scores after surgery. For reference, a symmetric distribution such as the normal distribution (bell curve) has a skewness of 0. Since the target (or independent) variables (i.e., post-operative ASES scores) are left-skewed, the machine learning model is trained on an imbalanced dataset and is less likely to accurately predict the outcome in patients with low post-operative ASES scores. Similarly, if the target variable were right-skewed, the model would be less likely to correctly predict cases with high post-operative scores. To reduce target variable skewness and improve model predictions, a mathematical transformation was performed by subtracting the pre-operative score from the post-operative score. This transformed variable demonstrates the improvement in post-operative ASES scores compared to the pre-operative scores. The post-transformation distributions for 3, 6, and 12 months post operation had a corresponding skewness of 0.06, −0.14, and −0.24. In other words, the skewness is closer to zero, indicating that these distributions are more symmetric and closer to a normal distribution. Other mathematical transformations such as log, square, and square root were not as effective on this dataset. The model building and model selection steps were undertaken using the transformed target variable described in the Methods section (Section 2.2 data preparation and model building). Root-mean-square error (RMSE) is a measure of the standard deviation of the prediction errors and is a commonly used heuristic to evaluate machine learning models. The average RMSE, calculated using 10-fold CV, for the various ML models (with default hyperparameters) studied in this work was between 15.3–17.2 for the 3-month post-operative ASES score predictions. For the XGBoost model, the RMSE along with $95\%$ confidence interval calculated using 10-fold cross validation of the training data was 15.90 ($95\%$ CI: 14.80–17.00), 16.36 ($95\%$ CI: 15.70–17.02), and 14.60 ($95\%$ CI: 12.84–16.36) for 3, 6, and 12 months post operation, respectively [18]. The RMSE for the test dataset prediction was 16.50, 14.75, and 12.94 for 3, 6, and 12 months post operation, respectively (Table 2). The minimal clinically important difference (MCID) and substantial clinical benefit (SCB) values were obtained from the literature and are also shown in Figure 3 [19]. MCID and SCB were used to characterize the extent of error in model predictions. MCID, in this case, is the smallest change in ASES score that a patient would perceive as meaningful. While MCID is defined as the minimum improvement threshold, SCB indicates a substantial change in clinical state as perceived by the patient. SHAP is a widely used, game-theory-based approach to explain global and local model behaviours [16]. SHAP was used to indicate which features are the most predictive of outcomes. SHAP mean value was used to rank variables or features by highest to least impact on the target variable (i.e., change in ASES score) for the dataset. ## 3. Results A total of 4729 patients who underwent ARCR were identified. There were 631 patients between the ages of 24–83 years who met inclusion criteria for analysis (mean age 61.5 years). Of the 631 patients, 362 were males ($57\%$), and 269 were females ($43\%$). Pre-operative ASES score distribution had a mean and standard deviation of 50 and 17, respectively (Figure 4). The mean post-operative scores increased with recovery time, with the data demonstrating a mean and standard deviation for the 12-month post-operative ASES scores of 87 and 12, respectively, demonstrating a mean improvement in functional outcomes after surgery. The model demonstrated reasonable performance in predicting recovery progression that healthcare providers could use in their decision-making process. A substantial number of the test dataset predictions using the XGBoost algorithm were within the MCID and SCB thresholds: $69\%$ of the 12-month post-operation predictions were within MCID, while $87\%$ were within SCB, which correlates with the clinical findings of ASES score improvement (Figure 3). The percentage of patients (in the test dataset) predicted within the MCID for 3, 6, and 12 months post operation was $52\%$, $54\%$, and $69\%$, respectively. Similarly, the percentage of patients predicted within SCB for 3, 6, and 12 months post operation was $73\%$, $78\%$, and $87\%$, respectively. The scatter plots comparing model prediction and observed data for 3, 6, and 12 months post operation are demonstrated in Figure 3. Based on pre-operative information, the model was able to predict 3-, 6-, and 12-month post-operative ASES scores following ARCR (Figure 5). Although the predictions did not exactly match the actuals, the predicted improvement in ASES score lies within the MCID value. Unlike linear regression models that provide statistical significance (p-value) of each feature as an output, black box ML models (e.g., random forest, XGBoost) output a feature importance score that ranks the relative contribution of each feature towards predicting the target variable. For feature importance, the black box models not only account for main effects but also account for interaction effects between different features. It is important to understand the trade-off between model accuracy and model interpretability. Simple models (e.g., linear regression) have good interpretability but may not have good accuracy. By contrast, complex models usually have better accuracy but poor interpretability (i.e., lack clear explanation of why the model made such a prediction). ML practitioners, having realized the importance of solving this problem, are actively working on various methods to address model interpretability [20]. SHAP is one such approach. As shown in the SHAP summary values (Figure 6), the features most predictive of post-operative ASES score included the pre-operative ASES score, pre-treatment VAPS, BMI, patient age, and tendon quality. The least predictive factor was patient smoking status (Figure 6). The SHAP summary plot also shows both the positive and negative relationships of the predictor variables. High pre-operative ASES scores negatively impacted SHAP values. In other words, a patient starting with a high pre-operative ASES score did not have as high of a potential to increase their score further and therefore demonstrated a smaller magnitude of change in the ASES score. In contrast, a patient starting with a low pre-operative ASES score has a larger potential for improvement. Similar conclusions can be drawn for the pre-operative VAPS. Additionally, poor tendon quality leads to slower recovery. ## Case Example Note that these SHAP summary results are global, i.e., overall trends observed based on this dataset. Interestingly, SHAP can also be used to understand the impact of each factor at an individual patient level. Figure 7 shows the explanation plot generated using the SHAP “force plot” method for one patient in the test dataset. The plot indicates the individual contribution of various factors that lead to the model prediction: improvement in ASES score of 46.99. The base score of 37.32 (the average change in 12-month post-operative ASES score) is also shown in the plot. As shown in Figure 7, the largest contributions in increasing the score come from the pre-operative ASES score and pre-operative VAPS. In addition, the plot attributes having good tendon quality and lower (than average in this dataset) BMI suggest better recovery. ## 4. Discussion One of the key findings from this study was that ML could forecast post-operative patient recovery over time using pre-operative factors (Figure 5). We identified pre-operative ASES score, pre-operative VAPS, BMI, age, and tendon quality as key factors impacting patient outcomes (Figure 6). These findings provide a better understanding of the factors influencing surgical outcomes, leading to better informed consent and personalized patient care with data-driven expectations for post-operative recovery. The application of ML in orthopaedics has recently increased [2,9,10,11,12,21]. While the number of orthopaedic studies utilizing ML is still limited, studies from other fields demonstrate its capability to surpass human performance [22,23,24]. Several orthopaedic studies demonstrate ML can optimize the use of pre-operative assessments and accurately predict the likelihood of patients achieving MCID or SCB in PROMs postoperatively [2,10,21]. One might expect that pre-operative assessment scores would be inversely related to post-operative outcomes; that is, that higher pain and lower functional scores pre-operatively would predict worse post-operative outcomes. Likewise, several studies demonstrate this to be the case [25,26,27,28]. Conversely, we found lower pre-operative ASES and VAP scores to be the most significant predictors of post-operative ASES score improvement, with higher pre-operative values negatively influencing post-operative scores. This finding is consistent with Jenssen et al. [ 29], who analysed factors predictive of post-operative functional outcomes following arthroscopic rotator cuff surgery. They found pre-operative pain scores to be negatively associated with post-operative shoulder function following shoulder arthroplasty. This could be attributed to the fact that patients with worse pre-operative assessments have the greatest potential for improvement. It could also be that patients with worse pre-operative pain can better appreciate their improvements, which reflects the more improved post-operative assessments. In the present study, individuals with a lower (than average in this dataset) BMI demonstrated better recovery. However, no significant association was found between diabetes mellitus and outcomes. The effects of high BMI, diabetes mellitus, and dyslipidaemia have been previously studied with varied findings. Several studies found that these variables predict worse clinical outcomes, recovery, and tendon healing following rotator cuff injury [30,31,32,33]. Conversely, other studies demonstrated no association of BMI with post-operative outcome scores following ARCR [29,34]. Younger age at the time of repair was positively associated with improved outcomes, though the prior literature on this topic is conflicting. Generally, younger age correlates with more successful recovery following rotator cuff repair [35,36]. Likewise, older age is negatively associated with successful tendon healing, longer recovery time, and increased risk of re-tear following ARCR [37,38,39]. Other studies demonstrate that when accounting for fatty infiltration, bone mineral density, or retraction of the rotator cuff tendon, there is no independent association between age and rotator cuff healing [40,41,42]. Furthermore, in other studies, younger age is associated with worse pain and functional outcomes [27,29,43]. In the present investigation, lower pre-operative tendon quality correlated with lower post-operative ASES scores. However, tendon tear size, the number of tendons torn, tear shape, and retraction stage was not strongly associated with a poor outcome. The literature also demonstrates that several measurements of pre-operative tendon quality are associated with worse post-operative outcomes [36]. A larger pre-operative rotator cuff tear size negatively impacts healing, recovery time, functional outcomes, and rate of retear [8,32,38,44,45,46]. Additionally, tendon retraction and fatty infiltration demonstrate a negative impact on healing [34]. Likewise, studies demonstrate that patients with multiple tendon injuries are more likely to develop a rotator cuff defect [47]. Our results did not demonstrate a strong association between gender and post-operative ASES scores, another area of conflicting findings in the literature. Several studies show female sex to be associated with worse post-operative quality of life, mental health, pain, and functional assessments [26,27,48,49,50,51]. Conversely, other studies demonstrate that gender does not influence post-operative outcomes [39]. We also did not identify a strong association between tobacco use and post-operative ASES scores. This finding correlates to a prior study demonstrating that tobacco use is not associated with post-operative structural failure following rotator cuff repair [52]. Nonetheless, other studies found that smoking is associated with an increased risk of rotator cuff tears and tear size and worse post-operative clinical outcomes [29,53]. Tobacco use is a modifiable risk factor, and until more definitive data are produced, it is feasible to recommend cessation for the purposes of undergoing ARCR. ## 5. Limitations Our results are encouraging, but we are aware of the limitations of the present study. While our sample size is relatively large, there still could be differences between predicted and actual results when applying the model to a real-world population. The model performance (i.e., RMSE) could be further improved by including additional input variables (e.g., compliance with physical therapy protocols and recovery exercises, pain management, and psychological factors) that could impact patient recovery. Additionally, the predicted values from the model may also not represent various regions around the world with different demographics, gender, or ethnic groups. Other features such as patient behavioural factors, medical risk factors, and chronic medical conditions before surgery should also be considered to more accurately predict patient recovery. Additionally, there could also be a recall bias when patients are reporting their outcome questionnaire. However, with more input data, new variables, and model-building refinement, this approach can significantly help surgeons customize their care plans. One potential application of the model is for surgeons to share the predicted recovery profile with their patients before surgery, thereby setting baseline expectations (Figure 7). Furthermore, the surgeon may run different scenarios—manually or algorithmically—to identify the best course of action for each patient along with any modifiable risk factors. This will allow surgeons to better modulate treatment and rehabilitation techniques with greater confidence. ## 6. Conclusions This proposed novel ML algorithm can predict the post-operative ASES scores after ARCR with satisfactory accuracy. While not intended for use in isolation, this model can be used as a critical tool for physicians to formulate better decisions and provide customized, evidence-based care for every patient. 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--- title: Proteomic Analysis Reveals Changes in Tight Junctions in the Small Intestinal Epithelium of Mice Fed a High-Fat Diet authors: - Hisanori Muto - Takashi Honda - Taku Tanaka - Shinya Yokoyama - Kenta Yamamoto - Takanori Ito - Norihiro Imai - Yoji Ishizu - Keiko Maeda - Tetsuya Ishikawa - Shungo Adachi - Chikara Sato - Noriko M. Tsuji - Masatoshi Ishigami - Mitsuhiro Fujishiro - Hiroki Kawashima journal: Nutrients year: 2023 pmcid: PMC10056729 doi: 10.3390/nu15061473 license: CC BY 4.0 --- # Proteomic Analysis Reveals Changes in Tight Junctions in the Small Intestinal Epithelium of Mice Fed a High-Fat Diet ## Abstract The impact of a high-fat diet (HFD) on intestinal permeability has been well established. When bacteria and their metabolites from the intestinal tract flow into the portal vein, inflammation in the liver is triggered. However, the exact mechanism behind the development of a leaky gut caused by an HFD is unclear. In this study, we investigated the mechanism underlying the leaky gut related to an HFD. C57BL/6J mice were fed an HFD or control diet for 24 weeks, and their small intestine epithelial cells (IECs) were analyzed using deep quantitative proteomics. A significant increase in fat accumulation in the liver and a trend toward increased intestinal permeability were observed in the HFD group compared to the control group. Proteomics analysis of the upper small intestine epithelial cells identified 3684 proteins, of which 1032 were differentially expressed proteins (DEPs). Functional analysis of DEPs showed significant enrichment of proteins related to endocytosis, protein transport, and tight junctions (TJ). Expression of Cldn7 was inversely correlated with intestinal barrier function and strongly correlated with that of Epcam. This study will make important foundational contributions by providing a comprehensive depiction of protein expression in IECs affected by HFD, including an indication that the Epcam/Cldn7 complex plays a role in leaky gut. ## 1. Introduction The prevalence of non-alcoholic fatty liver disease (NAFLD) has been increasing worldwide in recent years and is a major public concern [1]. Even in Japan, where obesity is less common than in Western countries, the prevalence of NAFLD is expected to increase in the future [2,3]. Among NAFLD patients, 12–$40\%$ develop non-alcoholic steatohepatitis (NASH), which can progress to cirrhosis and hepatocellular carcinoma [4]. During the progression of NASH, the multiple hit theory proposes that obesity and insulin resistance are the first hits, followed by increased oxidative stress, adipokine abnormalities, endotoxin, and other factors, leading to inflammation, which in turn promotes fibrosis and the activation of hepatic stellate cells [5]. Recently, abnormalities in the gut microbiota and the resulting increased intestinal permeability have been shown to promote the progression of NASH [6]. The gut-liver axis, which refers to the interdependent relationship between the gut, microbiota, and liver, is implicated in this process [7]. Increased permeability of the upper small intestine and tight junction (TJ) dysfunction has been reported in patients with NAFLD [8,9]. Dysfunctions of the intestinal barrier, including the TJ, were shown to be an important etiology of NASH progression in mouse studies [10]. This disruption of the TJ is thought to result from changes in the microbiota, including small intestinal bacterial overgrowth [11,12,13]. A high-fat diet (HFD) alters the microbiota, which compromises the intestinal barrier and promotes the influx of bacterial products into the portal vein [10]. Studies in rodents have shown that an HFD modifies the composition of the intestinal microbiota, which subsequently exacerbates intestinal barrier dysfunction and stimulates hepatic inflammation [14,15]. Because the liver is exposed to high concentrations of pathogen-associated molecular patterns, which are molecular motifs commonly found in microorganisms that promote inflammation, it is particularly vulnerable to their effects, particularly when primed by subclinical pathologies, such as the accumulation of lipids within hepatocytes [7]. Although recent studies have often attempted to ameliorate NASH by supplementation with prebiotics and probiotics to restore the intestinal bacterial balance caused by HFD [16,17,18], few studies have focused on changes in the host intestinal TJ itself due to HFD ingestion. Detailed study of changes in the host intestinal TJ induced by HFD ingestion may lead to the development of new biomarkers and therapies for the progression of NASH. Because TJ functions are regulated by vesicular trafficking and redistribution of its component proteins [19], transcriptome analysis may not be sufficient to investigate its effects. Deep proteomics using mass spectrometry detects and quantifies thousands of proteins in a single experiment, providing a comprehensive view of the proteome. This approach is suitable for studying complex biological processes, such as signaling pathways and protein-protein interactions, and for identifying novel biomarkers for disease diagnosis and treatment [20]. Therefore, we used high-throughput deep proteomic analysis to comprehensively analyze the protein expression profiles of mouse intestinal epithelial cells (IECs) fed an HFD to investigate the mechanism of TJ dysfunction. ## 2.1. Murine Model Nine-week-old male C57BL/6J mice from Japan SLC (Shizuoka, Japan) were fed an HFD containing 60 kcal% fat (D12492; Research Diet, New Brunswick, NJ, USA) or a control diet (CD, D12450J; Research Diet) for 24 weeks. Mice were housed with free access to water and food at a temperature of 23 ± 1 °C, a humidity of 50 ± $10\%$, and a 12-h light/dark cycle. After the animals were fully anesthetized and sacrificed, the liver, small intestine, and serum were collected for histological and serological analyses. Five mice were included in the HFD group and four mice in the CD group. Animal experiments were conducted in accordance with the NIH Guidelines for the Care and Use of Laboratory Animals. ## 2.2. Intestinal Permeability Intestinal permeability was assessed using previously described methods [21]. Briefly, 4-kDa fluorescein isothiocyanate (FITC)-dextran (Sigma-Aldrich, St. Louis, MO, USA; 20 mg/mL, PBS) was administered orally to mice after a 4-h fast (9:00 to 13:00) at a dose of 200 mg/kg body weight. Four hours later, blood was collected from the retrobulbar capillary plexus into heparinized tubes. Plasma was obtained by centrifugation at 2000× g for 5 min, followed by a 1:5 (v/v) dilution in PBS. Spectrophotometric measurement of fluorescence was performed using a SpectraMax iD5 (Molecular Devices, Tokyo, Japan) and a 96-well plate (excitation: 485 nm, emission: 528 nm) to determine the concentration of FITC-dextran. ## 2.3. Histological and Immunohistochemical Analyses Liver and intestine samples were fixed in $4\%$ paraformaldehyde, paraffin-embedded, sectioned at 4 μm thickness, and stained with hematoxylin and eosin (H&E). Tissue images were captured using a BZ-X800 microscope (Keyence, Osaka, Japan). The NAFLD activity score (NAS) was used to assess the activity and severity of fatty liver. The NAS is calculated based on three histologic features in the liver: steatosis (0–3), hepatocellular ballooning (0–2), and lobular inflammation (0–3) [22]. The lipid droplet area was calculated using a BZ-X800 analyzer (Keyence). For immunohistochemical analysis, 4 μm paraffin-embedded tissue sections were deparaffinized, rehydrated, and subjected to antigen retrieval by heating in preheated 10 mM sodium citrate (pH 6.0) at 98 °C for 20 min. The sections were blocked with $5\%$ (vol/vol) bovine serum albumin for 30 min and then incubated overnight with primary antibodies to either anti-zonula occludens-1 (ZO-1) (Abcam, Cambridge, UK) or anti-Epcam (rabbit IgG, Abcam). Anti-rabbit IgG Alexa Fluor 488 was used as the secondary antibody and counterstained with 4′,6-diamidino-2-phenylindole (DAPI; Sigma-Aldrich). Tissue images were captured with a BZ-X800 microscope. ## 2.4. Isolation of Intestinal Epithelial Cells The small intestine was divided into two equal parts: the upper small intestine and the lower small intestine. Isolation of IECs was performed as described previously [23]. Briefly, each piece was washed with PBS and cut into 1–2 cm pieces. The sections were incubated in isolation buffer (20 mM HEPES, 10 mM EDTA, 1 mM sodium pyruvate, $10\%$ FCS, and $1\%$ penicillin-streptomycin in PBS) at 37 °C for 25 min. After passing through a 100-μm cell strainer, the samples were centrifuged at 3500 rpm for 5 min, washed with PBS, and stored at −80 °C for protein mass spectrometry analysis. ## 2.5. Proteomic Assay The proteomic analysis technique was performed according to previously published methods [24]. For the sample used in mass spectrometry analysis, we made slight modifications to the Mass Spec Sample Prep Kit for Cultured Cells (Thermo Fischer Scientific, Waltham, MA, USA) protocol. Briefly, we suspended the pelletized cells in a lysis buffer, adding benzonase to degrade nucleic acids, and precipitating proteins using acetone. The precipitated protein was then re-dissolved in guanidine hydrochloride, reduced with TCEP, alkylated with iodoacetamide, and digested with lysyl endopeptidase and trypsin. The resulting digested peptides were analyzed using an Evosep One LC system (Evosep biosystems, Odense, Denmark) connected to a Q-Exactive HF-X mass spectrometer (Thermo Fischer Scientific). The mobile phases consisted of $0.1\%$ formic acid as solution A and $0.1\%$ formic acid/$99.9\%$ Acetonitrile as solution B. The analysis was performed in data-dependent acquisition mode, and the top 25 recorded mass spectrometry spectra between 380 and 1500 m/z were selected. All MS/MS spectra were searched against the protein sequences of the mouse Swiss-Prot database using Proteome Discoverer 2.2 with the SEQUEST search engine. The false discovery rate (FDR) was set to $1\%$ on peptide spectrum match. ## 2.6. Statistical Analysis The proteomic data obtained from the intestinal epithelial cells, which involved a comparison between two groups, were analyzed using the online statistical software MetaboAnalyst version 5.0 (https://www.metaboanalyst.ca/ accessed on 11 December 11) [25]. Functional analyses of the differentially expressed proteins (DEPs) were performed using DAVID (Database for Annotation Visualization and Integrated Discovery) version 2021 (https://david.ncifcrf.gov/ accessed on 11 December 2022) [26,27]. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and gene ontology (GO) functional annotation clustering were performed using DAVID. The software MetaboAnalyst was employed to identify DEPs and conduct Principal Component Analysis (PCA) utilizing the default settings. In instances of missing data, a value of $\frac{1}{5}$ of the minimum positive value for each variable was assigned. Multiple comparisons were performed, and proteins with an FDR value of less than 0.05 were considered DEPs. The DEPs were then subjected to KEGG analysis and functional annotation clustering using the DAVID. Other statistical analyses were performed using Graph Pad Prism version 9.2.0 (GraphPad Software, San Diego, CA, USA). Data were presented as bar graphs showing the mean ± standard error of the mean. Student’s t-test or Mann-Whitney U test was used for continuous variables to compare the two groups, as appropriate. A p-value < 0.05 was considered statistically significant. ## 3.1. Evaluation of the Liver and Changes in Intestinal Permeability Induced by an HFD in Mice To induce hepatic steatosis, mice were fed an HFD for 24 weeks, and a control group was maintained on a CD. The body weight of mice in the HFD group was significantly higher than that in the CD group (Figure 1A). An in vivo intestinal permeability assay showed a trend toward increased intestinal permeability in the HFD group mice compared with CD-fed mice (Figure 1B). All mice were sacrificed after 24 weeks of HFD. The liver weight and liver/volume ratio of the HFD group were significantly higher than those of the CD group (Figure 1C,D). The HFD group also showed elevated serum alanine transaminase (ALT) and total cholesterol (TC) serum levels compared with the CD group (Figure 1E,F). H&E staining of the liver showed hepatic steatosis and mild hepatocyte ballooning degeneration in HFD-fed mice and a significant increase in NAFLD activity score (Figure 1G–I). The upper small intestine and lower small intestine showed no obvious differences in H&E staining (Figure 2A,B). Fluorescence immunostaining showed that the localization of Zo-1 on the plasma membrane of IECs in the upper small intestine was decreased in the HFD group, suggesting TJ dysfunction (Figure 3). ## 3.2. Proteomic Changes in the Upper and Lower Small Intestines Induced by an HFD Proteomic changes in the IECs of the upper and lower small intestines of mice were analyzed by quantitative analysis using LC-MS/MS. Proteins in the upper and lower small intestinal IECs of the CD and HFD groups were analyzed twice per sample. PCA of the protein expression profiles by proteome analysis revealed that in IECs of the upper small intestine, the global pattern of the proteome was clearly different between the HFD and CD groups (Figure 4A). In the lower small intestine, there was no obvious difference in the global pattern between the HFD and CD groups (Figure 4B). In addition, the global pattern of PCA in the proteome of the upper and lower small intestines of the HFD group was clearly different (Figure S1). Because HFD-induced changes were more pronounced in the upper small intestines, the following analyses were performed in the upper small intestines. ## 3.3. Gene Ontology and KEGG Pathway Enrichment Analysis of Differentially Expressed Proteins in the Upper Small Intestine Overall, 3684 proteins were identified in IECs from the upper small intestine, and 1032 proteins showed significant differences in expression between the HFD and CD groups (FDR < 0.05) (Figure 5A). Further bioinformatic analysis was performed on the DEPs identified. Functional annotation clustering by DAVID revealed that functional clusters related to mitochondria, endoplasmic reticulum, protein transport, and GTP-binding proteins were enriched (Figure 5B). Fifty-six of the GTP binding (GO:0005525) proteins were identified as DEPs and contained several Rab family proteins (Figure 6A), which regulate intracellular membrane trafficking [28]. Many Rab family proteins, such as Rab4b, Rab7, Rab11b, and Rab35, were downregulated in the HFD group compared with the CD group (Figure 6A). In the KEGG pathway enrichment analysis, metabolic pathways, the PPAR pathway, and endocytosis were enriched, and TJ was also significantly enriched (Figure 5C). Twenty-two proteins in TJ (mmu 04530) were identified as DEPs (Figure 6B). ## 3.4. Correlations of TJ Proteins and Intestinal Permeability We then investigated the correlation between 22 TJ-associated proteins and intestinal permeability. The correlation between intestinal permeability in the in vivo FITC-dextran assay and protein expression quantified by LC-MS/MS is shown in Table 1. Significant inverse correlations were found for nine proteins, and interestingly, relatively strong correlations were found with actin-myosin fiber expression. Claudin-7 (Cldn7), a major membrane protein involved in TJ formation, also showed a significant correlation with intestinal permeability (Figure 7A). Based on the quantitative proteomics results, we searched for proteins that were strongly correlated with Cldn7 (Figure S2). Among these proteins, the expression levels of Cldn7 and Epcam showed a very strong correlation (Figure 7B), as both are known to interact with each other to regulate TJ functions [29,30]. Indeed, fluorescence immunostaining showed that the Epcam signal was reduced in the HFD group compared with the CD group (Figure 8). ## 4. Discussion In the present study, the protein expressions of IECs from the upper and lower small intestine of mice fed an HFD, or a CD were comprehensively analyzed by high-throughput deep proteome analysis to investigate the mechanism of TJ dysfunction. We found that HFD, especially in the upper small intestine, alters the protein expression profile of IECs and results in the dysregulation of several pathways related to protein transport, including endocytosis. In addition, several TJ-related proteins were differentially expressed in IECs of mice fed HFD compared to a normal diet. The TJs are complex protein structures that hold adjacent cells together, creating a barrier that regulates the passage of substances through the intestinal mucosa [31]. When the TJs become compromised, they can lead to intestinal permeability, also known as “leaky gut” syndrome [32]. The interaction between the intestine and the liver is mediated by the portal vein, which can transfer nutrients, as well as products of intestinal origin, such as microbial metabolites and microbial components, to the liver. This enterohepatic circulation provides the liver with continuous exposure to gut-derived factors. Furthermore, this gut-liver relationship is referred to as the “gut-liver axis”, and its importance in liver homeostasis and disease pathogenesis is being increasingly recognized [33]. TJ dysfunction has been observed in obese patients [34] and patients with NASH [9]. Intestinal barrier dysfunction is one of the main causes of NASH progression [7,35,36] and is an emerging therapeutic target [6,16]. Although HFD is known to damage the intestinal barrier directly or indirectly, with particular emphasis on the involvement of the microbiota [37], few reports have comprehensively investigated changes in host IECs. Therefore, the present data should be very valuable in elucidating the mechanism of HFD-induced intestinal barrier dysfunction for the following reasons. The results of this study revealed three important points. First, highly sensitive quantitative deep proteomics was a very valuable tool for the comprehensive analysis of TJ and related proteins expressed in IECs. Because the localization of TJ proteins is not necessarily dependent on transcription, it may be insufficient to assess their changes by messenger RNA expression analysis [38]. The structure of the TJ is composed of transmembrane adhesion proteins, cytoplasmic plaque proteins, and the actin cytoskeleton. Recent studies suggest that the TJ is not as rigid as previously thought but is a highly dynamic structure that can respond to various biochemical and mechanical stimuli by reforming and remodeling [39]. The dynamics of TJ proteins are mainly regulated by endocytosis [40]. It is highly suggestive that deep proteomics in this study revealed that HFD-induced changes in TJ proteins in mouse IECs occurred together with proteins associated with endocytosis and protein trafficking and their regulatory GTP-binding proteins. One potential pathway of endocytic sorting is recycling [41]. GTP-binding proteins that were decreased in the HFD group, Rab4, Rab7, Rab11, and Rab35, were reported to be associated with recycling endosomes [41], and dysfunction of this pathway may have affected TJ formation. Second, we found that the changes in the protein expression of IECs induced by an HFD differed significantly between the upper and lower small intestines. Indeed, HFD-induced changes in the global pattern of protein expression were more pronounced in the upper small intestine. Furthermore, we have shown that Cldn7 is significantly downregulated by HFD in upper small intestinal IECs, which correlates with intestinal barrier dysfunction. In our model, this suggests that the changes in the upper small intestine have a strong impact on intestinal barrier function. The region of the intestine where leaky gut occurs in the development of NASH remains unclear. Future studies of NASH focusing on intestinal barrier dysfunction should examine differences by the intestinal site. Although our data suggest that the HFD-induced intestinal barrier dysfunction is more pronounced in the upper small intestine, in clinical situations, the lower small intestine and colon may also be relevant because obesity, other habits, and genetic factors, as well as HFD, are intricately related to the progression of NASH [42]. Finally, we found that Epcam-mediated regulation of Cldn7 and its associate protein Epcam may play important roles in HFD-induced TJ dysfunction. Cldn7 is a transmembrane protein that plays an important role in maintaining TJ integrity and permeability but is characterized by a stronger basement membrane distribution than other claudins and has been shown to have functions in maintaining epithelial cell-matrix interactions and intestinal homeostasis [29]. Cldn7 is also known to interact with Epcam, a glycoprotein expressed in some epithelia, and regulate TJ functions in the intestine. Epcam interacts directly with Cldn7 at the lateral basement membrane and TJs of the intestinal epithelium, regulating the organization and function of the TJs [43,44]. A complex downregulation of claudin was observed in the intestinal epithelium of Epcam mutant mice, indicating that intestinal TJs were affected and intestinal permeability was increased [45,46]. Consistently, Epcam knockdown has also been shown to cause TJ dysfunction in the T84 and Caco-2 cell lines [47]. Here the expression levels of Cldn7 and Epcam showed a strong correlation, and Epcam-mediated regulation of Cldn7 seems to be associated with HFD-induced TJ dysfunction. The relationship between Epcam/Cldn7 interaction and protein trafficking, including endocytosis, has not been previously reported and requires further investigation. Nevertheless, the comprehensive protein expression analysis provides fundamental data to elucidate the functional relevance of endocytosis and GTP-binding proteins in HFD-induced intestinal barrier dysfunction and to elucidate the pathogenesis of NASH. Although additional mechanistic investigations and validation in humans are needed, these data may prove valuable in validating novel key factors and therapeutic approaches for NASH. This study has several limitations. First, the present study establishes a correlation between the expression of Cldn7 and the function of the intestinal barrier, but it does not prove a causal relationship. This study focused only on small intestinal IECs and did not investigate other mechanisms or factors that may contribute to HFD-induced leaky gut. Further studies are needed to fully understand the specific role of Cldn7 in the pathogenesis of leaky gut and the underlying mechanisms of TJ dysfunction. Second, this study did not examine the effects of different types of dietary fat or the duration of HFD on intestinal permeability. These factors may also contribute to the development of leaky gut and should be investigated in future studies. Third, this study did not investigate the role of the gut microbiota in relation to comprehensive protein expression in IECs. Further research is necessary to understand the potential influence of the gut microbiota on the development of leaky gut and its association with Cldn7 expression. Finally, because the study was conducted in C57BL/6J mice, the findings may not be directly applicable to humans. Further validation in humans is needed. ## 5. Conclusions We investigated protein changes associated with TJ by the comprehensive analysis of protein expression in IECs from HFD-fed mice using highly sensitive deep proteomics. Deep proteomic analysis is a powerful tool for investigating the underlying molecular mechanisms of NASH. This study provides new insights into the role of TJ dysfunction in the pathogenesis of these diseases and identifies potential candidate biomarkers for their diagnosis and treatment. We showed that Cldn7/Epcam in upper small intestinal IECs might be one of the key factors and a potential biomarker. Further studies are needed to validate these findings in humans and to develop targeted therapies based on these biomarkers. ## References 1. Younossi Z.M., Koenig A.B., Abdelatif D., Fazel Y., Henry L., Wymer M.. Global Epidemiology of Nonalcoholic Fatty Liver Disease—Meta-Analytic Assessment of Prevalence, Incidence, and Outcomes. Hepatology (2016) 64 73-84. DOI: 10.1002/hep.28431 2. 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--- title: Activation of AMPK Entails the Protective Effect of Royal Jelly against High-Fat-Diet-Induced Hyperglycemia, Hyperlipidemia, and Non-Alcoholic Fatty Liver Disease in Rats authors: - Alaa Hasanain Felemban - Ghedeir M. Alshammari - Abu ElGasim Ahmed Yagoub - Laila Naif Al-Harbi - Maha H. Alhussain - Mohammed Abdo Yahya journal: Nutrients year: 2023 pmcid: PMC10056733 doi: 10.3390/nu15061471 license: CC BY 4.0 --- # Activation of AMPK Entails the Protective Effect of Royal Jelly against High-Fat-Diet-Induced Hyperglycemia, Hyperlipidemia, and Non-Alcoholic Fatty Liver Disease in Rats ## Abstract This study examined the mechanism underlying the protective effect of royal jelly (RJ) against high-fat-diet (HFD)-mediated non-alcoholic liver disease (NAFLD) in rats. Adult male rats were divided into five groups ($$n = 8$$ each): control fed a standard diet, control + RJ (300 mg/kg), HFD, HFD + RJ (300 mg/kg), and HFD + RJ + CC (0.2 mg/kg). The treatment with RJ reduced weight gain, increased fat pads, and attenuated fasting hyperglycemia, hyperinsulinemia, and glucose tolerance in the HFD-fed rats. It also reduced the serum levels of liver function enzymes, interleukin 6 (IL-6), tumor necrosis factor-α (TNF-α), and leptin but significantly increased the serum levels of adiponectin. In addition, and with no effect on lipid excretion in stool, RJ significantly decreased the hepatic mRNA expression of SREBP1, serum, hepatic cholesterol, and triglycerides but increased hepatic mRNA levels of PPARα. Furthermore, RJ reduced the hepatic levels of TNF-α, IL-6, and malondialdehyde (MDA) in the livers of these rats. Of note, with no effect on the mRNA levels of AMPK, RJ stimulated the phosphorylation of AMPK and increased the levels of superoxide dismutase (SOD) and total glutathione (GSH) in the livers of the control and HFD-fed rats. In conclusion, RJ attenuates NAFLD via its antioxidant potential and adiponectin-independent activation of liver AMPK. ## 1. Introduction Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder associated with obesity, metabolic disorder, and insulin resistance (IR) [1]. The disease is characterized by the exaggerated synthesis and accumulation of triglycerides (TGs) and cholesterol (CHOL) in the liver (steatosis), which can then be progressively converted to more severe conditions such as non-alcoholic steatohepatitis (NASH), fibrosis, and cancer [1]. By 2030, NAFLD will be considered the most frequent cause of liver transplantation [2]. The pathogenesis of NAFLD is currently well-established [3]. It is widely accepted that peripheral IR, mainly due to stimulated lipolysis in the insulin-unresponsive adipose tissue, is the major cause underlying NAFLD [4]. In this regard, it was shown that IR leads to a massive increase in inflammatory cytokines and free fatty acids (FFAs) from the adipose tissue to the liver, which can then promote a state of inflammation and oxidative stress by activating resident inflammatory cells, mitochondria damage, and endoplasmic reticulum (ER) stress [5]. In addition, hepatic oxidative stress and inflammation can worsen the condition and result in sustained hyperglycemia by promoting hepatic IR [6]. Indeed, animals and patients with NAFLD exhibit the increased generation of reactive oxygen species (ROS) and high concentrations of inflammatory cytokines in their serum and livers [7,8]. However, suppressing adiposeness by stimulating peripheral insulin action, as well as treatment with anti-inflammatory and antioxidant drugs, has shown very promising results in alleviating NAFLD and its complications in both humans and animals [9]. During the last decades, several trials have been conducted to identify suitable safe drugs that prevent or treat NAFLD. To date, calorie restriction and exercise are the only available and approved options to treat NAFLD [10,11]. However, more research is now focusing on identifying safe pharmaceutical drugs that can manipulate the important key signaling pathways and mechanisms involved in the pathogenesis of NAFLD, such as de novo lipogenesis and mitochondria biogenesis [11]. One interesting target is the highly conserved heterotrimeric serine/threonine protein kinase complex known as 5′AMP-activated protein kinase (AMPK) [12]. AMPK depends on the liver, muscles, and adipose tissue and is activated during starvation with increasing ratios of adenosine monophosphate (AMP)/adenosine triphosphate (ATP). The ability of AMPK to treat NAFLD has been established in numerous animal and preclinical studies. It has been shown that the levels of AMPK activation are substantially decreased in animals and patients with NAFLD, whereas the activation of AMPK by genetic manipulation, exercise, activators (e.g., A-769662, EX$\frac{229}{991}$, MT47-100), and mitochondria inhibitors (e.g., metformin) showed protective effects [12,13,14,15,16,17]. Researchers have demonstrated that the protection afforded by AMPK is related to its ability to act using different mechanisms, including improving peripheral insulin sensitivity; suppressing de novo lipogenesis and lipogenic genes (e.g., sterol regulatory element-binding protein 1 (SREBP-1c) and acetyl-CoA carboxylase (ACC-1)); and stimulating adipose tissue, liver fatty acid oxidation, and mitochondria integrity/function [13]. Royal jelly (RJ) is one of the most nutritional foods available globally. It is produced by the mandibular glands of honeybees and is the main food for their queen [18]. The major constituents of RJ are water, protein, carbohydrates, lipids, polyphenols, and vitamins [19]. RJ is hypotensive and has potent antidiabetic activity due to its ability to stimulate insulin release, improve insulin sensitivity, and control glycemia [18,20,21]. In addition, RJ has been shown to alleviate neural, renal, and hepatic damage in several conditions, and it has potent antibacterial and antiaging effects due to its well-known antioxidant and anti-inflammatory properties [18]. Furthermore, RJ has been shown to attenuate dyslipidemia and to reduce circulatory levels of TGs, cholesterol (CHOL), and low-density lipoproteins (LDL-c) in obese and diabetic individuals, as well as in several experimental animal models [22,23,24,25,26,27]. However, the precise mechanism underlying RJ’s hypoglycemic and hypolipidemic effects is still unknown. In their most recent article, Malekia et al. [ 18] recommended looking at the effect of RJ on AMPK signaling. Therefore, in this study and using control and high-fat-diet-fed rats, we aimed to examine the effect of RJ on the major genes involved in lipogenesis and FA oxidation such as SREBP1 and the peroxisome proliferator-activated receptor alpha (PPARα). In addition, we tested the hypothesis that the hypolipidemic effect of RJ in these NAFLD animals is mediated mainly by regulating AMPK. ## 2.1. Animals Fifty-six adult male Wistar albino rats were supplied from the Experimental Animal Care Center at King Saud University (KSU), Riyadh, Saudi Arabia. All animals were housed under controlled ambient conditions (22 ± 2 °C and 55 ± $5\%$ relative humidity) and exposed to a daily light/night cycle of 12 h each. During the one-week adaptation period and throughout the experimental procedures, all rats had free access to their food and drinking water. All experimental procedures conducted in this study were approved by the Research Ethics Committee at KSU, Riyadh, Saudi Arabia, which follows the well-established guidelines of the National Research Council’s Guide for the Care and Use of Laboratory Animals [28]. ## 2.2. Diets Chronic feeding of an HFD is the most common protocol to induce NAFLD in rats [29]. Commercially available standard and high-fat diets (STD and HFD, respectively) were used in this study. The STD (Cat. No. Teklad 2014) was purchased from Envigo, Indianapolis, IN, USA, and has a total energy of $2.9\%$, of which $13\%$ is obtained from fat. The HFD (Cat. No. D12451) was purchased from Research Diets, New Brunswick, NJ, USA, and has a total energy of 4.73 kcal/g, where $45\%$ of this energy is obtained from fat. This diet was used previously to induce IR and NAFLD in rats [30]. ## 2.3. Experimental Design Fresh royal jelly (RJ) was purchased from a certified supplier in Riyadh, KSA, and was identified and its purity tested by an expert nutritionist at the department of nutrition at KSU. A total of 56 male rats were selected randomly and divided and classified as follows ($$n = 8$$ rats/group): [1] control group: fed STD for 16 weeks and received normal saline as a vehicle; [2] control + RJ (300 mg)-treated groups: fed STD for 16 weeks and co-treated with RJ at a final concentration of 300 mg/kg; [3] HFD model group: fed HFD for 16 weeks and co-treated with normal saline as a vehicle; [4] HFD + RJ (300 mg)-treated groups: fed an HFD for 16 weeks and co-treated with RJ at a final concentration of 150 mg/kg; [5] HFD + RJ (300 mg) + CC-treated rats: fed an HFD for 16 weeks and co-treated orally with RJ at a final concentration of 300 mg/kg and intraperitoneally (i.p.) with dorsomorphin (compound C/CC) (0.2 mg/kg). Normal saline and RJ were always given by gavage at 7:00 a.m. Body weight, food intake, and calorie intake were monitored every 2 days. The in vivo dose of CC was based on previous studies [30]. The dose of RJ was based on the study of several authors who have shown the ability of RJ to reduce fasting glucose, TGs, CHOL, and LDL-c improve insulin action in diabetic and hyperlipidemic rats [27,31,32,33]. ## 2.4. Oral Glucose Tolerance Test (OGTT) The oral glucose tolerance test (OGTT) was performed as described during the last 3 days of the experimental procedure. Briefly, blood samples were drawn from overnight fasted rats, and then all rats from all groups were orally administered glucose (2 g/kg). Blood samples were redrawn at different time intervals over 2 h after glucose administration in EDTA-containing tubes, centrifuged (1100× g/10 min), and then the plasma was collected. These samples were used to measure the levels of glucose and insulin using rat-specific ELISA kits (Cat. No. 10009582, Cayman Chemicals, Ann Arbor, MI, USA, and Cat. No. 589501, Ann Arbor, TX, USA, respectively). As a measure of IR, the homeostasis model assessment of IR (HOMA) was calculated from fasting glucose and insulin levels using the formula described by Yoon et al. [ 34]: HOMA-IR = ((glucose [mg/dl] × insulin (ng/mL))/405). All measurements were duplicated for eight samples/groups according to the manufacturer’s instructions for each kit. ## 2.5. Blood and Tissue Sampling By the end of the experimental procedure, all animals were fasted overnight and were anesthetized (ketamine/xylazine; 80:10 mg/mg). Blood samples (1 mL) were collected by cardiac puncture, centrifuged (1100× g/10 min), and the serum was collected. This serum was stored at −80 °C and used later for biochemical analysis. Then, all rats of all groups were killed by cervical dislocation, and their liver and white adipose tissue (inguinal, epididymal, peritoneal, and mesenteric) were collected on ice and weighed. Parts of the livers were fixed in $10\%$ buffered formalin for histological evaluation, and all remaining parts, as well as WAT pads, were snap-frozen in liquid nitrogen and stored at −80 °C until further use. The stools of each group of rats were collected during the last 2 weeks of the experiment, pooled, dried at 37 °C, and stored at 4 °C. ## 2.6. Hepatic and Stool Lipid Extraction and Preparation of Tissue Homogenates The chloroform–methanol described by Folch et al. [ 35] was used to extract various lipid fractions from the stools and livers. In brief, 0.25 g of the frozen liver tissues or collected stools were homogenized in a 10 mL methanol/chloroform mixture (1:2; v/v) and then incubated for 5 h. Normal saline (5 mL) was added to the mixture and then centrifuged at a rate of 2000× g for 20 min. This allows the separation of two layers. The lower layer was separated, and the solvent was evaporated by a rotatory evaporator where the solid lipids were redissolved in 1 mL of isopropanol, stored at 4 °C, and then used directly to measure the lipid levels. In addition, some parts of the frozen livers were homogenized in ice-cold phosphate-buffered saline (PBS/pH −7.4) and centrifuged at 1200× g for 15 min to collect supernatants. These supernatants were stored at −20 °C and used later for biochemical analysis in tissue homogenates. ## 2.7. Biochemical Analysis Serum, hepatic, and stool levels of TGs and CHOL were measured using commercial assay kits (Cat. No. ECCH-100, BioAssay Systems, Hayward, CA, USA and Cat. No. 10009582, Cayman Chemicals, Ann Arbor, MI, USA). The hepatic and serum levels of FFAs were measured using colorimetric kits (Cat. No. MBS014345, MyBioSource, San Diego, CA, USA). The serum levels of high-density lipoprotein–cholesterol (HDL-c) and low-density lipoprotein–cholesterol (LDL-c) were measured using the following assay kits (Cat. No. STA-394, Cell Biolabs, San Diego, CA, USA; Cat. No. 79960; Crystal Chemicals, Houston, TX, USA). The serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and gamma-glutamyl transpeptidase (GGT) were measured using rat-specific ELISA kits (Cat. No. MBS264975; Cat. No. MBS269614; Cat. No. MBS9343646, MyBioSource, San Diego, CA, USA, respectively). The serum levels of leptin and adiponectin were measured using rat-specific ELISA kits (Cat. No. ab100773 and Cat. No. ab239421, Abcam, Cambridge, UK). The hepatic levels of total glutathione (GSH), malondialdehyde (MDA), and superoxide dismutase (SOD) were measured using the following ELISA kits: Cat. No. MBS265966, Cat. No. MBS268427, and Cat. No. MBS036924, MyBioSource, San Diego, CA, USA, respectively. Finally, the hepatic levels of tumor necrosis factor-alpha (TNF-α) and interleukin 6 (IL-6) were measured by ELISA (Cat. No. BMS622, Thermo Fisher, Bremen, Germany; and Cat. No. R6000B R&D System, Minneapolis, MN, USA, respectively). All measurements were conducted for eight samples/groups as per the kits’ instructions. ## 2.8. Real-Time PCR The mRNA levels of markers of AMPKα, SREBP-1c, PPARα, and GAPDH (the reference gene) were measured in the liver of each rat. The primer pair sequences have been previously validated in our laboratories and described by us and other authors [30,36]. In brief, the total RNA was extracted using a commercial kit (Cat. No. 74004; Qiagen, Hilden, Germany). The purity of the RNA was determined using the absorbance $\frac{260}{280.}$ The first-strand cDNA was synthesized using the supplied commercial kit (Cat. No. K1621, Thermo Fisher kit). The amplification of the mRNA was conducted using the SsoFast EvaGreen Supermix kit (Cat. No. 172-5200, Bio-Rad, Hercules, CA, USA) and Bio-Rad qPCR amplification (model CFX96) as instructed by the kit. The following steps were followed for each target: [1] heating (1 cycle/98 °C/30 s), [2] denaturation (40 cycles/98 °C/5 s), [3] annealing (40 cycles/60 °C/5 s), and [4] melting (1 cycle/95 °C/5 s/step). The relative mRNA expression of all target genes was presented after the normalization of GAPDH using the 2ΔΔCT method. ## 2.9. Western Blotting For Western blotting, the liver tissues were homogenized in radioimmunoassay (RIPA) buffer (Cat. No. 89901, Thermo Fisher, Waltham, MA, USA). The total protein levels in all samples were measured using the Pierce™ BCA Protein Assay Kit (Cat. No. 23225, Thermo Fisher, Waltham, MA, USA). The proteins were diluted in the loading buffer, and then equal volumes of each sample were separated by the SDS-PAGE. The proteins were then transferred to nitrocellulose membranes, blocked with $5\%$ skimmed milk, and incubated with primary antibodies against total and phospho-AMPK (Cat. No. 2532 and Cat. No. 2531; Cell Signaling Technology, Danvers, MA, USA; 62 kDa, 1:1000 and 1:500, respectively) or β-actin (# 3700, 45 kD, 1:1000). The membranes were then incubated with the corresponding secondary antibodies and incubated with West Pico PLUS chemiluminescence substrate (Cat. No. 34580, Thermo Fisher, Waltham, MA, USA) for 5 min. The developed bands were scanned and analyzed using the C-Di Git blot scanner. Washing three times for 10 min with TBST buffer was conducted between steps. All antibodies, as well as the skimmed milk, were diluted in the TBST buffer. Incubations with the primary or secondary antibodies were performed at room temperature for 2 h and with continuous shaking. The expression of all target proteins was normalized against β-actin. ## 2.10. Liver Histopathological Evaluation Cuts of liver samples were immersed in $10\%$ buffered formalin for 24 h. All tissues were dehydrated in increasing concentrations of ethanol and were then cleaned with xylene. The tissues were subsequently coated in paraffin wax and sectioned with a microtome (5 µm). The tissues were then routinely stained with hematoxylin and eosin (HE) for overall morphology. All photos were captured using a light microscope at a magnification of 200×. ## 2.11. Statistical Analysis All data were fed into a computer and analyzed by two-way ANOVA using GraphPad Prism software. Normality was tested using the Kolmogorov–Smirnov test. The comparison between the various groups was conducted using Tukey’s post hoc test. The data were considered significantly different at $p \leq 0.05.$ ## 3.1. RJ Reduces Body and WAT Fat Weights with No Effect on Food Intake Final body weights, weekly food intake, and weights of the mesenteric and subcutaneous fats were significantly increased in the HFD-fed rats compared to the control rats (Figure 1A–E). However, treatment with RJ with or without CC did not alter the food intake in the HFD-fed rats (Figure 1A,B). In the same way, treatment with RJ had no effect on the food intake in the control rats (Figure 1A,B). In contrast, although no effect was shown in the control rats, RJ reduced the final body weights and the weights of subcutaneous and mesenteric fats in the HFD-treated rats. These effects were prevented when CC was co-administered with RJ (Figure 1B–F). ## 3.2. RJ Attenuates Fasting Hyperglycemia, Insulinemia, and Insulin Resistance in HFD-Fed Rats The serum glucose levels, as presented by the graph or area under the curve (AUC), were significantly increased from 0.0 min until the end of 120 min of the OGTT in the HFD-fed rats compared to the control and RJ-treated control rats (Figure 2A,B). The AUC for the glucose levels of the OGTT measured in the HFD + RJ-treated rats were not significantly different from the control or RJ-treated rats (Figure 2A,B). No significant variations in the glucose levels presented on the 120 min graph or calculated AUC were seen between the HFD-model rats and HFD + RJ + CC-treated rats. In accordance, the fasting glucose, insulin, and HOMA-IR index were significantly higher in the HFD-fed rats than in the control or RJ-treated rats but were significantly reduced in the HFD + RJ-treated rats (Figure 2C–E). On the other hand, no significant variations in the levels of these biochemical markers were seen when the HF-fed rats were compared to the HFD + RJ + CC-treated rats (Figure 2C–E). Of note, the serum levels of fasting glucose, insulin, and HOMA-IR were significantly decreased in the RJ-treated control rats compared to the control rats that received the vehicle (Figure 2C–E). ## 3.3. RJ Improves Adiponectin Levels and Ameliorates Systemic Inflammation and the Increase in Liver Marker Enzymes in HFD-Fed Rats The liver weights and the serum levels of leptin, TNF-α, IL-6, ALT, AST, and γ-GTT were significantly increased, but the serum levels of adiponectin were significantly decreased in the HFD-fed rats compared to the control rats and were significantly reversed in the HFD + RJ-treated rats (Table 1). Among these markers, only the serum levels of adiponectin were significantly increased in the RJ-treated control rats compared to the control rats administered the vehicle (Table 1). However, the liver weights and the levels of all these markers did not significantly vary between the HFD and HFD + RJ + CC-treated rats (Table 1). ## 3.4. RJ Reverses Hyperlipidemia and Reduces Hepatic Lipid Levels in HFD-Fed Rats The HFD-fed rats showed a significant increment in the serum and hepatic levels of CHOL, FFAs, and TGs compared to the control rats (Table 2). Their serum had higher levels of LDL-c, and their stool showed significantly higher levels of CHOL and TGs (Table 2). The fecal levels of CHOL and TGs were not significantly different when the control rats were compared with the RJ-treated control rats or when the HFD-fed, HF + RJ, and HFD + RJ + CC rats were compared with each other (Table 2). On the other hand, the RJ-treated control rats showed significantly lower serum and hepatic levels of TGS, FFAs, and CHOL as well as lower levels of LDL-c compared to the control rats (Table 2). In addition, a similar reduction in the levels of all these serum and hepatic lipid markers was seen in the HFD + RJ-treated rats compared to the HFD-fed rats (Table 2). Interestingly, no significant differences in the levels of these hepatic and serum lipids were seen between HFD-fed rats and HFD + RJ + CC-treated rats (Table 2). ## 3.5. RJ Attenuates Oxidative Stress and Inflammation and Improves Antioxidant Status in the Livers of HFD-Fed Rats The hepatic levels of TNF-α, IL-6, and lipid peroxides (MDA) were significantly higher in the levels of total GSH and SOD in the livers of the HFD-fed rats compared to the control or RJ-treated rats (Figure 3A–D). The levels of GSH and SOD were significantly increased but the levels of TNF-α, IL-6, and MDA were significantly reduced in the livers of HFD + RJ-treated rats compared to the HFD-fed rats (Figure 3A–E). In addition, the levels of SOD and GSH were significantly higher but the levels of MDA were significantly lower in the livers of the RJ-treated control rats compared to the control rats administered the vehicle (Figure 3A–E). However, the levels of all these inflammatory/oxidant/antioxidant markers did not vary between the HFD + RJ + CC-treated rats and HFD-model rats (Figure 3A–E). ## 3.6. RJ Enhances the Activity (Phosphorylation) of AMPK and PPARα and Downregulates SREBP1c in the Livers of Control and HFD-Fed Rats The mRNA levels and levels of p-AMPKα were significantly increased but the mRNA levels of SREBP1 and PPARα were significantly reduced in the livers of HFD-fed rats compared to the control and RJ-treated rats (Figure 4A–D). The total protein levels of AMPKα were not significantly different among all groups of the study (Figure 4D). The hepatic mRNA levels of AMPKα were not significantly different between the control and RJ-treated rats (Figure 4A). The hepatic mRNA levels of PPARα and SREBP1 were significantly reduced but the protein levels of p-AMPKα were significantly increased in the RJ-treated rats compared to the control rats (Figure 4A–D). This picture was reversed in the livers of HFD + RJ-treated rats compared to the HFD-fed rats. However, CC treatment did not alter the mRNA levels of AMPKα in the HFD + RJ + CC treated rats, but it significantly reduced the phosphorylation of AMPK and mRNA levels of PPARα and concomitantly increased the mRNA levels of SREBP1c (Figure 4A–D). ## 3.7. Histological Finding The livers of the control and RJ-treated rats showed normal features with intact central veins, sinusoids, and hepatocytes (Figure 5A,B). The livers from the HFD-fed rats showed an increased number of fat vacuoles of various sizes with dilated central veins. They also showed an increased number of necrotic cells (Figure 5C). Considerable improvement in the structure of the livers with very few fat vacuoles was seen in the livers of the HFD + RJ-treated rats (Figure 5D). A similar picture to that seen in the HFD-treated rats was also seen in the livers of the HFD-fed rats (Figure 5E). ## 4. Discussion The findings of this study revealed that the chronic feeding of RJ to HFD-fed rats not only reduces the gain in weight and improves IR, but also attenuates hyperglycemia and alleviates hepatic damage and steatosis. In addition, this study showed that the antidiabetic and anti-steatosis mechanisms by which RJ acts involve at least antioxidant potential, as well as the activation of the hepatic AMPK signaling-mediated upregulation of PPARα (fatty acid oxidation) and the suppression of SREBP$\frac{1}{2}$ (de novo lipogenesis). Chronic HFD feeding is the best model to induce obesity, metabolic features, and NAFLD in rats. Obesity and associated metabolic abnormalities are the best-known risk factors for NAFLD and NASH. In addition, the higher adipose tissue mass stimulates the release of leptin from the adipose tissue, stimulating further food intake and exaggerating the increase in body weight. Indeed, the polyphagic HFD-fed animals of this study showed typical features of type 2 diabetes mellitus (T2DM), including hyperglycemia, hyperinsulinemia, impaired OGTT, IR, obesity, and dyslipidemia. These data are similar to the findings of many other studies. On the contrary, the ability of RJ to reduce body weight and reverse the other metabolic symptoms was our strongest evidence for the antidiabetic effect of RJ. These effects were expected given the previously reported studies that found the ability of RJ at doses of 100–300 mg/kg to reduce fasting glucose levels, modulate circulatory insulin levels, improve IR and HOMA-IR, lower HbA1C, and attenuate obesity in diabetic rats [25,27,32,37]. Moreover, higher doses of 1000–3000 mg/kg showed hypoglycemic effects in diabetic subjects with varied effects on insulin and HbA1C [24,26,38]. Interestingly, RJ did not alter food or calorie intake in the control or HFD-fed rats, suggesting that its hypoglycemic and hepatic protective effect is independent of modulating food/calorie intake. Dyslipidemia remains the most common cause of the development of NAFLD [4]. In addition, oxidative stress and inflammation are the major triggers that can accelerate liver damage, hyperglycemia, and steatosis and facilitate the progression to NASH by altering enzyme activities, inducing lipid peroxidation, and promoting DNA damage and hepatic IR [39]. The relationship between oxidative stress/inflammation and IR is bidirectional. On the one hand, increased oxidative stress and inflammation in the adipose tissue induce peripheral IR [40,41]. On the other hand, the development of IR in the adipose tissue stimulates hepatic lipogenesis, oxidative stress, inflammation, and IR [39]. Within this view and in response to impaired insulin activity in the peripheral tissues, the stimulated lipolysis in the WAT enhances the influx of FFAs to the liver [39]. This stimulates de novo lipogenesis and results in the accumulation of TGs, mitochondrial damage, and the generation of ROS and inflammatory cytokines. In addition, adipose tissue IR promotes the release of numerous inflammatory cytokines that trigger mild systemic inflammation and induces hepatic inflammation and oxidative stress [4,39]. In the same line with these studies, the HFD-fed rats in this study showed increased serum and hepatic levels of FFAs, which could be attributed to the uptake, as well as increased lipolysis in the peripheral tissues of these rats due to the obvious IR. In addition, their livers showed increased fat vacuoles, which increased TGs, CHOL, IL-6, and TNF-α, concomitant with the higher serum levels of TGs, CHOL, and LDL-c. These data indicate increased lipogenesis and inflammation in the livers of these rats. This picture is similar to many previous studies that have also shown increased markers of inflammatory mediators in the livers of NAFLD animals. In addition, the livers of HFD-fed rats showed higher lipid peroxides that coincided with reduced levels of antioxidant markers (i.e., GSH and SOD), which indicates increased scavenging of these antioxidants due to high levels of ROS. Reduced levels of enzymatic and nonenzymatic antioxidants have been shown in numerous experimental animal models and in humans with NAFLD [8,42]. In contrast, treatment with both doses of RJ significantly improved the alterations in the serum and liver lipids, reduced hepatic levels of MDA, TNF-α, and IL-6, and increased hepatic levels of GSH and SOD in the HFD-fed rats. These data indicate the potent hypolipidemic and antioxidant potentials of RJ. Of note, treatment with RJ did not affect CHOL and TGs fecal levels in the control and HFD-fed rats, thus dissipating its hypolipidemic effect from altering intestinal lipid absorption. While the significant improvement could explain these data in peripheral IR and the subsequent amelioration in serum insulin levels, they may also suggest independent effects. Indeed, treatment with RJ of both doses also reduced the serum and hepatic levels of TGs and CHOL and stimulated levels of GSH and SOD, even in the control rats. However, since RJ did not affect the inflammatory markers in the control rats, it could be assumed that the anti-inflammatory effect of this nutritional food is secondary to its antioxidant effect. In addition, it could be suggested that RJ also stimulates peripheral IR by suppressing oxidative stress and inflammation. The effects of RJ on metabolism and serum lipoprotein levels have been reported in both human and animal studies. Indeed, treatment with RJ at a dose of 350 mg/kg reduced serum CHOL and LDL-c levels in hypocholesterolemic adults [43]. It also reduced serum TGs and CHOL in a similar study in diabetic women with no change in HDL-c levels [44]. In addition, diabetic patients chronically fed RJ showed higher circulatory levels of TGs, CHOL, LDL-c, and apolipoprotein (Apo) A-I and had a reduced ratio of ApoB/Apo AI [24]. Similarly, the uptake of lyophilized RJ (333 mg/kg) attenuated the increase in serum CHOL and TGs levels in overweight nondiabetic individuals [45]. A similar reduction was found in the serum levels of CHOL, TGs, and LDL-c in diabetic rats after treatment with 100, 200, or 300 mg/kg [27,32]. Similar to our data, treatment with RJ at doses of 100–450 mg also reduced the serum and hepatic levels of TGs and CHOL in animal models of NAFLD. In addition, the antioxidant and anti-inflammatory effects of RJ have been described in numerous experimental and clinical trials. Indeed, RJ inhibited inflammatory cytokine production in lipopolysaccharide (LPS)-treated BV-2 murine microglial cell line by suppressing NF-κB and p38/ c-Jun N-terminal kinase (JNK) signaling pathways [46]. Furthermore, reduced circulatory levels of Il-6 and c-reactive protein (CRP) were seen in asymptomatic overweight patients who had received RJ at a daily dose (333 mg/kg) [45]. RJ also reduced the bronchoalveolar lavage fluid levels of TNF-α in cancer and reduced cellular toxicity in patients treated with bleomycin [47]. It also reduced MDA levels, attenuated the reduction in glutathione peroxidase (GPx), reduced the expression of endothelial nitric oxide (eNOS) and Bax, and improved prostate histology in rodents with prostate cancer [48]. With no obvious toxicity, RJ also reduces the release of TNF-α, IL-1, and IL-6 from LPS-stimulated peritoneal macrophages in vitro [22]. RJ also reduced inflammatory cytokine production in animal models of colitis and renal inflammation [49,50]. By the same token, RJ has exceptional antioxidant activity mediated by chelating iron and scavenging superoxide, hydroxyl, and hydrogen peroxide radicals [51]. Others have also shown that RJ hydrolysate reduced the generation of ROS and stimulated the levels of SOD and GSH in LPS-treated macrophages [52]. It also inhibited ferric nitrilotriacetate (Fe-NTA)-induced lipid peroxidation in vitro [53]. In vivo, treatment with RJ attenuated cisplatin-mediated testicular damage by suppressing the generation of MDA and increasing the levels of GSH, SOD, and CAT [54]. It is also protected against carbon tetrachloride-induced hepatic damage by attenuating levels of MDA and stimulating levels of GSH and ascorbate [55]. In addition, the protective effect of RJ against cadmium-chloride-induced nephrotoxicity was associated with reduced lipid peroxidation, boosting GSH and antioxidant enzymes, and reducing the generation of TNF-α and IL-6 [56]. Yet, the mechanism by which RJ could exert its hypolipidemic, antioxidant, and anti-inflammatory effects is still not known. In previous research, the authors have shown the ability to increase the doses of RJ (150, 300, and 450 mg/kg) to alleviate NAFLD in rats and have attributed this to its antioxidant ability and regulation of circadian genes, including Per1 and Per 2, in the livers of ovariectomized rats [57]. In another study, the authors referred to the protective effect of RJ against NAFLD due to its antioxidant and anti-inflammatory effect, as well as regulating the metabolism of FAs such as α-linolenic acid, linoleic acid, arachidonic acid, and the biosynthesis of unsaturated fatty acids [58]. Although we have found similar antioxidant and anti-inflammatory effects, the ability of RJ to promote such beneficial effects on body weight, lipid and glucose metabolism, and liver pathology forced us to look further afield to identify other mechanisms. Therefore, we have targeted adiponectin signaling pathways based on the available data showing that adiponectin is a novel target for treating NAFLD [59]. Adiponectin is an adipokine that is released from adipose tissue. A negative relation between adiponectin levels and fat mass is reported. Levels of circulatory adiponectin are significantly reduced in obese animals and subjects, as well as in animals and patients with NAFLD. Adiponectin is an anti-inflammatory molecule and antidiabetic molecule that can improve insulin signaling and glucose and lipid metabolism by activating AMPK and suppressing the toll-like receptor-4 inflammatory pathway in the liver and muscles. Activating AMPK protected against NAFLD in several experimental studies [12,17]. In this regard, the pharmacological or genetic activation of AMPK inhibited hepatic lipogenesis by activating FA oxidation through activating PPARα [17]. It also inhibits hepatic TGs and CHOL synthesis by downregulating SREBP1 and SREBP2 and their target lipogenic genes, such as fatty acid synthase (FAS) and acetyl-CoA carboxylase (ACC-1) [17]. In addition, AMPK increases peripheral glucose uptake in the muscles by stimulating GLUT4 expression [60]. Furthermore, AMPK can inhibit cytokine production and stimulate antioxidant expression by suppressing the nuclear factor kappa beta (NF-κB) and activating the nuclear factor erythroid 2–related factor 2 (Nrf2) [61]. In this study, we have also seen a reduction in the circulatory levels of adiponectin and the reduced mRNA expression and phosphorylation of AMPKα in the livers of HFD-fed rats. In addition, these rats showed increased transcription of SREBP1, SREBP2, and FAS and a reduction in the mRNA levels of PPARα. These data support many other studies that also showed similar effects [12,17]. However, HFD did not change the total protein levels of AMPK. In the cell, AMPK is found in different forms, including AMPKα and AMPKβ. Unfortunately, we did not measure the mRNA of AMPKβ and the individual protein levels of each of these isoforms of AMPK, which may help us to explain this observation and why the livers of HFD-fed rats showed normal levels of total AMPK. In the same line with our study, several other authors have also shown reduced activities of AMPKα with no change in the total levels of AMPK [62,63]. Therefore, it could be possible that HFD exerts different effects on different isoforms of AMPK. On the other hand, treatment with RJ significantly increased the serum levels of adiponectin and concomitantly increased the hepatic phosphorylation of AMPK in HFD-fed rats. In parallel, it reduced the mRNA expression of SREBP1c, but stimulated the transcription of AMPKα and PPARα not only in the livers of HFD-treated rats but also in the livers of control rats. Interestingly, RJ did not affect circulatory levels of adiponectin or mRNA levels of AMPKα in the livers of control rats but significantly increased the rate of phosphorylation (activity) of MAPKα. This suggests that RJ stimulates the hepatic phosphorylation of AMPK without modulating the expression/levels of adiponectin, thus suggesting a novel independent mechanism of action. To support our findings, we treated the HFD-fed rats with 300 mg/kg RJ with CC, a well-known inhibitor of AMPK. In accordance, the treatment with AMPK abolished all the benefits of RJ on markers of oxidative stress, inflammation, glucose, insulin sensitivity, and lipids. Additionally, the treatment with CC did not alter the inhibitory effect of RJ on fat masses or its stimulatory effect on adiponectin in these HFD-treated rats. Therefore, these data suggest that RJ acts in an adiponectin-independent mechanism mainly through activating hepatic and possibly peripheral AMPK levels, a key mechanism that underlies its hypoglycemic, hypolipidemic, antioxidant, and anti-inflammatory effects. However, such an increase in adiponectin levels could be explained by the inhibitory effect of RJ on WAT adiposeness, which could be secondary to the improvement of insulin signaling. These data can be also supported by the study of Yoshida et al. [ 25], who have also shown the potential of RJ to alleviate hyperglycemia in obese diabetic KK-Ay mice through phosphorylating muscular and hepatic AMPK-mediated improvement in antioxidants and the suppression of inflammation. However, these authors have shown that treatment with RJ also increases the mRNA expression of adiponectin and its receptors. In conclusion, this study is unique as it is the first to investigate the molecular mechanistic effect behind the hypoglycemic and anti-hyperlipidemic effects of RJ in HFD-fed rats with NAFLD. In accordance, our data support a beneficiary effect of RJ on activating hepatic AMPK. In addition, RJ seems to act by boosting enzymatic and nonenzymatic antioxidant systems in the liver. However, these data need further clinical validation. ## References 1. Alswat K., Aljumah A.A., Sanai F.M., Abaalkhail F., Alghamdi M., Al Hamoudi W.K., Al Khathlan A., Al Quraishi H., Al Rifai A., Al Zaabi M.. 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--- title: Human Digital Twin for Personalized Elderly Type 2 Diabetes Management authors: - Padmapritha Thamotharan - Seshadhri Srinivasan - Jothydev Kesavadev - Gopika Krishnan - Viswanathan Mohan - Subathra Seshadhri - Korkut Bekiroglu - Chiara Toffanin journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10056736 doi: 10.3390/jcm12062094 license: CC BY 4.0 --- # Human Digital Twin for Personalized Elderly Type 2 Diabetes Management ## Abstract Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–$75\%$ to 86–$97\%$ and reduces insulin infusion by 14–$29\%$. ## 1. Introduction Type 2 diabetes (T2D) is characterized by reduced insulin secretion from the pancreas and is widely prevalent in low-and-middle-income countries, and also industrialized countries like the US [1]. Unlike type 1 diabetes (T1D), where patients are characterized by a complete lack of insulin secretion, in T2D relative insufficiency and/or inefficiency of insulin is common. Therefore, T2D requires continuous monitoring due to intermittent behavior and influences of certain factors: food, activity, etc., leading to frequent hyper and hypo events. This leads to many challenges in understanding and managing T2D (T2D). The problem becomes even more challenging in elderly people due to the changes observed with the aging, a.k.a geriatric conditions [2,3]. These geriatric-specific conditions are co-morbidity, multiple drug intake, reduced activity levels, pronounced diet influences, variability in nutrient absorption with age, hormonal changes, and other aspects. These factors evolve with age and progress as well, thereby making the blood glucose levels (BGL) highly unpredictable and intermittent. Similarly, insulin efficiency could also vary within a patient or among elderly patients with similar conditions. Therefore, personalization becomes the cornerstone for managing elderly T2D (E-T2D) effectively. In particular, precise insulin infusion considering the underlying geriatric conditions for managing E-T2D becomes important. However, this is cumbersome, as capturing certain geriatric factors to develop insights by itself is insufficient, and existing studies have not completely explored these specific aspects. This points out to an urgent need to propose a holistic approach for personalization that provides deep insights about various aspects and fuses them to decide the insulin dosing for BGL management. ## 1.1. Existing Methods Traditionally, oral hypoglycemic agents (OHAs) and insulin infusion are two widely used approaches in managing T2D. The OHA in the form of pills is recommended for patients who are newly diagnosed or not insulin dependent. Nevertheless, over long time periods, these patients become insulin dependent as well with age or other factors [4,5]. Therefore, eventually, insulin administration becomes the treatment option for most patients. In such cases, insulin infusion could be either manual or automatic. In manual infusion, the patient or caregiver infuses the insulin based on the diabetologist’s recommendation. The interventions are intermittent and open-loop in the sense that the BGL is not continuously monitored. Therefore, conventional insulin therapy will have multiple challenges in those with advanced T2D. Automated insulin delivery, also known as an artificial pancreas (AP), is touted as a solution for managing T2D [6]. The AP continuously monitors interstitial glucose level (IGL) by using a continuous glucose monitoring (CGM) sensor and infuses insulin to manage diabetes to be within 70–180 mg/dL (called the euglycemic range), using a control algorithm. Control algorithms for computing insulin dosing are the heart of AP systems. Different control algorithms for infusing insulin have been proposed both in practice and the literature. Existing control algorithms can be broadly discerned to be reactive and predictive. Reactive control action includes control algorithms such as proportional-integral and derivative (PID), fuzzy, and other approaches that compute insulin based on current measurements and lack predictive capabilities [7,8,9]. Some additional capabilities are available in the reactive controllers as well. For example, the Medtronic 722 has a PID controller with features such as automatic insulin shut-off when the BGL reaches closer to the lower target value and could include meal announcements in the insulin infusion plans. Still, these actions are based on measurements and not on patient-specific data, which is becoming a shortcoming in elderly patients. Predictive controllers, on the other hand, use patient models to study future behaviors and compute insulin infusion to manage T2D. The model predictive controller (MPC), a sophisticated control algorithm based on optimization routine and patient model, is revealed to be a promising solution [10,11]. This is mainly due to the fact that the MPC uses an approximate patient model to compute the insulin infusion and could embed real-time measurements in such computations. The role of an MPC to include aspects such as food influences and physical activity has been proposed in [12,13,14]. Though an MPC provides significant benefits and is suited for personalization, the need for patient models makes its design overwhelming. This is mainly due to model parameters whose computations require simulations or measurements (e.g., plasma insulin) that are difficult to obtain. Despite these difficulties, Omnipod 5 and Tandem t:slim X2 Control IQ are two successful MPC implementations available as commercial solutions [15]. Studies reporting on their safety and efficacy have been proposed in [16]. In spite of this success, patient-specific models and embedding aspects such as comorbidity still remain a challenge. Consequently, MPC implementation is relatively quite cumbersome to personalize for particular patients due to the need for computing model parameters. In practice, obtaining a patient model is difficult, and existing models rely on data such as plasma insulin that could be obtained from laboratory studies only. For this reason, simulators are being proposed and used for studying MPC efficacy. However, designing an MPC is limited due to the need for complex patient models (e.g., nonlinear), model parameters, optimization routines (nonlinear programming), and implementation in resource-constrained hardware (embedded processors with limited memory and processing power). This lends several challenges to personalization, and the focus is still open to obtaining models that are simple yet able to comprehensively capture BGL evolution, as this would also make optimization models simple and easier to be solved in embedded hardware. Recent research is more focused on personalizing the AP [17]. To this extent, patient-specific models are proposed that capture certain intricate aspects (e.g., insulin–glucose interaction). A personalized AP for T1D with validation trials up to 6 months on individual patients has been reported in [18,19]. More recently, several long-period trials lasting months have been outlined in [20,21]. However, to our best knowledge, these personalization efforts are not oriented towards E-T2D management. This is mainly due to the fact that obtaining personalized patient models factoring geriatric conditions is rather cumbersome, and multiple models may be required that captures specific viewpoint and provide various facets to the patient model. Clearly, handling E-T2D requires novel approaches going beyond traditional ones currently used for managing T2D. Moreover, as various facets need to be considered continuously, a framework is necessitated in a complex model, and certain modules can be turned off as required—a general approach that is widely used. Further, such a framework requires capturing sensor and patient-specific data, knowledge creation from raw data, discerning patterns, understanding outcomes, and using it to manage as well as simultaneously personalize the E-T2D treatment. A framework is required for depicting the patient from a different viewpoint and also having capabilities to obtain the data required for orchestrating these different models. Notwithstanding this, to our best knowledge, a framework to personalize and manage E-T2D is completely lacking and has not been studied in the literature. The digital twin (DT) is a framework that could fuse multiple models, data, and provide interfaces to virtualize physical entities with different viewpoints. In essence, the DT creates a digital replica of a physical object, process, or system orchestrated through a suite of models that exchange information with physical entities in real-time [22]. The DT was conceptualized around 2003 by Grieves for product life-cycle management in manufacturing, and it proposed a three-dimensional concept [23]. The concept was extended with five entities: physical object, digital representation, interconnections, data, and services [24]. The importance of the DT could be underlined by the fact that it is touted as one of the three emerging technologies in 2020 as per the IEEE Computer Society https://www.hpcwire.com/off-the-wire/ieee-computer-society-unveils-its-2022-technology-predictions/ (accessed on 2 December 2022). Major drivers for their proliferation are the Internet of Things (IoT), artificial intelligence (AI), machine learning, and big-data paired with digital and real-objects. The DT integrates these technologies appropriately for real-time data aggregation, system analysis, status monitoring, knowledge creation, providing deeper insights, risk management, bespoke displays, informed actions, agile situation management strategies, prediction, and others ([25,26,27] and references therein). Consequently, a generalized concept and notion for DT are still evolving. Notwithstanding this, the DT is proliferating other domains such as space cooling/heating [28,29], wind turbine fault diagnosis [30,31,32], bio-processes [33,34], energy [35], oil and gas [36], automotive [37,38,39], infrastructure [40,41], and defense [42] to name a few. More recently, DT concepts are being proposed in healthcare and medicine and are expected to provide significant advantages [43,44]. Mainly the DT is expected to personalize treatment pathways. ## Digital Twin in Healthcare and Medicine Human digital twins (HDTs) for healthcare are emerging as a cornerstone for personalizing treatments [45,46]. The prevalence of the IoT devices that can connect to user devices to collect, store, process, and notify in real-time through even mobile applications or embedded sensors is becoming a norm. By combining the patient replica with real-time data, it is possible to get deep insights into the patients’ conditions, manage conditions, predict future occurrences, monitor patients’ current conditions, and make informative decisions. Furthermore, simulations, dynamic models, and domain informed data-based models could help increase intelligence to manage, and alert users. Amongst the relevant research in this domain, a reference model for DT healthcare was proposed in [44]. The framework enabled self-adaptation and autonomic computing for continuous monitoring and forecasting of chronic disease diabetes. However, process implementation was lacking. A cloud-based DT for elderly healthcare was proposed in [47]. Cloud-DTH was a reference framework that combined the cloud model with the DT model. This enabled individualized healthcare. However, the case studies presented lacked a performance evaluation and results illustrating this aspect. Karkara et al. [ 48] proposed DT for hospital systems using discrete-event simulation and IoT devices. Flexsim HC software was used to test the feasibility of the proposed methodology with different scenarios. However, the DT has not been explicitly brought out in the investigation. Detecting seizures before they surface using machine learning models has been presented in [49] through data collected from drug-resistant epilepsy patients. The authors proposed a deep-epileptic seizure detection using a surrogate gradient descent-based approach. However, complexity and implementation aspects have not been studied. Personalized healthcare using an HDT for personalized healthcare has been reviewed in [50]. The HDT is a new direction in research that provides tools to provide proactive, prescriptive, accurate, and efficient personalized healthcare system (PHS) to patients using DT concepts. The HDT has three parts: patient (PT), virtual digital twin (VT), and PT–VT interactions. However, orchestrating an HDT requires novel data sources, new models, and techniques to achieve personalized management. The main advantage of an HDT is its ability to handle both historical and real-time data that is missing in current approaches. With an HDT, management could be tailored to focus on a particular individual’s contextual data. The role of digital twin technology for Healthcare 4.0 was studied in [43]. Existing works clearly point out an HDT’s ability to personalize treatment by combining various data sources, models, and techniques. An HDT could leverage data to optimize patient-specific treatment plans, recommend life-style modifications, improve management, and reduce overall costs incurred by E-T2D patients. Furthermore, an HDT provides prescriptions by predicting future patient states and preventing hyper and hypoglycemic events. However, there exist some pertinent questions to be answered for successful HDT-based personalized E-T2D management. These questions include whether underlying influences on E-T2D, such as comorbidity, multiple drug intake, diet influences, activity levels, and other patient-specific aspects, could be used for E-T2D management and whether inter- and intra-patient variations could be detected using temporal data patterns to provide deeper insights. In addition, extracting precise medical data and techniques to obtain models that could capture aspects that are contextual to a particular patient needs to be evolved. Our objective is to illustrate the role of an HDT in managing E-T2D through patient virtualization from different perspectives for delivering precision insulin. However, this requires personalization and modeling different aspects and fusing various patient-relevant data. To this extent, the HDT builds a suite of models to gain deeper insights on the patient. These models exploit the raw data and convert them to knowledge by mining them. The HDT’s ability to exploit historical, contextual, image, and other forms of data to create deeper insights about the patient becomes a major enabler for precision insulin. Finally, to prove an HDT’s ability to manage E-T2D, clinical trials to generate patient models and simulation studies to show that precision insulin delivery are used as illustrations. Our main contributions are:(i)A new HDT framework and architecture towards personalizing E-T2D management with capabilities to aggregate data, a suite of models that build intelligence on the data, and an interface between the VT and PT.(ii)An IoMT architecture to aggregate data vis-á-vis HDT for E-T2D management.(iii)Modules for forecasting, food nutrient predictions, time-series trending, and other intelligence required for managing E-T2D.(iv)An adaptive patient model that personalizes insulin infusion based on geriatric factors and learning-based MPC (LB-MPC) that could embed the deep-learning models to compute precise insulin infusion.(v)Illustrate the HDT’s capability to manage E-T2D by modeling a personalized patient model and embedding other aspects. To this extent, clinical data from patients are collected for 14 days to obtain patient model and patient-specific contextual data from 15 elderly patients. Using these models and data, simulations are performed to illustrate the HDT’s ability to deliver precision insulin considering various aspects. The paper is organized as follows: Section 1 introduces the concepts. Section 2 presents a detailed description of the HDT framework architecture, and its components. Section 3 presents the HDT implementation aspects for the various modules. Section 4 presents the clinical trial and simulation results. Section 5 summarizes the conclusion and future course of the investigation. ## 2. HDT Framework Architecture The paper proposes and implements an HDT framework for context-aware E-T2D to enhance management and achieve deeper insights. The proposed HDT framework uses IoT devices and sensors to aggregate data from elderly patients using an IoMT architecture. Similarly, it builds a suite of models to capture different aspects of the E-T2D patient. The models can be domain informed data-based models (e.g., machine learning), dynamical models based on differential equations, or hybrid models that can represent a complex nonlinear dynamical model of the elderly patient. Such models can capture specific aspects of the patient. The tools along with the IoMT architecture, create a virtual replica of the E-T2D patients and help to manage BGL within safe bounds. Furthermore, it enables learning inter- and intra-personal variations by cooperating to learn data from similar patients and within the same patient. It is to be noted here that such data is more frequent and has higher data rates than typical clinical measures. This helps healthcare professionals, patients, and caregivers to gain more insights to manage E-T2D. Three basic entities of the HDT framework are Patient (PT), Virtual Twin (VT), and interfaces. The PT is the patient who needs to be virtualized by using historical data, sensor measurements, contextual data, images, and other data sources. The VT is the digital replica of the PT that could be used for managing E-T2D. Interfaces map PT to VT and transfer data (e.g., sensor measurements) or inferences (e.g., food influences on T2D). The PT and VT need a reliable interface that enables the co-evolution of both the PT and VT. Together PT, VT, and interfaces offer personalized healthcare services for E-T2D. ## 2.1. HDT Framework Components The HDT framework shown in Figure 1 has four functional modules: (i) a data module that is responsible for aggregating the current data, (ii) a prediction module that provides future values based on historical and current data, (iii) a diagnostic module that uses current measurements and predictions to discern actions to predict inter and intra-patient variations and provides explanations for certain decisions and conditions, and (iv) a management module that personalizes insulin infusion and maintains BGL within bounds, whereas a module is a sub-block within the entity performing a particular task or providing a service. ## 2.2. Data Module The data module has devices to communicate, receive, store, and retrieve sensed data. To aggregate sensor data, an IoMT architecture is used to fuse IoT data with healthcare. Using IoMT architecture, vital signs, blood glucose levels, annotated food images, activity, and other sensed information are obtained. In addition, patient-centric contextual data such as diabetes history, family history, co-morbidity, drugs or multiple drug consumption, average calorie intake per day, and other data are collected. These data are aggregated and stored in the database in the data layer. The sensors are connected to the edge node and interfaced with the cloud. The database (DB) that contains all the patients’ data can be internal or external. Data can be stored, retrieved, and used from the DB. The details of the IoMT architecture, communication protocols, and communication services are discussed in the next section. ## 2.3. Prediction Module The prediction module uses the sensed information and historical data as inputs and provides future predictions of various aspects. Critically, providing predictions requires accurate data. However, aggregating accurate data with IoMT architecture and devices is challenging. This is mainly due to the fact that IoMT uses compact sensors and modules that have limited computing and memory power. Therefore, methods for data handling and preparing the data for extracting additional features to be used for predictions are required. The predictive module components—data handling and data preparation—are used to this extent. The data-handling component uses data imputation methods such as filling average values, past samples, or future samples. To this extent, it uses mathematical tools such as auto-correlation, partial auto-correlation, variance, and standard deviation to test the accuracy of the data imputation method. Second, the data preparation components provide scaling and feature extraction and study statistical aspects, such as average, minimum value, maximum value, variations from maximum to minimum, and others. These statistical measures provide valuable insights into individual patients and are important for E-T2D management and the detection of anomalies. The data profiling component uses a set of rules to provide the data required for prediction algorithms. The rules are implemented in the framework depending on the data to be used as input to a particular prediction algorithm. Temporal data predictions and pattern recognition from input data are required for understanding geriatric conditions’ effect on the BGL. In addition, the structural time-series component is a prediction method used to understand periodicity, cycles, average value, trend, and other information that could be obtained, which is critical for making predictions of the future states of the patient. Food image recognition obtains food images and recipe data to understand the nutrient intake of a particular patient. This information is used to understand the food influences on the BGL, and it estimates the carbohydrate (CHO) intake from the data. The level and type of physical activity (e.g., standing, sitting, and walking) detection are done by using the sensor measurements. The sensor tag is used to this extent. A detailed explanation of the activity detection is avoided here, considering the focus of the paper, and readers are referred to [12] for further data. ## 2.4. Diagnostic Module The diagnostic module provides valuable insights from the data module and prediction module. It has components that crunch predictions and measurement data to provide valuable insights. The diagnostic tool can provide insights into time-series patterns based on influencing factors or could even explain individual samples and their outcomes. To diagnose patterns, a matrix profile-based analysis is used, and to detect outcomes from individual samples, explainable artificial intelligence techniques are employed. These tools provide capabilities such as motif discovery, explanations of hyper and hypo conditions, and others. Similarly, the diagnostic tools have inference rules that help detect time-series patterns of different events. Similarly, to detect CHO and food nutrient-related aspects, queries are added to food images, and annotations are created for the images based on user feedback. This helps overarching current food image processing tools with feedback from the user. Consequently, nutrient predictions and their accuracy could be improved. The diagnostic module performs this action. Considering the brevity of the paper, discussions on food image processing are avoided here. The output of the diagnostic tool is the input to the management module. ## 2.5. Management Module The management module aids precision medicine by making suitable recommendations to physicians and caregivers. This way, the module helps personalize the insulin infusion, food recommendations, activity levels, and other aspects. Data fusion algorithms combine inferences from the personalization component with a personalized patient model to compute model parameters. However, these parameters capture the influences of the patient-specific aspect that could change over time. An adaptive patient model captures the dynamic influences of geriatric factors on patients. The personalized patient twin is obtained from the parameter estimation component. Here the contextual data is also embedded in the computation. Using the personalized E-T2D management twin, personalized insulin infusion is computed using the management component to maintain BGL within specified bounds. The metric estimator is used to compute time-in-range, hyper and hypo events, model errors, or other such factors. Insulin dosage reduction and other metrics can be evaluated using the time spent in hypo, hyper, and Time in Range (TIR) conditions The HDT framework presented above leads to new data, novel models that create additional knowledge, and improved decision support through new degrees of freedom. The three aspects are illustrated in Figure 2. Different components of HDT enable new data streams, novel models that use these data, and new variables that could be used for managing E-T2D. However, for the rest of our analysis, the treatment method is based on precision insulin infusion alone. Other aspects, such as behavioral interventions, are not considered in our analysis. ## 3. HDT Implementation This section describes the HDT implementation aspects. Detailed discussions on the components and their realization are provided in this section. ## 3.1. IoMT for Data Module The IoMT blends medical devices with the IoT as depicted in Figure 3. Medical devices have different communication requirements, such as personal area network (PAN)-based communication for transmitting patient data. Protocols such as Bluetooth low energy (BLE), near field communication (NFC), or bluetooth are examples of PAN. These devices generally transmit information reliably only over small distances. However, this information needs to be transmitted to longer distances for processing. Therefore, edge nodes are required to connect to PAN and be able to transmit the data to the cloud or other platforms. The edge nodes connect with sensors on one hand and have WiFi connections to connect with cloud platforms on the other. Further, as wearable sensors are commonplace today, the IoMT architecture should be flexible enough to integrate them on-the-fly. Measurements collected from IoMT architecture include vital signs, blood glucose levels, or other physical data about the patient. Moreover, HDTs require cloud services for data collection, and the IoMT architecture requires these capabilities. In what follows, the IoMT architecture and its components are discussed. The IoMT architecture for HDT has three layers: the perception, transfer, and application layers. The perception layer has compact sensors to measure BGL using continuous glucose sensors that use NFC and transmit the data to a mobile app. In addition, physical activity is detected through an accelerometer, gyros, and wifi probes, along with vital signs: (heart rate and blood oxygen saturation level) using MAX30100 based monitor, temperature (DS18B20). For detecting activity, Texas Instruments’ sensor tag CCS2210 is used. It is placed on the patient’s hip through suitable arrangements to prevent vibrations that cause noisy measurements. The sensed information is collected by the sensor node using RF and/or WiFi communication. The NRF24L01 and RFM69HCW modules are used for RF communication. The BGL is monitored using the FreeStyle Libre Pro sensor, which sends the IGL readings to a mobile app using the NFC protocol. The sensing layer is limited in communication range, and the sensor data needs to be communicated to the cloud to off-load HDT computations. The transfer layer is used for off-loading. The transfer layer has the edge node; a Raspberry Pi 3 is used in our case. It communicates with the mobile app through WiFi to collect data. The mobile app collects the data using NFC and BLE interfaces. Vital signs, BGL, activity levels, and other information are transmitted to the mobile app using the BLE. The mobile app uses WiFi connection to transmit the data to the edge node. Inside the edge node, a small database is created using Python scripts to read, store, and retrieve data every 15 min. In addition, the edge node can communicate with other edge devices and the cloud via WiFi interfaces. The patient, doctors, caregivers, and other stakeholders can connect to web interfaces to view their data in real-time or historical patterns. The IoMT architecture proposed in this work is shown in Figure 3. The edge node also provides various services. These include hardware interface services, monitoring services, hosting software (back-end and front-end), HDT-related services, and cloud-based services. These services are orchestrated through a back-end realized using Python scripts. In our implementation, our UI is created with Flask, a Python based front end development tool that updates hypertext modeling language (HTML) files. It uses the Jinja2 notation for coding the HTML files. The Flask application is used to connect the front end with the back end that implements the services required for managing the E-T2D interfacing with the cloud. A typical UI with data visualization is shown in Figure 4. Similarly, the edge-node has an auto-device discovery system through which sensors are connected to the edge devices. Even wearable sensors could communicate to the edge node through BLE connections. Device discovery is part of the monitoring services. As each sensor has a specific UUID for configuring and connecting to specific services in the IoMT, not only device discovery but mapping to services could also be achieved. The edge node with sensor and communication interfaces are shown in Figure 5. Each such edge unit is deployed for the patients. The IoMT layer provides the interface to update the VT model for the HDT. In addition, calibration, and device configuration are additional services provided by the monitoring services in the edge node. The storage extension services offered by the back-end manage the local MySQL database in the edge node and MongoDB database ported to the cloud using Healthdocx, a cloud platform. The application layer implements the actual HDT in the edge node, and a virtual version is available on the cloud. The back end uses the raw sensor data and transmits it to the cloud to provide various insights and insulin recommendations to the patient factoring in various personal aspects (e.g., co-morbidity). The different modules performing computation and the application layer of the IoMT architecture are discussed in the next section. ## 3.2. Prediction Module The prediction module has the following components (see Figure 1): (i) Time-series forecasting, (ii) Food image recognition algorithm, and (iii) Structured time-series analysis. These components use patient contextual data, clinical time-series data, and others to predict future blood glucose evolution. ## 3.2.1. Multi-Time Step and Multi-Variate Time-Series Prediction As stated earlier, blood glucose levels in elderly patients are influenced by various geriatric factors. Understanding future BGL variations requires predictions using these factors. Accurate prescriptions for personalized insulin to manage diabetes requires a BGL forecast. Moreover, a forecast is required for multiple time steps in the future. Consequently, multi-variate and multi-time step forecasting is required for predicting BGLs. However, time-series forecasting for multi-variate and multi-time steps is quite different from other regression models. First, the data sequencing and ordering are important. Second, multi-variate and multi-time step forecast with conventional mathematical methods is rather difficult. Third, patient-specific factors may introduce nonlinear and time-varying behavior. Existing approaches for time-series forecasting: auto-regressive moving average, auto-regressive integral moving average, auto-regressive exogenous, etc., lack the capability to handle multivariate and multi-time step models. These models work when there is a strong correlation between the current value and past sequences. However, with sudden food intake or insulin dosing, the influences can be varied. Artificial neural network (ANN) models could model time-series data. The recurrent neural network (RNN) is a widely used method for time-series predictions. The RNNs predict the future output based on past measurements and past outputs. Therefore, there is inherently a feedback mechanism. Nevertheless, RNNs lack the capabilities to handle long-term dependencies, as only past input is used as feedback. Further, their short memory makes them unsuitable for time-series forecasting tasks. Besides, the RNNs lack control over past samples and their relevance. In other words, the RNNs lack the capability to forget past samples whose contribution towards current prediction is low. Other computational issues, such as exploding and vanishing gradients, make them unsuitable for multi-time step and multi-variate forecasting. The Long–Short Term Memory (LSTM), a deep-learning model, is designed to overcome vanishing and exploding gradient issues with RNNs [51]. Further, LSTM can handle long delays, noise, and data loss, and has more degrees of freedom to tune the network. Yet, LSTM-based multi-time step and multi-variate forecasting are still not fully realized. This is primarily because current predictions have to be carried forward for computations and are feedback as well. Therefore, error propagation happens both in forward and feedback paths. Our idea is to use LSTM for forecasting blood glucose levels for multi-time variate and multi-time-steps. The inputs to the LSTM model are insulin infusion rate, food intake as carbohydrate (CHO), and past time samples of blood glucose levels. The output is the future blood glucose levels for the next 1 h. The LSTM captures the long-term temporal correlations towards predicting the BGL variations for a pre-defined duration (1 h) and sampling rate (15 min). In essence, an LSTM is a recurrent neural network capable of sequence learning, which combats the vanishing gradient problem that is characteristic of traditional recurrent neural networks. This is achieved through a set of gates including input gates, forget gates, and output gates. Thus, given a time series sequence {s1,…,st,…sT}, the objective of LSTM is to learn the temporal dependencies in {s1,s2,…,st−1} to predict st. The responses of the various gates are given by, [1]it=σwiht−1,st+bi [2]ft=σwfht−1,st+bf [3]ot=σwoht−1,st+bo The hidden layer response of the network for the sequence st is then given by [4]ht=ot∗tanhct where ot is given by Equation [3], and ct is the memory in the network that is given by [5]ct=ft∗ct−1+it∗tanhwcht−1,st+bc where wc and bc are the weights and biases for the memory gates, respectively. The memory in the network (ct) helps in capturing the long-term dependencies to remember the past. Thus, time-series data could be represented by LSTM through multiple gates, and it holds the temporal dependencies in the patients’ time-series BGL data [52,53]. To handle data loss the estimate from LSTM for the current time-instant is used as inputs. As shown in Figure 6, the LSTM model for the blood glucose estimator has two hidden layers, and it is unrolled in time to represent its sequence representation ability, as shown in Figure 6. One can observe that output at each time point could be derived in addition to the final time step. We use the past data over an observation window of 2 h to make a prediction after 15 min for 1 h (4 samples) or more time steps. Thus, the LSTM model represents the temporal correlations and helps us to predict BGLs after 1 h in advance so that this can be used to handle data loss and physiological delays with existing CGM and provide forecasting capability to the conventional BGL sensor. Figure 7, shows the flow of the LSTM prediction module, where y and y^ represent the BGL measurements and predicted value, respectively. Besides, NC denotes the prediction horizon. yi,yi+1,yi+2,…,yi+NC samples are used to predict the BGL value y^i+NC+1. The predicted BGL value y^i+Nc+1 is given as input to the next series to predict the BGL value. We denote such forecasting through predictions carried from the current step as time-series segmentation. Here, Nc is used as the prediction horizon with abuse of notation to denote that this differs from the conventional prediction horizon of patient models. The LSTM is trained on $70\%$ data omitting 4200–6000 min. Then during the testing phase, the remaining $30\%$ is used as with the usual approach used in the literature. Our LSTM model could forecast multiple time steps ahead based on multi-variate factors, and the forecasting step is repeated again during each horizon. This means that the forecast is corrected at each step. This is a multivariate and multi-time step LSTM with a moving horizon strategy. This means that in the current step, future time-step values are predicting incorporating recent measurements, and the procedure is repeated. The trained model was used to predict the final 30 % of the data. The performance metrics such as prediction error range, RMSE, and MSE for the blood glucose estimator, were computed using the test data, which is shown in Table 1, it shows that the LSTM can predict the BGL with an error percentage of 3.06–$5.16\%$ for the patients. Table 1 shows the prediction error % for the test data, and in Figure 8 and Figure 9 the red traces show actual measured values, blue traces show predicted value in a receding horizon manner as illustrated earlier. This means that the one-ahead sample that was estimated is plotted, and the rest are discarded. The procedure is repeated during each time-instant. The LSTM-based BGL forecasting tool needs to be deployed in the HDT framework. This means the model needs to be stored and provide an output when one sample instance is given as input, and output is obtained for the next few hours. In our framework, the model was stored as JSON files automated using existing packages, and then when instantiated, the forecast was provided from the model. The model is stored in JSON files as weights and through model callback functions, the forecasts are provided as output. Note 1: As with BGL forecasting, our HDT model also uses food nutrient estimation using AI tools. The idea is to use food image recognition and using annotations as a feedback mechanism to improve the food nutrient estimation accuracy. However, the complete module description is beyond the scope of this paper, and our assumption is that food nutrients are available either through a food atlas or with a nutritionist’s recommendation. However, in the implementation, a food nutrient estimator could be implemented and has not been presented here, considering the focus of the paper. ## 3.2.2. Structured Time-Series Analysis The structured-time series analysis is based on an unobserved components model (UCM). The idea is to decompose the time-series data into trend, seasonal, cycle, and regression components. Leading to a linear decomposition to understand the time-series data. The structured time series provides a way to analyze time-series data for trends that are useful for capturing aspects such as hyper and hypo conditions. In our HDT, the structured time-series analyzer and miner module use the time-series data on BGL, insulin, and food to inform trends, cyclic factors, seasonal term (daily/weekly) variations, and errors. In addition, it also provides variance in these parameters. The generalized UCM is given by, [6]yt=μt︸trend+γt︸Seasonal+ct︸cycle+∑$j = 1$kβjxjt︸explanatory+εt︸irregular, where yt (BGL versus Time) is the outcome at time instant t, xt is the input vector, βj is the explanatory variables as in a linear regression, and εt is the irregular component. Typically the trend component follows a normal distribution μt∼N(0,σ2) wherein σ2 models the variance. The trend in our analysis captures both (level and trend). Trends model the slope of the series in the absence of any other influencing variable. The UCM in our analysis is locally linear and has a slope term (positive or negative). The seasonal component in our analysis models the daily variations, and as the data samples are collected once in 15 min, the seasonal component is modeled for 96 samples. This means a daily seasonal component is considered in our analysis. The cycle component captures the cyclical aspects at time frames much longer than the seasonal component. The time-series components and their variance provide useful insights into inter and intra-patient variability. A snapshot of the structural time-series model is shown in Figure 10. The prediction tools presented in this section provide valuable forecasts and estimations on certain food and time-series evolution. To interpret patterns and their outcomes, diagnostic tools are required. The next section presents the diagnostic module used in our HDT. ## 3.3. Diagnostic Module The diagnostic module uses multiple tools to draw inferences based on data and prediction modules. Here, we illustrate the inferences from the time series that could be drawn from such analysis. We use motif discovery and anomaly detection as examples. Time-series motifs are sub-sequences of longer time series that have similar patterns. Motifs indicate patterns that get repeated or the presence of similar patterns between two time series. Using motifs, dictionaries of recurring series could be created that would provide more insights into inter-patient and intra-patient variations. The motif discovery methods in the literature are based on the sub-sequencing technique wherein certain time-series samples are grouped and compared with similar sub-sequences. A quadratic metric, usually their Euclidean distance, is measured to see whether the sub-sequences are similar. A matrix constructed with the euclidean distances between sub-sequence is called the distance matrix, and a matrix profile (MP) is a vector that stores Euclidean distance between any sub-sequence within a time series and its neighbor [54]. The MP can be used to study patterns (motif), detect anomalies, and discover shapelets, time-series chains, and other structures that discover patterns in the time series. Further, MPs are used to detect anomalies that could alert to possible wrong sensor readings or abnormal conditions, an important aspect for building robustness in the system. Using MP, similar patterns or periods having similar conditions (e.g., post-prandial periods with similar CHO intake) could be detected. Further, changes in Euclidean distances also reveal valuable information about the impact of influencing factors (e.g., activity signature on the BGL evolution). The inter-patient motif discovery in patient P1 is shown in Figure 11. It is evident that for similar CHO and insulin infusion profiles, the BGL patterns are similar. The Euclidean distance computed in this case is 2.01 (mg/dL in minutes), indicating similar patterns. An increase in Euclidean distance is observed for dissimilar patterns as shown in Figure 12. Such analysis provides the ability to discern different conditions. More detailed results with diagnostic tools based on time-series are presented in the results section. By identifying similar patterns, the patient can understand influences such as activities on BGL evolution. Still, patients require certain influencing factors posterior to their actual outcomes. The explainability of the hyper and hypo conditions is quite important in diagnosis and is the focus of the next section. Another aspect of the diagnostic tool is the explanations provided for hyper and hypo events through explainable artificial intelligence (XAI). While a matrix profile-based analysis provides ways to explain patterns, individual samples causing a particular outcome are explained using XAI. The XAI is a technique to explain decisions to stakeholders. The Local Interpretable Model-Agnostic Explanations (LIME) [55] is a tool that explains single data instances being model agnostic. Explanations generated by LIME for data instance x are defined as:[7]explanation(x)=argming∈GL(f,g,πx)+Ω(g) where f() is the model, g() is the local explanation for instance x, and L is the loss function that measures the fidelity between f() and g() while keeping the model complexity denoted by ω(g) low. The LIME uses a neighborhood πx* of the argument x_* in which approximation is sought. In general, g() denotes a class of interpretable models, G; such models could be decision trees or other simple linear models. Our explainable diagnostic tool has two components: the base-learner model and eXplainable Artificial Intelligence (XAI). The eXtreme Gradient Boost (XGBoost) classifier is used as the base learner, and the locally interpretable model agnostic explanations (LIME) tool is used to explain hyper/hypo events that are acquired using the data-instance profiler module. The XAI tool explains the factors that lead to hyper and hypo events in a patient, thereby helping personalization and improving patient behavior by highlighting behaviors that lead to hyper- and hypo-events. The results section shows explanations provided on single data points using XAI. ## 3.4. Personalization and Management Module This section describes the details of the E-T2D management module. ## 3.4.1. Adaptive Personalized Patient Model The personalization module is responsible for precise insulin infusion, maintaining BGL accurately within bounds, and handling patient-specific aspects. To handle BGL variations due to geriatric factors, a semi-parametric regression model is used. The basic idea here is to have an adaptive parameter estimation algorithm where the patient-specific parameters are identified using current measurements and predictions. The geriatric factors are embedded using parameters that denote these aspects, as explained in this section. We present the personalized patient model and personalized insulin computation algorithm based on the model predictive control technique. Our model is an adaptive model that is learned from data samples, and the personalization algorithm infuses insulin based on individual conditions. However, time-varying and nonlinear behaviors would require an adaptable model whose parameters change with time. Such changes also explain the influences of factors such as diet on the patient. Therefore, a patient model that is simple and adaptable needs to be proposed. This section presents an adaptable patient model that helps manage E-T2D toward building a personalized HDT [50]. The personal glucose dynamics is described as [8]G(k+1)=ζ1G(k)+ζ2I(k)+ζ3CHO(k) The model in [8] captures the patient’s complex dynamics by considering the influences of food and insulin. [ 9]G(k+1)=H(k)w(k)+ε H(k)=[G(k)I(k)CHO(k)] w(k)=[ζ1ζ2ζ3]T where ϵ is assumed to be normally distributed bounded additive noise with ∥(ε+εe)(k)∥2≤α with α∈R+. [ 10]minw(k)∑$k = 0$NPλNP−k[(G(k+1)−H(k)w(k))TΠ(k)(G(k+1)−H(k)w(k)]s.t. Π(k)∈R+In the recursive model parameter estimation, at each time instant k (k is the time step for 15 mins), the optimization problem in [10] is solved to identify the parameter with forgetting factor λ. Note that the forgetting factor might be adjusted for each patient. The model adaptation steps are shown in Algorithm 1. The initial input to the algorithm is inverse correlation matrix P and the forgetting factor λ. Once the new measurements from the patient are obtained, the model will predict the BGL, and the algorithm checks the difference between estimated and current BGL measurements, if there is an error, then the parameters will be updated; otherwise, it keeps as same as the previous. The algorithm uses the new measurements to adjust the model parameters w(k). Algorithm 1: Dynamic Patient Model Parameter Estimation for BGL Prediction. The adaptive model updates the parameters every computation time, i.e., 15 min. So first, the model is updated and used to compute the insulin infusion for the next hour, and the process repeats. During each time-instant, the model is adapted, and the optimal insulin infusion is computed. To illustrate the performance of the personalized patient module, estimates on P1–P5 and the actual measurements on CGM from clinical trial is shown in Figure 13. One can see that personalized patient module provides more accurate forecasts of the BGL variations as indicated by low RMSE (Table 2). Note 2: Adaptive patient model was validated against the data as well as conventional Type 2 models. As usual, T2D dynamics are nonlinear, and computing model parameters is quite cumbersome. However, the adaptive model presented in the paper mimics the nonlinear dynamics efficiently, and the results of the comparison are not provided in the paper due to space constraints. ## 3.4.2. Personalized Insulin Management Module The proposed personalized insulin management system uses the BGL measurements and patient model to compute the optimal insulin infusion to maintain BGL within recommended limits. To compute the optimal insulin infusion, it solves a multi-time step optimization model whose objective is to reduce the BGL excursion through proper choice of insulin, and the constraints for the problem are recommended BGL limits, patient dynamics, and limits on insulin infusion rates. ## Perturbation Terms for Geriatric Factors and Nutrient Intake Traditionally, an MPC works in three steps: (i) update measurements, (ii) compute control input using model, and (iii) apply the first among the computed control inputs in every iteration as with traditional MPC approaches. The rest of the computed control inputs are discarded by the MPC, and the procedure repeats during each time-step. This is called the receding horizon control. The LB-MPC is a new concept wherein the idea is to fuse factors obtained from domain informed data-based modeling tools (e.g., machine learning) to learn models or control actions (e.g., reinforcement learning). More recently, LB-MPC is proliferating various application domains due to their ability to model even abstract concepts within model or control actions [56]. To personalize and precisely compute insulin infusion considering patient’s geriatric conditions, fusing domain informed data-based models with conventional mathematical models is required. To this extent, the LB-MPC approach is used as the personalization module in our HDT. The idea is to use parametric models wherein during each computation there are parameters from mathematical models and perturbation parameters that model patient specific aspects (e.g., insulin sensitivity or diet influences). In our analysis, the perturbation parameter δa represents the effect of geriatric influences on BGLs. Suppose that the LSTM forecast is denoted by y^(k), whereas the model output is denoted by G^(k). The LSTM models capture future behaviors based on past measurements, whereas the mathematical model is used to observe the insulin infusion effect in the future on BGLs. At each time epoch k the model is updated and the future control moves are computed by solving a multi-time step optimization model. The LB-MPC uses the adaptive model of the patient and computes insulin infusion that manages BGL within specified limits. Therefore, by introducing the perturbation term, both past and future behaviors are included in the LB-MPC formulation. However, as the adaptive model is updated during each time epoch, and computation runs for the prediction horizon Np, the model is not updated during computation, but rather after each time epoch. With an abuse of notation to denote the model parameters to be varying, we include the index k. The mathematical disturbance parameter during each time epoch is given by, [11]ε1(k)=G(k+1)−ζ1(k)(G(k)−ζ2(k)I(k)−ζ3(k)CHO(k)∀{$k = 1$,2,⋯,Np},Equation [11] models the static disturbance term at time instant k+1. However, the disturbances could vary depending on the intermittent and time-varying aspects. Therefore, to model the geriatric effects, a perturbation term is added to the model parameters: [12]G(k+1)=(ζ1(t)+δa(k))G(k)+ζ2(t)I(k)+ζ3(k)CHO(k)+ε1(k),∀{$k = 1$,2,⋯,Np},The perturbation term is computed as, [13]δa=η1×y^(k+1)G(k+1)+η2×y^(k+1)−y^(k)h,∀{$k = 1$,2,⋯,Np}, where η1, η2 and h denote the adaptation parameters and sampling time, respectively. The values of adaptation parameters 0<η1<1,0<η2<1. The first term in the perturbation term [13] models the factor that adapts the perturbation parameter depending on the error between the model estimate and LSTM estimate. The second term models the adaptation w.r.t the slope of the BGL forecasts from the LSTM. These two terms together model the perturbation term that captures geriatric effects on the patient. In essence, the LSTM model provides predictions fusing food and insulin influences. These aspects are captured using the parameter. The LB-MPC tries to compute the optimal insulin that would maintain the BGL within recommended bounds for each patient. Therefore, the decision variable is insulin infusion. The objective aims to minimize insulin costs, and a slack cost term and variable are added to allow for small constraint violations due to numerical issues that could be avoided. The constraints are the personalized patient model, recommended BGL bounds for the patient, insulin infusion, and non-negative slack variables. The LB-MPC solves the optimization problem during each time-horizon and the optimization model is, [14]minI(k)∑$k = 1$k+NpCTI(k)+Cγγ(k)s.t. G(k+1)=(ζ1(k)+δa(k))G(k)+ζ2(k)I(k)+(ζ3(k)+δc(k))CHO(k)+ε(k)Gmin−γ(k)≤G(k)≤Gmax+γ(k)Imin≤I(k)≤Imaxγ(k)≥0∀k:1≤k≤Np where γ(k) is the slack variable, which introduces a soft constraint for modeling small variations in BGL over recommended limits. Here, CT and Cγ denote the cost for constraint violations, which is usually a very high value. The optimization model’s output is the insulin infusion for time instants (k+1)⋯(k+Np). In the MPC, the first among the computed insulin dosages—i.e., k+1 is used as the insulin dosage while the rest of the computed ones are discarded—and the procedure is repeated during the next time-instant. This is called the receding horizon or moving horizon approach that is used within the MPC. The prediction horizon Np is the duration for which the control inputs are computed, and Ts is the sampling period for the LB-MPC. In our approach, a sampling period of 15 min was selected, and a prediction horizon of 4 (one hour into the future) was used for most patients. The sampling period of 15 min was selected to capture the dynamics of the patient physiological model. The parameter variations model the patient-specific variations due to: food intake, insulin efficiency, comorbid conditions, and other aspects. By designing an MPC using these models and embedding knowledge of patients on the glycemic range, the proposed MPC becomes an LB-MPC. Due to this integration and modeling, the LB-MPC becomes E-T2D specific, i.e., it includes the geriatric influences inherently in the patient model. Further, by using contextual patient data on multiple drugs, the glycemic range is defined. Using these aspects the LB-MPC could not only integrate food influences on individuals but could also provide personalized optimal dosing to patients by considering their geriatric conditions. The software implementation aspects of the HDT are discussed in Appendix A. The detailed outcome of the LB-MPC managing E-T2D is provided in the results section. ## 4. Results This section illustrates the HDT’s efficacy in managing diabetes using personalized recommendations, providing diagnostics, predictions, and other management. This is done in two steps:(i)Clinical trials for 14 days on 15 elderly patients to collect patient-relevant data and blood glucose measurements. This is used for our modeling wherein an adaptive patient model, LSTM, STA, XAI, and other models are obtained. In this phase, infusions were done with insulin pumps pre-programmed based on diabetologist recommendations.(ii)Simulations with the model to compute precision insulin infusion to avoid BGL excursions in E-T2D patients exploiting the different HDT models. The MPC presented is a simulation result that uses the patient model obtained from clinical trials. The diabetologist verified these results and confirmed the findings. Moreover, its implementation with an insulin pump is feasible through pre-programmed inputs from insulin pumps, as with clinical trials. However, due to constraints in volunteer recruitment and re-admissions, only simulation results are provided in the paper. From clinical trials, data collected from 15 elderly diabetic patients is presented. The patients had co-morbid conditions and multiple drug intakes. The volunteers were recruited after initial clinical and ethical clearance. Then the data was collected for the initial study and is illustrated in the first section. This data had multiple facets: contextual data, medical records, patient-specific data, images, annotations, temporal data, and others. Similarly, time-series data was collected for blood glucose measurements. Alongside other data such as activity levels, nutrient intake, food timings, insulin infusion, etc., were used by HDT models to create knowledge from the aggregated data. The data was collected using the IoMT architecture proposed in our study. Similarly, using dynamical models, time-evolution and optimal insulin infusion could be computed by using the LB-MPC approach described in this paper. During insulin computations, the CHO, and current blood glucose levels are the inputs. However, for geriatric patients, the control BGL rate could vary depending on their personal conditions. Currently, such aspects are identified from historical information and diabetologist recommendations. The DT in this paper uses information from diabetologists that are contextual patient data: hyper and hypo constraints that could be achieved for a particular patient. However, by fusing dynamical models with domain-informed data-based models, the HDT obtains deeper insights and proposes suitable actions considering aspects of geriatric conditions of the individual patients. This extends the current capabilities for personalizing and managing diabetes using conventional tools. In what follows the results from these aspects are presented. ## 4.1. Clinical Data Description During the data collection phase, 15 elderly patients with varied geriatric conditions were recruited at Jothydev’s Diabetes Research Center (JDRC), Trivandrum, India. The data was collected from 2017–2018. The data collection had two phases: (i) patient recruitment and initial data collection and (ii) clinical data collection. In the patient recruitment phase, contextual data and medical data such as patient name, co-morbid conditions, multiple drug intake, patient-specific data, average calories, anthropometric data (e.g., body mass index), and others were recorded. These data were converted to contextual data as a JSON file (JavaScript Object Notation). Further, the data was entered and stored as medical records in the hospital. Note 3: The patient’s name and other personal aspects were coded with suitable protocols to avoid leakage of personal data. The registered ethics committee registration number for this data collection process is ECR/115/Indt/KL/2013/RR-16 issued under rule 122DD of the Drugs and cosmetics rules 1945, Government of India. Having recruited volunteers and collected contextual data, the next step was to formulate the clinical trials. As this involved patient data collection, a protocol needed to be established for the clinical trial. First, the protocol volunteers were defined. Only E-T2D patients were recruited for the clinical trials whose contextual data had already been obtained. Second, the testing procedure was to use the CGM for measuring continuously the BGL. In this condition, the patients would be continuously monitored in the hospital in a restrictive environment. Third, in the data acquisition phase, BGL was measured every 15 min with insulin infusion using an insulin pump. The basal and bolus values of insulin infusion were recorded from the pump. Additionally, information about the patient’s food intake: breakfast, lunch, snacks, and dinner was collected by a dietitian based on the food exchange list that was being served to the patients. The testing protocol used in our study is illustrated in Figure 14. ## 4.2. Clinical Data Collection During the clinical data collection period, blood glucose levels, CHO intake, and insulin infusion for the patients were collected for 14 days. The freestyle Libre Pro CGM sensor was used to collect the glucose level. The sensor insertion, removal, and troubleshooting were performed by a trained physician at JDRC. The insulin was administered by using the Medtronic 722 insulin pump. The data was collected every 15 min and was transmitted to the cloud to be stored/retrieved for modeling. The CGM sensor had to be calibrated with finger stick blood glucose measurements 2 or 3 times per day. The patient details and contextual data are shown in Table 3. Figure 15 shows the pie chart representation of 15 E-T2D patients’ data collected at JDRC. The first figure shows the BMI distribution among the volunteers used in our study. Based on BMI the patients are classified into three categories: patients’ BMI values between 18–25 are considered low BMI, between 25–30 are categorized as medium BMI, and above 30 is considered as high BMI. The distribution is indicative of the actual occurrence of patients. As for activity levels, $33\%$ of the people were active while the remaining $67\%$ were either moderate or sedentary. A close look at the co-morbidity of the patients under study: the distribution shows Hypothyroidism, dyslipidemia, hypertension, heart disease, kidney disease, post-kidney and heart surgery, and sigmoid colon, and multiple drug intake. Figure 16 shows the snapshot of one-day data collected from patients. It contains BGL variations, insulin infusion and CHO present in patient food. The CHO present in the food was determined and logged by a trained dietitian at the time of the study. Three meals, and snacks timed to vary around 07:30 (Meal 1), 13:30 (Meal 2), 16 (snack), 20:00 (Meal 3) ± 30 min. The number of meals may vary based on the patient’s condition and was recorded as contextual data. ## 4.3. Vital Signs and Activity Data As the BGL is influenced by the activity levels recording them helps identify their influences. Especially, activity influences patients having co-morbidity and multiple drug intake is very complex to understand. Therefore, by exploiting the activity data recorded through an integrated sensor that measures vital signs (e.g., body temperature) to activity levels using an accelerometer is used as described earlier. These values are recorded using suitable sensors and interfaced to a mobile app. The sensors and mobile app are an integral part of the IoMT architecture. The data displayed on the mobile app is shown in Figure 17. In our analysis, activity levels were classified into active, moderate, and sedentary. The lifestyle of a patient was detected by using the CC2650 sensor tag that has an accelerometer to detect the patients activity. This data is transferred to the cloud via the edge node using Wi-Fi. Activities can be detected by x, y, and z axis positions. This activity information about the patient is helpful to personalize the insulin infusion. ## 4.4. Prediction Module Data Analysis During the exploratory data analysis (EDA), studies were performed on contextual and temporal data collected from clinical and pre-clinical trial phases, and it would be helpful to enumerate the data types for the contextual and temporal data used in this analysis (e.g., CHO, BGL, activity, etc). Then the data was fused to perform EDA for analysis. First, the auto-correlation function (ACF) and partial auto-correlation function (PACF) were evaluated to understand the dependency of the current samples on the past values. The ACF and PACF plots for patient P1 are shown in Figure 18. An analysis of the ACF shows that there is a very strong correlation between the current value and past samples. This shows that regression-based models can estimate the current BGL from past samples for certain patients. However, the PACF plot shows that not more than two samples near to the current one may be useful in predicting it. Therefore, time-varying intermittent behaviors, causal variables, and exogenous factors may influence BGL predictions. ## 4.5. HDT Diagnostic Module Results As stated earlier, the HDT uses two types of models: domain-informed data-based and dynamical models. This section first describes the results of domain-informed data-based models and how fusing data helps understand the influences of various geriatric and influencing factors. This section illustrates HDTs’ ability to detect interpersonal variations considering time-series data patterns. Patients’ temporal data is analyzed for motifs that denote the existence of patterns in time series that could discern scenarios with similar patterns, but the magnitudes may differ due to certain factors. By detecting motifs influencing factors could be found. ## Motif Detection for Interpersonal Variations Detection This example illustrates the HDTs’ ability to detect changes in activity through data-driven techniques. As an example, time-series data for patient P1 is presented wherein two different scenarios are shown for day 9 and day 10 (see, Figure 19), and the euclidean distance is 5.31 (mg/dL in minutes) shown in Table 4. These are two simultaneous days with similar patterns of diet intake and insulin infusion. Nevertheless, the patient P1 had an exercise activity; walking for about 30 min on day 9 at morning 8:30 AM. Consequently, one can observe that the patterns for BGL are quite different or vary in magnitude. Day 9 with activity showed a lower BGL than day 10. This is primarily due to walking activity. Using such patterns the DT learns intricate aspects about the patient, such as sensitivity to activity or even a particular diet. This is a very important feature for personalization; as for managing BGL, the DT may suggest activities on a particular day whenever similar patterns are detected. Note though that, the result presented here is for illustrative purposes only. Similarly, patterns were observed for other aspects, such as multiple drug intake, nutrient variations, and others. The example demonstrates DTs’ ability to draw additional conclusions going beyond existing features with other techniques. Besides, such observation could be embedded in diabetes management as well. ## 4.6. Motif Based Intra-Personal Variations Detection This section presents the HDT data-driven model’s capability to detect intra-personal variations and draw conclusions about personalization. In this analysis, patient P1 has co-morbid conditions and multiple drug intake compared to patient P4 with a liver ailment. However, their age, activity levels, and diet intake are similar. Figure 20 shows the blood glucose level motifs for the two patients with similar CHO intakes. Even with their initial conditions being similar, the pattern indicates that patient P1 has a higher BGL compared to patient P4 in this time period. This is mainly due to co-morbidity and multiple drug influences. Similarly, the patient P4 maintains a better BGL profile than P1. Learning such aspects from data is not possible with the current models available in the literature. Similarly, comparison of the motif between two patients P4 and P13 is shown in Figure 21. The BGL magnitude for P4 is less compared to P13 this is because of activity levels, co-morbidity, and multiple drug intake. Even though the average CHO per day for P4 is high, the patient has a lower magnitude of BGL. These results not only illustrate the HDTs’ ability to use intra-personal variations to personalize diabetes management but also prescribe the infusing of insulin depending on their conditions. By fusing data with patient models in an HDT, such capabilities could be enabled, such aspects are unexplored for conventional models. ## 4.7. Personalization Module with XAI Personalized explanations based on recorded samples help avert critical conditions. The explanations provided for 3 different samples are shown in Figure 22a–c. The bars on the top provide the probability of the sample to within the euglycemic range or otherwise. Figure 22a shows a case with a higher probability (0.59) to be in the hypo/hyper range due to basal, food, and effective insulin. Whereas the second sample indicates food as a major factor (see, Figure 22b). Similarly, sample Figure 22c shows that food is a major factor pushing towards hyper-probability. This indicates that the patient is influenced by CHO intake and her bolus insulin has to be increased. Patient P3, a female aged 36 and having a kidney transplant, has an average daily CHO consumption of 300 g. A glycemic range of 80–140 [mg/dL] is recommended. Figure 23a shows a case with a higher probability of being in hypo or hyper conditions because of food, basal, and bolus insulin values. The second sample Figure 23b also indicates the higher probability of hypo/hyper conditions, the third sample shown in Figure 23c also indicates that higher probability in hypo/hyper conditions. From this, we can see that the personalization module with XAI reveals that both basal and bolus (effective insulin) are the main factors influencing the BGL of the patient as in Figure 23 suggesting a sensitive insulin infusion due to co-morbid conditions. ## 4.8. BGL Management through Precision Insulin Infusion This section describes the HDT-based BGL management module for E-T2D patients that handles their geriatric challenges. The module personalizes insulin infusion to manage BGL within the target range. To compute the insulin infusion, the HDT has a patient model with parameters modeling their food and insulin influences on BGL. The current BGL measurements are obtained from sensors; food influences are obtained from the mobile app and food nutrient prediction algorithm. The HDT-based BGL management module computes the precise insulin infusion for the patient considering geriatric conditions. The personalized optimal insulin dosage to maintain the BGL within limits is computed using the MPC approach proposed in this paper. The HDT will mimic the patient’s glucose dynamics, and the MPC will compute the BGL within the target range. The results were studied through simulations on 15 patient models for 14 days of data on food and insulin infusion. The results are validated against the clinical data. A control band of 80–180 mg/dL−1 is recommended for Patient P1 by the physician, mainly due to co-morbid conditions and patient history. Figure 24 shows the comparison between uncontrolled clinical trials and HDT-based control scenarios. During clinical trial, P1 had 363 hyper and 113 hypo events, whereas this was reduced to 99 hyper and 72 hypo events with LB-MPC. A target range of 80–150 mg/dL was recommended for Patient (P2) by the physician based on patient. Comparisons between clinical trials and personalized APs’ BGL regulation are shown in Figure 25. Patient P2’s clinical data were collected for 13 days because of the malfunction present in the CGM device. Insulin recommendation from HDT maintains the BGL within the bands specified by the physician even with high CHO consumption, co-morbid conditions multiple drug intake. During clinical trials, P2 is subjected to 478 hyper-glycemic and 104 hypoglycemic events. whereas this was reduced to 28 hyper events, and there are no hypo events with insulin recommendation from HDT. Furthermore, the patient used 350 Units of insulin in 13 days, but with LB-MPC, this is reduced to 250 Units, i.e., a $28.5\%$ reduction over 13 days, the number of insulin infusion events was reduced from 96 to 70 times per day. This result demonstrates personalized AP’s ability to regulate BGL in patients with high CHO intake, co-morbidity, and multiple drug intake. Patient (P13) suffers from hyperglycemia during clinical trials, even with insulin infusion. Moreover, P13 had heart surgery and hypertension. The target BGL range recommended by the physician is 90 to 180 mg/dL. Comparisons with clinical trials and personalized AP control are shown in Figure 26. While in uncontrolled trials, there were 1114 hyper-glycemic episodes, there were no events recorded with LB-MPC. Furthermore, patient (P13) was administered 550 units during the trial period, whereas 485 units were required for maintaining the BGL using the HDT, reduced by $14.5\%$, the insulin infusion events were reduced from 96 to 80 times per day. These results demonstrate that the insulin recommendation from the HDT can not only regulate the BGL for patients with significant hyper-glycemic events but with reduced insulin infusion. The time-in-range, time spent in hyper, and time spent in hypo conditions comparison between conventional insulin therapy and HDT-based personalized insulin recommendation is shown in Table 5. The Percentage of improvement in TIR for 15 patients with HDT-based personalized BGL management is shown in Figure 27. This shows that the TIR is increased from 3–$75\%$ to 86–$97\%$. Figure 28 and Figure 29 show the percentage of improvement in hypo and hyper conditions with HDT-based personalized BGL management. This shows that HDT-based personalized BGL management can reduce the time spent in hypo from 0–$22\%$ to 0–$9\%$, and percentage of time spent hyper is also reduced from 0–98 % to 0–$12\%$. ## 5. Conclusions This paper presented a Human Digital Twin (HDT) framework for Elderly Type-2 Diabetes (E-T2D). The HDT enables personalization and precise insulin infusion considering specific patient aspects. Further, the HDT provided deeper insights by aggregating various data: contextual, clinical, patient-specific, etc. Second, the HDT has a suite of models that can leverage this data and provide outcomes that can be used to obtain deeper insights into E-T2D. These models combined deep-learning models for time-series forecasting, image processing, and others. Finally, a mathematical model that can adapt its parameter based on clinical data is also proposed. Exploiting the deep-learning tools and mathematical models, a learning-based MPC is proposed that can personalize insulin infusion depending on the patient’s geriatric conditions. The HDT design and development aspects are also discussed. Finally, the HDT implementation is deployed and tested on 15 E-T2D patients through clinical trials and simulations. Our results suggest that by personalizing diabetes treatment through HDT enhances the time-in-range, reduces the hyper and hypo events, and more importantly, reduces insulin infusion showing its efficacy in managing diabetes. Extending the clinical trials for more co-morbid patients and studying it for a large number of population is the future course of this study. ## Software Implementation The software implementation aspects of the HDT are discussed in this section. The software architecture has two parts: front-end and back-end. The front-end was implemented with hypertext modeling language (HTML) pages orchestrated by Flask package from Python. Conditional statements and other aspects were implemented using Jinja2 templating engine for use with Python. The front-end and back-end were orchestrated using Python script. Data was stored in comma separated variable file and displayed using the graphical display tools in Python. The description module has interfaces to collect the data and store it in the cloud using the IoMT architecture. The implementation was done on a private cloud using Python scripts. It collects the sensed information and stores the data for retrieval. The Python was used to create these databases and display the data. Similarly, the prediction module is based on time-series modeling, and packages in Python such as Keras, Stumpy, Numpy, and other standard packages are used for analyzing the time-series data. LIME Python package was used to implement the explainable AI (XAI). LB-MPC implementation was done with GLPK linprog in a Python environment to personalize the insulin infusion. In the prediction module Python package, Keras was used to multi-variate and multi-time step prediction of BGL samples. Figure 7 shows the flow of BGL prediction using LSTM from Keras. The structured time series analysis was performed by using the TensorFlow package from the Python environment. This provides time-series trends to capture the hyper and hypo conditions. Similarly, the Python environment was used in the prediction module to analyze the food and time-series evolution. To interpret patterns and their outcomes in the diagnostic module, the Python package Stumpy was used to analyze the motif patterns. The results were used to analyze the inter and intra-patient variabilities. By analyzing activity inferences, pattern recognition, and contextual mapping this module can give alerts to the patient. To personalize the insulin infusion, the management module uses the LB-MPC. It was implemented using GLPK linprog from a Python environment to compute the personalized insulin infusion. The performance of the HDT-based personalized management was analyzed using various metrics (e.g., Time In Range (TIR), hypo, and hyper events). 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--- title: 'Embryonic Hyperglycemia Disrupts Myocardial Growth, Morphological Development, and Cellular Organization: An In Vivo Experimental Study' authors: - Ricardo Jaime-Cruz - Concepción Sánchez-Gómez - Laura Villavicencio-Guzmán - Roberto Lazzarini-Lechuga - Carlos César Patiño-Morales - Mario García-Lorenzana - Tania Cristina Ramírez-Fuentes - Marcela Salazar-García journal: Life year: 2023 pmcid: PMC10056749 doi: 10.3390/life13030768 license: CC BY 4.0 --- # Embryonic Hyperglycemia Disrupts Myocardial Growth, Morphological Development, and Cellular Organization: An In Vivo Experimental Study ## Abstract Hyperglycemia during gestation can disrupt fetal heart development and increase postnatal cardiovascular disease risk. It is therefore imperative to identify early biomarkers of hyperglycemia during gestation-induced fetal heart damage and elucidate the underlying molecular pathomechanisms. Clinical investigations of diabetic adults with heart dysfunction and transgenic mouse studies have revealed that overexpression or increased expression of TNNI3K, a heart-specific kinase that binds troponin cardiac I, may contribute to abnormal cardiac remodeling, ventricular hypertrophy, and heart failure. Optimal heart function also depends on the precise organization of contractile and excitable tissues conferred by intercellular occlusive, adherent, and communicating junctions. The current study evaluated changes in embryonic heart development and the expression levels of sarcomeric proteins (troponin I, desmin, and TNNI3K), junctional proteins, glucose transporter-1, and Ki-67 under fetal hyperglycemia. Stage 22HH Gallus domesticus embryos were randomly divided into two groups: a hyperglycemia (HG) group, in which individual embryos were injected with 30 mmol/L glucose solution every 24 h for 10 days, and a no-treatment (NT) control group, in which individual embryos were injected with physiological saline every 24 h for 10 days (stage 36HH). Embryonic blood glucose, height, and weight, as well as heart size, were measured periodically during treatment, followed by histopathological analysis and estimation of sarcomeric and junctional protein expression by western blotting and immunostaining. Hyperglycemic embryos demonstrated delayed heart maturation, with histopathological analysis revealing reduced left and right ventricular wall thickness (−$39\%$ and −$35\%$ vs. NT). Immunoexpression levels of TNNI3K and troponin 1 increased (by $37\%$ and $39\%$, respectively), and desmin immunofluorescence reduced (by $23\%$). Embryo-fetal hyperglycemia may trigger an increase in the expression levels of TNNI3K and troponin I, as well as dysfunction of occlusive and adherent junctions, ultimately inducing abnormal cardiac remodeling. ## 1. Introduction Diabetes mellitus (DM) is a chronic condition characterized by the dysregulation of physiological blood glucose levels, leading to periodic hyperglycemia. It is now considered a primary public health crisis owing to its rapidly growing prevalence in many regions of the world and the severity of DM-associated complications, especially cardiovascular diseases and neuropathies [1,2]. Pregnancy is associated with major physiological changes, including dysregulation of blood glucose [3,4]. According to the estimates of the International Diabetes Federation (IDF), $16.2\%$ of women worldwide who delivered live children in 2017 had some form of hyperglycemia during pregnancy, of which an estimated $86.4\%$ of cases were due to gestational DM (GDM), $6.2\%$ to pregestational DM, and $7.4\%$ to diabetes onset during pregnancy. In addition to high glucose levels, GDM is characterized by hyperinsulinemia and alterations in other hormones produced by the placenta. These changes can delay embryonic development and contribute to higher offspring morbidity and mortality [5,6]. Hyperglycemia during pregnancy can harm progeny in the short, medium, and long terms. In the short term, hyperglycemia is associated with various congenital defects, including heart malformations, which are a leading cause of intrauterine and perinatal death. Furthermore, maternal hyperglycemia and lifestyle during pregnancy can affect prenatal and postnatal development [2] and increase the risks of DM, obesity, and cardiovascular disease in both childhood and adulthood [4]. Despite the high incidence of hyperglycemia during gestation and its deleterious effects on offspring, the cellular and molecular mechanisms that lead to the development of these diseases have not been fully elucidated. Normal embryonic heart development depends on proper sequential specifications of the mesoderm, splanchnic mesoderm, and cardiogenic mesoderm, followed by morphogenesis of the embryonic cardiac chambers that ultimately form the mature four-chambered heart [7,8,9]. Ventricular growth and development in mammals are highly dependent on the changes that occur in the cardiomyocyte population [10,11]. Failure in any morphogenetic process or gene expression program during cardiogenesis can lead to various morphological and functional defects. Hyperglycemia is associated with multiple cardiovascular diseases, including cardiomyopathies, that result in insufficient ventricular strength for efficient circulation [11,12]. Cardiomyopathies may result from gross malformation or finer abnormalities in the microstructural organization of contractile elements and cellular pathways that transmit electrical excitation with the optimal spatiotemporal pattern for maximal force generation. In such disorders, the ventricular myocardium may become dilated, and the walls thinner, resulting in progressive heart failure and potential sudden death [13,14]. While the statistical links between hyperglycemia, diabetes, and myocardiopathies are well established among adults and children descended from diabetic women, the pathogenic processes leading to morphological and functional abnormalities in offspring are unclear. The domestic chicken (Gallus domesticus) embryo is a potentially valuable model for studying the pathogenesis of fetal cardiovascular abnormalities in a hyperglycemic environment because it allows for detailed morphological analyses throughout gestation [15], can be easily manipulated to mimic some of the microenvironmental conditions of human development independent of maternal status, including hyperglycemia [16], and is inexpensive enough to generate large sample numbers during a single incubation. The chicken embryo has been used as a model to investigate the effects of hypoxia on fetal development [17]. The aim of this study was to analyze the development of the embryonic ventricular myocardium under induced hyperglycemia, including morphological parameters and possible influences on sarcomeric proteins, cell junction proteins, cell proliferation, and cellular glucose transport. Collectively, these findings implicate dysregulation as a critical event in the disruption of cardiac structure and function under hyperglycemia and point out multiple targets for prenatal diagnosis. ## 2.1. Embryos Fertilized Bovans chicken eggs were obtained from a local poultry farm (ALPES, Puebla, Mexico) and incubated at 37.8 °C under $60\%$ relative humidity until they reached stage 22HH (3–3.5 days). The eggshells were then disinfected with $70\%$ alcohol and windowed (2 cm2) to stage the embryos, followed by exposure of the heart via dissection of the allantoidal and pericardial membranes (total $$n = 87$$ embryos). The eggs were then randomly divided into two groups: a hyperglycemic group (HG, $$n = 44$$) and a normoglycemic group with no treatment (NT, $$n = 43$$). To induce hyperglycemia, the HG embryos were treated according to the methodology of Zhang [2016] with modifications. In each HG group egg, a 450 µL volume of 30 mmol/L glucose in saline (NaCl $0.9\%$) was injected daily through the shell window using a 1 mL syringe to a maximum of 10 days (36HH stage), while the NT group eggs received daily injection of 450 µL saline. To verify the induction of hyperglycemia, the HG and NT embryos were obtained every 24 h starting on day 4 of incubation, separated from the yolk, and decapitated. A small blood sample was then taken to record blood glucose using test strips and a glucometer (Freestyle, Abbott Laboratories, Chicago, IL, USA). The animal use protocols and study procedures strictly conformed to Mexican Official Guidelines (NOM-062-ZOO-1999) and were approved by the research, ethics, and biosafety committees of the Children’s Hospital of Mexico Federico Gomez (HIM/$\frac{2018}{057.}$ SSA-156). ## 2.2. Weight and Body Development Embryonic development was assessed using an epifluorescence stereoscopic microscope (Zeiss, Jena, Germany) with Axiovision LE software. Once the embryos were photographed, they were weighed on an analytical balance and the heart was removed for imaging using the above-mentioned stereoscopic epifluorescence microscope (HG, $$n = 23$$; NT, $$n = 23$$). ## 2.3. Histology Following examination of gross morphology, hearts from stage 36HH embryos (HG, $$n = 8$$; NT, $$n = 9$$) were fixed in neutral formalin, dehydrated through a graded alcohol series (70–$100\%$), rinsed in xylene (Sigma-Aldrich, Burlington, MA, USA), embedded in Paraplast wax (Tissue-Tek, CA, USA), cut into 6 µm cross-sections at the level of the papillary muscles, and stained with hematoxylin and eosin (HE; Hycel, Jalisco, Mexico). The total diameter of each ventricular wall and the myocyte count in the three regions of interest were measured using a Digital Aperio Scanner (Leica, Wetzlar, Germany) and ImageScope software. ## 2.4. Scanning Electron Microscopy Stage 36HH hearts (HG, $$n = 6$$; NT, $$n = 7$$) fixed and photographed as described above were sectioned in transversal and longitudinal planes, dehydrated, desiccated under liquid CO2 in a critical-point drying apparatus (Samdri 789A, Tousimins Research Co., Rockville, MD, USA), and sputter-coated with 350 nm gold in a Denton Vacuum Desk 1A apparatus (Denton, Cherry Hill Industrial Centre, Cherry Hill, NJ, USA). Electron micrographs were acquired using a JSM 5300 Scanning Electron Microscope (JEOL, Tokyo, Japan) at 15 kV and various magnifications, as indicated in the figures. These electron micrographs were used to illustrate the organization of the ventricular trabeculae and to visualize the compaction process of the ventricular walls. ## 2.5. Immunostaining and Detection by Confocal Microscopy Protein expression levels were estimated by immunohistochemistry using primary antibodies (1:200 dilution) against TNNI3K, troponin I, desmin, N-cadherin,β-catenin, claudin-1, ZO-1 (all from Santa Cruz Biotechnology, Dallas, TX, USA), Cx43, and GLUT1 (Novus, Littleton, CO, USA). The samples were incubated with primary antibodies overnight at 4 °C before being treated with fluorescence-tagged anti-rabbit secondary antibodies (Santa Cruz, Dallas, TX, USA; SC2359) for 4 h at room temperature. The cell nuclei were counterstained with RedDot (1:150, Biotium, Freemont, CA, USA) diluted 1:150. Finally, the samples were mounted and viewed under a confocal microscope (Carl Zeiss, Oberkochen, Germany). Sections were obtained at the level of the papillary muscles in the 3 areas of interest and imaged at 10×, 40×, and 60× magnification using ZEN 2010 software (Carl Zeiss, Germany). The average optical density from each protein–antibody complex is reported as the mean (±standard deviation, SD) relative to the NT group (set to $100\%$) ($$n = 9$$ sections from HG and $$n = 10$$ sections from NT group hearts). ## 2.6. Western Blotting Ventricular tissues from 36HH stage hearts ($$n = 6$$ each from HG and NT groups) were placed in individual Eppendorf tubes with Tris-HCl lysis solution plus protease inhibitor, homogenized, and centrifuged. Total protein concentration in the supernatant was measured by the absorbance at 280 nm using a nanodrop spectrophotometer (Fisher Scientific, Waltham, MA, USA). Proteins were separated on $12\%$ polyacrylamide gels and transferred to PVDF membranes (Bio-rad) using a Trans-Blot apparatus (Fisher Scientific). The Precision Plus Protein Dual Color Standards (Bio-Rad, Hercules, CA, USA) were used as molecular weight markers. Membranes were labeled with primary antibodies against TNNI3K, troponin I, desmin, Cx43, N-cadherin, β-catenin, claudin-1, ZO-1, GLUT1 (suppliers provided in the previous section), and actin as the gel-loading control (all from Santa Cruz Biotechnology, Dallas, TX, USA). Protein bands were quantified using chemiluminescence and expressed as mean ± SD relative to corresponding bands from the NT group (set to $100\%$) (HG, $$n = 9$$ gels; NT, $$n = 10$$ gels). ## 2.7. Statistical Analysis Weight, height, blood glucose, morphometric parameters, and immunolabeling results expression measurements were first assessed for normality using the Shapiro–Wilk test and expressed as mean ± SD. Morphometric measurements were compared between groups using a two-tailed Student’s t-test. A $p \leq 0.05$ was considered significant for all tests. Morphometric measurements were compared between groups using one-way ANOVA and Student’s t-test using independent samples. A $p \leq 0.05$ (two-tailed) was considered significant for all tests. ## 3.1. Induction of Hyperglycemia In both HG and NT groups, embryonic development was allowed to continue for 10 days, or to stage 36HH under normal conditions, with daily injections of high glucose and normal saline, respectively. In the HG group embryos, blood glucose levels increased progressively ($$p \leq 0.0029$$), with the mean value surpassing 350 mmol/dL by day 10, while the NT embryos showed no significant changes (mean 198 mmol/dL) (Figure 1A). ## 3.2. Weight and Body Development Embryos from the HG group exhibited significantly reduced mean weight and size, and a general delay in maturation, compared to NT group embryos. Most NT embryos reached the expected 36HH stage by day 10, while the HG embryos reached only the 33HH stage. In addition, the HG embryos exhibited delayed limb and eyelid maturation, as well as defects in the phalanges of the legs (Figure 1B,C). Body weight (Scheme 1A) and body length (Scheme 1B) increased exponentially in both groups during embryonic development, but the mean body weight of day-10 HG embryos was reduced by $34\%$ compared to day-10 NT embryos ($$p \leq 0.034$$). The average weight of the HG embryos was 1.35 g compared to the average weight of the NT embryos of 2.11 g. The mean body length was reduced by $14\%$ compared to the NT group embryos ($$p \leq 0.041$$); the mean body length of the HG embryos was 35.6 mm, compared to the average for the NT embryos, 43.4 mm. ## 3.3. Morphometric and Histological Analyses of Embryonic Heart Morphometric analysis of day 10 embryos revealed that the whole heart was smaller in the HG embryos than in the NT embryos (Figure 2A–D). Cross-sections of the heart (Figure 2F,G) also revealed a slight decrease in ventricular diameter among the HG group embryos compared to the NT group embryos, in addition to a small reduction in cardiac circumference, albeit not statistically significant (Figure 2G,H). In addition, myocardial wall size was significantly smaller in the HG group than in the NT group at stage 36HH, with a $40\%$ reduction in left ventricular wall thickness ($$p \leq 0.0002$$), a $35\%$ reduction in right ventricle wall thickness ($$p \leq 0.0008$$), and a $38\%$ reduction in interventricular septum thickness ($$p \leq 0.0006$$) (Figure 3C–I). In addition to these deficits in gross morphology, the HG group hearts also exhibited a $34\%$ reduction in myocyte number per microscopic field across the anterior right and left ventricular walls ($$p \leq 0.006$$) and a $19\%$ reduction in the interventricular septum ($$p \leq 0.004$$) (Scheme 2). Scanning electron microscopy analysis of sections from longitudinally dissected hearts revealed delayed development and thickening of the ventricular walls in the HG group (Figure 4). Furthermore, delayed delamination and compaction of the trabeculae was observed in the HG hearts compared to the NT hearts, suggesting that the delay in the formation of the structural components of the heart led to the lower level of compaction in the ventricular walls and the thinner interventricular septum, persisting the delay in the formation of the structures that form the heart. ## 3.4. Immunodetection of Myocardial Damage Biomarkers To investigate the effect of hyperglycemia on myocardial damage markers, we compared the expression of TNNI3K, a kinase of the MAPK signaling cascade implicated in cardiomyopathies, between the NT and HG groups by immunofluorescence. Following hyperglycemia exposure, TNNI3K expression was upregulated by $30\%$ in the right ventricular wall ($$p \leq 0.003$$), $37\%$ in the left ventricular wall ($$p \leq 0.002$$), and $34\%$ in the interventriucular septum ($$p \leq 0.002$$) of the HG group compared to the corresponding tissues from the NT group. Moreover, immunostaining revealed atypically dispersed sarcomeric proteins in the cytoplasm rather than the usual fibrillar form (Figure 5A,B). Similarly, the TNNI3K binding partner troponin I was upregulated by $39\%$ in the left ventricular wall ($$p \leq 0.02$$), $24\%$ in the right ventricular wall ($$p \leq 0.02$$) (Figure 5D–F), and $41\%$ in the interventricular septum ($$p \leq 0.002$$) compared to corresponding tissues from NT embryos (Figure 5F). Again, this protein was not detected in the classic linear fibrillar pattern but rather in puncta throughout the HG group cardiomyocytes. In contrast, desmin expression was reduced by $23\%$ in the left ventricular wall of the HG heart compared to the NT heart ($$p \leq 0.003$$) (Figure 5G–I). Western blot analysis of sarcomeric protein expression revealed qualitatively similar changes as TNNI3K band density increased by $62\%$ ($$p \leq 0.003$$) and troponin density increased by $54\%$ ($$p \leq 0.004$$) in the heart lysates from the HG group embryos compared to the NT group embryos. Additionally, the desmin band density reduced by $23\%$ ($$p \leq 0.003$$) (Figure 6). ## 3.5. Evaluation of Cell Junction Proteins Communication between cells is essential for cardiogenesis, the proper spatiotemporal spread of excitation and contraction, and the transmission of vascular reflex signals, among other biological functions. Cellular organization is maintained and intercellular communication is mediated by a group of junctional proteins that form occluding, adherent, and gap junctions among myocytes, and deficiencies or increases in these proteins can lead to the development of congenital cardiomyopathies, arrhythmogenesis, myocardial ischemia, arterial hypertension, and abnormal myocardial remodeling [18]. To analyze changes in occluding junctions under hyperglycemia, we first measured the expression levels of ZO-1 and claudin-1 by immunostaining. The expression of ZO-1 reduced by $66\%$ in the interventricular septum (Figure 7A–C) of the HG heart compared to the NT heart, while claudin-1 expression reduced by $22\%$ in the RWV and $28\%$ in the IVS of the HG heart (Figure 7D–F). Similarly, immunostaining analysis of the adherent junction protein β-catenin revealed reductions of $41\%$ in the left ventricular wall, $38\%$ in the interventricular septum, and $45\%$ in the right ventricular wall of hearts from the HG embryos (Figure 7G–I), while N-cadherin immunoexpression reduced by $71\%$ in the left ventricular wall, $74\%$ in the interventricular septum, and $81\%$ in the right ventricular wall (Figure 7J–L). In contrast, there were no significant differences in the expression of the gap junction protein Cx-43 between the groups, and this result was confirmed by western blotting (Figure 7M–O). We then analyzed the expression of the insulin-regulated glucose transporter GLUT1, the primary glucose transporter during embryonic and fetal development. Immunostaining revealed a $14\%$ decrease in the left ventricular wall and an $11\%$ decrease in the right ventricular wall of the HG embryos compared to the NT embryos, while there was no difference in expression between the HG and NT interventricular septum (Figure 8). ## 3.6. Evaluation of Cell Proliferation Ki-67 is a nuclear protein associated with cell proliferation. During the interphase, Ki-67 is detected exclusively within the cell nucleus, whereas in mitosis, it mostly translocates to the surfaces of chromosomes [19]. Under normal conditions, embryonic cardiac development is characterized by high rates of cell proliferation. Given the atrophy and lower cardiomyocyte density of the HG group cardiac tissue, we examined Ki-67 expression as an index of the proliferation rate. Consistent with the observed smaller heart dimensions and lower cell density, heart tissues from the HG embryos showed a $19\%$ reduction in Ki-67-positive nuclei within the left ventricular wall and a $16\%$ reduction in the right ventricular wall, while there was no difference in the interventricular septum between the NT and HG groups (Figure 9). ## 4. Discussion The number of reproductive-age diabetic women is increasing worldwide, leading to a parallel increase in the number of fetuses and delivered children with congenital heart defects [20]. Both clinical and experimental evidence suggests that elevated glucose levels during pregnancy can cause embryonic or fetal death, as well as congenital diseases leading to prenatal death [21]. Moreover, there is accumulating evidence that maternal DM can increase cardiovascular and metabolic disease risks in adulthood as a direct consequence of the hyperglycemic intrauterine environment [22]. Furthermore, a substantial proportion of these cases may be prevented by improved glucose control during pregnancy [23]. Taking previous investigations into account, we aimed to distinguish the effects of a hyperglycemic environment on the chicken model from those on the human heart. We first established the similarities of embryonic development under a hyperglycemic environment in our embryonic model with maternal hyperglycemia, such as delayed embryonic development, as well as fetal and neonatal cardiomyopathies, which provide new information on the risks that gestational hyperglycemia may pose to the offspring [24,25,26]. In this study, we found that the impairments in cardiac growth in the chicken embryo model were similar to gestational diabetes in rodent models and humans. Macrosomia was not observed because factors such as nutrition and oxygen for embryo-fetal development, as well as growth factors of maternal and placental origin, were not involved in the current model mentioned in the study. Despite these grim statistics, the underlying molecular mechanisms remain largely unknown. Here, we present evidence that hyperglycemia during gestation induces cardiac microsomy regardless of maternal glucose status, possibly by inducing TNNI3K overexpression, as transgenic mouse studies have shown that TNNI3K overexpression reduces sarcomere length, leading to progressive cardiomyopathies and heart failure [27,28]. ## 4.1. Importance of the Chicken Embryo Hyperglycemia Model Experimental research on human embryos is limited by ethical considerations, while murine models do not allow for the analysis of fetal hyperglycemia independently of maternal hyperglycemia. As an alternative, the chick embryo permits precise control of fetal glucose and large-scale harvesting of heart tissue for molecular analyses. Furthermore, the results presented here are in accord with epidemiological observations in humans and experimental studies in model animals. Hyperglycemia resulted in delayed development of both the embryo as a whole and of the heart compared to the controls (Figure 1 and Scheme 1). Similarly, studies have reported that hyperglycemia in pregnant women stunts fetal growth and prenatal development, leading to microsomy [28]. In addition, recent research has suggested that in utero hyperglycemia can affect systolic and diastolic functions, leading to heart failure [29]. In rats, embryonic microsomy following hyperglycemia induced by the administration of streptozotocin on day 5 of gestation has been previously reported [30]. Therefore, the chick embryo appears to be a suitable preclinical model to study the molecular basis of congenital heart defects due to embryonic hyperglycemia. ## 4.2. Effects of Hyperglycemia on Cardiac Morphology and Histology A delay in the morphological and functional maturation of the heart can lead to maladaptive remodeling and even cardiomyocyte hypertrophy [27,31]. The fleshy trabeculae are irregular muscular structures that attach to the ventricular wall and provide resistance to increase the force of contraction. Delay or failure of trabecular delamination and compaction is associated with contraction deficits and arrhythmias [32]. We found that heart size was reduced by hyperglycemia (Figure 2), while trabecular delamination was delayed and myocardial compaction reduced (Figure 5 and Figure 6), indicating that effects of hyperglycemia on heart morphology (Figure 2 and Figure 3) can have deleterious repercussions at the molecular and functional levels. Proper myofibril organization is also critical for optimal myocardial contraction. Previous experimental studies have found differences in the histological characteristics of the heart at E18 among the offspring of diabetic female mice compared to controls [33]. Consistent with their findings, we found that the number of nuclei in ventricular walls markedly reduced in the HG group heart (Scheme 2) and that the number of nuclei immunopositive for the mitotic marker Ki-67 reduced substantially (Figure 9). Therefore, we conclude that hyperglycemia decreases ventricular size in part by reducing the rate of cardiomyocyte proliferation. TNNI3K is a heart-specific MAPK kinase that regulates myocardial contraction by binding to and phosphorylating cardiac troponin I [27,34]. Excessive expression of TNNI3K is strongly implicated in the progression of cardiomyopathies [27,31]. Additionally, a previous in vivo study reported cardiomyopathy and heart failure in transgenic mice overexpressing TNNI3K [35]. Subsequently, another study reported that, in addition to cardiomyopathy, transgenic mice overexpressing TNNI3K have high plasma levels of troponin I and a greater number of heart attacks [36]. Furthermore, they showed that increased expression of TNNI3K reduced sarcomere length and promoted changes in titin composition, indicative of cardiac remodeling. They also found that TNNI3K was located in the intercalary disks of the sarcomere. In accordance with the above-mentioned reports, this study further emphasizes a relation between increased TNNI3K expression and cardiomyopathies because TNNI3K expression was significantly elevated in chicken embryos exposed to hyperglycemia (Figure 5) and exhibited abnormalities in both gross heart morphology and histological structure (Figure 3 and Figure 4). Moreover, overexpression of TNNI3K was associated with altered expression of other sarcomeric proteins, namely troponin I and desmin, as evidenced by immunofluorescence and western blotting analysis (Figure 5 and Figure 6). ## 4.3. Altered Expression Patterns of Sarcomeric Proteins Associated with Myocardial Damage Cardiomyocytes contain linear arrays of sarcomeres aligned in parallel with the long axis of the cell, and disruption of this structural conformation by aberrant protein phosphorylation is associated with cardiac hypertrophy [37]. There is also evidence that abnormal remodeling of the postnatal heart is caused by the activation of protein kinase cascades converging on MAPKs, resulting in the phosphorylation of various cell growth and differentiation factors. The overexpression of TNNI3K is known to accelerate cardiac dysfunction in mice by inducing cardiac remodeling at the molecular level, including a reduction in sarcomere length and changes in the composition of the titin isoform [31]. Troponin I promotes actin–myosin coupling during cardiac contraction, and enhanced troponin I sensitivity has been detected following myocardial injury or infarction [38]. Troponin I and TNNI3K colocalize at the sarcomere of cardiac cells and act as effectors in the coupling of myosin and actin during contraction [31]. Overexpression of these proteins is considered an indicator of myocardial damage and a predictor of infarction [36,39]. Likewise, high troponin I expression has been reported in autopsy tissue of adult humans with diabetes and deficiency in cardiac function. Additionally, troponin I was associated with abnormal cardiac remodeling in a rat model of cardiomyopathy [40,41], which allows us to speculate that the elevated expression of troponin I, a heart-specific protein, could also be elevated in blood circulation. Based on these reports, we speculate that the elevated expression levels of TNNI3K (Figure 6A–C) and troponin I in the HG group (Figure 8) observed in this study may be indicative of myocardial damage and a consequent reduction in contractile strength. To further support this hypothesis, in this study, both TNNI3K and troponin I expression levels were increased and colocalized in a punctate pattern in hyperglycemic hearts rather than in the fibrillar arrays usually observed in healthy tissues of the NT group (Figure 5A–F). Desmin is an integral component of intermediate filaments found in the contractile apparatus, intercalary disks, nucleus, and other cellular organelles, and underexpression has been implicated in mechanical and structural abnormalities of the cytoskeleton underlying contraction deficits, underexpression is also associated with the abnormal propagation of electrical signals between heart muscle cells [41]. We found that desmin was expressed in a diffuse pattern following hyperglycemia (Figure 5) and was undetectable in intercalary disks, in contrast to some specimens from the NT group. Furthermore, overall expression was significantly reduced in the HG group compared to the NT group (Figure 5G–I). Emerging evidence suggests that mutations in the DES gene cause different musculoskeletal disorders and cardiomyopathies. The clinical phenotypes associated with DES mutations are heterogeneous, and some of these mutations are harmful. Clinical and experimental investigations have reported that mutations in the DES gene, such as p.A120D, a desmin variant, cause defective formation of intermediate filaments in ventricular cardiomyocytes, leading to the development of arrhythmias or cardiomyopathies [42]. This is also supported by studies in zebrafish embryos, where desmin knockout led to the formation of disorganized cardiac muscles, defective cardiac biomechanics, and disorders in Ca2+ signaling [43]. Taken together, these reports and the present study highlight the importance of low levels of desmin in our model. Cell-to-cell communication is essential for normal cardiac embryogenesis, transmission of electrical impulses, synchronization of myocardial contractile activity, and transmission of vascular reflex signals, among other biological functions [44]. Therefore, disruption of communication among cardiomyocytes, whether due to genetic mutations or acquired conditions, can lead to cardiac pathology. Both clinical investigations and experimental studies in murine and porcine models have reported that abnormal expression levels of junctional proteins responsible for cell-to-cell communication lead to the development of cardiomyopathies, arrhythmogenesis, myocardial ischemia, arterial hypertension, and abnormal myocardial remodeling [18]. In fact, these proteins are widely considered promising therapeutic targets for the treatment of cardiomyopathies [45]. The formation of intercalated disks is incomplete during embryo-fetal development of the heart; therefore, proteins that are components of cell junctions are predominantly found in the cytoplasm. For instance, the abnormal expression of protein components forming tight junctions or occluding junctions has been associated with arrhythmias in both human patients and animal models [46]. In addition, the major occludens junctional protein ZO-1 has been shown to bind multiple DI proteins, such as connexins, catenins, and vinculins [47]. Embryos exposed to hyperglycemia demonstrated a substantial reduction in ZO-1 expression within the interventricular septum (Figure 7A–C), concomitant with the aforementioned morphological and histological abnormalities, in accord with reports that loss of ZO-1 in cardiomyocytes impairs cardiac function in mice [48]. Claudins are also essential junctional proteins, as loss is associated with abnormal morphological development, remodeling, and myocardial dysfunction [48,49]. We found that claudin-1 expression was reduced in the left ventricular wall, right ventricular wall, and interventricular septum of hearts exposed to hyperglycemia (Figure 7D–F), which may underlie the observed morphological abnormalities. Furthermore, reduced cell-to-cell coupling could predispose to greater long-term functional deficits. Adherent junctions (AJs) are composed of proteins that form indirect associations with actin filaments and microtubules of the cytoskeleton [50]. Deficiencies or increases in the expression of these proteins can lead to the development of congenital cardiomyopathies, arrhythmogenesis, myocardial ischemia, arterial hypertension, and abnormal myocardial remodeling [51]. In the heart, AJs are responsible for mechanically docking cardiomyocytes and are closely associated with gap junction plates in intercalary disks [52]. The correct mechanical function of the heart muscle depends on the AJ component N-cadherin, which is highly expressed in both the developing and mature myocardium, where it is predominantly found in the transverse region of intercalated disks and regions of contact between neighboring myocytes [53]. In murine models, loss of N-cadherin in the embryonic myocardium resulted in embryonic lethality at midgestation, accompanied by multiple embryonic anomalies, including cardiovascular defects [54], highlighting the importance of N-cadherin for optimal heart development and function. Thus, the reduced expression levels of N-cadherin and β-catenin, another AJ protein, in the right ventricular wall, left ventricular wall, and interventricular septum (Figure 7) likely also contributed to the observed morphological abnormalities. Finally, gap junctions are arrays of transmembrane ionic channels that allow the passage of ions for electrical coupling as well as small molecules for metabolic coupling of adjacent cardiomyocytes. These structures are formed by connexins (Cx), with isoform 43 (Cx43) the most abundant in embryonic and adult cardiac tissue [55]. Connexins also form hemichannels, allowing the exchange of ions and small low molecular weight metabolites between the cytoplasm and extracellular environment, which is crucial for communication between more distant cardiomyocytes [56]. However, we did not find significant differences in expression between hyperglycemic and normoglycemic embryos (Figure 7M–O), possibly because, during the study period, intercellular coupling via gap junctions is not fully developed. ## 4.4. Effects of Embryonic Hyperglycemia on Glucose Transport in the Heart The mammalian heart is adapted to use multiple substrates for energy. The predominant fuel is fatty acids, followed by glucose, which accounts for around $25\%$ of ATP production in the myocardium. In the heart, the most abundant glucose transporters are GLUT1 and GLUT4 [57]. GLUT1 is located mainly in the plasma membrane and is responsible for basal cardiac glucose uptake, while GLUT4 is present mainly in intracellular vesicles at rest but translocates to the plasma membrane in response to insulin stimulation [58]. GLUT1 is the predominant glucose transporter in embryonic and neonatal hearts, and expression is constitutive. Nonetheless, the expression level is regulated by multiple physiological and pathological stimuli. For instance, hypoxia promotes an increase in GLUT1 expression. The apoptosis rate of ventricular cardiomyocytes is elevated in adult diabetics [59], potentially due to insufficient glucose transport via GLUT1 and GLUT4. This necessitates alternative energy production by the beta oxidation of free fatty acids and results in the reduced synthesis of pyruvate [60]. Expression of GLUT1 was reduced in both walls of the hyperglycemic embryonic heart (Figure 8), which, as previously reported, could paradoxically reduce glucose availability and impede development [61]. In our model, both compaction and thickening of the ventricular walls were delayed, potentially due to reduced proliferation rate in the ventricular walls, as evidenced by Ki-67 staining (Figure 9). In the intermediate and long term, these deficits could induce malformations and functional deficits in other tissues due to insufficient oxygen supply. All the aforementioned information highlights the importance of how hyperglycemia, such as in the case of gestational diabetes, where glucose can cross the placenta freely, results in the exposure of the fetus to high levels of glucose, thus leading to multiple complications in fetal development, such as defects in the formation of the nervous and circulatory systems. These changes could therefore increase the susceptibility of offspring to develop cardiometabolic diseases later in life due to epigenetic changes during fetal development [62,63]. In addition to the above, it has been shown that lipotoxicity resulting from maternal obesity is capable of activating a series of cascades of oxidative stress and proinflammatory signals that can exacerbate cardiovascular complications induced by maternal obesity in offspring during their adult life [64]. Reasons such as these make investigations that help clarify the pathological mechanisms around the complications derived from the development under a teratogenic environment of the progeny of great importance. ## 5. Conclusions Hyperglycemia delayed the gross development of the chicken embryonic heart and disrupted the normal organization of both cellular and molecular elements. Furthermore, hyperglycemia reduced the proliferation of cardiomyocytes. These abnormalities were associated with elevated expression levels of the sarcomeric proteins TNNI3K and troponin I, as well as with downregulation of multiple cell junction proteins, suggesting that the observed abnormalities in gross morphology arise in part from dysfunctional mechanical, electrical, and metabolic coupling among cells. In addition, the hyperglycemic heart exhibited downregulation of the main glucose transporter, GLUT1, suggesting a paradoxical deficit in glucose metabolism. These findings highlight the utility of the chicken embryo as a model for investigating the pathogenesis of congenital heart defects due to gestational diabetes, as well as the risks associated with poor glucose control during pregnancy. ## Figures and Schemes Figure 1: Delayed maturation of hyperglycemic Gallus embryos compared to normoglycemic embryos. (A) Changes in blood glucose levels after daily injections of 30 mmol/L glucose saline in the hyperglycemic (HG) group and daily injections of glucose-free saline in the control NT group. (B) Gallus embryo incubated for 10 days under normoglycemic conditions reaching stage 36HH (n = 32 independently treated embryos in each group). (C) Gallus embryos incubated under hyperglycemia for 10 days showing delayed maturation (typical example from HG-treated embryos). Arrowhead, delayed limb maturation; arrow, delayed eyelid maturation; star, defects in leg phalanges. () p < 0.005, (scale bar = 1 cm).* Scheme 1: Hyperglycemia stunts embryonic growth. (A) Mean body weight of the HG embryos was 34% lower than that of the NT embryos on day 10. (B) Mean body size of the HG embryos was 14% shorter than that of the NT embryos on day 10. Graphs represent mean ± SD of n = 23 HG and n = 25 NT embryos. () p < 0.05.* Figure 2: Delayed heart development in the hyperglycemic (HG) embryos compared to the control (NT) group embryos. Hearts from (A) a stage 32HH NT group embryo, (B) a stage 32HH HG group embryo, (C) a stage 36HH NT group embryo, and (D) a stage 36HH HG group embryo (all at 25× magnification). Scale bar = 1 mm. (E,F) Representative hematoxylin and eosin (HE)-stained cross-sections of (E) an NT embryonic heart at stage 36HH and (F) an HG embryonic heart at stage 36HH. Scale bar represents 1 mm. (G) Average ventricular diameter of the HG and NT embryonic hearts. (H) Average circumference of the HG and NT embryonic hearts (HG, n = 8 and NT, n = 9). Scale bar = 1 mm. Figure 3: Morphometric abnormalities in hearts in typical HE-stained cross-sections of Gallus embryos incubated for 10 days. (A) Left ventricular wall of an NT embryo. (B) Left ventricular wall of an HG embryo. (C) Average left ventricular wall thickness of the HG and NT embryos. (D) Right ventricular wall of an NT embryo. (E) Right ventricular wall of an HG embryo. (F) Average right ventricular wall thickness of the HG and NT embryos. (G) Interventricular septum of an NT embryo. (H) Interventricular septum of an HG embryo. (I) Average interventricular septum thickness of the HG and NT embryos. All images were acquired at 40× magnification (scale bar = 200 μm). () p < 0.01, (**) p < 0.0001. Scheme 2: Average myocyte count per field of the HG and NT hearts (40×). (A) Left ventricular wall. (B) Right ventricular wall. (C) Interventricular septum. () p < 0.01. Figure 4: Scanning electron micrographs of longitudinal sections of the Gallus embryonic heart at stage 36HH showing delayed delamination and compaction of the trabeculae under hyperglycemia. (A) NT heart (50×), (B) HG heart (50×), (C) NT heart (100×), and (D) HG heart (100×). The arrow head indicates the normal thickness of the cardiac walls in NT embryos, the arrowhead indicates the decreased thickness of the ventricular walls. Figure 5: Altered expression of sarcomeric proteins in hearts of Gallus embryos incubated for 10 days under hyperglycemic conditions. (A) Representative cross-sections of the left ventricular wall immunostained for TNNI3K in an NT embryo. (B) Representative cross-sections of the left ventricular wall immunostained for TNNI3K in an HG embryo. (C) Immunofluorescence intensities of TNNI3K in the HG and NT embryos (green = TNNI3K, red = nuclear marker). (D) Representative cross-sections of the left ventricular wall immunostained for troponin I in an NT embryo. (E) Representative cross-sections of the left ventricular wall immunostained for troponin I in an HG embryo. (F) Immunofluorescence intensities of troponin I in the HG and NT embryos (red = troponin, white = nuclear marker). (G) Representative cross-sections of the left ventricular wall immunostained for desmin in an NT embryo. (H) Representative cross-sections of the left ventricular wall immunostained for desmin in an HG embryo. (I) Immunofluorescence intensity of desmin (green = desmin, white = nuclear marker). All images were acquired at 40× magnification (n = 9 HG hearts and n = 10 NT hearts). Scale bar = 20 μm, () p < 0.05.* Figure 6: Altered expression of sarcomeric proteins in the hearts of Gallus embryos incubated under hyperglycemic conditions revealed by western blotting. (A) Representative western blots of sarcomeric proteins. (B) Densitometry of sarcomeric proteins. α-actin was used as the gel-loading control. () p < 0.05, (*) p < 0.01. Figure 7: Expression levels of cell junction proteins in the heart tissue of the Gallus embryos. (A) Representative sections of the left ventricular wall immunostained for the zona occludens protein ZO-1 in an NT embryo. (B) Representative sections of the left ventricular wall immunostained for ZO-1 in an HG embryo. (C) Immunofluorescence intensity of ZO-1 in the HG and NT embryos (green = ZO-1, white = nuclear marker). (D) Representative sections of the left ventricular wall immunostained for the zona occludens protein claudin-1 in an NT embryo. (E) Representative sections of the left ventricular wall immunostained for claudin-1 in an HG embryo. (F) Immunofluorescence intensity of claudin 1 in the HG and NT embryos (green = claudin-1, white = nuclear marker). (G) Representative sections of the left ventricular wall immunostained for the zona occludens protein β-catenin in an NT embryo. (H) Representative sections of the left ventricular wall immunostained for β-catenin in an HG embryo. (I) Immunofluorescence intensity of β-catenin in the HG and NT embryos (green = B-catenin, red = nuclear marker). (J) Representative sections of the left ventricular wall immunostained for the zona occludens protein N-cadherin in an NT embryo. (K) Representative sections of the left ventricular wall immunostained for N-cadherin in an HG embryo. (L) Immunofluorescence intensity of N-Cadherin in the HG and NT embryos (green = N-cadherine, red = nuclear marker). (M) Representative sections of the left ventricular wall immunostained for the zona occludens protein Cx-43 in an NT embryo. (N) Representative sections of the left ventricular wall immunostained for the zona occludens protein Cx-43 in an HG embryo. (O) Immunofluorescence intensity of Cx-43 in the HG and NT embryos (green = Cx43, red = nuclear marker). All images were acquired at 40× magnification (n = 9 HG and n = 10 NT hearts), scale bar = 20 µm. () p < 0.05, () p < 0.01, (*) p < 0.0001. Figure 8: Immunostaining in transverse histological sections probed for the primary fetal glucose transporter GLUT1. (A) Representative image of the left ventricular wall in an NT embryo. (B) Representative image of the left ventricular wall in an HG embryo (both 60×; green = Glut 1 and white = nuclear marker). (C) Immunofluorescence intensity of GLUT1 (Scale bar = 20 µm). () p < 0.05, (**) p < 0.001. Figure 9: Fetal cardiomyocyte proliferation, immunostained for the proliferation marker Ki-67, in the transverse histological sections of the hearts of Gallus embryos incubated for 10 days. (A) NT embryo nuclei. (B) Ki-67 signal from an NT embryo. (C) Merge of Ki-67 positive nuclei in NT embryos. (D) HG embryo nuclei. (E) Ki-67 signal from an HG embryo. (F) Merge of Ki-67 positive nuclei in HG embryos. (G) Number of Ki-67-positive cells (photos at 40×; red = nuclear marker, blue = Ki67 and pink = merge, marked with yellow arrows) Scale bar = 20 µm, () p < 0.05.* ## References 1. Zhao Z., Reece A.. New concepts in diabetic embryopathy. Lab. Med. Clin. (2013) 33 207-223. DOI: 10.1016/j.cll.2013.03.017 2. 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--- title: 'pH-Responsive and Mucoadhesive Nanoparticles for Enhanced Oral Insulin Delivery: The Effect of Hyaluronic Acid with Different Molecular Weights' authors: - Shuangqing Wang - Saige Meng - Xinlei Zhou - Zhonggao Gao - Ming Guan Piao journal: Pharmaceutics year: 2023 pmcid: PMC10056758 doi: 10.3390/pharmaceutics15030820 license: CC BY 4.0 --- # pH-Responsive and Mucoadhesive Nanoparticles for Enhanced Oral Insulin Delivery: The Effect of Hyaluronic Acid with Different Molecular Weights ## Abstract Drug degradation at low pH and rapid clearance from intestinal absorption sites are the main factors limiting the development of oral macromolecular delivery systems. Based on the pH responsiveness and mucosal adhesion of hyaluronic acid (HA) and poly[2-(dimethylamino)ethyl methacrylate] (PDM), we prepared three HA–PDM nano-delivery systems loaded with insulin (INS) using three different molecular weights (MW) of HA (L, M, H), respectively. The three types of nanoparticles (L/H/M-HA–PDM–INS) had uniform particle sizes and negatively charged surfaces. The optimal drug loadings of the L-HA–PDM–INS, M-HA–PDM–INS, H-HA–PDM–INS were 8.69 ± $0.94\%$, 9.11 ± $1.03\%$, and 10.61 ± $1.16\%$ (w/w), respectively. The structural characteristics of HA–PDM–INS were determined using FT-IR, and the effect of the MW of HA on the properties of HA–PDM–INS was investigated. The release of INS from H-HA–PDM–INS was 22.01 ± $3.84\%$ at pH 1.2 and 63.23 ± $4.10\%$ at pH 7.4. The protective ability of HA–PDM–INS with different MW against INS was verified by circular dichroism spectroscopy and protease resistance experiments. H-HA–PDM–INS retained 45.67 ± $5.03\%$ INS at pH 1.2 at 2 h. The biocompatibility of HA–PDM–INS, regardless of the MW of HA, was demonstrated using CCK-8 and live–dead cell staining. Compared with the INS solution, the transport efficiencies of L-HA–PDM–INS, M-HA–PDM–INS, and H-HA–PDM–INS increased 4.16, 3.81, and 3.10 times, respectively. In vivo pharmacodynamic and pharmacokinetic studies were performed in diabetic rats following oral administration. H-HA–PDM–INS exhibited an effective hypoglycemic effect over a long period, with relative bioavailability of $14.62\%$. In conclusion, these simple, environmentally friendly, pH-responsive, and mucoadhesive nanoparticles have the potential for industrial development. This study provides preliminary data support for oral INS delivery. ## 1. Introduction Diabetes is a chronic metabolic disease that lasts a lifetime, especially type I diabetes, which requires frequent subcutaneous injections of insulin (INS) to maintain blood glucose balance [1,2]. However, such a pathway may lead to poor patient compliance and hypoglycemia. Oral INS delivery, as a more convenient treatment for diabetes, has the characteristics of simulating physiological INS secretion while overcoming these difficulties [3]. Whereas various biological barriers in the gastrointestinal tract hinder the clinical development of oral INS [4], oral INS delivery has to overcome these physiological barriers. First, orally delivered INS should avoid degradation by gastrointestinal enzymes and be stable over a wide pH range (gastric pH 1–3, enteral pH 6–7.5) [5]. Secondly, as a biomacromolecule, INS cannot directly enter the blood circulation through the intestinal mucosa, nor can it be straight internalized by intestinal epithelial cells into the blood [6]. Based on this, it is necessary to design a safe and effective drug carrier to deliver INS, which can overcome the harsh physiological environment of the gastrointestinal tract and be endocytosed into the blood circulation by intestinal epithelial cells. With the continuous development of nanotechnology, there are many countermeasures to improve the bioavailability of oral INS [7,8]. pH-responsive delivery systems are the most widely reported INS carriers for targeted delivery and controlled release [9,10,11,12,13]. The pH-responsive delivery system protects INS from burst release and enzymatic degradation in gastric juices, and prolongs stability in vivo, thereby improving intestinal permeability and enhancing INS delivery efficiency [14]. pH-responsive polymers usually contain acidic or basic groups such as carboxyl, sulphonic acid, and amino groups. At different pH, pH-responsive polymers undergo protonation or ionization, changing the charge distribution and internal interactions, and thus changing the structure of the polymer [15]. Poly((2-dimethylamino)ethyl methacrylate) (PDM) is a well-known pH-sensitive polymer, and the presence of tertiary amine sites in its structure provides pH response characteristics [16]. The pKa of PDM is approximately 7.0. Thus, at pH < 7, PDM exhibits ionized forms of tertiary amine groups, and the polymer undergoes swelling, leading to a more efficient release of the loaded drug [17]. In this case, PDM acts as a cationic polymer. Thus, PDM can act as a carrier by interacting with anionic polymers, enzymes, or DNA through electrostatic interactions. Wang et al. [ 17] used photothermally sensitive polydopamine nanoparticles (NPs) modified with poly[(2-methacryloyloxy)ethyl phosphorylcholine-b-(2-dimethylamino)ethyl methacrylate] diblock copolymers to achieve near-infrared photothermal therapeutic and pH-sensitive drug release. Ghobashy et al. [ 18] prepared pH-responsive hydrogels using 2-(dimethylamino)ethyl methacrylate, polyethylene oxide, and ZnS NPs and investigated the release behavior of the hydrogels and the cumulative release of drugs increased from $40\%$ at pH 4 to $96\%$ at pH 7. Foss et al. [ 19] developed nanospheres of crosslinked networks of methacrylic acid grafted with poly(ethylene glycol), and acrylic acid grafted with poly(ethylene glycol) nanospheres for use as oral INS delivery devices. In addition, PDM contains carboxylic acid groups, which have the potential to open tight epithelial connections [20,21] and facilitate INS absorption. However, when PDM is used alone as a carrier to deliver drugs, it has a low drug loading capacity, unstable delivery system, and premature release, which may limit its further development [22,23,24]. The ideal drug delivery system should be versatile [14]. The introduction of another polymer is a feasible way to improve system stability and functional diversity [25]. The mucoadhesive delivery system adheres tightly to the epithelial mucosa of the intestinal, increasing the retention time of the preparation, and achieving the long-term stable release of drugs [26]. The rapid clearance of drugs at the gastrointestinal absorption site is also an oral absorption barrier [27]. Hyaluronic acid (HA) is a widely used adhesive carrier. HA, an anionic nonsulfated glycosaminoglycan natural polymer, is a viscous polysaccharide formed by the alternating connection of acetylglucosamine and glucuronic acid disaccharide [28]. HA is present in the extracellular matrix and synovial fluid of most human tissues and is also one of the main components of the extracellular matrix. It has many advantages as an adhesive carrier, such as ideal physicochemical properties, biocompatibility, non-immunogenicity, and biodegradability. The hydrophilic HA protects the activity of protein-based drugs and releases them slowly, making it a promising drug carrier. Using the negative charge carried by HA, it is self-assembled with cationic PDM using electrostatic interactions to form a new system for oral delivery of INS, which overcomes the disadvantages of rapid degradation and poor stability and improves the stability and bioavailability of INS in vivo. The molecular weight (MW) of HA ranges from 103 to 107 Da, and different MW of HA has different properties [29]. The MW of HA should be chosen carefully because if polymers with higher MW are used, the release of peptides may be delayed due to high steric hindrance and high viscosity [30]. The accumulation of HA and the HA-binding proteoglycan versican around smooth muscle cells suggests that these molecules play an important role in cell proliferation and migration [31]. Segura et al. [ 32] prepared hydrogels using 1.33 × 106 Da of HA with poly(ethylene glycol) diglycidyl ether using a crosslinking strategy. The hydrogel has low water content, a slow degradation rate, and ideal mechanical properties that could enhance its application in tissue engineering. Chiesa et al. [ 33] explored the role of the MW of HA (280, 540, 820 kDa) in the uptake of HA-based nanoparticles. In addition, some reports do not specify the MW of HA [34,35]. These result in low accuracy of experimental results and poor reference of the literature. Therefore, the effects of the MW of HA on carrier properties were explored in this study. Due to the pH variation in the gastrointestinal tract, the pH responsiveness and mucosal adhesion of PDM and HA were combined to prepare the HA–PDM nano-delivery system loaded with INS. Firstly, self-assembled composite NPs, HA–PDM, were formed using the electrostatic interaction between the anionic polymer HA and the cationic polymer PDM. Then, the structural characteristics of NPs were determined using FT-IR. The preparation process of HA–PDM was optimized by a single-factor approach, and the effect of the MW of HA on the drug delivery system was investigated by particle size and zeta potential. After that, the release characteristics of INS in vitro were studied in 1.2, 6.8, and 7.4 pH PBS. The biocompatibility of NPs was also studied using CCK-8 and live–dead cell staining. Next, the ability of NPs to promote INS penetration was examined with Caco-2 monolayer cells and an ex vivo intestinal permeation experiment. Finally, in vivo pharmacodynamic and pharmacokinetic studies were performed to characterize hypoglycemic effects in diabetic rats following oral administration. ## 2.1. Materials INS (I828365, derived from bovine pancreas, 27 u/mg), HA (1–2 × 105, 4–8 × 105, 1.5–2.5 × 106), and potassium persulfate (P823296, $99.9\%$) were obtained from Shanghai Macklin Biochemical Technology Co., Ltd. (Shanghai, China). The measured MW of the three HA were 1.5 × 105, 5 × 105, and 2.2 × 106, respectively. ( 2-dimethylamino) ethyl methacrylate (D111129, $99\%$), pepsin from porcine stomach (P128678, ≥2500 units/mg dry weight), and trypsin from bovine pancreas (T105531, potency ≥ 2500 units/mg) were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Insulin–FITC (MB5260, 7.14 mg/mL Insulin, 59.45 μg/mL FITC) was obtained from Dalian Bergolin Biotechnology Co., Ltd. (Dalian, China). Calcein AM Cell Viability Assay Kit (C203M, Lot No. 120121230207) was purchased from Shanghai Beyotime Biotech. Inc. (Shanghai, China). The other experimental reagents were of analytical grade and used without purification. Human intestinal cell lines Caco-2 were obtained from the Cell Resource Center, IBMS, CAMS/PUMC and passages 30–40 were used. Caco-2 cells were cultured in 75 cm2 T-flasks in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with $10\%$ (v/v) fetal bovine serum (FBS), $1\%$ (v/v) of a penicillin–streptomycin antibiotic blend, and $1\%$ (v/v) glutamine in 37 °C, $5\%$ CO2. Male Sprague–Dawley (SD) rats (160–180 g) were obtained from Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). All animal experiments were approved by the Laboratory Animal Ethics Committee in the Institute of Materia Medica and Peking Union Medical College. All procedures followed ethical standards during the experiment [36]. ## 2.2. Preparation of NPs The pH-responsive NPs were prepared by self-assembly of HA and 2-(Dimethylamino)ethyl methacrylate (DM). According to the single factor type in Table 1, the optimal prescription and the best process were screened. HA with three different MW was dissolved in distilled water, and some DM was added with stirring. The pH of the mixed solution was adjusted to 6.0 using hydrochloric acid. The initiator was added, and initiator was potassium persulfate (KPS) with a concentration of $2.5\%$ (w/v) to initiate the polymerization of DM. Nitrogen was used as a protective agent. After a certain time at 65 °C, the reaction solution was removed and placed in a dialysis bag (8–10 KDa) and dialyzed for 72 h using distilled water to remove unreacted monomers. NPs were mixed with $2.5\%$ (w/v) trehalose [37] and then frozen in a refrigerator at −40 °C overnight. Finally, the samples were lyophilized by using an EPSILON1-4LSC freeze dryer (Martin Christ, Osterode am Harz, Germany) to acquire freeze-dried NPs. Pressure (MPa) was 0.01 MPa. HA–PDM powder was obtained and stored in a moisture-proof cabinet. According to the different MW of HA, the three NPs were noted as L-HA–PDM, M-HA–PDM, and H-HA–PDM. ## 2.3.1. FT-IR The potassium bromide (KBr) pellet technique was used to obtain FT-IR spectra with Bruker Vertex 70 spectrometer (Bruker, Ettlingen, Germany). The samples were ground with KBr to prepare the pellets. The spectral width was 4000–400 cm−1, and the samples were scanned for 32 times. ## 2.3.2. Particle Sizes and Zeta Potentials of NPs Particle sizes, polydispersity indexes (PDI), and zeta potentials measurements were performed at 25 °C using a Zetasizer Nano ZS (Malvern, Worcestershire, UK) coupled with an MPT-2 accessory, fitted with a 532 nm laser at a fixed scattering angle of 173°. 1 mL of L-HA–PDM, M-HA–PDM, and H-HA–PDM (0.5 mg/mL) was filtered through a 0.8 μm syringe filter before performing the analysis [38]. The zeta potentials values (mV) were calculated from electrophoretic mobility using the Smoluchowski relationship ($$n = 3$$). ## 2.3.3. Encapsulation Efficiency (EE) and Drug Loading (DL) Firstly, the NPs loaded with INS were prepared. Briefly, a certain amount of INS hydrochloric acid solution (25, 50, 75, 100 mg) was first mixed with DM and then added dropwise to the HA solution. The optimal formulation and preparation process was used to obtain L-HA–PDM–INS, M-HA–PDM–INS, and H-HA–PDM–INS, respectively. The DL and EE of the NPs were measured using an indirect method [39]. Briefly, after the INS-loaded NPs were prepared, the reaction solution was centrifuged at high-speed (18,000 rpm, 30 min, 4 °C). The reaction solution contained the INS-loaded NPs, unloaded INS (free INS), and unreacted monomers. At the end of the high-speed centrifugation, the upper layer of the solution contained unloaded INS and unreacted monomers. HPLC was used to determine the INS concentration in the upper solution. The specific operation of HPLC is documented in Supplementary Materials. The total amount of INS added minus the amount of free INS (unloaded INS) was the dose of NPs-loaded INS. DL and EE were calculated using Equations [1] and [2], respectively [40]. DL (%) = total amount of INS added − amount of free INS/total amount of INS added[1] EE (%) = total amount of INS added − amount of free INS/weight of NPs[2] ## 2.3.4. Transmission Electron Microscopy (TEM) HT7700 Exalens Transmission Electron Microscopy (HITACHI, Chiyoda, Japan) was used to evaluate the roundness of NPs. The accelerating voltage was ×25.0 K Zoom-1 HC-1 80.0 Kv. The vacuum was less than 2 × 10−5 Pa. The NPs were uniformly dispersed in distilled water, and 6.0 μL sample was dropped onto a 300 mesh copper mesh and left for 5 min. Then, the excess liquid was blotted out with filter paper, dried, and observed, ×90,000. ## 2.3.5. Storage Stability The unloaded INS was removed using high-speed centrifugation (18,000 rpm, 30 min, 4 °C). After freeze-drying, the lyophilized NPs powder was obtained. Specific dialysis and freeze-drying process are described in Supplementary Materials. The lyophilized NPs were stored at 4 °C and protected from light. The NPs were redissolved at different time points, and the particle size, PDI, and DL of the NPs were detected. ## 2.4. Stability of NPs in Simulated Gastrointestinal Fluids The stability of L-HA–PDM–INS, M-HA–PDM–INS, and H-HA–PDM–INS was measured in simulated gastric fluid (SGF, pH 1.2) containing pepsin and simulated intestinal fluid (SIF, pH 7.4) containing trypsin. Proteins and peptides were highly susceptible to protease degradation in the gastrointestinal environment. SGF containing pepsin was prepared with 2.0 g sodium chloride and 3.2 g pepsin dissolved in water to 1000 mL, and pH was adjusted to 1.2 with hydrochloric acid. SIF containing trypsin was prepared with 6.8 g potassium dihydrogen phosphate, 77 mL 0.2 mol/L sodium hydroxide solution, and 10 g trypsin, then diluted to 1000 mL with water and adjusted pH to 7.4 with sodium hydroxide solution or hydrochloric acid solution. It is well known that the preparation is in the stomach for 1–2 h and in the intestine for 2–4 h after oral administration. Briefly [41], 1 mL of SGF and SIF solutions were added to 20 mL of the test formulations (INS content equivalent to 1 mg) separately. The samples were incubated at 37 °C, 100 rpm. Free INS in the presence of SGF and SIF was incubated and used as a control. A hundred microliters of samples were collected at 0.25, 0.5, 0.75, 1, 1.5, and 2 h after adding SGF and 0.25, 0.5, 0.75, 1, 1.5, 2, and 4 h after adding SIF. Equal volumes were added to maintain the sink conditions. The degradation reactions of pepsin and trypsin were halted by the addition of 100 μL ice cold acetonitrile solution containing $0.1\%$ (v/v) trifluoroacetic acid, respectively. The samples were extracted by incubation with pH 7.4 PBS and analyzed using HPLC to determine the amount of INS. ## 2.5.1. In Vitro INS Release The in vitro INS release properties of NPs were determined by the dialysis diffusion technique under different pH conditions. The release medium included pH 1.2 SGF, pH 6.8 simulated colonic fluid, and pH 7.4 SIF. The NPs suspension was placed in a dialysis bag (MWCO 8–10 kDa) and then separately into the dissolution medium. At 37 °C, 50 rpm, the sample (1 mL) was taken out at the desired time points, and the fresh solution was added. INS concentration was measured using HPLC. ## 2.5.2. Kinetics of Drug Release The mechanism of INS release from NPs were determined using various kinetics models based on in vitro release data, which include zero-order, first-order, Ritger–Pappas, and Higuchi. The INS release percent (%) was plotted as a function of time as follows Equation [3]:[3]MtM∞=Ktn Here, Mt was the amount of INS released in time t; M∞ was the total amount of INS released after unlimited time; K was the release rate constant; and n was the release index [42]. ## 2.5.3. Stability of INS in NPs The secondary structure of INS released from NPs were determined by circular dichroism (CD) spectroscopy to examine the stability of INS in NPs. INS solution and INS released from NPs were prepared at pH 1.2, 0.05 M hydrochloric acid solution so that the final concentration of INS was 100 μg/mL. The scan range was 195–250 nm, the precision was 0.2 nm, the bandwidth was 0.5 nm, and the scan rate was 50 nm/min. ## 2.6.1. Mucin Adhesion To evaluate the mucin adsorption and penetration of NPs, porcine mucin was mixed with an 8 mg/mL mucin reserve solution. The mixed solution was then mixed with NPs in equal volumes to obtain a 4 mg/mL NPs dispersion solution. After 1 h, the mixture was centrifuged at a high-speed of 13,000 rpm for 15 min, and the concentration of free INS in the supernatant was determined. The percentage of NPs with mucin aggregates was determined by calculating the ratio of free INS to total INS. ## 2.6.2. Ex Vivo Intestinal Permeation Experiment The absorption of NPs in vivo was simulated by an intestinal permeability experiment in vitro. The SD rats had fasted overnight. After inhalation of isoflurane at a concentration of $6\%$ for 10 min, the rats were anesthetized and then sacrificed. From these rats, a total of 4 cm and a penetration area of 7.85 cm2 of the small intestine, with both ends ligated, was removed. The isolated intestines were used for ex vivo intestinal permeation experiment (quantitative experiment) and a confocal laser scanning microscope (qualitative experiment). The 0.2 mL of 5 IU/mL INS solution, L-HA–PDM–INS, M-HA–PDM–INS, and H-HA–PDM–INS were injected into the isolated intestine, and then the intestine was placed in 5 mL pH 7.4 PBS at 37 °C and 50 rpm stirring speed [40]. The permeation experiment met the sink condition. At 0.5, 1, 2, 3, 4, 5, and 6 h, 0.5 mL of the solution was removed and supplemented with a fresh isothermal solution. The concentration of INS was determined ($$n = 6$$). The apparent permeability coefficient (Papp) reflects the ability of the drug to cross the intestinal, which is the drug transport rate. Papp = dQ/dt × A × C0[4] Here, dQ/dt was the steady state rate of permeated INS over time. A was the diffusion area. C0 was the initial concentration of INS. ## 2.6.3. Confocal Laser Scanning Microscope (CLSM) Fluorescently labeled INS (INS–FITC) was used instead of INS. L-HA–PDM–INS–FITC, M-HA–PDM–INS–FITC, and H-HA–PDM–INS–FITC were prepared using the same method as for HA–PDM–INS. Briefly [43], the 0.2 mL of 5 IU/mL INS–FITC solution, L-HA–PDM–INS–FITC, M-HA–PDM–INS–FITC, and H-HA–PDM–INS–FITC were injected into the isolated intestine, and then the intestine was placed in 5 mL pH 7.4 PBS at 37 °C and 50 rpm stirring speed. At 1 and 2 h, the intestines were removed and rinsed separately. The intestines were dehydrated, embedded, and sectioned (4 μm). Finally, intestines were stained with 4′,6-diamidino-2-phenylindole (DAPI), and the penetration was observed with a Cytation5 (Biotek, Winooski, VT, USA) ($$n = 3$$). The excitation wavelength was 495 nm and the emission wavelength was 525 nm. ## 2.7. Caco-2 Cells Caco-2 cells were used for assessment of the cytotoxicity and transintestinal epithelium delivery due to their morphological and functional similarity to the intestinal epithelium [44]. ## 2.7.1. CCK 8 CCK-8 assays were performed to evaluate the cytotoxicity of NPs on Caco-2 cells. In brief, Caco-2 cells were inoculated in 96-well plates for 24 h and 1 × 104 cells/well. After the cells were attached to the walls, the medium was discarded and replaced with 100 μL of fresh DMEM containing different concentrations of NPs (50, 100, 200, 300, 400, 500 μg/mL), which were cultured in an incubator for 24 h. Then, the medium was discarded and replaced with serum-free DMEM containing $10\%$ (v/v) CCK-8 solution. After 1.5 h of incubation, the OD value of every well was measured at a wavelength of 450 nm using a Synergy H1 microplate reader (Biotek, Winooski, VT, USA). ## 2.7.2. Live–Dead Cell Staining The survival of cells can be visualized using live–dead cell staining. The Caco-2 cell suspension concentration was adjusted to 1 × 105 cells/well. Cells were seeded in 12-well plates and cultured for 1 d. Then, cells were treated with a DMEM medium containing different samples for 24 h. Live cells were stained with 1 μmol/mL calcein AM, and dead cells were stained with 1 μmol/mL propidium iodide. The images were taken using a CLSM. ## 2.7.3. Transwell Corning® (Transwell pore diameter 0.4 μm, area 1.12 cm2) inserts with a 12-well plate were used to estimate the permeability. A quantity of 0.5 mL cell suspension was added to the donor chambers of the transwell plate at a density of 6 × 105 cells/well, and 1.5 mL fresh DMEM containing serum was added to the receptor chambers. The transwell plates were cultured in a C170 CO2 incubator (Binder, Neckarsulm, Germany). The transepithelial electrical resistance (TEER) was measured each time the medium was replaced. TEER, which is formed by the flow of ions through the paracellular space, has become a common indicator for detecting the integrity of cell monolayers due to its simple operation and repeatable measurement. Once the Caco-2 cell monolayer was fully established, the medium of the donor chambers and the receptor chambers was discarded. 0.5 mL NPs or INS solution was added to the donor chambers and 1.5 mL HBSS buffer was added to the receptor chambers, respectively. The transwell plates were placed in a CO2 incubator, and 200 μL solution was removed from the receptor chambers at different time points. At the same time, 200 μL HBSS was added. ## 2.7.4. Cellular Uptake of NPs CLSM can be used to observe the distribution of drugs in cells and qualitatively characterize cellular uptake. In brief, Caco-2 cells were seeded in 6-well plates at a density of 2 × 105 cells/well. The well plates were incubated for 24 h in a CO2 incubator. INS–FITC-loaded NPs with a concentration of 50 μg/mL were prepared using free DMEM and divided into INS–FITC, L-HA–PDM–INS–FITC, M-HA–PDM–INS–FITC, and H-HA–PDM–INS–FITC. After 1 and 2 h of culture, the medium was removed, and the cells were washed three times with PBS. One milliliter of $4\%$ paraformaldehyde solution was added to each well and fixed for 10 min. Finally, one milliliter of 1 μm/mL DAPI was added to each well and stained for 5 min. Cellular uptake was observed using a CLSM at 20×. ## 2.8.1. Establishment of the Diabetic Rat Model Male SD rats were intraperitoneally injected with 60 mg/kg streptozocin (STZ, citrate-buffered saline, pH 4.5) to establish the diabetic model. One week later, the fasting blood glucose of SD rats was monitored. When the fasting blood glucose level of the model rats remained higher than 16.7 mmol/L for a week, it was considered that the diabetic rat model was successfully established and could be carried out in vivo experiments. ## 2.8.2. Pharmacokinetics and Pharmacodynamics SD rats were deprived of food for 6 h before the experiment. Thereafter, rats were randomly grouped ($$n = 6$$) and separately administered with L-HA–PDM–INS, M-HA–PDM–INS, and H-HA–PDM–INS by oral gavage (40 IU/kg), or subcutaneous (SC) injection (5 IU/kg). As a control group, another group of rats was set up with free INS (40 IU/kg) by oral gavage. Plasma was collected from the fundus venous plexus and the changes in blood glucose were analyzed using a glucometer (Haier, Qingdao, China). INS concentrations in serum were measured using the Insulin Assay Kit (Nanjing Jiancheng, Nanjing, China). Relative bioavailability (F, %) was calculated according to Equation [5]:F% = AUC(oral) × Dose(S.C.)/AUC(S.C.) × Dose(oral) × $100\%$[5] Here, AUC(oral) was the area under the drug–time curve for the oral group, AUC(S.C.) was the area under the drug–time curve for the injection group, Dose(oral) was the dose administered for the oral group, and Dose(S.C.) was the dose administered for the injection group. ## 2.9. In Vivo Toxicity The treatment of chronic diseases needs to be carried out over a long time. Pharmacological safety is a key factor to be considered for innovative preparations. To determine the in vivo safety of NPs, healthy SD rats were orally administered 60 IU/kg INS of NPs for 7 d [45]. The L-HA–PDM–INS, M-HA–PDM–INS, and H-HA–PDM–INS were administered according to the body weight of the rats on the day. After the last dose, plasma (1 mL) was collected from the fundus venous plexus using a capillary tube. The SD rats were sacrificed. ## 2.9.1. Systemic Toxicity The systemic toxicity of NPs were evaluated by detecting the serum levels of alanine aminotransferase (ALT), aspartate transaminase (AST), urea nitrogen (BUN), and γ-glutamyl transpeptidase (γ-GT) in rats. The serum was separated by centrifugation at 4 °C, 4000 rpm. Serum ALT, AST, BUN, and γ-GT were measured according to the instructions of the kit (Nanjing Jiancheng, China). ## 2.9.2. Hemolysis Test The blood compatibility of NPs were evaluated using spectrophotometry. Briefly, one milliliter of fresh rats’ blood was centrifuged at 4000 rpm for 10 min to separate red blood cells (RBCs). RBCs were washed three times with pH 7.4 PBS. The different concentrations of NPs were added to $2\%$ (v/v) RBCs. Incubation was carried out at 37 °C for 1 h. Negative and positive controls were obtained by mixing RBCs with PBS and distilled water, respectively. Subsequently, the samples were centrifuged at 2000 rpm for 10 min. Approximately 100 µL supernatants for each sample were used to measure absorbance with a microplate reader ($$n = 3$$). Hemolysis was calculated using Equation [6]. Hemolysis (%) = (Asc − Anc)/(Apc − Anc)[6] Here, Asc, Anc, and Apc represent the absorbance of the sample, negative control (−) and positive control (+), respectively. ## 2.9.3. Hematoxylin and Eosin Staining (H&E) The heart, liver, spleen, lung, kidney, stomach, and small intestine of rats were fixed into $4\%$ paraformaldehyde solution and stained for H&E. The safety of the NPs were evaluated using histopathology. ## 2.10. Statistical Analysis All data were presented as mean ± standard deviation and experiments were performed at least three times. Statistical analysis was performed with the Prism 7.0 software (GraphPad Software) using Tukey’s multiple comparison tests and one-way analysis of variance (ANOVA). The differences were considered significant, when p values * <0.05, <0.01, and * <0.001. ## 3.1. Formulation Screening and Process Optimization In order to obtain safe, effective, stable, convenient, and economical preparations, formulation screening and process optimization are often needed [46]. Table 2 showed the results of particle size, zeta potential, and PDI during formulation screening and process optimization. Different MW of HA (L, M, and H) were used to explore the effects of MW on the physicochemical properties of NPs. Regardless of the MW of HA, the particle size of NPs increased with increasing DM addition in the formulation screening range. The particle size of NPs were negatively correlated with the addition of KPS. Within the scope of process optimization, there was no obvious rule for the change of the particle size of NPs with different speeds, possibly because the speed at this level had no significant effect on the particle size. Regarding the reaction temperature, the reaction temperature was proportional to the MW of HA. However, the particle size of the obtained NPs were inversely proportional, and the change was obvious. The best formulation and process results were showed in Table 3. Therefore, the MW of HA has a slight influence on the preparation process of HA–PDM. When the MW of HA was larger, the dose of KPS and reaction temperature were higher. This may be because the viscosity of HA increases with the increase of MW, and the high dose of KPS and reaction temperature can slightly reduce the viscosity of the polymer. ## 3.2.1. FT-IR Figure 1 showed the FT-IR results for the polymers and NPs. The stretching vibration absorption peak of the carboxyl group in HA appeared at 1616.35 and 1409.96 cm−1 [47]. The stretching peak near 2922.16 cm−1 belonged to -CH. The strong peak of 3385.07 cm−1 proved the existence of hydroxyl absorption (Figure 1A). The FT-IR of PDM showed two characteristic peaks at 1647.21 and 1728.22 cm−1, which were attributed to –C=C and –C=O, respectively, and 1350 cm−1 was the –CO stretch [18]. The peak located at 2848.86 cm−1 was attributed to the –CH bond stretching vibration of –N(CH3)2. In addition, the peak located at 2922 cm−1 was attributed to –CH stretching vibration (Figure 1B). In the HA–PDM system, the absorption peaks at 1728.22 and 1647.21 cm−1 moved to 1724.36 and 1639.49 cm−1, respectively, and the absorption peaks were enhanced (Figure 1B). This was due to the existence of –C=C and –C=O [48]. In contrast, this peak was not evident in HA and PDM, indicating an electrostatic interaction between HA and PDM. At about 3400 cm−1, the peak shape was wide and blunt, showing intramolecular or intermolecular hydrogen bond association of hydroxyl groups, indicating that HA and PDM form NPs through a hydrogen bond, and the structure was relatively stable. This hydrogen bonding ability increased with increasing MW of HA. ## 3.2.2. Morphology, Particle Sizes, and Zeta Potentials The TEM results (Figure 2A) showed that the three NPs had a nearly spherical structure with a compact structure and moderate dispersion. NPs were not completely monodispersed probably because the pH-sensitive NPs aggregate at pH 7.4 and above [49]. Some fragments were found in the TEM results (Figure 2A), which indicated that the pH-sensitive NPs also undergo swelling, relaxation, and erosion in neutral solutions for long time. By electrostatic adsorption, HA and PDM self-assemble to form NPs. NPs with appropriate particle size are more likely to break through the spatial barrier of mucosa and promote further internalization of NPs into intestinal epithelial cells. After the addition of INS, the particle sizes of the three NPs increased (Figure 2B) because the addition of INS expanded the internal space of the HA–PDM system. The surface charge of NPs is a significant factor in determining the efficiency of cellular uptake, which affects the adhesion of NPs and interactions with organelles [50]. The three kinds of NPs prepared in this study all have high negative potentials. The presence of anionic polymers confers better adhesion to NPs because of the presence of many surface carboxyl groups on anionic polymers, which produce strong bioadhesive interactions with enteric epithelium through hydrogen bonding [51], improving the retention and penetration of NPs. ## 3.3. EE and DL The EE and DL are characteristics of drug delivery systems and are vital for assessing the usability of the carrier. During agitation, HA–PDM adsorbs INS through hydrogen bonds or hydrophobic interactions. The DL and EE of NPs were indirectly determined using high-speed centrifugation (Figure 2C–E). When the total amount of INS was 25 mg, the DL of the L-HA–PDM–INS, M-HA–PDM–INS, and H-HA–PDM–INS was 5.41 ± $0.68\%$, 5.96 ± $0.85\%$, and 6.45 ± $0.93\%$ (w/w), respectively. When the total amount of INS was 100 mg, the DL of the L-HA–PDM–INS, M-HA–PDM–INS, and H-HA–PDM–INS was 9.21 ± $0.95\%$, 10.65 ± $1.09\%$, and 10.05 ± $1.15\%$ (w/w), respectively. When the total amount of INS was constant, the DL of HA–PDM–INS increased with an increase in the MW of HA. This is probably because HA with high MW occupies a large space and carries more carboxyl groups and negative charges [52]. Through charge interactions and hydrogen bonding, HA attracts a large amount of INS, increasing DL. Another phenomenon was also noted: the DL of the NPs increased with an increase in the INS dose, but the EE decreased significantly. Similar results have been reported previously [53,54]. The excessive INS perhaps approached the upper limit of the EE of the NPs and damaged the equilibrium of the loading process. When its loading capacity is exceeded, the INS can no longer be bound. Unloaded INS was dispersed in the free form in the reactive solution. The optimal amount of INS was determined by assessing the economic benefits. For the L-HA–PDM–INS and M-HA–PDM–INS, when the dosage was 50 mg, DL was 8.69 ± $0.94\%$ and 9.11 ± $1.03\%$ (w/w), EE was 76.81 ± $5.06\%$ and 86.61 ± $4.21\%$ (w/w), respectively. For the H-HA–PDM–INS, when the dosage was 75 mg, DL and EE were 10.61 ± $1.16\%$ and 87.42 ± $3.66\%$ (w/w), respectively. ## 3.4. Stability The stability of NPs plays a vital role. The M-HA–PDM–INS and H-HA–PDM–INS NPs prepared were stored at 4 °C for 60 d, and there was no significant change in particle size and INS leakage (Figure 3B,C). Nevertheless, there was a slight change in the L-HA–PDM–INS particle size (Figure 3A) and the DL decreased mildly over 60 d (Figure 3D). This indicated that L-HA–PDM–INS was slightly less stable compared to the NPs prepared from high MW of HA. ## 3.5. Anti-Protease Degradation Ability A major problem in oral INS delivery is that INS is susceptible to degradation by proteases in the gastrointestinal tract, particularly trypsin and pepsin. The results also showed that free INS was rapidly and completely degraded in SGF containing pepsin and SIF containing trypsin (Figure 4). All three NPs significantly delayed the degradation of INS by protease. In particular, H-HA–PDM–INS showed the most significant resistance to protease degradation. The INS of H-HA–PDM–INS remained at approximately $50\%$ retention at 2 h of pepsin action or 4 h of trypsin action, because the high MW of HA had a large viscosity, and the H-HA–PDM system was more stable, which tightly bound INS inside the NPs. The FT-IR results also showed that the H-HA–PDM system had higher hydrogen bonding (Figure 1B). The results of the antiprotease degradation experiments showed that after INS was encapsulated into the NPs, there was a certain “steric hindrance” between the NPs and the protease, which prevented the protease from contacting the INS and thus resisted the degradation. ## 3.6.1. pH-Responsive Release of NPs The intestine is the most critical site for the absorption of NPs. Oral preparations need to pass through the stomach to reach the intestine. The ability of the NPs to remain stable over a wide pH range plays an important role in the subsequent uptake of NPs by intestinal epithelial cells. Therefore, we investigated the INS release behavior of NPs in pH 1.2, 6.8, and 7.4 PBS, and the results are shown in Figure 5. At pH 1.2, all three NPs released only a small amount of INS within 2 h. The amount released was less than $30\%$, indicating that the NPs were more stable at pH 1.2 (Figure 5A). The cumulative release increases gradually with increasing pH (Figure 5B,C). Interestingly, at all three pH, the release of NPs were slower, and the cumulative release was lower as the MW of HA increased. The results are consistent with the results of NPs resistance to protease degradation. This is probably due to the high viscosity of the high MW of HA and the tighter internal network of NPs, which hindered the release of INS. A modeling study of release was vital for appraising and predicting INS release behaviors in vitro and in vivo [55]. The R2 was used as an index of the best fit of NPs release model. The diffusional exponent depended on the release mechanism of the NPs delivery. In vitro release behaviors were fitted, as shown in Table 4. The release of INS was a non-Fickian diffusion (0.45 < n < 0.89) and erosion release mechanism. The swelling and relaxation of polymer chains, the ionization reaction between carrier and INS, and the invasion of the matrix polymer, were involved during the release process. This greatly increased pore size and allowed for the continuous release of INS. ## 3.6.2. Structural Stability of INS INS has an inherent α-helix conformation, which is essential for the interaction between INS and its receptor and the biological activity of INS. The α-helix and β-sheet of INS correspond to the presence of two negative characteristic peaks at 209.5 nm and 221.5 nm, respectively (Figure 5D). INS released from all three NPs also showed obvious negative peaks at 209.5 nm and 222 nm, indicating that spatial conformation did not change significantly and had complete physiological activity. This indicates that INS can maintain a complete secondary structure in the NPs and had structural stability. The slight change in the absorption peaks at 209 nm and 222 nm may be due to a change in the pH of the system during the preparation of the NPs, which affected the hydrogen bonding between the carbonyl and amino groups on the polypeptide chain in the α-helical structure and affected the stability of the hydrogen bonds. ## 3.7. In Vitro Intestinal Penetration Study The mucoadhesive and penetration of the oral NPs is a key feature that must be sequential and balanced with each other to avoid mucin trapping, with the aim of better uptake by enterocytes. The aggregation results of different NPs and mucins are shown in Figure 6A. The results showed that the aggregation of free INS reached 89.67 ± $2.52\%$, which was due to van der Waals forces, hydrogen bonds, and hydrophobic interaction between INS and mucin [50]. In contrast, the presence of the strongly negatively charged HA–PDM–INS hindered the adsorption of mucin, and the aggregations were reduced to 22.33 ± $4.04\%$, 29.00 ± $5.00\%$, and 35.33 ± $4.51\%$, respectively. The aggregation of L-HA–PDM–INS was significantly lower than that of M-HA–PDM–INS and H-HA–PDM–INS. L-HA–PDM–INS showed the best antimucin adhesion. As the intestinal epithelium has been considered the most challenging barrier to the oral delivery of NPs, the present study further examined the ability of three NPs to permeate through the intestine in vitro. The cumulative permeation amount of the different NPs were time-dependent (Figure 6B). Among them, the cumulative permeability of L-HA–PDM–INS in the intestine was the highest, and the Papp was 34.01 ± 4.51 × 10−7 cm/s, which was significantly higher than that of H-HA–PDM–INS (Papp = 29.84 ± 3.85 × 10−7 cm/s) (Figure 6C). The penetration of NPs loaded with INS–FITC can be visually seen using CLSM. The greater fluorescence intensity indicated a higher INS–FITC concentration. As shown in Figure 6D, L-HA–PDM–INS–FITC had the largest penetration depth at 2 h, which was consistent with the results of in vitro intestinal permeation studies. ## 3.8. Caco-2 Study The Caco-2 cells aggregated in the logarithmic growth phase and continued to grow to differentiate into monolayer cells, forming a tight junction structure similar to the small intestine, which was consistent with the cell model for the study of oral drug absorption in vitro. ## 3.8.1. Biocompatibility For practical application of drug delivery systems, they must be nontoxic and have good biocompatibility. In this project, CCK-8 and live–dead cell staining were used to determine the biocompatibility of NPs in vitro. When Caco-2 cells were co-cultured with 500 μg/mL NPs for 24 h, the cell viability was still more than $80\%$ (Figure 7A). The results of the live–dead cell staining also showed a large number of normally growing cells (green fluorescence) (Figure 7B), which indicates that a high concentration of NPs still had good biocompatibility when co-cultured with cells and did not affect the activity of cells. ## 3.8.2. Transwell Experiments TEER was directly related to the tightness of the cell junction. With the prolongation of culture time, the TEER value gradually increased (Figure S1), indicating that the tight junction barrier of cells was gradually increased. The integrity of the tight junction barrier can be visually reflected by measuring the TEER of Caco-2 cell monolayers to determine the transport pathway of INS. Figure 7C shows a schematic diagram of Caco-2 cell monolayers. There was no significant effect on TEER for 6 h in the free INS group, suggesting that INS was transported via the transcellular pathway, and Papp was only 5.35 ± 0.51 cm/s, which also suggests that free INS has poor transmembrane capacity. TEER values in the NPs group were consistently lower from 1–6 h, suggesting that tight junctions are involved in causing paracellular transport of INS. L-HA–PDM–INS, M-HA–PDM–INS, and H-HA–PDM–INS increased the Papp of INS to 22.26 ± 0.62, 20.38 ± 0.71, and 16.58 ± 1.11 cm/s, respectively, which increased by 4.16, 3.81, and 3.10 times, respectively. These results further demonstrated the prominent ability of NPs to promote INS transport, particularly L-HA–PDM–INS. The Papp of NPs decreased with increasing MW of HA, which may be due to the larger particle size and slow release rate of NPs prepared by high MW of HA. ## 3.8.3. Cellular Uptake The uptake results of Caco-2 cells after treatment with preparations containing the same INS–FITC concentration were showed in Figure 7F. Only weak green fluorescence was observed in the free INS group because INS is a biological macromolecule with large MW, large polarity, and strong hydrophilicity, and its passive diffusion and transmembrane ability are poor. In contrast, the encapsulation of INS–FITC into NPs results in a stronger intracellular green fluorescence as the NPs have a stronger negative charge and interact better with the cell membrane. The results of the uptake study were similar to those of transwell experiments. At 1 h, the uptake of L-HA–PDM–INS–FITC was more than that of the other two NPs. At 2 h, there was no significant difference in the uptake of the three NPs, probably because the uptake and translocation of the cells reached a balance. Figure 7G was a quantitative analysis of INS–FITC uptake by Caco-2 cells. ## 3.9. In Vivo Study According to different administration methods, the blood glucose value of rats was detected to evaluate the activity of INS and its hypoglycemic effect in vivo. ## 3.9.1. Pharmacodynamics Figure 8A showed the blood glucose level changes in rats. Subcutaneous injection of INS solution could quickly take effect, and the blood glucose was reduced to the lowest level at 2–4 h, which was 34.67 ± $9.29\%$ of the initial blood glucose, and then the blood glucose of diabetic rats gradually rose to the initial level. After oral administration of the INS solution, there was almost no significant change in the blood glucose level of the rats, which indicates that unprotected INS is easily inactivated by oral administration and cannot achieve a hypoglycemic effect [56]. Compared with injectable administration, the NPs of oral delivery had a better hypoglycemic effect in diabetic rats, with a slow hypoglycemic trend that did not cause hypoglycemia and kept their blood glucose levels at normal levels for 12 h. Among them, H-HA–PDM–INS had a long-term hypoglycemic effect. This also indicates that INS can maintain good biological activity in NPs. NPs act as carriers to protect and maintain INS stability in the hostile environment of the stomach and promote increased cellular permeability to INS, thereby improving cellular uptake and intracellular delivery. Although all three NPs showed good hypoglycemic effects at this dose, there were still two major problems. Firstly, the hypoglycemic effect of the NPs was slow. Secondly, the blood glucose level of the rats in the oral NPs group was still higher than the normal range. Therefore, the dosage of NPs should be further increased if the clinical level is to be achieved. ## 3.9.2. Pharmacokinetic The pharmacokinetic experiment was directed to study the absorption of NPs in vivo. The Insulin Assay Kit (built in Nanjing, China) was used to assay the INS concentration in serum at different time points and the pharmacokinetic curves are shown in Figure 8B. The pharmacokinetic parameters obtained from the drug–time curves are shown in Table 5. The results showed that the serum INS concentrations of rats in the INS solution group were consistently low. That is the oral uptake of free INS was quite low. The INS solution was absorbed rapidly after subcutaneous injection, reached its peak at 1 h, and then metabolized rapidly. After oral administration of L-HA–PDM–INS, M-HA–PDM–INS, or H-HA–PDM–INS NPs, the relative bioavailability of INS increased to $12.27\%$, $13.16\%$, and $14.62\%$, respectively (Figure 8C). Blood glucose level—area on time curve (AAC) and Pharmacological activity (PA)—increased with increasing MW of HA, which is consistent with the results of the resistance to protease degradation assay. This phenomenon indicates that the NPs prepared with high MW of HA had a stronger protective capacity and a more significant sustained release effect, which prolonged the retention time of NPs at the absorption site and increased the oral absorption performance of INS. ## 3.10. Safety Evaluation After 7 d of continuous oral administration at a high dose, the rats still exhibited normal appearance and behavior. As shown in Figure 9A, compared with the control group, there were no significant changes in the activities of ALT, AST, ALP, and γ-GT ($p \leq 0.05$). No significant hemolysis occurred at different concentrations of NPs, and the hemolysis rate was less than $1.8\%$ (Figure 9B). According to the American Society for Testing and Materials, hemolysis of $2\%$ or less is considered to be non-hemolysis for biomaterials. The H&E results showed normal morphology of the main organs (heart, liver, spleen, lung, kidney, and stomach) and no significant abnormalities in the villi of the duodenum, jejunum, and ileum (Figure 9C). There was no inflammation, mucosal erosion, foreign bodies, and other abnormal phenomena, which practically completely maintained the integrity and health of the intestine. Therefore, we believe these NPs are nontoxic and can be used for further study. ## 4. Discussion For oral INS delivery, we developed INS-loaded pH-responsive and mucoadhesive NPs with different MW of HA and PDM. Depending on pH, HA–PDM–INS remained stable in the gastrointestinal environment. HA helps NPs adhere to the intestinal epithelium and may be a good choice to promote the interaction between the carrier and the intestinal wall, which prolongs the retention time of HA–PDM–INS at the absorption site and increases the dose across the intestinal mucosa. NPs are transported through the intestinal epithelium to reach systemic circulation and release bioactive INS. In this study, the formulation and preparation of NPs were investigated using the single-factor method. Three HA–PDM–INSs with suitable particle sizes, stable Zeta potentials, and good roundness were obtained. DM is a water-soluble cationic monomer that can be polymerized to form PDM via KPS in an aqueous solution. HA is a polyanionic electrolyte that can assemble with cationic compounds to form complexes via electrostatic interaction. Although HA has been widely used in drug delivery, only a few studies have focused on the effects of the MW on the properties of delivery systems. The MW of HA was found to have an influence on the formulation and preparation process of HA–PDM. As the MW of HA increases, the amount of initiator (KPS) required increases, and the reaction temperature increases, possibly because the viscosity of HA increases with increasing MW, and the initiator and reaction temperature can reduce the viscosity of the polymer. When the MW of HA increased, the DL of HA–PDM–INS also increased. This is due to the fact that during the self-assembly process, the high MW of HA occupies a larger space and carries more negative charges, which can adsorb many INS and improve the encapsulation performance. The encapsulated of INS in NPs prevents contact between INS and the protease, which in turn resists the degradation of INS by the protease. There are two possible reasons for the ideal protection provided by HA–PDM–INS. [ 1] INS was prepared in a hydrochloric acid solution with a pH below the isoelectric point of INS. At this time, INS and PDM were positively charged. On the other hand, HA contains a large number of –OH and –COOH and has a negative charge under weakly acidic conditions [53]. INS and HA are attracted to each other via electrostatic interactions. The INS is thus closely bound to the carrier and does not leak easily. [ 2] At a low pH, the carboxyl groups of HA were protonated [57]. An increasing number of carboxyl groups form intermolecular hydrogen bonds, which can lead to tight contraction of the polymer chain segments and highly protected INS in the NPs, thus reducing INS leakage. When the MW of HA increased, the space occupied by HA increased, the negative charge increased, and the internal space became more compact. The NPs system was more stable and could maintain its INS structure for a long time. The pH-responsive release of NPs was mainly due to the presence of PDM and HA. To understand the effect of pH on INS release, in vitro release experiments were performed at different pH (1.2, 6.8, and 7.4). At pH 1.2, the swelling rate of HA–PDM–INS was low. This was due to the ionization of PDM in acidic media, where the –N(CH3)2 group was fully protonated to –NH+–(CH3)2 [58]. – NH+–(CH3)2 forms intermolecular hydrogen bonds with the –OH and –COOH in HA, which contracted the network structure of the delivery system and limited the release of INS. The network structure became tighter as the MW of HA increased. At pH 6.8 and 7.4, the degree of protonation of the tertiary amine group decreased [59], and the number of intermolecular hydrogen bonds decreased, leading to an increase in the swelling of the HA–PDM–INS network. The highest release was observed at pH 7.4, which was due to the complete deprotonation of the –NH+–(CH3)2 group in alkaline media. The repulsion between –NH+–(CH3)2 and the electrostatic interactions was disrupted [17], causing the HA–PDM chain to expand. When the MW of HA was low, the expansion rate was higher, and the HA–PDM system was looser, thus INS was released to the maximum extent. Du et al. [ 60] also concluded that for HA matrix tablets, drug release results were consistent with swelling and erosion studies, whereas swelling and erosion rates were related to the MW of HA, ionic strength, and pH of the medium. HA has adhesive and penetration-promoting functions that prolong the retention time of NPs in the intestine, promote INS uptake by the epithelial cells, and increase the amount of INS entering the bloodstream. The carboxyl or sulfate groups of mucin render it negatively charged. By encapsulating INS inside negatively charged hydrophilic HA, electrostatic and hydrophobic interactions between the NPs and mucins can be avoided. Mucosal fluidity can be enhanced through charge repulsion, which greatly weakens the adsorption of mucin on NPs and improves their permeability. As the MW of HA increased, more HA was bound to INS and PDM, thereby reducing the repulsion of NPs from mucin. Therefore, as the MW of HA increased, the permeation of the NPs decreased. The amount of permeation is not only related to the potential but also the size of the NPs. The permeability of the INS decreased with increasing particle size. In addition, NPs with hydrophilic properties are more likely to penetrate mucus [61]. The in vivo pharmacodynamic and pharmacokinetic properties of HA–PDM–INS were consistent with the in vitro results in promoting the oral absorption of INS. Bioactive INS binds to INS receptors on the cell surface, allowing glucose transporter proteins to transport glucose into the cells. In adipose tissue, INS prevents the breakdown of triglycerides into free fatty acids, which can be used as fuel, and facilitates the absorption of glucose for storage [62]. In type I diabetes, the body’s immune cells destroy insulin-producing pancreatic beta cells, preventing glucose from entering fat or muscle cells, which in turn boosts the production of adenosine triphosphate. In our study, the delivered INS maintained its biological activity after crossing the Caco-2 monolayer. This is encouraging, as one of the most challenging issues in oral INS delivery is preventing the degradation of INS during absorption, allowing it to remain biologically active. NPs can avoid the influence of an acidic environment in the stomach, protect INS over a wide pH range, release INS slowly in the intestine and blood, promote the uptake of INS by intestinal epithelial cells, improve the oral bioavailability of INS, and maintain a long-term hypoglycemic effect. Rapid clearance of the drug from the site of absorption is also considered a barrier to INS absorption [63]. Therefore, increasing the retention time of INS in the mucosa may lead to better bioavailability, and encapsulation of INS into mucoadhesive NPs may help improve bioavailability [27]. Similarly, it has been suggested that mucosal adhesion of the carrier is an important property for determining the bioavailability of poorly absorbed drugs [64]. We found that NPs prepared with a high MW of HA had a stronger protective capacity and a more significant sustained release effect, which prolonged the retention time of NPs at the absorption site and increased the oral absorption performance of INS. Of course, the NPs we prepared also have some shortcomings, including large oral dose, low DL, and EE. This is a considerable point, since the EE of the developed NPs is crucial from a cost-effective point of view. ## 5. Conclusions In this study, pH-responsive and mucoadhesive NPs were developed with different MW of HA and PDM for oral INS delivery. These HA–PDM NPs have prominent biocompatibility, protect the delivery of INS in the gastrointestinal tract, enhance the uptake of INS by cells, and improve oral bioavailability regardless of the MW of HA. Among them, L-HA–PDM had the high permeability, whereas M-HA–PDM had protection and delivery efficiency. These simple, low-cost, and environmentally friendly NPs are worthy of further development and industrial production. Next, we will study the effects of these NPs on INS delivery with other mammals. ## References 1. Jeyagaran A., En-Lu C., Zbinden A., Birkenfeld A.L., Brucker S.Y., Layland S.L.. Type 1 Diabetes and Engineering Enhanced Islet Transplantation. Adv. Drug Deliv. Rev. (2022) 189 114481. DOI: 10.1016/j.addr.2022.114481 2. Mishra V., Nayak P., Sharma M., Albutti A., Alwashmi A.S.S.. 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--- title: Physicochemical, Morphological, and Cytotoxic Properties of Brazilian Jackfruit (Artocarpus heterophyllus) Starch Scaffold Loaded with Silver Nanoparticles authors: - José Filipe Bacalhau Rodrigues - Valeriano Soares Azevedo - Rebeca Peixoto Medeiros - Gislaine Bezerra de Carvalho Barreto - Maria Roberta de Oliveira Pinto - Marcus Vinicius Lia Fook - Maziar Montazerian journal: Journal of Functional Biomaterials year: 2023 pmcid: PMC10056764 doi: 10.3390/jfb14030143 license: CC BY 4.0 --- # Physicochemical, Morphological, and Cytotoxic Properties of Brazilian Jackfruit (Artocarpus heterophyllus) Starch Scaffold Loaded with Silver Nanoparticles ## Abstract Due to the physical, thermal, and biological properties of silver nanoparticles (AgNPs), as well as the biocompatibility and environmental safety of the naturally occurring polymeric component, polysaccharide-based composites containing AgNPs are a promising choice for the development of biomaterials. Starch is a low-cost, non-toxic, biocompatible, and tissue-healing natural polymer. The application of starch in various forms and its combination with metallic nanoparticles have contributed to the advancement of biomaterials. Few investigations into jackfruit starch with silver nanoparticle biocomposites exist. This research intends to explore the physicochemical, morphological, and cytotoxic properties of a Brazilian jackfruit starch-based scaffold loaded with AgNPs. The AgNPs were synthesized by chemical reduction and the scaffold was produced by gelatinization. X-ray diffraction (XRD), differential scanning calorimetry (DSC), scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), and Fourier-transform infrared spectroscopy (FTIR) were used to study the scaffold. The findings supported the development of stable, monodispersed, and triangular AgNPs. XRD and EDS analyses demonstrated the incorporation of silver nanoparticles. AgNPs could alter the scaffold’s crystallinity, roughness, and thermal stability without affecting its chemistry or physics. Triangular anisotropic AgNPs exhibited no toxicity against L929 cells at concentrations ranging from 6.25 × 10−5 to 1 × 10−3 mol·L−1, implying that the scaffolds might have had no adverse effects on the cells. The scaffolds prepared with jackfruit starch showed greater crystallinity and thermal stability, and absence of toxicity after the incorporation of triangular AgNPs. These findings indicate that jackfruit is a promising starch source for developing biomaterials. ## 1. Introduction Scaffolds are three-dimensional, porous structures that facilitate tissue growth/remodeling by supporting human cell adhesion, proliferation, differentiation, and orientation in a stable environment. They are commonly employed in biomedical tissue engineering and are composed of biocompatible and biodegradable materials that allow for the incorporation and transport of medicines and biological components [1,2,3,4]. Scaffolds composed of metals, ceramics, polymers, and composites are used in biomedical tissue engineering [5]. Composite scaffolds are produced by mixing two or more materials, each with its own set of desirable properties. The tensile and compressive mechanical strengths of composite polymeric scaffolds, for example, are significantly higher than those of polymeric scaffolds [6]. As some are produced from biodegradable polymers, such as cellulose, chitosan, and starch, they are not only more durable, but also less toxic [6,7]. Starch is one of the main biopolymers present in nature [8]. It is an abundant, low-cost, biodegradable, biocompatible, and natural polymer widely used in fabricating scaffolds [9]. Amylose (glucose units joined by α-1,4-glycosidic bonds) and amylopectin (glucose units linked by glycosidic bonds at the α-1,4 and α-1,6 carbons) chains make up starch’s molecular structure [10]. Starch, like other polysaccharides and proteins, is a thermoplastic polymer composed of linear chains linked by weak bonds and is capable of being processed into membranes [11], gels [12], nanofibers [13], microparticles [14], nanoparticles [15], and scaffolds [16]. It can also encapsulate pharmaceuticals [16] and metallic nanoparticles [15] that bond to the long polymeric chains and hydroxyl groups of molecular structures. Silver nanoparticles (AgNPs) stand out among metallic nanoparticles due to their distinctive physical, chemical, and biological features, particularly their anti-bacterial and antifungal activities [17], simple production, low cost [18], high conductivity, chemical stability, and catalytic activity [19]. They can be obtained with different morphologies, such as spheres, triangles, and rods; however, triangles are more biologically active [20,21]. AgNPs have a wide range of applications in biomedicine and are commonly coupled with biomaterials to provide bactericidal effects [22]. By attaching to peptidoglycans, AgNPs can permeate bacterial membranes, causing structural alterations, increased membrane permeability, and, eventually, death [23]. AgNPs can also interact with bacterial proteins through this mechanism, inhibiting DNA replication and bacterial growth [24]. AgNPs are used to improve the physical, chemical, and biological characteristics of biomaterials. A variety of studies have linked enhanced porosity, improved thermal and mechanical properties, increased water vapor absorption, anti-bacterial activity aggregation, and other biological aspects of biomaterials to the incorporation of AgNPs (see Table 1). Most of these studies used AgNPs with spherical forms due to their rapid synthesis and simplicity of acquisition [25] and employed more traditional polymer matrices, such as chitosan, collagen, cellulose, and alginate. Although the advanced properties of biomaterials loaded with spherical AgNPs produced from these biopolymers are well recognized, less is known about the impact that AgNPs of different morphologies can have on them, as well as their behavior in biomaterials produced from other polymer matrices, such as starch. A few different types of starches can be utilized to produce scaffolds and biomaterials. They are derived from many plant sources and have varying compositions and properties that are determined by the plants’ growing circumstances, area, harvest season, and climate [26]. Commercial starches produced from wheat, corn, potato, rice, and cassava have already been extensively explored and are widely exploited in industry [27] as films [8], hydrogels [28], micro/nanoparticles [29,30], composites [31], and scaffolds of starches extracted from these sources. For this reason, searching for novel forms of local starch sources, especially in Brazil, would be critical, given their importance and the differences in their qualities. jfb-14-00143-t001_Table 1Table 1Physical and biological properties of scaffolds, sponges, mats, and fibers loaded with silver nanoparticles prepared from starch and other polymers. MaterialAgNPs MorphologyPhysical PropertiesBiological PropertiesRef. Potato starch/PVA nanofibrous mats loaded with AgNPsSphericalHighly interconnected porous structure, relevant thermal stability, and fast release of AgNPsAbsence of toxicity against human fibroblast cells and AgNPs imparted anti-bacterial activity against E. coli and S. aureus[32]Carboxymethyl chitosan/oxidized starch sponges loaded with AgNPsSphericalImproved thermal propertiesBactericidal activity against E. coli and S. aureus; absence of toxicity and ability to promote the growth of L929 fibroblast cells; faster wound repair healing[33]Potato starch nanofibrous mats loaded with AgNPsSphericalReduced hydrophilicity and improved mechanical properties with increased tensile strength and reduced deformationBactericidal activity against E. coli; absence of toxicity against L929 fibroblasts cells up to AgNPs concentration of 2.5 mg.mL-1[34]Hybrid Ag nanoparticles/polyoxometalate–polydopamine nano-flowers Loaded chitosan/gelatin hydrogel scaffoldsSphericalHighly porous interconnected structureAnti-bacterial activity against E. coli and S. aureus; promoted wound healing; good biocompatibility with L929 cells and human umbilical vein endothelial cells (HUVEC)[35]Chitosan/Ag nanocomposite spongesSphericalHighly porous interconnected structure; improved mechanical properties and good water vapor transmission properties (in the range of 2000–2400 g/m2/d)Anti-bacterial activity against E. coli and S. aureus; non-cytotoxicity (cell viability values were greater than $90\%$)[36]Cellulose-based scaffolds containing orange essential oil and silver nanoparticlesSphericalUnexpected influence on water absorption and reduction in mechanical strength and elongation Anti-bacterial activity against B. subtilis and E. coli[37]3D cellulose nanofiber scaffolds decorated with silver nanoparticlesSphericalSilver nanoparticles significantly increased the thermal stability and mechanical properties, and reduced the scaffolds’ expansion process and swelling ratioAnti-bacterial activity against E. coli and S. aureus with 0.2 mM AgNPs content; low toxicity at concentrations of 0.2 and 1 mM AgNPs [38]3D hybrid scaffold based on collagen, chondroitin 4-sulfate, and fibronectin, functionalized with silver nanoparticlesSphericalMicrostructure with high number of interconnected poresIn vitro cytocompatible and non-genotoxic in human gingival fibroblast cultures; biocompatibility with chick embryos CAM; antimicrobial activity against F. nucleatum and P. gingivalis[39]Poly(ε-caprolactone) nanocomposite fiber scaffolds loaded with silver nanowiresWiresIncreased crystallinity and thermal properties, and retarded enzymatic biodegradation Significantly enhanced cell proliferation of C2C12 mouse myoblast cells before and after electrical stimulations [40]Chitosan/Bletilla striata polysaccharide composite scaffold loaded with silver nanoparticlesSphericalPreserved porous interconnected structure and improved mechanical propertiesEffective anti-bacterial and anti-biofilm properties both in vitro and in vivo against MRSA; potential to promote cell proliferation and angiogenesis[41] Jackfruit (Artocarpus heterophyllus) provides a different type of starch, showing potential among starch sources. The high amylose concentration [26], low gelatinization temperature [42], and small gelatinization enthalpy change [43] of jackfruit starch make it a promising starch resource. However, jackfruit starch’s potential applications have not been fully studied by the scientific community; a limited number of studies have focused on this material, making more investigation into its potential role in the development of biomaterials imperative. In this study, a jackfruit (Artocarpus heterophyllus) starch-based scaffold loaded with triangular AgNPs was synthesized and tested for its physicochemical, thermal, morphological, and cytotoxic properties. The production of such scaffolds utilizing readily accessible, inexpensive, and domestic starch sources was another primary objective of this study. ## 2.1. Chemicals The jackfruit used for starch extraction was obtained from the local market of Campina Grande, Paraiba, Brazil. All chemical reagents were of analytical grade. Silver nitrate (AgNO3, purity $99.0\%$), tribasic sodium citrate dihydrate (Na3C6H5O7·2H2O, purity $99.0\%$), and hydrogen peroxide (H2O2, $35\%$) were purchased from Neon. Sodium borohydride (NaBH4, purity ≥$96\%$) and glycerol (C3H8O3, purity ≥$99\%$) were obtained from Sigma-Aldrich. The aqueous solutions were prepared with ultrapure water (18.2 mΩ·cm), obtained from a GEHAKA Master System MS2000 System. The L929 Mouse Fibroblast Cell Line (ATCC NCTC clone 929) was acquired from the Rio de Janeiro Cell Bank, Brazil. ## 2.2. Starch Extraction from Jackfruit Seed Endocarp The starch extraction method was adopted from Perez, et al. [ 44]. In the first stage, jackfruit seeds were washed, peeled, and crushed in a blender until a uniform, thick, and homogenous mass was obtained, adding water in a 1:4 (m/v) ratio. The paste was filtered through organza bags (100 mesh). The filtered starch suspension was decanted for 24 h in a refrigerated atmosphere at 5 °C. The floating portion was removed, and the starch suspended in water was again decanted. This suspension and settling procedure was repeated until a white starch color was acquired. Following this, the starch was lyophilized (for 48 h) and sieved to 200 mesh. ## 2.3. Synthesis of AgNPs AgNPs were synthesized through the chemical reduction method described by Zhang, et al. [ 45]. Initially, 30 mL of ultrapure water was transferred to a beaker and magnetic stirring (500 rpm) was performed at room temperature (25 °C). The system was then filled with 30 µL of silver nitrate (0.1 mol·L−1), 1.5 mL of sodium citrate (0.9 mmol·L−1), 60 µL of hydrogen peroxide ($35\%$), and 200 µL of sodium borohydride (90 mmol·L−1). After adding sodium borohydride, the magnetic stirring was increased to 1150 rpm for 3 min, the period required for forming AgNPs. Our earlier study [46] provides more information on the effects of mixing intervals and variations in the volume and concentration of NaBH4 and H2O2 on the size, dispersion, and stability of AgNPs. ## 2.4. Synthesis of Jackfruit Starch Scaffold The starch scaffold was obtained according to the methodology proposed by Perez, et al. [ 47]. Amounts of 7.5 g of starch and 2.5 mL of glycerol were first added to a beaker containing 250 mL of ultrapure water while magnetic stirring (300 rpm) was performed at 85 °C. The starch solution was then cooled to room temperature (25 °C), transferred to Petri dishes, and frozen (24 h), defrosted (2 h), frozen (24 h), and lyophilized (72 h). ## 2.5. Synthesis of Starch–AgNPs Scaffold The starch–AgNPs scaffold was developed using the same process as the jackfruit starch scaffold, with a few modifications. After cooling to room temperature, 12.5 mL of the AgNP (a volume fraction of $5\%$ to the starch solution) solution (1 × 10−3 mol·L−1) was added, followed by freezing (24 h), defrosting (2 h), freezing (24 h), and lyophilizing (72 h). As the AgNPs were homogenized in the starch solution, this resulted in a uniformly AgNP-decorated scaffold. Figure 1 illustrates the steps followed during the scaffold’s synthesis. ## 2.6. Characterization of the Silver Nanoparticles (AgNPs) A UV-Vis spectrum model MB-102 (Bomem-Michelson, Vanier, Canada) was used to collect AgNPs spectra and confirm its synthesis. The scan was performed in the wavelength range of 250–1100 nm. The spectrums were collected using quartz cuvettes with a 10 mm optical path. The size, polydispersity, and stability of AgNPs are properties that directly influence their biological properties [48]. They were determined using dynamic light scattering (DLS) and zeta potential (PZ) techniques. The analyses were carried out on a Brookhaven ZetaPals (Brookhaven Instruments, New York, USA). The tests were performed at room temperature without diluting the samples, with a scattering angle of 90°, laser wavelength of 632.8 nm (He-Ne), average viscosity of 0.887 mPa·s, and refractive index of 1.330. All measurements were taken three times. The AgNPs’ morphology was investigated using a field-emission scanning electron microscope (FE-SEM) model S4700EI (Hitachi, Chiyoda, Japan) operating at a voltage of 15 kV. AgNPs were diluted in ultrapure water at a ratio of 1:10 and coated with platinum. ## 2.7. Scaffolds Characterization When developing a biomaterial that contains metallic nanoparticles, it is very important to check that the particles have been incorporated. Therefore, X-ray diffraction (XRD) was used to prove the inclusion of AgNPs in the starch–AgNPs composite and to verify the crystallinity of the starch. The experiment was carried out on a X-ray diffractometer model XRD-7000 (Shimadzu, Kyoto, Japan) with CuKα radiation (1.5418), 40 kV, and 30 mA current in the interval of 10–80° and a resolution of 2°/min. The General System Analyzer Structure (GSAS II) program was utilized for Rietveld refinement. The type of interaction that exists between a composite matrix and its filler provides information on how easily this filler will be released from the biomaterial. Thus, FTIR spectroscopy was employed to identify the vibration bands of starch and to assess the interaction between starch and AgNPs. The analysis was carried out on a Spectrum 400 FT-IR (Perkin Elmer, Waltham, MA, USA) device in the range of 4000–650 cm−1 with a resolution of 4 cm−1 in the diffuse reflectance mode for 32 scans at room temperature (25 °C) using an attenuated total reflectance (ATR) accessory equipped with a zinc selenium (ZnSe) crystal. To analyze the existence of AgNPs in the scaffold, evaluate morphological changes following AgNPs incorporation, and identify the starch microstructure, scanning electron microscopy (SEM) was conducted on a SEM microscope model TM-1000 (Hitachi, Chiyoda, Japan) coupled with energy-dispersive X-ray spectroscopy (EDS) was performed. All images were taken from uncoated samples fixed on an aluminum alloy sample holder at a 15 kV accelerating voltage, 1 mm depth of focus, 30 nm resolution, low vacuum, and variable pressure (1 to 270 Pa). Under the same conditions, EDS analyses were performed on an EDS Quantax 50 XFlash (Bruker, Billerica, MA, USA). The EDS spectra were collected at 3 different points of the starch–AgNPs scaffolds to detect the AgNPs. Image processing was carried out using the Quantax 50 program. The thermal stability of a biomaterial provides information on the conditions of use, storage, and application [49]. The thermal stability of the starch and starch–AgNPs scaffolds was evaluated using differential scanning calorimetry (DSC). A DSC, model 8500 (PerkinElmer, Waltham, USA), was employed in a temperature range of 25–300 °C with a heating rate of 10 °C/min under a nitrogen atmosphere and with a flow rate of 20 mL/min. an alumina crucible and a sample mass of 3.00 ± 0.05 mg were used. ## 2.8. Cytotoxicity Assessment Cytotoxicity tests determine whether a biomaterial is safe or not [50]. The cytotoxicity assessment of the AgNPs, starch scaffold, and starch–AgNPs scaffold was performed according to the direct contact method described in ISO 10993-5:2009 [50] and ISO 10993-12:1998 [51]. Initially, 0.5 g of scaffold was weighed and sterilized for 30 min via UV radiation. Next, the extracts were prepared by submerging the scaffolds in 1.25 mL of PBS solution (0.05 M) for 24 h. Then, 100 µL/well of L929 cell suspension was seeded in 96-well plates and incubated for 24 h at 37 °C ± 1 °C under a $5\%$ ± $1\%$ CO2 atmosphere. After cultivation, 50 µL of the medium extracted from the samples was added and the cells were incubated for another 24 h. For the AgNPs, five dilutions ($\frac{1}{1}$, $\frac{1}{2}$, $\frac{1}{4}$, $\frac{1}{8}$, and $\frac{1}{16}$) were performed to obtain final plaque concentrations ranging from 1 × 10−3 to 6.25 × 10−5 mol·mL−1. The method used for determining the concentration of AgNPs was solvent evaporation, which is well-known and widely used in the literature. In references [52,53], the researchers employed thermal gravimetric analysis to determine the weight of AgNPs and other metallic nanoparticles. Then, 100 µL/well of L929 cells suspension was seeded in 96-well plates and incubated for 24 h at 37 °C ± 1 °C under a $5\%$ ± $1\%$ CO2 atmosphere. Amounts of 20 µL of each of these dilutions were added to 96-well plates and the cells were incubated for another 24 h. The cytotoxicity assay was conducted following the MTT method. After the incubation time, the culture medium was removed and 100 µL of MTT solution (5 mg·mL−1) was added; the plates were then incubated under the same conditions for another 4 h. The cells were then treated with 100 µL of DMSO to dissolve the formazan crystals. The plates were read following the optical density method on a Victor X3 microplate reader (PerkinElmer, Waltham, USA) at 570 nm with 650 nm reference filters. Latex sheets were used as the positive control and high-density polyethylene (HDPE) as the negative control. ## 3.1. Characterization of the Silver Nanoparticles (AgNPs) The UV-Vis spectrum of the AgNPs colloidal solution is shown in Figure 2A. Three absorption bands were found in the UV-Vis analysis. According to the Schatz calculation, the first band at 333 nm corresponds to out-of-plane quadrupole resonance, the second shoulder-shaped band around 465 nm indicates dipole resonances characteristic of triangular nanoparticles [54], and the third band with maximum absorption at 744 nm resembles the plasmonic surface characteristic of the resonance band of almost perfectly triangular nanoparticles [54,55]. According to Mie’s hypothesis [56], anisotropic particles should be present because they have three absorption bands [55]. Furthermore, the colloidal solution of AgNPs acquired a blue color after synthesis, which can be attributed to the plasmonic excitation of the surface of triangular-shaped nanoparticles (nanoplates) [57]. The nanoparticles in the DLS result (Inset of Figure 2A) had an average size of 33.27 nm and a polydispersity index (PDI) of 0.205, which is typical of monodisperse solutions (0.300), which have values ranging from 0 to 1. As a result, the smaller the value, the more monodisperse the colloid [58]. The PZ findings revealed AgNPs with a surface charge of −33.39 mV, confirming stable AgNPs production [57]. The FE-SEM micrographs (Figure 2B–D) show the formation of nano-sized, anisotropic, and triangular monodisperse silver particles. These results are in agreement with those observed by others [59,60]. ## 3.2. Morphological Characterization SEM micrographs of the jackfruit starch granules, starch scaffold, and starch–AgNPs scaffold are shown in Figure 3A–F. The starch granules (Figure 3A,B) had a rounded and irregular bell shape ranging from 4–6 μm. Starch extracted from Brazilian jackfruit is typically of this size and form [61], whereas jackfruit starch extracted from Thai, Malaysian, or Chinese jackfruit is different in size and shape [26]. The variations in granule morphology are influenced by differences in cultivars and environmental factors [62] and affect starch’s functional characteristics [26]. The small grains and bell-shaped morphology of jackfruit starch developed for usage in the food and health industries contribute to its increased swelling capacity and viscosity. The SEM micrographs of the starch scaffold and starch–AgNPs scaffold shown in Figure 3C–F revealed that both scaffolds had a porous structure created by well-defined pores arranged with high interconnectivity. Water vaporization is one possible mechanism that results in the formation of this porous structure. When water is removed, as in the lyophilization process, a large amount of porosity is created [63]. For the functional properties of starch, this same type of interconnected porous structure could also be observed in scaffolds prepared with starch extracted from rice [64], a source of starch that has a similar size and morphology to starch extracted from jackfruit [26]. This implies that the interconnected porous structure presented by the jackfruit starch scaffolds is influenced by the morphology and size of the starch grains extracted from the jackfruit. For the starch–AgNPs scaffold (Figure 3E,F), it appears that adding AgNPs roughened the surface of the scaffold, facilitating cell attachment and proliferation [65,66]. Previous studies have reported a relationship between the incorporation of nanoparticles and increased total porosity of polymeric and ceramic composite scaffolds [67]. Herein, the incorporation of metallic nanoparticles into the starch polymer matrix was shown to modify the interactions between starch and glycerol [68,69], increasing its compactness and roughness. The EDS analysis revealed the presence of AgNPs in the scaffold (Figure 3G). Starch was associated with levels of $59.4\%$ carbon and $33.2\%$ oxygen. The Fe content was linked to the sample holder, which was composed of an aluminum alloy with iron. The silver ions in the nanoparticles were responsible for the $2.0\%$ silver content. The findings of Li, et al. [ 70] and Vaidhyanathan, et al. [ 71] are consistent with these results. ## 3.3. Phase Analysis Figure 4 depicts the XRD pattern of the jackfruit starch, starch scaffold, and starch–AgNPs scaffold. According to Figure 3B, the starch scaffold exhibited amorphous polymer structures with a crystallinity of $16.95\%$ according to the Rietveld refinement; there were diffraction peaks positioned at 15.86°, 17.38°, 21,04°, 22.94°, 23.88°, and 28.88°, confirming the type-A crystalline structure A [72], which, as other authors have pointed out, is consistent with the crystalline structure of jackfruit starch [73,74]. These peaks in the scaffold were identical to the peaks in the starch diffractogram (Figure 4A), although with amorphous characteristics and lower intensity, suggesting a disappearance of the crystalline structure of type-A jackfruit starch after processing. Studies indicate that plasticized starch tends to form a V-type crystalline structure, which was observed by the appearance of a slight shoulder around 17.00° and 19.18° (Figure 4B) [75]. In manufacturing starch scaffolds, glycerol was used, which acts as a plasticizer [76]. Therefore, it is suggested that, during processing, there was a change in the crystalline structure of starch through its interaction with glycerol. After adding AgNPs to the scaffold (Figure 4C), the crystallinity increased to $33.88\%$, representing a $16.93\%$ increase over the scaffold without AgNPs. It has been shown that the presence of AgNPs leads to a higher degree of crystallinity in polyimide films [77]. The starch–AgNPs scaffold exhibited four diffraction peaks at 37.80°, 44.02°, 64.36°, and 77.50°, which corresponded to planes [111], [200], [220], and [311] and correlated with the face-centered cubic structure of metallic silver (JCPDS file No. 03-0921). This validated the inclusion of AgNPs in the scaffold [78]. ## 3.4. Scaffold Chemistry The FTIR spectra of the starch, starch scaffold, and starch–AgNPs scaffold are shown in Figure 5. The vibration bands in the jackfruit starch spectrum (Figure 5A) represent features of the molecular deformations present in the starch molecules. The stretching deformation of –OH is responsible for the band located at 3400 cm−1 and angular deformation of the band at 1650 cm−1 [79]. The symmetrical stretching of –C–H is represented by the vibration band at 2926 and asymmetrical stretches at 2897 cm−1. The C–O–H bonds are represented by the bands at 1460–1400 cm−1 [80]. Absorptions at 1340 and 1024 cm−1 are due to –C–OH group deformations. Vibration modes associated with –C–C–H bonds were detected at 1418, 1205, and 1080 cm−1, whereas C–O, C–O–C, and C–C stretches corresponded to the 1153, 1107, and 933 cm−1 bands, which were typical of the vibration modes associated with pyranose rings found in natural polysaccharides [81]. All of these bands could be related to jackfruit starch bonds. The FTIR spectra of the starch (Figure 5B) and starch–AgNPs (Figure 5C) scaffolds matched the jackfruit starch spectrum very well. Such behavior shows that the interaction of the AgNPs with the starch was physical, rather than chemical [82,83]. This suggests that, while AgNPs were present in the scaffold (as shown by the XRD and EDS data), they did not chemically interact with the starch’s polymer chains. ## 3.5. Thermal Analysis Figure 6 depicts the thermograms of the starch and starch–AgNPs scaffolds, which display three endothermic peaks. The first referred to starch gelatinization, which had a gelatinization temperature of 81.3 °C in the starch scaffold and 71.3 °C in the starch–AgNPs scaffold [42,84]; the second peak, at temperatures of 110.2 °C and 105.8 °C in the starch scaffold and starch–AgNPs scaffold, could be attributed to the gelatinization of the amylose present in the starch, which was complexed by lipids [85,86]. The melting temperature (Tm, third peak) of the starch increased from 146.3 °C to 185.2 °C with AgNPs incorporation. This peak was caused by the melting of crystalline starch domains that were reorganized during retrogradation [87]. The results revealed that, after incorporating AgNPs, the gelatinization temperatures of starch and amylose decreased while the Tm rose. There is evidence that AgNPs and other metallic nanoparticles have a greater effect on the Tm than they do on changing the initial degradation temperatures, as was the case for the gelatinization temperatures [83,88]. The increase in Tm could be explained by the fact that AgNPs are more thermally stable [83,89]. Shameli, et al. [ 90] reported similar results, observing an $18\%$ increase in the enthalpy of starch–AgNPs composite films. The thermal stability was similarly enhanced when AgNPs were included in gelatin films [91] and sugar palm starch biocomposites [89]. ## 3.6. Cytotoxicity Analysis According to Figure 7A, it was observed that the tested dilutions of the AgNPs solution presented cytotoxicity values in the range of 99 to $86\%$. The highest dilution $\frac{1}{16}$, corresponding to a concentration of 6.25 × 10−5 mol·L−1, showed a cell viability of $99\%$. By gradually increasing the concentration of AgNPs, a slow, but proportional, reduction in cell viability was noted. This was due to the AgNPs causing apoptosis and cell death [92]. However, even in the sample with the highest concentration of $\frac{1}{1}$ (1 × 10−3 mol·L−1), a cell viability of $86\%$ was observed, which, according to ISO 10993-5, indicates the absence of toxicity [50]. Therefore, triangular anisotropic AgNPs with a diameter of ~30 nm in the concentration range of 6.25 × 10−5 to 1 × 10−3 mol·L−1 were not toxic against L929 fibroblast cells. Figure 7B demonstrates that the cell viability values of both scaffolds, one without AgNPs and one with, were $80.2\%$ and $80.3\%$, respectively. Additionally, adding AgNPs did not result in any reduction in the viability of the cells, suggesting that AgNPs did not have any cytotoxic effects. The veracity of the data was confirmed by the fact that the negative control showed a hundred-percent cell proliferation. According to ISO 10993-5, if the material has a value that is lower than $70\%$, it is considered to have a toxic effect [50]. Both scaffolds exhibited cell survival values higher than $70\%$, demonstrating that the jackfruit starch scaffold loaded with AgNPs was not toxic to L929 cells, which suggests that it is appropriate for application as a biomaterial in treating various conditions, such as healing wounds. ## 4. Conclusions In this study, we investigated the impact of incorporating silver nanoparticles (AgNPs) into a Brazilian jackfruit (Artocarpus heterophyllus) starch-based scaffold by analyzing its chemical, physical, thermal, and morphological characteristics. The chemical reduction technique yielded monodisperse, isotropic, and stable AgNPs with a size of 33.27 nm. The presence of AgNPs in the starch scaffold was verified by EDS and XRD analyses. The FTIR spectra of the jackfruit starch and starch–AgNPs scaffolds were identical, suggesting that the interaction of AgNPs with starch was purely physical. SEM revealed the scaffolds to have a very porous and three-dimensional structure. AgNPs inclusion maintained the scaffold’s porosity while increasing its surface roughness and crystallinity, which are all conducive to enhancing biological responses. In conclusion, the crystallinity, roughness, and melting temperature of the jackfruit starch scaffold were all improved by the addition of AgNPs. The scaffold could be developed using AgNPs to better survive temperature changes and demonstrate stronger biological interactions. The L929 cell survival rate was greater than $70\%$ for AgNPs at concentrations of 6.25 × 10−5 to 1 × 10−3 mol·L−1 and for both scaffolds, confirming that triangular anisotropic AgNPs with a 30 nm diameter and the scaffold loaded with AgNPs were non-toxic to L929 cells and clinically relevant for treating wounds, for example. These findings help to fill a gap in our understanding of the toxicity and impacts of triangular anisotropic AgNPs on the physicochemical and biological properties of polymeric matrices. 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--- title: Naringenin Attenuates Isoprenaline-Induced Cardiac Hypertrophy by Suppressing Oxidative Stress through the AMPK/NOX2/MAPK Signaling Pathway authors: - Yu Li - Bo He - Chao Zhang - Yanji He - Tianyang Xia - Chunyu Zeng journal: Nutrients year: 2023 pmcid: PMC10056776 doi: 10.3390/nu15061340 license: CC BY 4.0 --- # Naringenin Attenuates Isoprenaline-Induced Cardiac Hypertrophy by Suppressing Oxidative Stress through the AMPK/NOX2/MAPK Signaling Pathway ## Abstract Cardiac hypertrophy is accompanied by increased myocardial oxidative stress, and whether naringenin, a natural antioxidant, is effective in the therapy of cardiac hypertrophy remains unknown. In the present study, different dosage regimens (25, 50, and 100 mg/kg/d for three weeks) of naringenin (NAR) were orally gavaged in an isoprenaline (ISO) (7.5mg/kg)-induced cardiac hypertrophic C57BL/6J mouse model. The administration of ISO led to significant cardiac hypertrophy, which was alleviated by pretreatment with naringenin in both in vivo and in vitro experiments. Naringenin inhibited ISO-induced oxidative stress, as demonstrated by the increased SOD activity, decreased MDA level and NOX2 expression, and inhibited MAPK signaling. Meanwhile, after the pretreatment with compound C (a selective AMPK inhibitor), the anti-hypertrophic and anti-oxidative stress effects of naringenin were blocked, suggesting the protective effect of naringenin on cardiac hypertrophy. Our present study indicated that naringenin attenuated ISO-induced cardiac hypertrophy by regulating the AMPK/NOX2/MAPK signaling pathway. ## 1. Introduction Cardiac hypertrophy is a compensatory response to environmental stimuli, and it can be classified as physiologic and pathologic hypertrophy [1,2]. Cardiac hypertrophy involves multiple mechanisms such as long-term hemodynamic load, neurohormonal stimuli and cardiomyocyte metabolism. Perpetuated cardiac hypertrophy aggravates heart failure, affecting about 64.3 million people worldwide [3]. Currently, limited therapeutic options are available for patients with cardiac hypertrophy. Therefore, an effective therapeutic intervention for cardiac hypertrophy is urgently needed. Increased myocardial oxidative stress is an essential factor contributing to cardiac hypertrophy [4]. Under physiological conditions, reactive oxygen species (ROS) production and elimination are in a dynamic balance. However, oxidative stress occurs once the balance is broken [5]. Increased oxidative stress damages proteins, lipids, DNA, and RNA [6]. Meanwhile, excessive ROS significantly activates multiple hypertrophy-related genes, including NF-KB and NRF2 [5,7]. Therefore, antioxidant therapy has been proposed as an effective treatment for cardiac hypertrophy. However, the general antioxidant drugs, including vitamins C and E, do not yield satisfactory results in clinical experiments [8,9], whereas ROS targeted strategies and modulated downstream pathways have been proven to be better approaches. Hence, the exploration of new antioxidants is helpful in preventing cardiac hypertrophy. Naringenin is a natural flavanone found in citrus fruits such as oranges and shows many biological activities [10]. Naringenin appears in nature in two primary forms: aglycosylated (naringenin) and glycosylated (naringin or naringenin-7-O-glucoside) [11]. Flavonoids are widely found in fruits and vegetables and have been proven to alleviate cardiovascular disease, primarily due to their antioxidant properties. Epidemiological and prospective studies suggest that naringenin, one of the essential flavonoids, has beneficial effects on increasing cardiovascular risk [12]. The therapeutic effects of naringenin have been proven in several diseases due to its antioxidant, anti-inflammation, antiapoptotic, and antitumor activities, including chronic kidney disease, cancer, neurological disorders, and liver disease [13,14,15]. The pretreatment of naringenin before ischemia-reperfusion injury in the isolated hearts of rats effectively improved the systolic function of the left ventricle by activating the ATP-sensitive potassium channels on the cell membrane [16]. Naringenin also acts as an NADPH oxidase inhibitor. Naringenin alleviates diabetic nephropathy and high-cholesterol diet-induced endothelial dysfunction by inhibiting NADPH oxidase 4 (NOX4) and NOX2 [17]. In cardiovascular diseases, naringenin decreases NADPH oxidase activity, inhibits oxidative stress, and migrates hyperglycemia-induced myocardial fibrosis. Additionally, naringenin treatment inhibits the PI3K/Akt, ERK, and JNK signaling pathways to alleviate pressure overload-induced cardiac hypertrophy [18]. It has been noted that naringenin reduces LDL, increases HDL, inhibits macrophage inflammation, inhibits foam cell formation, and significantly down-regulates the expression of genes related to atherosclerosis, thus having a significant anti-atherosclerosis effect [19]. Due to insufficient data on pharmacokinetics, metabolic fate, and chemical instability, clinical trial registration is insufficient. Therefore, in this research, we evaluate the therapeutic effects of naringenin on isoprenaline (ISO)-induced cardiac hypertrophy induced by the continuous stimulation of β-adrenergic receptors, and determine the underlying mechanism relating to the inhibition of oxidative stress. ## 2.1. Animals All the mouse procedures were performed according to the Institutional Animal Care and Use Committee of the Army Medical University (Animal Ethical Statement AMUWEC20226269). C57BL/6J mice (at age 8–10 weeks) were housed in the animal center at a temperature of 18–22 °C with free access to food and water. Naringenin was suspended in carboxy methyl-cellulose ($0.7\%$). Mice in the control group were then administered $0.7\%$ carboxy methyl-cellulose via gavage. To induce cardiac hypertrophy, mice of either sex were treated with ISO (7.5 mg/kg/d, MedChemExpress, Princeton, NJ, USA) by subcutaneous injection for 2 weeks [20]. To determine the role of naringenin (NAR) in cardiac hypertrophy, naringenin was given by means of gavage administration with different dosage regimens (25, 50 and 100 mg/kg/d, Aladdin, Shanghai, China) for 1 week and then co-administration with ISO for another 2 weeks. The scheme of the experimental design is shown in Supplementary Figure S1. ## 2.2. Echocardiography The acquisition of echocardiographic images was performed through a Vevo 3100LT system (VisualSonics, Fujifilm, Tokyo, Japan), and the analysis of echocardiographic images was conducted by using Vevo LAB 3.1.0 software. The mice were anesthetized in an induction box by inhaling $2\%$ isoflurane and kept anesthetized at a lower concentration. Additionally, the heart rate of mice was controlled at 400–450 bpm. The representative M-mode images were captured, and M-mode tracings were used to measure end-diastolic and end-systolic left ventricular inner diameters (LVIDd, LVIDs). Left ventricular ejection fraction (EF) and fractional shortening (FS) were also obtained from the Vevo system. The operation and measurement of echocardiograph data were performed by two experienced echocardiographers blinded to the study design. ## 2.3. Cell Isolation and Culture As previously reported, the isolation of neonatal rat cardiomyocytes (NRCMs) was performed [21]. Briefly, neonatal rats were intraperitoneally injected with heparin and later anesthetized with isoflurane. The hearts were excised and fastened onto a Langendorff system. Subsequently, the harvested hearts were perfused with a pre-cooled medium for 8 min to remove as much blood as possible. The perfusion medium consisted of the following ingredients: 4 mM KCl, 140 mM NaCl, 1 mM MgCl2, 10 mM taurine, 10 mM HEPES, 10 mM glucose, and 10 mM 2,3-butanedione monoxime, with the medium’s pH adjusted to 7.3. Later the perfusion medium was replaced with a digestion buffer, which continued to infuse the heart for 15 min until the samples became soft. The digestion buffer was prepared by adding 0.12 mg/mL trypsin, 1 mg/mL collagenase II, and 0.02 mM CaCl2 to the perfusion buffer. Then, the hearts were transferred to a new culture dish with pre-cooled PBS solution, trimmed into smaller pieces, and moved into a cell suspension. The stop buffer was prepared by adding 0.1 mM CaCl2 to the perfusion buffer and terminating digestion with 5 mg/mL bovine serum albumin. The cells in the suspension were filtered with a 75 μm cell strainer, collected in a sterile centrifuge tube, centrifuged at 100× g for 5 min, and resuspended in DMEM medium containing $10\%$ FBS. Two hours later, non-adherent cells were removed, collected in a new sterile centrifuge tube, and centrifuged at 100× g for 5 min. Then, the supernatant was removed, and the non-adherent cardiomyocytes were resuspended, counted, and seeded into sterile plates for further study. To examine the cell viability of NRCMs after being treated with naringenin at different concentrations for 24 h, the cells were incubated with CCK8 solution (Beyotime, Shanghai, China), and their absorbance values were measured at 570 nm. ## 2.4. Masson Trichrome Staining and Immunofluorescence Staining After fixation, dehydration, and embedding, the heart tissues were sectioned at a thickness of 5 μm. Masson-trichrome staining was performed following the instructions provided with the Masson-trichrome staining kit (Solarbio, Beijing, China) to evaluate cardiac fibrosis. The areas where fibrosis occurred were stained blue. The proportion of fibrosis was calculated as the ratio of the blue area to the total area. Cell membrane staining was performed using Oregon Green 488 conjugated WGA (Invitrogen, Carlsbad, CA, USA) to assess the cross-sectional area of cardiomyocytes and cardiac hypertrophy. The sections were incubated with dilute WGA solution at 37 °C in the dark for 45 min, and later washed with PBS solution three times (each time for 5 min) and stained with DAPI for 2 min. cTnT staining was used to identify the isolated NRCMS and the hypertrophy of NRCMs induced by ISO via observing the size of the cell area. In brief, after washing with PBS solution, the treated cells were fixed in $4\%$ paraformaldehyde solution at room temperature for 10 min. After washing with PBS solution three times (each time for 5 min), the cells were incubated with the blocking solution for 30 min at room temperature and later incubated with diluted cTnT antibody solution at 4 °C overnight. The following day, the cells were washed with PBS solution three times (each time for 5 min), then incubated with the donkey anti-mouse IgG Alexa Fluor®488 (Invitrogen, Carlsbad, USA) at 37 °C for 1 h. Before the images were captured, the cells were washed with PBS solution three times (each time for 5 min), and stained with DAPI for 2 min. Images were obtained with a Zei ss LSM 880 upright confocal fluorescence microscope. The image results were analyzed using Fiji software (Image J version 1.52i). ## 2.5. Oxidative Stress Detection Dihydroethidium (DHE) staining was used to analyze ROS generation. The live cells or fresh frozen myocardial sections were incubated with DHE at 37 °C for 40 min. Subsequently, the representative fluorescence images were captured using a digital camera (Olympus DP80, Tokyo, Japan). Following the manufacturer’s instructions, the MDA level and SOD activity were measured with ELISA kits purchased from the Jiancheng Bioengineering Institute (Nanjing, China). ## 2.6. Real-Time Quantitative PCR Analysis Following the manufacturer’s instructions, total RNA was extracted from the heart tissues or cell samples with Trizol reagent. Then, the extracted RNA was reversely transcribed into cDNA according to the requirements of the reverse transcription kit (Takara Biotechnology, Dalian, China). The relative RNA expression level of target genes was quantified in each sample using Syber green-based quantitative PCR. Two wells were set up in each DNA sample, and the values were averaged. The PCR primers used are listed in the Supplementary Table S1. The relative expressions of the genes were calculated using the 2−ΔΔCT method. Additionally, the final data are represented as the fold change referring to 2−ΔΔCT treated/2−ΔΔCT control. ## 2.7. Western Blot Analysis Total protein was extracted from the heart tissues or cell samples using RIPA lysis buffer (Beyotime, Shanghai, China). In order to detect the expression of phosphorylated proteins, phosphatase inhibitors were also added to the protein extraction process. The total protein concentration in each sample was detected using a BCA protein assay kit (Beyotime, Shanghai, China). A suitable loading buffer was added to the protein according to the measured protein concentration, and the mixtures were denatured at 100 °C. The prepared protein was separated through SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and later transferred to the PVDF membranes. After incubating with a quick blocking solution for 30 min at room temperature, the membranes were incubated with different primary antibodies diluted in a diluent blocking solution at 4 °C overnight. The following day, after washing the membrane three times with TBST (each time for 15 min), the membranes were incubated with appropriate fluorescent second antibodies in the dark for 1 h at room temperature. Then, the results were examined and analyzed with the help of the Odyssey Infrared Imaging System (Li-Cor Biosciences, Lincoln, NE, USA) and software. The primary antibodies included phospho-P38, total-P38, phospho-JNK, total-JNK, phospho-ERK, total-ERK, and GAPDH (Proteintech, Wuhan, China). ## 2.8. Statistical Analysis All of the acquired numeric data are presented as the mean ± standard error (mean ± SE). Statistical analyses were performed using SPSS 21.0 software. Comparisons between multiple groups were performed using a one-way analysis of variance followed by Tukey’s multiple comparisons. Additionally, the comparisons between the two groups were analyzed using Student’s t-test. $p \leq 0.05$ was considered statistically significant. ## 3.1. Naringenin Attenuated Isoprenaline (ISO)-Induced Cardiac Hypertrophy Consistent with previous studies, ISO subcutaneous injection (7.5 mg/kg/d for 2 weeks) significantly induced pathological cardiac hypertrophy, as demonstrated by the increased cardiomyocyte cross-sectional area demonstrated in the HE and WGA staining results (Figure 1A,B), the increased ratio of heart weight/body weight (HW/BW) and heart weight/tibial length (HW/TL) (Figure 1C), and the increased expression of hypertrophic marker genes (ANP, BNP, and β-MHC) (Figure 1D), which were dose-dependently (ranging from 25 to 100 mg/kg/d) reduced by pretreatment with gavage-administrated naringenin (Figure 1A–D). We also investigated cardiac function through echocardiographic analysis. In ISO-induced cardiac hypertrophy mice, cardiac dysfunction was reflected by decreased EF and FS, and increased LVIDs and LVIDd (Figure 1E,F). However, naringenin pretreatment significantly attenuated ISO-induced cardiac dysfunction. Meanwhile, the results from Masson staining showed that naringenin pretreatment reduced the cardiac fibrotic area in ISO-induced hypertrophied hearts (Figure 1G). ## 3.2. Naringenin Ameliorated ISO-Induced Cardiomyocyte Hypertrophy by Inhibiting Oxidative Stress through AMPK/NOX2/MAPK Signaling Pathway Cell viability was assessed at different concentrations of naringenin to further determine the effect of naringenin on cardiomyocytes (Supplementary Figure S2). The anti-hypertrophic effect of naringenin was confirmed in vitro. ISO treatment (10 μM, 24 h) notably increased cell sizes and the expression of hypertrophic markers (ANP, BNP, and β-MHC) in NRCMs, which were attenuated by naringenin treatment (10 μM) (Figure 2A,B). It is reported that oxidative stress is essential in ISO-induced cardiac hypertrophy [22]. As expected, the MDA level in ISO-induced hypertrophied hearts was significantly increased, while the SOD activity was decreased. The pretreatment of naringenin effectively inhibited ISO-induced oxidative stress, as demonstrated by reduced MDA level and increased SOD activity (Figure 2C). The primary source of ROS in the heart is NADPH oxidase [18]. Of the NADPH oxidase isoforms expressed in the heart (NOX2 and NOX4), NOX2 may be responsible for β-AR agonist-induced cardiac hypertrophy [23,24]. The naringenin pretreatment significantly inhibited the elevation of NOX2 in ISO-induced hypertrophic NRCMs (Figure 2D). Previous studies demonstrated that the increased ROS generated by ISO treatment stimulated the activation of the MAPK signaling pathway [25]. Consistent with these results, our study found that ISO significantly stimulated the phosphorylation of P38, JNK, and ERK. On the other hand, naringenin inhibited the activation of MAPK signaling and the phosphorylation of P38, JNK, and ERK (Figure 2E). AMPK has been recognized as an energy sensor that plays a critical role in maintaining redox balance [26,27]. It has been verified that ISO-induced hypertrophy is accompanied by the inhibition of AMPK [28]. Naringenin has been reported to activate AMPK [29]. Therefore, we further investigated the characteristics of AMPK in the anti-hypertrophic effect of naringenin. We found that the pretreatment of the selective AMPK inhibitor compound C (20 μM, 4 h) almost eliminated the anti-hypertrophic effects of naringenin, as demonstrated by the enlarged cell size and increased expression of hypertrophic markers (Figure 2A,B). Meanwhile, naringenin-induced decreases in oxidative stress and inhibition of MAPK signaling were also mostly eliminated by compound C in NRCMs (Figure 2C–E). These results suggest that the anti-hypertrophic effect of naringenin on ISO-induced hypertrophied NRCMs is attributed to the AMPK/NOX2/MAPK signaling pathway. ## 3.3. Inhibition of AMPK Blocked the Anti-Hypertrophic Effects of Naringenin on ISO-Induced Cardiac Hypertrophy In Vivo Given the anti-oxidative effect of naringenin on ISO-induced cardiomyocyte hypertrophy, the impact of naringenin on oxidative stress in hypertrophic hearts was investigated. We found that in the ISO-induced hypertrophied hearts, the oxidative stress levels were significantly elevated, as demonstrated by the increased ROS production and MDA level and decreased SOD activity (Figure 3A,B). Meanwhile, the expression of NOX2 and the phosphorylation of P38, JNK, and ERK increased in hypertrophied hearts (Figure 3C,D). Naringenin effectively inhibited the levels of oxidative stress and the activation of MAPK; however, the inhibition of AMPK by compound C (20 mg/kg, 2 weeks) significantly blunted the anti-oxidative effects of naringenin (Figure 3A–D). As shown in Figure 4, ISO-induced cardiac hypertrophy was alleviated considerably by naringenin, as demonstrated by the reduced cell size, HW/BW ratio, HW/TL ratio (Figure 4A–C), and expression of hypertrophic marker genes (ANP, BNP, and β-MHC) (Figure 4D). Naringenin also resulted in cardiac function improvement, with improved echocardiography parameters (namely EF, FS, LVIDs, and LVIDd) (Figure 4E,F) and cardiac fibrosis area reduction (Figure 4G). However, the anti-hypertrophic effects of naringenin were mostly diminished by compound C in ISO-induced cardiac hypertrophy mice (Figure 4A–G). These results suggest that the activation of AMPK contributed to the cardioprotective effects of naringenin in ISO-induced cardiac hypertrophy mice. ## 4. Discussion Pathological cardiac hypertrophy in response to stress stimuli results in cardiac dysfunction, and eventually develops into heart failure [30,31]. Excessive activation of adrenergic receptors is one of the hallmarks of pathological hypertrophy in patients with heart failure, and sustained β-adrenergic receptor stimulation increases mortality in patients [32]. Evidence shows that oxidative stress plays a crucial role in cardiac hypertrophy [4,7]. Substantial studies demonstrated that ROS in cardiac redox signaling is associated with mitochondrial dysfunction and myocardial fuel utilization. In particular, most oxygen expenditure occurs in cardiomyocytes, where redox signaling facilitates its involvement in homeostatic and stress response pathways, physiological processes, and pathological changes. Thus, oxidative stress suppression represents an important therapeutic target for cardiac hypertrophy. However, in numerous clinical trials, conventional antioxidants, including beta carotene and vitamins C and E, were proven to be insufficient to prevent cardiac hypertrophy [33,34,35]. Although the reasons for this are unclear, conventional antioxidants lacking a direct target to reduce ROS generation might be a key point. NADPH oxidases are verified to be the primary source generating ROS in cardiac hypertrophy. Thus, naringenin, a natural antioxidant targeting NADPH oxidase inhibition, is a potential drug with which to suppress cardiac hypertrophy [36,37]. In the present study, our results found that the pretreatment with naringenin effectively alleviated ISO-induced cardiac hypertrophy and cardiac dysfunction in mice, which might be related to inhibiting oxidative stress, suggesting that naringenin may be a novel therapeutic approach for pathological cardiac hypertrophy. Naringenin is thought to be the predominant element in a diet high in vegetables and fruit for atherosclerosis [19]. The therapeutic effect of naringenin has been proven in several diseases associated with oxidative stress. In this study, cardiac hypertrophy was induced by the stimulation of abnormal neurohumoral factors through isoprenaline administration. The ISO-induced cardiac hypertrophy was assessed using increased cardiomyocyte cross-sectional area from HE and WGA staining, increased expression of cardiac hypertrophy marker genes (ANP, BNP, and β-MHC), and increased heart size determined with increased ratios of HW/BW and HW/TL. Meanwhile, the sustained ISO administration in mice resulted in cardiac dysfunction, mainly with contractile dysfunction, evaluated through echocardiographic examination with the EF, FS, LVIDs, and LVIDd measurements, as well as exaggerated fibrosis assessed using Masson staining. However, the pretreatment with naringenin significantly mitigated the morphological, pathological, and functional changes caused by ISO-induced pathological cardiac hypertrophy. Moreover, the anti-hypertrophic effect was confirmed in the in vitro experiment. Naringenin alleviated the increased cell area and gene expression of cardiac hypertrophy markers in NRCMs pretreated with ISO. Due to the essential role of oxidative stress in ISO-induced cardiac hypertrophy, we detected the level of oxidative stress in the heart tissues and NRCMs after naringenin treatment. We found that naringenin decreased SOD activity and increased MDA levels in both in vivo and in vitro experiments. These results implied that the inhibition of oxidative stress is a possible mechanism of the naringenin-mediated anti-hypertrophic effect. AMPK, a potent NOX inhibitor, has been proven to be essential in cardiac remodeling, such as cardiac hypertrophy, fibrosis, and inflammation [38]. The activation of AMPK reduced the generation of NOX-derived ROS and further inhibited cardiac hypertrophy [39,40]. The activation of AMPK by naringenin has been demonstrated in cardiovascular diseases [41,42]. Previous studies indicated that naringenin could activate the AMPK-SIRT3 signaling pathway, inhibit mitochondrial oxidative stress damage, and erase cardiac ischemia-reperfusion injury [41]. These studies indicated that the activation of AMPK was strongly implicated in the cardioprotective effect of naringenin in ISO-induced cardiac hypertrophy. Our present study showed that AMPK inhibitor compound C almost eliminated the anti-hypertrophic effects of naringenin, indicating that AMPK is involved in the pathological process. NADPH oxidases are the primary source of ROS. Among the isoforms of NADPH oxidase, NOX2 is closely associated with cardiac hypertrophy induced by the chronic stimulation of β-adrenergic receptors [23,24]. In our study, a remarkable increase in NOX2 expression was observed in NRCMs and hypertrophic hearts after ISO treatment, while naringenin pretreatment significantly reduced the expression of NOX2. These results implied that the therapeutic effect of naringenin on cardiac hypertrophy is related with the AMPK signaling pathway. The excessive ROS derived from NADPH oxidases activates various hypertrophy-related signaling pathways, for example, the MAPK signaling pathway [43]. Treatment with ISO has been reported to activate the MAPK signaling pathway [44], composed of P38, JNK, and ERK. The signaling pathway regulates a variety of biological behaviors, including cell proliferation, differentiation, and apoptosis [45]. The administration of ISO caused significant cardiac dysfunction in mice by activating MAPK-dependent oxidative stress [46]. Consistent with the reported results, our results show that ISO administration significantly upregulated the phosphorylation of MAPK-related signaling molecules (P38, JNK, and ERK) in heart tissues and NRCMs. Naringenin has been reported to prevent the activation of the MAPK signaling pathway, reduce oxidative stress, and protect the human bronchial epithelium from LPS-induced injury [47]. As with the previously reported results, we found that the pretreatment with naringenin reduced the phosphorylation of P38, JNK, and ERK. Meanwhile, the inhibition of AMPK by compound C mostly eliminated the inhibitory effect of naringenin on the MAPK signaling pathway, suggesting that naringenin exhibits an inhibition effect on cardiac hypertrophy via the MAPK signaling pathway (Figure 5). Several clinical trials provided bioactivity, tolerability, and safety data of naringenin [48]. Even after daily naringenin-enriched food intake (i.e., whole orange juice 400 mL to 1000 mL), the serum naringenin concentration is far lower than the minimal effective concentration 8 μM [49], implying that naringenin obtained from daily food is insufficient, and additional naringenin supplementation is necessary to increase its serum concentration and enhance its biological role in the body. We also noticed that there is a report showing that that 4-week oral administration of 100 mg naringenin twice a day significantly reduced the body mass index, visceral fat level, and systolic blood pressure [50]. With regard to its safety, a single ingested dose of 900 mg of naringenin capsules is still safe in humans [48]. These data identified the efficiency and safety of naringenin. However, more clinical trials are needed to promote the translation of naringenin to clinical practice. ## 5. Conclusions The present study suggested that pretreatment with naringenin attenuates ISO-induced cardiac hypertrophy. Additionally, the anti-hypertrophic effect of naringenin is mediated by inhibiting oxidative stress through the AMPK/NOX2/MAPK signaling pathway. Our results suggest an essential role of naringenin supplementation in treating myocardial hypertrophy. More studies are needed to confirm the effectiveness of naringenin in more animal models of cardiovascular disease. The encouraging results from animal experiments could promote naringenin into clinical trials as early as possible. Naringenin supplementation provides a new option for preventing and treating cardiovascular disease. ## References 1. Xu L., Su Y., Zhao Y., Sheng X., Tong R., Ying X., Gao L., Ji Q., Gao Y., Yan Y.. Melatonin differentially regulates pathological and physiological cardiac hypertrophy: Crucial role of circadian nuclear receptor RORalpha signaling. J. Pineal. Res. (2019) 67 e12579. 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--- title: Immobilization and Release of Platelet-Rich Plasma from Modified Nanofibers Studied by Advanced X-ray Photoelectron Spectroscopy Analyses authors: - Anton M. Manakhov - Elizaveta S. Permyakova - Anastasiya O. Solovieva - Natalya A. Sitnikova - Philipp V. Kiryukhantsev-Korneev - Anton S. Konopatsky - Dmitry V. Shtansky journal: Polymers year: 2023 pmcid: PMC10056793 doi: 10.3390/polym15061440 license: CC BY 4.0 --- # Immobilization and Release of Platelet-Rich Plasma from Modified Nanofibers Studied by Advanced X-ray Photoelectron Spectroscopy Analyses ## Abstract Platelet-rich Plasma (PRP) is an ensemble of growth factors, extracellular matrix components, and proteoglycans that are naturally balanced in the human body. In this study, the immobilization and release of PRP component nanofiber surfaces modified by plasma treatment in a gas discharge have been investigated for the first time. The plasma-treated polycaprolactone (PCL) nanofibers were utilized as substrates for the immobilization of PRP, and the amount of PRP immobilized was assessed by fitting a specific X-ray Photoelectron Spectroscopy (XPS) curve to the elemental composition changes. The release of PRP was then revealed by measuring the XPS after soaking nanofibers containing immobilized PRP in buffers of varying pHs (4.8; 7.4; 8.1). Our investigations have proven that the immobilized PRP would continue to cover approximately fifty percent of the surface after eight days. ## 1. Introduction The sequential process of wound healing, which includes inflammation, re-epithelialization, angiogenesis, granular tissue creation, wound closure, and the production of normal tissue, is well understood. It involves the interaction of many cell types, soluble mediators, and extracellular matrices [1] The violation of this cascade, including secondary infections, leads to ineffective healing, chronicity of the process, and the rejection of skin grafts [2]. The extracellular matrix (ECM) regulates cell migration, proliferation, and angiogenesis by acting as a signaling chemical reservoir, providing structural support, intercellular interaction, and angiogenesis. Fibroblasts’ secreted polysaccharides, proteoglycans, and fibrin fibers form a three-dimensional structure [3,4]. The creation of bioengineered skin substitutes aims to mimic the native ECM’s qualities, which are crucial to the course of normal healing. The development of matrices for the treatment of skin injuries must take into account factors including biocompatibility, extracellular matrix-like structure, atraumatic nature, stimulation of angiogenesis, and antibacterial activities. Effective neovasculogenesis is a crucial component of wound healing, hence improving the angiogenic capacity of biomaterials is a goal for their development. Vascular endothelial growth factors [5], a mixture of factors [6,7], chitosan derivatives with heparin, amino acids [8], copper ions, and other components are included in the biocomposite composition for this purpose [9,10,11]. Tissue engineering structures are constructed using biomaterials, which are organic or inorganic compounds that resemble the extracellular matrix. Collagen, chitosan, alginate polypeptides, hyaluronans, glycosaminoglycans, fibronectin, etc. are a few examples of natural polymers [12,13]. Natural polymers can cause immunogenic responses, have variable rates of degradation, and have limited capacity for modification. Polyglycolide, polylactide, polytetrafluoroethylene, polyethylene terephthalate, and polycaprolactone are examples of synthetic biodegradable materials. Tissue engineering structures based on bionanomaterials are now being actively developed. Synthetic polymers can be easily manipulated to create a regulated concentration and release rate of growth factors and/or antimicrobial chemicals. They are also less expensive, more homogeneous, and do not have immunogenic effects. Additionally, the fabrication of nanoparticles and the development of a technology that enables the modification and customization of the material’s structure and physicochemical properties under the circumstances of wound healing make nanomaterials ubiquitous. It should be highlighted that untreated synthetic polymer nanofibers, such as polycaprolactone and polylactide, have poor cell adherence and slow wound healing [14,15]. Additionally, it seems almost impossible for active biomolecules (such as antibiotics and growth factors) to adhere to untreated nanofibers. The electrospinning of biodegradable nanofibers from a solution of polymers (polycaprolactone, polyethylene glycol, polylactide, etc.) is the most promising technique [16]. The Nanospider method created by Elmarco (Elmarco, Libec, Czech Republic) at the same time made it possible to produce enormous nanofiber films (30 cm wide and any length) [17,18]. This method produces nanofibers with a regulated morphology and a low cost (the structure is depicted in the attached extra information file). Collagen, gelatin, and chitosan are examples of natural polymers that can be used in a solution to electrospin nanofibers (polycaprolactone, polyethylene glycol, etc.). Although natural polymer-derived nanofibers exhibit great biocompatibility, it can be challenging to produce stable, homogenous nanofibers of these materials [19]. It was impossible to obtain pure collagen and chitosan nanofibers for a long period; as a result, fibers made up of a blend of polymers, such as chitosan/polyethylene oxide, were produced [16]. Collagen is also a very expensive product, and gelatin and collagen nanofibers frequently degrade in aqueous environments. Consequently, creating bioactive nanofibers from synthetic polymers is a very promising endeavor. However, as the majority of these polymeric nanofibers have a high water contact angle (are superhydrophobic), additional processing is required to improve the cell adhesion and proliferation on such surfaces [20]. Currently, co-spinning of biopolymers (such as gelatin or collagen) with nanofibers, plasma treatment in a gas discharge combined with (or without) grafting growth factors, and plasma polymerization are the four most popular ways to modify nanofibers [21]. Since liquid processing causes nanofibers to degrade, the first option is the least promising. Because the surface of the nanofibers lacks active groups that may be further connected with active chemicals (such as growth factors or antibiotics), the second technique has the drawbacks of being non-universal and having too few application options. The third way is the most energy-effective: plasma treatment (treatment in a gas discharge such as air, oxygen, or argon) [22,23]. Wound healing has been markedly enhanced by the attachment of growth factors to plasma-treated polycaprolactone and polylactide nanofibers [24]. While it is currently uncertain how long the action of immobilized growth factors may be maintained on the surface, the effect of plasma treatment is lost very rapidly (within 1–2 days); hence growth factors should be applied immediately after plasma treatment. The latter technique (plasma polymerization, or the deposition of plasma polymers caused by a discharge in organic monomer vapor), which is also energy-efficient, is the most promising since the plasma polymer layers that are formed are extremely stable [19]. In addition, plasma polymers always have a larger concentration of active surface groups than plasma-treated surfaces. However, this method of processing nanofibers is the least studied. Additionally, the plasma-coated PCL nanofibers with immobilized PRP can be used to treat diabetic wounds [25,26]. By adding COOH groups to the surface of nanofibers, it is feasible to bind protein molecules through the creation of a covalent bond, in addition to improving the biocompatibility (decreasing hydrophobicity) [27]. Mesenchymal stem cells adhered and proliferated more strongly to the plasma-modified PCL nanofibers containing COOH groups. Still, PCL nanofibers with covalently bound bioactive compounds from platelet-rich plasma (PRP) had the greatest efficacy [25,27]. PRP is an ensemble of growth factors, extracellular matrix components, and proteoglycans that is naturally balanced. It is composed of platelet-derived growth factors (PDGF-AA, BB, and AB-isomers), transforming factor growth- (TGF-), platelet factor 4 (PF4), interleukin-1 (IL-1), platelet-derived angiogenesis factor (PDAF), vascular endothelial growth factor (VEGF), epidermal growth factor (EGF), epithelial growth factor cells (ECGFs), insulin-like growth factor (IGF), osteocalcin (TSP-1). The “reservoir” of PRP immobilized on a COOH plasma polymer layer may enhance the proliferation and migration of stem cells, attract macrophages, regulate the wound’s cytokine backdrop, and restrict inflammation. Promoting the formation of new capillaries accelerates epithelization in chronic wounds of various etiologies, hence enhancing wound healing. The immobilization of biomolecules onto plasma-modified surfaces is quite challenging to control. Despite the fact that the immobilization can be qualitatively confirmed by some changes in the spectra (e.g., by IR or X-ray Photoelectron Spectrospies, XPS), the quantitative analysis is challenging. In our previous works, we performed an attempt to model the XPS spectra and to calculate the amount of the proteins with well-known structures (Apoliprotein A1 and Angeigenin) [28]. However, if one is planning to model more complex bioactive substances, such as PRP, that approach will not be valid. In this work, for the first time, an investigation of the immobilization and release of PRP for plasma-modified nanofibers has been performed. The plasma-coated PCL nanofibers were used as substrates for the immobilization of PRP, and the amount of PRP immobilized was quantified by using a special XPS curve fitting coupled with the changes in the elemental composition. Then, the release of PRP was controlled by measuring XPS after soaking the nanofibers with immobilized PRP in buffers with different pHs. Our research has shown that even after 8 days, the immobilized PRP would still cover more than $50\%$ of the surface. ## 2.1. Preparation of Nanofibers Nanofibers were produced by electrospinning a 9-weight percent solution of PCL (80,000 g/mol) solution. The sample processing can be found elsewhere [29]. Briefly, acetic acid ($99\%$) and formic acid ($98\%$) were used to dissolve the granulated PCL. All substances were bought from Sigma Aldrich (Darmstadt, Germany). The acetic acid (AA) and formic acid (FA) had a weight ratio of 2:1. The samples were electrospun by a Super ES-2 machine produced by ESpin Nanotech, (ESpin Nanotech, Kanpur, India), which included both drum and static plate collectors. In this investigation, a static collector plate has been used for collecting the nanofibers. The flowrate of PCL solution was 1 mL/h. The samples were collected onto polypropylene fabric and placed at 12 cm distance from the nozzle. The electrospinining voltage was kept at 50 kV. The authors used the term PCL-ref to refer to the untreated, as-prepared PCL nanofibers. ## 2.2. Plasma-Coated COOH The plasma polymerization technique for Ar/CO2/C2H4 is discussed in full elsewhere [30,31]. On Si wafers and PCL nanofibers, COOH plasma polymer layers were deposited utilizing a UVN-2M vacuum system equipped with rotary and oil diffusion pumps. The reactor’s residual pressure was below 10−3 Pa. A radio frequency (RF) Cito 1310-ACNA-N37A-FF power supply (Comet, Flamatt, Switzerland) connected to an RFPG-128 disk generator (Beams and Plasmas, Moscow, Russia) located in the vacuum chamber was used to ignite the plasma. The duty cycle and RF power were, respectively, set to $5\%$ and 500 W. In the vacuum chamber, CO2 ($99.995\%$), Ar ($99.998\%$), and C2H4 ($99.95\%$) were introduced. The Ar gas flow was fixed at 50 sccm, whereas the CO2 and C2H4 gas fluxes were set to 35 and 10 sccm, respectively. They were managed with the aid of a 647C Multi-Gas Controller (MKST, Newport, RI, USA). Using a VMB-14 unit (Tokamak Company, Dubna, Russia) and D395-90-000 BOC (Edwards Vacuum, Sanborn, NY, USA) controllers, the chamber pressure was measured. In total, 8 cm was established as the distance between the RF electrode and the substrate. The time allotted for the deposition was 15 min. The plasma-coated nanofibers were denoted as PCL-COOH. ## 2.3. Sample Characterization The morphology of the material was studied using scanning electron microscopy (SEM). A JEOL Ltd. JSMF 7600 (JEOL, Tokyo, Japan) microscope equipped with an energy-dispersive X-ray spectrometer was utilized for SEM investigation. To compensate for surface charge, a 5 nm-thick Pt coating was applied to the samples. X-ray photoelectron spectroscopy (XPS), was used to characterize the chemical composition of the sample. The XPS examination was conducted with a PHI5500VersaProbeII (Ulvac PHI, Osaka, Japan) instrument equipped with a monochromatic Al K X-ray source ($h = 1486.6$ eV) at a pass energy of 23.5 eV and an X-ray power of 50 W. After deducting the Shirley-type noise, the spectra were matched with the CasaXPS software version 2.3.25 (Casa Software Ltd, Teignmouth, UK). The investigated region had a maximum lateral resolution of 0.7 mm. The literature [32,33,34,35,36,37] provides the binding energies (BEs) for all carbon and oxygen environments. In order to calibrate the BE scale, the CHx component was adjusted to 285 eV. ## 2.4. Preparation of PRP With slight adjustments, a platelet-rich plasma (PRP) was produced, as described in [38,39]. Blood was drawn from non-smoking, healthy women after receiving their informed consent. Platelet-rich plasma was collected and activated by a three freeze-thaw cycler after the blood was centrifuged in special tubes (Plasmolifting TM, Moscow, Russia). Growth factors produced from plasma were extracted by centrifugation at 12,000× g for 10 min at 4 °C, and then stored at 70 °C until further use. Each milliliter of PRP contained 102.5 mg of dry materials. Before participating in the study, all subjects provided their informed consent for inclusion. The protocol was approved by the Ethics Committee of the Research Institute of Clinical and Experimental Lymphology–Branch of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (RICEL-branch of ICG SB RAS), in line with the Declaration of Helsinki (Identifier: N115 from 24 December 2015). ## 2.5. PRP Immobilization Before PRP immobilization, all samples were sterilized for 45 min under UV light. It should be noted that the UV or VUV irradiation may induce the polymerization enhancing the immobilization [40]; however, our previous studies revealed that the irradiation of PCL-ref does not enhance the immobilization of PRP [41,42]. To induce the covalent attachment of PRP to the plasma-coated nanofibers, the samples were immersed for 15 min in a 2 mg/mL solution of 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) (Sigma Aldrich, $98\%$) in water. The samples were properly cleaned with PBS before being incubated with PRP for 15 min. The sample was carefully rinsed with PBS following the reaction. The sample designation was PCL-COOH-PRP. ## 2.6. Testing the Stability of PCL-COOH-PRP The stability of immobilized PRP was evaluated by soaking the PCL-COOH-PRP samples (sample size 1 × 1 cm) in PBS solutions (200 µL) at 3 different pH values (4.8, 7.0, and 8.1) for 1 h, 2 h, 1 day, 4 days, and 8 days at 37 °C. The selection of these pH values was aligned with the typically observed pH of chronic and acute wounds. The resulting samples were labeled PCL-COOH-PRP-“XXh”-“pHXX” and PCL-COOH-PRP-“XXd”-“pHXX”, where “XXh” (or “XXd”) represents the number of hours or days the samples were immersed, and pHXX was the solution’s pH. The samples after immersion were analyzed by XPS. ## 3.1. Plasma-Modified PCL Nanofibers Figure 1 depicts the morphologies of PCL-ref and PCL-COOH. The deposition of the plasma polymer resulted in minor alterations to the nanofibers’ thickness but no major changes to their topography. On the contrary, the chemistry of the PCL-ref and PCL-COOH were extremely different. Water contact angle (WCA) experiments clearly demonstrated the effect of plasma coating. The PCL-ref displayed a WCA of 123°. The WCA was significantly reduced during the plasma layer deposition (down to 10.0 ± 0.5°). Polar group grafting was responsible for the enhanced wettability of plasma-coated PCL nanofibers. XPS analysis revealed quantifiable differences in the surface chemistries. Table 1 displays the XPS-measured chemical compositions of PCL nanofibers (PCL-ref) and PCL-COOH samples. Using the high-resolution spectra of each component, the atomic percentages of the elements were determined. More details were obtained from the high-resolution spectra fitting presented in Figure 2. The XPS C1s spectrum of PCL-ref can be fitted with a sum of 3 components: hydrocarbons CHx (BE = 285 eV), ether group C-O (BE = 286.4 eV), and ester group C(O)O (BE = 289.0 eV), as shown in Figure 2a. The FWHM of the C-O peak was set to 1.35 eV, while the FWHMs of the CHx and C(O)O components were 1.1 and 0.85 eV, respectively. The fitting of the XPS C1s spectrum of PCL-COOH was completely different. PCL-COOH was fitted with the sum of 4 components: hydrocarbons CHx (BE = 285.0 eV, used to calibrate the BE scale), carbon singly bonded to oxygen C-O (BE = 286.55 ± 0.05 eV), carbon doubly bonded to oxygen C=O/O-C-O (BE = 288.0 ± 0.05 eV), and C(O)O (BE = 289.1 ± 0.05 eV). Figure 2b displays the concentrations of all constituents. Significant differences in the oxygen spectra of PCL-ref and PCL-COOH were also visible. Indeed, the PCL-ref exhibited two well-defined peaks with quite similar concentrations and a low FWHM (Figure 2c), while the PCL-COOH exhibited a broad spectrum with a higher FWHM for less well-defined peaks (Figure 2d). ## 3.2. Quantification of Immobilized PRP The immobilization of PRP bonding led to significant compositional changes. PCL-COOH-PRP did, in fact, have a significant nitrogen concentration of 11.8 at %. The increase in the nitrogen concentration was accompanied by a significant decrease in the oxygen concentration. All nitrogen atoms have amide functions (N-C=O, BE = 400.2 eV, FWHM 1.7 eV). Additionally, $0.4\%$ of sulfur was also detected. All these findings suggested that PRP was significantly incorporated into our layers. However, quantifying the percentage of the surface covered by PRP was not possible, neither from these simple observations nor from standard C1s curve fitting. As a result, we developed a novel method for quantifying PRP incorporated into the surface. Previously, this method was approbated for complex mixed polymers [35]. Using the CasaXPS software, the XPS C1s signal PCL-COOH-PRP was approximated by introducing a new line that replicated the form of the signal from the PCL-COOH. This method allowed for the quantification of the remaining plasma polymer surface. Finally, the C1s signal was fitted with a sum of 4 components: the plasma layer (PCL-COOH, BE = 285 eV), CHx (BE = 285.0 eV, FWHM = 1.3 eV), C-N (BE=286.4 eV, FWHM = 1.5 eV), and N-C=O (BE = 288.2 eV, FWHM = 1.1 eV). The fitted PCL-COOH-PRP is shown in Figure 3a. Hence, more than $50\%$ of the surface is covered by CHx, C-N/C-O, and N-C=O. The reliability of our fitting was confirmed by the fact that the atomic concentration of nitrogen was correlated with the N-C-O and C-N-C-O concentrations. Indeed, if one simply calculates the number of nitrogen atoms in the C-N groups, it will result in [C-N/C-O] × [C]/$100\%$ = 11.8 at %, i.e., perfectly matching the atomic concentration of nitrogen (Table 1). This methodology is what we have used to analyze all samples after immersion in different buffers. The variations of BE and FWHM for all introduced peaks (CHx, C-N/C-O and N-C=O) were less than 0.05 and 0.1 eV, respectively. ## 3.3. Stability of Immobilized PRP The role of the immobilized growth factors and viable proteins from the solutions of PRP is very significant, and, as previously shown, the modification of polycaprolactone fibers with human platelet-rich plasma significantly increases the number of cells on the nanofibers, enhances their adhesion, and facilitates the wound healing process [25,27,42]. The presence of these viable molecules on the surface is highly important to maintain sufficient enhancement of the wound healing process facilitated by the nanofibers. Hence, when one is developing a wound healing material with superior properties, the stability of the immobilized biomolecules should be tested. It is also important to mention that the acidity of the wound might be affected by many parameters or circumstances, i.e., inflammation, diabetes, the acute or chronic course of the disease, etc. Depending on the parameters, the pH of the wound might vary from slightly acidic (pH 4–5) to slightly basic (pH 8). Thus, it is very important to understand the behavior of porosified materials with immobilized PRP in this pH range at a reasonable temperature (37 °C). The atomic compositions of all samples are reported in Table 1. By following the concentration of nitrogen in the layer after soaking in the solutions, it is possible to have a semi-quantitative understanding of the remaining proteins and growth factors on the sample surface. For the sake of simplicity, the concentration of nitrogen is plotted as a function of immersion time in Figure 4a. The results indicated that the immobilized molecules remained on the surface for an extended period of time, as the nitrogen concentration was greater than $7\%$ in all samples. However, the changes in the nitrogen concentration over the immersion time were significantly dependent on the pH. The decrease in the nitrogen concentration during the first hours for acidic and neutral pH was significantly lower than for the samples immersed at pH = 8.1. Interestingly, longer immersion of samples in the solution at pH = 8.1 led to significant recuperation of the nitrogen concentration, while samples immersed at a neutral pH exhibited a significant decrease in nitrogen. The variation of nitrogen concentration has a direct relationship with the amount of remaining proteins at the surface, but why it is pH dependent can be attributed to the properties (stability) of protein-surface linkage, the stability of plasma polymers, or the physical properties of immobilized proteins and growth factors. In order to better understand the behavior of our samples at different pH levels, the XPS C1s curve fitting was performed. The XPS C1s curve fitting for all samples after immersion was performed using the same methodology presented in Section 3.2. The positions of CHx and plasma polymer components had a fixed BE position of 285 eV. The positions of C-N/C-O and N-C=O were within the ranges of 286.35–286.45 and 288.1–288.2 eV, respectively. The spectra for the samples immersed in solutions at neutral pHs are depicted in Figure 3. The evolution of N-C=O (amide environment), plasma polymer, and CHx (hydrocarbons from proteins or contaminations) contributions are shown in Figure 4b,c,d, respectively. The N-C-O contribution is a reasonably reliable indicator of the amount of immobilized biomolecules. Indeed, its position differs significantly from other peaks, and the amide contribution is directly attributed to peptide bonds in proteins, whereas the C-N/C-O contribution is related to alcohol groups and overlaps with the C-O contribution in the plasma polymer spectrum. The CHx contribution can be both related to proteins and contaminants, as adventitious carbon is always present at the surface. Therefore, our attention was focused on the N-C=O evolution (Figure 4b). In contrast to the samples immersed at pH = 8.1, immersion at an acidic or neutral pH did not result in a significant decrease in N-C=O. Moreover, at a neutral pH, the samples exhibited some gain in the N-C=O percentage; however, the increase from 8.7 to $10.9\%$ can be attributed to the heterogeneity of the samples or the low accuracy of such methodologies. It is important to note that the decrease in N-C=O for the samples immersed at pH = 8.1 for 1 and 2 h recovered after a longer immersion time (1 day), similar to the evolution of nitrogen concentration. For very long immersion times, the remaining N-C=O percentage tends to behave regardless of the pH. After 8 days of immersion, all samples had very similar N-C=O percentages (and nitrogen concentrations). More importantly, even after 8 days, the concentrations of nitrogen and N-C=O were very high, significantly higher than the initial values (before the immersion). Hence, our materials exhibited very good long-term stability in a wide pH range. ## 4. Discussion The reason for the significantly different behaviors during the first hours should be further discussed. The reason for different behaviors can be related to poor plasma polymer stability at high pHs. However, in this case, we should notice the features of PCL-ref and the lower plasma polymer contributions. In contrast, we have observed a very high increase in the percentage of plasma polymers for PCL-COOH-PRP-1h-pH8.1 and PCL-COOH-PRP-2h-pH8.1 (Figure 4c). Furthermore, the percentage of the plasma polymer contribution has been slightly increased at pH = 4.8. Hence, the decrease in N-C=O and nitrogen is not related to the faster dissolution of plasma polymers. A second possible reason could be the hydrolysis caused by the bonding of biomolecules with the surface. However, in this case, it is not possible to explain the recuperation of the nitrogen and N-C=O concentrations at a higher immersion time. Another explanation can be related to the physical properties of the proteins presented in the PRP. PRP is a rich cocktail of various protein molecules, among which several growth factors are most important for regeneration and are measured in picograms. As known, the main component of PRP is albumin, which has an isoelectric point of pH = 4.9. At the isoelectric point, the protein is highly unstable in solutions and changes its conformation. At a higher pH, the protein becomes more stable in solutions, and its release into the solutions can be more probable. Therefore, our observations are most probably related to the hypothetical dynamics of the release of albumin since this protein is the most common protein in human blood plasma (more than $60\%$ by mass). Albumin is a monomeric globular multicarrier of hydrophobic molecules such as fatty acids, hormones, growth factors, bilirubin, and fat-soluble vitamins. There is a dynamic structure to the interactions of albumin with other molecules (including growth factors), which are temporary, weak, multisite, and allosterically influence each other. At the isoelectric point, the protein is highly unstable in solutions and changes its conformation. At a higher pH, the protein becomes more stable in solutions, and its release into the solutions can be more probable. A possible reason for the difference in the dynamics of the content of amide bonds on the surface depends on the pH since its solubility significantly increases at an acidic pH. Hence, our immobilization procedure can be used to deliver therapeutic agents with a smart controlled release depending on the pH of the medium [43]. It should be noted that immobilization can occur not only towards the COOH groups of plasma polymers, but also to the pendent amino groups from proteins, as possibly in our case. Such bonding is less stable, but it can be released if it is stable in the solutions. Such a release would be more favorable at pH = 8.1 than at a lower pH. Hence, the decrease in nitrogen at pH = 8.1 in the first hours can be related to the faster release of “excess” PRP proteins. Later, these proteins can be redeposited onto the surface of the plasma polymer. At a lower pH, similar but much slower processes may occur. As for the functional activity of proteins after covalent binding, we do not directly demonstrate the specific activity of immobilized proteins; however, indirectly, we confirmed that covalent immobilization significantly increases the proliferative activity of cells and their viability. In addition, we compared these results with ionic interaction, which also showed similar results but over a shorter period of time (within 3 days), with the subsequent leveling of the effect. In turn, covalently attached PRP on the 7th day demonstrates significant differences in contrast to unmodified nanofibers and fibers coated with PRP upon covalent binding [44]. The “reservoir” of PRP immobilized on a COOH plasma polymer layer may enhance the proliferation and migration of stem cells, attract macrophages, regulate the wound’s cytokine backdrop, and restrict inflammation. Promoting the formation of new capillaries accelerates epithelization in chronic wounds of various etiologies, hence enhancing wound healing. ## 5. Conclusions The immobilization of PRP onto plasma polymers is a complex and challenging process. The quantification of grafted molecules can be performed using an advanced XPS fitting process, as shown in this paper. Our method revealed that more than $50\%$ of the surface is covered by biomolecules. The release of biomolecules is dependent on the pH during the first hours of immersion. However, our nanofibers with immobilized PRP exhibited very sufficient long-term stability regardless of the pH, and, thus, they will have significant potential in future applications. 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--- title: Associations of Three-Dimensional Anthropometric Body Surface Scanning Measurements and Coronary Artery Disease authors: - Ning-I Yang - Li-Tang Kuo - Chin-Chan Lee - Ming-Kuo Ting - I-Wen Wu - Shuo-Wei Chen - Kuang-Hung Hsu journal: Medicina year: 2023 pmcid: PMC10056801 doi: 10.3390/medicina59030570 license: CC BY 4.0 --- # Associations of Three-Dimensional Anthropometric Body Surface Scanning Measurements and Coronary Artery Disease ## Abstract Background and Objectives: The relationship between three-dimensional (3D) scanning-derived body surface measurements and biomarkers in patients with coronary artery disease (CAD) were assessed. Methods and Methods: The recruitment of 98 patients with CAD confirmed by cardiac catheterization and 98 non-CAD patients were performed between March 2016 and December 2017. A health questionnaire on basic information, life style variables, and past medical and family history was completed. 3D body surface measurements and biomarkers were obtained. Differences between the two groups were assessed and multivariable analysis performed. Results: It was found that chest width (odds ratio [OR] 0.761, $95\%$ confidence interval [CI] = 0.586–0.987, $$p \leq 0.0399$$), right arm length (OR 0.743, $95\%$ CI = 0.632–0.875, $$p \leq 0.0004$$), waist circumference (OR 1.119, $95\%$ CI = 1.035–1.21, $$p \leq 0.0048$$), leptin (OR 1.443, $95\%$ CI = 1.184–1.76, $$p \leq 0.0003$$), adiponectin (OR 0.978, $95\%$ CI = 0.963–0.994, $$p \leq 0.006$$), and interleukin 6 (OR 1.181, $95\%$ CI = 1.021–1.366, $$p \leq 0.0254$$) were significantly associated with CAD. The combination of biomarker scores and body measurement scores had the greatest area under the curve and best association with CAD (area under the curve of 0.8049 and $95\%$ CI = 0.7440–0.8657). Conclusions: Our study suggests that 3D derived body surface measurements in combination with leptin, adiponectin, and interleukin 6 levels may direct us to those at risk of CAD, allowing a non-invasive approach to identifying high-risk patients. ## 1. Introduction Cardiovascular disease, in particular coronary artery disease (CAD), is one of the main causes of morbidity and mortality in the world [1]. Obesity is described as an independent risk factor for CAD [2] and is generally defined by an excess of body fat with the most commonly used anthropometric index being the body mass index (BMI) [3]. Obese individuals with the same amount of total body fat can have markedly distinct risk factor profiles [4], with abdominal fat having strong associations with CAD, mortality [5,6,7,8], and type 2 diabetes [9]. Visceral adipose tissue is an important endocrine organ, responsible for secreting hormones involved in a range of processes, e.g., control of sensitivity to insulin and inflammatory process mediators, and vascular hemostasis [10,11]. Biomarkers which play a role in insulin resistance and inflammation have been found to be associated with cardiovascular diseases. Leptin is an important link between obesity and the development of cardiovascular disease partially due to its effects on arterial pressure, formation of arterial thrombosis, aggregation of platelets, and on inflammatory vascular response [12]. Low adiponectin levels have also been found to be an independent risk factor for CAD [13]. The inflammatory biomarker C-reactive protein is positively correlated to the risk of cardiovascular events [14]. Interleukin 6 (IL6) and interleukin 8 have also been shown to play an important role in atherogenesis and atherosclerotic plaque destabilization [15,16]. It has also been demonstrated that the induction of the cytokine transforming growth factor beta-1 is associated with myocardial infarction [17]. A noninvasive three-dimensional (3D) scanning technology has been developed to obtain anthropometric measurements with many advantages over traditional methods, such as computed tomography scanners, X-rays, and bioelectrical impedance [18]. The aim of our study is to explore the association between CAD, biomarkers, and body measures with the use of 3D body scanning, providing more information to be used in clinical practice, epidemiological studies, and preventative medicine. ## 2.1. Study Subjects From March 2016 to December 2017, a total of 98 patients found to have CAD as confirmed by cardiac catheterization exam at Chang Gung Memorial Hospital, Keelung, were recruited into our study CAD group. The same number of 98 sex- and age-matched patients presenting to our Department of Health Promotion and Examination were enrolled into the control group. Informed consent was obtained from all participants. This study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Chang Gung Medical Foundation (201405148B0). ## 2.2. Anthropometrical Parameters Three-dimensional body surface measurements were collected using a whole-body 3D laser scanner according to previously published methods. In addition to body weight, body height, and BMI, 35 measurements from four anatomical regions were made. The trunk region included the chest profile area, chest circumference, chest width (CW), waist profile area, waist circumference (WC), waist width, trunk volume, and trunk surface area. The head and neck region included the head surface area, head volume, head circumference, and neck circumference. The hip to the lower limb region included the hip profile area, hip circumference, hip width, left and right leg volume, left and right leg surface area, left and right calf circumference, left and right thigh circumference, and left and right leg length. The upper limb region included the left and right arm volume, left and right arm surface area, left and right arm length (RAL), left and right upper arm circumference, and left and right forearm circumference. The 3D laser scanning machine (LT3DCam) was built by Logistic Technology Company (LTC, Hsinchu, Taiwan), and was proven to have a high standard of accuracy due to the objective and comprehensive ways of measuring the human body surface. The standard procedure of measuring required the subject to remove all outer clothes except for underwear in preparation for scanning (women with bras in addition to pants) and to stand still on the stage for scanning (a total scanning time is about 10 s) [19]. The software system collected, realigned, constructed, and measured a subject’s whole-body digital stature and selected information. The measurement error of the 3D scanner in measuring the human body surface was checked; the error in the x- and y-axis was approximately 1 mm ($1.2\%$), and in the z-axis it was less than 0.1 mm ($0.2\%$) [20]. ## 2.3. Data Collection Upon recruitment, a questionnaire was given to acquire information on the following: date of birth; sex; occupation; education; marital status; history of cigarette smoking, alcohol drinking, and betel nut chewing; personal medical history (including hypertension, diabetes, heart disease, chronic kidney disease, liver cirrhosis, and chronic hepatitis). A medical chart review confirmed the answers provided. For those with no history of diabetes, a fasting blood glucose level was obtained. Diabetes was defined according to American Diabetes Association guidelines. For those without a history of hypertension, blood pressure was measured with a mercury sphygmomanometer on the left arm after the patient had been resting for 20 min in a seated position. Hypertension was defined according to the 2017 Hypertension Clinical Practice Guidelines (systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or the use of antihypertensive medication) [21]. ## 2.4. Laboratory Analysis Venous blood was sampled overnight. Assays for high-sensitivity C-reactive protein were carried out in the Department of Laboratory Medicine, Keelung Chang Gung Memorial Hospital. Biomarkers including IL6, IL8, leptin, adiponectin, and transforming growth factor beta-1 were measured using commercially available enzyme-linked immunosorbent assays (Boster Biological Technology, Pleasanton, CA, USA). ## 2.5. Statistics Two independent sample t-tests were used to compare differences between the continuous variables of the groups, and results were presented as the mean ± standard deviation (SD). The χ2 test was used to differentiate between the distribution of categorical variables, and results were expressed using frequencies and percentages between the groups. The 3D body surface measurements were screened using a two-sample t-test by comparing differences between CAD patients and controls. To avoid collinearity in the regression analysis, one body measurement with the lowest p value was selected from each anatomic dimension for subsequent multivariable analysis. A logistic regression model was used to determine the strength of the association between the selected body measurements and the presence of CAD. In addition to the forced-in sociodemographic variables, a backward model selection with $p \leq 0.1$ was used to determine variables, including lifestyle variables, to be retained in the regression model. The modulating effect was examined by comparing models with and without biomarkers while calculating the strength of association (odds ratio [OR]) between the body measurement combinations and CAD. In order to find associations with CAD in individual patients, biochemical and body shape variables that significantly differed between the non-CAD and CAD groups were further analyzed. This was done by calculating optimal cutoff values for continuous variables using a receiver operating characteristic (ROC) analysis. The statistical software used for the analyses in this study was SPSS 25.0 (IBM Corporation, Armonk, NY, USA). ## 3. Results A total of 98 patients were recruited into each of the CAD group and control group over a period between March 2016 and December 2017. Baseline characteristics for the study participants are shown in Table 1. Both groups were matched for age and sex, with $76.53\%$ of patients being male and $72.45\%$ equal to or greater than 50 years of age in both CAD and control groups. More patients in the CAD group had a lower educational level ($73.47\%$ vs. $37.76\%$, $$p \leq 0.001$$). Among lifestyle variables, the CAD group had more smokers ($56.12\%$ vs. $36.73\%$, $$p \leq 0.0099$$)), more patients that did not consume coffee ($54.08\%$ vs. $35.71\%$, $$p \leq 0.0097$$), and more that did not exercise ($52.04\%$ vs. $37.76\%$, $$p \leq 0.044$$). As regards to risk factors, more patients in the CAD group had hypertension ($50\%$ vs. $1.02\%$, $p \leq 0.0001$) and diabetes ($36.73\%$ vs. $3.06\%$, $p \leq 0.0001$), and fewer patients in the CAD group had hyperlipidemia ($20.41\%$ vs. $39.8\%$, $$p \leq 0.0031$$). Various biomarkers showed some differences between the CAD and control groups. ( Table 2) Levels of HsCRP, IL6, IL8, and leptin were higher and adiponectin lower in the CAD group. The 3D body surface scanning measurement results are shown in Table 3. The majority of measurements showed significant difference between the two groups, mainly with the CAD group having larger body measurements than controls. Associations between body measurements and biomarkers are presented in Table 4. ## Multiple Logistic Regression and ROC Analysis The associations between different body measurements and biomarkers on the occurrence of CAD were further assessed using a multiple logistic regression model adjusted for sex, age, education, exercise, smoking, alcohol drinking and coffee consumption, hypertension, diabetes, and hyperlipidemia. It was found that CW (OR 0.761, $95\%$ CI = 0.586–0.987, $$p \leq 0.0399$$), RAL (OR 0.743, $95\%$ CI = 0.632–0.875, $$p \leq 0.0004$$), WC (OR 1.119, $95\%$ CI = 1.035–1.21, $$p \leq 0.0048$$), leptin (OR 1.443, $95\%$ CI = 1.184–1.76, $$p \leq 0.0003$$), adiponectin (OR 0.978, $95\%$ CI = 0.963–0.994, $$p \leq 0.006$$), and IL6 (OR 1.181, $95\%$ CI = 1.021–1.366, $$p \leq 0.0254$$ were significantly associated with CAD (Table 5). The biomarker score, body measurement score, biomarker and body measurement score ware calculated based on the estimated values generated by the Table 5 model, and the scores were adjusted by risk factors. Receiver operating characteristic (ROC) curve analyses were adopted to estimate the predictive values of biomarker score, body measurement score and biomarkers combined with body measurements score for the occurrence of CAD. It was found that the combination of biomarker scores and body measurement scores had the greatest area under the curve and best association with CAD as shown in Figure 1 and Table 6. ( Area under the curve of 0.8056, $95\%$ CI = 0.7450–0.8662, $p \leq 0.0001$). ## 4. Discussion Our study results show that lower educational level, no coffee consumption, physical inactivity, low adiponectin, high leptin, and high IL6 levels were associated with CAD. In terms of 3D body measurements, compared to the traditional BMI assessment, smaller CW and RAL with higher WC were also associated with CAD. In addition, the combination of biomarker scores and body measurement scores had the highest predictive value for CAD as shown with ROC analysis. These findings have given us a novel method for assessing the risk of those who may have CAD. Inflammation contributes to CAD, among which IL6 plays an important role in atherogenesis and atherosclerotic plaque destabilization. IL6 is associated with vascular endothelial injury and tissue fibrosis, promotes angiogenesis, and increases vascular permeability [22]. Once IL6 levels are abnormally elevated, a series of pathological changes occurs including inflammatory injury, plaque formation and rupture, and thrombosis. Chronic exposure to IL6 also disturbs insulin action and body fat. Yet, despite having proinflammatory properties, IL6 also plays an important role in anti-inflammation. Enhanced fat oxidation occurs when IL6 is increased acutely, leading to improved insulin-stimulated glucose uptake with anti-inflammatory effects. With chronic secretion under obese conditions, these effects are not seen, probably due to the development of IL6 resistance [23]. In our study, it was found that IL6 was associated with CAD. Adipose tissue is associated with CAD, abdominal adiposity causes development of adipose cells that are enlarged and dysfunctional [24]. These dysfunctional adipose tissues secrete pro-inflammatory biomarkers including prostaglandins, C-reactive protein, and cytokines such as interleukins and leptin with a decrease in adiponectin levels [25,26]. Leptin can cause vascular smooth muscle hypertrophy and oxidative stress, and stimulates vascular inflammation which may then lead to the development of type 2 diabetes mellitus, hypertension, atherosclerosis, and CAD [27]. Some studies have shown that increased leptin levels in plasma are associated with adverse outcomes in heart failure and CAD [28]. In CAD patients, higher serum leptin levels were significantly related to an increasing number of stenotic coronary arteries and arterial stiffness [29]. Another adipokine, adiponectin, also has important effects on the cardiovascular system. Its levels are negatively correlated with metabolic and cardiovascular disorders [30], with low levels having been shown to be an independent risk factor for cardiovascular disease [31,32]. In contrast to leptin, adiponectin levels are directly correlated with insulin sensitivity and inversely correlated with adiposity [33,34,35]. Certainly, as shown in our study population, the above mentioned adipokines were found to be associated with CAD. By using a more accurate 3D body scanning method, we found that higher WC, lower CW and lower RAL were also associated with CAD. WC, which reflects abdominal obesity, has been suggested to be superior to BMI for CAD risk prediction [36], and this was similarly seen in our study. In addition to the important role it has in CAD, leptin has also been found to affect bone metabolism via both direct and indirect mechanisms [37]. Studies have shown that leptin resistance or insulin resistance as found in obesity may lead to poorer bone health [38,39]. Increased adiposity can also lead to decreased bone mass, affecting cortical bone more than trabecular bone [40,41]. These mechanisms may help explain the findings of shorter RAL associated with CAD in our study. Interestingly, CW was associated with CAD in our population. The thoracic cavity, when intact and closed, constrains the heart and lungs to a limited space, such that intrathoracic pressure changes throughout respiratory phases can have varying effects on cardiac function. Thus, one with a smaller chest width may have impaired pulmonary function or motion capacity of organs in the chest in addition to limitation of circulation flow rates. It has been shown that small whole heart volume predicts cardiovascular events in patients with stable chest pain [42]. During normal breathing, chest wall motion is determined by the displacement from respiration and the displacement by heart activity. There has been an interest in how chest wall motion provides information on the cardiorespiratory system with the design of different chest wall models [43]. A smaller CW may therefore also be an indicator that there is restricted cardiopulmonary displacement from cardiovascular impairment. Dynamic lung and chest wall compliance can be measured by the pressure–volume curve [44]. In fact, it has been documented that abdominal obesity preferentially depresses chest wall compliance resulting in a marked decrease in functional residual capacity and expiratory reserve volume [45]. This may very well explain the link between the high WC and lower CW we see associated with CAD in our population. Faced with CAD being such an important cause of death worldwide, we sought to explore its associations with the more accurate method of 3D-derived body measurements and biomarkers in an attempt to gain more mechanistic insight. ## Limitations As our study design was of a cross-sectional study, we are unable to infer causality from the results. The 3D body measurements were performed only at one point in time with no repeated estimations or data on changes over time. Our study population was of Chinese adults in a hospital setting so the results might not be applied to other ethnicities, age groups, or populations in the community. Therefore, it may be necessary to clarify these conclusions in further longitudinal studies and in a wider population. ## 5. Conclusions In our study, 3D anthropometrics provide incremental information regarding associations of body surface measurements with CAD. It has been shown that shorter RAL and CW, and longer WC measurements combined with lower adiponectin and higher leptin and IL6 levels were associated with CAD. 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--- title: 'Something to Snack on: Can Dietary Modulators Boost Mind and Body?' authors: - Mathilde C. C. Guillaumin - Boris Syarov - Denis Burdakov - Daria Peleg-Raibstein journal: Nutrients year: 2023 pmcid: PMC10056809 doi: 10.3390/nu15061356 license: CC BY 4.0 --- # Something to Snack on: Can Dietary Modulators Boost Mind and Body? ## Abstract The last decades have shown that maintaining a healthy and balanced diet can support brain integrity and functionality, while an inadequate diet can compromise it. However, still little is known about the effects and utility of so-called healthy snacks or drinks and their immediate short-term effects on cognition and physical performance. Here, we prepared dietary modulators comprising the essential macronutrients at different ratios and a controlled balanced dietary modulator. We assessed, in healthy adult mice, the short-term effects of these modulators when consumed shortly prior to tests with different cognitive and physical demands. A high-fat dietary modulator sustained increased motivation compared to a carbohydrate-rich dietary modulator ($$p \leq 0.041$$) which had a diminishing effect on motivation ($$p \leq 0.018$$). In contrast, a high-carbohydrate modulator had an initial beneficial effect on cognitive flexibility ($$p \leq 0.031$$). No apparent effects of any of the dietary modulators were observed on physical exercise. There is increasing public demand for acute cognitive and motor function enhancers that can improve mental and intellectual performance in daily life, such as in the workplace, studies, or sports activities. Our findings suggest such enhancers should be tailored to the cognitive demand of the task undertaken, as different dietary modulators will have distinct effects when consumed shortly prior to the task. ## 1. Introduction Food is classically perceived as a means to provide energy and building material to the body. In the past decades, it has become increasingly recognized that food has the ability to prevent and protect against various diseases [1]. This accumulated knowledge demonstrates that food can have a broad impact (positive as well as negative) on a range of molecular systems supporting neuronal function and plasticity. Until now, pharmacological cognitive enhancers have been used primarily to treat patients with cognitive difficulties, such as Alzheimer’s disease, Parkinson’s disease, and attention-deficit hyperactivity disorder [2]. However, interventions that aim to improve cognitive performance beyond what is necessary to sustain good health are also of interest to the general public. Although many recent studies emphasize the important effects of food on the brain, further work is necessary to determine causal links to cognitive performance and, crucially, the time windows when incorporating specific food components into the diet can improve cognition. During the last two decades [3], there were significant advances in elucidating the role of diet in improving cognitive and mental performance beyond merely exercise and physical performance. Historically, the targeted consumers were mainly athletes and bodybuilders, and the functional foods were sports nutrition products [4]. With the increase in consumer health consciousness, the interest in these functional foods expanded to recreational and lifestyle users [5]. Due to increased consumer interest in these foods and beverages, nutrition research shifted its direction to identify nutrients that support energy metabolism, improve overall performance, and increase muscle mass as well as physical performance [6,7]. This led to the development of functional foods and drinks that are sold in every gas station, supermarket, kiosk, and drug store. These products are advertised as a help, for example, to support exercise load, reduce exercise fatigue, or improve recovery following an intense workout. Due to the growing popularity of these functional foods, there is now increasing interest and demand in shifting their development towards the enhancement of behavioral and psychological functions. The next step will be to design targeted foods/beverages (from now on referred to as “dietary modulators”), which are tailored to the type of cognitive task, subjective mental state, gender, age group, and sleep-wake state. In principle, dietary modulators for behavioral and psychological responses could be directly identified, but the complexity of these responses makes this identification far from simple. There is also a clear need to distinguish between short-term postprandial responses to food ingestion and long-term effects of dietary adjustment, which requires different research methodologies. Therefore, research related to the cognitive and motivational effects of dietary supplements needs sensitive psychobiological assays. Thus, the aim of this study was to identify the short-term effects of the consumption of a certain dietary modulator—in the form of a snack—on specific cognitive functions, such as learning, memory, cognitive flexibility, motivation, and motor function. We tested four different dietary modulators: a control (balanced macronutrient composition), carbohydrate-, protein- or fat-rich snack. These modulators were similar in volume and sensory properties (taste, consistency, and color) and were given 20 min prior to testing. For cognitive evaluations, we employed automated, touchscreen-based operant chambers which allowed us to assess multiple cognitive domains within the same testing environment [8]. We chose a battery of tests comprising highly translatable paradigms to assess a range of cognitive abilities. This included a visual discrimination task to estimate cognitive flexibility, a progressive ratio task to evaluate motivation, and voluntary physical activity (wheel running) to measure motor function. The selected tests represent single components that form part of more complex skills and abilities (i.e., driving a car, ability to operate machinery, writing a test, etc.). Testing was performed within the same animals following different dietary modulators. ## 2.1. Subjects A total of 9 C57BL/6J mice were kept on standard chow (3430 Kliba Nafag, Kaiseraugst, Switzerland) and water ad libitum and on a reversed 12 h/12 h light/dark cycle. Experiments were performed during the dark phase. Adult mice (at least 8-week old), all group-housed, were used for the experiments. Prior to starting behavioral testing, water bottles were switched to $2\%$ citric acid water. This water regime was shown to have only subtle impacts on willingness to perform behavioral tasks utilizing plain water as a reward [9,10,11]. ## 2.2. Feeding Schedule and Preparation of Dietary Modulators The protein and carbohydrate dietary modulators were adapted from [12], the fat modulator was adapted from Kliba $60\%$ high-fat diet (2127 Kliba Nafag, Kaiseraugst, Switzerland), and the control diet replicated the normal laboratory chow (3430 Kliba Nafag, Kaiseraugst, Switzerland). All four dietary modulators were matched for micronutrient composition, and the ingredients used were kept the same with only proportions varying to account for the different macronutrient levels of each modulator. Modulators were prepared in-house and were designed in accordance with the information on the EFSA and FDA websites regarding the definition of high-fat, high-protein, and high-carbohydrate diets. Mice received the exact amount of food calculated for each dietary modulator so that the caloric intake for all of them was kept strictly the same. All animals were exposed to each modulator, but the order in which they were exposed to the different modulators was randomly assigned (using https://www.randomizer.org, accessed on 1 September 2020). This means that for each behavioral task, each mouse underwent four blocks of testing (one block per modulator). A block consisted of at least 5 days, during which the mice received the modulator once daily prior to the behavioral task to be performed on that day (mice had access to normal chow in their home cage during the rest of the day). Between the different dietary modulators, there was a two-day wash-out period during which all mice were only given their normal laboratory chow (i.e., no additional snack on those two days). This was necessary to rule out any potential transfer between the different dietary modulators consumed. All dietary modulators had similar taste, smell, and texture. ## 2.3. Compositions Proteins, carbohydrates, and fats were the three main macronutrient components in each dietary modulator, as some of the largest differences in one’s diet derive from changes in those three macronutrients. Consequently, the three modulators were prepared as follows: high-fat (‘Fat’) with $60\%$ of calories coming from fat, high-protein (‘Protein’) with $50\%$ of calories coming from proteins, high-carbohydrate (‘Carbohydrate’) with $80\%$ of calories coming from carbohydrates, and a control modulator (‘CTR’) (Table 1). The CTR modulator was prepared, mimicking the macronutrient composition of the normal laboratory chow diet (3430 Kliba Nafag, Kaiseraugst, Switzerland) to which mice had access in their home cage. All dietary modulator components were precisely measured, and the samples were kept in the freezer for the duration of the experiments. Before each meal, the modulators were taken out and let to come back to room temperature before being given to the animals. ## 2.4. Amounts All mice were habituated in their home cage to the dietary modulators prior to the start of the experiments with a small amount of the food given for 2 consecutive days. Food was fully eaten after each day. For dietary modulator consumption prior to each experiment, animals were placed in a feeding station where each mouse had its own serving plate with the exact amount of dietary modulator. All animals were habituated for 3 days to this setup prior to the start of experiments to reduce neophobia and novelty-related stress. For each task (VD/rVD, PR, RW), 20 min prior to the daily experiment, the dietary modulator (0.425 g ± 0.075 g containing 1.015 kcal) was given in individual feeding stations, and animals were allowed to eat the food for 10 min, after which, animals were placed back into their home cages until the beginning of the session. Due to the randomization of the order in which mice received the modulator, on a given day, all mice did not receive the same modulator. ## 2.5. Cognitive Assays We employed the touchscreen testing system (Campden Instruments Ltd., Loughborough, Leicestershire, UK), as it has several advantages over a classical operant testing apparatus, such as a high translational potential, similarity to psychological and cognitive tests used in human subjects, and lower stress levels for the animals. The cognitive assays we were interested in to test specific psychological domains all employed the same types of responses, reward (tap water), and stimuli (adapted from [13]). Eight mouse touchscreen chambers (Campden Instruments Ltd., Loughborough, Leicestershire, UK) were used for the visual discrimination (VD), fixed ratio (FR), and progressive ratio (PR) tasks. The behavioral programs were controlled by the ABET II Touch software (version 21.02.15.0, Campden Instruments Ltd., Loughborough, Leicestershire, UK) and the Whisker Server (version 4.7.7, Cambridge University Technical Services Ltd., Cambridge, UK, [14]). All animals were habituated to the chambers for 20 min over 2 consecutive days before testing commenced. For the VD/reverse VD and FR/PR tasks, an individual criterion was used so that immediately after reaching the acquisition phase criterion, a mouse was moved on to the next phase to avoid overtraining. In practice, this means that instead of waiting until all animals reached criterion before proceeding to the primary task/test phase (which would have meant that some animals would be overtrained and have obtained more rewards), animals moved on to the next phase at their own pace. This individual criterion approach allowed us to avoid carry-over effects. ## 2.6. Visual Discrimination (VD) Task The visual discrimination (VD) cognitive task (evaluating learning, cognitive flexibility, attention, and working memory) is based on the capability of a mouse to distinguish between two visual stimuli where one stimulus is reinforced (S+) and the second stimulus is not (S−). The S+ and S− were counterbalanced between animals. ## 2.6.1. Training Phase This phase was performed before the start of the dietary modulator schedule. Habituation—Animals were introduced to the touchscreen chambers over 2 days with each session lasting 30 min or 50 trials (whichever came first) and were given a reward (tap water) every 5 s. Initial touch—This stage lasted for 3 days with each session lasting either 30 min or until completion of 100 trials. Mice were given a reward every 30 s and were given the choice to touch a white square on the touchscreen, which resulted in receiving a reward 3 times larger. Must touch—This stage lasted for 3 days with each session lasting either 30 min or completion of 100 trials. Here, mice had to actively poke the screen in order to receive a reward. Must initiate—This stage lasted for 3 days with each session being either 60 min or completion of 100 trials. Here, mice needed to start each trial from the same position to avoid a location bias. The must-initiate step was activated by breaking the infrared beam in the food magazine, and the initiation was coupled with a sound so that the mice did not confuse it with reward collection. Punish incorrect stage—This stage lasted for 3 days with each session being either 30 min or completion of 100 trials. Incorrect responses resulted in a time-out period of 10 s which was coupled with turning on the house light during the first 5 s. Once that time had passed, mice were allowed to initiate a new trial. ## 2.6.2. Initial Acquisition Phase (VD/Reverse VD in the Absence of Dietary Modulators) The mouse needed to distinguish between two visual stimuli where one stimulus was always reinforced (S+), and the second stimulus was never reinforced (S−). The S+ and S− were counterbalanced between animals. Animals needed to successfully reach criterion which was to achieve $80\%$ correct responses. Once they achieved this stage, the contingency was reversed (the stimulus associated with reward became S− and vice-versa) until mice reached the criterion of $80\%$ correct trials again. Correction trials were introduced during the training and differed from ‘normal’ trials in that following an error (mouse pressed the S− stimulus), the previous stimulus was repeated (and not reassigned randomly) in the same location until the correct stimulus/location was selected. This step was important to prevent an animal from selecting one location by chance and being rewarded on $50\%$ of the trials. ## 2.6.3. VD and rVD under Dietary Modulators Following the conclusion of the acquisition phase, all animals were trained on a series of VD and reverse VD learning experiments using new visual stimuli and exposing mice each time to a different dietary modulator. Similar to the acquisition phase, the criterion to move on from VD to rVD was to reach an $80\%$ success rate within 20 days of training. If a mouse could not reach criterion by the 20th day, data from all 20 days were taken and analyzed. Once a mouse had achieved the $80\%$ correct responses for both VD and rVD phases, they waited until all mice reached completion, or the 20 days had passed. For each dietary modulator, a novel pair of stimuli was introduced. ## 2.7. Progressive Ratio (PR) Task The first phase of the task required an invariant number of responses (pokes) for a fixed quantity of reinforcer, i.e., a fixed ratio (FR) schedule. Later, a progressive ratio (PR) schedule was introduced where the response requirement incremented with each reinforcer earned. The PR schedule allows for measurement of reward strength [15], but it is also sensitive to detect reward magnitude, palatability, as well as reinforcer state [16,17]. Therefore, we employed the PR task to assess the motivational state of the mice in response to the different dietary modulators. ## 2.7.1. Training Phase The first four stages (habituation, initial touch, must touch, and must initiate) were the same as described for the VD task. ## 2.7.2. Fixed Ratio Schedule (FR) Animals were trained under FR1, FR2, FR3, and FR5. The amount of reward (tap water) stayed the same for all FRs; only the number of pokes required to obtain it changed. FR1, FR2, and FR3 were carried out for one session each, whereas FR5 was carried out over three sessions. Following the completion of the FR schedules, all animals started the PR schedule. ## 2.7.3. Progressive Ratio Schedule (PR) The PR protocol consisted of progressive increments of the number of pokes required in order to obtain the reward. A PR of 4 was chosen for the task; i.e., 1, 5, 9, 13, 17, 20, and so on, pokes were successively required to obtain one reward. Each mouse was given 5 days on each dietary modulator. After those 5 days, mice were kept with their normal diet with no addition of a modulator for 2 days before being introduced to the next dietary modulator. This served as a wash-out period and was important to eliminate residual effects of the previous modulator. The breakpoint was recorded after 90 min or if a mouse did not have any input (FM visit or screen poke) for 5 min. The breakpoint was defined as the last (largest) number of pokes necessary for the last reward received for each animal. This phase of the experiment was terminated after four weeks (4 dietary modulators). ## 2.8. Exercise Running Wheel Activity Mice were given access to a low-profile running wheel (ENV-047, MED Associates/OpCoBe) for 2 h per day over 5 days. Before each session, mice received a dietary modulator (same dietary modulator over the 5 days) in the same manner as in the FR/PR and VD/rVD tasks. There was a 2-day wash-out period before this 5-day protocol was repeated with another dietary modulator. All mice, therefore, performed this protocol 4 times (once for each modulator). Running activity was recorded wirelessly using a USB hub (DIG-807, Med Associates/OpCoBe) itself connected to the Running Wheel Manager Data Acquisition software (version 2.03, MED Associates, Fairfax, VT, USA) with the amount of running recorded in 1 min bins. Wheels were placed in standard IVC cages; mice were moved to these cages for their daily running session before being moved back to their own home cage at the end of each session. Mice had been habituated to the low-profile running wheels before the start of the protocol to ensure familiarity with running on these wheels. ## 2.9. Data Analysis Statistical analysis was conducted using SPSS (version 28.0.0.0, SPSS Inc., Chicago, IL, USA) with significance set at p ≤ 0.05. For the visual discrimination and/or reversal tasks, the measures include trials completed, response accuracy (% correct trials), number of correction trials, session duration, response and reward latencies, response omission, and the number of sessions required on each training stage to reach criteria. The effects of dietary modulators on these measures were examined by independent or paired t-tests, ANOVA, or repeated measures (RM) ANOVA, where appropriate, with consideration for multiple comparisons (Bonferroni correction if more than two modulators are compared). For all datasets, the z-score method was used to detect outliers (none were detected). ## 3.1. Visual Discrimination (VD) Task Here, we tested the acute effects of different dietary modulators on cognitive flexibility, which is a measure of higher cognitive abilities [18]. This type of behavioral flexibility can be studied experimentally by employing reversal learning tasks when subjects are required to adapt their responses to reversed reward contingencies. We were interested to identify whether short-term effects of certain dietary modulators would provide improvements for one phenomenon and not exert deleterious effects on others, as executive functions subserve the selection and processing of information necessary to plan, control, and direct behavior in a manner appropriate to changing environmental demands [19]. To evaluate the impact of the different dietary modulators on cognitive performance, we thus used a VD/reverse-VD (rVD) task (Figure 1a–c), allowing us to probe learning, cognitive flexibility, and working memory. Since most appetitive operant tests employ food as a reward, which, in our case, would confound assessing the effect of acute consumption of our dietary modulators, we used water as a reward instead. To avoid variation in the initial performance level during the acquisition of visual discrimination, each animal was trained to reach similar performance levels at the end of the training phase (performance criterion). This is important to assess alterations in performance in the reversal learning task due to the intake of the dietary modulator. When analyzing the accuracy of response, we found that the percentage of correct trials over sessions was not influenced by dietary modulators in the VD phase (Figure 1d). Additionally, the number of sessions needed to reach the performance criterion under each dietary modulator was used as a general measure of learning. We found a clear learning effect indicated by the main effect of the session for the percentage of correct trials (VD: F[11,88] = 131.44, $p \leq 0.001$, Figure 1d). The number of sessions to reach the criterion in the VD phase showed that under the carbohydrate modulator, animals seemed to need more sessions to reach the criterion, which would indicate difficulty in learning, but this did not attain significance (F[3,24] = 0.460, $$p \leq 0.750$$, Figure 1e). During the reversal learning phase (Figure 1f), the carbohydrate modulator led to increased performance during the first two sessions compared to the control (CTR) dietary modulator, which was made evident by a significant main effect of the dietary modulator when comparing those two modulators over the first two sessions (F[1,8] = 6.829, $$p \leq 0.031$$) in addition to the main effect of the session (F[1,8] = 10.413, $$p \leq 0.012$$). Pairwise comparisons showed a significantly higher percentage of correct trials for the carbohydrate vs. CTR modulator during the second session ($$p \leq 0.048$$; Figure 1f), indicating an initial beneficial impact of the carbohydrate modulator, leading to an initial increased performance that subsequently subsides. When further analyzing the general performance during reversal learning, the number of sessions needed to reach the performance criterion was highly similar across dietary modulators (F[3,24] = 0.021, $$p \leq 0.996$$, Figure 1g), although there was here again a clear learning effect with a significant main effect of the session (rVD: F[12,96] = 187.86, $p \leq 0.001$, Figure 1f). We further analyzed the differences in learning rates between acquisition and reversal phases across sessions (Figure 1h). RM ANOVA revealed a general performance difference between acquisition and reversal learning phases where all dietary modulators showed an increased number of sessions to reach the criterion during the reversal VD phase compared to the VD phase (F[1,8] = 32.40, $p \leq 0.001$). Pairwise comparisons indicated a significant difference in learning performance in CTR VD vs. rVD ($$p \leq 0.045$$) and protein VD vs. rVD ($$p \leq 0.05$$). These findings may indicate a cognitive flexibility deficit in the CTR and protein modulators. The fat and carbohydrate modulators were able to inhibit the prepotent response and instead allowed animals to learn the new contingency at a similar rate to the initial visual discrimination learning. ## 3.2. Progressive Ratio Schedule (PR) We next evaluated motivation to work for a reward—and how this can be enhanced or impaired by a dietary modulator—using the PR task (Figure 2a). The number of pokes needed to receive one reward was incremented by four after each reward collection during the PR phase of the task (see Methods, Figure 2b). To evaluate how the performance of the mice on the PR schedule was influenced by dietary modulators, we plotted the pattern of pokes under this schedule as a cumulative distribution curve (Figure 2c). An RM ANOVA showed a significant main effect of the dietary modulator (F[3,24] = 2.938, $$p \leq 0.05$$). Furthermore, pair-wise comparisons revealed that the carbohydrate modulator significantly produced lower poke numbers compared to the control dietary modulator ($$p \leq 0.025$$). Additionally, the breakpoint is traditionally used as an indicator of motivation; an RM ANOVA of the mean breakpoint over five PR sessions (Figure 3d) indicated a significant main effect of the dietary modulator (F[3,24] = 2.938, $$p \leq 0.05$$). Pair-wise comparisons revealed a significant difference between the CTR and carbohydrate modulators ($$p \leq 0.018$$), indicating that the carbohydrate modulator significantly reduced motivation to work for a reward in this task. Such a negative effect was not observed for the protein and fat modulators. In addition, our data revealed a significant difference between fat and carbohydrates, whereby the carbohydrate modulator significantly reduced motivation compared to the fat modulator, as shown by the lower cumulative number of pokes ($$p \leq 0.041$$, Figure 2c). ## 3.3. Exercise Running Wheel Activity Finally, to determine if different dietary modulators could alter physical performance, we recorded acute wheel running (Figure 3a,b) and analyzed running across the 5 2 h sessions completed for each modulator (see Methods, Figure 3c), looking at the mean running wheel activity (wheel revolutions) across 5 days (Figure 3d) as well as cumulative running across days (Figure 3e). We found that the different dietary modulators did not differentially influence the amount of wheel running either on a daily basis (Figure 3d) or if cumulating across all the sessions completed under a given modulator (Figure 3e). Additionally, one could hypothesize that the short-term effect of a given dietary modulator could alter the running activity only at the beginning (e.g., acute ‘boosting’ effect) or at the end (e.g., improves endurance) of a running session. However, we did not observe any significant effect for any of the modulators compared to the CTR (Figure 3c). It is interesting to note that the high-protein modulator yielded running levels that were always on the higher side for all three measures (Figure 3c–e), although this was not statistically significant. ## 4. Discussion It is known that chronic or constant inadequate intake of macronutrients, such as carbohydrates, proteins, and fats, can compromise the optimal function of the human body. Accumulating evidence suggests that a diet balanced in macronutrient composition can have an important role to prevent diseases [20,21] and can also affect psychological and mental states [22,23,24,25]. However, the acute effects of a meal or snack with a specific macronutrient composition on performance are still largely unresolved, especially in the general population (beyond athletes). Yet, there is an increasing demand for acute cognitive and motor function enhancers to improve both mental/intellectual and physical activity demands in daily life, such as in work environments, during studying, or sports activities. Therefore, here, we were interested to assess how specific macronutrient compositions in the form of a dietary modulator consumed prior to cognitive testing or exercise could acutely affect cognitive and motor performances in healthy adult mice. A high-fat dietary modulator was found to induce increased motivation compared to a carbohydrate-rich dietary modulator, which had the opposite effect. However, the high-carbohydrate modulator gave an initial advantage in a cognitive flexibility task compared to the other modulators. None of the tested dietary modulators had any effect on learning in the initial visual discrimination phase. Finally, no apparent effects of the different dietary modulators were observed on physical exercise despite a trend for a positive influence on running, which was observed following a protein-rich dietary modulator. Many rodent tests are fundamentally different from assessments used in human neuropsychological testing, which may be one of the reasons why preclinical findings often fail to translate to the clinical condition [26,27,28]. Therefore, in the present set of experiments, we used touchscreen operant boxes, which can assess complex cognitive abilities, such as discrimination learning, reversal learning, and motivation, similarly to human computer-based neuropsychological assessment tests [29,30,31]. Cognitive abilities can be often difficult to compare between species; however, an organism’s ability to adapt its behavioral repertoire to changing situations and environments is a translatable feature of cognition [32]. The visual discrimination learning task involves two processes: learning to discriminate between two stimuli and learning which of the two leads to a reward. In the initial visual discrimination acquisition performance phase, we did not observe any differences in learning between the different modulators. All animals were able to learn and perform the task properly and did not show any motivational decline. In reversal learning, the stimulus–reward contingency acquired during the initial discrimination phase is reversed, and animals need to inhibit the prepotent response to the previously correct stimulus and learn the new stimulus–reward contingency. Here, we detected initially enhanced learning in the carbohydrate group where animals performed significantly better across the first two reversal sessions and then evolved at a similar pace in the subsequent reversal sessions compared to the control modulator. A comparable behavioral profile was apparent when comparing the learning performance of each dietary modulator between the visual discrimination and reversal learning phases where animals needed more sessions to reach the performance criterion in the reversal phase; however, this difference was significant only in the control and protein modulator groups. This can indicate a general cognitive flexibility deficit in those two subgroups or enhanced flexibility with high-fat or high-carbohydrate modulators. In summary, because the visual discrimination phase performance did not differ between the different dietary modulators, the observed learning difference during the reversal phase cannot be attributed to impaired visual discrimination but is rather a consequence of deficits in cognitive flexibility in response to the control and protein modulators. In addition, the initial beneficial effect of the carbohydrate modulator on cognitive flexibility can suggest a possible role for carbohydrate consumption in early learning when the reversal was new but not in late learning when reversal learning was already familiar. This might be explained by the fact that ingestion of carbohydrate-rich foods produces only a short-lasting blood glucose spike since glucose is rapidly counter-regulated in the body and provides a ready source of energy. Studies in humans suggested that increased neural activity (e.g., the learning of a motor task and verbal working memory) was linked with increased use of glucose by the brain [33,34], which is consistent with the notion that cognitively demanding situations can deplete the brain of glucose [35,36]. When testing the acute and immediate effects of the different dietary modulators on motivation—again, utilizing water as a reward in the progressive ratio paradigm—the behavioral performance of all animals (as measured by a breakpoint) significantly increased during sessions (Figure 2c). However, intake of the carbohydrate modulator prior to testing significantly affected task performance with a decreased number of pokes and a decrease in average breakpoint (Figure 2d). In contrast, consumption of the fat modulator prior to testing significantly increased motivation relative to carbohydrate modulator consumption (Figure 2c). Importantly, the fat-mediated elevation in task performance did not appear to be the result of nonspecific locomotor hyperactivity, as no significant effects of fat modulator consumption affected wheel revolutions on the running wheel. Interestingly, a modulator high in carbohydrates improved cognitive flexibility in the early stage of a reversal-learning task (Figure 1f) but proved deleterious for a motivational task. Studies in the past decade have shown that certain diets consumed chronically over a long time period, where adaptation to a diet is being examined, can influence and maintain mental function. For example, diets enriched in omega-3 fatty acids (i.e., essential fatty acids) can enhance cognitive processes in humans [37,38,39,40] and promote restoration of brain homeostasis following brain injury in animals [39,41] by upregulating genes that are important for maintaining synaptic function and plasticity. In contrast, foods with a high content of both sugars and fats are known to negatively interfere with molecular substrates of cognitive processing and increase the risk of neurological dysfunctions [42,43] and produce changes in the hippocampus and negatively impact memory and learning [44,45,46,47,48]. Even a short period of a high-fat diet (a few days) was found to reduce working and episodic memory in adult humans [49]. However, the exact fatty-acid composition and ratios of other nutrients matter, as ketogenic diets, which are by definition high in fat, can have significant beneficial effects on cognition and some brain conditions, in particular, epilepsy [50,51,52]. What all studies seem to agree on and suggest is that poor dietary habits over longer time periods can lead to diverse negative health implications, including cognitive and mood dysfunctions. Here, we found that an acute intake of the modulator high in fat increased motivation. Our findings go in line with previous studies, among which, a study performed on young men who were given a fat-rich breakfast was found to positively influence different cognitive functions, which were assessed with the aid of computer tests for short-time memory, reaction time, and attentiveness [12]. The absence of effect of the high-protein modulator may appear surprising, as some amino acids are precursors of various neurotransmitters and neuromodulators (e.g., tryptophan is a precursor for serotonin and melatonin; tyrosine for dopamine, noradrenaline, or adrenaline) [53,54]. Thus, tyrosine, as a precursor of dopamine and noradrenaline, is involved in the regulation of attention, arousal, and motivation [55,56]. However, several factors need to be borne in mind. Amino acids must cross the blood–brain barrier to affect brain function, and for this, they require specific transporters for which amino acids can be in competition against one another [54]. Therefore, the link between the amino acid content of a meal and the subsequent levels of neurotransmitters in the brain is not straightforward. For example, a meal rich in tryptophan will not necessarily lead to an increase in serotonin; in fact, it was shown that a protein-rich meal decreased serotonin concentration, while a meal with a low protein:carbohydrate ratio increased those levels (as remaining tryptophan in the bloodstream no longer competes with other amino acids which are taken up by the muscles in response to insulin) [54,57,58]. In the case of glutamate, which is a neurotransmitter itself, the literature has shown that dietary glutamate can have both positive and negative effects on brain physiology, including both neuroprotective and neurotoxic effects, and can have both potentiating or inhibiting effects on learning and memory (see [59] for a review). Therefore, the absence of effect of the high-protein modulator may appear less surprising, especially as the mice were given this diet as a small addition to their daily food intake which always consisted of normal laboratory chow present in the home cage. Additionally, it was shown that in mice, gastric emptying takes around 90 min and gastrointestinal emptying about 3 h [60], at which time the behavioral tasks were mostly finished (as they lasted a maximum of 2 h). Moreover, protein, fats, and carbohydrates have been shown to have different effects on the speed of gastric emptying while also having different rates of absorption into the bloodstream [61]. While sugars and carbohydrates have been shown to slow down gastric emptying [62], they may also have a faster rate of absorption (depending on the form); thus, it is not easy to link rates of absorption to a direct effect on cognition and behavior, as many other factors come into play. The absence of significant effects of any of the dietary modulators on physical activity may be surprising. However, two important factors need to be borne in mind. First, we were evaluating motor function effects in the context of ‘casual’ exercise and not on the physical performance of highly trained mice (or human athletes) for whom the effect of acute dietary intake pretraining or pre-competition sessions has been extensively evaluated [63]. Our data, therefore, suggest that the motivation to exercise was not altered and thus that a high-fat modulator had distinct effects on behaviors motivated by an external reward (e.g., screen touches in the PR task) versus internally rewarding behaviors, such as voluntary wheel running. Our results also imply that a high-carbohydrate, high-protein, or high-fat snack or drink prior to exercise will not significantly affect performance in the recreational sportsman or woman but that a protein-rich modulator could result in a slight improvement in motor performance. This could be because of positive effects on glucose metabolism, as shown in diabetic patients with a high-protein snack [64], although a chronic high-fat diet could also have been expected to result in reduced blood glucose variations [65]. Second, it is important to distinguish the data we presented here from the effects of a longer-term dietary pattern on physical performance, which was not the aim of our study. In summary, we were able to demonstrate that mouse cognitive assays using specific time windows of behavioral testing are sensitive enough to successfully detect the short-term effects of different macronutrient ‘snacks’ on cognitive flexibility and motivation. 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--- title: In-Vitro Evaluation of Photofunctionalized Implant Surfaces in a High-Glucose Microenvironment Simulating Diabetics authors: - Supriya Kheur - Mohit Kheur - Vaibhav Madiwal - Ramandeep Sandhu - Tabrez Lakha - Jyutika Rajwade - Tan Fırat Eyüboğlu - Mutlu Özcan journal: Journal of Functional Biomaterials year: 2023 pmcid: PMC10056823 doi: 10.3390/jfb14030130 license: CC BY 4.0 --- # In-Vitro Evaluation of Photofunctionalized Implant Surfaces in a High-Glucose Microenvironment Simulating Diabetics ## Abstract The present study aimed to assess the efficacy of photofunctionalization on commercially available dental implant surfaces in a high-glucose environment. Discs of three commercially available implant surfaces were selected with various nano- and microstructural alterations (Group 1—laser-etched implant surface, Group 2—titanium–zirconium alloy surface, Group 3—air-abraded, large grit, acid-etched surface). They were subjected to photo-functionalization through UV irradiation for 60 and 90 min. X-ray photoelectron spectroscopy (XPS) was used to analyze the implant surface chemical composition before and after photo-functionalization. The growth and bioactivity of MG63 osteoblasts in the presence of photofunctionalized discs was assessed in cell culture medium containing elevated glucose concentration. The normal osteoblast morphology and spreading behavior were assessed under fluorescence and phase-contrast microscope. MTT (3-(4,5 Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) and alizarin red assay were performed to assess the osteoblastic cell viability and mineralization efficiency. Following photofunctionalization, all three implant groups exhibited a reduced carbon content, conversion of Ti4+ to Ti3+, increased osteoblastic adhesion, viability, and increased mineralization. The best osteoblastic adhesion in the medium with increased glucose was seen in Group 3. Photofunctionalization altered the implant surface chemistry by reducing the surface carbon content, probably rendering the surfaces more hydrophilic and conducive for osteoblastic adherence and subsequent mineralization in high-glucose environment. ## 1. Introduction The success of implant integration and function depends upon good bone-to-implant contact (BIC). One of the most important factors for good osseointegration is the physical and chemical nature of the implant surface. The purpose of modifying the implant surfaces is to improve osseointegration. This process has been a subject of continued research focus. It enhances the biological surface properties of implants, which in turn, favors osseointegration. Implant surfaces have been modified using different mechanical and chemical strategies such as etching using acid, blasting through a grit and/or sandblasting, biomimetic coating, and anodizing [1,2,3,4]. Additional modalities include bioactive molecules, including growth factors (platelet-derived growth factor, bone morphogenetic protein, vascular endothelial growth factor), inorganic materials (HA, calcium phosphate), extracellular matrix components (collagen, chondroitin sulfate, hyaluronic acid), and peptides [1,2,3,4,5]. Additionally, treating the titanium implant surface with plasma or UV irradiation (photo-functionalization) before its use has also been investigated, where UV-A and UV-C wavelengths were reported to increase the hydrophilicity of the surface by removing the carbon from the titanium surface and converting Ti4+ to Ti3+, thereby inducing nanoscale surface modification [6,7]. Plasma treatment of implants improves initial osseointegration of implants by increasing the wettability and subsequently osteoblastic cell adhesion [7]. Research over the past decade has shown the ability of photo-functionalization to transform a hydrophobic implant surface into a super-hydrophilic surface capable of enhanced osteoconductivity, and data reporting photofunctionalization of dental implants ranges from in vitro, in vivo animal-based studies (rats, beagle dogs) to human clinical trials [8,9,10,11]. In rat models, titanium samples (machined and acid-etched) subjected to UV irradiation for 48 h presented a substantial increase in osteoblast proliferation, differentiation, spread, and attachment after UV treatment [2]. The histomorphometry showed the implants to exhibit significant bone formation, with a maximum bone–implant ratio achievement as early as in the 4th week of healing [2], which can increase from $45\%$ to $95\%$ [12]. Systemic conditions, including diabetes mellitus, osteoporosis following bisphosphonate treatment and radiotherapy, are known to retard the osseointegration of dental implants by affecting bone quality and quantity [13]. Diabetes mellitus is a major metabolic disorder with a well-established global prevalence that causes a state of hyperglycemia, marked by a decrease in bone formation markers, specifically bone-specific alkaline phosphatase (bALP) [14,15,16,17] and procollagen type 1N- terminal propeptide (PINP) and osteocalcin [17]. In vitro studies have shown that increased blood glucose levels (fasting plasma glucose > 126 mg/dL) alter osteoblast function, including proliferation, migration, and adherence [17,18]. Osseointegration and its maintenance require a coordinated function of osteoblasts and osteoclasts, which in turn is regulated by the action of osteocytes [18]. Increased glucose level also leads to altered osteocyte function. Animal experiments have shown significantly lower BIC in diabetes [19,20]. Clinically, the success rate of implants is not altered in type 2 diabetic patients, but stringent glucose control is the primary criteria for achieving predictable osseointegration [20]. Others have reported significantly greater marginal bone loss at implants placed in diabetic subjects [5,21,22]. The potential benefits of photofunctionalization of dental implants placed in diabetic patients are not well established. One study reported improved strength of osseointegration at photofunctionalized implants at weeks 2 and 4 in diabetic rats [23]. Photofunctionalization as a pre-placement procedure applied to commercially available implant surfaces may counter, to an extent, the impaired osseointegration process inherent to diabetes [18]. Thus, the present in vitro study was formulated to assess the effects of photofunctionalization of dental implant surfaces on the response of osteoblasts under conditions that simulate diabetes. ## 2. Material and Methods Three commercially available titanium implant surface discs were provided by the respective manufacturer. Three samples of each group ($$n = 3$$ for each group) were randomly assigned to one of these groups to eliminate bias. These were provided in the sterile packages by the manufacturer in the dimension of diameter 10 mm and thickness of 1 mm. The packages were only opened at the start of the experiments. The study groups were as follows: Group 1—laser-etched implant surface (Laser Lok, BioHorizons, Birmingham, AL, USA); Group 2—titanium–zirconium alloy surface (Roxolid, Straumann Implants AG, Basel, Switzerland); Group 3—air-abraded, acid-etched surface (SLA dental implants, Straumann Implants AG, Basel, Switzerland). Untreated implant surfaces for each group were taken as control surfaces for that group. All the experiments were performed in triplicate to eliminate bias. ## 2.1. Photofunctionalization Unit The specimens in all the groups were treated using a customized photofunctionalization unit (Lelesil Innovative systems, Thane, India) for 60 and 90 min. The light source used was a 125-watt and 250-watt medium-pressure mercury (Hg) lamp with dual lamp monitoring. The wavelength of the light used for UV-A was 345–400 nm and UV-C was 250–260 nm [24]. The whole assembly was mounted on a UV-protected safety cabinet with a water-cooling facility. ## 2.2. X-ray Photoelectron Spectroscopy (XPS) Based Implant Surface Analysis All three groups of implants were subjected to XPS before and after photofunctionalization to assess for changes in the surface chemical composition. The spot, analyzed at 7.5 mm, was the center of the implants, and the spot size was 300 μm. The analysis was carried out using a custom-built ambient-pressure XPS system from Prevac equipped with a Scienta monochromator (MX650) using an Al Kα anode (1486.6 eV). The energy of the photoelectrons was determined using a Scienta R3000HP differentially pumped analyzer [25]. ## 2.3. Cell Viability and Attachment Assay The viability of MG63 (osteoblasts) cells was assessed by 3-(4, 5-dimethylthiazol-2-yl)-2-5 diphenyl tetrazolium bromide (MTT) (Sigma, Bangalore, India) assay. The MG63 cell line was procured from the National Centre for Cell Science (NCCS), Pune, India. During assay, 2 × 104 cells per implant were seeded in a 24-well plate. The cells were incubated at 37 °C in a humidified atmosphere containing $5\%$ CO2 for 48 h. After 48 h, the media were removed, followed by the addition of freshly prepared MTT reagent (stock concentration 5 mg/mL), and incubation was continued for 4 h. Subsequently, reagent containing medium was removed, and 0.5 mL DMSO (Sisco Research Laboratories, Pvt. Ltd., Mumbai, India) was added to dissolve the insoluble formazan crystals. A multi-well plate reader (Synergy HT, Bio-Tek Instruments Inc., Winooski, Vermont, USA) was employed to measure the absorbance at 570 nm. The formula used to calculate the cell viability percentage is as follows:[1]%Cell viability=OD570nm of treated cells×100OD570nm of control Cell morphology was assessed using an inverted phase-contrast microscope (Carl Zeiss Inc., Gottingen, Germany). Spreading behavior and cytoskeleton arrangement of osteoblasts seeded onto photofunctionalized titanium surfaces were examined using confocal laser scanning microscopy. After 24 h of seeding, cells were fixed in $4\%$ paraformaldehyde and stained using fluorescent dye rhodamine–phalloidin (actin filament, red color; Molecular Probes, Eugene, OR, USA). The MG-63 osteoblasts were inoculated in DMEM medium containing high-glucose concentration ~25 mM, indicating diabetic conditions. Microscopic observations and cell viability were determined as described earlier. ## 2.4. Mineralization Assay Alizarin red S (ARS) staining is a well-documented method to assess the deposition of calcium. For this assay, untreated (control) and photo-functionalized implant surfaces were seeded with 2 × 104 cells in 24-well plates containing DMEM ($10\%$ FBS and $1\%$ antibiotic supplementation) and incubated for 21 days. Cells were incubated in a humidified atmosphere with replenishment of culture media periodically. Cultures on day 21 were washed with PBS. The monolayer was then fixed using $4\%$ (w/v) paraformaldehyde (Sigma–Aldrich) for 15 min at 37 °C. Distilled water was used to wash the monolayers twice. This was followed by addition of 40 mM ARS (1 mL) (pH 4.1) to each of the wells. By shaking gently, incubation of the plates was carried out for 20 min at 37 °C. After incubation, the excess dye was removed. Distilled water was employed to wash the well 5 times. Before extraction of the dye, the samples were stored at −20 °C. ARS was extracted from the monolayer by incubating in 0.5 mL $10\%$ cetyl pyridinium chloride (CPC) solution for one hour. Then, 200 µL aliquots was transferred to a 96-well plate prior, and absorbance was recorded at 405 nm. ## 2.5. Statistical Analysis Data obtained were compiled in an MS Office Excel Sheet (v 2010, Microsoft Redmond Campus, Redmond, WA, USA). Statistical package for social sciences (SPSS v21.0, IBM, Bangaluru, India) was used for the data analysis. Descriptive statistics were determined, and non-parametric tests were used for comparisons. Mann–Whitney U test was employed for 2-group comparison. Kruskal–Wallis was used for multiple-group comparison. In XPS analysis, as the data were descriptive, no statistical analysis was performed. Means of the values were taken for comparative analysis in the group at different time frames. $p \leq 0.05$ was considered statistically significant in all tests. ## 3.1. XPS Implant Surface Analysis The XPS survey revealed the presence of various elements such as carbon (C), oxygen (O), and titanium (Ti) as the major components, whereas aluminum (Al,) calcium (Ca) and chloride (Cl) were the minor components, with traces of Si and N. There was a dominance of Ti and O signals, which are indicative of the presence of a titanium oxide layer on the surface. The strong C signal on the untreated surface showed contamination due to adsorbed carbon containing organic molecules. The C1s was found to vary somewhat in both intensity and shape from sample to sample. The C1s peak is always dominated by the peak at a binding energy of 285 eV, which corresponds to hydrocarbon (C-H and C-C) bonded carbon. XPS analysis revealed a significant reduction of the C (carbon) specific peak after UV treatment of the surfaces. Post UV photofunctionalization, a reduction in the C1s peak in Group 1 was observed ($48\%$ and $63\%$ reduction after exposure for 60 and 90 min, respectively). The UV photofunctionalization showed a reduction in carbon content on the surface of all three groups, with results more prominent at the 90 min time interval (Table 1). Broad O1s core level spectra consisting of two distinct features with a low intensity hump at high BE energy were observed in all the groups. The spectra were formally deconvoluted. The peak at 530.1 eV can be associated with metal oxide, such as TiO or CaO. The metal oxide feature was observed as the major peak component for Groups 2 and 3 implant surfaces. The groups at other time frames showed a reduction from the baseline samples as received (Table 1). The XPS spectra showed an increase in the peaks of O 1 s after 60 and 90 min of UV photofunctionalization in all groups, but significant results were observed only after 90 min. The tests were conducted in triplicates for each assessment. Thus, with the sample size being small, the results were not statistically significant, although we can appreciate the alteration in the elements analyzed in the study group. In addition, the probing depth of XPS was 8–10 nm, and changes observed before and after UV irradiation demonstrated a change in the surface composition only. The changes in surface concentration of various elements before and after UV irradiation are summarized in Figure 1. The values shown in Table 1 indicate the signal density. ## 3.2. XPS Core-Level Spectra of C, O, and Ti in the Study Groups The chemical alteration of the titanium surface plays an important role in osseointegration. This is attributed to the conversion of the titanium (IV) to titanium (III) and formation of Ti-OH on the surface. High-resolution spectra of the Ti2p core level show two distinct peaks at 458.5 and 464.3 eV for Ti2p$\frac{3}{2}$ and Ti2p$\frac{1}{2}$, respectively, for the photofunctionalized group samples (Figure 2). A systematic deconvolution was carried out to evaluate the change in the percentage of various Ti species present on the surface of all the study group implant surfaces. The peak deconvolution confirms the presence of Ti (IV) species as a major peak component; however, Ti (III), Ti (II), and Ti [0] were also found as minor peak components. The ratio of various peaks differs in the different study groups. All three groups showed a decrease in the peak area of Ti (IV) species, which was followed by the corresponding enhancement of Ti (III), Ti (II), and Ti [0] peaks. The above observation confirms that the UV treatment of all the groups induced the reduction of Ti (IV) species. ( Figure 2) ## 3.3. Morphology and Spreading Behavior of Osteoblasts Using Phase-Contrast Microscopy Osteoblastic morphology was evident after actin filament staining using a fluorescent dye, for all the implant surface groups, after photofunctionalization (Figure 3). Cell morphology and growth were found to be the same in Group 1 implant surfaces in the presence of a photofunctionalized implant, indicating that the presence of high-glucose and UV-PF implant did not increase and improve cell attachment. In the case of Group 2 and 3 implant surfaces, it was observed that UV-PF improved initial cell attachment. After 24 h of incubation, cells attained their typical osteoblast morphology as compared to the untreated surface where cells were yet to attain their typical morphology. In addition, the number of cells attached to the photofunctionalized surfaces seemed to be more when compared to the control surface (Figure 4). ## 3.4. Cell Viability Assay for the Dental Implant Surfaces Cell viability was assessed at 48 h for the control and the test groups (UV-treated implant surfaces). The test group at 48 h of incubation showed statistically higher cell viability than the control group. Only the 90 min UV-treated implants from Groups 1 ($p \leq 0.05$) and 3 ($p \leq 0.05$) showed significantly higher cell attachment compared to their untreated respective group. ( Table 2) ## 3.5. Alizarin Red S Assay for the Dental Implant Surfaces Quantification of the ARS staining showed that the amount of ARS (calcium deposits) was more in the test group than in the control group after 21 days of incubation. There was no statistical difference in any group between control and 60 min UV application. Only in 90 min UV-treated Group 2 did implant surfaces show a statistically higher mineralization than the corresponding control samples. ( Figure 5). ## 4. Discussion The key findings of the present study revealed a significant effect of UV photofunctionalization on the reduction of carbon content in all the examined surfaces, greater cell attachment, and spreading of osteoblasts, along with more mineralization in a simulated diabetic microenvironment. Together, these findings support the potential of UV photofunctionalization treatment to benefit implant osseointegration in the compensated milieu of a diabetic state. Photofunctionalization increases the hydrophilicity of surfaces broadly by two mechanisms: conversion of Ti4+ to Ti3+, which leads to the formation of titanium hydroxide groups, thereby generating oxygen vacancies that react with absorbed water [26]. The second mechanism is the elimination of the accumulated carbonyl moieties, especially hydrocarbons, from the surface [2,24,27]. Titanium surfaces are generally covered by carbon-containing molecules, the concentrations of which tend to increase during surface preparation and storage. The amount of surface carbon is known to increase to approximately $60\%$ to $75\%$ over some time as part of the aging of the implant surface. Varying amounts of contributions to the C1s peak are often found at other higher binding energies (286–290 eV), which is due to the presence of other bond types in the surface layer such as C-O and C-OH bonds [28,29]. Once the samples were irradiated with UV light, the relative UV treatment was found to reduce the carbon content and the contamination of the titanium surface by four times [30], and it has been observed that there is a direct correlation between surface carbon content and hydrophilicity of titanium surfaces [31]. In the present study, laser-etched implant surfaces (Group 1) showed the most reduction in carbon content upon UV treatment, i.e., $63\%$ after 90 min and around $48\%$ after 60 min. The air-abraded, acid-etched surfaces (Group 3) showed $19\%$ reduction after 60 min and around $29\%$ after 90 min of UV treatment. This is very close to observations reported earlier, showing a $20\%$ reduction of carbon content after photofunctionalization on acid-etched titanium surfaces. Similarly, the titanium–zirconium alloy surface (Group 2) showed 54.5 and $38.5\%$ reduction in carbon after 90 and 60 min of UV-PF, respectively. The XPS analysis conducted in the study showed significant reduction in the C1s peak with corresponding increases in the Ti2p and O1s peaks on all the examined surfaces after the UV treatment. There was also a decrease in Ti (IV) values on all surfaces. A formation of a thin TiO2 film was observed, which displayed more resistance to corrosion, and at a physiological pH value, it exhibited thermodynamical stability [32,33,34,35]. UV-treated titanium surfaces are also known to shift from biologically inert titanium to bioactive surfaces, which was supported by our cell culture experimental findings. The energy carried by UV rays breaks the contaminant molecules’ bonds from Ti atoms to O and/or N atoms, thereby increasing TiOH, which in turn is responsible for improved biocompatibility of the implant [30]. Our study groups showed a slight increase in oxygen peak after 90 min of UV photofunctionalization. There was a consistent increase in titanium peaks across all three surfaces and conversion into other Ti species such as Ti(3+) from Ti(4+). The magnitude of photofunctionalization effect on different test surfaces was different in the present study. This was plausibly due to different biological characteristics of individual surface properties. Overall, the data supported that photofunctionalization at both time intervals (60 and 90 min) probably led to increased hydrophilicity of the treated surfaces, leading to better biological responses. For effective osseointegration, attachment and proliferation of osteogenic cells is a primary requisite. Different surface topographies, including acid-etched, air-abraded, machined, and nano-featured surfaces were investigated in their studies [2,35,36]. Aligned with earlier reports, the number of osteoblasts attached to photofunctionalized surfaces was three- to five-fold higher than that attached to the untreated surfaces, especially for 3–24 h of incubation [2,9,24,27]. The phenomenon of increased biocompatibility and osteoblastic adherence to the implant surface has been attributed to a decrease in the amount of surface carbon molecules. Previously a negative correlation of surface carbon content with protein adsorption, cell attachment, cell spreading, and reduced calcium mineralization by osteoblasts was noted [37]. In the present study, a reduction in surface carbon content was accompanied by increased osteoblastic adherence. Superior osteoblastic adherence and proliferation were observed on the SLA surface after photofunctionalization. This was in accordance with earlier reports, as osteocalcin was reported to have the highest secretion at mod SLA or hydrophilized SLA surfaces in a previous study, where osteogenic response of MG63 cells to different surface modifications was evaluated [3]. The effect of different implant surface treatments, including mechanical abrasion, sandblasting + acid etching, sandblasting, and acid-etching surfaces, on cell viability in vivo and cell lines demonstrated high cell viability and adherence of osteoblasts on sandblasted surfaces [38]. Similarly, better cell attachment and adherence were observed on surfaces combining acid etching and grit blasting [39]. Irrespective of surface morphology, a higher osteoblastic proliferation was observed after photofunctionalization despite the detrimental effect of the high-glucose medium. All Surfaces demonstrated significantly higher cell attachment at 48 h. Earlier evidence shows that UV-treated surfaces induce a 20–$50\%$ augmentation of the proliferation compared to control group [32,35]. The potential of the osteogenic cells to proliferate and differentiate determines the bone formation speed and extent. Effects of photofunctionalization were well-documented using both in vitro and in vivo models [40,41,42]. The photofunctionalization process increased the osteogenic response on acid-etched or sandblasted surfaces [4]. The other surfaces tested included Ti alloys (Ti6Al4V and Ti-Ag), SLA, machined surfaces, micro-arc oxidation surface, and Ti nanotubes. In such cases, photofunctionalization had augmented mineralization [10,26,32,43]. In a previous study, UV-induced cell attachment was observed on day 2 and the greatest mineralization was reached after 21 days [2]. In contrast, higher mineralization in the Titanium-Zirconia surfaces upon photofunctionalization were observed in the present study. Notably, all the treated implant surfaces demonstrated higher Alizarin Red activity as compared to untreated surfaces in the high-glucose medium as reported earlier [26,44]. The present study showed that photofunctionalization altered implant surface chemistry, making the surfaces more hydrophilic and conducive to increased osteoblastic adherence. Photofunctionalization also enhanced osteoblastic cell proliferation, especially after 48 h, and increased mineralization. All five test implant surfaces exhibited a reduction in carbon content, conversion of Ti4+ to Ti3+, increased osteoblastic adhesion and viability in the stressed tissue medium, and increased mineralization in a simulated diabetic environment. The combination of chemical treatment through UV photofunctionalization with manufacturer-designed nano- and micro-surface modifications can increase osteoblastic response despite a diabetogenic environment. These in vitro data provide a basis for pre-clinical and clinical studies using randomized controlled designs to investigate how photofunctionalization may benefit dental implant outcomes in diabetes. One limitation of the study was that the sample size was small for each group. Another limitation was that there are other implants with different surface features that may react differently to photofunctionalization. Therefore, other implant surfaces should also be evaluated to better understand their success in a high-glucose environment. ## 5. Conclusions Photofunctionalization alters the surface chemistry of dental implant surfaces, enhancing hydrophilicity, and leading to increased osteoblastic adherence, cell proliferation, and mineralization in a high-glucose environment. Based on the results of this study, it can be suggested that UV photofunctionalization could be adopted to enhance dental implant osteointegration, especially in diabetic patients who are particularly at high risk for implant failures. ## 6. Clinical Relevance Photofunctionalization favored osteoblast attachment and mineralization on the implant surface, which in turn is responsible for the increased initial osteointegration. 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--- title: Protective Effect of Ergothioneine against 7-Ketocholesterol-Induced Mitochondrial Damage in hCMEC/D3 Human Brain Endothelial Cells authors: - Damien Meng-Kiat Leow - Irwin Kee-Mun Cheah - Zachary Wei-Jie Fong - Barry Halliwell - Wei-Yi Ong journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10056831 doi: 10.3390/ijms24065498 license: CC BY 4.0 --- # Protective Effect of Ergothioneine against 7-Ketocholesterol-Induced Mitochondrial Damage in hCMEC/D3 Human Brain Endothelial Cells ## Abstract Recent findings have suggested that the natural compound ergothioneine (ET), which is synthesised by certain fungi and bacteria, has considerable cytoprotective potential. We previously demonstrated the anti-inflammatory effects of ET on 7-ketocholesterol (7KC)-induced endothelial injury in human blood-brain barrier endothelial cells (hCMEC/D3). 7KC is an oxidised form of cholesterol present in atheromatous plaques and the sera of patients with hypercholesterolaemia and diabetes mellitus. The aim of this study was to elucidate the protective effect of ET on 7KC-induced mitochondrial damage. Exposure of human brain endothelial cells to 7KC led to a loss of cell viability, together with an increase in intracellular free calcium levels, increased cellular and mitochondrial reactive oxygen species, a decrease in mitochondrial membrane potential, reductions in ATP levels, and increases in mRNA expression of TFAM, Nrf2, IL-1β, IL-6 and IL-8. These effects were significantly decreased by ET. Protective effects of ET were diminished when endothelial cells were coincubated with verapamil hydrochloride (VHCL), a nonspecific inhibitor of the ET transporter OCTN1 (SLC22A4). This outcome demonstrates that ET-mediated protection against 7KC-induced mitochondrial damage occurred intracellularly and not through direct interaction with 7KC. OCTN1 mRNA expression itself was significantly increased in endothelial cells after 7KC treatment, consistent with the notion that stress and injury may increase ET uptake. Our results indicate that ET can protect against 7KC-induced mitochondrial injury in brain endothelial cells. ## 1. Introduction There has been much recent interest in the roles of oxysterols in cardiovascular and neurodegenerative diseases. Auto-oxidation of cholesterol esters present in low-density lipoprotein (LDL) leads to the formation of oxidised LDL (Ox-LDL), along with its by-products. 7-ketocholesterol (7KC) is a major oxidation product of cholesterol found in human atherosclerotic plaque, and it was more atherogenic than cholesterol in some animal studies [1]. 7KC can be derived from the diet or endogenously produced [2]. It has been demonstrated to disrupt membrane fluidity (increased permeability) before inducing numerous stress response pathways and cell death [2]. 7KC is also involved in mediating the inflammatory mechanisms involved in progression of fibroatheroma plaques (reviewed in [3]). 7KC also induces inflammation in ARPE-19 retinal pigment epithelium cells through elevated expression of the cytokines VEGF, IL-6, and IL-8 via the AKT-PKCζ-NFκB, p38 MAPK, and ERK signalling pathways [4]. Increased brain 7KC levels have been found in rats after excitotoxic brain injury induced by kainic acid [5,6]. Moreover, 7KC induces metabolic dysfunction [2] and blood brain barrier (BBB) dysfunction [7,8]; 7KC levels are increased in patients with type 2 diabetes mellitus [9], Alzheimer’s disease [10] and Parkinson’s disease [11], compared to healthy controls. 7KC levels are also correlated with the number of concurrent coronary risk factors [12]. 7KC is cytotoxic to cells of the vascular wall, and it is capable of inducing apoptosis in endothelial cells and smooth muscle cells and necrosis in fibroblasts [13]. Moreover, 7KC can also induce an increase in intracellular free calcium, either through release from internal stores or influx via calcium channels, and can trigger neurotransmitter release in PC12 cells [14]. In addition, 7KC can injure intracellular organelles, such as the mitochondria, through alteration of membrane fluidity and build-up of oxidation products [2,15]. In addition to generating energy for cells, mitochondria also play a role in intracellular calcium buffering [16]. Mitochondrial dysfunction can be characterised by damage leading to excessive generation of reactive oxygen species (ROS), a decrease in mitochondrial membrane potential, cytochrome c release, ATP depletion and caspase 3 activation [17]. Dietary polyphenols, such as resveratrol, quercetin, and apigenin, have been shown to reduce 7KC-induced oxidative stress and apoptosis in Neuro 2A (N2a) cells [18]. Ergothioneine (ET) is a unique low-molecular weight dietary thiol/thione that can cross the blood-brain barrier. This compound accumulates at high levels in the body from the diet and may play important physiological roles in human health and development and possibly in the prevention and treatment of disease [19]. Blood levels of ET decline with age and with the onset of various diseases [20], especially neurodegenerative diseases [21]. ET was taken up by human brain microvascular endothelial cells (HBMECs) consistent with the presence of the ET transporter OCTN1 in these cells, and it protected the endothelial cells against oxidative stress induced by high glucose, pyrogallol, and xanthine oxidase plus xanthine [22]. In addition, ET had a protective effect on high glucose-induced oxidative stress in endothelial cells [23]. ET also reduced endothelial cell senescence linked to hyperglycaemia through the regulation of SIRT1 and SIRT6 signalling [24]. Furthermore, ET reduced the proinflammatory induction of adhesion molecule expression associated with atherogenesis in human aortic endothelial cells (HAECs) [25]. We previously demonstrated that ET is able to mitigate 7KC-induced inflammation in brain endothelial cells [26]. However, the mechanism of cellular protection in endothelial cells has yet to be elucidated. 7KC is known to cause toxicity through damage to mitochondria [15]; hence, we sought to determine whether preservation of cell viability by ET could be related to protection against 7KC-induced mitochondrial injury. Human brain endothelial cells were used as a model of oxysterol-induced BBB dysfunction and to investigate the potential cytoprotective mechanisms of ET—especially in relation to mitochondrial damage. ## 2.1. Cell Death and Cell Viability Assay Trypan blue assay relies on cellular membrane integrity, whereas MTS assay is based on intracellular metabolic activity. Treatment of cells with 30 μM 7KC resulted in approximately $60\%$ death and loss of metabolic activity of brain endothelial cells visualised using the Trypan blue assay and MTS assay, respectively. Cell death was significantly reduced by cotreatment with ET (Figure 1). VHCL by itself did not have any effect on cell death. However, the protective effect of ET was abolished when cells were treated with an inhibitor of OCTN1 and VHCL, together with ET (Figure 1A). Comparable results were obtained using the MTS assay. ET was found to prevent 7KC-induced loss of metabolic activity; however, this effect was abolished when cells were pretreated with VHCL (Figure 1B). ## 2.2. OCTN1 Gene (SLC22A4) Expression and ET Uptake in Cells Treatment of cells with VHCL or ET did not significantly alter the expression of the SLC22A4 gene encoding the ET transporter OCTN1. However, 7KC induced a significant, 4-fold increase in OCTN1 mRNA expression in endothelial cells. This increase was unchanged by cotreatment with ET or VHCL (Figure 2A). ET was not detected in control cells, but intracellular ET levels significantly increased after treatment with ET. Addition of VHCL decreased ET uptake (Figure 2B). ## 2.3. Intracellular Free Calcium Levels 7KC induced an increase in intracellular free calcium as detected by Fluo-4 assay using flow cytometry. Addition of ET significantly decreased the 7KC-induced increase in intracellular free calcium, but addition of VHCL inhibited this effect (Figure 3). ## 2.4. Overall ROS Production 7KC appeared to induce an increase in overall ROS production in cells, as shown by DCFDA assay, which of course has some problems with specificity [27]. Positive control TBHP also induced overall ROS production in cells. The addition of ET significantly reduced the DCFDA signal from approximately $70\%$ to $35\%$; however, this effect was abrogated by VHCL tert-butyl hydroperoxide (TBHP), which is a positive control for the DCFDA assay (Figure 4A). To support the DCFDA assay results and verify mitochondrial ROS (mROS) production, MitoSOX assay was used. 7KC induced a significant increase in mROS production in cells, and this increase was reduced with ET (Figure 4B). ## 2.5. Mitochondrial Membrane Potential 7KC was found to significantly lower mitochondrial membrane potential, as seen with the lower mean fluorescence intensity (MFI) of rhodamine 123 (Figure 5A) and TMRM (Figure 5B) dyes. ET by itself had no significant effect on mitochondrial membrane potential. However, ET significantly restored mitochondrial membrane potential when 7KC was added, which was abrogated by coincubation with VHCL. Carbonyl cyanide 3-chlorophenylhydrazone (CCCP) and ethanol are positive controls for the TMRM assay (MMP levels) (Figure 5C). ## 2.6. ATP Levels Quantification of ATP levels could serve as an endpoint functional assay to determine mitochondrial dysfunction. 7KC treatment resulted in a significant decrease in ATP levels. Incubation of cells with ET partially restored ATP levels, which were significantly greater than 7KC-treated cells. Pretreatment with VHCL abrogated the protective effect of ET (Figure 6). ## 2.7. TFAM, NRF-1 and Nrf2 Gene Expression Mitochondrial transcription factor A (TFAM) is a nuclear-derived transcription factor involved in mitochondrial DNA maintenance that is inducible by mitochondrial nuclear respiratory factor 1 (NRF-1) transcription factor under stress [28,29]. Nuclear factor erythroid-derived 2-like 2 (Nrf2) is involved in cellular oxidative stress response and various protective mechanisms [30]. TFAM mRNA expression was induced by 7KC, but this increase was significantly reduced by ET (Figure 7A). As with TFAM, there was induction of NRF-1 (Figure 7B) and Nrf2 (Figure 7C), indicating an increased overall level of cellular stress after treatment of cells with 7KC. ET modulated the increased expression of Nrf2 but not that of NRF-1. ## 2.8. Pro-Inflammatory Gene Expression 7KC induced a significant increase in IL-1β, IL-6 and 1L-8 mRNA expression, and these increases were significantly reduced by ET (Figure 8). ## 3. Discussion The present study aimed to elucidate the effect of ET on 7KC-induced damage in hCMEC/D3 brain endothelial cells. The hCMEC/D3 model is suitable for neurovascular and neurodegenerative disease studies because the cell line stably maintains a BBB phenotype [31], and BBB dysfunction is now well recognised to be associated with various neurodegenerative diseases [32]. Studies using this cellular model in Alzheimer’s disease [33,34] and Parkinson’s disease [35,36] have provided crucial insights on disease pathology and possible treatments. 7KC was found to induce approximately $60\%$ cell death and loss of cell viability in hCMEC/D3 brain endothelial cells, using Trypan blue and MTS assay, respectively, and 7KC-induced cytotoxicity was significantly reduced by cotreatment with ET. The protective effect of ET was abolished when cells were treated with a nonspecific inhibitor of the ET transporter OCTN1, VHCL, together with ET. These results suggested that the protective effect of ET was dependent on cellular uptake of ET and not through, perhaps, extracellular neutralisation of 7KC. Moreover, our LC-MS results confirmed that VHCL decreased ET uptake into cells. To confirm that OCTN1 is present in brain endothelial cells, RT-PCR was performed on these cells. The results showed that VHCL or addition of ET did not alter the expression of OCTN1. In contrast, 7KC induced a significant, 4-fold increase in OCTN1 mRNA expression. This increase was not affected by the addition of ET or VHCL. The findings demonstrate upregulation of OCTN1 in the brain endothelial cells upon 7KC-induced injury. This finding could perhaps be a protective mechanism to increase ET uptake in injured tissues, as previously suggested [37,38,39]. Previous studies have also found that OCTN1 is important in the cellular uptake of ET in endothelial cells [22] and that OCTN1 levels can be upregulated in response to tissue injury, e.g., in fatty liver [40] or kidney disease [41]. One of the known effects of 7KC on cells is the disruption of calcium homeostasis, thereby causing an increase in intracellular free calcium levels. Excessive increases in cytosolic calcium concentration could trigger pathways leading to apoptosis [42,43,44]. Influx of calcium has been found in endothelial cells after treatment with 7β-hydroxycholesterol, cholesterol 5β, 6β-epoxide, cholesterol 5α, 6α-epoxide and 7KC [45]. The influx of calcium could occur via specific calcium channels on the cells. A calcium channel blocker, azelnidipine, reduced RelA (p65) nuclear translocation in 7KC treated human aortic endothelial cells [46]. In addition, 7KC-induced increases in intracellular free calcium could occur through release from intracellular stores [14]. We confirmed that 7KC treatment increased intracellular free calcium and that the increase was significantly modulated by ET. This effect could be due to the ability of ET to reduce oxidative stress [47] and/or chelate divalent metal ions [48,49,50], while the increase in chelation could increase sequestration of cytosolic calcium ions. Verapamil alone does not appear to affect intracellular free calcium levels [51], but coincubation with VHCL abrogated the ET-induced reduction in intracellular free calcium levels, indicating that modulation is likely to occur intracellularly, e.g., via modulation of intracellular stores in the endoplasmic reticulum or mitochondria, and is not due to external chelation of calcium by ET. Together, our results indicate that ET is effective in preventing 7KC-induced increases in intracellular free calcium. 7KC appeared to induce an increase in ROS production in brain endothelial cells, as shown by DCFDA assay, although the limitations of this assay are well known [27]. This outcome is very similar to the results of another study, which showed ROS/RNS production and mitochondrial DNA damage after 7KC treatment in human retinal pigment epithelial cells in vitro [52]. Addition of ET significantly reduced ROS production, and such protection was abrogated by VHCL. We validated the DCFDA results with MitoSOX assay, which appears to detect mitochondrial ROS production, and we confirmed that the 7KC-induced increase in mitochondrial ROS was significantly reduced by ET. These results highlight the importance of mitochondria in ROS formation induced by 7KC and the ability of ET to reduce such ROS production. Increased intracellular free calcium levels could trigger increased calcium buffering from mitochondria, contributing to mitochondrial calcium overload and a resultant loss of mitochondrial membrane potential (MMP) [16]. In this study, we demonstrated 7KC-induced damage to mitochondria, as determined by MMP assays. The deleterious effects of 7KC on MMP were ameliorated by ET, and this protective effect was abrogated by coincubation with VHCL. Loss of mitochondrial membrane potential could result in ROS production [53] and account for the increase in mitochondrial ROS production, as shown by MitoSOX assay above. The ability of 7KC to disrupt mitochondrial membrane potential has also been reported in human retinal pigment epithelial cells [52], 158N murine oligodendrocytes [54] and N2a mouse neuronal cells [18]. Damage to mitochondria could lead to a reduction in energy production critical for cellular metabolic processes. Defective energy metabolism has an important role in aging and neurodegenerative diseases [55,56,57]. In this study, we showed that ET was able to significantly inhibit 7KC-induced decreases in ATP levels in brain endothelial cells, and coincubation with VHCL abrogated the protective effect of ET, consistent with an intracellular effect of ET. 7KC-induced loss of mitochondrial membrane potential and ATP production have previously been shown in human aortic smooth muscle cells [58] and mouse endothelial cells [59]. In addition to oxidative phosphorylation, 7KC-induced loss of cellular ATP could be due to perturbations of the glycolysis pathway [60]. The decrease in mitochondrial stress induced by ET is evidenced by a drop in TFAM gene expression. TFAM is a downstream activation product of NRF-1 activation, and both NRF-1 and TFAM play key roles in regulation of oxidative stress and maintenance of mitochondrial DNA [28,29]. 7KC induced an increase in NRF-1 and TFAM expression, suggesting mitochondrial stress. As with NRF-1 and TFAM, there was induction of Nrf2, indicating an increased overall level of cellular stress [28]. An oxidative stress-induced increase in mitochondrial biogenesis genes was reported in HeLa cells [61]. The contribution of TFAM to intracellular calcium homeostasis has also been discussed [62]. Damage to mitochondria could also lead to activation of the inflammasome [63] and induction of inflammation [64]. Activation of NLRP3 inflammasome has been found in endothelial cells after treatment with 7KC [65,66,67], retinal pigment epithelial cells, and bone marrow-derived cells, including microglia [66]. In this study, we showed significant increases in the mRNA expression of the inflammatory cytokines IL-1β, IL-6 and IL-8 after 7KC treatment, and these increases were modulated by ET. The results are consistent with our previous findings that there are increases in NF-κB, IL-1β, IL-6, and TNF-α expression and COX-2 enzymatic activity after 7KC treatment of endothelial cells, and such increases are ameliorated by ET [26]. ## 4.1. Chemicals 7-ketocholesterol was purchased from Cayman Chemical (Ann Arbor, MI, USA). L-ergothioneine and L-ergothioneine-d9 (ET-d9; deuterated internal standard) were kindly provided by Tetrahedron (Paris, France). Tert-butyl hydroperoxide (TBHP), carbonyl cyanide 3-chlorophenylhydrazone (CCCP) and verapamil hydrochloride (VHCL), a nonspecific OCTN1 inhibitor, was purchased from Sigma Aldrich (St. Louis, MO, USA). Cells were cultured in Endothelial Basal Medium (EBM-2) (Lonza, Bend, OR, USA) with HyClone antimycotic/antibiotic solution (1×, Thermo Fisher Scientific, Waltham, MA, USA) and growth factors from the EGM-2MV kit (Lonza). The EGM-2MV kit contains gentamicin/amphotericin-B (GA), human epidermal growth factor (hEGF), ascorbic acid, vascular endothelial growth factor (VEGF), R3-insulin-like growth factor-1 (R3-IGF-1), hydrocortisone, human fibroblast growth factor-beta (hFGF-β) and foetal bovine serum ($5\%$ v/v). ## 4.2. Cell Culture Human brain endothelial cells (hCMEC/D3), derived from microvessels in the human temporal lobe, were purchased from EMD Millipore (Temecula, CA, USA) and cultured according to Weksler et al. [ 68]. hCMEC/D3 cells between passage 6 and 20, were utilised for all experiments. Dose response assays were performed, and 30 μM 7KC, 1 mM ET and 100 μM VHCL were determined to be the optimal concentrations for toxicity, protection, and inhibition, respectively, similar to prior in vitro studies [22,26,69,70]. In all cases, cells were preincubated with 1 mM ET for 1 h prior to coincubation with 7KC for 24 h. If VHCL was added, a 2-h preincubation time was allocated prior to addition of ET. ## 4.3. Trypan Blue Cell Viability Assay Trypan blue assay, which utilises an exclusion method, was performed to analyse the effects of 7KC and ET on the viability of cells, whereby nonviable cells take up the dye, but viable cells do not. Treated cells were detached with trypsin ($0.25\%$) (Omega Scientific, Los Angeles, CA, USA) for 3 min before centrifugation for 5 min at 1000× g. The supernatant was discarded, and the pellet resuspended in fresh EBM-2 media, and a portion of the cell suspension was mixed 1:1 with trypan blue ($0.4\%$) (Thermo Fisher Scientific) and loaded into cell counting chambers for analysis by an automated cell counter (TC10, Bio-Rad, Hercules, CA, USA). ## 4.4. MTS Assay MTS cell viability assay is based on the principle of MTS tetrazolium compound reduction by viable cells to generate coloured formazan dye, which can be measured by absorbance at 490 nm. Twenty microlitres of CellTiter Aqueous One Solution (MTS proliferation assay, Promega, Madison, WI, USA) were added to treated cells on a 96 well plate. Subsequently, a Synergy H1 Microplate Reader (BioTek, Winooski, VT, USA) was utilised to read the absorbance at 490 nm. ## 4.5. Cellular ET Uptake and Liquid Chromatography Mass Spectrometry Cells were grown to $80\%$ confluence and treated prior to ET extraction from cells. Samples were washed thrice with ice cold PBS before addition of methanol spiked with internal standard (ISTD) comprising of ET-d9. Next, samples were centrifuged at 20,000× g at 4 °C for 10 min, and the supernatant was collected in glass vials before being evaporated at 37 °C under an N2 stream. The contents of the glass vials were resuspended in methanol solution, and ET levels were analysed via liquid chromatography mass spectrometry (LC-MS/MS) using an Agilent 1290 UPLC system coupled with an Agilent 6460 ESI mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Samples were kept at 10 °C in the autosampler. Two microlitres of the processed samples were injected into a Cogent Diamond-Hydride column (4 µm, 150 × 2.1 mm, 100 Å; MicroSolv Technology Corporation, Brunswick, NC, USA) maintained at 30 °C. Solvent A was acetonitrile in $0.1\%$ formic acid, and Solvent B was $0.1\%$ formic acid in ultrapure water. Chromatography was performed at a flow rate of 0.5 mL/min using the following gradient: 1 min of $20\%$ Solvent B, followed by a 3-min gradient increase in Solvent B to $40\%$ to elute ET. The retention time for ET is 4.2 min. Mass spectrometry was performed using the positive ion, electrospray ionisation mode, with multiple reaction monitoring (MRM) for quantification of specific target ions. The capillary voltage was set at 3200 V, and the gas temperature was kept at 350 °C. The nitrogen sheath gas pressure for nebulising samples was at 50 psi, and the gas flow was set at 12 L/min. Ultra-high purity nitrogen was used as collision gas. The precursor to product ion transitions and fragmentor voltages (V)/collision energies (eV) for each compound were as follows: ET; 230.1 → 186, 103 V/9 eV and ET-d9; 239.1 → 195.1, 98 V/9 eV. ## 4.6. Measurement of Intracellular Free Calcium, Mitochondrial Membrane Potential, and Cellular/Mitochondrial Reactive Oxygen Species (ROS) Flow cytometry and fluorometry were employed to assess mitochondrial membrane potential and ROS production. Cells were first detached, and cell pellets were washed twice with PBS. Cells were incubated for 30 min with their respective stains in the absence of light before conducting flow cytometry analysis. Intracellular free calcium was quantified using Fluo-4 (Thermo Fisher Scientific). Mitochondrial membrane potential (MMP) was quantified using rhodamine 123 (Thermo Fisher Scientific) and tetramethyl rhodamine methyl ester perchlorate (TMRM) (Sigma Aldrich). For the controls, pretreatment with 100 mM ethanol (ETOH) and 50 μM CCCP for 30 min was used to induce hyperpolarisation and depolarisation of mitochondrial membrane potentials, respectively. Cellular ROS production was quantified using cell-permeant 2′,7′-dichlorodihydrofluorescein diacetate (H2DCFDA) (Thermo Fisher Scientific) and mitochondrial ROS (mROS) production was quantified using MitoSOX assay (Thermo Fisher Scientific). Tert-butyl hydroperoxide (TBHP) at a concentration of 50 μM was used as a positive control to induce oxidative stress. Flow cytometry measurements were performed using a Cytoflex LX flow cytometer (Beckman Coulter Life Sciences, Brea, CA, USA), using 106 cells per sample for analysis with 10,000 events per sample recorded. The FL1 channel was used to quantify intracellular free calcium, MMP, ROS, mROS (Fluo-4, Ex/Em = $\frac{488}{525}$ nm; rhodamine 123, Ex/Em = $\frac{507}{529}$ nm; TMRM, Ex/Em = $\frac{555}{575}$ nm; H2DCFDA, Ex/Em = $\frac{495}{527}$ nm; MitoSOX, Ex/Em = $\frac{510}{580}$ nm). Fluorometry measurements were performed using a Synergy H1 Microplate Reader (BioTek). For flow cytometry, fluorescent signals were measured on a logarithmic scale of four decades of log. Raw data were processed using FlowJo software, version 10.5.3. ## 4.7. ATP Assay ATP levels in cells were quantified using a commercial ATP determination kit (Thermo Fisher Scientific). This bioluminescence assay is based on the reaction of ATP with recombinant firefly luciferase and its substrate luciferin. Cells were collected via trypsinisation and quantified by Trypan blue assay to ensure all samples contained equal numbers of cells (106 cells). Upon centrifugation and removal of supernatant, 1 mL of boiling ultrapure water from an Arium pro® ultrapure system was added into the cell pellet and incubated in a water bath for 10 min at 100 °C [71]. Samples were then cooled on ice for 30 s, and the supernatant was utilised for ATP assay per the manufacturer’s instructions. Luminescence readings of samples were performed using a Synergy H1 Microplate Reader (BioTek) alongside ATP standards. ## 4.8. Quantitative RT-PCR TRIzol Reagent (Thermo Fisher Scientific) was used for RNA extraction per the manufacturer’s instructions. cDNA was produced from reverse transcription of 1000 ng of RNA (High-Capacity cDNA Reverse Transcription Kit; Applied Biosystems, Waltham, MA, USA) using a T-Personal Thermocycler (Biometra, Gottingen, Germany) programmed at 25 °C for 10 min and 37 °C for 30 min, followed by 85 °C for 5 min. qPCR was performed to quantify OCTN1, NRF-1, Nrf2, TFAM, IL-6, IL-8, and IL-1β mRNA expression (OCTN1: Hs.310591_m1; NRF-1: Hs.654363_m1; Nrf2 or NFE2L2: Hs.744006_m1; TFAM: Hs.594250_m1; IL-1β: Hs.1555410_m1; IL-6: Hs.174131_m1; IL-8: Hs.174103_m1), using TaqMan Gene Expression Assay Probes (Applied Biosystems) and TaqMan Universal PCR Master Mix (Applied Biosystems). GAPDH (Hs99999903_m1). The qPCR condition was performed using a 7500 Real-time PCR System (Applied Biosystems) with conditions of 95 °C for 10 min, followed by 95 °C for 15 s and 60 °C for 1 min, for 40 cycles. Subsequently, the relative mRNA expressions for the respective genes of interest were quantitated via comparative CT (ΔΔCT) method. ## 4.9. Statistical Analysis All data are presented as the mean ± standard deviation (SD). Comparisons in the Trypan blue, MTS, gene expression, flow cytometry, and fluorometry assays were performed by one-way ANOVA with Bonferroni’s post-hoc correction, using GraphPad Prism software, version 9. A p-value < 0.05 was considered significant. ## 5. Conclusions The above studies showed a cytoprotective effect of ET on 7KC-induced toxicity in human brain endothelial cells. 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--- title: Characterization and Proteomic Analysis of Plasma EVs Recovered from Healthy and Diseased Dogs with Canine Leishmaniosis authors: - Sofia Esteves - Clara Lima - Inês Costa - Hugo Osório - Carmen Fernandez-Becerra - Nuno Santarém - Anabela Cordeiro-da-Silva journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10056832 doi: 10.3390/ijms24065490 license: CC BY 4.0 --- # Characterization and Proteomic Analysis of Plasma EVs Recovered from Healthy and Diseased Dogs with Canine Leishmaniosis ## Abstract Dogs are highly valued companions and work animals that are susceptible to many life-threatening conditions such as canine leishmaniosis (CanL). Plasma-derived extracellular vesicles (EVs), exploited extensively in biomarker discovery, constitute a mostly untapped resource in veterinary sciences. Thus, the definition of proteins associated with plasma EVs recovered from healthy and diseased dogs with a relevant pathogen would be important for biomarker development. For this, we recovered, using size-exclusion chromatography (SEC), EVs from 19 healthy and 20 CanL dogs’ plasma and performed proteomic analysis by LC-MS/MS to define their core proteomic composition and search for CanL-associated alterations. EVs-specific markers were identified in all preparations and also non-EVs proteins. Some EVs markers such as CD82 were specific to the healthy animals, while others, such as the Integrin beta 3 were identified in most samples. The EVs-enriched preparations allowed the identification of 529 canine proteins that were identified in both groups, while 465 and 154 were only identified in healthy or CanL samples, respectively. A GO enrichment analysis revealed few CanL-specific terms. Leishmania spp. protein identifications were also found, although with only one unique peptide. Ultimately, CanL-associated proteins of interest were identified and a core proteome was revealed that will be available for intra- and inter-species comparisons. ## 1. Introduction Several studies have shown that extracellular vesicles (EVs) contain proteins and nucleic acids, associated with several disorders, including infectious diseases [1]. EVs carry cell-specific molecular information protected by a double membrane structure. Compared to other conventional soluble biomarkers detected in biological samples, the EVs provide the promise of specificity and sensitivity comparable, or even higher, due to their excellent biological stability. It should also be stated that EVs are a privileged source of membrane-associated proteins, that are not found free in biological fluids. Moreover, EVs can be recovered from easily obtained bio fluids such as blood, saliva, tears, or urine making them invaluable in clinical applications. Thus, EVs have been the subject of many scientific efforts to provide the next generation of clinically relevant biomarkers [2]. Still, few studies in veterinarian sciences report EVs characterization and evaluation as potential biomarkers. Particularly, studies using EVs recovered from dogs are scarce. Some reports studied EVs produced in vitro, such as the proteomic characterization of EVs derived from canine oviductal cells [3]. In addition, a proteomic profile of exosomes from healthy osteoblasts and osteosarcoma cell lines was described as well as their impact on healthy T cells [4]. Tissue explants were also taken from dogs with osteosarcoma and EVs were recovered in vitro after 24 h. A posterior proteomic analysis followed and two molecular targets for this disease were identified—PSMD14/Rpn11 [5]. In the context of biological fluids, plasma-derived exosomes from osteosarcoma-bearing dogs, healthy dogs, and dogs with traumatic fractures were characterized, in conjunction with patients with osteosarcoma prior and 2 weeks after amputation. This characterization allowed the identification of a group of proteins capable of distinguishing healthy dogs with natural fractures from dogs with osteosarcoma [6]. EVs recovered from obese female dogs’ serum were recovered and their miRNAs profile was evaluated [7]. The available information suggests that, much like EVs from humans, EVs from dogs might be used to address the clinical condition of the animals and unravel disease-specific biomarkers. In this context, vector-borne canine leishmaniosis (CanL), caused by Leishmania infantum protozoan parasites, is a major concern for the veterinary community, having high mortality and morbidity rates [8]. In endemic areas, domestic dogs are considered the main reservoir of L. infantum, as they allow parasite life cycle perpetuation. The relation between the incidence of CanL and human zoonotic leishmaniasis has been described, so CanL is not only considered a veterinary problem but also a public health issue [9]. Therefore, considering the zoonotic potential of Leishmania infection, disease management in dogs is essential from the One Health perspective. The available tools, in conjunction with clinical evaluation, are adequate to detect diseased dogs but present limitations when trying to diagnose infection in asymptomatic dogs [10]. Thus, the real prevalence and overall risk of infection are believed to be underestimated [11]. The current diagnosis options are suboptimal, so there is a need for alternative tools that are able to address complex situations such as asymptomatic disease, co-infection, and treatment assessment [12]. To achieve this, we must overcome the limitations concerning the availability of biological samples from infected tissues and mixed serology results that are highly dependent on the antigens used [13]. Molecular approaches using host/parasite-specific molecules would be the ideal scenario. In this context, EVs are a resource with great potential [2], as these small lipidic vesicles of cellular origin are present in all biological fluids and have great stability. In fact, the potential of EVs as a source of disease-relevant biomarkers has been proposed for other parasitic pathogens [14,15,16,17,18]. EVs from Leishmania have been previously characterized [19,20] and parasite-associated proteins were also identified in infected macrophages [21,22,23]. Moreover, circulating parasite material was found in the urine of visceral leishmaniasis patients [24] as well as in immunocomplexes [25]. Specifically, in the context of CanL, EVs were recovered from the canine serum of healthy and diseased dogs and the exosomal miRNA 122 expression was evaluated. The levels of miRNA 122, a marker of liver impairment, decreased in diseased dogs [26]. Nonetheless, the protein cargo of plasma-derived EVs was not yet characterized in healthy or CanL dogs. This study represents the first systematic report of characterization and proteomic analysis of circulating EVs recovered from canine plasma with the goal of defining the core proteome of these EVs and evaluating plasma EVs as a potential source of biomarkers relevant for CanL. This study will pave the way to the field of EVs in these companion animals. It will enable future inter-species comparisons and also help define EVs as a source of potentially relevant biomarkers for CanL and other relevant clinical conditions. ## 2.1. Generic Sample Characterization Thirty-nine dogs were included in this study. These were characterized clinically and grouped into CanL and/or Healthy groups (Table 1; Figure S1). The Healthy group was composed of dogs presenting unremarkable physical examination, negative serological detection of anti-Leishmania antibodies performed by a rapid chromatographic test validated for anti-Leishmania antibodies detection and an in-house ELISA for SPLA and rK39, besides negative blood-PCR for L. infantum. In this group, 14 dogs resided in CanL non-endemic regions (11 dogs from Ireland, 3 dogs from Azores) and 8 dogs were from Portugal. The samples included in the CanL group belonged to dogs living in CanL-endemic regions of Portugal that were found polysymptomatic for CanL (Figure S1). The diagnosis was confirmed either through commercially available serological tests validated for CanL diagnosis, together with in-house ELISA for SPLA and rK39, or parasite isolation from blood, lymph nodes, or bone marrow. A complete blood cell count (CBC) was performed in 18 CanL dogs (Figure S1). ## 2.2. EVs recovery and Characterization After sample selection, plasma was processed by size exclusion chromatography (SEC) using 1 mL sepharose columns. The obtained fractions were characterized by protein quantification and bead-based assay, using CD9, CD71 and CD5L as EV-markers. The CD9 is a classical tetraspanin, used in several studies to characterize EVs including in the context of exosomes of dogs serum characterization [26], CD5L is a marker previously shown to be present in human plasma-derived EVs and in the context of isolation of EVs from a patient with Chagas disease [17,27] and CD71 is an exosomal marker previously used in the context of characterization of plasma-derived EVs in Plasmodium vivax infected patients [28,29]. The fractions with the highest median fluorescent intensity (mfi) in the bead-based assay, enriched in the EV markers, were pooled (Figure S3). The pooled fractions were further characterized by NTA for size determination, by TEM and mass spectrometry for protein identification of Canis lupus familiaris and L. infantum proteins (Figure 1). ## 2.3. Qualitative Evaluation of Canine Proteins The average number of proteins identified in each sample was 305 and 230 for the Healthy group and CanL group, respectively, while the average number of Peptide-Spectrum Matches (PSMs) was 15,859 and 14,390 (Figure 2a,b). When comparing the CanL and the Healthy groups, no significant differences in the number of proteins or PSMs were observed (Figure 2a, Table S1). Identifications with 1 unique peptide (UP) were not considered in the analysis, although represented most identifications, $32.38\%$ (Figure 2c). More than $53.6\%$ of the proteins were identified with 3 or more UP. A comparative proteomic analysis was performed using the merged data from each group (merged data from the 20 CanL EVs and the merged data from the 19 healthy dogs EVs) (Figure 2d, Table S2). Considering the total identifications from the merged CanL compared to the merged Healthy groups, a total of 1148 canine proteins were identified with high confidence. A total of 529 canine proteins were common to both conditions, corresponding to $46.1\%$ of the total proteome identified. Moreover, 154 canine proteins were only identified in the CanL EVs, and 465 proteins were only identified in the Healthy dogs’ EVs (Table S2). To confirm that the fractions sent to proteomics were enriched in EVs, the proteomic characterization was performed according to MISEV2018 guidelines [30] (Figure 3). In all preparations, at least one protein of categories 1, 2 and 3 was detected. Concerning the recommended EVs markers, among the transmembrane or GPI-anchored proteins associated to plasma membrane and/or endosomes, the Integrin alpha (ITGA) and beta (ITGB) were the most consistently identified in the individual samples. In fact, ITGB3 was the most consistently detected. Most markers were only sporadically detected, while others such as CD82 were mostly detected in the Healthy cohort. Among the cytosolic proteins recovered in EVs, the Heat Shock Protein family A (HSP70) member 8 (HSPA8) was the most consistently detected. Among proteins with promiscuous incorporation in EVs, Gliceralehyde-3-phosphate dehydrogenase (GAPDH) was the most consistently detected. Several non-EV co-isolated structures were also detected, such as the apolipoprotein E that was detected in all samples (Figure 3 and Figure S4). Among the other proteins recommended by the MISEV2018 guidelines, CD5L was identified in all preparations. To evaluate the biological impact of CanL in the EVs containing fractions, a GO enrichment analysis using the Database for Annotation, Visualization, and Integrated Discovery (David 2021) was performed (Figure 4, Table S3). This analysis was performed at two levels; first, a simple comparison of the Healthy and CanL group, then, a subsequent analysis based on common (core proteome) and group-specific protein identifications. The proteins identified in the EVs containing fractions from healthy dogs were associated to 29 significantly enriched biological processes, while for the CanL group there were 20. From the biological processes significantly enriched in CanL, 18 were present also in the Healthy group. Considering the proteins identified in the core proteome, 20 biological processes were found significantly enriched, matching perfectly the biological processes enriched in CanL samples. Considering the analysis performed using the proteins that were uniquely identified in each group, nine GO terms were found to be significantly enriched in the Healthy group, and a single GO term associated to CanL EVs, the antigen processing and presentation of peptide antigen via MHC class I (GO:00002474). Among the Healthy-specific GO terms, three were not significantly enriched when considering the complete Healthy group data set: endocytic recycling (GO:0032456), Golgi organization (GO:0007030) and protein transport (GO:0015031). When molecular function was considered, 14 GO terms were found significantly over-represented in the complete Healthy data set, and 12 for the complete CanL data set. Among the 12 GO terms associated to molecular function that were enriched in the complete CanL data set, three were not found significantly enriched in the complete Healthy data set: serine-type endopeptidase inhibitor activity (GO:0004867), calcium ion binding (GO:0005509) and ATPase activity (GO:0016887). The latter, GO:0016887, was also significantly enriched in the core proteome. Calcium ion binding (GO:0005509) was also the only term significantly over-represented in the CanL unique proteins. Considering the Heathy unique proteins, nine GO terms associated to molecular functions were found to be enriched. Among these, eight were also found in the core proteome. Only the GO term associated with serine-type endopeptidase activity (GO:0004252) was not significantly enriched in the core proteome. Considering the GO analysis for cellular compartment, 22 terms were found enriched in the Healthy group, and 15 on the CanL. Among these 15, only 1, extracellular region (GO:0005576), was not present in the core proteome. Among the GO terms significantly enriched in the group-specific proteins, four were associated to the unique Healthy proteins. These terms were also enriched in the core proteome. For the unique CanL proteins only one GO term was enriched, MHC class I protein complex (GO:0042612). To evaluate the protein identifications in individual runs, the entirety of the 100 injections (48 injections for 19 healthy samples and 52 for 20 CanL samples) in the merged file were used to evaluate the capacity to detect the individual proteins (Table S4). The protein that was most consistently differentially detected in the injections was Myo-inositol oxygenase (E2QTD8), detected in 44 injections associated to healthy samples and none of the CanL (Figure 5). On the contrary, Carboxylesterase 5 A (A0A5F4BXK6) was detected in 40 out of the 52 injections of the CanL and only 4 of the Healthy injections. Moreover, stromal cell derived factor 4 (SDF4) and biorientation of chromosomes in cell division 1 (BOD1L1) were only detected in CanL injections, with 32 and 31 spectra identifications, respectively. ## 2.4. Quantitative Evaluation of Canine Proteins A quantitative analysis was also performed to compare the protein abundance between the 2 conditions, using all 100 individual injections. This analysis resulted in an abundance ratio (CanL/Healthy) and a p value associated with each protein. A distinct protein profile was revealed between the 2 groups and proteins with significantly altered abundance were identified: 5 proteins were significantly more abundant in CanL group and 24 more abundant in the Healthy group (Figure 6 and Table S5). The GO terms associated with the proteins more abundant in the Healthy group are mostly associated with red blood cells and oxygen/carbon dioxide transport. ## 2.5. Canine Derived Biomarkers of CanL A retrospective analysis was then performed to evaluate the capacity to detect the most promising proteins from Figure 5 and Table S5 in the individual animals using as criteria for detection the presence of at least 1 UP (Figure 7). Among all the proteins the carbonic anhydrase (F1PBK6) was the best performer, being identified in $\frac{17}{19}$ healthy animals and none of the CanL animals. Several other proteins of interest can be associated to the Healthy group, including biliverdin reductase B, present in $\frac{18}{19}$ healthy samples and only 1 CanL sample, followed by glycophorin C present in all healthy samples although also present in 2 CanL samples. These three proteins cover all Healthy samples and only one CanL sample was associated with more than one of these proteins. ## 2.6. Qualitative Evaluation of Leishmania Proteins We also investigated the presence of L. infantum associated proteins in the samples, as the identification of parasite proteins would be relevant for diagnosis. No protein identification was supported by more than 1 UP. All identifications associated to the Healthy samples were excluded and only identifications unique to the CanL group were considered (Table S6). To evaluate if there was an enrichment of Leishmania-specific identifications, two other pathogens databases were used as controls in the proteomic data analysis. Considering that no identification with more than 1 UP was obtained, we evaluated the total number of spectra associated with the peptides detected in the Healthy and CanL and compared it with two other unrelated pathogens, Neospora caninum, a possible dog pathogen endemic to Portugal and Plasmodium falciparum, a non-dog pathogen non-endemic to Portugal (Figure S6). Only for L. infantum, the number of PSM/unique peptides was at least two times higher for the CanL group when compared to the Healthy group. For the two other pathogens databases, the identifications were similar in both groups. Considering the above mentioned constraints, 88 proteins were identified in the 20 CanL samples. Twelve proteins were identified in more than one dog (Figure 8 and Table S7). Putative DNA Polymerase epsilon subunit b (LINJ_35_1780) was detected in six dogs. Three other proteins were detected in more than two animals, the uncharacterized proteins (LINJ_14_1540) and (LINJ_16_1500) were detected in 4 and 3 animals respectively while the Putative copper-transporting ATPase-like protein (LINJ_33_2210) was also detected in three animals. The detected peptides associated with the identification of these 12 proteins were subjected to homology evaluation to further support the confidence of their association with Leishmania spp. ( Table S8). Finally, the DNA Polymerase epsilon subunit b was also produced as a recombinant protein (Figure 9) and the presence of antibodies that recognize this protein was confirmed in the CanL group by a significant seroreactivity. Furthermore, a cut-off was determined and 9 samples were considered above the cut-off, of which 5 identified putative DNA Polymerase epsilon subunit in CanL EVs through proteomic analysis. ## 3. Discussion The present study described the recovery, characterization and proteomic analysis of plasma-derived EVs purified by SEC from healthy and canine leishmaniosis dogs. No differences were seen in the mode size of EVs when comparing the two groups. However, different EVs sizes were already reported in the context of inflammation, tissue damage, cellular stress and infection [31,32]. This suggests that CanL does not cause a significant shift in EVs size as was observed for viral infections caused by HTLV-1. Considering the overall protein identifications, no differences were observed comparing the total number of protein identifications and PSM, suggesting that the diseased condition did not impact the overall capacity to detect proteins in the fractions of interest. Interestingly, the healthy group presented an apparent clustering into lower protein identifications and higher protein identifications. This apparent clustering was not attributable to any evaluated characteristic of the samples. The approach selected enabled a reproducible and consistent recovery of EVs-enriched preparations. These EVs-enriched populations are expected to constitute a mixture of EVs from different origins such as exosomes and microvesicles. Still, these preparations contained several abundant proteins in plasma, such as albumin and fibrinogen. This was a limitation of the approach. In fact, using the list from Menezes-Neto [27] about $25\%$ of the detected spectra are associated to common plasma contaminants or non-EVs co-isolated proteins such as apolipoproteins. Moreover, no consistent group-specific enrichment in plasma-soluble proteins was observed. The only exception was albumin, which is in agreement with the hypoalbuminemia often observed in the context of physiological stress or inflammation. In fact, hypoalbuminemia is often an altered parameter observed in CanL dogs [33,34]. These contaminants probably diminished the capacity to detect more EVs-associated proteins. In fact, $32\%$ of the protein identifications were associated with 1 UP and therefore excluded from most analyses. Still, the presence of plasma contaminants did not prevent the detection of EVs-specific markers in each individual sample [30]. Most individual markers were not detected in all samples. This might be explained by the heterogeneity of the cohort. Still, Integrin alpha and beta were consistently identified in the individual samples, with ITGB3 being the most prevalent one. This integrin, mostly associated with platelets, has been recently involved in metastatic development in cancer [35]. The Heat shock protein family A (HSP70) member 8 (HSPA8) was also detected in most samples. Considering that no prior constraints on age, sex, or dietary conditions, the consistency of detection of markers such as ITGB3 is relevant. Unexpectedly, some markers were only consistently detected in one of the groups. The clearest example of EVs marker segregation was CD82, mostly found in healthy samples. This might indicate that EVs populations differ upon infection. A previous study analysing the tetraspanin expression in peripheral blood leukocytes from healthy controls and in patients with bacterial infections was altered [36]. Notably, the tetraspanin with the highest significant differences in expression was also CD82, since no expression in B cells was detected in the patients with infections. It is plausible that this change in cells’ tetraspanins expression will impact the vesicles in circulation. Moreover, CD82 has been shown as an important player in phagocyte infiltration restraining and controlling inflammation. CD82 knockout macrophages lead to larger lesions upon L. mexicana infection, demonstrating the role of this tetraspanin in infection control [37]. Furthermore, when analysing the abundance of classical tetraspanins in the quantitative analysis, it was observed that CD9 and CD81 are less abundant in the CanL group. Overall, the modification of most relevant EVs markers observation can have a significant impact on future studies, especially considering positive selection approaches. The CD5L was also identified in all preparations. It is described by MISEV2018 as a secreted protein recovered with EVs; it was also described as an exosomal marker of plasma-derived EVs in human samples [27]. Although the abundance ratio of CD5L was not significantly different in both groups, it was previously found to be increased in patients with lung cancer, with its expression correlated to cancer tissues, and CD5L was suggested as a non-invasive biomarker for this type of cancer [38]. CD5L has been shown to have a role in several pathologies, mostly inflammatory diseases, ranging from infections to obesity or cancer [39]. Moreover, a 10-fold increase in CD5L plasma levels has been described 3 weeks after murine infection with M. tuberculosis [40]. Similar to what was observed in albumin and CD82, several proteins were either enriched or detected in one specific group. The GO analysis of core proteins revealed that these proteins were responsible for most of the GO term enrichment detected in the individual groups. In fact, the GO enrichment analysis revealed 20 biological processes associated with the core proteome, which matched perfectly the GO terms enriched in the CanL group. Most GO enrichment deviations from the core proteome were associated to the Healthy group, with 12 GO terms enriched only in the Healthy dataset. Most Healthy-specific GO enrichment was associated with generic biological processes, such as endocytic recycling, Golgi organization and protein transportation. The lack of enrichment of these GO terms in the CanL group might be related to a shift from homeostatic conditions caused by the infection. Considering the CanL-specific GO enrichment, only the antigen processing and presentation of peptide antigen via MHC class I (GO:00002474) was observed. Moreover, the cellular compartment GO associated with the MHC class I protein complex was enriched. A possible cause for this enrichment might be the diminution of EVs associated with red blood cells. This is supported not only by the clinical evidence of anaemia, but also by the decreased detection of red-blood-cells-related proteins. Still, it is intriguing that this was the only biological process that was significantly more represented in the CanL group. Nonetheless, MHC class I presentation through exosomes has been observed in other pathogens. For example, M. tuberculosis antigens presentation through infected cells exosomes can function as an alternative cross-presentation mechanism to induce acquired immune response. These exosomes activate CD8 T-cells, indicating that these antigens can be processed through MHC class I presentation pathways, and can help mount the CD8 T-cell response upon M. tuberculosis infection [41]. Therefore, a similar biological phenomenon might happen in the context of Leishmania infection, especially because, as with M. tuberculosis, Leishmania is also an intracellular pathogen that infects macrophages. Furthermore, also upon H. pylori infection, exosomal miR-155 released from infected macrophages promotes cytokine production to regulate inflammatory response that consequently leads to an expression of cellular signal transduction proteins, among them MHC-I, to regulate the immune response [42]. However, the downregulation of MHC-I is a common mechanism of different pathogens to invade the host. In human herpesvirus-6, this might be achieved through the transfer of MHC-I and MHC-II to exosomes [43]. A similar immune evasion mechanism mediated by the release of EVs could also justify the increased detection of EVs associated with MHC-I in CanL. Two other CanL-specific Molecular function GO-related observations were noteworthy, the enrichment of calcium ion binding and serine-type endopeptidase inhibitor activity. Phagocytes intracellular calcium concentration was shown to be increased upon Leishmania infection, probably as a response to a transient decrease of intracellular calcium storage upon infection [44]. This enrichment in the frequency of calcium ion binding proteins might be related to enhanced signalling needs in response to infection. Concerning the identification of serine-type endopeptidase inhibitor GO enrichment, it should be highlighted that in the unique healthy serine-type endopeptidase activity is enriched. Serine endopeptidase inhibitors are a protein superfamily associated with anticoagulant properties [45]. Thus, serine-type endopeptidase inhibitor activity GO term in CanL dogs might be associated with the prevention of coagulation processes. In fact, disseminated intravascular coagulation was previously observed in dogs infected with L. infantum [46]. Furthermore, the presence of circulating immunocomplexes, frequently observed in diseased dogs, activates coagulation pathways in the context of visceral leishmaniasis [47,48]. Thus, a possible mechanism of protection against these harmful coagulation events might be an increased presence of proteins that prevent coagulation. Nonetheless, the relative abundance of the individual proteins associated with these GO terms was not significantly increased in CanL. The detection of group-specific proteins was further exploited by a qualitative analysis through the identification of spectra in individual runs. This allowed the identification of two particularly interesting proteins, Myo-inositol oxygenase, detected in 44 out of the 48 injections associated with the Healthy group; and carboxylesterase 5 A, detected in 40 out of the 52 injections of the CanL group and in only 4 Healthy injections [10]. Myo-inositol oxygenase is a renal proximal tubular–specific enzyme, that catalyzes the oxidation of Myo-inositol to D-glucuronate, being considered an essential enzyme for inositol metabolism. The upregulation of this enzyme in the kidney is associated with increased production of inflammatory cytokines and ROS [49]. The plasma levels in humans were associated with the progression of chronic kidney disease [50]. Still, the biological implications of the reduced detection of Myo-inositol oxygenase are not evident because kidney damage is a frequent clinical sign in CanL dogs. Interestingly, increased Myo-inositol, the natural substrate of MIOX can cause in vitro depolarization of macrophages, altering the phagocytic potential of macrophages. This was shown in the context of antibiotic-resistant E. coli. [ 51]. Carboxylesterase 5 A can be associated with xenobiotic metabolism [52]. It is highly secreted in cats being found in the urine of these animals. We cannot exclude that the diseased animals have increased circulating carboxylesterase 5A associated with veterinary medical interventions with drugs. Moreover, reports on naturally infected dogs demonstrated changes in plasma concentration of lipids, such as cholesterol. These changes might be associated with hepatic disorders caused by infection and the deposition of immunocomplexes. The presence of carboxylesterase 5A associated with the EVs fractions recovered in diseased animals might be a sign of these perturbations. The qualitative approach, although invaluable, does not convene the full biological picture associated with the EVs preparations. Thus, a quantitative approach comparing the Healthy group with the CanL was performed. The reduced number of proteins with significantly altered abundance might be due to the already mentioned cohort heterogeneity and the significant amount of non-EVs contaminants. None of the proteins identified as more abundant in CanL presented a high PSM count, thus, the real biological relevance is difficult to ascertain. Concerning the proteins significantly less abundant in CanL EVs, several had a high number of PSMs. Three of them overlapped with the 10 most abundant proteins in the merge of the healthy samples—GLOBIN domain-containing protein, protein 4.1 and haemoglobin subunit alpha. Among the other proteins significantly less abundant in CanL with a detection profile with more than 2 UP and more than 50 PSMs carbonic anhydrase, biliverdin reductase B and glycophorin C were mostly identified in the Healthy group. GO assessment of the proteins revealed that the terms enriched are associated with red blood cell biology. These observations were in agreement with previous studies that report a haemoglobin downregulation in the context of infection [53]. It has been reported that inflammation associated with CanL can cause anaemia and consequently prevent the body from using the stored iron reservoirs, which may cause a decrease in haemoglobin. Decreases in haemoglobin levels were verified in different situations. Among them, haemoglobin subunit beta levels were downregulated in dogs’ serum after treatment with anti-Leishmania drugs [54]. The downregulation of haemoglobin subunit-α has previously been described in Leishmania-panamensis-infected human skin lesions [55]. Haemoglobin and globin, together with the carbonic anhydrase, another protein also significantly less abundant in the CanL group, were also downregulated in the saliva of experimentally L. infantum infected dogs with clinical signs [53], which is in agreement with the findings made in CanL EVs. Anaemia can be caused by chronic renal failure, haemorrhage, haemolysis, bone marrow hypoplasia or aplasia and decreased erythrocyte membrane lipid fluidity [56,57,58]. Infection can also impact erythropoiesis due to changes in the bone marrow [59] and kidneys [56]. CanL dogs frequently present alterations in the erythrogram status. Moreover, studies have described a correlation between the clinical signs and severity of anaemia [60,61,62]. In fact, 6 out of the 20 CanL dogs had anaemia, which corroborates with the results obtained in this study. It has also been shown that CanL-associated anaemia can result from impaired erythrocyte membrane fluidity [57]. Other less abundant proteins, such as protein 4.1, anion exchange protein, and erythrocyte membrane protein 4.2 are all integrant parts of the membrane skeleton of erythrocytes. Rh protein, glycophorin A, and glycophorin C are all proteins present in the red blood cell membrane. In fact, alterations in glycophorin C, a protein that interacts with the erythrocytes cytoskeleton, are involved in hematopoietic homeostasis [63]. Erythrocytes membrane structural integrity is necessary for their function. Thus, perturbations of the membrane characteristics, such as fluidity, oxidative changes, or ligand-specific interactions often result in pathology [64]. Moreover, the aforementioned carbonic anhydrases were shown to be also diminished in dogs’ saliva in CanL dogs [53] and have antimicrobial activity and contribute to the maintenance of pH homeostasis in the mouth [65]. The downregulation of this protein was suggested to be an organism’s response against the parasite by diminishing antioxidant compounds. Few proteomic studies analysing dogs with CanL are reported [53,66,67], two of which analysed canine serum, one of them from infected dogs and healthy dogs [67] and the second one compared the proteins from asymptomatic and symptomatic stages of infection [66]. None of the differentially abundant proteins found in these studies were significantly enriched in our samples. Although the samples chosen in this study present variability in different parameters such as age and breed, it was possible to identify possible EVs-associated proteins such as carbonic anhydrase, biliverdin reductase B and glycophorin C that were consistently detected in the Healthy animals. The identification of proteins associated with L. infantum would be of high value not only for a better understanding of the infection but also as possible exploitable biomarkers. However, only one unique peptide was identified for each protein and most proteins only appeared in one sample. This can be explained by the presence of other highly abundant peptides from the host that prevent the detection of multiple peptides from the same protein. That was also observed for T. cruzi when trying to identify biomarkers from plasma-derived EVs in a heart transplant patient with chronic Chagas disease [17]. Nonetheless, when comparing the PSMs/UP associated with CanL and Healthy groups when using databases from two other pathogens, N. caninum and P. falciparum, the data suggests Leishmania-specific protein identification. Interestingly, the PSMs/UP ratio for the Healthy animals obtained using the Leishmania database was similar to the one obtained for the other pathogens, further strengthening the Leishmania-specific identifications for the CanL group. Another limitation of the study was the possibility of subclinical infections. We tried to limit this by using also animals from non-endemic regions, but we cannot exclude that some negative identifications might be indeed Leishmania-specific. Among the parasite proteins identified in more than one sample, putative DNA Polymerase epsilon subunit b was the one most consistently detected being detected in six dogs. The catalytic subunit of this protein was previously identified in circulating immunocomplexes of VL patients [25]. A significant difference in seroreactivity was verified when the Healthy group and the CanL group were compared suggesting that the protein is present during the infection. Significantly, DNA Polymerase epsilon subunit b peptides were identified in five seropositive animals. Overall, this study is the first characterization and proteomic analysis of EVs recovered from dogs’ plasma. The core proteome of this type of EVs was described and changes associated with CanL were identified. It was established that EVs can be useful to understand the pathology since several known Leishmania-specific haematological and biochemical alterations were also possible to associate with the EV molecular cargo. In addition, some intriguing protein identification such as Myo-inositol and carboxylesterase 5 A might be proteins of interest for CanL management that were previously unknown. Interestingly, it was possible to find with some consistency some parasite-specific proteins. Among these, the most relevant was the putative DNA polymerase epsilon subunit b. The value of these identifications is still uncertain because they are not supported by more than one UP. Further experiments with dogs afflicted with other pathologies are needed to evaluate if the markers found are adequate for CanL management. The presented data paved the way for canine plasma EV studies and enables inter-species comparisons. ## 4.1. Parasites and Cell Culture L. infantum (MHOM/MA/67/ITMAP-263) promastigotes were maintained in standard RPMI 1640 medium supplemented with $10\%$ Fetal Bovine Serum (FBS), 2 mM L-glutamine, 100 U/mL penicillin, 100 mg/mL streptomycin and 20 mM HEPES buffer (all products from Lonza, Basel, Switzerland) at 26 °C. Cultures were grown with a starting inoculum of 1 × 106 parasites. ## 4.2. Antigens For SPLA production, 5 days old promastigotes were washed three times with PBS and centrifuged at 3500× g, 10 min, at 4 °C. The pellet was suspended in PBS containing 1 mM phenylmethylsulfonyl fluoride (PMSF) protease inhibitor and submitted to 10 freeze–thaw cycles for the parasites’ rupture. The suspension was centrifuged at 13,000× g, 30 min, at 4 °C and the supernatant was recovered, quantified by DC (detergent compatible) protein assay (Bio-Rad Laboratories, Hercules, CA, USA), and stored at −80 °C in single aliquots. The rK39, obtained from Dr Steven Reed (Infectious Disease Research Institute, Seattle, WA, USA), was suspended H2O, quantified and stored at −80 °C in single-use aliquots. ## 4.3. Collection, Characterization, and Selection of Biological Samples During this study, thirty-nine samples were selected (20 CanL dogs and 19 healthy dogs). The 20 CanL dogs were from continental Portugal in regions endemic to L. infantum infection. Healthy samples 1–11 were from regions without known active Leishmania transmission, Azores, and Ireland, the remaining 9 were from endemic areas. Peripheral blood was collected by venepuncture in EDTA K3 tubes (Sarstedt, Sarstedtstraße, Germany). Samples were centrifuged at 400× g for 10 min at room temperature. Plasma was collected and posteriorly centrifuged twice at 2000× g for 10 min at 4 °C. Supernatant was recovered and frozen at −80 °C. Dogs were included in CanL group if they were polysymptomatic for CanL and presented at least two of the three criteria [1] seropositive for at least one Leishmania-specific antigens (SPLA and rK39) by the in-house ELISA; [2] positive result on a rapid-chromatographic test validated for CanL; [3] positive parasite culture from blood, lymph node or bone marrow cells seeded in Schneider media. Healthy dog samples were obtained from animals fulfilling the criteria to become eligible for blood donation programs. All healthy dogs met the following criteria: [1] absence of clinical evidence of disease; [2] seronegative results for Leishmania-specific antigens (SPLA and rK39) by ELISA and commercially available rapid chromatographic tests validated for CanL diagnosis; [3] negative Leishmania blood PCR. ## 4.4. EVs Separation EVs were isolated from canine plasma samples by size-exclusion chromatography (SEC) as previously described by de Menezes-Neto et al., 2015 [27], with some modifications. Aliquots of plasma were thawed on ice and processed by centrifugation at 2000× g for 10 min at 4 °C. One hundred microliters of supernatant were then loaded on the top of a 1 mL Sepharose CL-2B (Cytiva, Marlborough, MA, USA) column pre-equilibrated with PBS. Ten fractions of 100 µL were collected immediately after sample loading and frozen at −80 °C. A small aliquot of each was kept to measure protein concentration by BCA (Thermo Scientific, Waltham, MA, USA) by measuring absorbance an automatic reader (Synergy2, BioTek, Winooski, VT, USA) and to perform the bead-based assay. This procedure was repeated at least 3 times, using new columns for a total volume of processed plasma of 300 µL minimum (100 µL/column). ## 4.5. Bead-Based Flow Cytometry The bead-based flow cytometry assay was performed based on conjugation of latex microbeads to EVs followed by antibody coupling for FACS analysis [68]. The EVs obtained from canine plasma, and isolated by SEC, were coupled to latex microbeads and then incubated with antibodies to detect the presence of at least two of the three EVs markers, CD9, CD5L [27] and CD71 [28,29], in the preparations. Briefly, 50 µL of the SEC fractions were coupled to Aldehyde/Sulfate Latex Beads, $4\%$ w/v, 4 µm (Invitrogen, Waltham, MA, USA) by incubation of 15 min, with agitation every 5 min. Coupled beads were then blocked with 1 mL of BCB Buffer (PBS 1X/BSA $0.1\%$/NaN3 $0.01\%$—from Sigma-Aldrich, St. Louis, MO, USA), incubating overnight in a rotation device. Beads were further centrifuged at 2000× g for 10 min, the supernatant was removed, and the pelleted beads were suspended in 150 µL of BCB buffer. 45 µL of bead suspension was incubated with anti-CD5L antibody (Abcam: ab45408, Cambridge, UK) at 1:5000 dilution, anti-CD71 antibody (Abcam: ab84036) at 1:1000 dilution or anti-CD9 (Immunostep 9PU-01MG, Salamanca, Spain) at 1:500 dilution for 30 min at 4 °C in a round bottom plastic microplate. After washing, samples previously incubated with CD9 were incubated with α-mouse secondary antibody conjugated to Alexa 488 (Invitrogen: A11001) and fractions incubated with CD5L and CD71 were incubated with α-rabbit secondary antibody conjugated to Alexa 488 (Invitrogen: A11008) both at 1:500 dilution for 30 min at 4 °C, protected from light. After two wash steps, the beads were suspended in 100 µL of PBS and analysed by flow cytometry using a BD FACSLyric flow cytometer. MFI and bead count data were obtained using FlowJo V10 Software (Tree Star, Woodburn, OR, USA). As a control for specificity, a pool of fraction 5 and 6 obtained from SEC coupled to beads was incubated with the secondary antibody Alexa 488 at dilution 1:500. ## 4.6. Nanoparticle Track Size mode determination ($$n = 8$$ for the Healthy group; $$n = 12$$ for the CanL group) was performed resorting to Nanoparticle Track Analysis (NTA) in a NanoSight LM1012 instrument (Malvern Instruments Ltd., Malvern, UK) using the NTA software (version 3.2). The instrument is equipped with a 638 nm laser, a system of video capture and a particle-tracking software. The fractions with highest fluorescence values to CD5L/CD71/CD9 were analysed in this equipment. Size determination was considered when the number of particles per frame was in a range from 10 to 120. ## 4.7. Transmission Electron Microscopy Representative images of canine plasma-derived EVs recovered by SEC were obtained with negative staining transmission electron microscopy, using Jeol JEM 1400 transmission electron microscope (JEOL, Tokyo, Japan) and images were digitally recorded using a CCD digital camera Orius 1100W (Tokyo, Japan). Digital images of EVs from each sample were taken. ## 4.8. Proteomic Analysis and Protein Identification After physical characterization of Evs, the proteomic analysis proceeded. Digestion of the samples (10–100 µg protein) was performed with trypsin/LysC (1:50) overnight following solid-phase-preparation (SP3) previously described [69]. The concentration of the resulting peptides was measured by fluorescence. The proteomic analysis of EVs was performed by injection of 500 ng of peptides in a nano LC (Ultimate 3000, Thermo Fisher Scientific, Bremen, Germany) connected to a Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) as previously described [70]. The LC-MS/MS raw data was analysed by Proteome Discoverer 2.5.0.400 (Thermo Fisher Scientific). For protein identification, the proteomes from four taxonomic selections were considered, Leishmania infantum (8045 entries, 2019_11), Canis lupus familiaris (45,301 entries, 201_11), Plasmodium falciparum (129,297 entries, 2022_01) and Neospora caninum (6933 entries, 2022_02) from UniProt, together with a database of common contaminants from MaxQuant. ## 4.9. Data Analysis Canine peptides were selected based on a maximum false discovery rate (FDR) of $1\%$. Canis lupus familiaris proteins identified only with one unique peptide were also excluded. Common contaminants were excluded from the analysis. The data for canine proteins was analysed using the merged data from the CanL dogs EVs and the merge data of the healthy dogs EVs. Moreover, for quantification purposes, the data was analysed using the data from both groups to perform the ratio of abundances (CanL/Healthy). The data was transformed into logarithmic scale (Log2). Subsequently, a statistical analysis was performed considering the ratio of abundances. Thus, a fold change and a correspondent p-value were reported for each protein. This data allowed the construction of a volcano plot that highlighted the upregulated and the downregulated proteins. Enrichment analysis of canine proteins with statistically different abundance in CanL dogs were performed with the Database for Annotation, Visualization, and Integrated Discovery (David 2021) [71]. L. infantum proteins were evaluated in an individual sample level. L. infantum proteins with one unique peptide identified were considered. Proteins identified in Healthy dogs EVs were considered false positives and were excluded from the analysis. All peptides identified were subjected to homology analysis in NCBI, and peptides only identified in Leishmania species were valued. Moreover, the proteins were only considered as potential candidates if they appeared in several individual samples. ## 4.10. DNA Isolation Leishmania infantum (MHOM/MA/67/ITMAP-263) genomic DNA was extracted using DNAzol (Invitrogen, Waltham, MA, USA), following the manufacturer’s instructions. DNA quality and concentration were determined with NanoDrop (Thermo Fisher Scientific, lmington, DE, USA), and then stored at −20 °C until use. ## 4.11. Putative DNA Polymerase Epsilon Subunit b Cloning Fragments of the open reading frames of putative DNA Polymerase epsilon subunit b (LinJ.35.1780; chromosome LinJ.35; 666433-668091) were PCR-amplified, using primers 1 (5’ CATATGATGAGGGAGCCCAAGGATG 3’) and 2 (5’ GGATCCTTACTCCAGCTATTGAGGCTGC 3’) PCR conditions used for the amplification of putative DNA Polymerase epsilon subunit b were: initial denaturation (2 min at 94 °C), 10 cycles of denaturation (15 s at 94 °C), annealing (30 s at 53.8 °C,) elongation (2 min at 72 °C), followed by 20 additional cycles of denaturation (15 s at 94 °C), annealing (30 s at 53.8 °C,) elongation (2 min + 5 s per cycle at 72 °C) and a final extension step (7 min at 72 °C). All the PCR products were obtained using a Taq DNA Polymerase with proofreading activity (Roche, Basel, Switzerland). The fragments were isolated and cloned into pGEM-T Easy vector (Promega, Madison, WI, USA) and posteriorly sequenced. The putative DNA Polymerase epsilon subunit b genes were excised from the pGEM-T Easy vector using NdeI/BamHI, gel purified and subcloned into pET28a(+) expression vector (Novagen—Sigma-Aldrich, St. Louis, MO, USA). The resulting plasmids presented a hexa-histidine tag in the N-terminal and were transformed into E. coli BL21 DE3 cells (Invitrogen, Life Technologies, Carlsbad, CA, USA). ## 4.12. Protein Production The recombinant protein was grown in at least 1 L of LB broth medium supplemented with 5 g/L of NaCl and 50 µg/mL Kanamycin. Precultures were grown overnight at 37 °C. Cells were induced with 1 mM IPTG and were grown at 37 °C. After 4 h, the cells were harvested at 4 °C, maximum speed for 30 min. The pellet was stored at −20 °C. ## 4.13. Inclusion Bodies Isolation and Solubilization The pellet corresponding to 200 mL of culture was resuspended in 8 mL of resuspension buffer [20 mM Tris-HCl, pH 8.0]. Lysozyme was added to a final concentration of 0.2 mg/mL, as well as 10 μg/mL DNase and 1 mM MgCl2 and incubated 30 min on ice. The cells were disrupted with sonication using S-250A Model Sonifier Analog Cell Disrupter (Branson Ultrasonics, Brookfield, Connecticut, United States) using the following conditions: macrotip; 10 cycles; 10 pulses; output control 4; duty cycle $50\%$. A centrifugation step followed, and the supernatant was discarded. The pellet was suspended in 6 mL of cold isolation buffer [20 mM Tris-HCl, pH 8.0; 2 M Urea; 0.5 M NaCl; $2\%$ Triton-X 100]. A sonication step followed with the conditions: microtip; 5 cycles; 10 pulses; output control 3; duty cycle $50\%$. A centrifugation was performed, the supernatant was removed and the pellet resuspend pellet in cold isolation buffer. The sonication, centrifugation and resuspension steps were repeated two more times, in the same conditions. The remaining pellet was stored at −20 °C for the solubilization protocol. The pellets were resuspended in 10 mL of 8 M urea buffer [20 mM Tris-HCl pH 8.0; 0.5 M NaCl; 8 M Urea; 1 mM β-mercaptoethanol] and were incubated for 1 h at room temperature on the rotation device. A centrifugation was performed to remove any particles and the protocol was completed with a filtration with a 0.45 μm filter (Millipore, Merck, Burlington, MA, USA). The sample was kept to use in the preparative SDS-PAGE protocol. ## 4.14. Semi-Quantification To determine the concentration of putative DNA Polymerase epsilon subunit b, prior and post protein extraction protocol, SDS-PAGE analyses were performed, using bovine serum albumin (BSA) as a standard. ImageJ software (ImageJ, National Institutes of Health) was used to determine the mean gray values through the intensity of the bands. The calibration curve was determined by plotting the mean gray value (MGV) against BSA mass. ## 4.15. Preparative Gels and Protein Extraction Four $10\%$ (m/v) poliacrilamide 1.5 mm SDS-PAGE gel were prepared and loaded with 390 µg of putative DNA Polymerase epsilon subunit b. The gels were run with an upper buffer [Tris/Glycine + $0.05\%$ SDS + 25 mg/L Coomassie R250], to avoid posterior staining. The bands of interest were cut and put in a single 50 mL falcon tube. 6 mL of protein extraction buffer [2 mM DTT, 0.5 mM PMSF, $0.02\%$ SDS] was added to gel. The gel was homogenized into small beads with a polytron and incubated overnight at 4 °C on rotation. A centrifugation at 4000× g followed and the supernatant was kept. Another round of protein extraction succeeded (incubation overnight at 4 °C and supernatant recovery). The supernatants were frozen at −80 °C and posteriorly lyophilized. The protein was suspended in 1 mL of water, and cold acetone in a 4:1 ratio was added. The falcon was frozen overnight, and the protein was pelleted the next day at 4000 g for 30 min. The pellet was washed with ice cold $90\%$ acetone, centrifuged and dried at 37 °C. Finally, the protein was resuspended in PBS and homogenized with weak sonication. ## 4.16. Enzyme-Linked Immunosorbent Assay Flat-bottomed 96 well high-binding microtiter plates (Greiner, Kremsmünster, Austria) were coated with 5 µg/mL of recombinant proteins rK39 and putative DNA Polymerase epsilon subunit b; or 10 µg/mL of soluble promastigote Leishmania antigens. All antigens/extracts were diluted in carbonate buffer [0.04 M NaHCO3, 0.01 M Na2CO3, pH = 9.6] and dispensed 50 µL/well. The plates were incubated overnight at 4 °C. Posteriorly, the plates were washed several times with PBS-Tween ($0.05\%$) and blocked with PBS-low-fat-milk ($3\%$), for 1 h at 37 °C. The plates were washed with PBS-Tween ($0.05\%$) and canine sera were diluted (1:1500) in PBS-Tween ($0.05\%$) and incubated for 30 min at 37 °C. After washing the plates, 30 min at 37 °C incubation with the secondary antibody, in the dark, occurred. α-dog IgG conjugated to horseradish peroxidase (A6792; Sigma, St. Louis, Missouri, United States) was diluted 1:1500 (for the rK39 and SPLA) or 1:1000 (for putative DNA Polymerase epsilon subunit B) in PBS-Tween ($0.05\%$). The plates were washed and developed for 10 min in the dark at room temperature (RT) using 0.5 mg/mL ο-phenylenediamine (OPD; Sigma, St. Louis, Missouri, United States) in citrate buffer [0.05 M C6H8O7.H2O; 0.024 M Na2HPO4; pH = 5.3] as substrate and hydrogen peroxide as reactive oxygen metabolic by-product. Reaction was stopped with HCl 3 M and absorbance values were read at 492 nm in a Synergy 2 automatic reader (BioTek, Winooski, VT, USA). ## 4.17. Statistical Analysis The cut-offs for SPLA and rk39 were calculated elsewhere [72]. 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--- title: Current Consumption of Traditional Cowpea-Based Dishes in South Benin Contributes to at Least 30% of the Recommended Intake of Dietary Fibre, Folate, and Magnesium authors: - Lorène Akissoé - Youna M. Hemery - Yann E. Madodé - Christèle Icard-Vernière - Isabelle Rochette - Christian Picq - Djidjoho J. Hounhouigan - Claire Mouquet-Rivier journal: Nutrients year: 2023 pmcid: PMC10056862 doi: 10.3390/nu15061314 license: CC BY 4.0 --- # Current Consumption of Traditional Cowpea-Based Dishes in South Benin Contributes to at Least 30% of the Recommended Intake of Dietary Fibre, Folate, and Magnesium ## Abstract Regular consumption of legumes is recommended worldwide for its environmental and health benefits. Cowpea, the most frequently consumed pulse in West African countries, is rich in nutrients and health-promoting bioactive compounds. A one-week retrospective food frequency questionnaire was used to estimate the contribution of the cowpea-based dishes to the recommended nutrient intake (RNI), based on their consumption frequency, intake, and nutritional composition. Participants were 1217 adults (19–65 years) from three urban or rural areas in southern Benin. Out of all respondents, $98\%$ reported that they usually consumed cowpea-based dishes. The mean consumption frequency was 0.1 to 2.4 times/week, depending on the type of cowpea-based dish. The mean amount consumed was 71 g and 58 g of seeds/adult/day in urban and rural areas respectively. The mean daily contribution of cowpea-based dishes to RNI was $15\%$ for energy, $42\%$ for fibre, $37\%$ for magnesium, $30\%$ for folate, $26\%$ for protein, and just above $15\%$ for zinc and potassium. Thus, such regular cowpea consumption should be maintained. ## 1. Introduction The increasing industrialization and urbanization worldwide are associated with ‘nutrition transition’, characterized by diets richer in fats and sugars, and with more sedentary lifestyles [1]. As a consequence, the prevalence of overweight and obese populations, and related chronic diseases, is constantly increasing [2]. In Benin, the prevalence of diabetes in 2014 and high blood pressure in 2015 were $7\%$ and $28\%$, respectively, in adults. Moreover, in 2016, obesity (body mass index ≥30) concerned $23\%$ of women and $6\%$ of men [3]. Therefore, strategies are needed to prevent these dietary-related noncommunicable diseases. For instance, the consumption of recommended food groups, such as pulses, should be encouraged due to their health benefits [4]. Pulses (e.g., dry beans, peas, chickpeas, cowpeas) are particularly interesting for fighting against malnutrition [5], because they are rich in key nutrients and in some health-promoting bioactive compounds [6]. Pulses are an important source of dietary proteins with a good essential amino acid profile, complementary to that of cereals. They are rich in minerals (particularly magnesium, potassium, zinc, and possibly also iron) [7] and vitamins (E and B groups) [8]. They have a low lipid content [9], and provide large amounts of dietary fibre and various bioactive compounds that help prevent metabolic disorders. Several studies suggested that the consumption of legumes could help aid weight loss, possibly due to their low lipid and high dietary fibre contents that favour satiation and satiety [6]. Pulse-based foods have low glycaemic index values, recommended for people with type 2 diabetes [10]. Regular pulse consumption helps reduce LDL cholesterol levels and lower blood pressure [11]. Wang et al. [ 12] showed associations between legume consumption and gut microbiome diversity. The ongoing nutritional transition in many urban areas of sub-Saharan Africa, characterized by an increased demand for processed and quick-to-prepare foods, leads to a gradual shift from the traditional diet (high in fibre, low in fat and sugar), to a diet rich in refined foods, low in fibre, and high in fat, salt and sugar [13]. The long time required to prepare cowpea-based dishes and the presence of anti-nutritional factors that cause intestinal disorders could also lead to turn away from traditional cowpea-based dishes in these areas. In addition, their nutritional composition could be affected by processing (as reported for the traditional processing of cowpeas into Ata and Ata-doco, two cowpea-based doughnuts consumed in Benin [14]), leading to a reduction of their nutritional value. Ten years ago, in Benin, Madode et al. [ 15] investigated the consumption patterns of ten traditional cowpea-based dishes (among the 18 dishes identified as available in the southern part of the country) and their qualitative consumption frequency (regularly, often, occasionally). They found that Ata (doughnut), Abobo (stew), and Atassi (mixed dish of cowpeas and rice) were the most frequently consumed cowpea-based dishes. They also determined some nutritional traits of these dishes; however, they did not have data on the consumed quantities and could not evaluate their contribution to the recommended nutrient intake (RNI). The aims of our study were: (i) to update data on cowpea consumption patterns in South Benin, and (ii) to estimate the nutritional intakes linked to the consumption of cowpea-based foods. Among the 18 cowpea-based dishes listed in the work by Madode et al. [ 15], the nine that are currently the most popular were selected for this study. These dishes can be classified into three groups: doughnuts (Ata, Ata-doco, Ataclè), mixed dishes (Abla, Atassi, Djongoli), and stews (Adowè, Abobo, Vêyi) (Figure 1 and Table S1). First, we carried out a food consumption survey among 1217 adults (19–65 years of age) in an urban area (Cotonou) and two rural areas (Adjohoun, Allada) in the southern part of Benin, using a food frequency questionnaire (FFQ) that we validated. Then, we sampled the nine dishes from street food vendors in Cotonou to determine their nutritional composition (proximate composition, minerals, folate, and thiamine). Using the FFQ and nutritional composition data, we determined the contribution of these dishes to the adult RNI of dietary fibre, proteins, magnesium and folate, zinc, potassium, and thiamine. ## 2.1.1. Study Areas and Cluster Sampling We carried out this study in three different municipalities in the southern part of Benin, Cotonou (an urban area of ~680,000 inhabitants), at the end of 2017, and Adjohoun and Allada (two rural areas of ~75,000 and ~128,000 inhabitants, respectively) in early 2019. In Benin, municipalities encompass administrative units called ‘arrondissements’ that, in turn, contain city districts (for urban municipalities) or villages (for rural municipalities). Adjohoun is a rural municipality in the Ouémé Department, that produces $65\%$ of the total amount of cowpea seeds grown in Benin [16]. Conversely, Allada, a rural municipality of the Atlantique Department, is a low-cowpea production area. In each study area, we carried out representative two-step random sampling by considering city districts or villages as clusters (Figure 2). In the first sampling step, we selected two or three clusters per arrondissement using random numbers generated by the Excel software. In the second step, we randomly chose a direction by spinning a pen at the centre of each cluster for household selection. One adult, aged between 19 and 65 years, was interviewed in each selected household. An equal number of men and women were selected by cluster. The sample size was chosen based on previous studies that used FFQs in Africa [17]: 641 adults in Cotonou were surveyed and 576 in Adjohoun and Allada (288 respondents per rural area). ## 2.1.2. Development of the Food Atlas A food atlas was developed for the nine cowpea-based dishes described in Table S1 and presented in Figure 1, following the guidelines proposed by Nelson and Haraldsdóttir [18]. For each cowpea-based dish, photographs of four portion sizes were included in the food atlas (an example is provided in the supplementary data, Figure S1). The photographs illustrate portions corresponding to the four best-selling prices for each cowpea-based dish, as sold by street vendors. After determining the best-selling prices by recording the consumers’ purchase prices at different selling places, we weighed the portion size for each best-selling price at several selling places and used the mean value as the portion size in the atlas. For each cowpea-based dish, there were nine possible portion size choices, with four presented as photographs (B, D, F, H) and five intermediate portion sizes without photographs (A, C, E, G, and H) (Figure S1). This allowed covering a wide range of cowpea-based dish portions consumed by the participants. ## 2.1.3. Validation Studies We carried out a validation study in Cotonou to compare the portion sizes chosen by respondents listed in the food atlas to the corresponding weighed records. A convenient sample size of 50 customers per cowpea-based dish produced as street food was initially targeted. Finally, the number of people we actually surveyed varied between 19 and 52 respondents per dish, because for some dishes, there were very few street vendors. The study was performed on two days at various selling places for each cowpea-based dish. On day 1, the quantity of the cowpea-based dish consumed by the respondent was weighed using a kitchen scale (Soehnle, weighing capacity 5 kg, weight accuracy 1 g). An appointment was made to perform the 24 h recall (Figure 2) using the food atlas on the next day (day 2). Respondents were not aware that the portion size consumed the day before would be asked again on day 2. We validated the FFQ with a sample of 50 respondents in each of the three study areas. Briefly, three 24 h recalls were carried out in the same week (two recalls during weekdays and one in the weekend), followed by the FFQ at the end of the week (Figure 2). The usual portion size determined with the FFQ data was then compared with the mean estimated weights during the three 24 h recalls. ## 2.1.4. FFQ Study Description The FFQ included two parts. In the first part, data on the household head’s socio-professional characteristics, household income and expenses (feeding and clothing), household belongings, and housing characteristics were collected to characterize the household socio-economic index. In the second part, data on the consumption of cowpea-based dishes and the estimation of the quantities consumed over one week were recorded. During the FFQ-based interviews, we first asked general questions about the consumption of various legume species (results published elsewhere [19]). This was followed by questions on the cowpea-based dish types consumed by the respondent during the previous week (named 7-day recall, thereafter). For each cowpea-based dish, we recorded how many times it was consumed, the last place of consumption, and the quantity usually consumed (estimated using the food atlas). ## 2.1.5. Data Collection Data were collected on digital tablets, using surveyCTO™-generated Excel forms to allow their quick and correct transfer to the SurveyCTO™ platform. For the FFQ validation study, data were collected on paper forms and then entered in the Epidata software (version 3.1). ## 2.2. Sampling of Cowpea-Based Dishes The nine most frequently eaten cowpea-based dishes were sampled at 27 street food vendors (i.e., three vendors for each type of dish) in Cotonou and transported in a cooler containing ice to the laboratory, where they were stored at −20 °C until analysis. ## 2.3. Nutritional Composition Analysis Lipids (AOAC Official Method 2003.06) and proteins (NF V03-050, AFNOR, 1970) were determined for each cowpea dish using standard methods [20]. Ash contents were determined by calcination in a furnace at 530 °C. Dry matter contents were determined by oven drying at 105 °C for 24 h. The total dietary fibre (TDF) was determined using an enzymatic–gravimetric method (Megazyme K-TDFR Kit), as described by Njoumi et al. [ 21]. Available carbohydrates were determined by difference using the following formula: (100 − (Water content + Lipid + Protein + Ashes + TDF)), and the energy value using the Atwater coefficients [22]. Minerals were extracted with a closed-vessel microwave digestion system (ETHOS-1, Milestone, Italy) [23]. Extracts were then analysed for total iron, zinc, calcium, potassium and magnesium by optical emission spectrometry, using an ICP-OES 5100 apparatus (Agilent Technologies, Les Ulis, France). Total folate content was analysed by trienzymatic extraction followed by a microbiological assay, using Lactobacillus rhamnosus ATCC 7469 as the growth indicator microorganism [24]. Total thiamine content was determined by chromatographic analysis using the Waters AcquityTM Ultra Performance LC (UPLC) system (Waters, Milford, MA, USA), equipped with an AcquityTM fluorescence detector and an Acquity UPLCTM column, using a method adapted from Schmidt et al. [ 25]. ## 2.4.1. Analysis of Data from the Validation Studies For both validation studies, we used Spearman’s correlation tests after checking the data normality. We assessed the concordance between methods using the Bland–Altman test. For this, we log-transformed data [26] to narrow the limits of agreement. We calculated the antilogarithms of the limits of agreement to obtain the ‘24 h recall over-weighed record’ and ‘usual portion size from the FFQ over the mean value of the three 24 h recalls’ ratios for the food atlas and FFQ validation, respectively. These ratios were then expressed as percentages of agreement [27]. ## 2.4.2. Determination of the Socio-Economic Index The household socio-economic index (SEI) was determined separately for the rural and urban populations by considering 17 variables related to the housing quality, assets owned, household income and expenses, and household head’s socio-professional characteristics. A multiple-correspondence analysis with the selected variables was performed using the Rstudio software (version 3.5.1). We chose the axis that accounted for the highest percentage of the inertia, and performed an ascending hierarchical classification with the coordinates of the individuals on this axis. The dendrogram partitioning after the ascending hierarchical classification allowed the generating of socio-economic classes. Then, the surveyed households were clustered in three different SEI classes in each area: low, middle, and high (Table 1). ## 2.4.3. Estimation of the Contribution to the RNI We determined the contribution of each cowpea-based dish to the RNI using the daily consumed quantity for each traditional dish during the 7-day recall (estimated with the usual portion size) and the daily frequency of consumption (number of consumption times over a week divided by 7). For all respondents, we calculated the daily nutrient intake and compared them to the mean RNI for men and women. We used the reference values provided by FAO and WHO [28,29] for mineral, vitamin and protein requirements, and the values by ANSES [30] for dietary fibre (Table S2). We divided the daily nutrient intakes (calculated as the amount of nutrients contained in the usual portion multiplied by the daily frequency) by the reference values, to obtain the contribution to the RNI, expressed as a percentage. ## 2.4.4. Calculation of the Weighting Coefficients for the Statistical Analyses As the number of city districts/villages or their population density may vary from one arrondissement to another, we calculated a design weight (Dw) that corresponded to the representativeness of each city district/village in its cluster, and applied it to each respondent. The Dw is defined as the inverse of the probability to include a respondent in the sample [13]. In our study, considering the two-step random sampling method used (Figure 2), the Dw corresponded to the product of the inverse of the probabilities at each step. We calculated these probabilities using the following equations: First step: sampling of city district/rural villages in the arrondissement [1]P1i=Number of city districts or villages selected for the FFQTotal number of city districts/villages in the arrondissement Second step: sampling of subjects within city districts/rural villages [2]P2i=Number of subjects selected in the city district or village Eligible adult population of the city district or village The formula to determine the design weight (Dwi) per respondent (i) was:[3]Dwi=1P1iP2i To perform the statistical analysis with a weighting factor that corresponded to the actual size of the sampled population (n), we calculated a weighed design weight (WeDwi) for each respondent as follows:[4]WeDwi=Dwin∑$i = 1$nthDwi; with ∑$i = 1$nthWeDwi=n The weighting was realized for urban and rural data separately to perform the statistical analyses per area. ## 2.4.5. Statistical Analyses Data were analysed with the Rstudio software (version 3.5.1). We used the weighting variables as a correction factor for the statistical analyses with the package ‘Survey’, to ensure that the results were representative of all studied areas. To identify the factors (SEI, sex, age, education level, place of living) that influenced the quantity of cowpea-based dishes consumed, we used general linear models (GLM) adapted to the weighted data and also the one-factor analysis of variance, t-test, and chi-2 test. The level of statistical significance was set at $p \leq 0.05.$ ## 3.1. Validation of the Food Frequency Questionnaire The validation of the portion sizes in the food atlas, using the 24 h recall data and the recorded weights, gave Spearman’s correlation coefficient values that ranged between 0.2 and 0.8 (Table S3). Moreover, the comparison of the usual portion size consumed in one week (7-day recall, estimated with the FFQ) with the mean portion size value of the three 24 h recalls gave correlation coefficient values of at least 0.4 for all dishes, except for Ataclè and Abla. This was due to the limited number of respondents that did not allow for establishing a statistical relation. Moreover, the concordance percentages between the 24 h recall and the recorded weights ranged between 74 and $128\%$, except for Vêyi ($162\%$), because it was consumed with other foods. The concordance percentages between FFQ and 24 h recall data varied between 66 and $96\%$, based on the Bland–Altman method. ## 3.2. Socio-Economic and Demographic Characteristics of FFQ Respondents The respondents’ mean age was 39 years in Cotonou and 36 years in the two rural areas (Table 1). As data were weighted for the statistical analysis, we observed some small (not significant) differences between the number of interviewed women and men, which normally should have been the same in both area types. The education level was significantly higher in Cotonou than in the two rural areas, where almost half of participants never went to school and only $2\%$ of respondents had a higher education diploma. ## 3.3. Consumption of Cowpea-Based Dishes Nearly all respondents ($98\%$) reported consuming cowpea-based dishes regularly and more than $70\%$ at least once per week (Table 2). Between 90 and $95\%$ of all respondents said that they had consumed at least one cowpea-based dish during the 7-day recall. The mixed-dish Atassi and the stew Abobo were the most consumed cowpea-based foods in all study areas (Table 3). We observed some variations in the cowpea-based dish consumption patterns in the function of the area. Specifically, we found significant differences in the number of consumers between urban and rural areas, except for Atassi and Ataclè. Ata, Vêyi, and Adowè were more consumed by respondents in the urban area. Conversely, the number of consumers of Ata-doco, Abla and Djongoli in the rural areas was twice as much compared to the urban area. Similarly, the percentage of Abobo consumers was $15\%$ higher in the two rural areas than in the urban area. By grouping the different cowpea-based dishes consumed by each respondent during the 7-day recall, the total number of combinations reached 102 in the rural areas and 113 in the urban area. The most consumed combinations were a two-dish combination that included Abobo and Atassi ($18\%$ of all consumers) in the rural areas and a three-dish combination (Ata, Abobo, and Atassi; $11\%$ of all consumers) in the urban area. ## 3.4. Consumption Frequency and Intake of Cowpea-Based Dishes Among people who said they had consumed cowpea-based dishes during the 7-day recall, the frequency of consumption was significantly different between urban and rural consumers, except for Atassi, Abobo and Djongoli (Table 3). The frequency varied between 1.4 and 3.1 times per week, depending on the dish. However, when we took into account the people who said they did not consume these dishes during the 7-day recall, the mean consumption frequency decreased to 0.1–1.5 times per week for most dishes, except for Abobo and Atassi that were consumed 1.9–2.4 times per week. Based on these mean frequency values for each dish, we estimated the mean consumption frequency of cowpea-based dishes, all dishes included, at 7 times per week (7.2 ± 4.9 and 7.3 ± 5.4 in rural and urban areas, respectively): approximately once per day. The mean portion size varied from one dish to another. Abobo, Atassi and Djongoli were consumed in quite large quantities (272 to 311 g/meal) (Table 3). Doughnuts (Ata, Ata-doco and Ataclè) and the stew Adowè were consumed in smaller portions. Among all survey participants, the mean intake per day of Adowè, Ata, Ata-doco, and Vêyi was significantly higher, and the mean intake per day for Atassi and Djongoli was lower in the urban area than in the two rural areas. When we converted the daily intake of cowpea-based dishes into ‘cowpea seed equivalent’ (Table 3), the mean total quantity of cowpea seed equivalent consumed per day was 71 g in Cotonou and 58 g in the two rural areas ($p \leq 0.05$). This conversion into ‘cowpea seed equivalent’ also allowed us to calculate that, with cowpea consumption alone, $51.5\%$ and $49.7\%$ of the surveyed population, in urban and rural areas, respectively, reached the recommendations of the EAT-Lancet Commission on Healthy Diets from sustainable food systems [31] in terms of pulse consumption (i.e., at least 50 g pulses /day). We observed some associations between socio-economic and demographic factors and (i) the daily intake of the studied dishes and (ii) the total quantity of cowpea seed equivalent consumed (Table S4) in both area types. In rural areas, the daily intake (in cowpea seed equivalents) in Allada (54 g), even if substantial, was lower than in Adjohoun (67 g), in agreement with the local level of production (Adjohoun being an important cowpea production area). In addition, Figure S3 shows that, in rural areas, the source of supply influences cowpea consumption: people who can buy directly from farmers have higher consumption than those who have to buy from the market. This influence of the supply source was not observed in urban areas. The total daily amount of cowpea seed equivalents consumed was significantly lower in women than in men (Table S4), due to smaller portion sizes. In the rural areas, the consumption of cowpea seed equivalents per day was lower in the high compared to the low and middle socio-economic index classes. Moreover, in Cotonou, the quantity of cowpea seed equivalent consumed per day was lower in respondents with a higher education level than in the other education level groups (Table S4). ## 3.5. Nutritional Composition of Traditional Cowpea-Based Dishes Table 4 presents the mean proximate composition, mineral, folate, and thiamine contents of the traditional cowpea-based dishes, as consumed. We observed high and significant variations ($p \leq 0.05$) among dishes for all the quantified nutrients: protein (3.0–9.9 g/100 g), dietary fibre (2.7–7.2 g/100 g), lipid (0.4–33.3 g/100 g), magnesium (13–68 mg/100 g), potassium (90–500 mg/100 g), zinc (0.3–1.6 mg/100 g), iron (0.4–8 mg/100 g), thiamine (28–175 µg/100 g) and folate (12–84 µg/100 g). Moreover, for each dish, we detected significant differences for most nutrients in function of the street food vendor (Table S5). The doughnuts had a high lipid content, and consequently higher energy values, compared to the other dishes. The nutritional density of the three stews was higher than that of the other cowpea-based dish groups (Table S6, supplementary data). ## 3.6. Contribution of Cowpea-Based Dishes to the RNI The mean contribution of all cowpea-based dishes to the RNI of the studied nutrients (except iron, calcium, and thiamine) was higher than that for the energy. The consumption of the cowpea-based dishes during the 7-day recall allowed for the covering $42\%$ of the RNI of dietary fibre, the highest percentage among the studied nutrients. Their contribution to the RNI of magnesium ($37\%$), folate ($30\%$), protein ($26\%$), zinc ($18\%$) and potassium ($17\%$) was also interesting, being higher than $15\%$ [32]. Conversely, their contribution to the RNI of calcium, iron, and thiamine was lower than $15\%$ (Figure 3). Independently of the area (rural vs. urban), the Abobo stew, widely consumed and in large quantities by the adult population according to the 7-day recall data, was the dish that most contributed to the RNI of the studied nutrients (Table S7). We observed that the number of different dishes consumed was strongly and positively associated with the amount of cowpea seed equivalent consumed (the correlation coefficient R was 0.64 and 0.81 for rural and urban areas, respectively). Consequently, the contribution to the RNI also increased with the number of different dishes consumed (Figure S2). ## 4. Discussion In this study, we developed a FFQ to identify the consumption patterns of cowpea-based dishes and estimate their contribution to the RNI in adults in Cotonou and in two rural areas in the south of Benin. First, we performed two validation studies to assess the accuracy in the estimation of the quantity of the traditional cowpea-based dishes consumed, using a food atlas and a FFQ as tools. For this we used correlation tests and the Bland–Altman method that are commonly employed in validation studies at the individual and group level, respectively [33,34,35]. The correlation coefficients ranged between 0.2 and 0.8. Lombard et al. [ 36] showed that with correlation coefficients from 0.2 to 0.49, the relation between data is considered acceptable, and above 0.5 is considered good. Moreover, based on the results obtained using the Bland–Altman method, we can consider that the assessment of the usual intake with the FFQ can generate accurate data [27]. In our study, more than $70\%$ of respondents said to consume cowpea-based dishes at least once per week in both urban and rural areas. This percentage was higher than that reported in 2009, in the survey (three non-consecutive 24 h food recalls) carried out among 200 urban adults in Benin [37], in which $56\%$ of respondents were estimated to be cowpea consumers. It shows an increase in the number of consumers in the last decade instead of the decrease that was rather expected due to the nutritional transition in urban areas. Among the cowpea-based dishes, the higher consumption of the stew Abobo and the mixed-dish Atassi could be explained by the fact that their processing involves few steps and that they can be easily prepared at home. Many other cowpea-based dishes are often bought as street food and consumed out of the house, particularly in urban areas where street food consumption is more developed than in rural areas [19]. This latter reason could explain the higher consumption frequency of Ata, Abobo, Vêyi and Adowè in urban areas than in rural areas. The consumption frequencies observed in our study were comparable with the results by Madodé et al. [ 15], showing that the current cowpea consumption frequency had not changed much compared with a decade ago. Most respondents declared consuming cowpea-based dishes at least once per week (Table 2). The total daily intake, expressed in cowpea seed equivalent, estimated at 71 g in the urban and 58 g in the rural areas, was in agreement with the value (77 g) reported by Delisle et al. [ 13] for legume consumption in south Benin. This corresponded to $49.7\%$ of the population in rural areas and $51.5\%$ in urban areas who consumed at least 50 g of pulses per day, as recommended by the EAT-Lancet Commission on healthy diets from sustainable food systems [31]. However, the chance to meet this recommendation was significantly lower for women living in the urban area (Figure S3). This observed consumption was also much higher than the 24.7 g for Benin in the country nutrition profiles by the Global Nutrition Report [38]. This important discrepancy could be partially due to consumption differences between the south and the other regions of Benin. Using the daily cowpea intakes of our study, the cowpea amount consumed could be estimated at 26 and 21 kg/adult/year in the urban and rural areas, respectively. This annual consumption is quite higher than in previous studies. Langyintuo et al. [ 39] reported that cowpea consumption was 9 kg/year/capita in Benin between 1990 and 1999, suggesting a doubling of the quantity consumed in the last three decades. The nutritional composition of traditional cowpea-based dishes varied widely depending on the street food vendors, reflecting variations due to cowpea cultivars, as concluded by Madode et al. [ 40], and processor practices. A previous study highlighted the influence of processors’ practices on the nutritional composition variability of cowpea-based doughnuts [14]. Despite these observed differences in composition, the high nutritional contribution, above $30\%$ of the RNI of dietary fibre, magnesium and folates, showed that traditional cowpea-based dishes are rich in these compounds/nutrients. They can also be considered as a source (above $15\%$ of the RNI) of protein, zinc and potassium. However, their contributions to the RNI of calcium and iron are low. It should be noticed that the reference values used to estimate the contribution to the recommended iron and zinc intake were those for low bioavailability, due to the presence of some mineral-chelating factors that reduce bioavailability (e.g., phytate) in cowpeas. Our findings raise questions about considering cowpea-based dishes as good sources of iron and calcium [8]. These results show that the consumption of cowpea-based dishes in southern Benin contributes to the nutritional security of the adult population, and undoubtedly helps reach adequate protein intakes. The very high daily contribution (above $40\%$) of the consumed cowpea-based dishes to the dietary fibre requirements shows that cowpea consumption can play a positive role in the prevention of overweight and obesity, and some dietary-related chronic diseases [11,41,42]. In addition, the high contribution to folate requirements may help prevent folate deficiency, which affects healthy foetal development in women of childbearing age, and is a public health problem in many countries around the world. Our results finally show a positive relationship between the number of different cowpea-based dishes consumed and the total seed equivalent consumption. Moreover, we observed that for both urban and rural areas, the higher the number of different cowpea-based dishes consumed per week, the higher the overall contribution to the RNI. Thus, the availability of a wide variety of traditional cowpea-based dishes is also a factor that promotes total cowpea consumption: one cowpea-based dish not only replaces another cowpea-based dish, but also replaces other types of dishes. Our study presents some strengths and limits. ## 4.1. Strengths The FFQ was validated for the estimation of the consumed quantities and consumption frequency of the nine traditional cowpea-based dishes. The nutritional composition of the nine cowpea-based dishes used for the study were determined by laboratory analyses on samples collected in the study areas, and therefore reflected the actual nutritional value of these composite dishes. The resulting data sets are made available and may be used to complete food composition tables. ## 4.2. Limitations Food intake estimation using a FFQ might lead to the underestimation or overestimation due to social desirability, as observed by Steinemann et al. [ 43]. Several studies reported overestimation when intakes were estimated with a FFQ compared with food records, especially for foods known to be beneficial for health, such as legumes, vegetables and fruits [44,45]. Therefore, some respondents might have overreported their usual consumed portion size or consumption frequency. Moreover, our surveys were focused on foods based on the same raw material and this could have led to an overestimation of the total cowpea seeds consumed per day due to a cumulative effect. The surveys in Cotonou and in the two rural sites were separated by a few months, which could have led to seasonal variability. However, cowpea seeds are available all-year-round for purchase, and when directly questioned, respondents did not indicate any variation in consumption during the year. ## 5. Conclusions Cowpea was highly consumed in the form of various dish types in all the study areas, resulting in important daily intakes. Indeed, with cowpea consumption alone, 51.5 and $49.7\%$ of the surveyed population in urban and rural areas, respectively, reached the recommendations of the EAT-Lancet Commission on Healthy Diets from sustainable food systems, in terms of pulse consumption. The current consumption level by the adult population in the study areas corresponded to an estimated contribution by traditional cowpea-based dishes, all dishes combined, to the RNI of ~$15\%$ for energy, above $20\%$ for protein, magnesium and folate, and over $40\%$ for dietary fibre. The amount of cowpea seed equivalents consumed, and hence the contribution to nutritional requirements, was higher in participants who consumed a greater number of different cowpea dishes. This shows the value of having a wide variety of dishes. Thus, this regular and high cowpea consumption, together with that of other pulses, should be encouraged and maintained, as it may contribute to the nutritional security of the population, and may help prevent malnutrition. ## References 1. 1. WHO FAO Diet, Nutrition, and the Prevention of Chronic DiseasesWHO Technical Report SeriesWorld Health OrganizationGeneva, Switzerland2003978-92-4-120916-8. 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(2016.0) 19 1184-1194. DOI: 10.1017/S136898001500275X
--- title: 'Anti-Inflammatory, Antioxidant, and WAT/BAT-Conversion Stimulation Induced by Novel PPAR Ligands: Results from Ex Vivo and In Vitro Studies' authors: - Lucia Recinella - Barbara De Filippis - Maria Loreta Libero - Alessandra Ammazzalorso - Annalisa Chiavaroli - Giustino Orlando - Claudio Ferrante - Letizia Giampietro - Serena Veschi - Alessandro Cama - Federica Mannino - Irene Gasparo - Alessandra Bitto - Rosa Amoroso - Luigi Brunetti - Sheila Leone journal: Pharmaceuticals year: 2023 pmcid: PMC10056895 doi: 10.3390/ph16030346 license: CC BY 4.0 --- # Anti-Inflammatory, Antioxidant, and WAT/BAT-Conversion Stimulation Induced by Novel PPAR Ligands: Results from Ex Vivo and In Vitro Studies ## Abstract Activation of peroxisome proliferator-activated receptors (PPARs) not only regulates multiple metabolic pathways, but mediates various biological effects related to inflammation and oxidative stress. We investigated the effects of four new PPAR ligands containing a fibrate scaffold—the PPAR agonists (1a (αEC50 1.0 μM) and 1b (γEC50 0.012 μM)) and antagonists (2a (αIC50 6.5 μM) and 2b (αIC50 0.98 μM, with a weak antagonist activity on γ isoform))—on proinflammatory and oxidative stress biomarkers. The PPAR ligands 1a-b and 2a-b (0.1–10 μM) were tested on isolated liver specimens treated with lipopolysaccharide (LPS), and the levels of lactate dehydrogenase (LDH), prostaglandin (PG) E2, and 8-iso-PGF2α were measured. The effects of these compounds on the gene expression of the adipose tissue markers of browning, PPARα, and PPARγ, in white adipocytes, were evaluated as well. We found a significant reduction in LPS-induced LDH, PGE2, and 8-iso-PGF2α levels after 1a treatment. On the other hand, 1b decreased LPS-induced LDH activity. Compared to the control, 1a stimulated uncoupling protein 1 (UCP1), PR-(PRD1-BF1-RIZ1 homologous) domain containing 16 (PRDM16), deiodinase type II (DIO2), and PPARα and PPARγ gene expression, in 3T3-L1 cells. Similarly, 1b increased UCP1, DIO2, and PPARγ gene expression. 2a-b caused a reduction in the gene expression of UCP1, PRDM16, and DIO2 when tested at 10 μM. In addition, 2a-b significantly decreased PPARα gene expression. A significant reduction in PPARγ gene expression was also found after 2b treatment. The novel PPARα agonist 1a might be a promising lead compound and represents a valuable pharmacological tool for further assessment. The PPARγ agonist 1b could play a minor role in the regulation of inflammatory pathways. ## 1. Introduction Peroxisome proliferator-activated receptors (PPARs) are important targets in metabolic diseases including obesity, metabolic syndrome, diabetes, and non-alcoholic fatty liver disease (NAFLD). PPARs are transcription factors that belong to the nuclear receptor superfamily. In particular, the activation of PPARs was found to modulate various biological effects mainly related to inflammation [1,2], oxidative stress [3], and obesity [4]. In particular, PPARs are implicated in the control of inflammatory processes induced by obesity, through their modulatory effects on the expression of proinflammatory cytokines in adipose cells [5]. PPARs have also been found to modulate the acute phase response in the liver as well as the mechanisms of inflammation in the vasculature [6,7]. There are three subtypes of PPARs, designated as PPARα, γ, and β/δ, which exhibit different tissue expression profiles and modulate specific physiological functions [5]. In this regard, a wide body of evidence suggests that PPARα plays a key role in reducing inflammation. Accordingly, mice lacking PPARα showed a prolonged inflammatory response [8,9]. Moreover, elevated levels of inflammatory markers, including vascular cell adhesion molecule-1 (VCAM-1) and serum amyloid A (SAA), were found in both mouse endothelial cells (EC) and hepatocytes lacking PPARα [10,11]. PPARγ was also shown to play a key role in the control of the inflammatory response, especially in macrophages [12]. The excessive intake of macronutrients stimulates the adipose tissue to release inflammatory mediators such as tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6), and reduces the production of adiponectin [13], leading to the development of a pro-inflammatory state and oxidative stress [14,15]. On the other hand, recent studies have shown that obesity increases the risk of developing various medical conditions, including type 2 diabetes, dyslipidemia, cardiovascular diseases, and NAFLD [16,17,18]. In this context, PPARα is expressed at much higher levels in brown, relative to white, fat cells and is known to be a validated marker of brown fat cells. In addition, PPARα is involved in the activation of brown fat selective genes, such as uncoupling protein-1 (UCP1) and PR-(PRD1-BF1-RIZ1 homologous) domain containing 16 (PRDM16) [19,20]. Furthermore, PPARγ has also been shown to play a key role in inducible brown fat [21], and the activation of the browning process could be a new strategy to fight obesity and metabolic syndrome-related diseases. In this regard, PPARγ agonists were found to increase the expression of thyroid hormone activating type 2 deiodinase (DIO2), another important player in brown adipose tissue-mediated adaptive thermogenesis in animals [22]. Moreover, PPARγ has been recently suggested as a putative target for epilepsy treatment [23]. PPARγ could also be involved in the modulation of the anticonvulsant effects of EP-80317, a ghrelin receptor antagonist [24]. Finally, the antiseizure effects of cannabidiol were associated with the upregulation of PPARγ in the hippocampal CA3 region [25]. However, PPARα and PPARγ agonist drugs are known to induce side effects, including edema, weight gain, cancerogenic effects, heart failure, and renal fluid retention leading to edema [26,27,28,29,30]. Moreover, thiazolidinediones were found to reduce bone formation and stimulate bone loss in both healthy and insulin-resistant subjects [31]. These side effects have strongly restricted the clinical use of these drugs, as well as having limited the development of various PPAR ligands [32]. In previous work, we studied different PPAR agonists and antagonists in an in vitro transactivation assay. Some of these compounds showed interesting activities, with EC50 or IC50 in the low micromolar range [33,34,35,36]. In particular, we investigated the effects of PPAR ligands containing a fibrate scaffold [26], the PPAR agonists 1a (αEC50 1.0 μM) and 1b (γEC50 0.012 μM), and PPAR antagonists 2a (αIC50 6.5 μM) and 2b (αIC50 0.98 μM, with a weak antagonist activity on γ isoform) (Figure 1). Compounds 1a-b are stilbene derivatives, with stilbene bound to the 2,2-dimethylpentanoic chain, typical of gemfibrozil [33], or a tyrosine scaffold typical of the potent and selective γ agonist GW409544 [37,38]. Compounds 2a-b are acylsulfonamide derivatives containing a benzothiazole [39,40]. In the present study, we evaluated, by ex vivo and in vitro studies, the anti-inflammatory and antioxidant effects of these novel PPAR ligands and their role in directly activating the thermogenic program by differentiating WAT into BAT. Ligands 1a-b and 2a-b were tested on isolated liver specimens treated with lipopolysaccharide (LPS), a validated ex vivo experimental model of inflammation [41,42], and proinflammatory and oxidative stress biomarkers, including prostaglandin (PG) E2, 8-iso-PGF2α, and lactate dehydrogenase (LDH), were measured. In a second step, we evaluated the effects of these compounds on the gene expression of adipose tissue markers of browning (UCP1, PRDM16, and DIO2), as well as on PPARα and PPARγ in white adipocytes. ## 2. Results and Discussion Initially, we evaluated the potential biocompatibility of the novel investigated PPAR ligands (1a, 1b, 2a, and 2b (1 μM)) in human fibroblast (HFF-1) cells. The in vitro evaluations were conducted in both basal and LPS-induced inflammatory conditions (Figure 2 and Figure 3). The effects induced by the novel PPAR ligands were compared to those of WY-14643 (PPARα agonist; 1 μM) and pioglitazone (PPARγ agonist; 1 μM), two well-known reference compounds. WY-14643 (1 μM), pioglitazone (1 μM), 1a (1 μM), 1b (1 μM), 2a (1 μM), and 2b (1 μM), were well tolerated in human fibroblast (HFF-1) cells, in both basal and LPS-induced inflammatory conditions (Figure 2 and Figure 3). On the basis of these results, the ex vivo experiments were carried out to evaluate the effects of compounds 1a-b and 2a-b in modulating LPS-induced LDH, PGE2, and 8-iso-PGF2α production in isolated liver samples from adult male Sprague-Dawley rats, mimicking the inflammation induced by a metabolic disease [43]. In this context, we previously reported that isolated tissues that were ex vivo treated with LPS represent a validated experimental model to determine the modulatory activities of potential new drugs on inflammation and oxidative stress [44,45]. LDH is a well-known marker of tissue damage [46]. In particular, LDH production in hepatocytes increased in acute liver failure [47]. Compared to LPS treated controls, both 1a and 1b decreased the LPS-induced LDH activity, showing hepatoprotective effects (Figure 4). In particular, 1a was more effective in decreasing LDH activity than the reference compound WY-14643, at the same dose, (Figure 4). Moreover, the antagonists 2a-b did not modify the LPS-induced LDH activity at any of the tested doses. Our findings agree with previous studies showing that PPARα agonists decreased the LDH levels in hepatic tissue [48]. Furthermore, increased LDH levels were found in liver slices from mice with a targeted deletion of PPARγ in macrophages, compared to control animals [49]. Contextually, we also found a significant reduction in LPS-induced PGE2 levels after the 1a (0.1–10 μM) treatment (Figure 5). In agreement with the present study, PPARα activation in liver samples has been demonstrated to decrease hepatic inflammation induced by an acute exposure to cytokines and other compounds [50]. The gene expression of pro-inflammatory markers, including cyclooxygenase (COX)-2, was also suppressed by PPARα agonists, in response to cytokine activation [6]. Schaefer and collaborators [2008] also showed that WY14643 inhibited the LPS-induced production of a number of pro-inflammatory mediators, including PGE2, and TNF-α, further confirming the beneficial effects induced by PPARα activation on tissue damage [51]. On the other hand, 1b does not affect LPS-induced PGE2 production after treatment at any of the selected concentrations (Figure 5). Our findings agree with those of Yoon and collaborators [2007] showing that 15d-PGJ2, a natural ligand of PPARγ, does not reduce COX-2 gene expression and PGE2 production in rabbit articular chondrocytes [52]. Similarly, 2a and 2b did not decrease LPS-induced PGE2 production in liver tissues (Figure 5). Subsequently, we studied the potential effects of the novel PPAR ligands on oxidative stress. Oxidative stress describes the cellular damage caused by excess reactive oxygen species not adequately scavenged by antioxidants. Oxidative stress has been implicated in the development of many disorders. In this regard, the lipid peroxidation end product 8-iso-PGF2α has been extensively studied as a marker of oxidative stress [53]. Therefore, following the same experimental protocol as above, we tested the effects of the new compounds on 8-iso-PGF2α production. The PPARα agonist 1a (0.1–10 μM) decreased the LPS-stimulated 8-iso-PGF2α levels (Figure 6). Similarly, 1b and 2a-b did not exert any effect on LPS-induced 8-iso-PGF2α production in liver tissues (Figure 6). Our findings showed the selective PPARα agonist 1a as the most promising compound involved in the regulation of inflammatory and lipid peroxidation pathways. Accordingly, PPARα has been implicated in modulating the activity of superoxide dismutase (SOD) and oxidative stress, as confirmed by the up-regulation of antioxidant enzymes such as mitochondrial SOD2 induced by PPARα activation [54]. In addition, PPARα hypothetically protects against oxidative damage in hepatocytes, developed during starvation [55], further confirming its role in modulating oxidative stress. In addition to their role in regulating inflammatory processes, PPARs are also studied as markers of the brown adipose tissue phenotype [19]. In mammals, WAT is the largest energy reserve, while BAT has a high mitochondrial content and is known to play a key role in thermogenesis via UCP1 [13,56,57]. Variations in BAT activity could contribute to differences in energy expenditure in young and adult humans [58]. PRDM16 is a 140 kDa transcriptional co-regulator selectively expressed in brown or beige, with respect to white, adipocytes. Importantly, PRDM16 plays a critical role in modulating the differentiation-linked brown fat gene program [59,60]. Previous studies showed that the loss of PRDM16 in brown fat preadipocytes causes a loss of brown fat characteristics and induces muscle differentiation. Conversely, the ectopic expression of PRDM16 in myoblasts has stimulated brown adipogenesis [61]. The interaction with multiple DNA-binding transcriptional factors, including PPARs, is critically involved in the stimulation of brown adipogenesis induced by PRDM16 [61]. DIO2 is an enzyme playing a key role in the modulation of thyroid hormone signaling and the activation of BAT. DIO2 is also implicated as one of the major players in WAT browning [62]. Therefore, we evaluated the role of the novel PPAR ligands in the thermogenic activation of brown fat. The gene expression of adipose tissue markers of browning (UCP1, PRDM16, and DIO2), as well as PPARγ and PPARα, was evaluated after the treatment of 3T3-L1 cells, derived from 3T3 mouse cells, with testing compounds. As shown in Figure 7, compared to control, the gene expression of UCP1 (panel A), DIO2 (panel B), and PRDM16 (panel C) were significantly enhanced, in a dose-dependent manner, when white adipocytes were incubated with 1a at all tested doses, with the most effective dose at 10 μM. Similarly, treatment with 1b caused a significant increase in the gene expression of UCP1 and DIO2 (Figure 7A,C) at both the 1 and 10 μM doses, with the most effective dose at 10 μM, whereas it did not change the expression of PRDM16 (Figure 7B). Ligands 2a-b caused a significant decrease in the gene expression of UCP1, PRDM16, and DIO2 (Figure 7A–C) when tested at 10 μM. Our present findings confirm previous studies showing that the expression of UCP1, DIO2, and PRDM16 were increased by GW7647, another PPARα agonist, in human white adipocytes [19]. Furthermore, treatment with PPARγ agonists has been shown to increase the UCP1 expression in various WAT depots, in mice [63]. UCP1 gene up-regulation is also associated with adipogenic differentiation via PPARγ or with the fatty acid oxidation required for active thermogenesis via PPARα [20]. Both PPARα and PPARγ can modulate the expression of UCP1 and both receptors are important regulators of energy balance [64]. Moreover, PRDM16 was reported to induce the thermogenic program in the subcutaneous WAT of rodents [65], and PPARα is a direct activator of PRDM16 production [19]. Furthermore, we evaluated PPAR gene expression, as reported in Figure 8. Compared to the control, 1a significantly increased PPARα gene expression, with a maximum effect at 10 μM (Figure 8A). 1a also stimulated PPARγ gene expression (Figure 8B), uncorrelated with the dose range, compared to the control. These results confirm a selectivity for PPARα/γ of 1.4, as calculated for this compound and previously reported [33]. Similarly, compared to control, we found a significant increase in PPARγ gene expression (Figure 8B) after 1b treatment in white adipocytes, at all tested doses. These results, together with the absence of PPARα gene expression, is in accordance with the previously reported data for this γ-selective agonist [37]. On the other hand, compared to the control, 2a-b significantly decreased PPARα gene expression (Figure 8A), in the dose range 0.1–10 μM, and 2b decreased PPARγ gene expression in a dose dependent manner (Figure 8B). ## 3.1. Chemistry All selected compounds (1a-b and 2a-b) were synthesized in the Laboratories of Medicinal Chemistry of the Department of Pharmacy, “G. d’Annunzio” University, Chieti, Italy, following procedures reported in the literature [33,34,37,40]. Melting points were determined with a Buchi Melting Point B-450 and were uncorrected. NMR spectra were recorded on a Varian Mercury 300 spectrometer with 1H at 300.060 MHz and 13C at 75.475 MHz. Proton chemical shifts were referenced to the TMS internal standard. Chemical shifts are reported in parts per million (ppm, δ units). Coupling constants are reported in units of Hertz (Hz). Splitting patterns are designed as: s, singlet; d, doublet; t, triplet; q, quartet; dd, double doublet; m, multiplet; and b, broad. Infrared spectra were recorded on a FT-IR 1600 Perkin Elmer. All commercial deuterated solvents for spectra were reagent grade and were purchased from Sigma Aldrich. The following deuterated solvents have been abbreviated: dimethyl sulfoxide (DMSO) and chloroform (CDCl3). The chemical physical properties of the studied PPARs ligands are described as follows: 1a. 5-{4-[(E)-2-(4-chlorophenyl)ethenyl]phenoxy}-2,2-dimethylpentanoic acid. White solid, mp 193–194 °C. 1HNMR (CDCl3) δ 1.95 (s, 6H, CH3), 1.63–1.77 (m, 4H, CH2CH2), 3.94 (t, 2H, CH2), 6.93 (q, 2H, CH=CH), 6.84 (d, 2H, CHAr), 7.27 (d, 2H, CHAr), 7.36–7.41 (m, 4H, CHAr); 13C NMR (CDCl3) δ 25.25 (CH2), 25.44 (CH3), 37.2 (CH2), 42.4 (C), 68.35 (CH2), 114.9 (CAr), 125.4 (CH=CH), 127.6 (CAr), 127.99 (CH=CH), 129.0 (CAr), 129.07 (CAr), 129.8 (CAr), 132.8 (CAr), 136.4 (CAr), 159.1 (CArO), 178.1 (CO); IR (neat) 3430, 3023, 2950, 1693, 1605, 1511 cm−1 [33]; 1b. 2-(((E)-4-Oxo-4-phenylbut-2-en-2-yl)amino)-3-(4-(3-(4-((E)-4-chloro styryl)phenoxy)propoxy)phenyl)propanoic acid. Amorphous yellow solid, mp 137–139 °C dec. 1H NMR (DMSO) δ 1.60 (s, 3H, CH3), 2.11 (qnt, 2H, OCH2CH2CH2O, $J = 6.0$ Hz); 2.66 (dd, 1H, PhCHH, $J = 13.8$ Hz, $J = 8.7$ Hz), 3.07 (dd, 1H, PhCHH, $J = 13.8$ Hz, $J = 3.6$ Hz), 3.83–3.90 (m, 1H, CHN), 4.05 (t, 2H, OCH2(CH2)2O, $J = 6.0$ Hz), 4.11 (t, 2H, O(CH2)2CH2O, $J = 6.0$ Hz), 5.47 (s, 1H, =CHCO), 6.79 (d, 2H, CHAr, $J = 9.0$ Hz); 6.93 (d, 2H, CHAr, $J = 9.0$ Hz), 7.03–7.21 (m, 4H, CHAr, PhCH=CH), 7.34–7.39 (m, 5H, CHAr), 7.48 (d, 2H, CHAr, $J = 8.7$ Hz), 7.55 (d, 2H, CHAr, $J = 8.7$ Hz), 7.73–7.77 (m, 2H, CHAr), 11.35 (d, 1H, NH, $J = 9.0$ hz); 13C NMR (DMSO) δ 19.86, 27.01, 37.06, 62.03, 64.68, 64.99, 90.98, 114.73, 115.39, 127.11, 128.49, 128.61, 128.72, 129.16, 129.34, 129.61, 130.10, 131.25, 132.06, 137.07, 140.78, 141.33, 157.48, 159.05, 172.121, 184.65; IR (KBr) 3397, 2924, 2849, 1602, 1536, 1405, 1246, 831 cm−1 [37]; 2a. 2-[(5-Chloro-1,3-benzothiazol-2-yl)thio]-2-phenyl-N-(phenylsulfonyl)acetamide. White solid, mp 140–142 °C. 1H NMR (CDCl3) δ 5.43 (s, 1H, CHS), 7.33–7.60 (m, 8H, CHAr), 7.67 (d, 1H, $J = 8.4$ Hz, CHAr), 7.78 (d, 1H, $J = 1.8$ Hz, CHAr), 7.91–7.97 (m, 3H, CHAr), 11.05 (bs, 1H, NH); 13C NMR (CDCl3) δ 54.6 (CHS), 121.8, 122.1, 126.0, 126.6, 128.5, 129.0, 129.1 and 129.3 (CHAr), 132.2, 133.0 and 133.5 (CAr), 134.1 (CHAr), 138.3, 152.6 and 166.7 (CAr), 167.9 (C=O). IR (KBr) 3256, 1709, 1449, 1422, 1363, 1183 cm−1 [34]; 2b. 2-[(5-chloro-1,3-benzothiazol-2-yl)thio]-N-({4-[(phenylacetyl)amino]phenyl}sulfonyl) pentanamide. Colourless solid, m.p. 202–204 °C (dec); 1H NMR (DMSO) δ 0.82 (t, 3H, $J = 7.2$ Hz, CH3CH2), 1.19–1.35 (m, 2H, CH2CH3), 1.72–1.85 (m, 2H, CH2CH), 3.65 (s, 2H, CH2), 4.49 (t, 1H, $J = 7.2$ Hz, CHS), 7.22–7.36 (m, 6H, CHAr), 7.65 (d, 1H, $J = 2.1$ Hz, CHAr), 7.71 (d, 2H, $J = 9.0$ Hz, CHAr), 7.82 (d, 2H, $J = 9.0$ Hz, CHAr), 7.96 (d, 1H, $J = 8.4$ Hz, CHAr), 10.55 (bs, 1H, NH), 12.56 (bs, 1H, NHAr); 13C NMR (DMSO) δ 14.0, 20.2, 33.9, 43.9, 51.6, 119.0, 121.2, 123.9, 125.4, 127.3, 129.0, 129.6, 129.8, 131.8, 133.0, 134.2, 136.1, 144.5, 153.7, 166.7, 169.4, 170.5. IR (KBr) 3310, 3267, 1706, 1671, 1540, 1403, 1363, 1173 cm−1 [40]. ## 3.2. Cell Viability Assay The cell viability was evaluated by MTT assay [3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide] (Sigma, St. Louis, MO, USA) as previously described [66]. Briefly, HFF-1 cell lines were seeded in 96-well plates (5 × 103 cells/well) and pretreated with 10 μg/mL lipopolysaccharide (LPS) for 24 h. Subsequently, both LPS-pretreated and non-LPS-pretreated HFF-1 cells were subjected to the PPARα agonist WY-14643 (1 μM), PPARγ agonist pioglitazone (1 μM), 1a (1 μM), 1b (1 μM), 2a (1 μM), and 2b (1 μM), or treated with a vehicle (control) for a further 24 h. After treatment, the MTT solution was added to each well and incubated at 37 °C for at least 3 h, until purple formazan crystals were formed. In order to dissolve the precipitate, the culture medium was replaced with dimethyl sulfoxide (DMSO, Euroclone). Absorbance of each well was quantified at 540 and 690 nm, using a Synergy H1 microplate reader (BioTek Instruments Inc., Winooski, VT, USA). ## 3.3. Ex Vivo Studies Male adult Sprague–Dawley rats (200–250 g) were housed in Plexiglass cages (40 cm × 25 cm × 15 cm), with two rats per cage, in climatized colony rooms (22 ± 1 °C; $60\%$ humidity), on a 12 h/12 h light/dark cycle (light phase: 07:00–19:00 h), with free access to tap water and food for 24 h/day throughout the study and no fasting periods. Rats were fed a standard laboratory diet ($3.5\%$ fat, $63\%$ carbohydrate, $14\%$ protein, and $19.5\%$ other components without caloric value; 3.20 kcal/g). The housing conditions and experimentation procedures were strictly in agreement with the European Community ethical regulations (EU Directive no. $\frac{26}{2014}$) on the care of animals for scientific research. In agreement with the recognized principles of “Replacement, Refinement and Reduction of Animals in Research”, liver specimens ($$n = 5$$ for each treatment group) were obtained as residual material from vehicle-treated rats randomized in our previous experiments approved by the local ethical committee (‘G. d’Annunzio’ University, Chieti-Pescara) and Italian Health Ministry (Project no. $\frac{885}{2018}$-PR). Rats were sacrificed by CO2 inhalation ($100\%$ CO2 at a flow rate of $20\%$ of the chamber volume per min) and liver specimens were immediately collected and maintained in a humidified incubator with $5\%$ CO2 at 37 °C for 4 h, in a RPMI buffer with added bacterial LPS (10 μg/mL) (incubation period), as previously reported [41]. During the incubation period, tissues were treated with the PPARα agonist WY-14643 (1 μM), and the PPARγ agonist pioglitazone (1 μM), and scalar concentrations of 1a-b and 2a-b (0.1–10 μM). Tissue supernatants were collected, and PGE2 and 8-iso-PGF2α levels (ng/mg wet tissue) were measured by radioimmunoassay (RIA), as previously reported [67,68]. Briefly, specific anti-8-iso-PGF2α and anti-PGE2 were developed in the rabbit; the cross-reactivity against other prostanoids is <$0.3\%$. One hundred microliters of prostaglandin standard or sample were incubated overnight at 4 °C with the 3H-prostaglandin (3000 cpm/tube; NEN) and antibody (final dilution: 1:120,000; kindly provided by Prof. G. Ciabattoni), in a volume of 1.5 mL of 0.025 M phosphate buffer. Free and antibody-bound prostaglandins were separated by the addition of 100 μL $5\%$ bovine serum albumin and 100 μL $3\%$ charcoal suspension, followed by centrifuging for 10 min at 4000× g at 5 °C and decanting the supernatants into scintillation fluid (Ultima Gold™, Perkin Elmer, Waltham, MA, USA) for β emission counting. The detection limit of the assay method is 0.6 pg/mL. Additionally, tissue supernatants were assayed for LDH activity [44]. LDH activity was measured by evaluating the consumption of NADH in 20 mM HEPES-K+ (pH 7.2), $0.05\%$ bovine serum albumin, 20 μM NADH, and 2 mM pyruvate using a microplate reader (excitation 340 nm, emission 460 nm) according to the manufacturer’s protocol (Sigma-Aldrich, St. Louis, MO, USA). The LDH activity was measured by evaluating the consumption of NADH in 20 mM HEPES-K+ (pH 7.2), $0.05\%$ bovine serum albumin, 20 μM NADH, and 2 mM pyruvate using a microplate reader (excitation 340 nm, emission 460 nm) according to manufacturer’s protocol. ## 3.4. Adipocyte Culture The 3T3-L1 cells, derived from 3T3 mouse cells, were used for the in vitro study. 3T3-L1 cells have a fibroblast-like morphology, but, under appropriate conditions, the cells differentiate into an adipocyte-like phenotype. Mouse 3T3-L1 preadipocytes were cultured in Dulbecco’s modified Eagle medium (DMEM) supplemented with $10\%$ fetal bovine serum (FBS), 100 U/mL penicillin-streptomycin (Sigma–Aldrich, Milan, Italy) at 37 °C in a humidified atmosphere containing $5\%$ CO2. Upon reaching confluence, the cells were maintained in M-1 containing glutamine (4.0 mM), sodium pyruvate (1 mM), and $10\%$ of FBS for 1 to 2 additional days. This medium was replaced by M-2 containing M-1, insulin (1.5 µg/mL), 3-isobutyl-1-methylxanthine (IBMX) (0.5 mM), and dexamethasone (1.0 µM), for inducing adipocyte differentiation. The cells were cultured for 2 days to achieve an adipose-like phenotype. After 2 days, M-2 was replaced by M-3 containing only insulin (1.5 µg/mL), necessary for the maintenance of an adipocyte phenotype. M-3 was replaced every 2 days for 8 days; at day 8 adipocytes were treated with scalar concentrations of 1a-b and 2a-b (0.1–10 µM) for 24 h. ## 3.5. PCR Assay Total mRNA was extracted from adipocytes using the Trizol LS reagent (Invitrogen, Carlsbad, CA, United States), according to the manufacturer’s protocol. Total RNA was quantified with a spectrophotometer (NanoDrop Lite, Thermo Fisher, Waltham, MA, USA) and 1 μg was reverse transcribed using the SuperScript™ IV Reverse Transcriptase (Invitrogen, Carlsbad, CA, USA) and random primers, following the manufacturer’s protocol in a volume of 20 µL. First-strand DNA (1 μL) was added to the BrightGreen qPCR Master Mix (Applied Biological Materials Inc, Richmond, BC, Canada) in a total volume of 20 µL per well to evaluate the gene expression of the target genes: UCP1, PRDM16, DIO2, PPARα, and PPARγ using specific mouse primers. For UCP1 the following primers were used: forward (ATGGTGAACCCGACAACTTC) and reverse (CAGCGGGAAGGTGATGATA). For PRDM16 the following primers were used: forward (CGAGGAGGAGACCGAAGAC) and reverse (GAAGTCTGGTGGGATTGGAA). For DIO2 the following primers were used: forward (ATGGGACTCCTCAGCGTAGA) and reverse (GGAGGAAGCTGTTCCAGACA). For DIO2 the following primers were used: forward (ATGGGACTCCTCAGCGTAGA) and reverse (GGAGGAAGCTGTTCCAGACA). For PPARα the following primers were used: forward (TCTGTCCTCTCTCCCCACTG) and reverse (CCCGGACAGCTTCCTAAGTA). For PPARγ the following primers were used: forward (CCAACTTCGGAATCAGCTCT) and reverse (CAACCATTGGGTCAGCTCTT). The samples were loaded in duplicate and GADPH was used as housekeeping gene [for GAPDH the following primers were used: forward (GTCAAGGCTGAGAATGGGAA) and reverse (ATACTCAGCACCAGCATCAC); the reaction was performed using the 2-step thermal protocol suggested by the manufacturer (Applied Biosystems, Foster City, CA, USA). The results were quantified using the 2−ΔΔCt method and expressed as an n-fold increase in gene expression using untreated white adipocytes as the calibrator. ## 3.6. Statistical Analysis The statistical analysis was performed using GraphPad Prism version 5.01 for Windows (GraphPad Software, San Diego, CA, USA). Means ± S.E.M. were determined for each experimental group and analyzed by a one-way analysis of variance (ANOVA), followed by the Newman–Keuls comparison multiple test. Statistical significance was set at $p \leq 0.05.$ ## 4. Conclusions In conclusion, the novel PPARα agonist 1a might be a promising lead compound and represents a valuable pharmacological tool for further assessment, opening new perspectives on PPARα as a molecular target to afford anti-inflammatory, antioxidant, and thermogenic effects. On the other hand, the PPARγ agonist 1b could play a minor role in the regulation of inflammatory pathways. A main limitation of our study is that we have not evaluated PPAR antagonists as reference compounds. Further studies are needed to accurately evaluate the in vivo activities of these compounds. ## References 1. 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--- title: Drug Candidate BGP-15 Prevents Isoproterenol-Induced Arrhythmias and Alters Heart Rate Variability (HRV) in Telemetry-Implanted Rats authors: - Brigitta Bernat - Rita Erdelyi - Laszlo Fazekas - Greta Garami - Reka Maria Szekeres - Barbara Takacs - Mariann Bombicz - Balazs Varga - Fruzsina Sarkany - Arnold Peter Raduly - Dana Diana Romanescu - Zoltan Papp - Attila Toth - Zoltan Szilvassy - Bela Juhasz - Daniel Priksz journal: Pharmaceuticals year: 2023 pmcid: PMC10056898 doi: 10.3390/ph16030359 license: CC BY 4.0 --- # Drug Candidate BGP-15 Prevents Isoproterenol-Induced Arrhythmias and Alters Heart Rate Variability (HRV) in Telemetry-Implanted Rats ## Abstract Multi-target drug candidate BGP-15 has shown cardioprotective and antiarrhythmic actions in diseased models. Here, we investigated the effects of BGP-15 on ECG and echocardiographic parameters, heart rate variability (HRV), and arrhythmia incidence in telemetry-implanted rats, under beta-adrenergic stimulation by isoproterenol (ISO). In total, 40 rats were implanted with radiotelemetry transmitters. First, dose escalation studies (40–160 mg/kg BGP-15), ECG parameters, and 24 h HRV parameters were assessed. After, rats were divided into Control, Control+BGP-15, ISO, and ISO+BGP-15 subgroups for 2 weeks. ECG recordings were obtained from conscious rats, arrhythmias and HRV parameters were assessed, and echocardiography was carried out. ISO-BGP-15 interaction was also evaluated on an isolated canine cardiomyocyte model. BGP-15 had no observable effects on the ECG waveforms; however, it decreased heart rate. HRV monitoring showed that BGP-15 increased RMSSD, SD1, and HF% parameters. BGP-15 failed to counteract 1 mg/kg ISO-induced tachycardia, but diminished the ECG of ischemia and suppressed ventricular arrhythmia incidence. Under echocardiography, after low-dose ISO injection, BGP-15 administration lowered HR and atrial velocities, and increased end-diastolic volume and ventricle relaxation, but did not counteract the positive inotropic effects of ISO. Two weeks of BGP-15 treatment also improved diastolic function in ISO-treated rats. In isolated cardiomyocytes, BGP-15 prevented 100 nM ISO-induced aftercontractions. Here, we show that BGP-15 increases vagally mediated HRV, reduces arrhythmogenesis, enhances left ventricle relaxation, and suppresses the aftercontractions of cardiomyocytes. As the drug is well tolerated, it may have a clinical value in preventing fatal arrhythmias. ## 1. Introduction The incidence of sudden cardiac death (SCD) due to lethal arrhythmias is increasing, despite the slight decline in cardiovascular deaths [1]. A considerable proportion of cardiovascular disease-related deaths are the direct or indirect result of cardiac arrhythmias [1]. Ventricular arrhythmias cause death in around fifty percent of individuals with chronic heart failure (HF) [2]. Compared to certain other therapeutic areas, cardiovascular drug research has had relatively little investment during the previous years. In addition, many antiarrhythmic drug candidates failed to show a clear benefit in reducing mortality; therefore, there is still a need for new antiarrhythmic treatments. [ 3,4]. In the last decade, it became apparent that the activity of the autonomic nervous system (ANS) and cardiovascular mortality, especially SCD, have a strong association. The idea of measuring heart rate variability (HRV) as a quantitative indicator of autonomic activity has been inspired by experimental findings that link the susceptibility to fatal arrhythmias either to increased sympathetic or decreased vagal tone [5,6]. Low HRV is associated with an increased risk of SCD and all-cause mortality in post-myocardial infarction sufferers and HF patients [7,8]. According to several studies, low vagally mediated HRV has been postulated as a predictive marker of increased mortality and susceptibility to fatal ventricular arrhythmias in post-myocardial infarction and HF patients [9,10]. Subsequently, beta-adrenergic overstimulation (i.e., by isoproterenol) is a useful tool to dose-dependently generate stress-induced cardiomyopathy, heart failure, and arrhythmias in rodent models and is also used for arrhythmia induction in human patients as a diagnostic aid in basic electrophysiology studies [11,12]. Murine models have been widely used in arrhythmia research and translational studies aimed to evaluate the effects of drugs on HRV. However, as anesthetics alter the autonomic regulation of the heart, radiotelemetry is the only acceptable method to assess HRV in conscious, unrestrained experimental animals, which makes these experiments more complicated and expensive [5,13]. Nonetheless, to date, various computer-based radiotelemetry systems are available on the market to assess short- and long-term HRV in different animal models. Substantial evidence suggests that BGP-15 drug candidate (3-piperidino-2-hydroxy-1-propyl)-nicotinic amidoxime) exerts various cardioprotective actions and can improve cardiac function in animal models of heart disease, although its main mode of action has remained elusive [14,15]. It has been shown that BGP-15-treatment reduces the incidence of atrial fibrillation (AF) episodes in a transgenic mouse model of HF, and this effect has been associated with the increased phosphorylation of the cardiac-specific insulin-like growth factor 1 (IGF1) receptor [16]. Additionally, BGP-15 acts as an inhibitor of poly(ADP)-ribose polymerase 1 (PARP-1) [17]. According to a recent study, PAPR-1 inhibition protects against contractile dysfunction and atrial fibrillation, preserving cardiomyocyte function [18]. It has been described that BGP-15 protects against mitochondrial ROS production and oxidative damage by modulating PARP-1 in ischemia-reperfusion injury performed in the Langendorff heart perfusion system [19]. Several reports suggest that BGP-15 modulates plasma membrane composition through lipid-raft reorganization and increases membrane fluidity, which is crucial because stiff membranes are limiting the cellular stress response [20]. Furthermore, it has been demonstrated that BGP-15 has a beneficial effect against hypertension-induced cardiac remodeling and cardiac fibrosis by decreasing the activity of transforming growth factor beta (TGFβ/Smad) and mitogen-activated protein kinase (MAPK) signaling. It favorably influences the prosurvival signaling pathways and activates mitochondrial biogenesis, thereby resulting in an increase in mitochondrial mass [21]. In addition, our team recently showed that BGP-15 decreases cardiovascular mortality, recovers disrupted mitochondrial homeostasis, and increases mitochondrial ATP production in obese rats [22]. It has been recently stated that developing agents that diminish the mitochondrial ROS overload has enormous potential in preventing lethal ventricular arrhythmias [23]. Based on the above-described effects of BGP-15 on mitochondria, the drug candidate may play an essential role in reducing arrhythmogenesis. This assumption is further reinforced by the study, which reports that the drug candidate reportedly stabilizes Kir2.1 current in long-QT7 channelopathy [24]. Based on previous results, the drug candidate BGP-15 delays the onset of diastolic dysfunction and elevates the level of phosphorylation of VASP and phospholamban in Goto-Kakizaki rats with diabetic cardiomyopathy [25]. As phospholamban is a key regulator of cardiac contractility and modulates SR Ca2+ sequestration by inhibiting the sarco-endoplasmic reticulum Ca-ATPase (SERCA) in its dephosphorylated state, BGP-15 may act by restoring SERCA activity [26,27]. Our team has previously shown that BGP-15 enhances cyclic guanosine monophosphate (cGMP)—protein kinase G (PKG) signaling, alters myocardial mechanics, and furthermore, decreases inotropy in paced human right atrial samples [28,29]. Based on the above, we investigated the effects of BGP-15 on the electrocardiogram (ECG), isoproterenol-induced cardiac arrhythmias, heart rate variability, and echocardiographic heart functions in a telemetry-implanted rat model. Here, we show that BGP-15 reduces arrhythmogenesis, increases vagally mediated HRV, improves diastolic function, and suppresses the aftercontractions of cardiomyocytes. As the substance is proven to be well-tolerated in phase II human studies [30], we propose to further evaluate its cardioprotective actions in patients suffering heart failure and cardiac arrhythmias. ## 2.1. Effects of BGP-15 Dose-Escalation on Heart Rate and ECG Parameters of Naïve Rats Oral administration of 20 mg/kg, 40 mg/kg, 80 mg/kg, and 160 mg/kg BPG-15 slightly and dose-dependently reduced the heart rate of conscious rats. BGP-15 (20 mg/kg, the average dose based on previous studies) lowered HR compared to the Control (saline-treated) rats ($$p \leq 0.0037$$, unpaired t-test), and also compared to corresponding baseline values of the animals ($$p \leq 0.005$$, paired t-test; Figure 1a). BGP-15 administration (40–160 mg/kg) failed to acutely counteract 1 mg/kg ISO-induced tachycardia (ISO was injected i.p. 10 min after BGP-15 administration; Figure 1b). No significant effects on ECG waves were attainable on the lead-II electrocardiogram of naïve BGP-15-treated rats (data not shown). ## 2.2. Effects of BGP-15 Dose-Escalation on ECG Parameters of ISO-Treated Rats Although oral BGP-15 administration did not alter normal lead-II ECG waveforms, 40–160 mg/kg BGP-15 pre-treatment significantly counteracted ischemic signs (depression of the ST-segment) caused by 1 mg/kg ISO ($$p \leq 0.029$$, BASE vs. BASE+ISO; and $$p \leq 0.0062$$, BGP40 vs. BASE+ISO; $p \leq 0.0001$, BGP80 vs. BASE+ISO; and $p \leq 0.0001$, BGP160 vs. BASE+ISO, respectively; Figure 1c,d). A significant prolongation of the QTc interval (modified Bazett’s formula) was shown on the lead-II ECG of 1 mg/kg ISO-treated rats after the wear-off of the drug-induced tachycardia ($$p \leq 0.0003$$, BASE vs. BASE+ISO), which was again counteracted by 40–160 mg/kg BGP-15 administration (Figure 1e). In our preliminary studies, higher doses of ISO (2 mg/kg and 5 mg/kg) caused significant mortality by triggering torsade de pointes-type lethal arrhythmias, recorded by the telemetry system (Figure 1f, upper panel). We note that in one ISO-treated animal, overdosing with BGP-15 (160 mg/kg BGP-15) caused 2nd-degree AV block (Wenkebach type; Figure 1f, bottom panel). ## 2.3. Effects of Single-Dose BGP-15 on 24-h HRV Parameters BGP-15 treatment (40 mg/kg) had an observable effect on 24 h HRV parameters of telemetry-implanted rats (paired t-test, Table 1). The average HR was lower during the treatment ($$p \leq 0.0497$$, BASE vs. BGP-15). In the time domain measurements, the standard deviation of R-R intervals (SDNN) and the proportion of the successive NNs that differed by more than 10 ms (pNN10) tended to increase. The root mean square of the successive differences (RMSSD, indicative of parasympathetic activity) increased during the BGP-15 treatment ($$p \leq 0.0294$$, BASE vs. BGP-15). In nonlinear analyses (Poincaré plot), we found that SD1 (the parameter correlated to RMSSD) increased significantly ($$p \leq 0.0270$$, BASE vs. BGP-15). In frequency-domain measurements, there was no significant change in the VLF band; however, the LF band % decreased, and the HF band % (another index of vagal activity) significantly increased ($$p \leq 0.0016$$, BASE vs. BGP-15). ## 2.4. Ehocardiographic Results of the Acute Treatment Groups Echocardiography was performed using a high-resolution ultrasound machine under ketamine/xylazine ($\frac{50}{5}$ mg/kg) anesthesia. Baseline traces were recorded 15 min after the injection of the anesthetics, after ensuring that the heart rate was under 300 bpm (HR: 270.3 ± 20.31 bpm) to properly assess diastolic waves. Isoproterenol administration (0.1 mg/kg, i.p.) elevated HR (405.5 ± 65.97 bpm; $$p \leq 0.0025$$ vs. baseline; Figure 2a). All tested doses of BGP-15 (40 mg/kg, 80 mg/kg, and 160 mg/kg cumulative doses, i.p.) decreased ISO-induced tachycardia (with p-values: $p \leq 0.0001$, $$p \leq 0.0003$$, and $$p \leq 0.0018$$ vs. ISO 0.1, respectively). Ejection fraction (EF) dramatically increased after ISO administration ($$p \leq 0.0006$$, reaching 96.9 ± $2.186\%$; Figure 2b). All doses of BGP-15 increased LV end-diastolic volume ($$p \leq 0.0115$$, $$p \leq 0.0021$$, and $$p \leq 0.0064$$ vs. ISO 0.1 mg, respectively; Figure 2e), and a trend toward increase was seen in LV end-systolic volume (Figure 2d). Despite this, BGP-15 injections failed to significantly counteract the positive effects of ISO on EF. Diastolic function was evaluated by pulsed-wave Doppler and tissue velocity imaging (TDI). The transmitral E/A ratio decreased after ISO dosing (E and A wave fusion was seen in some cases), but recovered after BGP-15 administration ($$p \leq 0.0054$$, BGP-15 40mg/kg vs. ISO 0.1 mg; Figure 2c), as the drug decreased A wave velocity ($$p \leq 0.0024$$, BGP-15 40mg/kg vs. ISO 0.1 mg; Figure 2i). Similar results were obtained from tissue Doppler measurements: the TDI e′/a′ ratio slightly increased (Figure 2h), and septal a′ wave velocity significantly decreased after BGP-15 injections (Figure 2j). The ratio of E/e′ (indicative of LV filling pressure) tended to decrease after 40 mg/kg BGP-15 administration (Figure 2f,g). ## 2.5. Effects of 2-Week ISO and BGP-15-Treatments on 2-h HRV Parameters Short-term HRV parameters were evaluated on the last day of the 2-week-long treatments, at resting conditions (after the wear-off of the ISO-induced tachycardia). Interestingly, the most significant decrease in mean HR was observed in the BGP-15+ISO group (Figure 3a; $$p \leq 0.0022$$ vs. control) along with the highest increase in pNN10 (Figure 3d; $$p \leq 0.0007$$ vs. control). Total variability (defined as SDNN, Figure 3b) increased in the BGP-15 treated rats ($$p \leq 0.0084$$ vs. control). RMSSD and SD1 (indices of vagal activity) increased in the BGP-15-treated ($$p \leq 0.036$$ and $$p \leq 0.018$$, respectively) and BGP-15+ISO-treated animals ($$p \leq 0.0064$$ and $$p \leq 0.0022$$, respectively) compared to controls (Figure 3c,e). ## 2.6. Effects of the 2-Week-Long ISO and BGP-15 Treatments on Echocardiographic Parameters Endpoint echocardiographic parameters were evaluated on the last day of 2-week long treatments (Table 2). No significant differences were found in LV dimensions and systolic parameters (EF, CO) between the groups. Left ventricle mass increased in the ISO group ($$p \leq 0.0147$$ vs. control). BGP-15 treatment did not counteract this effect of ISO. However, more distinct changes were found in diastolic parameters. ISO treatment lengthened deceleration time ($$p \leq 0.0291$$ vs. control) and tended to decrease the e′/a′ ratio ($$p \leq 0.09$$ vs. control). The ratio of e′/a′ improved in the BGP-15+ISO group ($$p \leq 0.0318$$ vs. ISO), moreover, BGP-15 treatment increased TDI s’ velocity ($p \leq 0.0001$ vs. control). Finally, the ratio of E/e′ (indicative of LV filling pressure) improved in both the BGP-15-treated group compared to the control ($$p \leq 0.0041$$), and in the ISO+BGP-15 group compared to ISO ($$p \leq 0.0307$$). ## 2.7. Effects of BGP-15 on ISO-Induced Arrhythmogenesis The ventricular arrhythmic events were evaluated on 10 min long telemetric Lead II ECG recordings on the last day of the 2-week-long ISO and BGP-15+ISO treatments ($$n = 17$$). We counted the number of premature ventricular complexes (PVCs), salvos, duration (s) of monomorphic and polymorphic ventricular tachycardia (VT), ventricular fibrillation (VF), and the duration (s) of VF episodes. The total number of arrhythmic events was significantly lower in the BGP-15+ISO group compared to ISO ($$p \leq 0.045$$; Figure 4g). PVCs and VT duration tended to decrease in the BGP-15-treated rats. ( Figure 4a–d). The number of VF episodes was significantly decreased in BGP-15+ISO vs. ISO ($$p \leq 0.0308$$; Figure 4e), along with the duration of VF episodes ($$p \leq 0.0185$$; Figure 4f). ## 2.8. Results of Isolated Cardiomyocyte Experiments Changes in sarcomere length and Ca2+-transient amplitude were evaluated in single, isolated canine cardiomyocytes at a stimulation frequency of 0.5 Hz. In the presence of physiologic saline solution, 100 nM ISO elicited aftercontractions (in almost all cardiomyocytes following ISO applications; Figure 5a,b,f) and increased the amplitudes of Ca2+-transients to 190.6 ± $65.16\%$ from baseline values (100 ± $40.43\%$) (Figure 5g). Pre-treatment with 100 µM BGP-15 prevented ISO-induced aftercontractions in all cardiomyocytes tested (Mann–Whitney test, $$p \leq 0.0006$$; Figure 5c,d,f), although it did not counteract the effect of ISO on Ca2+-transient amplitude (Figure 5g) (p = ns, BGP-15 vs. BGP-15+ISO). Interestingly, BGP-15 treatment increased the resting (diastolic) sarcomere length ($$p \leq 0.0342$$, Figure 5e) of the isolated cardiomyocytes. ## 3. Discussion Here, we demonstrate that the small molecule BGP-15 reduces arrhythmogenesis, prevents ECG changes resulting from beta-adrenergic overstimulation, increases vagally mediated HRV, and improves diastolic function in telemetry-implanted rats. At the cellular level, we showed that BGP-15 suppresses isoproterenol-induced aftercontractions and the reactivation of Ca2+ transients in isolated cardiomyocytes. Previous human studies have shown that BGP-15 is well tolerated and does not exert any side effects on cardiac electrophysiology [30]. Our results corroborate these findings. As a result of BGP-15 administration to telemetry-implanted, conscious rats, BGP-15 did not have any visible effects on resting lead II ECG waves; however, it slightly (but significantly) decreased heart rate. In contrast, BGP-15 application failed to counteract 1 mg/kg ISO-induced tachycardia; thus, we conclude that BGP-15 possesses no (or very weak) antagonistic effects on beta-adrenergic receptor-mediated positive chronotropy, despite showing some similarity to propranolol [28]. ISO administration causes severe tachycardia with transient ischemia, which is apparent on the ECG (T-wave inversion, ST segment changes [31,32,33]. Accordingly, in our studies, ST-segment depression was seen on the ECG recordings under the influence of ISO (we note that it is difficult to detect the ST segment in the rat ECG as it is very short and the T wave often rises in continuity with the S wave) [34]. BGP-15 pre-treatment counteracted ISO-induced ST-segment changes, while unaffected acute tachycardia. In addition, after the wear-off of the ISO effect, significant QT-interval prolongation was detected on the resting ECGs, possibly due to the resulting myocardial ischemia that affects repolarization [35]. This was also prevented by BGP-15 pre-treatment, as the resting QTc interval was normalized. We were also able to record one torsade de pointes type fatal arrhythmia during the preliminary experiments (Figure 1f). We note that in rats treated with high dose (2 mg/kg) ISO and BGP-15 (160 mg/kg), AV blocks were seen after BGP-15 application, which might be the result of BGP-15 overdose on injured hearts. In the following series of experiments, we evaluated the effects of BGP-15 (40 mg/kg) on heart rate variability (HRV). HRV itself describes the fluctuation in the interval between subsequent heartbeats and is generated by heart–brain interactions and dynamic autonomic nervous system activities. An optimal level of HRV is linked to good health, self-regulation, adaptation, and resilience [36,37]. Elevated sympathetic activity, accompanied by age-related deterioration in cardiac vagal regulation, impairs the electrical stability of both the atria and the ventricles and triggers arrhythmogenesis [38,39]. Low vagally mediated HRV has been postulated as a predictive marker of increased mortality and susceptibility to fatal ventricular arrhythmias [9,10]. In contrast, interventions (various drugs, exercise training, and vagal nerve stimulation) that increase vagally mediated HRV are proposed to exert significant cardioprotection and decrease the risk of SCD [40,41,42]. We found that over a 24 h period, BGP-15 treatment decreased the resting mean heart rate in undisturbed rats, while it did not affect total variability (SDNN), but increased RMSSD. Since feedbacks driven by the vagal nerve operate quickly, the RMSSD represents high-frequency oscillations of R-R intervals and therefore estimates vagal activity [37]. According to the non-linear analyses, we found that BGP-15 also increased the SD1 value (indicative of short-term parasympathetic (PS) variability). Spectral analysis of 24 h HRV data revealed similar findings as BGP-15 increased the relative power of the high-frequency band (HF%), another significant measure of PS tone [43]. Thus, we conclude that BGP-15 treatment increases vagally mediated HRV. These findings also corroborate previous results showing some similarity in the cardiac actions of BGP-15 and beta-blockers (propranolol) [28]. Of note, in the beta-blocker heart attack trial, propranolol treatment also increased the recovery of PS tone in patients with acute myocardial infarction as measured by HF power and RMSSD, thus, improving survival [44]. We also obtained short-term HRV data on rats treated with vehicle, BGP-15, ISO, or BGP-15+ISO after a 2-week treatment period. In these studies, similar findings were obtained, as BGP-15 treatment slightly increased total variability (SDNN) and significantly elevated indices of vagal tone (RMSSD and SD1). The resting heart rate of the ISO group was normal, but variability was slightly increased, in accordance with literature data [45]. We conclude that BGP-15 increases vagally mediated HRV, which may contribute to arrhythmia prevention. Echocardiographic changes were also evaluated after single-bolus and 2-week-long BGP-15 and ISO treatments, with congruent results. ISO significantly elevated HR and dramatically increased LV ejection fraction (LVEF). However, because of the severe tachycardia, E and A waves were fused, and end-diastolic volume and e′/a′ ratio decreased. BGP-15 lowered HR (we note that animals were under ketamine-xylazine anesthesia in this case), and also counteracted tachycardia induced by a minimal dose (0.1 mg/kg) of ISO, without any significant effect on LVEF. Despite this, BGP-15 administration (40 mg/kg i.p.) increased LV end-diastolic volume, E/A and e′/a′ ratios, indicative of enhanced diastolic function. We note that, as BGP-15 injection lowered HR, the increased EDV might be a consequence of the increased filling time. The increase in the E/A ratio might also be a result of the decreased heart rate; however, BGP-15 also decreased TDI a′ wave velocity, a parameter relatively independent of HR [46]. Transmitral A wave was also decreased after the treatment, corresponding to our previous findings that BGP-15 has negative tropic effects on the human atria [28]. Similar outcomes were obtained from the 2-week studies, as BGP-15 treatment did not affect systolic parameters (EF, CO), but improved diastolic function, corresponding to our previous results in different animal models [25,29]. In addition, in the 2-week studies, we found worsened diastolic function (E/A, DecT) and significantly elevated LV mass in the ISO group, in accordance with the literature data [47]. Notably, we found that BGP-15 treatment significantly counteracted these effects of ISO. To summarize, we conclude that BGP-15 improves diastolic function and exerts cardioprotective actions against constant beta-adrenergic damage. The main result of this telemetric study is that BGP-15 administration is able to prevent various types of ventricular arrhythmias resulting from adrenergic stimulation. We found that the total number of arrhythmic events was significantly lower in the BGP-15+ISO group compared to ISO. The number and duration of VF episodes significantly decreased, and the incidence of premature complexes and ventricular tachycardia also tended to decline. To our knowledge, this is the first study showing that BGP-15 may have value in preventing ventricular arrhythmias. Nevertheless, Sapra and colleagues demonstrated the antiarrhythmic actions of BGP-15 in two different mouse models of heart failure and atrial fibrillation (HF+AF), but they focused only on the supraventricular events (AF) [16]. Here, we show that BGP-15 may also prevent ventricular arrhythmic episodes resulting from adrenergic stimulation. To elicit experimental Ca2+-mediated afterdepolarizations in cardiac cells, ISO is widely used (100 nM-1µM), as it causes Ca2+ overload [48,49]. High-dose isoproterenol causes alterations in action potential dynamics in cardiomyocytes, causing either delayed or early afterdepolarizations (DAD or EAD, respectively). Beta-adrenergic EAD shares a common ionic mechanism with DAD in terms of cellular Ca2+ overload and spontaneous sarcoplasmic reticulum (SR) Ca2+ release [50,51,52]. ISO elevates PKA-dependent phosphorylation of L-type calcium channels (LTCC) and ryanodine (RyR2) receptors, which results in enhanced Ca2+ signals [53]. Thus, EADs and DADs induced by beta-adrenergic stimulation mediate arrhythmogenesis in the intact and diseased myocardium, which is associated with calcium overload, impaired contractility, and increased arrhythmia incidence [54]. Here, we showed aftercontractions as a result of re-activated Ca2+-transients in all cardiomyocytes after 100 nM ISO treatment. ( We note that as we did not directly measure the action potentials but the sarcomere length of the cells, the term ‘aftercontraction’, instead of ‘afterdepolarization’, is more appropriate, as suggested by Egdell [55]). The amplitude of the Ca2+ signal was significantly increased compared to baseline conditions, which corroborates to literature data [49,56]. BGP-15 pre-treatment (100 µM) before ISO application completely prevented aftercontractions of isolated cells, which might be the cellular basis of its antiarrhythmic properties. The Ca2+-transients measured in the presence of BGP-15 tended to decrease when compared to ISO controls, showing that BGP-15 may partly affect Ca2+ overload in cardiomyocytes. Interestingly, BGP-15 caused an increase in diastolic (and resting) sarcomere length, which might be a result of its actions on titin phosphorylation, which was previously shown by our team [29]. Considering these results, we propose some possible mechanisms by which BGP-15 prevents arrhythmogenesis. Firstly, we have recently shown that BGP-15 increases cardiac cGMP-PKG signaling [29]. PKG exerts cardioprotection by improving the diastolic function [57], but also modulates ATP-sensitive potassium channels (K(ATP) channels) via the inhibition of the (Ca2+/calmodulin-dependent protein kinase II (CaMKII) pathway, a major contributor to arrhythmogenesis [58]. K(ATP) activation reduces Ca2+ oscillations and cell contractility, thus stabilizing energy consumption [58]. The reduced Ca2+ peaks and aftercontractions observed in the BGP-15 pre-treated cells might be the result of the above processes, however, this hypothesis needs further evaluation. The cGMP-dependent signals activate SERCA via PKG. The increase in SERCA activity appears to diminish Ca2+ peaks and Ca2+ oscillations and attenuate hypercontracture [59]. This effect of BGP-15 on the SERCA pump was also shown by our team; thus, we suggest that this mechanism also contributes to suppressing aftercontractions [29]. PKG activation also takes part in mitochondrial mitoK(ATP) channel regulation, transmitting cardioprotective signals, and restoring mitochondrial respiration [60]. Substantial evidence suggests mitochondrial protective actions of BGP-15, while the disrupted mitochondrial Ca2+ homeostasis is a major contributor to arrhythmogenesis [21,23,25,61]. Mitochondrial ATP ensures that SR Ca2+-handling proteins operate properly, providing that mitochondria receive an appropriate supply of Ca2+ ions. Myocytes from HF models showed an increased mitochondrial Ca2+ peak, AP prolongation, EADs QT prolongation, and a high incidence of ventricular arrhythmia following the application of ISO [62]. Moreover, genetic knockdown of mitochondrial Ca2+ uniporters inhibited mitochondrial Ca2+ uptake, reduced Na+-Ca2+ exchange currents, and suppressed EADs, reducing ventricular fibrillation [63]. Thus, the mitoprotective actions of BGP-15 may further contribute to suppressing aftercontractions and arrhythmias [61]. Another possible explanation is that BGP-15 reportedly stabilizes the Kir2.1 current in Andersen–Tawil syndrome (long-QT7) channelopathy, thus protecting the electrical stability of the cardiac cells [24]. Lastly, on the organism level, we demonstrated that BGP-15 administration increases vagally mediated HRV, thus increasing the adaptation capacity of the heart while decreasing its vulnerability to arrhythmias [64]. There are several limitations to this current report. For instance, testing multiple doses of isoproterenol might have provided a better insight into the potential influence of BGP-15 on isoproterenol-induced tachycardia. Furthermore, animal models themselves have various limits on assuming translational perspectives in preclinical drug research. Here, we used a rat model which may resemble the human heart less closely than larger mammals, e.g., rabbits or swine models. Last, further studies are needed to clarify the exact molecular mechanism of action of BGP-15. To summarize the above, substantial evidence supports the cardioprotective abilities of BGP-15. As this drug candidate is proven to be well tolerated, we suggest further evaluating its clinical value in patients suffering from heart failure and cardiac arrhythmias. ## 4.1. Animal Model and Chemicals All experimental protocols were approved by the local Ethics Committee of the University of Debrecen ($\frac{12}{2022}$ DEMÁB, August 12, 2022), and the animals received humane care in accordance with the “Principles of Laboratory Animal Care” by EU Directive (citation). Animal experiments are reported in compliance with the ARRIVE guidelines [65]. In total, 40 male adult Sprague-Dawley rats (493.77 ± 17.56 g, Charles River Laboratories Inc., Germany) were housed in a room with controlled temperature and kept under a $\frac{12}{12}$ h dark/light cycle. A 2-week adaptation period was provided before the initiation of the treatments. BGP-15 (Sigma-Aldrich-Merck KGaA, Darmstadt, Germany) was dissolved freshly in saline before oral gavage or intravenous (i.v.) bolus administration (depending on the study protocol). Myocardial injury was induced by isoproterenol (Sigma-Aldrich-Merck KGaA, Darmstadt, Germany), dissolved freshly in saline. For antibiotic prophylaxis and postoperative care, cefuroxime-sodium (50 mg/kg) was used intraperitoneally. Metamizole-sodium (100 mg/kg) was administered on the first 5 postoperative days as analgesic therapy. ## 4.2. Study Design In Protocol I (dose escalation and single-dose treatments), isoproterenol (ISO) was administered intraperitoneally (i.p.) to telemetry implanted rats ($$n = 8$$, in 0.1, 1, 2, and 5 mg/kg cumulative doses), and ECGs were recorded during the 15 min preceding, and 3 h after the injections. BGP-15 (20, 40, 80, and 160 mg/kg) was orally administered to another population of implanted rats, and an ECG was recorded. After a wash-off period (1 week), BGP-15 dose escalation was performed, which was followed by the injection of ISO (1 or 2 mg/kg, i.p) to evaluate the interactions of the two drugs on heart rate and ECG parameters. Finally, another population of rats was subjected to 0.1 mg/kg i.p. ISO treatment and then BGP-15 dose escalation (40, 80, and 160 mg/kg, i.p.) under echocardiographic monitoring (ketamine/xylazine: $\frac{50}{5}$ mg/kg anesthesia, continuous ECG recording). Data was evaluated, and the ISO (1 mg/kg) and BGP-15 (40 mg/kg) doses were chosen to perform the long-term studies. The 1 mg/kg ISO dose, according to our ECG findings and literature data [66,67], is sufficient to produce arrhythmias, transient myocardial ischemia, and injury. In Protocol II (2-week follow-up), rats ($$n = 32$$) underwent telemetry implantation, and a 10-day long recovery period was provided. Following a 24-h ECG recording, a single oral BGP-15 bolus (40 mg/kg) was administered, followed by another 24-hour ECG recording of conscious animals to assess the effects of BGP-15 on HRV parameters. After, rats were divided into 4 groups: Control (vehicle-treated), BGP-15 (40 mg/kg), ISO (1 mg/kg), and ISO+BGP-15 (1 mg/kg and 40 mg/kg, respectively). ISO was administered i.p. on the 1st, 4th, 8th, and 12th days. ECG recordings were performed all day for 2 h, and, on the days of the ISO treatment, during the 15 min that preceded and the 3 h that followed the injections. All injections were performed between 10.00 and 12.00 h. On the 14th day, echocardiography was performed, and rats were sacrificed. On the lead II ECG recordings of the 13th day, short-term HRV parameters were determined, and arrhythmic events were counted. In addition, isolated canine cardiomyocyte experiments were carried out to evaluate BGP-15—ISO interactions (see Section 4.7). ## 4.3. Surgical Implantation of Radiotelemetry Transmitters Aseptic conditions were assured by using autoclaved instruments and sterilized materials and by disinfecting the workbench. Following antiseptic processes, the telemetry transmitters (Stellar Implantable Transmitter, Type PBTA-L1; Stellar Telemetry, TSE Systems Inc., Chesterfield, MO, USA) were first prepared. After being removed from their sterile package, the leads of the transmitters were shortened to a length appropriate for the size of the animals, considering their growth during research as well. Telemetry device implantations were performed under ketamine-xylazine anesthesia ($\frac{100}{15}$ mg/kg, i.m.). After the loss of the righting reflex, hair clipping was performed on both the thoracic and interscapular regions of the animals. Transmitter implantation into the animal’s back is considered to be less invasive than intraperitoneal implantation, ensuring a higher survival rate and fewer movement-based artifacts on the ECG [13]. Next, three skin incisions were made: a mid-clavicular longitudinal incision (about 1 cm in length) located on the right side of the thorax at the level of the 2nd and 3rd ribs approximately; a 2 cm long transverse incision located on the left side around the level of the 7th rib; and a transverse incision ≤5 cm on the dorsal skin in the interscapular region. The areas of the wounds were s.c. prepared, as well as the route from the skin incisions on the chest to the dorsal incision. Following this, the telemetry devices (ECG sampling frequency: 1000 Hz) were placed under the dorsal skin into the pouch formerly created by preparing the subcutaneous connective tissue. From here, the electrodes were subcutaneously transferred to the chest through the incisions and positioned according to the 2nd ECG chest lead. Real-time ECG recording was used to find the optimal placement of the electrodes. After fixing the electrodes in the right position with holding stitches (polypropylene, $\frac{5}{0}$), disinfection was performed, and wounds were closed with continuous horizontal mattress sutures ($\frac{4}{0}$, polyglactin 910). A heated table was used to maintain body heat during and after the surgery. Post-operative care consisted of the administration of antibiotics (cefuroxime 50 mg/kg, i.p.) and analgesic medications (metamizole-sodium 50 mg/kg, i.m.), observation, wound care, and weight measurements for 3 consecutive days after surgery in individually housed rats. Animals were provided with a 10-day long recovery period before the initiation of the treatments. ## 4.4. ECG Monitoring and Detection of Arrhythmias ECG (sampling frequency: 1000 Hz) and locomotor activity (LOC) were recorded from undisturbed rats in their cages. Radio signals were picked up by a receiver (Stellar Receiver and Antenna, Stellar Telemetry, TSE Systems Inc., Chesterfield, MO, USA) and recorded by a computer instrumented with the AcqKnowledge ACK100-STL Biopac Software (BIOPAC Systems Inc., Goleta, CA, USA). Signals were recorded for 35 s in every 2 min for 2 h (short-time HRV protocol) or 5 min every hour for a day (24 h protocol). The frequency of spontaneous and drug-induced cardiac arrhythmias was determined and quantified offline. The incidence of premature ventricular complexes (PVC), salvos, monomorphic and polymorphic ventricular tachycardia duration, ventricular fibrillation and its duration, and the total number of arrhythmic events (reported as the number of events per 10 min) were identified and quantified, according to the most recent guideline (The Lambeth Conventions II) [68]. ## 4.5. Heart Rate Variability (HRV) Analyses From the continuous ECG data, R-R interval raw data (tachogram) were generated for each 2 min recording period. ECG R waves were automatically identified and exported by the AcqKnowledge ACK100-STL Biopac Software (BIOPAC Systems Inc., CA, USA). Each raw ECG tachogram was first visually examined to make sure that all R-waves had been appropriately identified. ECG recording segments that showed artifacts (the animal was moving, the signal was noisy, or an arrhythmic event occurred) were eliminated without replacement and were excluded from further investigation. A filter was applied to ensure the exclusion of abnormally low or high R-R intervals (100–300 ms). Heart rate (as beats per minute, bpm) and HRV parameters were quantified using the KUBIOS HRV software (version 3.4.2., Kubios Inc., Finland) with modifications in accordance with Thireau and colleagues’ criteria for assessing HRV parameters in rodents [13]. Subsequent R-R differences greater than 30 ms were also excluded by the software, and the remaining arrhythmias were automatically filtered again. The normal (N) R-R intervals were considered N-N intervals. Time-domain measures included the standard deviation of the time between normal-to-normal beats (SDNN), the root mean square of successive beat-to-beat interval differences (RMSSD), and the proportion of the number of pairs of successive NNs that differ by more than 10 ms (pNN$10\%$) [13]. For frequency-domain HRV data (only for the 24 h HRV experiments), a power spectrum was generated by a fast Fourier transform-based method (Welch’s periodogram: 256 points, $50\%$ overlap, and Hamming window), automatically by the KUBIOS software. The powers (ms2) of the very-low-frequency (VLF: <0.04 Hz), low-frequency (LF: 0.04–0.78 Hz), and high-frequency (HF: 0.78–2.5 Hz) bands were calculated [43]. Non-linear analyses included the automatic generation of a Poincaré plot and the determination of the SD1 parameter (standard deviation of the width of the Poincare plot ellipse, indicative of short-term variability). Each tachogram was split into 2 min epochs (0–2 min, 2–4 min, etc.), and, for each epoch, separate indices of HRV and ΔHR values were generated. Subsequently, parameters were averaged as the mean values of animals [42]. To assess the effects of BGP-15 and ISO, HR and HRV values were calculated for baseline conditions (pre-injection) and after the wear-off of the ISO-induced tachycardia, at resting conditions, in two-hour long ECG signals. HR and HRV data were analyzed by means of paired t-tests (Protocol I, to estimate differences between two groups) or one-way ANOVA, Tukey’s post-test (Protocol II, to estimate differences between 4 groups). ## 4.6. Echocardiography In both acute and long-term studies, echocardiography was carried out with the Vevo 3100 ultrasound imaging system (Fujifilm VisualSonics Inc., Toronto, ON, Canada) equipped with a high-frequency transducer (MX250, 14–28 MHz), designed for cardiac imaging of rodents. The imaging was performed under ketamine-xylazine anesthesia ($\frac{50}{5}$ mg/kg, i.m.) after chest hair removal and placing the animals onto a heated table (Visualsonics SR200, adjusted to 39 ± 0.5 °C). Data acquisition was performed in accordance with the recommendations of the American Society of Echocardiography [69]. The database was built from the parasternal long- and short-axis, as well as from the apical four-chamber view (PSLAX, PSAX, and A4C, respectively). The aortic arch was visualized from a modified suprasternal view. After acquisition, data were stored and offline analyzed by a blinded reader using the VevoLAB software (version 5.1, Fujifilm VisualSonics Inc., Toronto, ON, Canada). Left ventricle (LV) internal diameter in end-diastole (LVIDd, mm) and end-systole (LVIDs, mm), anterior, and posterior LV wall thickness (mm, LVAWd, LVAWs, and LVPWd, LVPWs, respectively) were determined by manually tracing the endo-and epicardial borders in M-mode traces. Left ventricular volume in end-diastole and end-systole (LVVOLd, LVVOLs, respectively; µL) was calculated by the software. LV ejection fraction (LV EF, %) was calculated as 100(LVVOLd-LVVOLs)/LVVOLd. Left atrial (LA) size (mm) and aortic (Ao) diameter (mm) were measured from M-mode recordings. Heart rate (HR, bpm), stroke volume (SV, µL), cardiac output (CO, ml/min), and corrected LV mass (mg) were determined (1.053((LVIDd+LVPWd+IVSd)3-LVIDd3)0.8). Diastolic function was evaluated by pulsed-wave Doppler (PW) and tissue Doppler imaging (TDI) from apical 4-chamber views. PW Doppler echocardiography was used to measure the ratio of the peak early (E) and atrial (A) transmitral flow velocities (E/A ratio) and the E wave deceleration time (DecT, ms). Wall motion velocity was defined as the e′/a′ ratio by measuring e′ and a′ waves at the septal annulus by TDI. The E/e′ ratio (indicative of LV filling pressure) was also calculated. Aortic flow velocities (Vel, peak and mean, mm/s) and pressure gradients (PGrad, peak and mean, mmHg) were measured after visualizing the aortic arch from a modified suprasternal view. Three cardiac cycles were averaged for each parameter; data are presented as the mean ± SD. ## 4.7. Isolated Canine Cardiomyocyte Experiments Canine cardiomyocytes were donated by the Department of Physiology, University of Debrecen. The cells were isolated from the midmyocardial region, as described elsewhere [70]. Briefly, hearts were isolated from anesthetized (ketamine: 10 mg/kg, xylazine: 1 mg/kg) adult beagle dogs and single cardiomyocytes were obtained by enzymatic dispersion technique using Joklik solution (Minimum Essential Medium Eagle, Joklik Modification; Sigma-Aldrich Co., St. Louis, MO, USA) supplemented with type II collagenase (1 mg/mL) for 35 min. Isolated cells were kept at 15 °C and were subjected to ex vivo calcium transient measurements. Cardiomyocytes were loaded with 5 μM Fura-2 AM for 30 min in Tyrode solution, in the presence of Pluronic F-127. Cardiomyocytes were then allowed to rest for 30 min to let the intracellular esterases cleave Fura-2 AM, then stored at 15 °C until the measurements. Cells were then placed in a tissue bath on the stage of an inverted microscope (Nikon Eclipse TS100, Nikon Corp., Tokyo, Japan). The final volume of the chamber filled with Tyrode solution (containing 144 mM NaCl, 5.6 mM KCl, 2.5 mM CaCl2, 1.2 mM MgCl2, 5 mM HEPES, and 11 mM dextrose, at pH = 7.4) was 1 mL. After sedimentation, a single rod-shaped cardiomyocyte with a clear striation pattern was chosen to record the physiological parameters. The cardiomyocyte was paced (Experimetria, MDE, Heidelberg) by 0.5 Hz. Alternating excitation wavelengths of 340 and 380 nm were used to monitor the fluorescence signals of Ca2+-bound and Ca2+-free Fura-2 dye, respectively. Fluorescent emission was detected at 510 nm, and traces were digitized at 200 Hz using the FeliX software (Ratiomaster RM-50 system, Horiba, New Brunswick, NJ, USA). The experimental protocol was the following: after a 5 min resting period, cardiomyocytes were paced at 0.5 Hz for 30 secs, resulting in a steady-state condition. The baseline resting sarcomere length was determined after a 1 min stabilization period following the pacing. Then, 1 µL BGP-15 (100 µM, dissolved in distilled water) or 1 µL saline (control) was added to the chamber, followed by 5 min of incubation. Sarcomere length was determined again, and a 0.5 Hz pacing was initiated for at least 30 s. Finally, 1 µL isoproterenol (ISO, dissolved in distilled water) was added at a final concentration of 100 nM, and pacing was initiated for another 30 s cycle with the recording of the above parameters. Background fluorescence intensity was obtained at the end of the measurements in a region without cells and was subtracted from the original data. Sarcomere length and Ca2+-transient amplitude of cardiomyocytes were exported to GraphPad Prism software, and the curves were analyzed with appropriate curve-fitting methods. ## 4.8. Statistics and Data Analysis Data are presented as the mean value of the group ± standard error of the mean (SEM), unless stated otherwise. Group numbers were determined based on the minimum numbers required for statistical analyses, also considering our previous studies and possible losses due to the treatments. Experiments were designed to generate groups of equal size using randomization and blinded analysis. Statistical analyses were carried out only for experiments where each group size was at least $$n = 5$$, both in animal and cellular studies. First, the Gaussian distribution of data was estimated by the Shapiro–Wilk normality test. Statistical analysis was then performed using one-way analysis of variance (ANOVA), followed by Tukey’s multiple comparisons post-test (only if F in the ANOVA achieved $p \leq 0.05$, the normality test was passed, and there was no significant variance inhomogeneity) or Kruskal–Wallis test followed by Dunn’s post-test (when the normality test was not passed). Student’s t-test or Mann–Whitney test (non-Gaussian data) was used to estimate differences between two treatment groups (unpaired or paired, depending on the control/self-control design). Statistical analyses were carried out using GraphPad Prism software for Windows, version 8.00 (GraphPad Software Inc., La Jolla, CA, USA). Outliers were identified as mean ± 2SD when it was necessary. Probability values (p) less than 0.05 were considered significantly different and marked with an asterisk (: $p \leq 0.05$, relative to controls) or a hash symbol (#: $p \leq 0.05$, relative to ISO). 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--- title: Neuroprotective and Antiherpetic Properties of Polyphenolic Compounds from Maackia amurensis Heartwood authors: - Darya V. Tarbeeva - Dmitry V. Berdyshev - Evgeny A. Pislyagin - Ekaterina S. Menchinskaya - Natalya Y. Kim - Anatoliy I. Kalinovskiy - Natalya V. Krylova - Olga V. Iunikhina - Elena V. Persiyanova - Mikhail Y. Shchelkanov - Valeria P. Grigorchuk - Dmitry L. Aminin - Sergey A. Fedoreyev journal: Molecules year: 2023 pmcid: PMC10056899 doi: 10.3390/molecules28062593 license: CC BY 4.0 --- # Neuroprotective and Antiherpetic Properties of Polyphenolic Compounds from Maackia amurensis Heartwood ## Abstract In this study, we isolated a new isoflavanostilbene maackiapicevestitol [1] as a mixture of two stable conformers 1a and 1b as well as five previously known dimeric and monomeric stilbens: piceatannol [2], maackin [3], scirpusin A [4], maackiasine [5], and maackolin [6] from M. amurensis heartwood, using column chromatography on polyamide, silicagel, and C-18. The structures of these compounds were elucidated by NMR, HR-MS, and CD techniques. Maksar® obtained from M. amurensis heartwood and polyphenolics 1–6 possessed moderate anti-HSV-1 activity in cytopathic effect (CPE) inhibition and RT-PCR assays. A model of PQ-induced neurotoxicity was used to study the neuroprotective potential of polyphenolic compounds from M. amurensis. Maksar® showed the highest neuroprotective activity and increased cell viability by $18\%$ at a concentration of 10 μg/mL. Maackolin [6] also effectively increased the viability of PQ-treated Neuro-2a cells and the value of mitochondrial membrane potential at concentrations up to 10 μΜ. Maksar® and compounds 1–6 possessed higher FRAP and DPPH-scavenging effects than quercetin. However, only compounds 1 and 4 at concentrations of 10 μM as well as Maksar® (10 μg/mL) statistically significantly reduced the level of intracellular ROS in PQ-treated Neuro-2a cells. ## 1. Introduction Maackia amurensis Rupr et Maxim. is an endemic woody plant of the Fabaceae family, widespread in the Primorsky and Khabarovsky regions of the Russian Federation. The heartwood of M. amurensis is included in the state register of medicinal products and its polyphenolic complex is used to produce the drug Maksar®. Maksar® was developed at the G.B. Elyakov Pacific Institute of Bioorganic Chemistry (PIBOC FEB RAS) and registered in the Russian Federation as a hepatoprotective drug (registration number P N$\frac{003294}{01}$). The active compounds of this complex were isoflavones, pterocarpans, flavanones, isoflavans, isoflavanones, chalcones, lignans, and monomeric and dimeric stilbenes [1]. In contrast to M. amurensis heartwood, the main polyphenolic metabolites of M. amurensis root bark were shown to be glycosides of isoflavones and pterocarpans as well as prenylated flavanones [2,3]. In addition to possessing hepatoprotective properties, Maksar® also reduced the total lipid content in blood [4,5]. It prevented the increase in the total serum lipid content and the development of hyperlipoproteinemia in experimental animals [6] and in clinical trials [7]. Maksar® also possessed antiplatelet [8] and antitumor properties [9], as well as demonstrated antioxidant activity in vitro [10]. This drug enhanced the antioxidant defense system of the body and reduced the lipid peroxidation level [5,11]. The neuroprotective activity of the polyphenolic compounds from M. amurensis may be associated with their ability to inhibit monoamine oxidase (MAO), an enzyme that catalyzes the oxidative deamination of monoamines and exists in two isoforms: MAO-A and MAO-B. In the mammalian brain, MAO-B activity increases with aging. Hence, the inhibitors of this enzyme can be used to treat Parkinson′s disease [12,13]. The ability of 19 polyphenolic compounds from M. amurensis to inhibit MAO-B was studied, and the isoflavones calicosin and 8-O-methylretusin as well as the pterocarpan (—)-maakiain were shown to be effective MAO-B inhibitors [14,15]. The isoflavones genistein and formononetin significantly inhibited both MAO-A and MAO-B [15]. Maakiain was also shown to protect dopaminergic neurons from damage in several strains of transgenic worms [16]. The pathogenesis of neurodegenerative diseases, including Parkinson′s disease, involves the death of neurons from oxidative damage. An increase in the content of intracellular reactive oxygen species (ROS) causes DNA damage [17]. Many researchers also suggested a correlation between neurodegeneration and herpes infection [18,19]. Herpesvirus infections are also associated with the generation of oxidative stress in infected cells. It was reported that herpes simplex virus type 1 (HSV-1) caused an increase in the level of ROS and lipid peroxidation and also reduced the content of glutathione, the main tool of the body′s antioxidant defense [20,21]. It was shown that HSV-1 disrupted the functional capacity of mitochondria [22,23,24]. The impaired mitochondrial function, elevated ROS levels, and reduced mitochondrial membrane potential due to the herpes infection subsequently led to the development of neurodegenerative diseases [25,26,27]. The antiherpetic properties of the polyphenolics from M. amurensis have not been studied so far, except for resveratrol and piceatannol. Their antiherpetic activity was studied both in vitro and in vivo [28,29,30,31]. The aim of this study was to assess the antiherpetic activity of polyphenolic compounds, the constituents of Maksar®, as well as their ability to reduce the level of intracellular ROS and increase mitochondrial membrane potential using a model of Parkinson′s disease. ## 2. Results Here, we isolated a new compound 1 as well as five previously known dimeric and monomeric stilbens: piceatannol [2], maackin [3], scirpusin A [4], maackiasine [5], and maackolin [6] from M. amurensis heartwood (Figure 1). Compounds 2–6 were identified by comparison of their HPLC-PDA-MS and NMR spectra with previously published data [32,33,34]. ## 2.1. Structure Determination of Conformers 1a and 1b The mixture of compounds 1a and 1b was obtained as a white amorphous powder. We observed only one peak in the HPLC profile of the mixture of 1a and 1b (Figures S1 and S2, Supplementary data). The molecular formulas of 1a and 1b were determined to be C30H26O8 based on the presence of [M-H]− and [M+H]+ ions at m/z 513.1573 (calculated for [C30H25O8]− 513.1555) and 515.1687 (calculated for [C30H27O8]+ 515.1700), respectively, in the HR-MS-ESI spectra of 1a and 1b (Figures S3 and S4, Supplementary Data). Sixteen carbon atoms belonged to the vestitol skeleton (rings A–C) and methoxy group of 1a, whereas the other fourteen atoms formed a piceatannol moiety (rings D and E). The 1H NMR spectrum of 1a showed the presence of an ABX spin system: the signals at δH 6.64 (d, $J = 8.4$ Hz, 1H), 6.29 (dd, $J = 8.4$ Hz, 2.4 Hz. 1H), and 6.42 (d, $J = 2.4$ Hz, 1H) were attributed to protons H-5, H-6, and H-8 of ring A, respectively (Table 1). The signals of another ABX system at δH 6.30 (d, $J = 2.4$ Hz, 1H), 6.27 (dd, $J = 8.5$ Hz, 2.4 Hz, 1H), and 7.25 (d, $J = 8.5$ Hz, 1H) in the 1H NMR spectrum of 1a were assigned to H-3′, H-5′, and H-6′ of ring B in 1a, respectively. A singlet at δH 3.63 (s, 3H) in the 1H NMR spectrum of 1a and a carbon signal at δC 54.9 in the 13C NMR spectrum of 1a were ascribed to OCH3-group at C-4′. We concluded that the methoxy group was located at C-4′, because we observed the correlation between the singlet signal of its protons at δH 3.63 and the signal of C-4′ at δC 159.8 in the HMBC spectrum of 1a (Table 1). The proton signals at δH 4.24 (dd, $J = 10.3$ Hz, 3.5 Hz 1H), 4.03 (t, $J = 10.3$ Hz, 1H), 4.16 (td, $J = 11.6$ Hz, 3.5 Hz, 1H), and 5.36 (d, $J = 11.6$ Hz, 1H) belonged to H-2a, H-2b, H-3, and H-4 of ring C in 1a, respectively (Table 1). Two doublets at δH 6.82 (d, $J = 16.0$ Hz, 1H) and 6.57 (d, $J = 16.0$ Hz, 1H) were due to H-7″ and H-8″, which formed a double bond in the piceatannol moiety of 1a. The value of the coupling constant ($J = 16.0$ Hz) confirmed trans configuration of the double bond. Two doublets in the 1H NMR spectrum of 1a at δH 6.26 (d, $J = 2.5$ Hz, 1H) and 6.51 (d, $J = 2.5$ Hz, 1H) were assigned to H-3″ and H-5″ of ring D in 1a, respectively. The ABX-system of ring E in 1a was formed by H-10″, H-13″, and H-14″, which gave signals at δH 6.80 (d, $J = 2.4$ Hz, 1H), 6.73 (d, $J = 8.1$ Hz, 1H), and 6.67 (dd, $J = 8.1$ Hz, 2.4 Hz 1H) in the 1H NMR spectrum of 1a, respectively. The carbon atom C-4 (δC 35.8) of the vestitol moiety in 1a was linked to C-1″ (δC 119.5) of the piceatannol moiety, because in the HMBC spectrum of 1a we observed cross-peaks between the proton signal at δH 5.36 (d, $J = 11.6$ Hz, 1H) of H-4 and the carbon signals of C-1″, C-2″, and C-6″at δC 119.5, 157.4, and 139.6, respectively. The chemical shift values in the 1H and 13C spectra of compound 1a did not differ considerably from those of 1b except for C-4. The vestitol (rings A–C) skeleton, including methoxy group, and piceatannol (rings D and E) moiety in 1b were formed by other sixteen and fourteen carbon atoms, respectively. The 1H NMR spectrum of 1b revealed the presence of an ABX spin system: the signals at δH 6.50 (d, $J = 8.8$ Hz, 1H), 6.20 (dd, $J = 8.8$ Hz, 2.4 Hz. 1H), and 6.27 (d, $J = 2.4$ Hz, 1H) were due to protons H-5, H-6, and H-8 of ring A, respectively (Table 2). The protons of another ABX system gave signals at δH 6.40 (d, $J = 2.4$ Hz, H), 6.22 (dd, $J = 8.5$ Hz, 2.4 Hz, 1H), and 6.99 (d, $J = 8.5$ Hz, 1H) in the 1H NMR spectrum of 1b (H-3′, H-5′, and H-6′ of ring B in 1b, respectively). A singlet at δH 3.61 (s, 3H) in the 1H NMR spectrum of 1b and a carbon signal at δC 54.9 in the 13C NMR spectrum of 1b were ascribed to the OCH3-group at C-4′. The correlation between protons at δH 3.61 and the carbon signal at δC 159.9 in the HMBC spectrum of 1b confirmed that the methoxy group was located at C-4′ (Table 2). The signals at δH 4.20 (dd, $J = 10.3$ Hz, 3.2 Hz 1H), 4.44 (t, $J = 10.3$ Hz, 1H), 4.36 (td, $J = 10.9$ Hz, 3.2 Hz, 1H), and 4.95 (d, $J = 10.9$ Hz, 1H) were assigned to H-2a, H-2b, H-3, and H-4 of ring C in 1b, respectively (Table 2). Two doublets at δH 7.25 (d, $J = 15.9$ Hz, 1H) and 6.62 (d, $J = 15.9$ Hz, 1H) were due to H-7″ and H-8″, which formed a double bond in the piceatannol moiety of 1b. The value of the coupling constant ($J = 15.9$ Hz) also confirmed trans configuration of the double bond in the piceatannol moiety of 1b. Two doublets in the 1H NMR spectrum of 1b at δH 6.20 (d, $J = 2.4$ Hz, 1H) and 6.44 (d, $J = 2.4$ Hz, 1H) were assigned to H-3″ and H-5″ of ring D in 1b, respectively. The ABX-system of ring E in 1b was formed by H-10″, H-13″, and H-14″, which gave signals at δH 7.09 (d, $J = 1.9$ Hz, 1H), 6.78 (d, $J = 8.5$ Hz, 1H), and 6.84 (dd, $J = 8.5$ Hz, 1.9 Hz 1H) in the 1H NMR spectrum of 1b, respectively. C-4 (δC 39.7) of the vestitol moiety of 1b was also linked to C-1″ (δC 119.4) of the piceatannol moiety, because the cross-peaks between the proton signal at δH 4.95 (d, $J = 10.9$ Hz, 1H) of H-4 and the carbon signals of C-1″, C-2″, and C-6″ at δC 119.4, 157.1, and 141.7, respectively, were observed in the HMBC spectrum of 1b. All signals in the 1H and 13C NMR spectra of compounds 1a and 1b were completely assigned on the basis of COSY, HMBC, and ROESY spectral data. The NMR spectra for 1 can be found in Supplementary Data (Figures S5–S68). Thus, compounds 1a and 1b were determined to be dimeric compounds composed of vestitol moiety and piceatannol moiety. In order to determine the absolute configuration of compound 1, we used the approach based on the combination of theoretical and experimental UV, ECD, and NMR spectroscopy methods. The experimental NMR spectra (δH, JH–H, and ROESY data) definitely gave evidence that the sample of 1 under study was a mixture of compounds, which might be two different stereoisomers of 1 or two different conformers of one stereoisomer. Thus, we had to distinguish between these two possibilities. We performed the extended quantum-chemical investigation of the conformational mobility of 3R,4S and 3S,4S stereoisomers of 1 using density functional theory (DFT) with the B3LYP exchange-correlation functional set, polarizable continuum model (PCM), and 6-311G(d) basis set implemented in the Gaussian 16 package of programs [35]. The details of the calculations are described in Supplementary Data (Figures S70–S83). Many large amplitude motions (LAM) may proceed in each stereoisomer: internal rotations of different hydroxyl groups, the internal rotation of the 4-vinylbenzene-1,2-diol fragment of the piceatannol substituent at C-4, the inversion of ring C, and the internal rotations of substituents at C-3 and C-4. Due to these intramolecular motions, a number of rotameric forms for each conformation of ring C may occur for both 3R,4S and 3S,4S configurations. Some of them are favorable for the creation of intramolecular O-H…O hydrogen bonds (Figures S79 and S80, Supplementary Data), which stabilize these rotameric forms for about several kcal/mol relative to other rotameric forms. The values of vicinal spin–spin coupling constants JH4−H3, JH2a−H3, and JH2b−H3 strongly depend on the values of dihedral angles θ4 ≡ ∠ H-4–C-4–C-3–H-3, θ2a ≡ ∠ H-2a–C-2–C-3–H-3, and θ2b ≡ ∠ H-2b–C-2–C-3–H3, respectively. According to the Karplus equation, JHi−H3 ≈ 10–11 Hz when θi ≈ 0 ± 20° or 180 ± 20°. The values of JH4–H3 in the experimental 1H NMR spectra of 1a and 1b were 11.6 Hz and 10.9 Hz, respectively (Table 1 and Table 2). According to the results of the conformational analysis, the values of dihedral angle θ4 for stable conformations of 3S,4S stereoisomer of 1 varied in the diapason of \|θ4\| ≥ 35°. The most abundant conformations of 3S,4S stereoisomer of 1 with total statistical weight of more than $93\%$ (S79, Supplementary data) arose when θ5 ≡ ∠ C-2″–C-1″–C-4–H-4 ≈ 180 ± 30°. The most stable ones with total amount ≈ $81\%$ arose when ring C had conformation with axial orientation (Ax) of the atom H-3 (θ4 ≈ −43°). In the most stable conformations, the atom H-7″ stayed in the proximity to the atom H-4 (the distance (H-4…H-7″) ≈ 2.1 Å) and far from the atom H-3 (the distance (H-3…H-7″) ≈ 3.4 Å). The second most stable conformation arose when ring C had conformation with equatorial (Eq) orientation of the atom H-3 (θ4 ≈ +36°) and the atom H-7″ stayed far from both the H-3 and the H-4 atoms (the distance (H4…H-7″) ≈ 3.8Å and the distance (H3…H-7″) ≈ 4.1Å). Based on these geometries, we were able to suppose that 3S,4S- stereoisomer of 1 poorly matched the experimental values of JH4−H3 constants and the ROESY data. To prove this, we calculated these constants quantum-chemically using the B3LYP/6-311G(d)_GIAO_PCM//B3LYP/6-311G(d)_PCM level of theory. For 3S,4S-1_Ax, the obtained values were: JH4–H3 = +6.5 Hz, JH3–H2a = +3.1 Hz, and JH3–H2b = +9.4 Hz. For 3S,4S-1_Eq, the conformation theoretical values were: JH-4–H-3 = +8.1 Hz, JH-3–H-2a = +3.7 Hz, and JH3–H2b = +1.1 Hz. The statistically averaged values were: JH4–H3 = +6.2 Hz, JH3–H2a = +2.9 Hz, and JH3–H2b = +7.7 Hz. Thus, the theoretical NMR data for the 3S,4S- stereoisomer of 1 (and, hence, for the 3R,4R-stereoisomer as well), did not match the experimental NMR data. Two main intramolecular rearrangements were considered when analyzing the NMR, UV, and ECD spectra of 3S,4R stereoisomer of 1 (Figure 2). The big size of the 4-vinylbenzene-1,2-diol fragment at C6″ resulted in high steric hindrances, which inhibited the internal rotation of piceatannol fragment around C4−C1″ bond. The scans of the potential energy surface along θ(6″) ≡ ∠ 6″ − 1″ − C-4 − H-4 are given in Supplementary Data (S77). Our calculations showed that 3R,4S-1a ↔3R,4S-1b rearrangement could proceed only when ring C had equatorial (“Eq”) conformation. Additionally, even in this case, the transfer was required to overcome the wide and high potential energy barrier: ΔV#1a↔1b ≥ 20.0 kcal/mol. The rate constant calculated for this ΔV#1a↔1b value was k1a↔1b = ≤ 10−1 c−1. The inverse value of k1a↔1b was a lifetime τ of conformations 1a and 1b. The lifetimes τ ≈ 101 ÷ 102 c were about 3 ÷ 8 orders of magnitude larger than the characteristic time in NMR experiments was. Thus, the conditions of the very slow exchange between two stable states were fulfilled and we were able to detect experimentally two different rotameric forms (1a and 1b) of the 3R,4S stereoisomer of 1 by NMR technique. In contrast to the 1a↔1b rotamerism, the inversion of ring C proceeded with overcoming of relatively low potential energy barrier: ΔV#inv ≥ 4 kcal/mol (S73–S75, Supplementary Data). The rate constant for this process was kinv~109 ÷ 1010 c−1 and τAx/Eq ≈ 10−10 ÷ 10−9 c. These lifetimes corresponded to the conditions of quick exchange and, hence, conformations, differing in the conformation of ring C, could not be distinguished by NMR method. The performed conformational analysis (accounting for rotamerism of substituents at C-3 and C-4, rotamerism of OH groups, the inversion of ring C and the formation of O−H…O intramolecular hydrogen bonds) allowed us to select conformations with Gibbs free energies in the region ΔGim ≤ 5 kcal/mol. The structures of these conformations and their statistical weights are presented in Supplementary Data (S80). According to the performed analysis, the 3R,4S-1a and 3R,4S-1b rotameric forms should be treated as two different compounds. According to the calculated Gibbs free energies, compound 3R,4S-1a existed predominantly in “Ax” conformation, whereas compound 3R,4S-1b existed as a mixture of “Ax” and “Eq” conformations in the ratio g(Ax): g(Eq) ≈ 1.63. The “Ax” conformation of 3R,4S-1a was characterized by dihedral angles θ4 ≈ 180°, θ2a ≈ +175°, and θ2b ≈ −64°. This geometry coincided well with the geometry, which might be expected according to relative values of experimental JH-3-H-4, JH-3-H-2a, and JH-3-H-2b constants. In this conformation, the atom H-3 stayed in the proximity to atoms H-7″ (the distance (H3…H-7″) ≈ 2.4 Å), whereas H-4 stayed in the proximity to atoms H-6′ (the distance (H4…H-6′) ≈ 2.1 Å). These data also were in accordance with the ROESY experiments (Table 1 and Table 2). The “Ax” conformation of 3R,4S-1b was characterized by dihedral angles θ4 ≈ −174°, θ2a ≈ +173°, and θ2b ≈ −65°. This geometry also coincided well with the relative values of experimental JH3-H4, JH3-H2a, and JH3-H2b constants (Table 1 and Table 2). In this conformation, the atom H-4 stayed in the proximity to atoms H-7″ (the distance (H4…H-7″) ≈ 2.0 Å) and the atom H-3 stayed in the proximity to atoms H-2a (the distance (H3…H-2a) ≈ 2.4 Å). These data also were in accordance with the ROESY data (Table 1 and Table 2). Then, we calculated theoretical values of vicinal spin–spin coupling constants for both stable rotameric forms of 3R,4S stereoisomer of 1: 3R,4S-1a_Ax: JH-4−H-3 = +10.4 Hz, JH3−H2a = +9.4 Hz, JH3−H2b = +3.2 Hz; 3R,4S-1b_Ax: JH-4−H-3 = +9.7 Hz, JH3−H2a = +8.9 Hz, JH3−H2b = +2.9 Hz; 3R,4S-1b_Eq: JH-4−H-3 = +5.7 Hz, JH3−H2a = +2.1 Hz, JH3−H2b = +2.7 Hz; The statistically averaged values for 3R,4S stereoisomer (1b) were: JH-4−H-3 = +7.8 Hz, JH3−H2a= +6.0 Hz, and JH3−H2b = +2.7 Hz. Thus, the theoretical NMR data calculated for the 3R,4S stereoisomer of 1a corresponded well for one set of signals in the experimental NMR 1H spectrum (Table 1). On the contrary, for 1b, the theoretical values of vicinal spin–spin coupling constants were smaller than the experimental values. The relative values of JH-4−H-3, JH3−H2a, and JH3−H2b constants were reproduced correctly (Table 2). The linear regression analysis showed that calculated J values were systematically underestimated: Jcalc(3R,4S-1a) = 0.017 + 0.879 · Jexp; Jcalc(3R,4S-1b_Ax) = -0.013 + 0.839 · Jexp. When scaling factor η = $\frac{1}{0.839}$ = 1.192 was used, the recalculated mean values of constants for 3R,4S-1b became: JH-4–H-3 = +9.3 Hz, JH3–H2a = +7.2 Hz, JH3–H2b = +3.2 Hz. These values were still lower than the experimental values. Thus, we were able to suppose that the theory we used overestimated to some extent the amount of 3R,4S-1b_Eq conformations. According to NMR data, the concentrations of 1a and 1b in the sample related as η ≈ 1: 0.85. Figure 3 shows the averaged ECD spectrum, calculated for the 3R,4S stereoisomer of 1 as a superposition of spectra, obtained for 3R,4S-1a and 3R,4S-1b. The variations of Δε (3R,4S-1) contour dependent on relative amounts of “Ax” and “Eq” conformations are presented in Supplementary Data (Figure S84). A good correspondence between theoretical and experimental spectra was obtained for the 3R,4S-1 stereoisomer, but a poor one for the 3S,4R-1 stereoisomer. These data confirmed that the absolute configuration of 1 was 3R,4S. Based on the results of the performed quantum-chemical calculations and NMR data, we found that compound 1 was a mixture of two stable conformers 1a and 1b with 3R,4S absolute configuration (Figure 2). Hence, compound 1 was named maackiapicevestitol and its structure was determined to be 4-(E)-3,5-dihydroxy-2-[(3R,4S)-7-hydroxy-3-(2-hydroxy-4-methoxyphenyl)-chroman-4-yl)-styryl]-benzene-1,2-diol. The absolute configurations of compounds 3, 4, and 6 were determined by comparison of the experimental and theoretically calculated ECD spectra. The details of the procedure were the same as for 1. First of all, we used the B3LYP/6-311G(d)_PCM method to investigate the relative thermodynamic stability of different conformations for all compounds. The most stable conformations were selected for further calculation of the UV and ECD spectra. The TDDFT approach along with the cam-B3LYP density functional were used for calculation of excitation energies and the rotatory and oscillatory strengths for a number of vertical electronic transitions ($$n = 50$$ for each conformation). The comparison of the calculated UV spectra with the experimental ones was used to obtain the UV shifts and the bandwidths that gave good coincidence between theoretical and experimental UV spectra. The analysis of the theoretically obtained UV spectra (Figures S85 and S86, Supplementary Data) showed that the TDDFT_cam-B3LYP/6-311G(d)_PCM//B3LYP/6-311G(d)_PCM method allowed us to reproduce well experimental UV spectra in the short-wave region λ ≤ 260 nm. These data forced us to choose the λ ≤ 260 nm region as a reference region for determination of the UV shifts and bandwidths for all compounds. The spectra calculated for compound 3 are presented in Supplementary Data (Figure S85). The UV shift Δλ = +12 nm and the bandwidth ζ = 0.38 eV were used for simulations of the UV and ECD spectra. The comparison of experimental and theoretical ECD spectra showed that the correct sign of the band in λ ≤ 260 nm region reproduced the ECD spectrum, calculated for 2S,3S stereoisomer of 3. Figure S86 shows the theoretical and experimental UV and ECD spectra of 6 (the UV shift Δλ = +10 nm and the bandwidth ζ = 0.32 eV were used). Good correspondence between calculated and experimental ECD spectra occurring for 3S,3aS,8R,8aS configuration of 6 was observed. The determination of the configurations of asymmetric centers for compounds 4 and 5 is in progress. ## 2.2. Antiradical Activity and Ferric Reducing Power (FRAP) of Polyphenolic Compounds from M. amurensis Heartwood Compounds 1–6 exhibited considerably higher DPPH-scavenging effect and FRAP compared to those of the reference compounds quercetin and ascorbic acid (Table 3). In the DPPH assay, the IC50 values for 1–6 were in the range from 2.0 to 4.3 µM compared to 9.3 µM for quercetin. Maackolin [6] showed the highest antiradical activity (IC50 2.0 µM) among the tested compounds, as well as significant FRAP (23.10 CFe2+(µM)/Cmaackolin). Maackiasine [5] exhibited quite significant DPPH-scavenging effect, but its FRAP was the lowest among the tested stilbenes. The DPPH-scavenging effect and FRAP of Maksar® were lower than those of individual polyphenolics 1–6 (Table 3). ## 2.3. Cytotoxic Activity of Stilbenes from M. amurensis against Neuro-2a Cells The evaluation of cytotoxic activity of stilbenes 1, 2, 4–6 against Neuro-2a cells showed that they did not affect cell viability at concentrations up to 100 μM. It was also shown that only maackin [3] at the maximum studied concentration of 100 μM reduced cell viability with EC50 value of 87.7 µM (data not shown). ## 2.4. Effect of Polyphenolic Compounds from M. amurensis Heartwood on the Viability and ROS Level in PQ-Treated Neuro-2a In this study, we performed the MTT assay to assess the percentage of living cells after treatment with paraquat (PQ). Maksar® showed the highest neuroprotective activity and increased cell viability by $18\%$ at a concentration of 10 μg/mL compared to PQ-treated Neuro-2a cells (Figure 4a,b). Maackolin [6], at a concentration of 10 μM, also effectively increased the viability of PQ-treated cells (by $16\%$). Piceatannol [2] and maackiasine [5] did not show any effect in this test. Maackiapicevestitol [1] and scirpusin A [4], at a concentration of 10 μM (5.1 and 4.9 μkg/mL, respectively), as well as Maksar® (10 μg/mL) reduced the level of intracellular ROS in PQ-treated Neuro-2a cells by $17\%$, $7\%$, and $12\%$, respectively (Figure 4c). Maackin [3] at the minimum studied concentration of 0.1 μM inhibited the level of ROS by $17\%$. Maackolin [6] did not decrease the ROS level in PQ-treated Neuro-2a cells (Figure 4c). ## 2.5. Effect of Polyphenolic Compounds from M. amurensis Heartwood on Mitochondrial Membrane Potential in PQ-Treated Neuro-2a We studied the effect of stilbenes from M. amurensis as well as Maksar® on PQ-induced mitochondrial dysfunction in Neuro-2a cells. The tetramethylrhodamine methyl (TMRM) fluorescence decreased by $16\%$ after a 1 h exposure of Neuro-2a cells with PQ, which indicated that PQ caused depolarization of the mitochondrial membrane (Figure 4d). All tested compounds were able to prevent depolarization and restored mitochondrial membrane potential to almost baseline values. We observed a significant dose-dependent effect of increasing mitochondrial potential when Neuro-2a cells were incubated with scirpusin A [4] compared to PQ-treated cells. The maximum effect was observed when Neuro-2a cells were incubated with 4 at a concentration of 10 μM ($26\%$). Maackolin [6] at a concentration of 10 μM also increased the value of mitochondrial membrane potential compared to PQ-treated cells by $9\%$. When maackin [3] was added to PQ-treated Neuro-2a cells, an inverse dose-dependent effect was observed. The most effective concentration was 0.1 μM (the value of mitochondrial membrane potential increased by $16\%$ compared with PQ-treated cells) (Figure 4d). ## 2.6. Cytotoxic Activity of Stilbenes from M. amurensis against Vero Cells The study of cytotoxicity of polyphenolic complex from M. amurensis heartwood against Vero cells was carried out using the MTT assay. Maksar® showed the lowest cytotoxicity (CC50 > 1200 μg/mL) (Table 4). Piceatannol [2] also demonstrated low cytotoxicity (CC50 > 1020 µM). The cytotoxicity of polyphenolic compounds 3–6 ranged from 300 to 560 μM. To study the antiviral effect of stilbenes 1–6, we applied the compounds at concentrations that were below the CC50 values. ## 2.7. Anti-HSV-1 Activity of Polyphenolic Compounds from M. amurensis (CPE assay) The antiviral activity of stilbenes from M. amurensis against HSV-1 was assessed using the cytopathic effect inhibition (CPE) assay. To study the inhibitory effect of polyphenolic compounds on the early stage of viral infection, these were added to Vero cells simultaneously with the virus. Maksar® inhibited virus replication more effectively than stilbenes 1–6 (IC50 and SI values were 13.9 µg/mL and 87, respectively; Table 4). Polyphenolic compounds 1–4 also showed significant antiviral activity (IC50 values ranged from 27 to 90 µM). Maackiasine [5] and maackolin [6] showed moderate activity with SI values of 6.6 and 6.4 and IC50 values of 45.0 and 88.9, respectively (Table 4). ## 2.8. Anti-HSV-1 Activity of Polyphenolic Compounds from M. amurensis (RT-PCR) The anti-HSV-1 activity of the polyphenolic compounds from M. amurensis was also studied using the real-time PCR (RT-PCR) technique (Figure 5 and Table S69, Supplementary Data). Vero cells were simultaneously infected with 100 TCD50/mL HSV-1 and treated with polyphenolic compounds 1–6 at concentrations of 50 and 5 μg/mL. After 48 h of cell incubation, viral DNA was extracted from the supernatants, and the relative level of HSV-1 DNA was determined using RT-PCR. The effect of stilbenes on the relative level of viral DNA was assessed by 2−ΔCt method and reported as fold reduction relative to virus control, to which the value of 1.0 was assigned. We showed that polyphenolic compounds from M. amurensis significantly inhibited the replication of HSV-1 at a concentration of 50 μg/mL, when Vero cells were simultaneously treated with these compounds and infected with HSV-1 (Figure 5 and Table S69, Supplementary Data). At this concentration, compounds 1–4 and Maksar® caused a 4.2 log10 reduction in viral DNA compared to virus control ($p \leq 0.001$). However, at a concentration of 5 µg/mL, only compounds 1, 4, and Maxar® reduced the relative level of HSV-1 DNA by 1.3 log10 relative to virus control on average ($p \leq 0.05$). At the same time, the reference medicine acyclovir, at a concentration of 5 µg/mL, caused a 4 log10 reduction in the viral DNA (data not shown). The obtained results showed that the tested polyphenolic compounds inhibited HSV-1 infection in a dose-dependent manner in both the CPE inhibition and RT-PCR assays. We found that compounds 1–4 and Maksar® were the most effective in reducing viral replication when added simultaneously with the initiation of viral infection. ## 3. Discussion In this study, we investigated antioxidant, neuroprotective, and antiherpetic activities of polyphenolic compounds that were constituents of Maksar® obtained from M. amurensis heartwood. The DPPH-scavenging effect and FRAP of polyphenolic compounds 1–6 were significantly higher compared to the reference compounds quercetin and ascorbic acid (Table 3). In order to evaluate the neuroprotective properties of polyphenolic compounds from M. amurensis heartwood, including their ability to reduce intracellular ROS level and increase mitochondrial membrane potential, we used a model of PQ-induced neurotoxicity in Neuro-2a cells. Maksar® showed the highest neuroprotective activity and increased cell viability by $18\%$ at a concentration of 10 μg/mL compared to PQ-treated cells. Stilbenolignan maackolin [6], at a concentration of 10 μM, also effectively increased the viability of PQ-treated Neuro-2a cells (by $16\%$) at a concentration up to 10 μM, which may be due to the ability of this compound to increase the value of mitochondrial membrane potential. Although compounds 1–6 possessed high DPPH-scavenging effect and FRAP values, only compounds 1 and 4 at a concentration of 10 μM as well as Maksar® (10 μg/mL) statistically significantly reduced the level of intracellular ROS in PQ-treated Neuro-2a cells. Dimeric stilbene scirpusin A [4] effectively increased mitochondrial membrane potential. Maackin [3] at the minimum studied concentration of 0.1 μM significantly inhibited the level of ROS and increased mitochondrial membrane potential. The inverse dose-dependent effect was mainly due to the cytotoxic activity of 3 against Neuro-2a cells. Monomeric stilbene piceatannol [2] and isoflavonostilbene maackiasine [5] did not significantly increase the viability of Neuro-2a cells. In addition to the neuroprotective properties, polyphenolic compounds 1–6 showed moderate antiherpetic activity. We found that these compounds affected the early stage of the HSV-1 life cycle. Some studies also reported multiple mechanisms of anti-HSV-1 action of stilbenes isolated from various plants, including inhibition of virus adsorption and entry, reduction in gene expression, inhibiting the late viral protein synthesis, and stimulation of ROS production [29,36,37,38]. However, the most probable mechanism of antiherpetic activity of Maksar® and its components 1 and 4 (Table 4, Figure 2) may be due to their ability to reduce ROS level, induced by HSV-1 in cells [39]. Hence, further study of the mechanisms of HSV-1 inhibition by stilbenes from M. amurensis is necessary. ## 4.1. Plant Material M. amurensis was collected in September 2021 by academician P.G. Gorovoy (Andreevka village, Khasansky District) of the Primorsky region (Russian Far East). Voucher specimen (No. 103539) was deposited into the herbarium of the Laboratory of Chemotaxonomy (G.B. Elyakov Pacific Institute of Bioorganic Chemistry, FEB RAS). ## 4.2. Extraction and Isolation We extracted the heartwood of M. amurensis (350 g) twice under reflux with a CHCl3–EtOH solution system (3:1, v/v) for 3 h at 60 °C. The obtained extract (11 g) was subjected to a polyamide column (100 g, 50–160 µm, Sigma-Aldrich, St. Louis, MI, USA). The column was eluted with a hexane–CHCl3 solution system with gradually increasing CHCl3 amounts (hexane/CHCl3, 1:0, 10:0, 5:1, 2:1, 1:1, 1:2) to obtain fractions 1–6 and then, with a CHCl3–EtOH solution system with gradually increasing EtOH amounts (CHCl3/EtOH, 1:0, 100:1, 50:1, 40:1, v/v) to obtain fractions 7–16. We subsequently purified the fractions containing stilbenes according to the HPLC data. Fraction 12 (CHCl3/EtOH, 1:1, 1.16 g) (Figure S87, Supplementary Data) was subsequently subjected to a silica gel column (40–63 µm) and eluted with a hexane–CHCl3 solution system with gradually increasing CHCl3 amounts (hexane/CHCl3, 20:0, 10:1, 8:1, 5:1, v/v) twice to obtain the mixture of compounds 5 and 6 (27.6 mg). This mixture was then applied to a C-18 column to obtain individual compounds 5 (11.5 mg), and 6 (13.6 mg). Fraction 14 (EtOH, 940 mg) (Figure S88, Supplementary Data) was chromatographed twice over a silica gel column (40–63 µm, Sigma-Aldrich, St. Louis, MI, USA). The column was eluted with a CHCl3–EtOH solution system with gradually increasing EtOH amounts (CHCl3/EtOH, 1:0, 200:1, 100:1, 50:1, 40:1, v/v) to yield compounds 1 (9.1 mg), 2 (28.7 mg), and the mixture of compounds 3 and 4 (32.5 mg). Compounds 1 and 2 were subsequently purified using a C-18 column. The mixture of compounds 3 and 4 was then applied to a C-18 column to obtain individual compounds 3 (15.1 mg) and 4 (14.4 mg). Maackiapicevestitol [1]: white, amorphous powder; UV (MeOH) λmax 202, 284, 332 nm; ECD (2.55 × 10–4 M, MeOH) λmax (Δε) 202 (+2.01), 215 (−4.26), 236 (−1.99), 258 (+0.13), 280 (−0.48), 300 (+0.89), 322 (+0.78); 1H and 13C NMR data, see Table 1 and Table 2; HR-ESI-MS m/z 513.1573 [M-H]− (calculated for [C30H25O8]− 513.1555), m/z 515.1687 [M+H]+ (calculated for [C30H27O8]+ 515.1700). ## 4.3. General Experimental Procedures We measured the UV spectra using a UV-1601 PC spectrophotometer (Shimadzu, Kyoto, Japan). The CD spectra were obtained on a Chirascan-plus Quick Start CD Spectrometer (Applied Photophysics Limited, Leatherhead, UK) (acetonitrile, 20 °C). We recorded the 1H, 13C, and two-dimensional NMR spectra in acetone-d6 using a Bruker AVANCE III DRX-700 NMR instrument (Bruker, Karlsruhe, Germany). ## 4.4. HPLC Analysis We used an Agilent Technologies 1260 Infinity II HPLC system (Agilent Technologies, Waldbronn, Germany) equipped with a VWD detector (λ = 280 nm) to perform the HPLC analysis of extracts and fractions. The flow rate was 0.8 mL/min using a Supelco Analytical HS-C18 (Supelco Analytical, Bellefonte, PA, USA). The column (3 μm, 4.6, 75 mm) was thermostated at 30 °C. The mobile phase consisted of $1\%$ aqueous acetic acid (A) and acetonitrile containing $1\%$ acetic acid (B). The following gradient steps were programmed: 0–2 min—$10\%$ B, 2–4 min—10–$20\%$ B, 4–21 min—20–$30\%$ B, 21–26 min—30–$40\%$ B, 26–31 min—40–$50\%$ B, 31–34 min—50–$90\%$ B, and 34–36 min—90–$50\%$ B. The data were analyzed using OpenLab CDS software v. 2.4 (Agilent Technologies, Waldbronn, Germany). ## 4.5. HR-ESI-MS Analysis We recorded HR-ESI-MS spectra on a Shimadzu hybrid ion trap–time of flight mass spectrometer (Shimadzu, Kyoto, Japan). The electrospray ionization (ESI) source potential was 3.8 and 4.5 kV for the negative and positive ion mode, respectively; the drying gas (N2) pressure was 200 kPa; the nebulizer gas (N2) flow was 1.5 L/min; the temperature for the curved desolvation line (CDL) and heat block was 200 °C; the detector voltage was 1.5 kV, and the detection range was 100–900 m/z. The mass accuracy was below 4 ppm. Shimadzu LCMS Solution software (v.3.60.361, Shimadzu, Kyoto, Japan) was used to acquire and process the data. ## 4.6. Antiradical Activity The DPPH (2,2-diphenyl-1-picrylhydrazyl) scavenging effect of polyphenolic compounds 1-6 from M. amurensis heartwood was evaluated as described previously in [40]. Stilbenes were added to DPPH solution in MeOH (10−4 M) at a concentration range from 1 to 34 µM. The mixture was kept in the dark at room temperature for 20 min. Then, we measured the absorbance at 517 nm using a Shimadzu UV 1240 spectrophotometer (Shimadzu, Kyoto, Japan). The DPPH radical-scavenging effect (%) of the stilbenes was calculated using Equation [1]: [1]DPPH scavenging effect, %=A0−AxA0×100,where: A0 is the absorbance of DPPH solution without polyphenolic compounds (blank sample);Ax is the absorbance of DPPH solution in the presence of different concentrations of polyphenolic compounds. Quercetin and ascorbic acid were used as reference compounds. All experiments were performed in triplicate. The half maximal inhibitory concentration (IC50) for polyphenolic compounds was calculated by plotting the DPPH scavenging effect (%) against the concentrations of polyphenolic compounds. IC50 values are given as the mean ± SEM. ## 4.7. Ferric Reducing Antioxidant Power (FRAP) Assay The FRAP assay was carried out as described in [41]. We prepared FRAP reagent by mixing 2.5 mL of TPTZ (2,4,6-tris(2-pyridyl)-s-triazine) solution (10 mM) in 40 mM HCl and 25 mL of FeCl3 solution (20 mM) in acetate buffer solution (300 mM, pH 3.6). Then, we added stilbenes 1–6 to 3 mL of FRAP reagent at concentrations from 1 to 34 µM. The mixture was kept in the dark at room temperature for 4 min. Then, we measured the absorbance at 595 nm using a Shimadzu UV 1240 spectrophotometer. Equation [2] was used to calculate the FRAP values of stilbenes 1–6:[2]FRAP=CFeCx, where:CFe is the concentration of Fe2+ (µM) formed in the reaction;Cx is the concentration of polyphenolic compounds in the reacting mixture. The concentration of Fe2+ (µM) formed in the reaction was determined using the calibration curve obtained for different concentrations of FeSO4·7H2O. ## 4.8. Neuro-2a Cell Line and Culture Conditions We purchased the murine neuroblastoma cell line Neuro-2a (CCL-131) from the American Type Culture Collection (ATCC®) (Manassas, VA, USA). We cultured Neuro-2a cells in Dulbecco′s Modified Eagle Medium (Biolot, St. Petersburg, Russia), which contained $10\%$ fetal bovine serum (Biolot, St. Petersburg, Russia) and $1\%$ penicillin/streptomycin (Biolot, St. Petersburg, Russia). We incubated the cells at 37 °C in a humidified atmosphere containing $5\%$ (v/v) CO2. ## 4.9. The Viability of Neuro-2a Cells We prepared the stock solutions of stilbenes in DMSO at a concentration of 10 mM. Compounds 1–6 were added to the wells of the plates in a volume of 20 μL diluted in PBS at the following final concentrations: 0.1, 1.0, and 10.0 µM (final concentration of DMSO <$1\%$). Neuro-2a cells (1 × 104 cells/well) were kept in a CO2 incubator at 37 °C for 24 h until they formed an adherent monolayer. Then, 20 μL of stilbenes solution was added to the cells. After incubation for 24 h, the medium containing the stilbenes was replaced by 100 μL of fresh DMEM medium. Then, we added 10 μL of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) (Sigma-Aldrich, St. Louis, MO, USA) stock solution (5 mg/mL) to each well and incubated the microplate for 4 h. After that, we added 100 μL of SDS–HCl solution (1 g SDS/10 mL dH2O/17 μL 6 N HCl) to each well. After incubation for 18 h, we measured the absorbance of the converted dye formazan on a Multiskan FC microplate photometer (Thermo Scientific, Waltham, MA, USA) at a wavelength of 570 nm [42]. We performed all experiments in triplicate. We expressed the cytotoxicity of the stilbenes as percentages of cell viability. ## 4.10. In Vitro Model of PQ-Induced Neurotoxicity After 24 h of adhesion, we treated Neuro-2a cells (1 × 104 cells/well) with stilbenes (0.01–10 μM) for 1 h and added 1 mM of PQ (Sigma-Aldrich, St. Louis, MO, USA). Cells incubated without PQ or with PQ were used as positive and negative control, respectively. After 24 h, we measured the cell viability using MTT assay. The results are presented as percentages of the positive control value. ## 4.11. Reactive Oxygen Species (ROS) Analysis in PQ-Treated Cells After 24 h of adhesion, Neuro-2a cells (1 × 104 cells/well) were incubated with stilbenes (0.01–10 µM) for 1 h. After that, we added PQ (1 mM) to each well and incubated the cells for 3 h. To study ROS formation, we added 20 µL of 2,7-dichlorodihydrofluorescein diacetate solution (10 µM, H2DCF-DA, Molecular Probes, Eugene, OR, USA) to each well, so that the concentration was 10 µM, and kept the microplate in a CO2–incubator for additional 30 min at 37 °C. ## 4.12. Mitochondrial Membrane Potential (MMP) Evaluation We incubated the cells for 1 h in a 96-well plate (1 × 104 cells/well) with stilbenes (1 and 10 µM). Then, we added PQ (500 µM) and kept the cell suspension in a CO2—incubator for 1 h. Cells incubated without PQ and stilbenes and with PQ only were used as positive and negative control, respectively. We added the tetramethylrhodamine methyl (TMRM) (Sigma-Aldrich, St. Louis, MO, USA) solution (500 nM) to each well and kept the cells for 30 min in a CO2–incubator at 37 °C. The intensity of fluorescence was measured with a PHERAstar FSplate reader (BMG Labtech, Ortenberg, Germany) at λex = 540 nm and λem = 590 nm. We processed the data by MARS Data Analysis v. 3.01R2 (BMG Labtech, Ortenberg, Germany) and presented the results as percentages of the positive control value. ## 4.13. HSV-1 Virus and Vero Cell Culture The HSV-1 strain L2 and Vero cell culture (kidney epithelial cells of the African green monkey Chlorocebus sp.) were obtained from the N.F. Gamaleya Federal Research Centre for Epidemiology and Microbiology (Moscow, Russia). HSV-1 was grown in Vero cells, using Dulbecco′s Modified Eagle′s Medium (DMEM, Biolot, St. Petersburg, Russia) supplemented with $10\%$ fetal bovine serum (FBS, Biolot, St. Petersburg, Russia) and 100 U/mL of gentamycin (Dalkhimpharm, Khabarovsk, Russia), at 37 °C in a CO2 incubator. In the maintenance medium, the FBS concentration was decreased to $1\%$. The tested compounds were dissolved in DMSO (Sigma, St. Louis, MO, USA) at a concentration 10 mg/mL and stored at −20 °C. For cytotoxicity and anti-HSV-1 activity determination, the stock solutions were diluted with DMEM so that the final concentration of DMSO was $0.5\%$. ## 4.14. Cytotoxicity of the Tested Compounds against Vero Cells The cytotoxicity evaluation of the studied compounds was performed using the MTT assay, as described previously [39]. In brief, confluent Vero cells in 96-well microplates (1 × 104 cells/well) were incubated with tested compounds at various concentrations (1–500 μg/mL) at 37 °C for 48 h ($5\%$ CO2). Untreated cells were used as controls. Then, MTT solution (methylthiazolyltetrazolium bromide, Sigma, St. Louis, MO, USA) was added to cells at a concentration of 5 mg/mL and the cells were incubated at 37 °C for 2 h. After dissolution of formazan crystals, optical densities were read at 540 nm (Labsystems Multiskan RC, Vantaa, Finland). Cytotoxicity was expressed as the $50\%$ cytotoxic concentration (CC50) of the tested compound that reduced the viability of treated cells by $50\%$ compared with control cells. [ 42]. Experiments were performed in triplicate and repeated three times. ## 4.15. Anti-HSV-1 Activity of Stilbenes The anti-HSV-1 activity of stilbenes was evaluated using cytopathic effect (CPE) inhibition assay in Vero cells. We infected the monolayer of cells grown in 96-well plates (1 × 104 cells/well) with 100 µL/well of virus suspension (100 TCD50/mL) and we simultaneously treated it with stilbenes 1–6 (100 µL/well) at various concentrations (from 1 to 400 μg/mL) for one hour at 37 ◦C. After virus absorption, we removed the mixture of virus and stilbenes, washed the cells, and added the maintenance medium with $1\%$ FBS. We kept the plates at 37 °C in a CO2-incubator for 48 h until $90\%$ CPE was observed in virus control compared to cell control. We used MTT assay to evaluate the antiviral activity of stilbenes. The viral inhibition rate (IR, %) was calculated according to Equation [3] [43]:[3]IR, %=Atv−AcvAcd−Acv×100,where:Atv is the absorbance of cells infected with virus and treated with a polyphenolic compound;Acv is the absorbance of the untreated virus-infected cells;Acd is the absorbance of control (untreated and non-infected) cells. The concentration of the compound that reduced the virus-induced CPE by $50\%$ ($50\%$ inhibitory concentration, IC50) was calculated using a regression analysis of the dose–response curve [44]. The selectivity index (SI) was calculated as the ratio of CC50 to IC50. Experiments were repeated three times. ## 4.16. Extraction of HSV-1 DNA from Infected Vero Cells For the viral DNA extraction assay, Vero cells grown in 96-well plates were infected with HSV-1 and simultaneously treated with polyphenolic compounds and incubated at 37 °C in a CO2-incubator for 48 h. After incubation, the culture media and the cells scraped from the plate were transferred to centrifuge tubes. The cell debris was removed by centrifugation at 300× g for 10 min. Then, supernatant was collected and kept at −20 °C. The HSV-1 DNA was extracted from supernatant by using the AmpliSens® DNA-sorb-AM (K1-12-100-CE) nucleic acid extraction kit (Moscow, Russia) according to the manufacturer′s instructions. The supernatants were treated with a lysis solution that contained chaotropic agent (guanidine chloride) in the presence of sorbent (silica particles). As the elution solution was added, the DNA was adsorbed on silica particles and then, separated from the sorbent particles by centrifugation. ## 4.17. DNA HSV Detection by the Real-Time Polymerase Chain Reaction (PCR) Method The DNA HSV was detected in the obtained DNA samples using the AmpliSens® HSV I, II-FRT-100F PCR kit (Moscow, Russia) on a real-time PCR instrument Rotor-Gene Q (Qiagen, Hilden, Nordrhein-Westfalen, Germany) according to the manufacturer′s instructions. The HSV-I detection by this PCR kit was based on the amplification of the pathogen genome specific region (DNA-target and target gene–gB gene) using specific HSV I primer. In the real-time PCR, the amplified product is detected with the use of fluorescent dyes. The PCR reaction was carried out under the following conditions: initial denaturation at 95 °C for 15 min; then 5 cycles: at 95 °C for 5 s, at 60 °C for 20 s, at 72 °C for 15 s; then 40 cycles: at 95 °C for 5 s, at 60 °C for 20 s, at 72 °C for 15 s. A negative sample was used as the amplification control for each run. The threshold cycle number, Ct, was measured as the PCR cycle, where the amount of amplified target reached the threshold value. The reduction in the HSV-1 DNA levels in culture supernatants was assessed by the change in the threshold cycle (ΔCt) (Equation [4]):[4]ΔCt=Cttv−Ctcvwhere:Cttv is the average Ct value for the infected samples after treatment with polyphenolic compounds;Ctcv corresponds to the average Ct value for the virus control. For each compound concentration, the viral inhibition rate (IR, %) was calculated according to Equation [5]: [5]IR, %=Cttv−CtcvCtcc−Ctcv×100,where:Cttv is the average Ct value for the infected samples after treatment with polyphenolic compound;Ctcv corresponds to the average Ct value for the virus control;Ctcc corresponds to the average Ct value for the cell control. The concentration of the compound that reduced the level of HSV-1 DNA by $50\%$ (IC50) and the SI of the compound were calculated. ## 4.18. Statistical Analysis All the experiments were carried out in triplicate. Student’s t-test was performed using SigmaPlot 14.0 (Systat Software Inc., San Jose, CA, USA) to determine statistical significance. ## 5. 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--- title: 'Alternate-Day Fasting Combined with Exercise: Effect on Sleep in Adults with Obesity and NAFLD' authors: - Mark Ezpeleta - Kelsey Gabel - Sofia Cienfuegos - Faiza Kalam - Shuhao Lin - Vasiliki Pavlou - Krista A. Varady journal: Nutrients year: 2023 pmcid: PMC10056902 doi: 10.3390/nu15061398 license: CC BY 4.0 --- # Alternate-Day Fasting Combined with Exercise: Effect on Sleep in Adults with Obesity and NAFLD ## Abstract Objective: This study investigated how alternate-day fasting (ADF) combined with aerobic exercise impacts body weight and sleep in adults with non-alcoholic fatty liver disease (NAFLD). Methods: Adults with obesity and NAFLD ($$n = 80$$) were randomized into one of four groups for 3 months: combination of ADF (600 kcal “fast day,” alternated with an ad libitum intake “feast day”) and moderate-intensity aerobic exercise (five sessions per week, 60 min/session); ADF alone; exercise alone; or a no-intervention control group. Results: By month 3, body weight and intrahepatic triglyceride content decreased ($p \leq 0.001$, group × time interaction) in the combination group versus the exercise group and control group, but not versus the ADF group. Sleep quality, measured by the Pittsburgh Sleep Quality Inventory (PSQI), did not change in the combination group (baseline: 6.0 ± 0.7; month 3: 5.6 ± 0.7), ADF group (baseline: 8.9 ± 1.0; month 3: 7.5 ± 0.8), or exercise group (baseline: 6.4 ± 0.6; month 3: 6.7 ± 0.6), versus controls (baseline: 5.5 ± 0.7; month 3: 4.6 ± 0.5). Wake time, bedtime, sleep duration, and insomnia severity did not change (no group x time interaction) over the course of the study in any group. Risk for obstructive sleep apnea was present in $30\%$ of combination subjects, $75\%$ of ADF subjects, $40\%$ of exercise subjects, and $75\%$ of controls, and did not change in the intervention groups, versus controls, by month 3. No associations were observed between changes in body weight, intrahepatic triglyceride content, and any sleep outcome. Conclusions: The weight loss induced by ADF combined with exercise does not improve sleep quality, duration, insomnia severity, or risk of obstructive sleep apnea in individuals with NAFLD. ## 1. Introduction Non-alcoholic fatty liver disease (NAFLD) is defined as the presence of $5\%$ or more fat in the liver, confirmed by hepatic imaging or biopsy [1]. Approximately $25\%$ of adults in the United States are afflicted by NAFLD [2]. Poor sleep may adversely affect insulin sensitivity and inflammatory status [3], thereby contributing to the development and progression of NAFLD. A recent cohort study of nearly 150,000 adults followed over 4 years showed that short sleep duration (≤5 h) was independently associated with an increased risk of incident of NAFLD [4]. In addition, low sleep quality and the presence of obstructive sleep apnea have been shown to exacerbate the severity of NAFLD [5,6]. These findings underscore the importance of healthy sleep in preventing the progression of NAFLD. Lifestyle interventions that produce 5–$10\%$ weight loss have been shown to improve various sleep measures and resolve steatohepatitis [7]. The most commonly implemented lifestyle therapy in patients with NAFLD is daily calorie restriction combined with aerobic exercise. However, more recently, there has been mounting interest in exploring how intermittent fasting may benefit people with fatty liver disease. Evidence from two studies show that alternate-day fasting (ADF; 600 kcal “fast day” alternated with an ad libitum intake “feast day”) is effective for reducing liver steatosis score, circulating levels of alanine transaminase (ALT), and body weight in patients with NAFLD [8,9]. In addition, we recently performed a randomized controlled trial showing that ADF combined with aerobic exercise decreased body weight by $5\%$, intrahepatic triglyceride content by $5\%$, and ALT concentrations in adults with NAFLD [10]. While these studies are valuable to the field, they are limited in that none of them examined the underlying role of sleep in mediating these effects. Accordingly, the goal of this study was to investigate how intermittent fasting combined with exercise impacts body weight and sleep measures in adults with NAFLD. We hypothesized that ADF combined with aerobic exercise would produce the greatest weight loss, and in turn, the most pronounced improvements in sleep quality, duration, and insomnia severity, when compared to ADF alone, exercise alone, or controls. ## 2. Methods This is a secondary analysis of a 3-month randomized, controlled, parallel-arm study. The trial examined the effects of ADF combined with exercise, to each intervention alone, on intrahepatic triglyceride content and metabolic disease risk factors in patients with NAFLD [10]. Participants were randomized to 1 of 4 intervention groups: ADF plus exercise, ADF alone, exercise alone, or a no-intervention control group. Randomization was performed by a stratified random sampling procedure based on sex, age, BMI, and intrahepatic triglyceride content. The trial was not blinded, but study staff who analyzed the outcome variables were unaware of the participant’s group assignment. ## 2.1. Subject Selection Participants were recruited from the University of Illinois Chicago Medical Center. The trial was conducted between January 2020 and March 2022. Subjects were enrolled in four separate rounds (~20 subjects per round). Adults between the ages of 18 and 65 years with a BMI between 30 and 60 kg/m2 were screened by survey and ALT blood test. Women with ALT levels greater than 17 U/L and men with ALT levels greater than 25 U/L were admitted for further NAFLD screening by magnetic resonance imaging (MRI). Specifically, their intrahepatic triglyceride (IHTG) content was quantified by magnetic resonance imaging proton density fat fraction (MRI-PDFF). Patients who previously had ultrasound- or biopsy-diagnosed NAFLD also had their diagnosis confirmed by MRI-PDFF. In order to be included in the study, the intrahepatic triglyceride content needed to exceed $5\%$ of liver weight. Exclusion criteria were as follows: history of acute or chronic viral hepatitis, autoimmune hepatitis, or drug-induced liver diseases; alcohol consumption greater than 5 alcoholic drinks per week for women and greater than 10 drinks per week for men in the past 6 months; history of diabetes, cardiovascular disease, or chronic kidney disease; weight instability, i.e., more than $4\%$ weight loss/gain in the past 3 months; or a medical condition or injury that would prevent participation in the aerobic training. The protocol was approved by the Office for the Protection of Research Subjects at the University of Illinois at Chicago, and informed consent was obtained from all participants (IRB #2019-0300). This trial was registered at ClinicalTrials.gov (NCT04004403). ## 2.2. Alternate Day Fasting Protocol As described previously [10], subjects in the combination group and ADF group were asked to eat 600 kcal as a dinner (between 5 and 8 pm) on fast days and eat food ad libitum on alternating feast days. The feast and fast days began at 12 am each day. Therefore, subjects fasted for approximately 17–20 h on the fast day (i.e., from 12 am to 5 pm or 12 am to 8 pm). On each fast day, subjects were asked to consume lots of water and were allowed to drink energy-free beverages such as black coffee, herbal tea, black tea, and sugar-free sodas (max 2 sugar-free sodas per day). Combination group and ADF group subjects were given pre-packaged fast day meals during the first 4 weeks of the study. After this, these subjects received diet counseling to learn how to meet calorie goals on fast days. The pre-packaged fast day meals complied with the American Diabetes Association nutrition guidelines for macronutrient composition (i.e., $30\%$ fat, $55\%$ carbohydrates, and $15\%$ protein). The exercise group and control group were asked to not change their eating habits and did not receive pre-packed foods or any dietary advice during the trial. ## 2.3. Exercise Protocol Subjects in the combination group and exercise group performed moderate-intensity aerobic training 5× per week for 3 months. Every exercise session was supervised by the study coordinator. Treadmills, stationary bikes, or elliptical machines were used for the aerobic training. Each training session was performed at the research center. The maximum predicted heart rate (HRmax) was calculated as [(210/min-age)] for women and [(220/min-age)] for men. An activity monitor was used to evaluate Hrmax. Exercise intensity increased over the first 4 weeks of the trial (i.e., from 65 to $80\%$ of Hrmax). The exercise lasted for 60 min per session. Approximately 3 months into the study, the COVID pandemic hit, and subjects transitioned to at-home training using their own exercise equipment. If they did not have any equipment at home, they were instructed to watch aerobic exercise videos on the internet. The training sessions at home were supervised by the study coordinator using video conference platforms. Subjects in the ADF group and control group did not perform the exercise intervention. These subjects were asked not to change their daily activity routines, so as to not confound the study findings. ## 2.4. Control Group Protocol Control participants were instructed to maintain their body weight during the 3-month trial by not changing their eating habits or activity routines. The controls received no pre-packaged foods or dietary advice but visited the research center at the same frequency as the other study groups to provide clinical assessments. ## 2.5. Body Weight, Body Composition, Intrahepatic Triglyceride Content, and Liver Fibrosis All variables were assessed at baseline and month 3. Body weight was assessed without shoes, in light clothing, using a digital scale (HealthOMeter) at the research center. Height was assessed using a wall-mounted stadiometer. BMI was calculated as kg/m2. Fat mass, lean mass, and visceral fat mass were measured after an 8-hour fast by dual X-ray absorptiometry (iDXA, GE). Intrahepatic triglyceride content was measured by MRI-PDFF [10]. These scans were carried out at the UIC Center for Magnetic Resonance Research. A SIEMENS 3.0-Tesla MRI scanner was used for the baseline and month 3 liver fat estimations. T1 volumetric interpolated breath-hold examination (VIBE) Dixon sequence was used to obtain fat–water separation images. The following parameter settings were employed: TE1 = 2.5 ms; TE2 = 3.7 ms; repetition time = 5.47 ms; 5° flip angle; ± 504.0 kHz per pixel receiver bandwidth; slice thickness = 3.0 mm. Irregular-shaped regions of interest covering the entire liver were used to quantify liver fat content. MRIs were performed by a trained radiologist for each subject. MRI-PDFF maps were generated by placing circular ROIs with diameters of 20 mm centrally in each of the liver segments. The average fat content values were calculated for the entire liver. The degree of liver fibrosis was estimated using the Fibrosis-4 (FIB-4) index, as follows: Age (years) × AST (IU/L)/(√ALT (IU/L) × Platelet count (109/L)) [11]. A FIB-4 score below 1.30 is an indicator for low risk for advanced fibrosis, while a score above 2.67 is an indicator for high risk for advanced fibrosis. ## 2.6. Energy Intake and Physical Activity Energy intake was assessed by the National Cancer Institute (NCI) web-based system, Automated Self-administered 24-hour Dietary Assessment Tool (ASA24), over 7 days at baseline and month 3. Habitual physical activity (not including the aerobic exercise program) was measured by a pedometer (Fitbit Alta) worn for 7 days at baseline and at month 3. ## 2.7. Sleep Measures Sleep quality, duration, and timing were measured using the Pittsburgh Sleep Quality Index (PSQI) [12]. The PSQI is a self-report survey with 19-items that measures sleep quality in the past month, resulting in a total score of 0–21. Scores above 5 can be used as an indicator of poor sleep quality. Insomnia severity was measured by the Insomnia Severity Index (ISI) [13]. The ISI is a 7-item self-report questionnaire that rates each item by a 5-point Likert scale. The ISI produces a total score of 0–28 points. Scores fall into the following categories: no clinically significant insomnia (score of 0–7); subthreshold insomnia (score of 8–14); moderate-severity insomnia (score of 15–21); and severe insomnia (score of 22–28). The risk of obstructive sleep apnea (% occurrences) was estimated in all subjects by the 10-item self-report Berlin Questionnaire [14]. ## 2.8. Statistical Analysis All data are presented as means ± SEM. At baseline, differences between groups were tested by one-way ANOVA (continuous variables) or the McNemar test (categorical variables). Repeated-measures ANOVA with groups (combination, ADF, exercise, and control) as the between-subject factor and time (baseline and month 3) as the within-subject factor was used to compare changes in dependent variables between the groups over time. Pearson correlations were performed to assess the relationships between changes in body weight, intrahepatic triglyceride content, and sleep measures. Differences were considered significant at $p \leq 0.05.$ All data were analyzed using SPSS software (version 27, SPSS Inc., Chicago, IL, USA). ## 3.1. Subject Baseline Characteristics and Dropouts As previously reported [10], 132 individuals were assessed for eligibility, and 52 of these individuals did not meet one or more of the inclusion criteria. A total of 80 participants were randomized into the combination group ($$n = 20$$), ADF group ($$n = 20$$), exercise group ($$n = 20$$), or the control group ($$n = 20$$). The number of completers were as follows: combination group, $$n = 20$$; ADF group, $$n = 19$$; exercise group, $$n = 15$$; and control group, $$n = 20$.$ Table 1 displays the baseline characteristics of the participants. At baseline, there were no significant differences between groups for body weight, body composition, intrahepatic triglyceride content, liver fibrosis score, energy intake, physical activity, or any sleep variable. ## 3.2. Body Weight, Body Composition, Intrahepatic Triglyceride Content, and Liver Fibrosis By month 3, body weight decreased ($p \leq 0.01$, group x time interaction) in the combination group versus the exercise group and controls, but not versus the ADF group (Table 1, Figure 1A). Likewise, fat mass was reduced ($$p \leq 0.02$$, group × time interaction) in the combination group versus the exercise group and controls, but not versus the ADF group (Table 1). Lean mass, visceral fat mass, and BMI did not change significantly between groups. By month 3, intrahepatic triglyceride content decreased ($$p \leq 0.02$$, group × time interaction) in the combination group versus the exercise group and controls, but not versus the ADF group (Table 1). The liver fibrosis score did not change significantly between groups by month 3. ## 3.3. Energy Intake and Physical Activity By month 3, energy intake decreased ($p \leq 0.05$, group x time interaction) in the combination group versus the exercise group and controls, but not versus the ADF group (Table 1). Regular physical activity (excluding the exercise intervention program) did not change in any of the groups over time (Table 1). ## 3.4. Sleep Measures Sleep quality, timing, and duration were measured by the PSQI survey. A PSQI total score greater than 5 indicates poor sleep quality. At the beginning of the study, the average scores for PSQI were 6.0 ± 0.7 for the combination group, 8.9 ± 1.0 for the ADF group, 6.4 ± 0.6 for the exercise group, and 5.5 ± 0.7 for controls, indicating poor sleep quality in all groups at baseline (Table 1). After 3 months, the sleep quality scores did not change significantly (no group × time interaction) in any intervention group, relative to controls (Figure 1B). Wake time, bedtime, and sleep duration did not change (no group × time interaction) over the course of the study in any group (Table 1). By month 3, the insomnia severity scores did not change significantly (no group x time interaction) in the intervention groups, relative to controls (Table 1, Figure 1C). The risk for obstructive sleep apnea was present in $30\%$ of combination subjects, $75\%$ of ADF subjects, $40\%$ of exercise subjects, and $75\%$ of controls, and at baseline (Table 1). By the end of the trial, the risk of obstructive sleep apnea did not change in the intervention groups versus controls (Figure 1D). There were no associations between changes in sleep quality, duration, or insomnia severity and changes in body weight, intrahepatic triglyceride content, or liver fibrosis. ## 4. Discussion This is the first study to examine the effects of intermittent fasting combined with aerobic exercise on sleep in adults with NAFLD. Our results show that this combination intervention produced significant reductions in body weight and intrahepatic triglyceride content but no changes in sleep quality, duration, insomnia severity, or risk of obstructive sleep apnea. Weight loss by dietary restriction may improve sleep quality and duration by reducing sleep fragmentation and alleviating sleep-disordered breathing [15,16]. In the present study, the combination of ADF and aerobic exercise produced significant reductions in body weight (~$5\%$) and liver fat (~$5\%$), but no change in sleep quality or duration in adults with NAFLD. Our findings are complementary to those of other fasting trials showing no impact on these sleep measures. For instance, in the trial by Kalam et al. [ 17], 6 months of ADF combined with a high-protein/low-carbohydrate diet produced $6\%$ weight loss but no change in sleep quality or duration in participants with obesity. Likewise, Gabel et al. [ 18] and Cienfuegos et al. [ 19] reported no change in sleep quality or duration after 2–3 months of time-restricted eating, despite $3\%$ weight loss. Moreover, Wilkinson et al. [ 20] demonstrated no change in sleep quality after 2 months of time-restricted eating, even though their participants reduced body weight by $4\%$. In contrast, studies examining the impact of aerobic exercise on sleep quality and duration in patients with obesity generally report improvements, even in the absence of significant weight loss [21,22]. There are several reasons why sleep quality and duration may not have improved in our trial. First, our participants were on the cusp of being “good sleepers” at baseline (based on PSQI scores [12]). If their sleep quality was worse at the beginning of the study, we may have been more likely to observe improvements. Second, the study was conducted during the COVID-19 pandemic, and the stress induced by lockdown conditions may have had detrimental effects on sleep quality and duration [23]. Lastly, our participants were averaging 7.5 h of sleep per night, which is in line with what is considered healthy by the National Sleep Foundation [24]. Changes in insomnia severity were also assessed. At baseline, participants in the combination and ADF groups portrayed sub-clinical insomnia (ISI score 8–14), while subjects in the exercise and control groups displayed no clinically significant insomnia (ISI score 0–7). By the end of the trial, no significant changes in insomnia scores were noted in the intervention groups versus controls. This finding is not surprising as our subjects did not portray clinically significant insomnia at baseline; thus, it would be unlikely for this sleep measure to improve. These findings are in accordance with other trials of ADF [17], time-restricted eating [18,19], and aerobic exercise [25], which show no change in insomnia severity in those who are not afflicted by this condition. The risk of obstructive sleep apnea did not change during the trial. At the beginning of the study, close to half of our cohort (~$50\%$) was at high risk of obstructive sleep apnea. While we observed that the risk for sleep apnea decreased numerically in all the intervention groups, these changes were not significant relative to controls. However, it is possible that our interventions did not achieve enough weight reduction to improve this sleep metric. Accumulating evidence suggests that at least $10\%$ weight loss may be necessary to decrease the risk of obstructive sleep apnea in people with obesity [26]. This study has several limitations. First, our sample size was small (i.e., $$n = 80$$ in total, $$n = 20$$ per group). Moreover, our power calculation was based solely on intrahepatic triglyceride content, so it is likely that this study was not powered adequately to identify significant changes in sleep parameters, such as the insomnia and PSQI score. Second, all sleep outcomes were quantified via self-report. This study would have benefitted from the use of wrist actigraphy to provide more objective assessments of rest and activity patterns. Third, this study was conducted during the coronavirus pandemic, which most likely impacted our participants’ daily routines and regular sleep habits [23]. Fourth, the trial duration was short (3 months). Thus, the longer-term effects of intermittent fasting, alone or combined with exercise on sleep parameters, remain unknown. Fifth, the degree of weight loss produced by the combination and ADF interventions was moderate and fell short of being clinically significant (i.e., >$5\%$ weight loss from baseline). Lastly, subjects were permitted to drink caffeinated beverages during their fasting window. As such, some participants may have consumed caffeine late into the evening, which may have impacted their sleep. In summary, these findings suggest that the weight loss induced by ADF combined with exercise does not improve sleep quality, duration, insomnia severity or risk of obstructive sleep apnea in individuals with obesity and NAFLD. However, these findings will need to be confirmed by a well-powered randomized controlled trial specifically designed to assess the impact of these lifestyle interventions on sleep in this population group. ## References 1. Chalasani N., Younossi Z., Lavine J.E., Charlton M., Cusi K., Rinella M., Harrison S.A., Brunt E.M., Sanyal A.J.. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology (2018) 67 328-357. DOI: 10.1002/hep.29367 2. Arshad T., Golabi P., Henry L., Younossi Z.M.. Epidemiology of Non-alcoholic Fatty Liver Disease in North America. Curr. Pharm. Des. (2020) 26 993-997. DOI: 10.2174/1381612826666200303114934 3. 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--- title: The U-Shape Relationship between Triglyceride-Glucose Index and the Risk of Diabetic Retinopathy among the US Population authors: - Yu Zhou - Qiong Lu - Min Zhang - Ling Yang - Xi Shen journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10056904 doi: 10.3390/jpm13030495 license: CC BY 4.0 --- # The U-Shape Relationship between Triglyceride-Glucose Index and the Risk of Diabetic Retinopathy among the US Population ## Abstract Objective: To explore the association of diabetic retinopathy (DR) with TyG index and TyG-related parameters among the United States population. Methods: This cross-sectional study is conducted in adults with diabetes mellitus based on the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Multivariate logistic regression, restricted cubic spline, trend test, receiver operating characteristic curve and subgroup analysis are adopted to uncover the association of DR with TyG index and TyG-related parameter levels in diabetics. Results: An aggregate of 888 eligible participants with diabetes is included, involving 263 ($29.6\%$) patients with DR. The participants are stratified according to the quartile of TyG index and TyG-related parameters (Q1–Q4). Following the adjustments of the confounding factors, a multivariate logistic regression analysis finds that TyG-BMI, TyG index and Q4-TyG index are significant risk factors for DR. The restricted cubic spline shows that TyG index and the DR risk of diabetes patients are proved to be U-shaped related (p for nonlinearity = 0.001). Conclusions: The triglyceride-glucose index has a U-shaped correlation with the risk of diabetic retinopathy, which has potential predictive value. ## 1. Introduction The prevalence of diabetes is increasing worldwide due to rapid population aging and unhealthy lifestyles characterized by smoking, excessive drinking, sedentary behavior and high-calorie diet intake. Diabetes retinopathy (DR) is a pervasive microvascular complication of diabetes that often leads to blindness, with a global prevalence of $34.6\%$ [1]. The pathological changes of DR are often concealed, leading to delayed medical intervention, and advanced stages of the disease, which results in irreversibly impaired vision and unfavorable treatment prognosis. Consequently, early prediction, diagnosis and treatment of DR hold significant clinical importance. Currently, the pathogenesis of DR remains insufficiently understood, and hyperglycemia is typically regarded as the primary cause of DR. In the past, the prevention and treatment of DR focused mainly on managing blood glucose levels and glycosylated hemoglobin levels in diabetic patients. Nevertheless, the pathogenesis of DR is a multifaceted process, and several mechanisms and factors contribute to its occurrence and progression, such as hypertension, abnormal lipid metabolism, inflammation and insulin resistance [2,3,4]. At present, the hyperinsulinemic normoglycemic clamp (HIEC) is considered the gold standard for assessing insulin resistance [5], as it measures peripheral tissue sensitivity to insulin. Nonetheless, due to its complexity and cost, this technique is not widely used in clinical practice. Alternatively, the homeostasis model assessment of insulin resistance (HOMA-IR) is the most commonly used method for assessing insulin resistance in clinical practice and has a good correlation with HIEC [6]. However, fasting insulin needs to be measured when calculating HOMA-IR, which can be difficult to obtain in some primary medical institutions. In 2008, it was first reported that the triglyceride–glucose (TyG) index could be used as a substitute index for insulin resistance [7], which does not rely on fasting insulin levels. Compared to other insulin resistance evaluation indexes, the advantages of the TyG index lie in its lower cost, simpler operation and wider applicability. In recent years, numerous studies have corroborated the relationship between the TyG index and the risk of IR-related metabolic diseases such as diabetes [8], nonalcoholic fatty liver disease (NAFLD) [9], cardiovascular disease [10] and metabolic syndrome [11]. What is more, obesity is commonly acknowledged to trigger or worsen the presence of insulin resistance [12]. Several studies have evaluated that TyG-related parameters are more effective than the isolated TyG index [13], such as TyG combined with body mass index (TyG-BMI), TyG combined with waist circumference (TyG-WC) and TyG combined with waist-height ratio (TyG-WHtR). However, the effect of applying the TyG index and TyG-related parameters to predict the risk of diabetic retinopathy in diabetes patients is still unclear. Therefore, this study aims to determine the predictive value of the TyG index and TyG-related parameters for the DR among the US population with diabetes. ## 2.1. Data Source The National Health and Nutrition Examination Survey (NHANES) is a nationwide cross-sectional study aimed at assessing the health and nutrition status of the general population of the United States, using a complex sampling strategy. National Center for Health Statistics granted the study procedures of the Ethics Review Board (Protocol #2005-06, #2011-17, #2018-01). Informed consent of the participants was obtained before collecting any data. Centers for Disease Control and Prevention (CDC) provided health statistics and details of the NHANES protocol [14]. All participants were required to take part in standardized home interviews which obtained demographic and health related issues, meanwhile comprehensive physical and laboratory examinations were carried out at the mobile examination center (MEC). In our study, we used seven cycles of the open NHANES database (2005–2018). Figure 1 depicts the selection process. Missing data and subjects younger than 20 years of age or pregnant or without diabetes, are excluded. For more information on the data, please visit www.cdc.gov/nchs/nhanes/ (accessed on 29 October 2022). ## 2.2. Data Source During the home interview, in the face of the question “have you ever been told by a doctor or health professional that you have diabetes or sugar diabetes?”, the participants who answer “yes” were defined as diabetes patients. Digital images of the retina, obtained using Topcon non-mydriatic fundus photography (TRC-NW6S, Topcon, Tokyo, Japan) in the 2005–2006 and 2007–2008 cycles, were sent to contract graders of the University of Wisconsin-Madison for reading. DR participants included those diagnosed by retina image and self-reported DR individuals. ## 2.3. Study Variables Information on age, gender, race/ethnicity, education, history of comorbidities (coronary heart disease (CHD), stroke, hypertension and nephropathy) and ratio of family income to poverty (PIR) was collected through demographic questionnaires in family interviews. The height, waist circumference (WC) and weight of all participants were collected by trained health technicians at the mobile examination center (MEC). Body mass index (BMI) was calculated by the following formula: BMI = body weight (kg)/height (m2) [15]. Participants’ had fasting venous blood drawn after at least an 8-h overnight fast, and the measurements, including high-density lipoprotein (HDL, mg/dL), low-density lipoprotein (LDL, mg/dL), total cholesterol (TC, mg/dL), triglyceride (mg/dL), fasting glucose (mg/dL), fasting insulin (uU/mL) and glycohemoglobin (HbA1c, %) were obtained. TyG index is equal to ln [triglyceride (mg/dL) × glucose (mg/dL) ÷ 2] under fasting conditions [16], HOMA-IR = fasting glucose (mmol/L) × fasting insulin (uU/mL)/22.5 [6], TyG-WC = TyG index × waist circumference, TyG-BMI = TyG index × body mass index, waist-to-height ratio (WHtR) = WC/height, TyG-WHtR = TyG index × WHtR [17]. ## 2.4. Statistical Analysis A χ2 test and independent sample t-test were used to compare the differences in the characteristics between categorical variables and continuous variables at baseline in the non-DR group and DR group, respectively. Continuous variables are represented as mean ±standard deviation, and the categorical variables are shown as frequencies. A logistic regression analysis was carried out to evaluate the relationship between the risk of DR and TyG-related parameters, and to calculate the odds ratio (OR) and $95\%$ confidence interval (CI), which showed the outcomes of several models modifying confounding factors. Among them, the crude model did not include any adjustment for covariates, model 1 adjusted the general demographic variables, and model 2 added HDL, LDL, TC, hypertension history and retinopathy history on the basis of model 1. In addition, the tendency test was conducted with the first quartile as a reference. Restricted cubic splines (RCSs) were used to identify nonlinear relationships. The diagnostic efficacy of the TyG index and its related parameters for DR were analyzed and drawn using the receiver operating characteristic (ROC) curve, evaluating the screening value of each method by the area under the ROC curve (AUC). A hierarchical logistic regression model carried out an exploratory hierarchical analysis on some subgroups and determined whether interactions occurred. $p \leq 0.05$ (bilateral) was considered to have statistical significance. All analyses were conducted through R language 4.2.2 and SPSS 22.0. ## 3.1. Baseline Characteristics of the Participants The study samples include 888 adults, 443 females and 445 males. The average age is 62.2 ± 12.1 years. Two-hundred and sixty-three ($29.6\%$) patients have DR. Among the diabetes patients, only $13.7\%$ have a normal BMI, and $85.6\%$ are overweight or obese. Table 1 shows the comparison between non–DR and DR adults. The result shows that the glucose, HbA1c and TyG index of the DR participants increase. Meanwhile, it suggests participants with retinopathy, CHD and stroke history are more likely to have DR. Age, race, education, PIR, LDL, HDL, triglyceride, total cholesterol, insulin, waist circumference, body mass index and hypertension history show no significant differences between diabetic patients with/without DR. ## 3.2. Logistic Regression Analyses for the Relationship between Various TyG-Related Parameters and DR in Different Models The logistic regression model depicts the relationship between the various TyG-related parameters and DR, as shown in Table 2. In the crude model, the TyG index (OR 1.412, $95\%$ CI 1.024–1.947, $$p \leq 0.035$$) and Q4-TyG index (OR 1.603, $95\%$ CI 1.069–2.404, $$p \leq 0.022$$) are important risk factors for DR. The ORs of DR increase along with the TyG index quartiles (p for trend = 0.027). Following an adjustment for the demographic characteristics (age, sex, race, education, PIR), model 1 shows that the ORs are 1.603 ($95\%$CI 1.146–2.242, $$p \leq 0.006$$) and 1.826 ($95\%$ CI 1.200–2.779, $$p \leq 0.005$$) for the TyG index and Q4-TyG index, respectively. Based on our exploration, there are dose-response relationships between the quartiles of the TyG index which takes the first quartile as a reference and the risk of DR (p for trend = 0.004). This trend remains significant even after further modification of the confounding factors (HDL, LDL, TC, hypertension and retinopathy) in model 2 (p for trend = 0.002). Upon modifying potential confounding variables (model 2), the ORs of DR are 1.182 ($95\%$ CI 0.756–1.848), 1.327 ($95\%$ CI 0.831–2.121) and 2.186 ($95\%$ CI 1.323–3.613) for the second, third and fourth TyG index quartile, respectively. TyG-BMI becomes an unignorable risk factor for DR (OR 1.014, $95\%$ CI 1.001–1.027, $$p \leq 0.035$$), TyG index ($$p \leq 0.002$$) and Q4-TyG index ($$p \leq 0.002$$) remain critical risk factors for DR. ## 3.3. Restricted Cubic Splines for the Relationship between the TyG Index and DR An approximately U-shaped association between the TyG index and DR, demonstrated and modeled by the restricted cubic splines with four knots, is displayed among diabetes participants, which suggests that the TyG index is non-linearly associated with DR participants (Figure 2). In the crude model (Figure 2a), when the TyG index is greater than 9.21, the risk of DR increases (p for non-linearity = 0.001). Following further adjustments of confounding factors (Figure 2b), this diagram demonstrates a reduction of the risk of DR when the TyG index is beneath 9.18, then it increases afterward (p for non-linearity = 0.001). ## 3.4. Subgroup Analysis of the Correlation between the TyG Index and DR To verify the impact of the TyG index on DR, the study examined the interaction terms of effective variables that may lead to changes in DR risk. A subgroup analysis was conducted according to demographic factors, laboratory examination, history of hypertension (yes or no) and history of kidney disease (yes or no). Table 3 shows the results of a subgroup analysis of the correlation between the TyG index and the risk of DR, there is no difference in the TyG index among most pre-specified subgroups in DR participants, except for gender (p for interaction = 0.013), total cholesterol (p for interaction = 0.013) and retinopathy history (p for interaction = 0.032). The TyG index has a significant interaction relationship with DR in the female group (OR 2.669, $95\%$ CI 1.395–5.109), high total cholesterol group (OR 1.004, $95\%$ CI 1.001–1.006) and retinopathy history group (OR 2.096, $95\%$ CI 1.328–3.307) after adjusting for the confounding variables. In addition, according to the history of coronary heart disease (CHD) and stroke, diabetic patients are classified into vasculopathy($$n = 171$$) and non-vasculopathy groups ($$n = 717$$). Table 4 shows the results of the vasculopathy subgroup analysis of the correlation between the TyG-related parameters and the risk of DR after adjusting for the confounding factors in model 2. The TyG index is a risk factor for a DR event in participants without vasculopathy (OR:2.656, $95\%$ CI: 1.643–4.294, $p \leq 0.01$). ## 3.5. Diagnostic Efficacy of Various Parameters for DR Using a receiver operating characteristic (ROC) curve to analyze the diagnostic efficacy of the TyG index, TyG-WC, TyG-BMI, TyG-WHtR and HOMA-IR for DR (Figure 3). The optimum cut-off value of the TyG index for DR diagnosing is 9.86 (AUC 0.543, sensitivity = $23.2\%$, specificity = $86.56\%$). In addition, the study also calculates the best cut-off value of TyG-WC as 961.0 (AUC = 0.517, sensitivity = $69.6\%$, specificity = $37.3\%$). Moreover, the sensitivity, specificity, AUC and the best cut-off value of TyG-BMI to diagnose DR are $81.8\%$, $21.8\%$, 0.494 and 247.3, respectively. The sensitivity, specificity, AUC and the best cut-off value of TyG-WHtR are $93.16\%$, $11.04\%$, 0.504 and 5.05. While the sensitivity, specificity, AUC and the best cut-off value of HOMA-IR are $5.7\%$, $94.9\%$, 0.454 and 41.04, respectively. An AUC greater than 0.5 is considered to have diagnostic applications, these results show that the diagnostic value of the TyG Index for DR patients is higher than that of TyG-WC, TyG-BMI, TyG-WHtR and HOMA-IR. ## 4. Discussion This research further explores the relation and application of the TyG index and its related parameter in DR patients, using the NHANES database based on the national representative population distributed throughout the United States. This study finds that the TyG-BMI, TyG index and Q4-TyG index are significant risk factors for DR. Additionally, the TyG index exhibits a significant dose-response relationship with DR risk. Notably, this study is the first to demonstrate a U-shaped relationship between the TyG index and DR risk after adjusting for confounding factors, and the risk of DR bottoms out when the TyG index is approximately 9.18. These findings suggest that the TyG index is a robust indicator of DR risk and can facilitate the identification and monitoring of diabetic patients who are at risk for DR. Research has explored the relation between the TyG index and DR. A nested case-control study carried out by Yao et al. on Chinese T2DM inpatients shows that the TyG index is highly correlated with severe levels of DR [18]. However, another study found that the rise of the TyG index was intimately related to microalbuminuria and the risk of cerebrovascular disease, which is irrelative to DR [19]. Diverse research designs, sample sizes and statistical methods may account for the discrepancies in research findings. Notably, the aforementioned studies primarily focus on inpatients, thus necessitating further validation from the community population. The TyG index has been reported to be a composite biochemical indicator that reflects the combined effect of glucose and lipids [20]. The relationship between DR and abnormal glycolipid metabolism indicators has been extensively discussed [21,22]. DR is a progressive eye disease that poses a threat to vision. Hyperglycemia damages the retinal microvascular system, leading to diabetic macular edema (DME), neovascularization, tractive retinal detachment, vitreous hemorrhage and ultimately blindness [23]. In spite of hyperglycemia as the core of diabetic retinopathy development, abnormal lipid metabolism exacerbates the condition [24]. Montgomery et al. observed that a severe decrease in b/a wave ratio and retinal function is considered to be caused by dyslipidemia and its related lipid oxidation and increased oxidative stress [25]. Therefore, lipid abnormalities and oxidative stress likely aggravate the damage caused by diabetes to the retina. However, experiments conducted by Acharya et al. on rats revealed that hyperglycemia and aging exacerbate inflammation and oxidative stress induced by dyslipidemia [26]. The research indicates an intertwined pathogenesis between abnormal glucose and lipid metabolism. The TyG index is used as a substitute measurement for evaluating insulin resistance [27]. Insulin resistance is mainly manifested by decreased insulin sensitivity, which is prevalent in a variety of metabolic-related diseases. Insulin resistance runs throughout diabetes, and many studies have demonstrated that chronic low-level inflammation due to obesity promotes the occurrence and development of diabetic complications by aggravating insulin resistance [28]. Obesity is one of the established risk factors for diabetes mellitus [29], and it is characterized by abnormal or excessive fat accumulation. In this study, $85.6\%$ of diabetic patients are overweight or obese (BMI ≥25 kg/m2) [30]. Previous studies show that hypertrophy or an increased number of adipose cells results in the enhanced or weakened expression of its secreted hormones and adipokines, which affects the effects of insulin from different levels, and further induces or exacerbates the presence of insulin resistance [31,32]. These possibly explain the mechanism of the TyG index related to diabetes retinopathy, but the specific mechanism still needs further study. Multiple studies have demonstrated that combining the TyG index with obesity-related indicators, such as waist circumference, BMI, and WHtR, enhances the ability to predict insulin resistance. A large-scale cross-sectional study concluded that TyG-BMI shows the best discriminative power for assessing insulin resistance in clinical settings [33]. Taiwo et al. concluded that TyG-WHtR is a superior predictor of metabolic syndrome risk in Nigerians compared to the TyG index and other TyG-related parameters [34]. In contrast, another study indicated that the TyG index is the better predictor of coronary heart disease risk and coronary atherosclerosis severity in NAFLD patients compared to the TyG-BMI [17]. This study found that the TyG-BMI and TyG index are important risk factors for DR after correcting for related confounders. Our study showcases the initial evidence of a U-shaped nonlinear relationship between the TyG index and DR. Furthermore, even after controlling for confounding factors, a significant correlation between the TyG index and DR persists. This discovery will be of great help to clinicians, as it suggests that the TyG index could potentially serve as a straightforward, reliable and practical measure in the treatment and management of DR. Our subgroup analysis also indicates that IR-related diabetic retinopathy may affect female diabetic patients more acutely. However, this does not necessarily imply a higher prevalence of female patients. Therefore, clinicians should pay more attention to insulin resistance in female diabetic patients during clinical practice, while also taking into account the blood lipids and kidney status. Moreover, the vasculopathy subgroup analysis highlights the TyG index as a critical risk factor in diabetic patients without vasculopathy. As the TyG index could detect retinal damage earlier than cardiovascular and cerebrovascular damage in diabetic patients, monitoring this index during the disease could help reduce the risk of incident DR and resultant healthcare burdens. Nonetheless, the complexity of the disease and the presence of numerous combined risk factors in diabetic patients with vasculopathy may weaken the correlation of the TyG index. Future studies should thus aim to determine the safety threshold of the TyG index to guide medication and treatment in diabetic individuals, thereby delaying the onset and progression of diabetic retinopathy. This study also has some limitations. [ 1] This is a cross-sectional study. Therefore, it can only illustrate a correlation between DR and the TyG index, but it requires further prospective research to clarify the causal relationship. [ 2] The lack of DR severity and other residual confounding factors that are difficult to measure or evaluate probably affect our conclusions [35]. However, these limitations might be balanced by our strengths, including the large sample size, diverse ethnicities in the United States, wide age range, precise data and information on covariates, etc. ## References 1. Shahulhameed S., Vishwakarma S., Chhablani J., Tyagi M., Kaur I.. A Systematic Investigation on Complement Pathway Activation in Diabetic Retinopathy. Front. Immunol. (2020.0) 11 154. DOI: 10.3389/fimmu.2020.00154 2. Busik J.V.. Lipid metabolism dysregulation in diabetic retinopathy. J. Lipid Res. (2021.0) 62 100017. DOI: 10.1194/jlr.TR120000981 3. Li Y.Y., Yang X.F., Gu H., Snellingen T., Liu X.P., Liu N.P.. 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--- title: Pharmacology-Based Prediction of the Targets and Mechanisms for Icariin against Myocardial Infarction authors: - Zunping Ke - Yuling Wang - Guzailinur Silimu - Zhangsheng Wang - Aimei Gao journal: Medicina year: 2023 pmcid: PMC10056910 doi: 10.3390/medicina59030420 license: CC BY 4.0 --- # Pharmacology-Based Prediction of the Targets and Mechanisms for Icariin against Myocardial Infarction ## Abstract Background and Objectives: This study aims to illustrate the mechanisms underlying the therapeutic effect of Icariin after myocardial infarction (MI). Materials and Methods: Based on the network pharmacology strategy, we predict the therapeutic targets of Icariin against MI and investigate the pharmacological molecular mechanisms. A topological network was created. Biological process and Kyoto Encyclopedia of Genes and Genomes pathway enrichment were also performed. We also conducted the molecular docking analysis to stimulate the component–target interaction further and validate the direct bind effect. Results: Network pharmacology analysis identified 61 candidate genes related to the therapeutic effect of Icariin against MI. EGFR, AKT1, TP53, JUN, ESR1, PTGS2, TNF, RELA, HSP90AA1, and BCL2L1 were identified as hub genes. The biological processes of the candidate targets were significantly involved in the reactive oxygen species metabolic process, response to hypoxia, response to decreased oxygen levels, response to oxidative stress, regulation of reactive oxygen species metabolic process, and so forth. Overall, biological process enrichment analysis indicated that the protective effect of Icariin against MI might be associated with oxidative stress. Moreover, the pathway analysis showed that the candidate targets were closely associated with lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, etc. We identified the conformation with the lowest affinity score as the docking conformation. The simulated molecular docking was displayed to illustrate the topical details of the binding sites between Icariin and TNF protein. Conclusions: This study provides an overview of the mechanisms underlying the protective effect of Icariin against MI. ## 1. Introduction Myocardial infarction (MI) is a common ischemia heart disease caused by the obstructed arterial inflow to the heart and subsequent myocardium impairment [1,2]. Currently, MI is one of the most important risk factors for heart failure (HF), and it has greatly reduced the population life expectancy across the globe [3]. Over the last decade, MI has become one of the leading causes of death worldwide [4]. In the US, it is estimated that the annual incidence of MI is approximately 605,000 new cases and 200,000 recurrent cases a year [5]. The primary pathological processes leading to MI include artery inflammation, lipid deposition, and metabolic disorders caused by many risk factors [6]. Reactive oxygen species (ROS) have been well-demonstrated in aggravating the adverse effects of myocardial infarction and many other diseases [7,8,9,10]. Despite the advances in the cellular and molecular mechanisms underlying myocardial ischemia/reperfusion injury, treating ischemic heart disease remains challenging. Epimedii Herba, also referred to as Horny Goat Weed, is a traditional Chinese herbal medicine [11]. Epimedii Herba has shown various health benefits, including therapeutic potential on cardiovascular diseases [11]. Icariin is the major bioactive pharmaceutical component isolated from Epimedii Herba. Many potential mechanisms underlying the cardioprotective effects of Icariin have been revealed, including inhibiting oxidative stress, attenuating DNA damage, correcting endothelial dysfunction, preventing platelet activation, and so forth [12,13]. Importantly, Icariin can inhibit oxidative stress injury, prevent mitochondrial oxidative damage, and reduce cardiomyocyte apoptosis [1,2]. These characteristics could potentially protect the heart cell from the damage caused by free radicals and improve the blood flow. Although some pharmacological studies reported the beneficial effect of Icariin in preventing cardiac ischemia/reperfusion injury [14], improving cardiac remodeling [15], and inhibiting ferroptosis of cardiomyocytes [16], it remains unclear whether Icariin could provide clinical benefits for MI patients. The computational network pharmacology-based investigation on the potential effect of MI could provide more preliminary evidence for Icariin administration. Network pharmacology is an integrative computational method that integrates systematic medicine with information science and establishes a protein–compound and disease–gene network [17]. Combining the network analysis and computational methods, network pharmacology can be used to understand the complex interactions between drugs, targets, and biological pathways. The goal of network pharmacology is to identify the most effective combinations of drugs for treating various diseases by analyzing the complex interactions between drug targets and biological pathways. To comprehensively understand of the interaction between drugs and diseases in the human body, multiple disciplines were involved in network pharmacology, including biology, chemistry, computer science, and mathematics. Network pharmacology can also be used to develop new drugs, improve existing treatments, better understand the underlying mechanisms of disease, and re-purpose approved drugs, which provides an opportunity to systematically extend the druggable space of proteins implicated in various diseases [18,19]. The analysis based on network pharmacology usually follows these steps: identify drug compounds and disease-related target genes, construct a protein–protein interaction network based on the identified genes, and visualize the gene network [17]. To date, network pharmacology has been widely used for the unbiased elucidation of drug mechanisms and systematic prediction of effective therapeutic combinations [20,21]. This study used network pharmacology methods to investigate the mechanisms of Icariin on MI. ## 2.1. Data Sources and Targets Fishing PubChem (https://pubchem.ncbi.nlm.nih.gov/, accessed on 14 October 2022) is a free online database of chemical information created by the National Institutes of Health. It provides a repository of over 39 million chemical compounds with associated data such as chemical structures, 3D models, bioactivities, reactions, and spectra. Compounds can be searched and visualized using the PubChem Structure Search and PubChem Compound Search tools, and data can be downloaded in several different file formats. PubChem also provides access to related resources, such as databases of potential drug targets, gene expression data, and toxicity profiles. We acquire Icariin’s 2D Structure and Canonical SMILES (Simplified Molecular Input Line Entry System) from the PubChem database using the keyword “Icariin”. Canonical SMILES is a molecular representation format used to describe the structure of a chemical compound. It is a text-based representation of a molecular structure, using a string of characters to represent the bonds and atoms in a molecule. It is a standardized system which is used to uniquely identify molecules and aid in searching databases. The predictive targets of icariin were harvested from the Swiss Target Prediction (www.swisstargetprediction.ch, accessed on 15 October 2022) [22] and STITCH (stitch.embl.de, accessed on 15 October 2022) [23] databases. Swiss Target Prediction is a database that contains predicted targets for small molecules [22]. It is based on a machine learning approach and is designed to provide an easily accessible source for target prediction of small molecules. The database provides a comprehensive overview of the predicted targets for a given small molecule, which can be used to inform preclinical drug development. The Swiss Target Prediction database contains over 1.5 million predicted targets for small molecules and is updated regularly with new compounds and targets. STITCH is a database that provides access to interactions between chemical compounds and proteins [23]. It is a comprehensive resource of over 29 million interactions between small molecules and proteins, which can be used to generate hypotheses about compound–protein associations and potential drug targets. The database also contains information about protein–protein interactions, protein families, pathways, and protein post-translational modifications. Moreover, target genes related to MI were collected with the keyword “myocardial infarction” in TTD (db.idrblab.net/ttd, accessed on 15 October 2022) [24], DrugBank, DisGeNET (www.disgenet.org, accessed on 15 October 2022) [25], and CTD database (ctdbase.org, accessed on 15 October 2022) [26] databases, which contain genes and variants associated with human diseases. The TTD is an online resource created to provide comprehensive and up-to-date information about therapeutic targets and their associated drugs [24]. It contains information on over 7500 drug targets and more than 7000 drug products, including drugs in clinical and pre-clinical development, as well as approved drugs. The TTD includes detailed information about the interaction between drugs and target molecules, including the type of interaction, mechanism of action, and the effects of the drug–target interaction. The database also includes information about drug metabolism, toxicity and other pharmacological data. DrugBank Database is a comprehensive, publicly accessible, online drug information resource that contains detailed information on thousands of drug molecules. It includes detailed information on small molecules, biologics, and drug-to-drug interactions. DisGeNET is an open access database that provides comprehensive information on human diseases and their associated genes [25]. It is a comprehensive and integrated resource that provides access to a large collection of data related to the genetic factors involved in the etiology of human diseases. It is an open-access database that contains data from more than 200 sources, including both curated and automatically generated information. DisGeNET also provides access to a variety of tools to help researchers identify and analyze disease-related genes. The Comparative Toxicogenomics Database is a web-based, open-access resource created to help researchers better understand the complex relationships between chemicals, genetic variants, and diseases [26]. It provides detailed information on gene–chemical, gene–gene, gene–disease, and other biological interactions. CTDbase.org also provides access to curated biological pathways, pathway-based analysis tools, and chemical–disease associations. This resource is designed to facilitate the integration of toxicogenomics and environmental health research. After acquiring target genes, we used the UniProt (www.uniprot.org, accessed on 15 October 2022) [27] database to match gene symbols with UniProt ID, and we removed duplicated genes and standardized all the results. Only the genes from “Homo sapiens” were selected for the following analyses. Finally, we overlapped the Icariin targets and MI-related genes to acquire candidate therapeutic targets of Icariin against MI. ## 2.2. Construction of Protein–Protein Interaction Network and Topological Analysis STRING (http://string-db.org, accessed on 15 October 2022) [28] is a protein–protein interaction database, which aims to collect, score, and integrate all publicly available sources of protein–protein interaction (PPI) information. It contains information from diverse sources, including experimental databases, computational prediction methods, and interactions extracted from the literature [28]. The database allows researchers to explore the protein–protein interactions of a particular gene or protein and to visualize the network of interactions. This can facilitate research in network pharmacology by providing researchers with a comprehensive view of a drug’s target proteins and their interactions, which can help in predicting its mechanism of action, identifying potential off-target effects, and elucidating the pharmacological pathways involved. STRING database has provided the researchers with a useful tool to create PPI networks, which can be used to draw insights into the pharmacological pathways involved and to identify potential drug effects [28]. We input the candidate genes into the STRING database and set the organism as Homo sapiens. Then, the STRING database matched the candidate genes with proteins automatically. It should be noted that one gene would be only matched to one protein in the STRING database. We used the STRING database to collect possible interactions of target proteins with a medium confidence score of >0.4. Both functional and physical protein associations were analyzed. Resultant PPI data were visualized by Cytoscape software (v3.7.2) [29]. An analyzer (a plug-in of Cytoscape) was utilized in analyzing topological parameters of mean and maximum degrees of freedom in the PPI network. Further, based on mixed character calculation, we identified the top 10 hub genes using the Cytoscape plug-in software “cytoHubba”. ## 2.3. Biological Process and Pathway Enrichment Analysis To reveal the biological function of the target genes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. GO is a controlled vocabulary of biological terms used to describe gene products in any organism. The GO vocabulary is used to classify genes and gene products into three distinct categories: molecular function, biological process, and cellular component. GO is used to aid in the interpretation of gene expression data, to provide insight into the underlying molecular mechanisms of a given phenotype, and to identify network and pathway relationships between genes and gene products. The KEGG pathway database is an online resource that provides comprehensive information on molecular-level pathways, networks and interactions in various organisms, including humans. It contains manually curated information on metabolic, genetic, and regulatory networks, as well as on the evolution and development of biological systems. The information is organized into a series of pathways and maps that connect genes and proteins to their metabolic, regulatory, and signaling functions. KEGG pathways are used to analyze and visualize large-scale omics data, and to infer underlying biological networks and functions. The enriched biological processes and KEGG signaling pathways related to the candidate targets of Icariin against MI were visualized. We used default parameters in the study unless otherwise specified. ## 2.4. Component–Target Molecular Docking Computational molecular docking is a computational technique used to predict the binding affinity of a given molecule to a target protein or ligand [30]. Since it can provide insight into the binding mechanisms of drug–target interactions, computational molecular docking has become a vital method to explore the component–target interaction and predict the binding model [30]. It is a powerful tool for understanding the structure–activity relationships of molecules and helps in the design of new molecules with desired binding properties. Component–target molecular docking involves the use of a scoring function to evaluate the binding affinity of a given molecule to its target. This scoring function is a mathematical representation of the interaction energy between the molecule and target. The molecule is placed at various orientations relative to the target, and the energy of the interaction is evaluated for each orientation. The orientation that yields the lowest energy is chosen as the optimal binding conformation. The process of component–target molecular docking typically involves two steps. First, the receptor–ligand complex is modelled using a physical model. This model is used to generate the coordinates of the ligand and the receptor molecule. Second, the ligand is docked into the receptor molecule using a scoring function. This scoring function is a combination of the energy of the receptor–ligand complex and the entropic contribution due to the internal motions of the ligand molecule. We performed the molecular docking analysis to stimulate the component–target interaction further and validate the direct bind effect. We obtained the three-dimensional structure of the potential hub gene (treated as a receptor) from the RCSB PDB database (https://www.rcsb.org/, accessed on 15 October 2022). The AutoDock Tool software was applied to pre-process the protein structure by removing the water molecules and adding the nonpolar hydrogen atoms. The 2D structure of Icariin was obtained from PubChem, transformed by the Open Babel software, and saved in the PDBQT format as a ligand via the AutoDock software. Next, we used the Autodock Vina software to predict the molecular docking site, and the affinity score (binding energy) of each conformation pair was calculated. The lower affinity score indicates a stronger bond. We identified the conformation with the lowest affinity score as the docking conformation. Finally, the conformation between Icariin and the potential target was visualized by PyMOL software. ## 3.1. Candidate Targets of Icariin against MI The canonical SMILES of Icariin is CC1C(C(C(C(O1)OC2=C(OC3=C(C2=O)C(=CC(=C3CC=C(C)C)OC4C(C(C(C(O4)CO)O)O)O)O)C5=CC=C(C=C5)OC)O)O)O. The 2D Structure of Icariin is illustrated in Figure 1. The PubChem CID is 5318997. After standardization and duplicate removal, 940 MI-related targets and 109 Icariin targets were obtained. The detailed MI-related genes and Icariin target genes are provided in Supplement Tables S1 and S2, respectively. We overlapped MI-related genes and Icariin targets, and 61 candidate drug–disease interaction genes were acquired (Figure 2). The candidate drug–disease interaction genes included PRKCE, XDH, ADRA2C, NOS2, PRKCZ, SLCO1B3, ALOX5, CYP1A2, BACE1, PRKCB, SLCO2B1, EGFR, IKBKB, ADORA1, ALDH2, IL2, BCHE, BCL2, OPRD1, ADRA2A, PDE5A, F7, KCNH2, F10, LDHB, CYP1A1, BCL2L1, TNNI3, ADORA3, HSP90AB1, RELA, PRKCD, NOS3, SLC29A1, ABCB1, PLG, SERPINE1, CYP19A1, TP53, TNF, DRD2, LDHA, AKT1, PRKACA, F2, PTGS2, SQLE, KLK1, NFKB1, ABCC1, NOX4, BAD, ESR1, SCARB1, JUN, ACHE, PRKCA, CYP1B1, APP, RPS6KA3, HSP90AA1. ## 3.2. Network and Topological Analysis We imported the 61 potential targets into the STRING database and obtained the PPI network, which contained 61 nodes and 362 edges with an average node degree of 12.1 (Figure 3A). The cytoHubber revealed the top 10 hub genes ranked by maximal clique centrality (MCC) according to the topological parameters of the interaction network: EGFR, AKT1, TP53, JUN, ESR1, PTGS2, TNF, RELA, HSP90AA1, BCL2L1 (Figure 3B). ## 3.3. Biological Process and KEGG Pathway Enrichment Analysis Biological process and KEGG pathway enrichment analyses were performed to clarify the characteristics of Icariin-related targets. As illustrated in Figure 4A, the biological processes of the candidate targets were significantly involved in the reactive oxygen species metabolic process, response to hypoxia, response to decreased oxygen levels, response to oxidative stress, regulation of reactive oxygen species metabolic process, and so forth. Overall, biological process enrichment analysis indicated that the protective effect of Icariin against MI might be associated with oxidative stress. Moreover, the KEGG pathway analysis showed that the candidate targets were closely associated with lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, etc. ( Figure 4B). ## 3.4. Molecular Docking Based on the integration of the results from the PPI network, we selected the TNF as the potential target in molecular docking. The structure of the human protein encoded by the TNF gene was obtained from the RCSB Protein Data Bank (PDB DOI: 10.2210/pdb1TNF/pdb). After pre-processing, we performed molecular docking between Icariin and TNF protein. Conformation with high binding energy (affinity score below −5 kcal/mol) was considered a stable structure. We identified the conformation with the lowest affinity score as the docking conformation. The simulated molecular docking is displayed in Figure 5 to illustrate the topical details of the binding sites between Icariin and TNF protein. ## 4. Discussion MI is a clinical event characterized by the acute reduction in or cessation of blood flow to a portion of the heart muscle, leading to ischemic injury and cell death. It is typically caused by the occlusion of a coronary artery by a thrombus or embolus, which is usually formed on top of a ruptured vulnerable plaque. The incidence of MI has been declining, but it remains a leading cause of death and disability worldwide [5,31]. The risk factors for MI include advanced age, male gender, smoking, hypertension, dyslipidemia, diabetes, family history, sedentary lifestyle, and so forth [5,31]. Although the advances in diagnosis and treatment strategies have reduced the mortality caused by MI, most MI patients still suffer from an irreversible pathological evolution, which might eventually result in left ventricle dysfunction or heart failure [32]. The exploration of drugs against MI is a hot spot of cardiovascular research. Epimedii Herba is a perennial herb native to China and other parts of Asia [11]. It belongs to the Berberidaceae family and has been used for centuries in traditional Chinese medicine for various purposes. The potential cardiovascular benefits of Epimedii Herba include reducing blood pressure, improving blood circulation, and reducing the risk of heart disease. Epimedii Herba includes multiple active compounds, such as include flavonoids, icariin, and prenylflavonoids [33]. Among the pharmacological constituents of Epimedii Herba, pharmacokinetic studies have identified Icariin as a major bioactive pharmaceutical component [33], which exhibits a wide range of pharmacological activities [2,12,34,35,36]. The pharmacokinetic analysis of Icariin metabolism in rodents discovered the main metabolites of icariin: icaritin, icariside I, icariside II, and desmethylicaritin [37]. Although the exact metabolism of Icariin is not fully understood, it is now known that Icariin is metabolized in the body primarily in the liver through various pathways. Icariin is metabolized to several metabolites in the liver, which show a wide range of cardioprotective effects by inhibiting oxidative stress, reducing inflammation response, regulating cell apoptosis, and attenuating cellular senescence [38,39,40]. These cardiovascular protective characteristics make Icariin a candidate for treating MI. Network pharmacology is an interdisciplinary field that seeks to understand the complex interactions between drugs, biological pathways, and disease mechanisms [17]. This is achieved through the use of network analysis and computational methods. The objective of network pharmacology is to identify the most effective combinations of drugs for treating various diseases [18,19]. This is accomplished by analyzing the interactions between drug targets and biological pathways. The result of this analysis can lead to the development of new drugs, improvements in existing treatments, and a deeper understanding of the underlying mechanisms of disease. In terms of methodology, network pharmacology utilizes complex data sets, including information on drug targets, biological pathways, and molecular interactions, to build models of drug–target interactions. These models can be used to identify new drug targets, predict potential side effects, and optimize drug delivery. The application of network pharmacology has the potential to greatly impact the field of drug discovery and development. Its interdisciplinary approach can contribute to a more comprehensive understanding of the relationships between drugs and the human body, leading to the advancement of treatments for various diseases [17]. Based on the network pharmacology strategies, our study revealed the potential targets of Icariin on MI, including EGFR, AKT1, TP53, JUN, ESR1, PTGS2, TNF, RELA, HSP90AA1, BCL2L1. The biological process and KEGG pathway enrichment analysis suggested that the protective effect of Icariin against MI might be associated with inhibited oxidative stress. In MI, the lack of oxygen and nutrients delivered to the heart tissue leads to cellular damage and death, resulting in the formation of an infarct. Many pathological processes were involved in myocardial infarction, including platelet activation and aggregation, ischemia, inflammation, necrosis, and so forth. Importantly, ROS plays a key role in contributing to the development of many cardiovascular diseases. ROS are highly reactive molecules that are produced by cells as a byproduct of normal cellular metabolism and are also generated by various sources of oxidative stress, such as hypoxia, ischemia, and inflammation. In the development of MI, ROS contribute to the injury and death of heart muscle cells through multiple mechanisms. One of the key mechanisms by which ROS contribute to MI is through the oxidative modification of proteins and lipids, leading to cellular dysfunction and death. ROS can also induce the activation of various signaling pathways that contribute to the development of an inflammatory response, further exacerbating the injury to heart muscle cells. Our results indicated that the potential antioxidative effect of Icariin might benefit MI patients, probably from the polyphenol structure similar to other antioxidant properties. Zhao and colleagues [41] used a cell-free system to examine the anti-oxidant effects of Icariin, and they observed reduced DNA damage after Icariin treatment. Xia et al. [ 42] reported that Icariin could inhibit oxidative stress, inflammation, and apoptosis in chemotherapy-induced cardiotoxicity. The myocardial injury was significantly reduced via activating PI3K/Akt, inhibiting the MAPKs signaling pathway, and downregulating the secretion of inflammatory factors in H9c2 cells and mice models after Icariin treatment [42]. In addition, Icariin can inhibit ROS-induced JNK and p38 signaling pathways and thus protect cardiomyocytes from apoptosis [43]. Moreover, the anti-atherosclerosis effect of Icariin was also observed in ApoE-/- mice [44,45], rats [46], and rabbit models [47]. TNF, tumor necrosis factor, is a pro-inflammatory cytokine that plays a crucial role in the pathological processes involved in cardiovascular diseases [48,49]. The secretion, release, and transformation of TNF is a mediator in the development and progress of MI. TNF is produced by various cell types, including immune cells, and is involved in the regulation of the immune response. It is reported that TNF contributes to the injury and death of heart muscle cells by multiple mechanisms. One of the key mechanisms by which TNF contributes to myocardial infarction is through the induction of oxidative stress. TNF can induce the production of reactive oxygen species (ROS), which are highly reactive molecules that contribute to the oxidative modification of proteins and lipids and the death of heart muscle cells. TNF can also induce the activation of various signaling pathways that contribute to the development of an inflammatory response, further exacerbating the injury to heart muscle cells. Based on the high-cholesterol diet-induced atherosclerosis rat model, Hu et al. [ 46] reported that Icariin could inhibit atherosclerosis by reducing the circulating levels of TNF-α and IL-6 in a dose-dependent manner via p38/MAPK signaling pathway. Another study also revealed that Icariin could downregulate the mRNA levels of TNF-α, ICAM-1, IL-2, and IL-6, which attenuates myocardial inflammation [50]. This study identified the TNF as a hub gene in the Icariin treatment of MI. The molecular docking simulated the binding between Icariin and TNF protein. Last but not least, this study was not validated in a cell or animal model, which is a significant limitation. In the following studies, more external experiments should be performed to validate the results. ## 5. Conclusions This study provides preliminary evidence for Icariin administration to treat MI. Further research is required to demonstrate the clinical benefit of Icariin on the development and progress of MI. ## References 1. Mehta A., Mahtta D., Gulati M., Sperling L.S., Blumenthal R.S., Virani S.S.. Cardiovascular Disease Prevention in Focus: Highlights from the 2019 American Heart Association Scientific Sessions. Curr. Atheroscler. Rep. (2020) 22 3. DOI: 10.1007/s11883-020-0822-6 2. Benjamin E.J., Muntner P., Alonso A., Bittencourt M.S., Callaway C.W., Carson A.P., Chamberlain A.M., Chang A.R., Cheng S., Das S.R.. 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--- title: Potential α-Glucosidase Inhibitors from the Deep-Sea Sediment-Derived Fungus Aspergillus insulicola authors: - Weibo Zhao - Yanbo Zeng - Wenjun Chang - Huiqin Chen - Hao Wang - Haofu Dai - Fang Lv journal: Marine Drugs year: 2023 pmcid: PMC10056930 doi: 10.3390/md21030157 license: CC BY 4.0 --- # Potential α-Glucosidase Inhibitors from the Deep-Sea Sediment-Derived Fungus Aspergillus insulicola ## Abstract Three new phenolic compounds, epicocconigrones C–D (1–2) and flavimycin C [3], together with six known phenolic compounds: epicocconigrone A [4]; 2-(10-formyl-11,13-dihydroxy-12-methoxy-14-methyl)-6,7-dihydroxy-5-methyl-4-benzofurancarboxaldehyde [5]; epicoccolide B [6]; eleganketal A [7]; 1,3-dihydro-5-methoxy-7-methylisobenzofuran [8]; and 2,3,4-trihydroxy-6-(hydroxymethyl)-5-methylbenzyl-alcohol [9], were isolated from fermentation cultures of a deep-sea sediment-derived fungus, Aspergillus insulicola. Their planar structures were elucidated based on the 1D and 2D NMR spectra and HRESIMS data. The absolute configurations of compounds 1–3 were determined by ECD calculations. Compound 3 represented a rare fully symmetrical isobenzofuran dimer. All compounds were evaluated for their α-glucosidase inhibitory activity, and compounds 1, 4–7, and 9 exhibited more potent α-glucosidase inhibitory effect with IC50 values ranging from 17.04 to 292.47 μM than positive control acarbose with IC50 value of 822.97 μM, indicating that these phenolic compounds could be promising lead compounds of new hypoglycemic drugs. ## 1. Introduction According to the International Diabetes Federation, 537 million people worldwide were diagnosed with diabetes mellitus in 2021, and about 90 percent of them were type 2 diabetes mellitus (T2DM) [1,2]. T2DM is a chronic metabolic disease that is characterized by postprandial hyperglycemia in the case of insulin resistance and relative lack of insulin [3]. The inhibition of α-glucosidase can reduce the cleavage of glucose from disaccharides or oligosaccharides to inhibit postprandial hyperglycemia [4]. Therefore, α-glucosidase is a common therapeutic target for the treatment of T2DM [5]. Currently available α-glucosidase inhibitors, such as acarbose, voglibose and miglitol, have been used to treat T2DM patients. Nevertheless, the use of these drugs has been associated with serious side effects, such as abdominal distension and diarrhea [6,7]. For this reason, the search for natural, efficient and non-toxic α-glucosidase inhibitors provides an attractive strategy for the development of new hypoglycemic drugs. Phenolic compounds have been proved to be effective α-glucosidase inhibitors [8,9,10,11]. Marine phenolic compounds are far less researched than those from terrestrial sources, which could suggest great potential in the ocean to develop novel diabetes drugs [12]. Some marine phenolic compounds isolated from seaweed [13,14] and seagrass [15] have been confirmed to have wonderful α-glucosidase inhibitory activity. In order to find more marine phenolic compounds with α-glucosidase inhibitory activity, our team studied marine fungi from the South China Sea. Aspergillus insulicola, a fungi previously not extensively studied, had great development and utilization value. Previous chemical studies of A. insulicola have discovered many peptides [16,17,18] and nitrobenzoyl sesquiterpenoids [19,20], which showed significant biological activities, including anti-bacteria [16] and cytotoxic [19,20]. During our ongoing research in finding new compounds with potential bioactivities [21,22,23], a chemical investigation of the deep-sea sediment-derived fungus A. insulicola led to the isolation and identification of three new phenolic compounds, epicocconigrones C–D (1–2) and flavimycin C [3], together with six known phenolic compounds (4–9) (Figure 1). All compounds were investigated for their α-glucosidase inhibitory activity. Herein, we describe the structure elucidation of the new metabolites as well as the α-glucosidase inhibitory activity of the isolated compounds. ## 2.1. Structure Elucidation of New Compounds 1–3 Epicocconigrone C [1] was isolated as a yellow solid, and its molecular formula was determined to be C19H16O9 with 12 degrees of unsaturation by HRESIMS data at m/z 411.0698 (calcd. 411.0687 for C19H16O9Na, [M + Na]+), which was supported by the 13C NMR and DEPT spectral data. The IR spectrum of 1 featured typical absorption bands for hydroxyl (3413 cm−1) and conjugated ketone (1670 cm−1). The 1H NMR spectrum (Table 1) of 1 revealed two methyls (δH 2.26 and δH 2.31), one methoxy (δH 3.70), two oxymethines (δH 6.38 and δH 6.83), one aldehyde proton (δH 10.34), and one hydroxyl proton (δH 11.33). The 13C NMR (Table 2) and DEPT spectra showed 19 well-resolved carbon atom signals, including one ketone carbonyl (δC 196.9), one aldehydic carbonyl (δC 191.2), two oxygenated tertiary carbons (δC 89.8 and δC 68.6), one methoxy carbon (δC 60.2), two methyls (δC 11.8 and δC 10.2), and twelve olefinic quaternary carbons at δC 156.9−104.5, accounting for 8 degrees of unsaturation. Thus, compound 1 was thought to possess a tetracyclic skeleton. The strong heteronuclear multiple-bond correlation (HMBC) correlations from H-17 (δH 2.31) to C-12 (δC 121.7), C-13 (δC 121.9), and C-14 (δC 144.3), from H-18 (δH 10.34) to C-11 (δC 112.6), C-12, C-13, and C-14, as well as the weak signals from H-17 to C-11, C-15 (δC 138.4), and C-16 (δC 135.8) confirmed the existence of ring A (Figure 2). The HMBC correlations from H-19 (δH 2.26) to C-3 (δC 130.8), C-4 (δC 115.8), and C-5 (δC 156.9), as well as the HMBC correlations from 7-OH (δH 11.33) to C-6, C-7 (δC 153.6), and C-8 (δC 104.5) established the substitution of the aromatic ring D. Furthermore, the HMBC correlations from H-2 (δH 6.83) to C-10 (δC 68.6) and C-16, from H-10 (δH 6.38) to C-2 (δC 89.8), C-11, C-12 and C-16 suggested the presence of two oxygen bridges between C-16/C-2 and C-2/C-10 in ring B, which could be confirmed by the low field chemical shift signal of CH-2 (δC 89.8, δH 6.83). Ring C was established by the HMBC correlations from H-2 to C-4 and C-8, and from H-10 to C-8 and C-9 (δC 196.9). The comprehensive NMR analysis indicated that 1 shared the same oxygen-bridged skeleton with epicocconigrone A [4] [24], with the exception that the appearance of 6-OCH3 in 1 replaced 6-OH in 4, which was supported by the HMBC correlation from 6-OCH3 (δH 3.70) to C-6 (δC 134.7). Thus, the planar structure of 1 was elucidated as shown (Figure 1), named epicocconigrone C. In the nuclear Overhauser effect spectroscopy (NOESY) spectrum of 1, the correlation between H-2 and H-10 was indicative of their cis relationship (Figure 2). The absolute configuration of 1 was confirmed by the ECD calculation. Its experimental ECD curve for the absolute configurations of 2S and 10R was consistent with the calculated ECD curve of (2S, 10R) (Figure 3). Epicocconigrone D [2] was obtained as a yellow solid. The molecular formula of 2 was determined as C20H20O9 with 11 unsaturated degrees by HRESIMS data at m/z 427.1004 (calcd. 427.1000 for C20H20O9Na, [M + Na]+), which was supported by the 13C NMR and DEPT spectral data. The IR spectrum of 2 featured typical absorption bands for hydroxyl (3446 cm−1) and conjugated ketone (1626 cm−1). The 1H NMR spectrum (Table 1) of 2 indicated two methyl groups (δH 2.09 and δH 2.18), two methoxy groups (δH 3.57 and δH 3.69), one methylene (δH 4.32, d, $J = 12.1$ Hz; 4.81 d, $J = 12.1$ Hz), two oxymethines (δH 5.65 and δH 6.76), and two hydroxyl protons (δH 8.92 and δH 11.46). The 13C NMR (Table 2) and DEPT spectra revealed 20 carbon atom signals, including one ketone carbonyl (δC 196.8), two oxygenated tertiary carbons (δC 89.9 and δC 70.3), two methoxy carbons (δC 60.3 and δC 60.0), one methylene (δC 55.8), two methyls (δC 11.0 and δC 10.3), and twelve olefinic quaternary carbons. Detailed analysis of 2D NMR spectra of 2 revealed that it had a similar structure to 1. The major differences in 2 were a hydroxymethylene group and a methoxy group substituted at C-12 and C-15, instead of the aldehyde group and the hydroxyl group, respectively, when compared to 1 (Figure 2), which were further confirmed by the HMBC correlations from H2-18 (δH 4.32, 4.81) to C-11 (δC 108.3), C-12 (δC 132.1), and C-13 (δC 118.1), and from 15-OCH3 (δH 3.69) to C-15 (δC 134.6). Thus, the planar structure of 2 was elucidated as shown (Figure 1), named epicocconigrone D. The ROESY correlation between H-2 and H-10 indicated their cis orientation (Figure 2). The absolute configuration of 2 was understood to be 2S, 10R by comparing the experimental and simulated ECD curves (Figure 3). Flavimycin C [3] was isolated as a white solid. It had a molecular formula of C20H22O8 with 10 degrees of unsaturation, as determined by HRESIMS data at m/z 391.1385 (calcd. 391.1387 for C20H23O8, [M + H]+), which was supported by the 13C NMR and DEPT spectral data. The IR spectrum of 3 featured typical absorption bands for hydroxyl (3449 cm−1). The 1H NMR spectrum (Table 1) of 3 exhibited one methyl (δH 1.88), one methoxy (δH 3.64), one methine (δH 4.30), one methylene (δH 4.54 d, $J = 15.0$ Hz; 4.65 d, $J = 15.0$ Hz), and two hydroxyl protons (δH 8.55, δH 8.68). The 13C NMR (Table 2) and DEPT spectra displayed 10 well-resolved carbon atom signals, dividing into six quaternary carbons that were assigned to one benzene ring, one methylene (δC 65.8), one methine (δC 66.1), one methoxy carbon (δC 60.2), and one methyl (δC 9.5). The NMR data of 3 were very similar to those of 8 except for the absence of the methylene signal, and instead, the presence of the methine signal of C-3 (δH 4.30/δC 66.1) in 3. Combined with molecular formula, 3 was deduced to be a symmetrical dimeric derivative. The above data suggested 3 was a symmetrical dimer of 8, connecting at C-3/C-10 between the two units (Figure 2), which was further confirmed by the HMBC correlation from H-3 to C-10. Thus, the planar structure of 3 was confirmed as shown (Figure 1), and named flavimycin C. The 1H and 13C NMR spectra (Table 1 and Table 2) of this aromatic polyketide dimer only exhibited a set of signals of aromatic polyketide monomer. There were three possible absolute configurations of two chiral carbons C-3 and C-10 in 3. The obvious negative optical activity (αD20 = −70.0) and the Cotton effect indicated that compound 3 was not a mesomer, which implied the possibility of 3R, 10S was excluded. Consequently, the absolute configurations of C-3 and C-10 were the same (3S, 10S or 3R, 10R). The absolute configuration of 3 was understood to be 3R, 10R by comparing the experimental and simulated ECD curves (Figure 3). The known compounds: epicocconigrone A [4] [24]; 2-(10-formyl-11,13-dihydroxy-12-methoxy-14-methyl)-6,7-dihydroxy-5-methyl-4-benzofurancarboxaldehyde [5] [25]; epicoccolide B [6] [26]; eleganketal A [7] [27]; 1,3-dihydro-5-methoxy-7-methylisobenzofuran [8] [28]; and 2,3,4-trihydroxy-6-(hydroxymethyl)-5-methylbenzyl-alcohol [9] [29] were identified by comparing their NMR data with those reported in the literature. The new compounds 1–3 are all aromatic polyketide dimers, particularly compounds 1 and 2 simultaneously featuring consistent $\frac{6}{6}$/$\frac{6}{6}$ heterotetracyclic ring cores and compounds 1–3 co-occurrence in the same marine-derived fungus suggest that they should originate from the same biogenetic pathway. A plausible biosynthetic pathway toward the formation of compounds 1–3 can be proposed by detailed analysis of their structures (Scheme 1). ## 2.2. In Vitro Evaluation of α-Glucosidase Inhibitory Activity All compounds were tested for their α-glucosidase inhibitory activities using a reported method [30], with acarbose as the positive control. The results revealed that compounds 1, 4–7, and 9 showed more potent inhibitory activity (IC50 values ranging from 17.04 ± 0.28 to 292.47 ± 5.87 μM) than acarbose (IC50, 822.97 ± 7.10 μM) (Table 3). The potent α-glucosidase inhibitory activity of epicocconigrone A [4] and epicoccolide B [6] has been already reported [31]. It could be noted herein that the number of hydroxyl groups of polyhydroxy phenolic compounds was important for α-glucosidase inhibitory activity, as reflected by the low IC50 values of compounds 4 and 6, while structures with fewer hydroxyl groups (compounds 1 and 5) exhibited little activity. ## 3.1. Fungal Material and Fermentation The fungal strain A. insulicola was isolated from deep-sea sediments, which were collected from the South China Sea at the depth of 2500 m. After grinding, the sample (1.0 g) was diluted to 10−2 g/mL with sterile H2O, 100 μL of which was spread on potato dextrose agar medium (200.0 g potato, 20.0 g glucose, and 20.0 g agar per liter of seawater) plates containing chloramphenicol as a bacterial inhibitor. It was identified by its morphological characteristics and ITS gene sequences (GenBank accessing No. ON413861), the used primers of which were ITS1 (TCCGTAGGTGAACCTGCGG) and ITS4 (TCCTCCGCTTATTGATATGC). A reference culture of A. insulicola was deposited at the Hainan Provincial Key Laboratory for Functional Components Research and Utilization of Marine Bio-resources, Haikou, China. ## 3.2. Culture Conditions The fungal strain A. insulicola was cultured in potato dextrose broth medium (consisting of 200.0 g/L potato, 20.0 g/L glucose, and 1000.0 mL deionized water), and incubated on a rotary shaker (150 rpm) for 72 h at 28 °C. Thereafter, 3 mL of seed broth was transferred to fifty 1000 mL Erlenmeyer flasks containing solid rice medium (each flask contained 80 g rice and 120 mL seawater), used for fermentation. The flasks were incubated under static conditions at room temperature for 30 days. ## 3.3. General Experimental Procedures Optical rotation was measured using a Modular Circular Polarimeter 5100 polarimeter (Anton Paar, Austria). The NMR spectra were measured on Bruker Avance 500 NMR spectrometer (Bruker, Bremen, Germany) and Bruker DRX-600 spectrometer (Bruker Biospin AG, Fällanden, Germany) using TMS as an internal standard. HRESIMS were determined with an API QSTAR Pulsar mass spectrometer (Bruker, Bremen, Germany). ECD and UV spectra were recorded on a MOS-500 spectrometer (Biologic, France). IR data were measured on a Nicolet 380 infrared spectrometer (Thermo Electron Corporation, Madison, WI, USA). Analytic HPLC was performed with an Agilent Technologies 1260 Infinity II equipped with an Agilent DAD G1315D detector (Agilent, Palo Alto, CA, USA), the separation columns were (COSMOSIL-packed C18, 5 mm, 4.6 mm × 250 mm). Semi-preparative HPLC was performed on reversed-phased columns (COSMOSIL-packed C18, 5 mm, 10 mm × 250 mm). Silica gel (60–80, 200–300 and 300–400 mesh, Qingdao Marine Chemical Co. Ltd., Qingdao, China) and Sephadex LH-20 (Merck, Germany) were used for column chromatography. TLC was conducted on precoated silica gel GF254 plates (Qingdao Marine Chemical Co. Ltd., Qingdao, China), and spots were detected by spraying with $10\%$ H2SO4 in EtOH followed by heating. ## 3.4. Extraction and Isolation After the fermentation of the strain, the cultures were extracted with EtOAc, then filtered with filter paper. After repeating the procedure three times, the EtOAc extract was evaporated under a reduced pressure to obtain a crude extract (124.0 g). The crude extract was dispersed in water and extracted with petroleum ether, ethyl acetate and n-butanol three times, respectively. After vacuum concentration, the petroleum ether extract (11.3 g), ethyl acetate extract (34.0 g) and n-butanol extract (20.0 g) were obtained, respectively. Then, the EtOAc extract (34.0 g) was subjected to silica gel vacuum liquid chromatography using step gradient elution with CHCl3/MeOH (1:0, 200:1, 150:1, 100:1, 80:1, 50:1, 20:1, 10:1, 0:1, v/v) to obtain 13 fractions (Fr.1–Fr.13). Fr.4 (425.0 mg) was applied to Sephadex LH-20 gel chromatography eluted with CHCl3/MeOH (1:1, v/v) to give six subfractions (Fr.4.1–Fr.4.6). Fr.4.3 (150.5 mg) was subjected to silica gel column chromatography (petroleum ether/EtOAc, 10:1, v/v) to afford nine subfractions (Fr.4.3.1–Fr.4.3.9). Fr.4.3.9 (40.5 mg) was separated by semi-preparative HPLC, eluting with $45\%$ MeOH/H2O to yield compound 2 (tR 11.5 min, 4.5 mg), and Fr.4.3.7 (29.8 mg) was separated by semi-preparative HPLC, eluting with $35\%$ MeOH/H2O to give compound 8 (tR 15.3 min, 4.2 mg). Fr.6 (1.15 g) was applied to ODS chromatography eluting with MeOH/H2O ($10\%$–$100\%$) to give thirteen subfractions (Fr.6.1–Fr.6.13). Fr.6.11 (91.5 mg) was subjected to Sephadex LH-20 (eluted with $100\%$ MeOH) and then purified by semi-preparative HPLC (eluted with $48\%$ MeOH/H2O) to give compound 1 (tR 21.9 min, 11.1 mg). Fr.6.12 (58.9 mg) was subjected to Sephadex LH-20 (eluted with $100\%$ MeOH) and then purified by semi-preparative HPLC (eluted with $65\%$ MeOH/H2O) to give compound 5 (tR 10.0 min, 4.2 mg). Fr.6.6 (72.3 mg) was purified on silica gel (petroleum ether/EtOAc, 3:2, v/v) to yield compound 3 (7.5 mg). Fr.9 (10.0 g) was subjected to Sephadex LH-20 gel chromatography eluted with MeOH to give ten subfractions (Fr.9.1–Fr.9.10). Fr.9.7 (2.1 g) was subjected to silica gel column chromatography (CH2Cl2/MeOH, 100:1, v/v), and subsequently purified by semi-preparative HPLC, eluting with 50 % MeOH/H2O to yield compounds 4 (tR 12.0 min, 5.1 mg) and 6 (tR 18.5 min, 2.7 mg). Fr.9.6 (1.67 g) was separated by Sephadex LH-20 column chromatography eluted with MeOH and then purified by silica gel column chromatography eluting with petroleum ether/EtOAc (3:1; v/v) to obtain compound 9 (5.1 mg). Fr.9.8 (0.8 g) was subjected to silica gel column chromatography (CH2Cl2/MeOH, 35:1, v/v), and subsequently purified by semi-preparative HPLC, eluting with 55 % MeOH/H2O to yield compound 7 (tR 6.8 min, 8.1 mg). Epicocconigrone C [1]: Yellow film. αD20 = +98.0 (c 0.10, MeOH); UV (MeOH) λmax (logε): 237 (4.31) nm; 261 (3.91) nm; 309 (4.27) nm; 359 (4.06) nm; IR (KBr) vmax (cm−1): 3413, 1669, 1466, 1395, 1355, 1296, 1117. 1H and 13C NMR data see Table 1 and Table 2; HRESIMS [M + Na]+ m/z 411.0698 (calcd. for C19H16O9Na, 411.0687). Epicocconigrone D [2]: Yellow film. αD20 = +57.0 (c 0.10, MeOH); UV (MeOH) λmax (logε): 234 (4.26) nm; 260 (4.04) nm; 309 (4.21) nm; 365 (4.19) nm; IR (KBr) vmax (cm−1): 3446, 2931, 1626, 1469, 1359, 1226, 1154, 1115. 1H and 13C NMR data see Table 1 and Table 2; HRESIMS [M + Na]+ m/z 427.1004 (calcd. for C20H20O9Na, 427.1000). Flavimycin C [3]: White film. αD20 = −70.0 (c 0.10, MeOH); UV (MeOH) λmax (logε): 232 (4.15) nm; 284 (3.56) nm; IR (KBr) vmax (cm−1): 3449, 2928, 1606, 1478, 1376, 1264, 1110, 1027. 1H and 13C NMR data see Table 1 and Table 2; HRESIMS [M + H]+ m/z 391.1385 (calcd. for C20H23O8, 391.1387). ## 3.5. ECD Calculation The conformers of compounds were generated using the Confab [32] program ebbed in the Openbabel 3.1.1 software, and further optimized with xtb at GFN2 level [33]. The conformers with population over $1\%$ were subjected to geometry optimization using the Gaussian 16 package [34] at B3LYP/6-31G(d) level and proceeded to calculation of excitation energies, oscillator strength, and rotatory strength at B3LYP/TZVP level in the polarizable continuum model (PCM, methanol). The ECD spectra were Boltzmann-weighted and generated using SpecDis 1.71 software [35]. ## 3.6. α-Glucosidase Inhibitory Activity All the assays were carried out under 0.1 M sodium phosphate buffer (PH = 6.8). The samples were dissolved with DMSO and diluted into a series of gradient concentrations (final concentrations of 6.25, 12.5, 25, 50, 100, 200, 400, and 800 μM). The 10 μL sample was mixed with 100 μL α-glucosidase solution (0.2 U/mL, Sigma) and shaken well, then added to a 96-well plate and placed at 37 °C for 15 min. Subsequently, 40 μL of 2.5 mM 4-nitrophenyl-α-D-glucopyranoside was added and further incubated at 37 °C for 15 min. Finally, the OD value of each well was detected at 405 nm wavelength of microplate reader. Acarbose was used as a positive control. The control was prepared by adding DMSO instead of the sample in the same way as the test. The blank was prepared by adding sodium phosphate buffer instead of 4-nitrophenyl-α-D-glucopyranoside using the same method. The percentage inhibition was calculated using the following equation: ## 4. Conclusions In summary, two new tetracyclic cores of integrastatins, named epicocconigrones C–D (1–2), one new dimeric isobenzofuran, named flavimycin C [3], and six known compounds (4–9) were isolated from fermentation cultures of the deep-sea sediment-derived fungus A. insulicola. The biological evaluation revealed compounds 1, 4–7, 9 exhibited significant α-glucosidase inhibitory with IC50 values ranging from 17.04 ± 0.28 to 292.47 ± 5.87 μM, among which compound 6 was the most potent α-glucosidase inhibitor, with an IC50 value 48-fold stronger than positive control acarbose. Comparing the structure of compounds 1, 4, 5 and 6 revealed the α-glucosidase inhibitory activity was greatly enhanced after the hydroxyl group replaced the methoxy group, which further confirmed that polyhydroxy phenolic compounds were efficient α-glucosidase inhibitors, and provided a reference value for the synthesis of novel α-glucosidase inhibitors. In conclusion, the study has enriched the structural diversity of phenolic compounds and provided a promising lead toward the development of novel α-glucosidase inhibitors. ## Figures, Scheme and Tables Figure 1: Structures of compounds 1–9 from Aspergillus insulicola: epicocconigrones C–D (1–2); flavimycin C (3); epicocconigrone A (4); 2-(10-formyl-11,13-dihydroxy-12-methoxy-14-methyl)-6,7-dihydroxy-5-methyl-4-benzofurancarboxaldehyde (5); epicoccolide B (6); eleganketal A (7); 1,3-dihydro-5-methoxy-7-methylisobenzofuran (8); and 2,3,4-trihydroxy-6-(hydroxymethyl)-5-methylbenzyl-alcohol (9). Figure 2: Key HMBC correlations of compounds 1–3 and key NOESY/ROESY correlations of compounds 1–2. Figure 3: Experimental and calculated ECD spectra of compounds 1–3. Scheme 1: Putative biosynthetic pathways toward the formation of compounds 1–3. TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 ## References 1. Kashtoh H., Baek K.H.. 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--- title: A Study on Pharmacokinetic Functionalities and Safety Margins of an Optimized Simvastatin Nanoformulation authors: - Aftab Ahmad - Unnikrishnan Meenakshi Dhanalekshmi - Kailasam Koumaravelu - Arul Prakash Francis - Shah Alam Khan - Mohammed F. Abuzinadah - Nandakumar Selvasudha journal: Pharmaceuticals year: 2023 pmcid: PMC10056947 doi: 10.3390/ph16030380 license: CC BY 4.0 --- # A Study on Pharmacokinetic Functionalities and Safety Margins of an Optimized Simvastatin Nanoformulation ## Abstract A pharmaceutical formulation with favorable pharmacokinetic parameters is more likely to be efficacious and safe to overcome the failures of the drug resulting from lack of efficacy, poor bioavailability, and toxicity. In this view, we aimed to evaluate the pharmacokinetic functionalities and safety margin of an optimized CS-SS nanoformulation (F40) by in vitro/in vivo methods. The everted sac technique was used to evaluate the improved absorption of a simvastatin formulation. In vitro protein binding in bovine serum and mice plasma was performed. The formulation’s liver and intestinal CYP3A4 activity and metabolic pathways were investigated by the qRT-PCR technique. The excretion of cholesterol and bile acids was measured to demonstrate the formulation’s cholesterol depletion effect. Safety margins were determined by histopathology as well as fiber typing studies. In vitro protein binding results revealed the existence of a high percentage of free drugs (22.31 ± $3.1\%$, 18.20 ± $1.9\%$, and 16.9 ± $2.2\%$, respectively) compared to the standard formulation. The controlled metabolism in the liver was demonstrated from CYP3A4 activity. The formulation showed enhanced PK parameters in rabbits such as a lower Cmax, clearance, and a higher Tmax, AUC, Vd, and t$\frac{1}{2.}$ qRT-PCR screening further proved the different metabolic pathways followed by simvastatin (SREBP-2) and chitosan (PPAR-γ pathway) in the formulation. The results from qRT-PCR and histopathology confirmed the toxicity level. Hence, this pharmacokinetic profile of the nanoformulation proved it has a unique synergistic hypolipidemic modality. ## 1. Introduction Comprehensive knowledge of in vitro and in vivo characteristics is vital in developing novel pharmaceuticals. This study describes various steps tangled for polymeric nanoparticles to reach systemic circulation after oral administration. Nanoparticles that permeate the gut wall can enter the systemic circulation and be distributed to target organs if they do not bind to plasma proteins. Still, plasma protein binding is vital for other pharmacokinetic parameters [1]. Hence, it would be beneficial to understand the in vitro parameters of binding and permeability, which could provide information on the in vivo biodistribution of polymeric nanoparticles. Numerous research studies have been explored in the past to establish a correlation between in vitro and in vivo methods. During the drug discovery and development process, data generation from Absorption, Distribution, Metabolism, and Excretion (ADME) has considerably advanced through automated technology platforms combined with high-throughput liquid chromatography–mass spectrometry (LC/MS/MS) bioanalysis. Assessment of passive permeability, P-gp substrate, metabolic stability, liver microsomes, and whole hepatocyte models are the commonly methods used for in vitro ADME studies. In vivo pharmacokinetic (PK) data, including drug clearance (Cl), bioavailability (F%), exposure (AUC), half-life (t$\frac{1}{2}$), and distribution volume (L), are acquired using animal models [2]. It Is prudent to gain further understanding of in vitro/in vivo correlation and PK/PD reports at an early stage. Some marketed drugs approved by the FDA such as atorvastatin and simvastatin, with an absolute bioavailability of $14\%$ and $5\%$, respectively [2,3], have become the highest selling drugs despite their poor PK profiles. Interestingly, more than $30\%$ of marketed drugs have a relatively low bioavailability (F < $10\%$), almost $50\%$ of the drugs have moderate-to-high clearance, while $17\%$ show a high clearance rate [4]. Hence, considering only PK data to screen compounds might exclude some potential drug candidates such as atorvastatin, which could not have become developed further based on its PK profile. PD reports could be facilitated by rodent and a non-rodent toxicology studies of at least a 14-day duration and ADME/PK safety pharmacology studies. Simvastatin, initially made available in 1988, is a well-known hydroxy-methylglutaryl co-enzyme A (HMG-CoA) reductase inhibitor. At a maximum dosage of 80 mg per day, it results in an average decrease of $47\%$ in LDL cholesterol (LDL-C), along with decreases in extremely LDL cholesterol, triglycerides, and apolipoprotein B, and a slight increase in HDL cholesterol [5]. Simvastatin has few undesirable properties in terms of solubility, Log D, in vitro microsomal instability, and permeability/efflux data, with high in vivo clearance and low bioavailability [6]. Hence, this research study aimed to focus on third-generation controlled drug delivery systems based on rectifying both formulation and biological barriers. In this study, nanotechnology and a pH-sensitive smart polymer were utilized to overcome the formulation barriers, which in turn were proposed to overcome the biological barriers for better pharmacological activities. Therefore, the optimized nanoformulation F40 (simvastatin-loaded chitosan nanoparticles, which were previously optimized) is hypothesized to have improved physicochemical properties than conventional drugs and current research mainly focused on evaluating this optimized nanoformulation F40 in terms of its pharmacokinetic profile (ADME/PK) and its safety margins. ## 2.1. In Vitro Absorption The experiment performed by an everted intestinal sac method showed that absorption of simvastatin from formulation F40 was markedly elevated as compared to standard simvastatin as shown in Table 1. Although absorption of standard simvastatin was initially high, it was decreased till the end of the experiment. However, formulation F40 showed decreased absorption in mucosae from 27 ± 0.90 to 16 ± 0.41 μg/mL after 75 min, indicating increased intestinal absorption. This is because simvastatin has a low in vivo availability not only due to its poor solubility and first-pass metabolism but also its inhibition of absorption by efflux transporters such as P-gp in the intestine, which was markedly decreased by the excipients added in formulation F40, thereby enhancing intestinal passage and permeability of simvastatin, increasing its bioavailability. ## 2.2. In Vitro Plasma Protein Binding (Distribution) The results of the BSA binding test showed that at a given concentration, the average protein binding of nanoformulation F40 was 77.02 ± $4.58\%$ and that of pure simvastatin was 95.00 ± $3.1\%$. In addition, pure simvastatin resulted in a higher percentage of protein binding compared to nanoformulation F40 coated with chitosan. The free drug present in the supernatant of nanoformulation F40 was found to be 22.31 ± $3.1\%$, which was higher than pure simvastatin. In the experiment involving human plasma, the free drug available for nanoformulation F40 was found to be 18.20 ± $1.9\%$ (for pure simvastatin 4.3 ± $1.0\%$), whereas, for mice plasma, it was found to be 16.9 ± $2.2\%$ for formulation F40 and for pure simvastatin 4.0 ± $0.7\%$. As shown in Table 2, there are variations in the results of plasma as compared to the BSA test and the percentage of binding was less. It was observed that the free drug available in mice plasma was slightly less than in human plasma which might be due to the presence of plasma esterase in mice plasma that hydrolyzes the simvastatin released from formulation F40, whereas the absence of this enzyme in human plasma renders more free drug availability. However, deviation in the results was less which might be due to the controlled release of simvastatin from formulation F40 as well as due to differences in species. Therefore, there might be differences in the percentage of free drugs and other PK parameters, which should be further confirmed by clinical studies. Nevertheless, our studies showed that the percentage of the free drug for formulation F40 was higher as compared to standard simvastatin, which indicates that it can produce a beneficial biodistribution effect. ## 2.3.1. CYP3A4 Activity of Standard Simvastatin and Formulation F40 The mRNA levels of CYP3A4 in the liver and intestines were measured using qRT-PCR to investigate whether there was any alteration in the pharmacokinetics of simvastatin in formulation F40. This was assumed by observing the modified metabolism of F40-treated mice in the liver as simvastatin is primarily eliminated in the liver via metabolism. Results showed that, as compared to the standard simvastatin-treated group, the formulation F40-treated group had less CYP3A4 expression in both the intestine and liver. Metabolism via the intestine was less as the expression of CYP3A4 mRNA was minor in the intestine as compared to the liver. The decreased expression of hepatic CYP3A4 mRNA (threefold) in the formulation F40-treated group as compared to the standard simvastatin group (fold expression—7.90) might be due to the size as it is a nanoformulation and formation of intermolecular bonding for the presence of novel excipients due to a controlled release of simvastatin. ## 2.3.2. Metabolic Pathway of Standard Simvastatin and the Formulation This study showed that fold expression for SREBP-2 was higher for standard simvastatin (5.02) than formulation F40 and transcription factor PPAR-γ fold expression (6.06) was greater for formulation F40 than for standard simvastatin as shown in Figure 1, indicating an increased lipid metabolism through this pathway due to the presence of chitosan. This also indicates that both simvastatin and chitosan follow different metabolic pathways, which could avoid possible toxicity. Simvastatin also expresses PPAR-γ due to the anti-inflammatory effect exerted by it and indirectly induces the activity of PPAR-γ. ## 2.4.1. Food Intake and Body Weight Animals in all groups were healthy and active for up to ten weeks of study. At the end of this study, some of the group II animals were inactive and group III animals were observed to have some behavioral changes, which were suggested due to certain muscular dystrophy produced by standard simvastatin. All groups gained weight during the experimental period except the formulation F40-treated group as depicted in Table 3. There was a minor difference in food consumption among experimental groups. Therefore, any differences among groups in the present study can be attributed to the fiber effect. ## 2.4.2. Fecal Dry Weight As shown in Table 3, during this study, the fecal dry weight was not significantly altered in groups I, II, and III. In contrast, the total fecal dry weight excreted by animals in the formulation F40-treated group was higher after treatment and increased from 0.32 ± 0.002 (8 weeks) to 0.49 ± 0.005 g/day (16 weeks). ## 2.4.3. Neutral Sterol and Bile Acids This study showed that only trace amounts of total cholesterol and bile acid were excreted in feces of control, high-fat diet, and standard simvastatin-treated animals during the whole experimental period, whereas certain amounts of fecal total cholesterol and bile acid were found in feces of formulation F40-treated animals. ## 2.4.4. HPTLC for Individual Bile Acid and Sterols HPTLC revealed that cholesterol excretion in feces increased from 1.71 ± 0.28 to 2.89 ± 0.19 mg/day/animal in the formulation F40-treated group compared to other groups. These results were compared using HPLC peaks. The bile acid chenodeoxycholic acid increased from 0.19 ± 0.001 to 4.02 ± 0.21 mg/day/animal of fecal matter in the formulation F40 group compared to the control group as shown in Figure 2, Figure 3 and Figure 4. The lithocholic acid (a secondary metabolite of chenodeoxycholic acid) and the ratio of secondary to primary metabolite decreased from 5.89 ± 0.84 to 3.78 ± 0.61 mg/day/animal and from 4.67 ± 0.42 to 2.42 ± 0.18, respectively, in the formulation F40 group. The major metabolic products of cholesterol, viz. corprostanol and cholestanol, were not significantly increased and peaks were not seen in the densitogram for groups IV as shown in Figure 4. This indicates that chitosan in formulation F40 reduced the conversion of cholesterol and primary bile acids. The concentrations of simvastatin/simvastatin metabolite (18.98 ± 0.20 ng/mL/23.12 ± 1.3 ng/mL) were found to be higher in standard simvastatin-treated mice feces, as shown in Figure 5, whereas, in the formulation F40-treated group, the concentrations were very low. Simvastatin was not detected and simvastatin metabolite was found in traces as depicted in Figure 6. The reduced concentration of simvastatin and its metabolite in feces is due to the encapsulation of simvastatin in the polymer shell of nanoformulation F40, which inhibits its exposure to gut metabolism, rendering a controlled-release pattern. In contrast, standard simvastatin is involved in gut wall metabolism and acid degradation, causing more excretion in feces, thereby leading to low bioavailability. ## 2.5. In Vivo Pharmacokinetic Study The pharmacokinetic study conducted in rabbits was used to quantify SS and its active metabolite SSA. The mean plasma concentration profiles of SS and SSA as a function of time obtained after a 10 mg oral dose of both standard and test are shown in Table 4. Further, all the pharmacokinetic parameters of nanoformulation F40 were determined by software (Kinetica 5.0, Thermo Fisher Scientific, Waltham, MA, USA). Due to extravascular administration, non-compartmental analysis has been opted. Lower plasma levels of simvastatin and simvastatin acid were observed with formulation F40 than with standard drug after oral administration. The AUC of formulation F40 (53.17 ng/mL, 73.11 ng/mL) and the Tmax F40 (10 h, 14.56 h) were significantly higher than for standard simvastatin (36.38 ng/mL, 51.11 ng/mL and 4.72 h, 5 h). The Cmax was lower for formulation F40 (4.33 ng/mL, 3.98 ng/mL) than for standard simvastatin (21.12 ng/mL, 19.42 ng/mL). The higher Tmax, AUC, and a lower Cmax indicate the sustained-release properties of formulation F40. Generally, the drug molecules are absorbed rapidly from GIT due to an improved dissolution rate by a reduced particle size, an increased surface area, and diffusional layer thickness (nanoparticle formation and intermolecular hydrogen bonding). The mucoadhesion and controlled delivery of the drug from formulation F40 were responsible for sustained release, leading to a low Cmax but a prolonged Tmax and AUC. Half-life and MRT were also higher for nanoformulation F40 than for standard drug (Figure 7). A higher Vd (378.90 ± 112.3 ng/mL, 404.00 ± 134.98 ng/mL) and KE and a lower Cl (135.78 ng/mL, 180.34 ng/mL) for nanoformulation F40 (Figure 8) than for standard simvastatin also confirmed the sustained-release property and lower plasma protein binding. In the case of standard simvastatin, a lower Cmax and Cl, and a higher Tmax, AUC, Vd, t$\frac{1}{2}$, KE, and MRT were observed, showing a steady-state concentration. p values were found to be significant for the Cmax, Tmax, t$\frac{1}{2}$, KE, Vd, AUC last AUC(0–∞), Cl, and MRT at $p \leq 0.05.$ Relative bioavailability or bioequivalence is the most common measure for comparing the bioavailability of one formulation of the same drug to another. The mean responses such as the Cmax and the AUC were considered to determine relative bioavailability. The AUC refers to the extent of bioavailability, while the Cmax refers to the rate of bioavailability. The relative bioavailability of formulation F40 was $154\%$, and $209\%$ compared to that of the pure drug simvastatin. There was only a little increase in the bioavailability of formulation F40. As simvastatin is a narrow therapeutic-indexed drug, an increase in drug concentration in the plasma might enhance toxicity. When the results of PD studies were compared with PK studies, a reduction in the TC level and an increase in relative bioavailability in the case of nanoformulation F40 were observed. Thus, the results obtained in the PK study are well supported by the PD studies, which showed the same hypolipidemic activity of nanoformulation F40 compared with standard drugs with reduced doses and negligible toxicity. ## 2.6.1. Observation In the control/HFD group of mice, the incidence of fiber necrosis was not observed (Figure 9. In the standard simvastatin-treated (group 3), all aspects of induced muscle necrosis were remarkably similar in all three mice. Most of the organs and muscles sampled from the hind limb except the soleus were affected by necrosis. Muscle necrosis was segmental, affected individual fibers, and characterized by loss of cytoplasmic structure, vacuolation, and little or no inflammatory infiltrate. In the nanoformulation F40-treated (group 4), muscle necrosis or negligible necrosis was not detected (Figure 10). ## 2.6.2. Fiber Typing and Necrosis in Standard Simvastatin and Nanoformulation F40 The muscle fiber type of mammals is determined by the particular myosin heavy chain (MHC) isoform expressed. The limb muscles of the adult mice express one slow and three fast MHC isoforms [7]. Most fibers normally express only one isoform and are referred to as pure, although fibers are present that contain more than one [8]. The pure and mixed fiber types constitute a continuum from the slowest twitch type I fibers to the fastest twitch type IIB: I ↔ IC ↔ IIC ↔ IIA ↔ IIAD ↔ IID ↔ IIDB ↔ IIB. Statins cause type II muscle fiber degeneration particularly type II B is more sensitive than type I. Results showed that several muscles were totally or relatively saved in both the control and nanoformulation F40-treated groups. In the case of the standard simvastatin-treated group, the soleus muscle was insensitive to statin-induced necrosis. The soleus muscle consisted predominantly of type I fibers and a smaller proportion of type II A and a few II C fibers with no type II D or II B fibers. For type I and type II, fibers clearly showed that in muscles containing mixtures of these fibers, when early necrosis was present, then type I fibers were spared. Even when a substantial proportion of the type II fibers were necrotic, the type I fibers retained their normal histological appearance (Figure 10). This was consistent for the muscles which contained type I oxidative fibers biceps femoris. In fact muscle fiber, in which glycolysis is the major process, size is closely related to their metabolic characteristics; the lower the oxidative activity the greater the diameter of the fiber, with the largest fibers being type IIB. In this study, some muscles showed acute changes which contain type IIB fibers. Myosin ATPase staining confirms severe necrosis as observed in biceps brachii, vastus medialis. ## 2.7. Hemolysis Assay for Biocompatibility This study showed that all the tested concentrations of formulation F40 neither showed hemolytic activity nor thrombus formation, making it a biocompatible systemic application. In addition to this, the group treated with SLS (positive control) showed $100\%$ hemolysis marked by complete lysis of the red blood cells (RBCs). The PBS (negative control), drug, and nanoformulation F40 groups did not show any hemolysis or toxicity to the RBCs, revealing its possible biocompatibility. Hemolytic activity was further confirmed on a blood agar plate. ## 3. Discussion Simvastatin is a medication with a significant problem of substantial first-pass metabolism, which results in a very low bioavailability of only $5\%$. The use of innovative medicine delivery techniques can boost this [9]. Presently, statin medications are typically taken orally by patients. In fact, this is the only method of taking statins that has received FDA approval. In addition to difficulties with poor absorption, which has driven novel statin formulations and different dose forms of statin administration, statins also carry a small but real risk of negative side effects [1,9]. In general, simvastatin is a P-gp inhibitor and due to this property, in the present study, the absorption of standard simvastatin was initially high, and it was decreased till the end of the experiment due to the efflux mechanism [10]. However, an increased absorption of the drug from nanoformulation F40 was due to its nano size, the presence of tween 80 as a surfactant in the formulation, and the encapsulating agent chitosan, which synergistically inhibits the P-gp efflux mechanism [11,12]. The integrity of tight junctions (TJs) can be altered by its opening through chitosan when delivered in the form of nanoparticles, rendering enhanced paracellular permeability in vivo. The gastrointestinal transit time is also altered by nanoformulation F40 due to its mucoadhesive property influencing its absorption and carrier-mediated uptake due to a decrease in the gut degradation of simvastatin. CYP3A44 is the most abundant cytochrome P450 enzyme within the intestinal enterocytes, responsible for metabolic elimination of simvastatin, causing retarded bioavailability. Nanoformulation F40 with encapsulation of the drug by chitosan demonstrated decreased gut metabolic elimination of simvastatin, thereby increasing oral bioavailability. As mentioned earlier, nanoformulation F40, due to the presence of chitosan, can adhere to epithelial surfaces, which in turn causes transient opening of tight junction (TJs) between adjacent cells. Transmembrane proteins and claudins regulate the specificity of tight junction permeability. Literature suggested that transmembrane protein CLDN4 plays a key role in chitosan-mediated reversible epithelial TJ opening [13]. The increased use of Polysorbate 80 for lipophilic drug candidates such as simvastatin which are P-gp substrates is needed [14,15]. Nanoformulation F40 demonstrated lower protein binding as compared to standard simvastatin due to the encapsulation by chitosan. This indicated more availability of the free drug to elicit the pharmacological action. As simvastatin is a narrow therapeutic index drug either higher protein binding or greater free drug concentration would be detrimental as it precipitates toxicity. The present study adopted one of the most effective strategies of encapsulation of simvastatin for better bioavailability and prolongation of the circulation time without any toxicity. Nanoformulation F40 demonstrated successful encapsulation and hydrophilicity due to the use of chitosan, PVA, and Tween 80 in the formulation, which in turn imparted reduced protein binding [16]. In this study, the assay was conducted over a 2 h period, resulting in less drug concentration being released. This was compared to the bulk of the drug that was available at this time with the unencapsulated drug. Therefore, the mechanism of protein binding for nanoformulation F40 may be considered as a function of the affinity of the polymeric nanoparticles for plasma proteins as well as the concentration of the drug available for binding which, at a specific time, is decreased by encapsulation [17]. The binding of albumins to the hydrophilic chitosan surface exerts the long residency of nanoformulation F40, thereby offering longer half-lives as evident from the PK studies. The chitosan coating in nanoformulation F40 also minimized opsonization, which will eventually prolong the systemic circulation of the nanoparticles. More specifically, in our study, the protein interaction was reduced at the highly curved surfaces of the nanoparticle. The spherical NPs have a higher association in the cell as compared to rod-shaped NPs, which is among the reasons in this study for almost $77\%$ protein binding of nanoformulation F40 [18,19]. This study, however, focused on the effect of surface-coated simvastatin on in vitro protein binding to validate biodistribution nanoformulation F40 after in vivo evaluation. It is generally accepted that simvastatin and simvastatin acid in the liver are metabolized via CYP3A4. The results clearly demonstrated marked detraction of the activity in liver of the formulation F40-treated group compared to the standard simvastatin group, which was consistent with the decrease in simvastatin metabolism. This result suggested that the decreased activity and expression of hepatic CYP3A4 in the formulation F40 group were the main contributors to the reduced systemic clearance of simvastatin and simvastatin acid which increases the half-life (pharmacokinetic parameters). Nanoformulation F40 demonstrated the downregulation of intestinal and liver CYP3A4 mRNA levels, which were responsible for systematic metabolism and first-pass metabolism of simvastatin, respectively. As the simvastatin and simvastatin acid were encapsulated by chitosan, this left no scope for metabolism by intestinal CYP3A4, leading to suppression of CYP3A4. All the results support the conclusion that the downregulation of both CYP3A4 activity and expression decreases the hepatic metabolism of simvastatin in formulation F40, thus leading to long exposures of simvastatin and simvastatin acid. The metabolic pathway of nanoformulation F40 was observed to be increased more with the activity of PPAR-γ than the standard simvastatin group, which is contradictory to the previously observed downregulation of PPAR-γ expression in hypercholesterolemic animals [20]. There was also a significant increase in the expression of PPAR-γ mRNA by nanoformulation F40 compared to the control and standard groups as evident from the qRT-PCR profiling. This study confirmed that simvastatin in formulation F40 can also improve the downregulated PPAR-γ mRNA expressions in the liver indirectly through the induction of SREBP-2. The study findings revealed a combination effect of chitosan and simvastatin, mechanizing through different pathways to synergize the cholesterol depletion effect. SREBP-2 mRNA expression was increased in the liver of standard simvastatin mice compared to the control and formulation F40 groups and this was accompanied by a significant increase in the mRNA expression of LDLr and HMG-CoAR, two SREBP-2 target genes. The cholesterol depletion effect observed due to nanoformulation F40 induces proteolytic activation of the SREBP family [21], cascade of which causes induction of PPAR-γ levels. This increase in the PPAR-γ transcriptional activity is expected to cause the expression of several genes responsible for triglyceride clearance [22,23]. Hence, this mode of interaction between transcription factors controlling different lipid pathways may provide somewhat unexpected/LDL-lowering effects. At this point, some well-designed studies need to be initiated to prove this. The present study demonstrated a significant increase in the excretion of cholesterol (4-fold) and bile acids in feces. For the nanoformulation-treated group, the cholesterol concentration was found to be 0.49 ± 0.005 mg/day/animal, which was higher when compared to the standard-treated group (0.13 ± 0.008 mg/day/animal). Nanoformulation F40 feeding has been reported to increase the fecal excretion of cholesterol, its non-polar derivatives and bile acids. The present study demonstrated two different mechanistic pathways for serum cholesterol reduction, the SREPB-2 pathway being followed by simvastatin and the PPAR-γ pathway being followed by chitosan. The higher efficacy of nanoformulation F40 in terms of fecal elimination of cholesterol and bile acids was due to the synergistic effect produced by simvastatin and chitosan. Chitosan is primarily responsible for the above effect as it upregulates fecal excretion upon hypercholesterolemia. Previous studies in animal models [24] as well as in humans [13] also supported the present findings. In this study, using a low-molecular-weight, high-DD chitosan, nanoparticle size added value in high binding and excretion of lipids in feces. This study also indicated that chitosan lowered the plasma total cholesterol level by enhancement of the hepatic LDL receptor mRNA and chitosan in formulation F40 might have the potential to increase the excretion of fecal bile acids. The use of chitosan, PVA, and surfactant Tween 80 in the nanoformulation is responsible for the increased dissolution of simvastatin and influx against P-gp activity. The previous in vitro studies showed that the lactone ring in formulation F40 showed maximum stability in gastric pH, and no degradation of the lactone in 24 h was observed [25,26]. However, in the present research, significantly higher MRT values of metabolite SSA from nanoformulation F40 in comparison to the standard drug are responsible for the prolonged residence of metabolite in rabbits. Thus, it might be expected that the enhanced residence would have a positive effect on the efficacy of the active metabolite. It appeared that nanoformulation F40 delivered SS in a more sustained fashion, providing smoother plasma concentration profiles and lower maximum plasma concentrations compared with those of standard simvastatin. Since increased peak concentrations of SS are related to the incidence of adverse events, the obtained smooth plasma concentration coupled with a lower Cmax and a higher AUC values could potentially reduce the incidence of such toxic events and could sustain the efficacy of SS at the same time. This nano-controlled drug delivery system resulted in a lower plasma concentration, but it provides a constant pharmacological availability of the drug which might reduce toxic side effects [27]. Mucoadhesive functionalities of chitosan and PVA in the formulation amplify the potentiality in terms of drug transport across the intestinal barrier to produce its hypolipidemic action. The studied nanoformulation F40 proved to be an optimized and balanced delivery modality for simvastatin in terms of a persistent, controlled PK profile and reduced adverse effects as well as safety. Compared to the conventional drug, nanoformulation F40 indicated reduced muscle toxicity as evidenced by histology. Chitosan encapsulation prevents the opsonization process and improves the biocompatibility of nanoformulation F40, which was further evidenced by the results of biocompatibility studies. ## 4.1. Materials Nanoformulation F40 was designed, formulated, and optimized in our laboratory and the results are published [6]. Briefly, the nanoparticles of simvastatin were prepared by a solvent evaporation method using the different mass ratio of the drug to chitosan solution ($2\%$ acetic acid). The $0.5\%$ polyvinyl alcohol and $0.2\%$ tween 80 were used as stabilizers and surfactants, respectively. By subjecting to various steps of methodology viz., magnetic stirring, homogenization, centrifugation, and freeze-drying, nanoparticles were prepared. Nanoparticles exhibited a narrow size distribution, a higher positive zeta potential, and greater encapsulation efficiency with amorphous conversion. The modified physicochemical properties of simvastatin in the nanoformulation were attributed to the decrease in LDL, TG, and total cholesterol and increase in HDL with a several-fold reduced dose of simvastatin when compared to pure drug. The present study deals with the PK functionalities to prove the efficiency and efficacy of nanoformulation F40 CS-SS. The drug simvastatin used to formulate F40 was a gift from Biocon Pvt. Ltd., India. All other chemicals, polymers, and solvents utilized in the present work were of analytical grade and have been purchased from Sigma Aldrich, Germany. ## 4.2.1. The Everted Sac Technique: In Vitro Absorption Study Female albino mice (24–30 g) were used for this study and were obtained from King Institute, Chennai. The animals were kept in a controlled environment of 25 °C with a 12 h light/12 h dark cycle and all protocols and procedures used were approved by the IAEC PRIST University, Thanjavur (Project 1 Pon/Phar$\frac{1}{2015}$). The mice were fasted overnight before experimentation and had access to water ad libitum. The experiment was performed as per the reported method [28,29]. Briefly, 5 cm of the jejunum of the intestine was maintained with an ice-cold physiological solution and everted. Then, it was filled with 600 µL Krebs solution and sealed. It was then transferred into the incubation flask containing test samples in 25 mL oxygenated media at 37 °C. The sampling was performed at different time intervals and evaluated. ## 4.2.2. In Vitro Plasma Protein Binding: Distribution Studies The in vitro bovine serum albumin binding test was performed by the most popular method of equilibrium dialysis with the use of an activated 10–12 kDa molecular cut-off dialysis membrane [29]. It was filled with standard BSA solution and required a concentration of samples (pure simvastatin and nanoformulation F40) and the volume was maintained up to 4 mL. The membrane bags were immersed in conical flasks containing phosphate buffer solution and were shaken gently at 37 ± 0.5 °C for approximately 6 h in a shaking incubator. After shaking, the absorbance of free drug in the buffer (outside the membrane bags) was measured at 238 nm using the UV–VIS spectrophotometer, and the concentrations of the bound and unbound drugs were determined using a standard curve. The pure drug was taken as control. % Bound=1−Concentration of free drug in test sampleConcentration of free drug in control×100 In vitro protein binding of nanoformulation F40 was calculated using the above equation [30]. Mice plasma and human plasma were used for the assay with the approval of the ethical committee. ## Separation of m-RNA For separation of m-RNA, the mice were anesthetized for collecting the blood and other organs were dissected and immediately frozen in liquid nitrogen. ## RNA Isolation by Trizol Method For direct lysis of the cells, 1 mL of TRI reagent was added per 1000 mg of the tissue sample. The processed samples were centrifuged at 12,000× g for 15 min at 2–8 °C, which separated the mixture into three phases: a red organic phase (containing protein), an interphase (containing DNA), and a colorless upper aqueous phase (containing RNA). The upper aqueous phase was processed further and allowed to stand for 5–10 min at room temperature and centrifuged at 12,000× g for 10 min at 4 °C. The supernatant was carefully aspirated off the tube, leaving behind the precipitated RNA pellet on the sides of the tube, which was washed by adding a minimum of 1 mL of $75\%$ ethanol per 1 mL of TRI reagent. The sample was then vortexed and then centrifuged at 7500× g for 5 min at 2–8 °C. The RNA pellet was air-dried and suspended in 20–30 μL of RNase-free water [31]. ## qRT-PCR (Quantitative Reverse Transcriptase Polymerase Chain Reaction) for CYP3A4 Microenzyme Analysis qRT-PCR was used to measure mRNA levels of CYP3A4 in the liver and intestine by quantitative RT-PCR analysis using a cDNA input converted from 2 μg of total RNA. Primer sequences of mice mRNA for CYP3A4 are Forward 5′-CAGGAGGAAATTGATGCAGTTTT-3′; Reverse 5′ GTCAAGATACTCCATCTGTAGCACAGT-3′. After denaturing at 95 °C for 2 min, the amplification was obtained by 40 cycles of 95 °C for 5 s and 60 °C for 30 s. Melting curves were obtained to investigate the specificity of the PCR reaction. For normalization of the gene levels, β-actin (primer sequence—Forward 5′-CAGTCGGTTGGAGCGAGCAT-3′ Reverse 5′-GGACTTCCTGTAACAACGCATCT-3′) was used to correct minor variations in the input RNA amount or inefficiencies of the reverse transcription. The relative quantification (RQ) of each gene expression was calculated according to the comparative Ct method using the formula: RQ = 2−ΔCt [31]. ## Determination of Metabolic Pathway of Simvastatin and Chitosan in the Standard Simvastatin and Formulation F40-Treated Groups—qRT-PCR After extraction of RNA using TRI reagent as explained above, the mRNA expression for PPAR-γ, and SREPB2 was carried out in the liver using an RT-PCR kit from PROMEGA by using a standard protocol. The PCR reaction was performed in the thermal cycle with RT reaction at 40 °C for 30 min, initial PCR activation at 94 °C for 2 min, followed by 35 cycles of 94 °C (denaturation) for the 30 s, 58 °C (annealing) for 30 s, and 72 °C (extension) for 1 min. Finally, the reaction mixture was incubated at 72 °C for 10 min to extend any incomplete single strands. Optimal oligonucleotide primer pairs for RT-PCR were selected with the aid of the software Gene Runner. The primer sequence (5′ to 3′) for mice gene coding (+) strand was: PPARγ (Sequence ID: NM_001145366.1); Forward: 5′-GCCCTTTGGTGACTTTATGG-3′; Reverse: 5′CAGCAGGTTGTCTTGGATGT-3′; β-actin (Sequence ID: NM_031144); Forward: 5′-CACCCGCGAGTACAACCTTC-3′; Reverse: 5′-CCCATACCCACCATCACACC-3′; SREBP2; Forward primer 5′ to 3′ CCAAAGAAGGAGAGAGGCGG; Reverse primer 5′ to 3′ CGCCAGACTTGTGCATCTTG. Melt curve analyses were obtained with each series to confirm the specificity of the primers and products were amplified by heating the amplified products from 658 °C to 958 °C at 0.58 °C steps for 5 s. Relative quantification of mRNA expression was analyzed by the 2−ΔCt method [31]. ## 4.2.4. Fecal Matter Evaluation—In Vitro Excretion For the fecal matter evaluation, mice were individually housed in polypropylene cages at a controlled room temperature at 25 °C, under a 12 h light/12 h dark cycle and with free access to food and water. The mice were randomly divided into four experimental groups ($$n = 6$$). Group 1 was fed a standard laboratory diet (CD). Group 2 was fed a cholesterol-rich diet (HFD). Groups 3 and 4 received HFD with standard drug and formulation F40 (10 mg/kg). The duration of the treatment was 16 weeks. Each treatment group received a standard cholesterol diet daily orally in the morning throughout 16 weeks to induce hyperlipidemia except the control treatment group. A high-cholesterol diet was prepared by mixing cholesterol $2\%$, sodium cholate $1\%$, and coconut oil $2\%$ with animal food. Throughout this study, various parameters were monitored and measured which includes bodyweight, food intake, fecal dry weight, total cholesterol concentration in feces (mg/day/animal), total bile acids in feces (mg/day/animal), simvastatin concentration in ng/mL, and simvastatin metabolite in ng/mL. ## Determination of Fecal Cholesterol and Bile Acid Contents Fecal total sterol and total fecal bile acids were extracted from dry feces and quantified enzymatically according to the procedures provided in the assay kit purchased from Sigma Aldrich. Neutral sterols (NSs) in feces were determined as previously described [32], with some modifications. Briefly, after extraction of sterol, it was incubated with 5.0 mL alcoholic KOH solution and 10 mL of petroleum ether. Then, 5.0 mL of distilled water was added. An aliquot (0.5–2.0 mL) of the supernatant was taken into test tubes and petroleum ether was evaporated under nitrogen. To each of the samples, 3.0 mL of glacial acetic acid, and 0.1 mL distilled water were added and then 2.0 mL of the FeC13-H2SO4 reagent was added. The intensity of immense purple color formed was measured in a Shimadzu-UV spectrophotometer at 560 nm. The bile acids and total cholesterol were determined by an enzymatic method [15]. Working Reagent was prepared by mixing an appropriate quantity of Assay Buffer, NAD, Probe, Enzyme A, and Enzyme B. The specified amount of working Reagent was added to Internal Standard (sodium cholate) and sample wells, and Blank Reagent was added to the sample blank wells. The plate was taped to mix and incubated for 20 min in the dark. The fluorescence intensity at 585 nm was read. The bile acid concentration of a sample was calculated using a formula. The extract was mixed with mobile phase (chloroform-isopropyl alcohol-ammonium hydroxide in the ratio 20:25:1 for bile acid separation; benzene-diethyl ether in the ratio 85:5 for sterol separation) and HPTLC analysis was carried out ## 4.2.5. In Vivo Pharmacokinetic Study Albino rabbits of either sex weighing 1.5 to 3.0 kg were used for the estimation of pharmacokinetic parameters of nanoformulation F40 and standard simvastatin. The animal study protocol was approved by IAEC in presence of the CPCSEA nominee with Approval no. Project 1 Pon/Phar/2015 at PRIST University, Thanjavur. Pharmacokinetic studies were carried out based on a single dose complete cross-over method in twelve healthy albino rabbits. The rabbits were weighed and randomly divided into two groups, standard and test with six animals in each group. The data were acquired and calculated on Shimadzu controlled using a software analyst. The pharmacokinetic parameters for pure simvastatin and nanoparticles following oral administration were determined from plasma concentration data. The area under the concentration–time curve AUC(0–t) was estimated according to the trapezoidal rule. The area under the curve extrapolated to infinity AUC(0–∞) was calculated by formula. AUC0−∞=AUC0−t+ClastKe where Clast and Ke are the last measurable concentration and the elimination rate constant, respectively [16]. The elimination rate constant, half-life, mean residence time, and relative bioavailability were calculated using the below formula. Ke=−2.303×Slope t$\frac{1}{2}$=0.693Ke MRT=1.44×t$\frac{1}{2}$ Fr %=AUC0−∞ Formulation F40 AUC0−∞Reference×100 ## 4.2.6. Histopathological Analysis (Toxicity) The animals were sacrificed as per CPCSEA protocol. The heart, kidney, liver, stomach, brain, and a range of muscle tissues were sampled for necropsy and histology. The muscle tissues sampled were biceps femoris, soleus, tibialis cranialis, vastus medialis from the left hind limb; biceps brachii from the left forelimb. Tissues were fixed in buffered $10\%$ formalin, processed to wax blocks, and then sectioned and stained with hematoxylin and eosin for examination by light microscopy. During the histopathological examination under microscopy, the presence or absence of necrosis was observed for all four groups. ## 4.2.7. Biocompatibility Analysis: Hemolytic Activity on Human Blood Agar Plate In 95 mL of sterile nutrient agar placed in Petri dishes, 5.0 mL of human blood was added aseptically and allowed for solidification. Then, wells were cut into the agar plate using a corkscrew borer (8 mm diameter) and loaded with 50 μL (1 mg/mL) of samples. The plates were observed for hemolysis after overnight incubation at room temperature [33]. ## 4.3. Statistical Analysis All the data were presented as the mean ± SD and analyzed by the statistical software package GraphPad Prism 5 version (GraphPad Software, San Diego, CA, USA). The statistical analysis included a one-way analysis of variance (ANOVA) followed by the Tukey post hoc test. The difference between the two parameters was considered statistically significant for $p \leq 0.05.$ ## 5. Conclusions The present study represents an important contribution since it demonstrated a promising pharmacokinetic profile and ideal safety of nanoformulation F40 as a unique synergistic hypolipidemic modality. Pure simvastatin has less absorption due to dissolution-limited absorption, the P-gp efflux mechanism, gut/liver metabolism, being highly protein bound, and less elimination at half-life. Although its AUC is not particularly high, it produces toxicity in tissues through liver and muscle. In the present study, the use of a hydrophilic polymer in the nanoformulation improves the wettability of the drug and dissolution. Utilization of surfactant Tween 80 modulates the P-gp efflux mechanism, allowing penetration of more drug and a higher distribution volume. This increase in absorption and protective effect of chitosan decrease gut/liver metabolism. Therefore, a higher percentage of the drug is distributed in body fluids entering plasma. When a positive surface charge reduces protein binding, an increase in the volume of distribution (Vd, 378.90 ± 112.31 and 404 ± 134.98 for simvastatin and its acid in nanoformulation, respectively) was noticed. In addition, in plasma, controlled metabolism of simvastatin to simvastatin acid occurs. This all causes a higher Tmax of 10.00 ± 2.78 (simvastatin) and 14.56 ± 2.19 (simvastatin acid), and a lower Cmax. Drug elimination by biliary and feces was also altered due to increased drug transporter P-gp activity present in the intestine, liver, and kidney exerted by Tween-80. This causes decreases in the V area (volume at the terminal phase of elimination), clearance, and an increase in half-life (12.29 ± 4.57 for simvastatin and 16.87 ± 3.91 for simvastatin acid) on the whole representing a higher AUC and relative bioavailability. The reduced dose subsequently decreased muscle-related toxicity, which was confirmed by histopathological analysis. Finally, it can be concluded that this hypolipidemic action of the F40 CS-SS nanoformulation may be due to inhibition of the absorption of dietary cholesterol by the biliary secretion of cholesterol and cholesterol excretion in the feces caused by chitosan and its reduced production by the liver induced by simvastatin. 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--- title: 'Nailfold Videocapillaroscopy for Non-Invasive Assessment of Microcirculation and Prognostic Correlation with Endothelial Dysfunction, Cardiovascular Risk Factors, and Non-HLA Antibodies in Heart Transplant Recipients: A Pilot Study' authors: - Dorota Sikorska - Dorota Kamińska - Rusan Catar - Dashan Wu - Hongfan Zhao - Pinchao Wang - Julian Kamhieh-Milz - Mirosław Banasik - Mariusz Kusztal - Magdalena Cielecka - Michał Zakliczyński - Rafał Rutkowski - Katarzyna Korybalska - Harald Heidecke - Guido Moll - Włodzimierz Samborski journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10056970 doi: 10.3390/jcm12062302 license: CC BY 4.0 --- # Nailfold Videocapillaroscopy for Non-Invasive Assessment of Microcirculation and Prognostic Correlation with Endothelial Dysfunction, Cardiovascular Risk Factors, and Non-HLA Antibodies in Heart Transplant Recipients: A Pilot Study ## Abstract Early identification of allograft vasculopathy and the concomitant elimination of adverse risk factors is essential for improving the long-term prognosis of heart transplant (HTx) recipients with underlying cardiovascular disease (CVD). The major aim of this pilot study was to conduct a non-invasive imaging evaluation of the HTx patient microcirculation by employing nailfold video-capillaroscopy (NVC) in a well-characterized patient and control cohort, and to correlate these data with endothelial cell function, accompanied by studies of traditional cardiovascular risk factors and non-HLA antibodies in HTx recipients. Ten patients undergoing HTx (mean age of 38 ± 14 years) were recruited for the study and compared to a control group of 12 well-matched healthy volunteers (mean age 35 ± 5 years) with normal body mass index (BMI). Detailed medical records were collected from all individuals. NVC was performed using CapillaryScope 200 MEDL4N microscope. For functional readout and correlation analysis, endothelial cell network formation in conjunction with measurements of patient serum levels of vascular endothelial growth factor (VEGF) and non-HLA autoantibodies directed against the angiotensin II type-1-receptor (anti-AT1R-Ab), endothelin-1 type-A-receptor (anti-ETAR-Ab), protease-activated receptor-1 (anti-PAR-1-Ab), and VEGF-A (anti-VEGF-A-Ab) were studied. Our NVC analysis found that the average apical loop diameter of nailfold capillaries was significantly increased in HTx recipients ($$p \leq 0.001$$). In addition, HTx patients with more prominent changes in capillaroscopic patterns were characterized by the presence of traditional cardiovascular risk factors, and HTx patients had increased levels of anti-AT1R-ab, anti-ETAR-ab, and anti-VEGF-A-Ab ($$p \leq 0.017$$, $$p \leq 0.025$$, and $$p \leq 0.003$$, respectively). Capillary diameters most strongly correlated with elevated serum levels of troponin T and triglycerides ($R = 0.69$, $$p \leq 0.028$$ and $R = 0.81$, $$p \leq 0.004$$, respectively). In conclusion, we found that an abnormal NVC pattern in HTx patients is associated with traditional CVD risk factors and that NVC is a useful non-invasive tool to conveniently monitor changes in the microvasculature of HTx patients. ## 1. Introduction Heart transplantation (HTx) is the gold standard for the treatment of terminal heart failure that is refractory to conventional medical treatment [1]. Although the procedure is effective, chronic rejection and cardiac allograft vasculopathy (CAV) still continue to limit the long-term success of HTx (1-year and 10-year survival of $85\%$ and $60\%$, respectively) [1,2,3,4]. Thus, novel diagnostic and prognostic tools for the long-term monitoring of HTx patients at risk of developing allograft vasculopathy are needed. One such method that may be of both diagnostic and prognostic value and may also be suitable for convenient non-invasive long-term imaging of transplant patients is the monitoring of microvascular morphology with nailfold videocapillaroscopy (NVC) [5,6,7]. The endothelium plays a key role in the onset and progression of CAV [1]. Endothelial dysfunction is a typical feature of many cardiovascular diseases (CVDs) [8], and it is also an early feature of CAV, and it was found to progress with increasing time post-HTx [9,10]. Typically, coronary angiography is used to monitor the progress of CAV [11]. However, the diagnostic accuracy of angiography in the context of CAV after HTx has not been clearly established yet [12]. For the long-term prognosis to improve, the identification of CAV at an earlier clinical stage would be desirable [13]. To avoid the progression to detrimental (non-reversible) pathology, it is of crucial relevance to monitor and identify early on, in particular, patients with both traditional and non-traditional risk factors for the development of endothelial dysfunction and vasculopathy and to best treat them early in a preventive fashion for ameliorating any negative outcomes and to maintain graft function. Diverse markers of endothelial (dys)function must be taken into account in HTx [14]. In particular, factors that have already proven their usefulness in related clinical settings of endothelial dysfunction are of interest, such as the modulation of vascular endothelial growth factor (VEGF) levels in patients with renal failure undergoing hemodialysis (HD) or peritoneal dialysis (PD) [8,15]. Importantly, particularly the monitoring of the microcirculation through NVC may be an interesting and feasible option for convenient long-term monitoring of patients [5]. This non-invasive microvascular imaging approach is already established in other clinical settings, e.g., in the monitoring of rheumatoid arthritis (RA), systemic sclerosis (SSc), Raynaud’s syndrome, and transplantation [6,7,16,17,18,19]. Indeed, the results of previous studies indicate that patients with coronary heart disease exhibit functional and structural disturbances in the cutaneous microcirculation, and evaluation of the cutaneous capillary NVC may represent a simple method for investigating the presence of subclinical atherosclerosis [20,21]. Both immunologic and nonimmunologic (CVD) risk factors contribute to the development of CAV [22,23,24]. Intriguingly, there appears to be a complex interplay between both factors, ultimately leading to endothelial injury and an exaggerated repair response [2,9,10]. Traditional risk factors for the development of both CVD and CAV include dyslipidemia, diabetes, and hypertension [25]. Non-traditional risk factors include mode of donor brain death, cytomegalovirus infection, HLA mismatch, and HLA-antibody-mediated rejection. In addition, non-HLA antibodies are now recognized as a potential source of antibody-mediated rejection following transplantation [26,27]. The intricate role of non-HLA antibodies in allograft rejection and vasculopathy has been described in our earlier studies [27,28,29,30,31,32,33]. The epitopes that lead to the production of these antibodies typically result from tissue disruption, specifically of the endothelium, secondary to prior inflammation and injury [29,34]. Interestingly, most non-HLA antibodies are also considered to be autoantibodies [27,35,36,37,38], as they are often directed against cryptic autoantigens of vascular endothelium, which are expressed following both transplant and vascular injury. The primary aim of this pilot study was to establish feasibility for non-invasively evaluating microcirculation by employing NVC in well-characterized HTx patients and a well-matched control cohort. In addition, we aimed to evaluate the correlation of our obtained NVC data with the experimental evaluation of endothelial (dys)function [8,15,39], traditional CVD risk factors, and the presence of non-HLA antibodies in HTx recipients. ## 2.1. Patients Ten patients with normal body mass index (BMI) that had HTx (5 males and 5 females, mean age was 38 ± 14 years, BMI 24.2 ± 5.1 kg/m2) were included in the study. The primary cause of heart failure and the reason for HTx was either myocardial inflammation ($$n = 8$$) or idiopathic dilated cardiomyopathy ($$n = 2$$). All patients underwent the standard qualification for the HTx procedure, and there were no major complications after the operations. The patients received standard immunosuppressive therapy with basiliximab as the induction, then steroids, mycophenolate mofetil, and tacrolimus. The patients in this pilot study were examined at 1 to 24 years (mean 9 ± 8 years) after transplantation. They were all in a stable clinical condition with no evidence of acute transplant rejection. The patients were routinely examined for allograft condition, including via echocardiography, coronary angiography, and protocol biopsies. Detailed demographic and medical data as well as the results of all routinely performed additional tests were collected from all patients (Table 1). A more detailed description of the individual HTx patients is included in the results chapter. As a control group, 12 healthy volunteers of a similar age (5 males and 7 females, mean 35 ± 5 years, BMI 23.4 ± 4.1 kg/m2) were recruited from the general population. The participants in the control group did not have any chronic diseases (was not taking any medications) or traditional CVD risk factors (age < 50, no family history of cardiovascular disease, normal weight, healthy lifestyle, non-smokers, normal blood pressure, normal serum lipid, and glucose levels), while all 10 HTx patients had either underlying chronic diseases or CVD risk factors ($p \leq 0.001$, Table 1 and Table S1). ## 2.2. Experimental Section Nailfold videocapillaroscopy (NVC) was performed using the CapillaryScope 200 MEDL4N microscope (Dino-Lite; Europe), as described previously [6,7]. The NVC evaluation of all patients was carried out by the same experienced operator (DS) to reduce operator bias. The examination took place at room temperature after 20 min of rest and involved both hands and all fingers (excluding thumbs). A global examination of the entire nailfold area was performed under low (50×) magnification. Then, 3 pictures were taken at high magnification (200×) from each finger (12 pictures for one hand) to assess [1] morphology [2] density, and [3] diameter of nailfold capillaries. Disorganized or branching capillaries, avascular areas, and microhemorrhages were all considered incorrect morphology, while tortuous and crisscrossing capillaries could also be observed in the normal vasculature. The capillary density was estimated by quantifying the number of capillaries per linear millimeter. The diameters of all capillaries were assessed, and then, the average of all measurements was calculated as described previously [6]. At the time of NVC, an additional blood sample was collected from each patient. The following parameters were chosen for the assessment of endothelial function: [1] patient serum VEGF concentrations, and [2] standardized assessment with the EC tube formation assay to study EC branching and network formation in vitro [8,15,39,40,41,42]. Serum concentrations of VEGF were measured using the DuoSet® Immunoassay Kit (R&D Systems; Bio-Techne, Warsaw, Poland) with an estimated sensitivity of 13 pg/mL. Human microvascular endothelial cells (HMECs; catalogue no. CRL-3243, used at passages 2–6) were purchased from ATCC (Manassas, VA, USA), and EC tube formation assays were performed as described previously [8,15,39,40,41,42]. Briefly, Matrigel (Corning, Tewksbury, MA, USA) was poured into a 96-well culture plate (50 μL/well) and solidified at 37 °C for 30 min. HMECs were seeded onto the Matrigel at a density of 2 × 104 cells/well and cultured in MCDB131 medium (Thermo Fisher Scientific, Waltham, MA, USA) either with or without $10\%$ (v/v) human control or patient serum, as described in detail in the figure legends. Capillary networks of tubes formed were photographed under the microscope (Zeiss Axiovert 40 CFL Oberkochen, Germany), and five randomly selected fields from each well were analyzed for the number of newly formed segments, junctions, and meshes by using the Angiogenesis Analyzer on ImageJ 1.43 software (National Institutes of Health, Bethesda, MD, USA), as outlined previously [8,15,39,40,41,42]. The levels of the following prominent non-HLA (auto)antibodies directed against G-protein-coupled receptors (GPCRs) were quantified: anti-angiotensin II type 1 receptor (anti-AT1R-Ab), anti-endothelin-1 type-A-receptor (anti-ETAR-Ab), anti-protease-activated receptor 1 (anti-PAR-1-Ab), and anti-VEGF-A antibody (anti-VEGF-A-Ab). Non-HLA antibodies against AT1R-Ab and ETAR-Ab were considered to be detected positive when the result was above 10 U/mL. The entire immunoassay was performed as per the manufacturer’s instructions, as previously described [31]. ## 2.3. Statistical Methods Statistical analyses were performed using the Statistica 15.0 software (StatSoft Polska, Krakow, Poland). Since the number of patients was too small to ascertain normality of the data distribution, the data were presented as medians (and interquartile ranges), and non-parametric tests were applied for statistical analysis. The data were analyzed with the Mann–Whitney (for continuous variables) or the χ2 test (for categorized data), as required. The relationship between variables was analyzed with the Spearman’s rank correlation coefficient. The differences were considered significant at $p \leq 0.05.$ ## 3.1. Comparison of NCV Results between the Patient Groups (HTx vs. Healthy Controls) As outlined at the start of the methods sections, both the HTx and control group of this small pilot study are fairly well matched considering their size (10 vs. 12), age (38 ± 14 vs. 35 ± 5; $$p \leq 0.495$$), and sex (5 males and 5 females vs. 5 males and 7 females; $$p \leq 0.682$$) (Table 1, $$n = 10$$ and $$n = 12$$ patients, respectively). Considering the assessment of the NVC pattern in both patient groups, we found that the parameters obtained from the HTx recipients were within a normal range similar to that of the healthy controls (Figure 1 and Table 2), with no major avascular areas ($$n = 0$$ each), no capillary disorganization, and no branching or giant capillaries ($$n = 0$$ each), and similar capillary density (mean of each group: $$n = 8$$ capillaries/millimeter). The HTx patients displayed a trend for slightly elevated levels of hemorrhages, ectatic, and tortuous capillaries (1 vs. 0, 6 vs. 4, and 7 vs. 5, not significant in each case). Interestingly, the average apical loop diameter of the capillaries was significantly larger in the HTx patients than in the control group (Median 18 vs. 12 µm, Table 2), as also visible to some degree in the representative NVC images (Figure 1). No major differences in serum VEGF concentrations were found between the groups (median 70 and 50 pg/mL, Table 3), although there was a weak trend for higher levels in the HTx group. Similarly, the groups showed comparable angiogenesis parameters in the endothelial tube formation assay (similar median for total length, total branching length, total segment length, and total branch length, Table 3). Interestingly, the HTx recipients displayed significantly higher concentrations for three of the four non-HLA antibodies tested (Anti-AT1AR-Ab, anti-ETAR-Ab, and anti-VEGF-A-Ab, Table 4). ## 3.2. Correlations within the HTx Group Despite the small number of patients in this pilot study, we aimed to assess any correlations between key parameters, especially those that differed significantly between the HTx and the control group (Table 5 and Table 6). Indeed, a significant correlation was found between the average apical loop diameter of the capillaries and patient troponin T serum concentrations ($R = 0.69$, $$p \leq 0.028$$) and triglycerides ($R = 0.81$, $$p \leq 0.004$$). Surprisingly, capillaroscopic parameters did not correlate with serum VEGF concentrations or with angiogenesis results in cell culture. Interestingly, the serum concentrations of the three non-HLA antibodies (anti-AT1AR-Ab, anti-ETAR-Ab, and anti-VEGF-A-Ab) correlated with angiogenesis parameters in cell culture (Table 5), but not with any other clinically relevant parameters. Additionally, angiogenesis parameters in cell culture did not correlate with other significant CVD risk factors. ## 3.3. Detailed Analysis of Individual HTx Recipients Due to the small size of the analyzed group, it was impossible to divide the patients after HTx into subgroups. Therefore, the authors analyzed each patient in detail (Table 6). All analyzed patients were in a stable condition. None of the patients showed signs of acute rejection, which was also confirmed in protocol biopsies: histopathological evaluation of the biopsy material showed no features of acute rejection (neither cellular nor humoral). One of the patients presented atherosclerotic changes in coronary angiography (patient 3), while the other two patients (patients 4 and 5) had subtle atherosclerotic changes that were not hemodynamically significant. These were the patients with the longest post-transplant period (21–24 years). Interestingly, they presented a normal picture of microcirculation in NVC (although two of the patients had a slightly reduced density of microcapillaries and one of the patients had tortuous capillaries) and tended to have indirect levels of non-HLA antibodies (although both non-HLA-AT1R-Ab and ETAR-Ab were considered positive). Two patients (patients 1 and 2) showed significantly dilated capillaries in NVC (21 and 26 µm, normal range < 20 µm, Table 6). Both of these patients had tortuous capillaries, and one of them (patient 2) had hemorrhages. At the same time, these patients had traditional CVD risk factors: higher Troponin T values, features of metabolic syndrome (increased glycemia, cholesterol, and triglycerides), the tendency to have higher inflammatory markers (CRP, WBC, neutrophils), and additionally reduced glomerular filtration rate (eGFR). They presented no particular profile of angiogenesis parameters in cell culture, and they did not show any changes in coronary angiography. However, these were patients assessed only one year after the transplant, which may explain the lack of changes. Surprisingly, these patients tended to have significantly lower non-HLA antibody values (early after HTx). Almost all of the HTx recipients (8 of 10: patients 3–10) had levels of non-HLA antibody that were considered positive, while one of the patients (patient 2) had close to cut-off values (in transplantation, non-HLA-AT1R-Ab and ETAR-Ab are considered positive when the result is above 10 U/mL). The two patients with the lowest levels of non-HLA antibodies (patients 1 and 2) were those with the shortest post-transplant period but with the most intensive immunosuppressive therapy. No other significant relationships were found between the concentrations of non-HLA antibodies and clinical parameters. ## 4. Discussion In this small pilot study, we explored the value of non-invasive nailfold videocapillaroscopy (NVC) as a potential diagnostic and prognostic tool for the early detection and long-term monitoring of cardiac allograft vasculopathy (CAV) and endothelial dysfunction in stable patients that underwent prior heart transplantation (HTx) in conjunction with the assessment of (non)conventional cardiovascular diseases (CVD) risk factors to correlate the outcome of NVC analysis with the detailed individual patient pathophysiology. First of all, we found that the average diameter of capillaries assessed with NVC was significantly greater in patients after HTx than in the control group. Patients after HTx also seemed to have a trend for more frequent tortuous capillaries, and HTx recipients with atherosclerotic changes in coronary arteries tended to have a reduced density of microcirculation vessels, although these findings were not statistically significant. Surprisingly, the NVC pattern of microvessels did not correlate with serum VEGF concentrations or other parameters of angiogenesis/endothelial tube formation, as assessed in the cell culture experiments, which may be due to the small size of the patient groups or, alternatively, due to the inability to resolve rather small differences in serum VEGF levels with this assay. Nevertheless, we think that the NVC pattern of HTx recipients suggests microcirculatory disorders in this group. We conclude at this stage that NVC is a safe and useful investigational tool for the assessment of the microcirculation. Abnormal findings in NVC, e.g., tortuous and dilated capillaries or reduced density of capillaries, even without a specific pattern, could be related to clinical parameters in various pathologies and could be assessed for the early detection of endothelial dysfunction, as previously described in detail elsewhere [43,44]. Importantly, HTx patient cohorts may intrinsically exhibit many (risk) factors that may adversely affect the endothelium and can cause visible changes on NVC [45], which may become more evident only in larger cohorts than our small pilot study and should, thus, be assessed in future studies. We herein tried to analyze potential causes of changes in NVC patterns in HTx in an exemplary fashion to serve as a template for future larger verification studies. In our study, the patients with dilated capillaries were characterized by the presence of classical CVD risk factors, e.g., features of metabolic syndrome (hyperglycemia, increased levels of cholesterol and triglycerides), a tendency to have higher inflammatory markers (CRP, WBC, neutrophils), higher troponin T values, and reduced glomerular filtration rate (eGFR). Additionally, capillary diameters correlated with serum levels of troponin T and triglycerides. HTx patients with atherosclerotic coronary artery changes displayed a tendency for reduced microcirculatory vessel density. In addition, earlier studies have also shown significant changes of the NVC pattern in patients with diabetes mellitus. Indeed, it appears that NVC allows for the assessment of the advancement of microangiopathy in diabetes mellitus and that NVC also indicates the presence of other complications, e.g., retinopathy [41,46,47]. The NVC pattern may also be influenced by other traditional CVD risk factors, such as arterial hypertension or dyslipidemia [48,49], as well as sleep apnea [48] and lifestyle [50]. Based on our results that were obtained previously [31], we expected that in HTx recipients, additional immunological factors, e.g., non-HLA antibodies, may affect the endothelium and the NVC image. Therefore, we evaluated the presence of non-HLA antibodies and their impact on the microcirculation in HTx recipients. Almost all of the HTx recipients had levels of non-HLA antibodies that were considered positive (AT1R-Ab and ETAR-Ab > 10 U/mL), and the levels of three non-HLA antibodies (AT1AR-Ab, ETAR-Ab, and VEGF-A-Ab) out of the four tested were higher in patients post-HTx than in the healthy population. The serum concentrations of non-HLA antibodies correlated with angiogenesis parameters in the endothelial cell culture experiments that employed human serum, but they did not translate into any clinically relevant parameters. Surprisingly, the two patients with dilated capillaries tended to have lower non-HLA antibody values. Due to the short post-Tx time and the associated high risk of rejection, these patients had the most intense immunosuppression (triple therapy). This may explain the low non-HLA antibody levels and vascular changes [51,52]. ## 5. Conclusions We found that NVC is a useful tool to conveniently and non-invasively monitor changes in the microvasculature of HTx patients. Most prominently, we found in our small pilot study that in NVC, the average diameter of capillaries was significantly increased in the HTx recipients compared to the matched control group, and the microcapillary diameters correlated with the patient serum levels of troponin T and triglycerides. In addition, patients with changes in the NVC pattern post-HTx were also characterized by the presence of classic CVD risk factors. Altogether, these are meaningful results that should be verified in larger studies. NVC could be a non-invasive diagnostic method that could help in the early identification of patients at risk of cardiovascular complications. Heart transplant recipients displayed significantly higher concentrations of non-HLA antibodies. Although these autoantibody concentrations correlated with the angiogenesis parameters in cell culture, this did not translate into correlation with any clinical parameters and NVC patterns. Thus, their exact contribution to the development of vasculopathy in patients after HTx needs to be verified in future, more detailed studies. In our opinion, the presence of abnormal microcirculation in HTX patients at different intervals post-HTx indicates endothelial dysfunctions primarily associated with the presence of traditional CVD risk factors, such as diabetes, hypertension, and hyperlipidemia, and immunosuppressive therapy (including glucocorticoids), but it is not yet clear what the influence of the other factors (such as non-HLA antibodies) was. Our research has many limitations. First of all, it is a small pilot study. In addition, anti-HLA antibodies were not routinely assessed. There is no comparison of the results before HTx to different intervals post-HTx, and our results indicate an impact of study time. The most valuable would be a comparison of the results before and after HTx. Therefore, more research is needed on larger study groups to draw more detailed conclusions. ## References 1. Moll G., Dai Z., Camara N.O.S.. Editorial: Advances in Heart Transplantation. Front. Immunol. (2022) 13 960800. 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--- title: Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based on 18F-FDG PET authors: - Can Cui - Xiaochen Yao - Lei Xu - Yuelin Chao - Yao Hu - Shuang Zhao - Yuxiao Hu - Jia Zhang journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10056979 doi: 10.3390/jpm13030539 license: CC BY 4.0 --- # Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based on 18F-FDG PET ## Abstract Background: The characteristic magnetic resonance imaging (MRI) and the positron emission tomography (PET) findings of PCNSL often overlap with other intracranial tumors, making definitive diagnosis challenging. PCNSL typically shows iso-hypointense to grey matter on T2-weighted imaging. However, a particular part of PCNSL can demonstrate T2-weighted hyperintensity as other intracranial tumors. Moreover, normal high uptake of FDG in the basal ganglia, thalamus, and grey matter can mask underlying PCNSL in 18F-FDG PET. In order to promote the efficiency of diagnosis, the MRI-based or PET/CT-based radiomics models combining histograms with texture features in diagnosing glioma and brain metastases have been widely established. However, the diagnosing model for PCNSL has not been widely reported. The study was designed to investigate a machine-learning (ML) model based on multiple parameters of 2-deoxy-2-[18F]-floor-D-glucose (18F-FDG) PET for differential diagnosis of PCNSL and metastases in the brain. Methods: Patients who underwent an 18F-FDG PET scan with untreated PCNSL or metastases in the brain were included between May 2016 and May 2022. A total of 126 lesions from 51 patients (43 patients with untreated brain metastases and eight patients with untreated PCNSL), including 14 lesions of PCNSL, and 112 metastatic lesions in the brain, met the inclusion criteria. PCNSL or brain metastasis was confirmed after pathology or clinical history. Principal component analysis (PCA) was used to decompose the datasets. Logistic regression (LR), support vector machine (SVM), and random forest classification (RFC) models were trained by two different groups of datasets, the group of multi-class features and the group of density features, respectively. The model with the highest mean precision score was selected. The testing sets and original data were used to examine the efficacy of models separately by using the weighted average F1 score and area under the curve (AUC) of the receiver operating characteristic curve (ROC). Results: The multi-class features-based RFC and SVM models reached identical weighted-average F1 scores in the testing set, and the score was 0.98. The AUCs of RFC and SVM models calculated from the testing set were 1.00 equally. Evaluated by the original dataset, the RFC model based on multi-class features performs better than the SVM model, whose weighted-average F1 scores of the RFC model calculated from the original data were 0.85 with an AUC of 0.93. Conclusions: The ML based on multi-class features of 18F-FDG PET exhibited the potential to distinguish PCNSL from brain metastases. The RFC models based on multi-class features provided comparatively high efficiency in our study. ## 1. Instruction The use of imaging techniques to assess brain lesions is crucial in diagnosing and managing neurological disorders. MRI and CT have commonly used imaging modalities, but they have limited usefulness in providing information on the metabolic activity of brain lesions. In contrast, 18F-FDG PET-CT is a functional imaging modality that can provide valuable information on the metabolic activity of brain lesions, particularly brain tumors. A review of the literature suggests that 2-deoxy-2-[18F]-floor-D-glucose (18F-FDG) PET-CT is a valuable tool in identifying metabolically active brain tumors and monitoring treatment response [1]. Zhao et al. [ 2014] reported that 18F-FDG PET-CT had high sensitivity and specificity for detecting brain tumors and differentiating them from non-neoplastic lesions [2]. However, the accuracy and usefulness of 18F-FDG PET-CT in CNS diagnosis are still debated among researchers and clinicians. Some studies have reported lower accuracy rates for differentiating between benign and malignant brain tumors. The usefulness of 18F-FDG PET-CT may be affected by the lesion’s type and location, surrounding inflammation or edema, and the patient’s metabolic state. Despite these limitations and controversies, the available evidence suggests that 18F-FDG PET-CT remains a valuable tool for assessing brain lesions, particularly in the context of brain tumors. Yang et al. [ 2019] [3] reported that 18F-FDG PET-CT and MRI had similar diagnostic accuracy in differentiating between high-grade and low-grade gliomas. Primary central nervous system lymphoma (PCNSL) is a rare type of non-Hodgkin lymphoma that affects the brain, eyes, leptomeninges, or spinal cord. The incidence of PCNSL was 7 cases per 1,000,000 people in the USA in 2013 [4]. The PCNSL accounts for 2–$3\%$ of all brain tumors [5] (pp. 971–977). A study reported that the 2-year age-adjusted relative survival rate of PCNSL was $33\%$, and the corresponding 5-year survival rate of PCNSL was $26\%$ [6]. An accurate diagnosis is crucial for the effective treatment of PCNSL. Currently, combination chemotherapy regimens that include high-dose methotrexate are considered the standard of care for newly diagnosed PCNSL [7]. In contrast, patients with brain metastases require a multidisciplinary approach that involves surgical resection, various radiation treatment modalities, cytotoxic chemotherapy, and targeted molecular treatment [8]. Neuro-imaging using cranial MRI with fluid-attenuated inversion recovery (FLAIR) and T1-weighted sequences before and after contrast injection is the preferred method for diagnosing and monitoring PCNSL [9]. However, distinguishing between PCNSL and brain metastases can be challenging since both present similar MRI signs, such as non-enhancing core and perifocal edema [10]. Moreover, a particular part of PCNSL can demonstrate T2-weighted hyperintensity as other intracranial tumors [11]. 18F-FDG PET can be helpful for differential diagnosis, but it has insufficient specificity [9,12]. Recent, more inspiring studies of MRI-based or PET/CT-based radiomics models combining histograms with texture features have been widely reported in diagnosing and managing glioma and metastases in the brain [13,14]. Nonetheless, due to the low morbidity of PCNSL, the relevant diagnosing model has not been widely investigated yet. Therefore, we aim to establish several models based on 18F-FDG PET/CT and find an estimator with the best-predicted performance to identify PCNSL to improve diagnosis, affect patients’ management, decrease the number of indications to surgical interventions, direct the patient to the most accurate therapy, and, therefore, affect their quality of life. ## 2. Materials and Methods Our study follows the guideline, transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) [15]. The statement adhered to the supplement materials as a part of the study (Table S1). ## 2.1. Study Participants The study retrospectively reviewed patients with intracranial mass who received an 18F-FDG PET/CT at Jiangsu Cancer Hospital from May 2016 to May 2022. Patients with PCNSL confirmed by pathology and brain metastases confirmed by pathology or clinical history without receiving systemic therapy or brain radiotherapy for the past six months. Due to patients’ compliance, the biopsy of brain metastases cannot be feasible for all the patients whose primary tumor was pathologically confirmed. All the lesions were not postoperative or post-biopsy (Figure 1). ## 2.2. 18F-FDG PET/CT Protocol 18F-FDG PET/CT protocol followed the European Association of Nuclear Medicine’s guidelines [16]. Patients fasted for at least 6 h. The plasma glucose level of all the patients was in a range from 4.0 mmol/L to 8.3 mmol/L. For patients with diabetes, additional restrictions were applied. Only intermediate-acting or short-acting insulin was allowed within 12 h before the administration of 18F-FDG, and the application of metformin was compromised. The radioactivity of 18F-FDG for intravenous injection was calculated by body weight, 4.1 ± 0.82 Mbq/kg (range from 2.96 MBq/kg to 5.55 Mbq/kg). The acquisition of the brain starts at 77 ± 2.9 min (range from 74 to 82 min) after 18F-FDG injection when the PET scan of the torso (from the canthus line to the thigh) was completed. The brain scan is a separate procedure. The PET/CT (Discover 710 STD GE Healthcare, Waukesha, WI, USA) image acquisition consisted of a 10-min emission scanning with one bed for the brain and low-dose CT for attenuation correction. The voxel size was 3.65 × 3.65 × 3.75 in mm with a matrix of 192 × 192. The reconstruction is Vue Point FX with 24 subsets and 2-times iterations. Low-dose CT used 3.75 mm slice thickness, pitch 1.375:1, 140 kV with Auto-mA. ## 2.3. Segmentation of Images All the PET/CT images, relevant MRI, and related contrast-enhanced CT images were reviewed using PET VCAR with Integrated Registration, a component of the Advantage Workstation (version 4.6, GE Healthcare, Waukesha, WI, USA). Segmentation of lesions was performed by two clinical radiologists with over five years of experience. The volume of interest (VOI) was checked by radiology and nuclear medicine physicians with a career in oncological PET/CT interpretation over ten years. Segmentation of PET volumes was based on the iterative image thresholding method (ITM), which yielded reliable PET volume estimation as previously reported [17]. Relevant MRI and contrast-enhanced CT were used as the reference to adjust the edge of VOIs manually. VOIs were saved and exported as the radiotherapy structure set (RTSS). ## 2.4. Feature Extraction All the characters were divided into two groups, the group of density features and the group of multi-class features (Table S2). Briefly, the density-features group contains $10\%$ percentile, $90\%$ percentile, energy, maximum, minimum, and range. The multi-classes-features group includes all first-order characters and the texture characters, such as the gray-level co-occurrence matrix (GLCM), modification of grey-level difference matrix (GLDM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and neighboring gray-tone difference matrix (NGTDM), 93 features in total. As the unit of the pixel value is Becquerel per mL, PET images were normalized by the SUV factor Formula [1] and resampled to a uniform voxel size of 2 × 2 × 2 mm3. PyRadiomics (V3.01) (https://pyradiomics.readthedocs.io/en/latest/index.html, accessed on 3 May 2022) was used to extract all features [18]. The bin width of 0.5 was derived by dividing the maximum range by 64 [19]. [ 1]SUVfactor=WD×2−t/TFormula [1]. W: Body weight (g), D: Injection dose (Bq), t: Delay between injection time and scan time (s), T: Half-life of the isotope (s). ## 2.5.1. Statistical Analysis The present study employed a statistical analysis of three primary steps: resampling, dimensionality reduction, and estimator establishment (Figure 2a). Specifically, to address the issue of imbalanced datasets, the researchers utilized the synthetic minority over-sampling technique and edited nearest neighbors (SMOTEENN) algorithm. SMOTEENN is a hybrid approach that combines the synthetic minority over-sampling technique (SMOTE) and edited nearest neighbors (ENN) algorithms. SMOTE generates synthetic minority class samples to balance the class distribution, while ENN removes examples considered noisy or belonging to the majority class. By combining these two techniques, SMOTEENN can oversample minority class examples and remove potentially noisy or irrelevant examples from the dataset. The tools were provided by mbalanced-learn (Version: 0.9.1) (https://imbalanced-learn.org/stable/, accessed on 18 May 2022) Principal component analysis (PCA), a linear method known for reducing the dimensions of a dataset while retaining the most relevant information, was employed to achieve the aim mentioned above. The PCA was achieved by transforming the original n-dimensional dataset into a new dataset using an orthogonal transformation [14]. For the last step, three classification algorithms were selected: support vector machine (SVM), logistic regression (LR), and random forest classification (RFC). SVM is a particularly effective classifier for small machine-learning tasks [20]. The LR classifier, while running faster, places greater emphasis on feature engineering [21]. On the other hand, RFC is known to reduce overfitting by averaging decision trees, making it a relatively stable classification method. However, it requires more time to train the model due to its complex calculation process [22]. All the tools above were provided by the scilearn-kit (Version: scikit-learn 1.1.2) (https://scikit-learn.org/stable/ accessed on 6 August 2022). ## 2.5.2. Pre-Process of Datasets Two groups of original datasets were separately resampled by imbalanced-learn (Version: 0.9.1) (https://imbalanced-learn.org/stable/, accessed on 18 May 2022). The method of SMOTEENN was used to balance the datasets [23,24]. Two datasets were divided into training sets and testing sets with a ratio of 2:1 using the scilearn-kit (Version: scikit-learn 1.1.2) (https://scikit-learn.org/stable/, accessed on 6 August 2022. All of the data were normalized by Standard-Scaler provided by scilearn-kit. ## 2.5.3. Dimensionality Reduction Principal component analysis (PCA) was used for dimensionality reduction for the multi-class features group. PCA reduces high-dimensional features into a small number of principal components (PCs). The PCs will be retained until the cumulative-explained variance is over 0.9. The dimension of the density-features group was not reduced. Because only six dimensionalities exist in the datasets, dimensionality reduction is unnecessary. ## 2.5.4. Fitting the Model and Internal Cross-Validation Two groups of data were fitted to logistic regression (LR), support vector machine (SVM), and random forest classification (RFC) models. Hyperparameters were determined by grid search with five-fold cross-validation (Figure 2b,c) [25,26]. Briefly, the dataset was split into five folds. In the initial iteration, the first fold was used to validate the model, and the rest folds were used for the training of the model. In the second iteration, the second fold is used as the validation set, while the rest is the training set. This process was repeated five times. The precision Formula [2] of each iteration was averaged. All of the hyperparameters were traversed by grid search. The hyperparameters of each model with the highest precision were selected. Finally, trained by training sets with the best hyperparameters, the six estimators were established from three different models with two data sets. ## 2.5.5. Evaluation of Estimators The testing sets and original datasets (the dataset without resampling) were used to evaluate the estimator. The receiver operating characteristic curve (ROC) with the area under the curve (AUC) is presented. The F1 score Formula [2] is a machine-learning metric used in classification models [27]. For imbalanced data, we use the weighted average F1 score to compare the efficiency of the estimators. [ 2]Precision=True positivesTrue Positives+False positives [3]Recall=True positivesTrue positives+False Nagetives [4]F1 score=2×Precision·RecallPrecison+Recall Formula [2]. The definition of precision [2], recall [3], and Average F1 score [4]. ## 3.1. Study Participants The characteristics of patients are demonstrated in Table 1. In total, 8 patients with PCNSL and 43 patients with metastases in the brain were included, with 14 lesions of PCNSL and 112 lesions of metastases in the brain (Figure 1). The primary tumor of all the brain metastases patients was pathologically confirmed. One of the patients, whose primary tumor was adenocarcinoma of the lung, underwent a craniotomy biopsy. Finally, the brain metastases of the lung carcinoma were confirmed. The pathology result of all patients with PCNSL was confirmed by stereotaxic needle biopsy. There is no significant difference in sex and age. The SUVmax of PCNSL and metastases is significantly different. ## 3.2. Dimensionality Reduction The study used PCA to project 93 features in the multi-classes-features group to six dimensions. The data of the first three principal components in the training set of the multi-class-features group is shown in Figure 3a. The individual-explained variance ratio and cumulative-explained variance ratio for each principal component are shown in Figure 3b. The cumulative-explained variance ratio of the third principal component is $82.6\%$, and the sixth is $91.6\%$, meaning the first 6 principal components contained $91.6\%$ of the information of all 93 features. The PCA loading vectors are shown in Figure 2c and Supplement Table S3. The multi-features dataset was converted from its original dimension to the reduced PCA dimension by using the vectors in the linear transformation. ## 3.3.1. Fit the Model and Internal Cross-Validation The hyperparameters of all the estimators are shown in Table 2. The precision between different models ($$p \leq 0.0137$$) and between datasets ($$p \leq 0.0174$$) are discrepant. In multiple comparisons between values of precision, only the difference between the SVM model trained by multi-class features and the LR model trained by density features is observed ($$p \leq 0.0025$$). The recall of the LR model trained by density features is lower than the others ($p \leq 0.0001$), while there is no difference was found between the others (Figure 4). ## 3.3.2. Evaluation of Estimators The weighted average F1 score of estimators is shown in Table 3. Although all the ROC of estimators shows a nearly perfect performance in the testing set, only SVM and RFC trained by multi-class features exhibit acceptable results, of which the AUCs are 0.92 and 0.93 (>0.9) (Figure 5). ## 4. Discussion The study established a model to classify PCNSL and neuro-metastases, combining histogram and high-order characteristics from lesions in 18F-FDG PET images. The technique, dimensionality reduction and the balance of data sets, was adopted to reduce the possibility of overfitting. The SVM and RFC models trained by the multi-class features data set and the RFC models trained by density features show the highest F1 scores and AUCs validated by the testing set. However, evaluated by the data sets without resampling, the F1 scores and AUCs’ reduction of all six estimators can be observed. Nevertheless, the F1 score and AUC of the RFC models trained by the multi-class features were still acceptable and relatively higher than others evaluated by the testing and original data set. 18F-FDG PET-CT is a sensitive screening tool for PCNSL patients suspected of systemic involvement [7]. However, A low diagnostic yield of PCNSL for initial staging has been reported [28]. Even if the limitation of 18F-FDG PET in neuro-oncology is widely accepted, some studies argued that the different SUVmax and tumor-normal ratios could be observed in PCNSL and metastases in the brain [12,29]. A similar result can also be drawn from our data; sensitivity and specificity are $71.43\%$ and $73.21\%$, with a cut-off of 14.42. However, the change SUVmax and tumor-normal ratios may not be conspicuous in atypical PCNSL [30]. Precisely as we noticed, some lesions of PCNSL can be concealed by the high metabolism of the cerebral cortex. In recent years, 18F-FDG PET or MRI-based radiomics features have been reported to distinguish the PCNSL and glioblastoma, which provides a reliable noninvasive method [31,32,33,34]. The multi-feature-based diagnosing method should potentially promote the performance in the differential diagnosis between PCNSL and brain metastases. It is just what we discussed in our study to establish a method based on radiomics to increase the diagnosis accuracy of the PCNSL and brain metastases interpreted from 18F-FDG PET. Due to the disparate incidence of PCNSL and brain metastases, the data set can be highly unbalanced. The incidence of PCNSL was 7 cases per 1,000,000 people in the USA in 2013 [4]. The PCNSL accounts for 2–$3\%$ of all brain tumors [5] (pp. 971–977). Relatively, brain metastases develop in approximately $10\%$ to $30\%$ of adults and $6\%$ to $10\%$ of children with cancer [35]. Training with unbalanced datasets may lead to overfitting and underfitting. The synthetic minority over-sampling technique (SMOTE) can be an appropriate option for dealing with imbalanced datasets [24]. The SMOTE is a way to deal with the minority classes in a dataset. This algorithm’s fundamental idea is to analyze, simulate, and add the new sample simulated artificially into the original dataset to balance the classes in the original data. In our study, the hyper-sampling method was used. The method combines SOMTE with edited nearest neighbors (ENN), an under-sampling technique that removes the majority class to match the minority class [36]. The method has been used in several clinical studies [37,38,39,40]. Actually, for the sure size of the training set, the predictive performance of models decreases with increasing dimensionality [41]. The six visually recognizable features were defined as the group of density. Ninety-three features in the multi-class features group were extracted for PET imaging. The multi-class features can be redundant, and some features can be highly related, which may lead to the over-fitting of the models. It is vital to reduce dimensionality without losing information. PCA determines a set of orthogonal vectors called principal components, defined by a linear combination of the original variables and ordered by the amount of variance explained in component directions [42]. The cumulative-explained variance ratio, the summary of explained variance ratio, has been set to 0.9, which means more than 90 percent of variation from the 93 features has been retained. In our study, besides the AUCs of ROCs, the weight-average F1 scores were used to evaluate the predicted performance of estimators. While ROC was unaffected by skew, precision–recall curves suggest that ROC may mask poor performance [43]. The weight-average F1 score is the harmonic mean of precision (also called positive predictive value) and recall (indicated the sensitivity), widely used in information retrieval and information extraction evaluation [44]. In our study, the weighted average F1 scores were used to evaluate the performance of estimators, which calculates the weighted mean of all per-class F1 scores while considering each class’s support, eliminating the effect of unbalanced data sets. For the five sixths estimators, the F1 scores resulting from the testing set are more prominent than 0.9. The result indicated that precision and sensitivity could be excellent in the testing set. ROC and AUC can also display similar results. In order to evaluate the predicted performance in the real world and the generalization ability of the estimators, we used the original data sets (without resampling) to re-evaluate all estimators. We noticed that all estimators’ F1 scores or AUCs have a decrease in a certain degree tested by original data sets (without resampling) while considering the class imbalance. Especially in the RFC model trained by density features, the F1 score is 1.00 in the testing set and decreases to 0.82 in the original data. We conjecture that overfitting this estimator may decrease the estimators’ performance, as reported [45].On the other hand, the estimator generated from the RFC model trained by multi-class features performs well for both the testing data set and the original data set (without resampling). We conjecture that the characters of the random forests algorithm decrease the possibility of overfitting. Because the random forests deal with the problem of overfitting by creating multiple trees, with each tree trained slightly differently, it overfits differently. The sufficient diagnostic information provided by the multi-class features and the combination of each decision tree offset the effect of overfitting each decision tree. The low incidence of PCNSL and restricted enrollment criteria restrict the sample size, and further multicenter studies are urgently required. The utilization of various machine learning algorithms has significantly enhanced the efficacy of identifying primary central nervous system lymphoma (PCNSL) and brain metastases. However, it has concurrently augmented the complexity of the practical implementation of these techniques in clinical settings. The random forest model exhibits superior accuracy when dealing with high-dimensional data. Nevertheless, the random forest model’s interpretability is greatly diminished by the utilization of multiple decision tree models to determine the final classification outcome through voting. The ML model based on 18F-FDG can improve the diagnosis of brain lesions by providing clinicians with more precise and consistent information, which can lead to faster and more effective treatment decisions. Radiomics models, which use AI algorithms to analyze medical images, have shown promise in differentiating between brain lesions, including PCNSL and brain metastases. However, while The ML model based on 18F-FDG has shown potential in improving the diagnosis of brain lesions, more research is needed to fully understand their clinical impact and how to integrate them into clinical practice. Clinicians must be aware of these tools’ limitations and potential biases and ensure their use is evidence-based and clinically relevant. ## 5. Conclusions The SUVmax of 18F-FDG PET is a proven semi-quantitative indicator; the combination of radiomics and machine learning promotes the performance of PCNLS and brain metastases diagnosis. The F1 score and AUC of the RFC model trained by multi-class features are 0.85 and 0.93. The RFC model trained by multi-class features has the potential to revolutionize brain lesions diagnosis and improve patient outcomes. However, they need to integrate into clinical practice cautiously and consider their limitations and biases. More research is needed to fully understand the clinical impact of the model and how it can be best utilized in clinical settings. ## References 1. Pietrzak A., Marszałek A., Kunikowska J., Piotrowski T., Medak A., Pietrasz K., Wojtowicz J., Cholewiński W.. Detection of clinically silent brain lesions in [18F]FDG PET/CT study in oncological patients: Analysis of over 10,000 studies. Sci. Rep. (2021.0) 11 18293. DOI: 10.1038/s41598-021-98004-w 2. 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--- title: Human Brain Microvascular Endothelial Cells Exposure to SARS-CoV-2 Leads to Inflammatory Activation through NF-κB Non-Canonical Pathway and Mitochondrial Remodeling authors: - Carolline Soares Motta - Silvia Torices - Barbara Gomes da Rosa - Anne Caroline Marcos - Liandra Alvarez-Rosa - Michele Siqueira - Thaidy Moreno-Rodriguez - Aline da Rocha Matos - Braulia Costa Caetano - Jessica Santa Cruz de Carvalho Martins - Luis Gladulich - Erick Loiola - Olivia R. M. Bagshaw - Jeffrey A. Stuart - Marilda M. Siqueira - Joice Stipursky - Michal Toborek - Daniel Adesse journal: Viruses year: 2023 pmcid: PMC10056985 doi: 10.3390/v15030745 license: CC BY 4.0 --- # Human Brain Microvascular Endothelial Cells Exposure to SARS-CoV-2 Leads to Inflammatory Activation through NF-κB Non-Canonical Pathway and Mitochondrial Remodeling ## Abstract Neurological effects of COVID-19 and long-COVID-19, as well as neuroinvasion by SARS-CoV-2, still pose several questions and are of both clinical and scientific relevance. We described the cellular and molecular effects of the human brain microvascular endothelial cells (HBMECs) in vitro exposure by SARS-CoV-2 to understand the underlying mechanisms of viral transmigration through the blood–brain barrier. Despite the low to non-productive viral replication, SARS-CoV-2-exposed cultures displayed increased immunoreactivity for cleaved caspase-3, an indicator of apoptotic cell death, tight junction protein expression, and immunolocalization. Transcriptomic profiling of SARS-CoV-2-challenged cultures revealed endothelial activation via NF-κB non-canonical pathway, including RELB overexpression and mitochondrial dysfunction. Additionally, SARS-CoV-2 led to altered secretion of key angiogenic factors and to significant changes in mitochondrial dynamics, with increased mitofusin-2 expression and increased mitochondrial networks. Endothelial activation and remodeling can further contribute to neuroinflammatory processes and lead to further BBB permeability in COVID-19. ## 1. Introduction Coronavirus disease 2019 (COVID-19), caused by infection with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2), remains a major health threat globally. The USA continues to lead the world with a total of 102 million COVID-19 cases and 1.1 million deaths by the end of January 2023 [1]. Even though vaccines, which mostly prevent serious illness and death, have been widely available in the U.S. and many countries, only $59\%$ of the overall population is fully vaccinated. This is below the estimated 85–$90\%$ threshold assumed to be needed to stop the spread of SARS-CoV-2 and make the virus endemic. Globally, COVID-19 cases continue rising, and new variants and subvariants (e.g., omicron BA.1, BA.2 [BA.2.12, BA.2.12.1]) were recently identified in different countries, spreading globally [2]. SARS-CoV-2 is a member of the family of β-coronaviruses, similar to two other highly pathogenic coronaviruses, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Initial SARS-CoV-2 infection investigated cases led to the isolation of the virus in human respiratory epithelial cells [3,4] and its genome sequencing deposited (GISAID accession IDs: EPI_ISL_402119, 402120 and 402121). SARS-CoV-2 is an enveloped, positive-sense, and single-stranded RNA virus. Its genome encodes non-structural proteins (such as 3-chymotrypsin-like protease, papain-like protease, helicase, and RNA-dependent RNA polymerase; all key enzymes in the viral life cycle), structural proteins (spike [S] protein, membrane [M] protein, envelope [E] protein, and nucleocapsid [N] protein), and accessory proteins. It is now known that SARS-CoV-2 interacts with and infects human cells through the ligation of the S1 subunit of the S protein with host cell receptors, especially the angiotensin-2 converting enzyme (ACE2) that serves as an entry receptor to the virus, representing its main route of entry into the host cell [5]. Pulmonary, cardiac, and intestinal epithelia and endothelial cells express high levels of ACE-2 [3]. Upon S1-ACE2 interaction, a transmembrane serine protease 2 (TMPRSS2) is required for priming the S protein and viral entry into the cell [4,6,7]. Along with ACE2 and TMPRSS2, several other proteins have been suggested to participate in SARS-CoV-2 entry into human cells, such as ADAM metallopeptidase domain 17 (ADAM17) [8,9], dipeptidyl peptidase 4 (DPP4) [10,11], angiotensin II receptor type 2 (AGTR2) [12,13], basigin (BSG, also called extracellular matrix metalloproteinase inducer [EMMPRIN] or cluster of differentiation 147 [CD147]) [14,15], aminopeptidase N (ANPEP) [16], and cathepsin B/L [5,17]. Among the most commonly observed symptoms in COVID-19 patients, alterations of neural functions are frequently detected, from mild cases with loss of taste and smell, dizziness, and headaches, to more extreme cases with the occurrence of acute cerebrovascular disease, including episodes of vascular encephalic accidents, loss of consciousness, ataxia, and epilepsy [18]. The Central Nervous System (CNS)-related symptoms are also prominent for so-called chronic or long COVID. The CNS is a well-documented target of β-coronavirus infections, such as SARS-CoV-2, and to date, several studies detected SARS-CoV-2 in the brain and the cerebrospinal fluid of COVID-19 patients [19,20,21,22]. Distinct routes of SARS-CoV-2 entry into the brain have been proposed, such as the olfactory nerve [23,24,25], the choroid plexus, and the blood–brain barrier (BBB) [26]. The BBB represents a physiological interphase between systemic blood circulation and the brain parenchyma. The BBB is primarily formed by endothelial cells that are surrounded by astrocytes, neurons, pericytes, and microglia cells that, by coordinating functions with endothelial cells, form the structural elements of the BBB known as the neuro-vascular unit [27]. However, the contribution of the BBB may be particularly important due to the presence of the virus in the bloodstream allowing the passage of viral particles through the wall of brain capillaries to brain parenchyma. While the mechanisms of SARS-CoV-2 neuroinvasion are not fully understood, it has been suggested that infection of BBB capillary-composing cells could be critical to triggering CNS impairment [28,29]. Among the NVU-forming cells, endothelial cells are especially important for ensuring BBB function, and several studies have recently described EC as key players in SARS-CoV-2-induced pathogenesis [30,31,32]. Several reports have correlated the infection outcome with vascular dysfunction, establishing vascular inflammation and cytokine storms promoted by immune responses as critical factors contributing to the worsening of the clinical condition and even death. Endothelial dysfunction may have important consequences, which include ischemia, altered angiogenesis and coagulation, inflammation, and tissue edema. Therefore, COVID-19-related endothelitis could explain the systemic microcirculatory dysfunction observed in patients, including a chronic form of this disease. It was previously demonstrated that the treatment of human brain microvascular endothelial cells with recombinant S1 protein resulted in the endothelial permeability and altered the levels of pro-inflammatory cytokines [33]. However, little is known about the involvement of brain microvasculature in brain infection by SARS-CoV-2, which may result in endothelial activation and hyper-inflammatory responses. Even less is known if damages to the BBB could be propagated to neural tissue and, therefore, be the triggering mechanism of neural abnormalities that promote neurological symptoms observed in COVID-19 patients. In the present work, we describe the cellular and molecular effects of HBMEC exposed to SARS-CoV-2 in order to gain insight into possible routes by which the virus affects the BBB and invades the brain parenchyma. HBMECs susceptibility to infection was compared to that of gold-standard African monkey kidney epithelial Vero cells, including viral production and activation of caspase-3. Further characterization of SARS-CoV-2 effects on HBMECs was performed by two unbiased analyses of gene expression and angiogenic factor secretion. Transcriptomic analyses revealed activation of noncanonical NF-κB signaling pathway and changes in mitochondrial quality control, with increased mitochondrial networks and mitofusin-2 expression in SARS-CoV-2-challenged cultures. Our data demonstrate that exposure to SARS-CoV-2 leads to brain endothelium activation, thus contributing to promoting increased neuroinflammation in Neuro-COVID-19. ## 2.1. Cell Culture Human brain endothelial cells (HBMECs) were a gift from Prof. Dennis Grab (Department of Pathology, Johns Hopkins School of Medicine). Cells were immortalized using an SV40-LT plasmid [34] and were maintained in 199 medium with $10\%$ fetal bovine serum (FBS) and $1\%$ antibiotics (penicillin/streptomycin, ThermoFisher, Carlsbad, CA, USA) up to passage 38. Vero E6 cells (African green monkey kidney epithelial cells) were used as the gold standard for viral isolation and propagation and were used in a few experiments as a positive control for efficient SARS-CoV-2 infection. Vero E6 cells culture medium consisted of Dulbecco’s Modified Eagle Medium (DMEM, ThermoFisher, Carlsbad, CA, USA) formulated with D-glucose (4.5 g/L, Sigma-Aldrich, St. Louis, MO, USA), L-Glutamine (3.9 mM, Sigma-Aldrich, St. Louis, MO, USA) supplemented with 100× penicillin-streptomycin solution (to final 100 U/mL and 100 μg/mL, respectively, ThermoFisher, Carlsbad, CA, USA), and inactivated FBS (USDA-qualified region FBS) at $10\%$. Both cell and viral cultures were incubated at 37°C and $5\%$ CO2 atmosphere. ## 2.2. SARS-CoV-2 Isolate All the procedures associated with the viral isolation and further infection assays were performed in a biosafety level-3 laboratory in accordance with the WHO guidelines [35]. The SARS-CoV-2 isolate used in this study was previously obtained from a nasopharyngeal swab sample collected from a COVID-19 patient diagnosed at Fiocruz COVID-19 regional reference center for WHO, in March 2020, in Brazil, as part of the Brazilian Ministry of Health surveillance system. The clinical sample was recovered from a patient that developed a mild disease and fully recovered. Viral isolation was performed in Vero E6 cells, as previously described [36]. In addition, the isolate was characterized by transmission electron microscopy [37]. The viral titer of the isolate was increased by an additional passage in Vero E6 cells to obtain a working stock. The $50\%$ Tissue Culture Infectious Dose (TCID50) titer of the viral working stock was determined by limiting dilution and infection of Vero E6 cells. Genetic characterization of the isolate was performed by whole-genome sequencing, and its genome is available in the Global initiative on sharing all influenza data (GISAID) under the accession numbers EPI_ISL ID 427294 (https://www.epicov.org/ accessed on 10 August 2020), confirming its classification as the original Wuhan strain (Pango lineage B.1.1.33). All procedures involving patient samples were approved by the Committee of Ethics in Human Research of the Oswaldo Cruz Institute (registration number CAAE 68118417.6.0000.5248). ## 2.3. SARS-CoV-2 Challenge Cells were previously cultured to obtain confluent monolayers for the moment of infection. After that, cells were washed once with PBS and further incubated with SARS-CoV-2 inoculums diluted in non-supplemented DMEM or medium 199, corresponding to indicated multiplicities of infection (MOI) for one hour. After that, inoculums were removed from cells and replaced by their appropriate supplemented medium with N-tosyl-L-phenylalanine chloromethyl ketone (TPCK)-treated trypsin (Sigma-Aldrich, St. Louis, MO, USA) at 1 µg/mL. ## 2.4. Viral Quantification We evaluated the SARS-CoV-2 replication of infected cell cultures over time by measuring the number of viral RNA copies in their culture media over time. Viral RNA was extracted from 140 μL of cell-free culture media via QIAamp Viral RNA mini kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Reverse transcription and SARS-CoV-2 gene amplification were performed in one-step reactions with a quantitative real-time PCR kit developed by Biomanguinhos Institute (Fiocruz, Rio de Janeiro, Brazil) in an ABI 7500 thermocycler (Applied Biosystems, Carlsbad, CA, USA). As a quantification standard, we used a SARS-CoV-2 plasmid control containing the reference sequence of the viral envelope (E) gene with a known number of copies (IDT, Newark, NJ, USA). Therefore, a concentration curve was prepared by performing serial dilutions of the plasmid. ## 2.5. RNA Libraries and Sequencing (RNA-Seq) For RNA-Seq analysis, three independent replicates were prepared for each treatment group, Mock, MOI 0.01-, and MOI 0.1-exposed HBMEC cultures, after 6 and 24 h. Total RNA was isolated via the miRNeasy micro kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The RNA was quantified by O.D. measurement before being assessed for quality by chip-based capillary electrophoresis using Agilent 2100 Bioanalyzer RNA 6000 Pico assays (Agilent Technologies, St Clara, CA, USA; Part # 5067-1513). Libraries were prepared from 150 nanograms (ng) of DNA-free total RNA using the Universal Plus mRNA-Seq Library Prep Kit (NuGEN Technologies, Inc., San Francisco, CA, USA; Part # 0508-96). The quality and size distribution of the amplified libraries was determined by chip-based capillary electrophoresis on Agilent 2100 Bioanalyzer High Sensitivity DNA assays (Agilent Technologies; Part # 5067-4626). Libraries were quantified using the Takara Library Quantification Kit (Shiga, Japan; Part # 638324). The libraries were pooled at equimolar concentrations and diluted prior to loading onto a P3 flow cell (Illumina, San Diego, CA, USA; Part # 20027800) with the P3 300 Cycle reagent kit (Illumina, San Diego, CA; Part # 20038732) on the NextSeq2000 instrument (Illumina, San Diego, CA, USA). ## 2.6. RNA-Seq Data Analysis Reads: R1 and R2 were trimmed 12 nucleotides (nt) to remove low-quality sequences. Bases with a quality score of less than Q20 were trimmed off the right end of each R1 and R2. Illumina adapter sequences were trimmed from the 3′-end of both R1 and R2 reads. Read pairs, in which the mate in the pair was less than 30 nt after trimming, were discarded. These quality-filtered reads were then used for alignment. Sequence alignment was performed using HISAT2 [38] version 2.0.5 with the following settings: hisat2 --end-to-end -N 1 -L 20 -i S,1,0.5 -D 25 -R 5 --pen-noncansplice 12 --mp 6,3 --sp 3,0 --time --reorder --known-splicesite-infile [SPLICESITES] --novel-splicesite-outfile splicesites.novel.txt --novel-splicesite-infile splicesites.novel.txt -q –x [hsa38 HISAT2 INDEX] -1 [FASTQ1] -2 [FASTQ2] -S [SAMOUT]. The read summarization program featureCounts [39] version 1.5.1 was used for exon- and gene-level counting. An Ensembl human version 83 GTF file (downloaded from Ensembl Biomart on 22 January 2016) was used for the determination of exon boundaries and the exon–gene relationship during counting. The summarization level used for exon and gene counting was the feature and the meta-feature, respectively. The feature-Counts is available in the Subread package at http://subread.sourceforge.net, accessed on 20 November 2021 [39]. To determine differential gene expression and due to the low coefficient of biological variation, paired comparisons were performed between the untreated control (UC) and MOIs 0.01- and 0.1-treated HBMEC cells at the 6 and 24 h timepoints, using an additive linear model with the untreated group as the blocking factor. Differential gene expression analysis was performed using the EdgeR R package [40]). The top differentially expressed genes have consistent UC vs. MOI 0.1 changes for the three replicates at $5\%$ FDR, and an absolute log2 fold change of 0.6 was considered a cut-off to generate the DEG list. Computed z-scores of significant genes are represented in the heatmap. Heatmap was plotted using the ComplexHeatmap R package [41]. ## 2.7. Downstream RNA-Seq Analysis We used a list of genes differentially expressed between MOI 0.1 SARS-CoV-2-exposed and untreated HBMEC cells. The pathway enrichment and interaction networks analysis were performed using clusterProfiler and gprofiler2 R packages [42,43]. Overlapping gene sets from reactome pathway terms were visualized as a chord plot using the GOplot R [44]. ## 2.8. RT-qPCR Cells were grown in 60 mm2 dishes, and total RNA was extracted with Trizol reagent (ThermoFisher, Carlsbad, CA, USA), according to the manufacturer’s instructions. One microgram of total RNA was reversely transcribed into cDNA via the SuperScript III system (ThermoFisher, Carlsbad, CA, USA), and 0.5 µL of cDNA was used per RT-qPCR reaction with Power SYBR Green (ThermoFisher, Carlsbad, CA, USA) master mix. Reactions were read in 7500 StepOne Plus from the Oswaldo Cruz Institute. Primer sequences for Drp1, Fis1, ZO-1, claudin-5, HIF-1α, Mfn2, MFF, and TOMM20 are provided in Table S1. For the E gene and Spike1 RT-qPCR, we used the protocols described in [45] and [46], respectively. For the remaining genes, 100 ng of total RNA was used for Taqman reactions using primer probes from ThermoFisher (Carlsbad, CA, USA): Hs00242739_m1 (LTB); Hs00174128_m1 (TNF); Hs00232399_m1 (RELB); Hs00357891_s1 (JUNB); Hs00759776_s1 (FOSL1); Hs00765730_m1 (NFKB1); Hs00174103_m1 (CXCL8); Hs00601975_m1 (CXCL2); Hs00236937_m1 (CXCL1); Hs00173615_m1 (PTX3); Hs00174961_m1 (EDN1); Hs00299953_m1 (SERPINE2); and Hs01028889_g1 (NFKB2). GAPDH (Hs02786624_g1) was used for sample normalization. Gene expression variations were assessed by the 2ΔΔCt method, with Ct as the cycle number at the threshold. Desired PCR result specificity was determined based on melting curve evaluation. ## 2.9. Western Blotting HBMECs were cultivated in 60 mm2 dishes and, at desired times, were washed with PBS and lysed in the presence of 1× Laemmli Buffer (0.0625M Tris, 0.07M SDS, $10\%$ glycerol, $5\%$ β-mercaptoethanol, and bromophenol blue). Protein concentration was measured with BCA Protein Assay Kit according to the manufacturer’s instructions (Thermo Fisher Scientific, Carlsbad, CA, USA). Then, 30 µg of protein were loaded onto 4–$20\%$ gradient acrylamide gels (Bio-Rad Laboratories, Hercules, CA, USA). Membranes were blocked with bovine serum album (BSA) $5\%$ in TBS-$0.05\%$ Tween20 and incubated overnight at 4 °C with the primary antibodies at 1:1000 dilution in TBST (Table S2). The next day, blots were washed with TBS-$0.05\%$ Tween20, incubated for 1 h at room temperature with secondary antibodies (Lincoln, NE, USA), and analyzed using the Licor CLX imaging system and the Image Studio 4.0 software (LI-COR, NE, USA). ## 2.10. Immunofluorescence Cells were grown on 13-mm round glass coverslips and fixed at desired times with $4\%$ paraformaldehyde in PBS for 10 min at 20 °C, permeabilized with $0.5\%$ Triton x-100 (Sigma–Aldrich, St. Louis, MO, USA), blocked with $3\%$ bovine serum albumin (BSA, Sigma-Aldrich, St. Louis, MO, USA), and incubated overnight with primary antibodies at 4 °C. Cells were washed with PBS and incubated with fluorescently labeled secondary antibodies for 1 h at 37 °C. For nuclear visualization, cells were incubated with DAPI (4′,6-diamidino-2-phenylindole) and mounted in a solution of glycerol and DABCO (1,4-diazabicyclo [2.2.2]octane, Sigma-Aldrich, St. Louis, MO, USA) in PBS. The list of primary and secondary antibodies used in this study is detailed in Table S2. ## 2.11. Quantitative Analysis of Mitochondrial Network Morphology Mitochondrial network morphology was analyzed using the Mitochondrial Network Analysis Tool (MiNA) for the Fiji distribution of ImageJ [47]. Images were cropped into individual cells. To enhance contrast and sharped mitochondrial images, several pre-processing tools were applied to each image prior to MiNA analysis. First, an unsharp mask (sigma = 3) was used to sharpen images by subtracting a blurred version of the image (i.e., unsharp mask) from the image. The unsharp mask is created by Gaussian blurring the original image and multiplying the blurred image by the mask weight (0.8). Second, a median filter (radius = 1) was applied to each image. The median filter functions by replacing each pixel with the neighborhood median, where the neighborhood size is determined by the radius. Following pre-processing, images underwent thresholding using the Otsu thresholding to produce a binary image [48]. The mitochondrial footprint is calculated as the total number of mitochondria-signal positive pixels from the binarized image. A morphological skeleton is then produced from the binarized image using the Skeletonize 2D/3D plug-in [49,50]. This method employs iterative thinning to create a skeleton of mitochondrial structures, one pixel wide. Length measurements of the mitochondrial structures are then measured using the Analyze Skeleton plug-in, resulting in two additional parameters: mean branch length and mean summed branch length. Mitochondrial form branching networks, in which branches intersect at a node. The mean branch length is calculated as the mean length of mitochondrial structure between two nodes. Mean summed branch length is calculated by determining the sum of branch lengths within an independent network structure and dividing by the total number of independent networks within a cell. ## 2.12. Angiogenesis-Related Protein Secretome For the generation of HBMEC Conditioned Medium (CM), cells were plated on 6-well plates and, after infection, were maintained in a total volume of 1 mL per well. Conditioned culture media were collected at 24 h post-infection (hpi) and centrifuged for 5 min at 10,000 rpm at 4 °C and stored at −80 °C until use. Secretion of angiogenesis-related protein levels was detected using a Proteome Profiler™ Human Angiogenesis Antibody Array kit (R&D Systems) according to the manufacturer’s instructions. Membranes were incubated with pools of two independent experiments as follows: Membrane 1: Mock culture, experiment #1 + Mock culture, experiment #2; Membrane 2: MOI 0.01 experiment #1 + MOI 0.01 experiment #2; Membrane 3: MOI 0.1 experiment #1 + MOI 0.1 experiment #2). Spots were developed with chemoluminescence, and X-ray films were exposed for 1, 5, 10, and 15 min to detect differentially expressed proteins. Densitometric analysis was performed with UN-SCAN-IT gel analysis software version 7.1, and relative intensity values for each spot of the 1-min exposed film were analyzed via GraphPad Prism software version 9.0.1. ## 2.13. Transmission Electron Microscopy Cells were grown on 35 mm Petri dishes and infected or treated as described above. At the desired time, cultures were washed in PBS, fixed with $2.5\%$ glutaraldehyde diluted in 0.1 M cacodylate buffer with $3.5\%$ sucrose and CaCl2 for 1 h at 20 °C, followed by washes in cacodylate buffer and post-fixation with $1\%$ osmium tetroxide with potassium ferricyanide for one hour at 4 °C in the dark. Cells were dehydrated in a crescent acetone gradient and embedded in Epon resin at 60 °C for 72 h. Ultrathin sections were obtained with Leica ultramicrotome and collected in 300-mesh copper grids, stained with uranyl acetate and lead citrate, and visualized at Hitachi Transmission Electron Microscope at Centro Nacional de Bioimagem (CENABIO-UFRJ). ## 2.14. Statistical Analyses For RT-qPCR and western blotting, a minimum of 5 independent cell culture preparations were used and analyzed with Two-Way ANOVA with Bonferroni post-test in GraphPad Prism Software v 9.3.1. Morphometrical analysis of ZO-1 immunostaining was performed with ImageJ software for fluorescence intensity and Tight Junction Organization Rate (TiJOR) using the TiJOR macro for ImageJ, which is an index of localization of tight junction proteins in the membrane–membrane contact region of adjacent cells as described by [51]. ## 3.1. Characterization of HBMEC Challenge by SARS-CoV-2 In order to characterize the profile of host cell infectivity by SARS-CoV-2, HBMEC and Vero E6 cells were challenged with viral particles in the presence or not of serine endoprotease TPCK trypsin (1 μg/mL), which was shown to increase infectivity in Calu-3 cells, a permissive cell line for the efficient replication of SARS-CoV-2 [52]. Cultures were exposed to different multiplicities of infection (MOIs) of 0.01, 0.1, 1, and 2, and supernatants were collected after 6, 24, 48, and 72 h and analyzed by RT-qPCR for quantification of viral E gene (Figure 1A). We found that HBMECs showed no increase in viral replication or release in the supernatant over time, whereas Vero E6 cells had a time-dependent release of SARS-CoV-2 in the supernatant, as expected (Figure 1A). TPCK trypsin treatment did not affect the cell infectivity rates; however, for consistency, all subsequent assays were performed in the presence of TPCK. In the same context, HBMECs challenged with different MOIs did not show any increase in the expression of SARS-CoV-2 Spike1 and E genes after 6 and 24 h (Figure 1B). We showed recently that HBMEC cells express, to some extent, several SARS-CoV-2 receptors at both RNA and protein levels [8]. Therefore, we evaluated the possible effect of SARS-CoV-2 exposure on the expression of ACE2 and TMPRSS2 in HBMECs and found that exposure to MOI 0.1 induced a $40\%$ decrease in ACE2 mRNA expression ($p \leq 0.05$), which did not result in ACE2 protein level alterations (Figure 1C). However, TMPRSS2 showed a 1.77-fold increase in protein levels in MOI 0.1-infected cultures at 24 hpi ($$p \leq 0.0782$$). Despite the apparent non-productive infection of HBMEC, a challenge with SARS-CoV-2 for 24 h was able to increase immunoreactivity to cleaved caspase-3, an executioner of apoptosis (Figure 1D). MOIs 0.01 and 0.1 resulted in 2.27 and $4.1\%$ of caspase-3-positive cells, respectively, whereas non-infected dishes showed a physiological rate of $0.7\%$ of stained cells. The apoptotic stimulus was also observed in Vero E6 cells 24 h after the challenge with MOI 0.1 (Figure 1D). Positive control with 0.5 and 2.0 µg Staurosporine for 2 h led to 1.9 and $9\%$ of caspase-3 positive HBMECs, respectively (not shown). ## 3.2. SARS-CoV-2 Affects Tight Junction Genes Expression in BBB-Forming Cells The barrier property of BMECs is mostly conferred by the expression and function of tight junction proteins, such as ZO-1 and claudin-5 [53]. HBMEC and Vero E6 cells were infected as described above and analyzed at 6 and 24 hpi. ZO-1 immunoreactivity was drastically altered in infected Vero cultures (Figure 2A) and showed discontinuous staining in cell-cell contacts, as compared to uninfected controls. SARS-CoV-2 viral particles were clearly detected in Vero cells, as revealed by Spike1 immunoreactivity. Conversely, infected HBMEC cultures did not present significant differences in the distribution of ZO-1 along cell membranes at 24 hpi (Figure 2A). To better evaluate ZO-1 organization in TJ, we performed densitometric (ZO-1 fluorescence intensity) and tight junction organization rate (TiJOR) [51] analyses in SARS-CoV-2-exposed HBMECs. We observed that ZO-1 presented a significant 1.29-fold increase in TiJOR index with exposure to MOI 0.1 at 6 hpi, concomitantly with a 1.3-fold increase in fluorescence signal, and such effects were lost after 24 h. In parallel, MOI 0.01 affected the ZO-1 fluorescence signal in HBMECs after 24 h of exposure by 1.19-fold (Figure 2B). ZO-1 and claudin-5 mRNA expression remained unaltered in HBMECs after 6 and 24 h of the SARS-CoV-2 challenge (Figure 2C), but their protein levels were significantly increased by 2.0- and 1.17-fold by the MOI 0.1 at 24 h, respectively (Figure 2D). ## 3.3. Exposure to SARS-CoV-2 Promotes Endothelial Activation and Hyper-Inflammatory Response In Vitro We performed RNA-Seq analyses 6 and 24 h after exposing HBMECs to SARS-CoV-2 to determine their transcriptional profiles. At 6 h, biological replicas had high variability across experiments, as determined by the square root of the common dispersion and visualized by principal component analysis (not shown). Exposure to both MOIs 0.01 and 0.1 led to minimal effect on HBMEC’s transcriptome, with few significantly differentially expressed genes (DEGs) and no pathways enrichment found (Supplementary Material). However, at 24 h, we observed a significant impact on host cell transcriptome: exposure to SARS-CoV-2 MOI 0.1 led to the up-regulation of 23 and down-regulation of four genes. The volcano plot and heatmap in Figure 3A,B, respectively, depict the transcriptomic profile of HBMECs exposed to the MOI 0.1 at 24 hpi. Data obtained from RNA-Seq was consistent with endothelial activation, with high expression levels of cytokines (IL-6, IL-8, TNF) and chemokine (CXCL1, -2, -8, and CCL20) encoding genes (Figure 3). Accordingly, functional enrichment analysis revealed that the main genes found related to “Cytokine signaling in the immune system”, “TNF signaling”, and “TNFR1-induced NFkappaB signaling pathway”, among other Reactome terms (Figure 3C). In fact, TNF was the most up-regulated gene, with a 104-fold increase, followed by TNF-c (Lymphotoxin beta, LTB), with a 32.8-fold change (Figure 3, Table 1). Interestingly, LTB is a known inducer of the noncanonical NFκB inflammatory pathway [54] and was found to be up-regulated both at 6 and 24 h in SARS-CoV-2-exposed HBMEC by RT-qPCR (Figure 3E). Although our RNA-Seq data revealed an increase in NFκB2 (p100/p52) and NFκBIA (IκBα), we performed RT-qPCRs with additional biological samples for NF-κB1 (p105/p50) and NF-κB2 and observed that due to biological variability such genes remained unaltered in challenged cultures (Figure 3D). However, RELB, the main activator of the noncanonical NF-κB signaling pathway [54], was shown to be up-regulated by confirmatory RT-qPCR at 24 h (Figure 4). We also further confirmed by RT-qPCR the up-regulation of inflammation-related genes, including LTB, TNF, IL-6, CXCL1, CXCL2, and CXCL8 (Figure 3E). Pentraxin3 (PTX3) is a glycoprotein involved in the innate immune response and has a relevant role in FGF2-dependent angiogenesis [55]. We found PTX3 to be 19.6-fold-increased in SARS-CoV-2-exposed HBMECs (Figure 3A and Figure 4). Apart from the inflammatory transcriptomic response, KEGG pathways related to ribosomal structure/function and mitochondrial biology were found to be altered by SARS-CoV-2-exposed HBMECs at 24 h (Figure 3D). ## 3.4. Angiogenic Profiling of SARS-CoV-2-Exposed HBMEC Cells Dysfunctional angiogenesis is a common phenomenon observed in neuroinflammatory states and can be a result of BBB damage [56]. We analyzed the profile of angiogenesis-related secreted proteins by HBMECs during the challenge with SARS-CoV-2 and observed that out of 55 spotted targets, 15 had the most significantly detectable signals (Figure 3A,B, Table 2). The highest signals were observed for uPA, serpin-E1 (PAI-1), IL-8, thrombospondin-1, VEGF, TIMP-1, endothelin-1 (ET-1), PTX3, angiogenin, and amphiregulin, with at least 5000 pixels each. SARS-CoV-2-exposed cultures (MOI 0.1) had the most pronounced increase in the secretion of PTX-3 and TIMP-1 as compared to Mock-treated cultures, with 113 and $112\%$ levels, respectively. Accordingly, PTX-3 was also one of the most up-regulated genes as determined by RNA-Seq (Figure 3). We further assessed the expression levels of PTX3 by RT-qPCR and found it to be increased in HBMEC cultures after 6 and 24 h exposure with the MOI 0.1, whereas VEGF and ET-1 showed no significant changes at the transcriptional level (Figure 4C). Insulin-like growth factor binding protein-3 (IGFBP-3), a member of the IGFBP family, was shown to be 166 and $125\%$ more abundant in the HBMEC-conditioned media in MOI 0.01 and 0.1-treated dishes, respectively. Interestingly, monocyte chemoattractant protein 1 (MCP-1) had a selective increase in secreted levels in HBMEC cultures exposed to MOI 0.01, shifting from 4549 pixels in Mock-treated cultures to 7624 pixels in MOI 0.01-exposed cultures, which corresponds to a 1.67-fold increase; whereas MOI 0.1-exposed cultures showed a 4759-pixel signal for MCP-1. We performed scratch-wound healing migration assays in infected HBMECs; however, no effect in cellular migration was noticed in challenged cultures compared to Mock-treated cultures (not shown). Interestingly, hypoxia-inducible factor-1 alpha (HIF-1α) was also increased by SARS-CoV-2 challenge at both MOIs at 24 hpi (Figure 4C). ## 3.5. Mitochondrial Plasticity Is Affected by Exposure to SARS-CoV-2 Because mitochondria play a role in cellular homeostasis and pathology, we sought to investigate the effects of the SARS-CoV-2 challenge on mitochondrial plasticity in HBMECs. Cells were immunostained for mitochondrial import receptor subunit TOMM20 (Figure 5A). Our first observation was that MOIs 0.01 and 0.1 induced a denser mitochondrial network profile when compared to the Mock-treated condition (Figure 5A–C). Mitochondrial Network Analysis (MiNA) [47] revealed that mitochondrial footprint (Figure 5C), which measures the mitochondria signal in a 2-dimensional image of a cell, was found to be significantly increased in HBMECs challenged with the MOI 0.1 at 6 h and with both MOIs at 24 h. We next measured the mean mitochondrial branch length mean, which is the average length of mitochondrial structures that are either independent or connected to networks (Figure 5C). We observed a slight, yet significant, increase in cells exposed to MOI 0.01 at 6 h and with MOI 0.1 at 24 h, with a 7 and $3\%$ increase, respectively. Furthermore, MiNA revealed that SARS-CoV-2 induced an overall increase in mitochondrial networks, with a significant increase in summed branch length mean values at 6 (34 and $33\%$ increase for MOIs 0.01 and 0.1, respectively) and 24 hpi (38 and $45\%$ increase for MOIs 0.01 and 0.1, respectively). Mitochondrial morphological analyses were further assessed by TEM (Figure 5B), and we found that challenged HBMECs displayed larger, swollen mitochondria with reduced cristae and, to some extent, associated with multivesicular bodies. Moreover, MOI 0.1-treated cultures displayed 356 mitochondria/mm2, while Mock-infected cultures had 266 mitochondria/mm2 ($p \leq 0.05$), which corresponded to a $33\%$ increase (Figure 5D). Since changes in mitochondrial networks could be influenced by abnormal fission or fusion events [57], we evaluated the expression of markers of such processes. TOMM20, used to determine mitochondrial networks by confocal microscopy and MiNA analysis (Figure 5A,C), had a four-fold increase in its mRNA level ($p \leq 0.05$) in MOI 0.01-exposed cells. However, no changes were detected in TOMM20 protein levels by western blotting (Figure 5E). We then assessed the expression of mitochondrial fission-related genes. Fis1 and Drp1 mRNA were significantly increased by 4-fold and 3-fold, respectively, at 24 hpi in HBMECs exposed to the MOI 0.01, which did not translate to changes in Fis1 and Drp1 protein content (Figure 5E). Drp1 phosphorylation at serine 616 (Drpi1S616) residue, which is responsible for directing mitochondrial fission [58], was not altered in SARS-CoV-2-exposed cultures. Mitochondrial Fission Factor (MFF) showed a 0.84-fold reduction induced by MOI 0.1 at 24 hpi, which also did not reflect in altered protein expression. However, Mitofusin-2 (Mfn2) mRNA levels showed a 4-fold increase by MOI 0.01 at 24 h ($$p \leq 0.02$$), while Mfn2 protein levels were increased by 1.5-fold in MOI 0.1-exposed cultures as compared to Mock-treated (Figure 5E). ## 4. Discussion Neurological consequences of COVID-19 still pose a relevant puzzle to the medical and scientific community. Since its first cases, CNS invasion has been described [59,60], but the routes and mechanisms by which SARS-CoV-2 gains entry to brain parenchyma remain elusive [61]. In the present study, we aimed to deepen previous observations of our group that exposure to SARS-CoV-2 proteins led to HBMEC cellular responses, such as tight junction protein remodeling [8]. Despite our previous observation that primary HBMECs express several receptors for the virus [8], we found little to no indication of productive viral replication in the supernatants of HBMECs, which is in accordance with previous reports that described that several endothelial cell types are not permissive for SARS-CoV-2 productive infection [62,63]. Krasemann et al. [ 64] observed infection of iPS-derived HBMECs but only at MOIs 10 and 100, which are unlikely to have pathological significance. Exposure to SARS-CoV-2 did not affect the expression of ACE2 and TMPRSS2, and, despite the apparent lack of productive infection, exposure to SARS-CoV-2 increased cleaved caspase-3 immunoreactivity, an indicator of apoptotic cell death. This effect was observed both in HBMECs and Vero epithelial cells and is consistent with what was described in the literature [65]. In fact, HBMECs have been shown to undergo cell death in response to viral infections, including Dengue [66] and Zika [67,68] viruses, followed or not by changes in BBB permeability. These results suggest that the interaction of host cells with viral surface proteins may be sufficient to trigger programmed cell death cascades even in the absence of a productive infection. Tight junction proteins have a crucial role in maintaining BBB integrity and its selective paracellular permeability [53]. In our study, SARS-CoV-2-infected Vero E6 cells showed marked disorganization of paracellular tight junctions, as shown by ZO-1 immunostaining. Previous studies described that treatment of HBMECs in 2D or 3D cultures with the S1 subunit Spike1 protein led to mislocalization of ZO-1, concomitantly with cytokine secretion [33,69]. Not only ZO-1 [70] but also β-catenin, cadherin-5ccludingcludin junctional proteins have recently been shown to be affected by SARS-CoV-2 proteins in HUVECs [71]. ZO-1 possesses a PDZ domain, which is responsible for binding and interacting with other proteins [72,73]. Interestingly, ACE2 possesses a PDZ-binding domain [74,75], and it has been suggested that epitopes of viral proteins, such as 1–60: M1Lys60 and 241–300: Ala240-Glu300 could directly bind to ZO-1 and VCAM-1 PDZ domains, thus suggesting a possible alternative route of CNS entry. We found ZO-1 and claudin-5 protein levels to be increased after 24 h of SARS-CoV-2 exposure in HBMECs, and this increase is consistent with what our group recently demonstrated with S1 treatment [8]. Whether ZO-1 increase can be directly related to BBB permeability may vary among experimental models and infectious agents. We have described that the Honduras isolate of Zika virus selectively up-regulated ZO-1 expression in vitro, while BBB permeability was increased in vivo [67]. In fact, proper BBB functioning relies on the combined expression, phosphorylation, and/or localization of ZO-1, -2, and -3, claudin-5, occludins, and tricellulin [76,77]. Direct infection with higher MOIs of SARS-CoV-2 or treatment with plasma from COVID-19 patients failed to induce significant increases in permeability in BMECs in vitro [78]. Conversely, using the K18 mouse model and hamster infection, Zhang et al. [ 79] showed that SARS-CoV-2 effectively infects and replicates in HBMECs, but leads to no change in BBB permeability and TJ proteins. Interestingly, a massive inoculation of iPS-derived HBMECs (MOIs 10 and 100) showed active viral replication, whereas MOIs 0.1 and 1 described infectivity near $0.6\%$ cells, which is similar to what we observed herein [64]. Following the initial characterization of the cellular effects of HBMECs after the SARS-CoV-2 challenge, we sought to characterize the transcriptomic landscape of BBB-forming cells after such treatment. Previous data from our group have demonstrated that primary HBMECs exposed to the S1 subunit of SARS-CoV-2 Spike1 protein led to alterations in tight junction gene/protein expression [8]. Therefore, our aim was to deepen the knowledge of what molecular pathways are affected by SARS-CoV-2 proteins. We performed RNA-Seq analyses of HBMECs after 6 and 24 h challenges with SARS-CoV-2 MOIs 0.1 and 0.01. Due to the low change in the overall host cell transcriptome with most of the experimental conditions used in this study, we focused our subsequent analyses of the MOI 0.1 treatment at 24 hpi. The majority of the significantly up-regulated genes corresponded to known endothelial activation pathways, including CXCL1, -2, -3, CCL20, PTX3, ICAM1, and TNF. Combined upregulation of LTB, TNF, and RELB by SARS-CoV-2 provided evidence that activation of the non-canonical NF-κB pathway activation may be taking place. NF-κB is a family of transcription factors that can be activated by several ligands and activates the expression of proinflammatory cytokines and chemokines [80]. Interestingly, the main protease of SARS-CoV-2 (Mpr°) cleaves a member of the NF-κB family, NEMO, which, in turn, leads to HBMEC cell death in vitro and in vivo [81]. RELB can form heterodimers with p50/p105, p52/p100, and p65 [54,82] but can also bind to sirtuin1 to direct epigenetic silencing of inflammatory gene expression [83,84]. In fact, among KEGG pathways enriched in our datasets, we found ribosomal structure and function as possible candidates for epigenetic regulation induced by SARS-CoV-2. These observations suggest that host epigenetic factors may be key for the outcome of COVID-19 and/or long COVID-19. Indeed, the promoters of the genes involved in inflammation, including NF-κB, can be demethylated, thereby resulting in an increased expression of interferons (IFNs), possibly leading to a “cytokine storm” [85]. The expression of IL-6, another important player in the so-called cytokine storm occurring in the most severe COVID-19 patients, was also significantly increased in infected HBMEC, and it is known to be modulated by its promoter methylation. It was also observed that oxidative stress induced by viral infections, including SARS-CoV-2 infection, can inhibit the maintenance of DNA methyltransferase-1 (DNMT1), thereby aggravating the DNA methylation defects [86,87,88]. Our preliminary results (not shown) indicate that HBMEC exposure to SARS-CoV-2 results in a decrease in DNA methylation, supporting a recent study of genome-wide DNA methylation analysis in peripheral blood of COVID-19-infected individuals, which identified marked epigenetic signatures, such as hypermethylation of IFN-related genes and hypomethylation of inflammatory genes [89]. Such observations further suggest the involvement of epigenetic regulatory mechanisms in COVID-19 [90]. MCP-1, a pro-inflammatory chemokine well-known to be increased in COVID-19 patients [91,92,93,94,95], was also found to be increased in SARS-CoV-2-exposed HBMEC conditioned media, as detected by mini-proteome assays. Accordingly, recent findings from our group show that both delta and D614G Spike1 proteins are capable of inducing MCP-1 release from HBMECs (Stangis and Toborek et al., unpublished data). MCP-1 plays a key role in leukocyte migration to the brain parenchyma [96] and can be used as a biomarker of HIV-1-induced neuroinflammation/neuropathogenesis [97,98]. As stated above, PTX3 was one of the main hits found in the transcriptomic analyses. Pentraxins are a superfamily of multifunctional proteins with conserved phylogeny [99], divided into two groups based on their primary structure: short and long pentraxin, where c-reactive protein and PTX are examples of short and long pentraxins, respectively. PTX3 is expressed in several neural cell types [100,101,102,103,104] and in endothelial cells and can be upregulated by inflammatory stress, such as cytokine stimulation [105]. We performed profiling of angiogenesis-related panels in the supernatants of SARS-CoV-2-challenged HBMEC and confirmed that PTX3 was increased by this treatment. PTX3 is known to be produced in high amounts by blood vessels in vascular inflammatory conditions [106] and inhibits FGF2-dependent angiogenesis [55,107]. Pathological vascularization and angiogenesis have been described as a unique comorbidity associated with SARS-CoV-2 infection in the pulmonary endothelium [108], including microvascular distortion and increased intussusceptive angiogenesis [109,110]. Moreover, VEGF, as well as other angiogenic-related analytes, were found to be increased in COVID-19 patients (including PTX3), which correlated with disease severity [111]. Accordingly, VEGF levels were $8\%$ increased in the supernatants of SARS-CoV-2-exposed HBMECs, even though vegf transcripts remained unaltered. It is well-known that inflammation, especially IL-6-dependent, can stimulate defective angiogenesis [112], and our data further contributes to the notion that following SARS-CoV-2 infection, there is an intense brain endothelial activation, which leads to defective angiogenic signaling and possibly endothelial permeability. Additionally, we found HIF-1α to be greatly increased after 24 h of treatment. HIF-1α is a major angiogenesis inductor and is known to be up-regulated by distinct viral infections (reviewed by [113]). HIF-1α is activated and translocated to the nucleus upon hypoxic conditions [114], and it has been shown that COVID-19 patients present massive hypoxia due to vasoconstriction and coagulopathy [115]. Interestingly, ACE2 expression is decreased in pulmonary smooth muscle cells upon HIF-1α accumulation [116], whereas hypoxia leads to a biphasic modulation of both ACE2 and TMPRSS2 expression on brain microvascular endothelial cells (hCMEC/D3), with an initial increase at 6 h and a decrease at 48 h of hypoxic stimulus cells [117]. These observations are in accordance with our present data, that ACE2 is decreased while HIF-1α is increased at 24 hpi. Although VEGF is one of the most described downstream targets of HIF-1α activation, apoptotic cell death, and IFN-stimulated gene expression are additional targets of HIF-1α activation [113], which can also be dependent on NF-κB signaling pathway [118]. Our data indicate that HIF-1α up-regulation can be a part of a SARS-CoV-2-induced endothelial activation, along with cytokine/chemokine stimulation and NF-κB non-canonical activation. Our final series of experiments focused on mitochondrial morphology and dynamics in HBMECs following SARS-CoV-2 exposure. It is well known that mitochondria are gatekeepers of BBB endothelium physiology and correspond to higher cytoplasmic volume as compared to non-cerebrovascular endothelial cells [119,120]. Moreover, mitochondrial function is important for BBB maintenance and integrity [121]. It was described that SARS-CoV encodes a protein named open reading frame-9b (ORF-9b), which localizes to mitochondria, increases Drp1-mediated mitochondrial elongation, and activates innate cellular response via MAVS signalosome [122]. We first employed a morphological/morphometrical approach to determine the mitochondrial contents and cellular distribution. Herein we demonstrated that direct exposure to SARS-CoV-2 led to a remodeling of mitochondrial networks. By using the MiNA plugin, we verified that challenged HBMECs had increased mitochondrial footprint as an estimation of the overall TOMM20 pixel signal. Recent reports have also shown an effect of SARS-CoV-2 and COVID-19 on mitochondrial biology: monocytes isolated from COVID-19 patients display reduced mitochondrial membrane potential, and SARS-CoV-2 viral load was positively correlated with the generation of ROS [123]. Importantly, endothelial cells exposed to SARS-CoV-2 Spike1 protein showed decreased tubular and increased fragmented mitochondrial networks in vitro, which was accompanied by a decrease in oxygen consumption rate and increased extracellular acidification rate [124]. Confirming observation was recently described by Domizio et al. [ 125], in which pulmonary endothelial cells in a lung-on-a-chip infection model displayed increased mitochondrial networks. Similarly, we demonstrated that mitochondrial networks were increased, as determined by summed branch length analyses, which indicates that SARS-CoV-2-exposed HBMECs had longer mitochondrial ramifications. Changes in endothelial mito-morphology are well described in several models of inflammatory diseases and/or aging [126,127,128] and are correlated with abnormalities in the mitochondrial quality control system, which in turn, can lead to increased ROS production. Mitochondrial quality control encompasses biogenesis, fission, fusion, and mitophagy processes, which are essential for its biology and function. We analyzed mitochondrial fusion and fission markers after exposing HBMECs to different concentrations of SARS-CoV-2. Our results showed a significant increase in the fission and fusion-related gene expression Fis1, Drp1, and Mfn2 and a trend to increase MFF when cells were exposed to MOI 0.01. Interestingly, this effect is opposite when cells are exposed to MOI 0.1, showing a significant decrease in the MFF mRNA levels as well as a trend to decrease Fis1, Drp1, and Mfn2, possibly as a compensatory mechanism. However, Mfn2 was the only mitochondrial plasticity marker, which showed a significant increase in protein levels in HBMECs exposed to MOI 0.1, which could explain the increased values in morphometric MiNA data that revealed increased branch length means. Confirming our results, several studies had shown the relationship between endothelial cell dysfunction and mitochondria fusion and fission balance in response to cellular damage [128,129,130,131]. Moreover, we found mitochondria associated to some extent with multivesicular bodies, which has also been described as another pathway for mitochondrial quality control [132,133]. Interestingly, several reports have linked NF-κB-mediated inflammation with mitochondrial responses [134,135,136,137], which could indicate that, in fact, mitochondrial remodeling observed in infected HBMECs could be due to (or lead to) SARS-CoV-2-induced inflammatory response. ## 5. Conclusions Despite no active replication or signs of productive infection, exposure to SARS-CoV-2 leads brain microvascular endothelial cells to a proinflammatory activation, possibly mediated by NF-κB non-canonical pathway activation. These events would result in mitochondrial and tight junction remodeling and endothelial apoptosis. Taken together, our data point to a relevant role of circulating SARS-CoV-2 viral particles or proteins on BBB-forming endothelial cells, which could contribute to a neuroinflammatory state. These events reflect important aspects of clinical observations of neurological and cerebral vascular manifestations of COVID-19. ## References 1. COVID Data Tracker 2. 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--- title: Laser Therapy Changes the Expression of Matrix Metalloproteinases in Bleomycin-Induced Skin Fibrosis authors: - Anna G. Soboleva - Vladimir V. Sobolev - Mari M. Karapetyan - Alexandre Mezentsev - Olga I. Rud’ko - Evgenia D. Davydova - Julia A. Mogulevtseva - Olga V. Zhukova - Irina M. Korsunskaya journal: Life year: 2023 pmcid: PMC10056988 doi: 10.3390/life13030810 license: CC BY 4.0 --- # Laser Therapy Changes the Expression of Matrix Metalloproteinases in Bleomycin-Induced Skin Fibrosis ## Abstract Matrix metalloproteinases (MMPs) are often considered biomarkers of skin fibrosis. At the early stages of the pathological process, an elevation of their enzymatic activity causes significant changes in the composition of the extracellular matrix. MMPs secreted by immune cells facilitate their migration to the site of damage. Then, the immune cells eliminate the affected cells and biomolecules. Moreover, bidirectional changes in the activity of proteolytic enzymes, including MMPs, accompany wound healing. This study aimed to assess changes in the expression of Mmp2, Mmp3, and Mmp9 after treating mice with laser therapy using the experimental model of bleomycin-induced skin fibrosis. Using immunohistochemistry, we characterized the histological features of scarred skin. We also analyzed changes in the expression of MMPs using real-time polymerase chain reaction before and after laser irradiation. We showed that treatment of the mice with a CO2 laser partially normalized the histological features of scarred skin. We also noticed a decrease in the expression of Mmp2, Mmp3 (both $p \leq 0.05$), and Mmp9 ($$p \leq 0.065$$) during scar healing. The obtained results suggest that normalization of skin homeostasis requires control of MMP activity via induction of genes. ## 1. Introduction Fibrosis is a pathological condition characterized by an expansion of connective tissue that compromises the integrity of the affected organ [1,2]. Trauma is a cause of cutaneous fibrosis. It may also appear at sites of burns or frostbite due to the developing inflammatory response. Scarring is one of the complications associated with skin fibrosis [3,4]. In the skin, the deposition of the extracellular matrix, primarily collagens, is an indispensable and reversible part of wound healing. In turn, dysregulation of the tissue homeostasis may lead to an irreversible fibrotic response, especially if the tissue injury is either severe or repetitive. Fibrosis is a hallmark of numerous systemic autoimmune disorders, such as rheumatoid arthritis, scleroderma, and progressive systemic sclerosis [5]. In cancer patients, fibrosis may follow chemo- and radiotherapy [6,7]. Presently, more than a dozen animal models, such as genetically modified mice and mice with chemically induced fibrosis, are available. These experimental models are used to explore the histological features of the scarred tissue and the associated changes in the expression of disease-relevant genes (rev. in [8]). Although some aspects of fibrosis in humans and mice differ, the animal models would help us explore the early stages of this pathological condition. Due to slow, almost asymptomatic initial onset, they frequently remain undiscovered during the evaluation of human patients. Furthermore, the animal models also would let us assess the role of specific genes in the pathogenesis of human fibrosis. In addition, they would allow us to evaluate new experimental therapies. Obtaining the experimental model of bleomycin-induced skin fibrosis requires multiple consequent injections of mice skin with bleomycin [9,10]. Bleomycin is an antibiotic with a robust antiproliferative effect. In mammalian cells, bleomycin interferes with the inclusion of thymidine deoxyribonucleoside triphosphate into the DNA and causes the appearance of DNA breaks. To establish the named model, the experimenters inject bleomycin regularly until they observe the desired pathological changes, such as epidermal hypertrophy and dermal fibrosis, consisting of an accumulation of collagen and dense extracellular matrix material, and adipose atrophy becomes evident. Then, there is a need to wait until the inflammatory response subsides and fibrosis occurs to perform the necessary experimental procedures. Upon completion of the experimental protocol, the experimenters evaluate the expected therapeutic effects and analyze the role of selected genes in the pathological process using histological methods and data on gene expression in the collected samples. Along with transforming growth factor-β (TGF-β) and decorin (DCN), matrix metalloproteinases (MMPs) are the most characteristic biomarkers of cutaneous fibrosis. In the affected area, MMPs maintain the balance between the accumulation of the extracellular matrix (ECM) and its breakdown [2]. TGF-β activates the biosynthesis of collagen and inhibits the degradation of ECM proteins. Furthermore, it stimulates the migration of macrophages and fibroblasts to the wound. At the site of damage, TGF-β promotes the interactions of cells with the ECM and the differentiation of myofibroblasts to fibroblasts [11]. It also downregulates the biosynthesis of collagen. DCN downregulates collagen fibrillogenesis via the inactivation of TGF-β. In turn, a suppression of DCN distorts the structure and organization of collagen fibers [12]. MMPs are responsible for changes in the composition of the ECM. Being directly involved in the pathogenesis of fibrosis, they contribute to skin remodeling and re-epithelization. MMPs secreted by immune cells facilitate their migration to sites of inflammation to degrade damaged cells and malfunctioned biomolecules. The recovery of scarred tissue is also accompanied by significant changes in the activity of proteolytic enzymes, including MMPs [13,14]. These make MMPs valuable biomarkers of cutaneous fibrosis and prove their relevance for experimental studies and clinical applications. The previous experimental works performed on animal models suggest that modulating active MMPs can be beneficial for patients with skin fibrosis to restore normal tissue homeostasis [15]. In this regard, careful and cautious management of MMPs in disease-affected tissues and organs will help to suppress the inflammatory response and downregulate the infiltration of immune cells [16]. For this reason, manipulation of MMPs requires an understanding of their role at various stages of the disease, including the early stages when it may not be easy to diagnose. This paper aimed to evaluate changes in the expression of MMP genes: Mmp2, Mmp3, and Mmp9 caused by laser therapy in the experimental model of bleomycin-induced skin fibrosis. ## 2.1. Lab Animals Mice were purchased from the Stolbovaya Research Center for Biomedical Technologies at the Federal Medical and Biological Agency of Russia (Stolbovaya, Moscow region, Russia). The study involved 21 female Balb/c mice (7–8 w. o.). The weight of the mice ranged from 20 to 30 g. Mice were housed in an air-conditioned room at a temperature of 21–23 °C on 14 h light cycles since day 1, with 4–5 animals per cage. The air humidity was between 50 and $65\%$. Mice received a balanced diet (Laboratorkorm, Moscow, Russia). They also had unlimited access to drinking water. The quarantine lasted for two weeks. Before the experiment, the mice were anesthetized with a combination of ketamine and xylazine (80–100 and 7.5–16 mg/kg, respectively) delivered intraperitoneally in 100 μL. Before sampling the skin, the animals received a lethal dose of sodium pentobarbital (500 mg/kg) administered through the tail vein. For the experiment, the animals were divided into three groups (7 mice per group) Animals were ranked by weight and distributed into three treatment groups after randomly settling the first animal. The breeders preselected the mice to exclude littermates from the same shipment. We did not perform a paternity test on the mice in the lab. The animals were injected with either saline or bleomycin intradermally by a staff veterinarian who had not been informed of the following treatment options. The first group of animals received injections of 100 µL of saline ($0.9\%$ sodium chloride) intradermally. The second and third groups of mice received injections of 0.1 U bleomycin in 100 µL of saline. The injections continued every other day for 19 days in the interscapular region of the back. On the 30th day of the experiment, animals that belonged to the third group were irradiated with a SmartXide DOT® CO2 laser (DEKA, Manchester, NH, USA). The dose of irradiation was 18.2 J/cm2. The laser was preset to discrete mode. Samples were collected 48 h after irradiation by a lab technician who was unaware of the treatments received by the animals and the principles of their grouping. Specimens of healthy skin assigned to the correlation analysis were collected 2 cm away from the site of damage. Skin samples assigned to real-time PCR were rinsed in ice-cold phosphate-buffered saline (PBS) to remove excess blood, frozen in liquid nitrogen, and stored at −96 °C until needed for experiments. Samples designated for histology were fixed in $10\%$ formalin and embedded in paraffin. Paraffin blocks were sectioned at eight μm thickness and stained with hematoxylin–eosin (H + E) following a standard protocol by a lab member unaware of the experimental design and used treatment options. Briefly, the prepared sections were incubated at 37 °C for 30 min and deparaffinized by consequent washing in xylene (2 times, 30 min each) and $100\%$ ethanol (2 times, 10 min each). Deparaffinized sections were hydrated by washing in $90\%$ ethanol for 5 min and then in 70, 50, and $30\%$ ethanol (1 min each). After rinsing in PBS for 5 min, sections were stained with hematoxylin for 5 min and eosin for 30 s and washed with tap water after each incubation. After removing the excess water, sections were dehydrated with 2 changes of xylene (10 min each), mounted in VectaMount® Express mounting medium (Vector Laboratories, Newark, CA, USA), coverslipped, and sealed with transparent nail polish. After staining, the sections were examined using bright field microscopy with an AxioVert. A1 microscope (Zeiss, Oberkochen, Germany). The thickness of the epidermis and the total thickness of the skin were measured using ImageJ freeware (NIH, Bethesda, DE, USA) by a lab member unaware of treatments received by animals and the principles of their grouping. ## 2.2. Purification of Total RNA Total RNA was isolated using the TRIZOL reagent (Thermo Fisher Scientific, Waltham, MS, USA) according to the protocol provided by the manufacturer. Briefly, the tissue samples were homogenized in TRIZOL. Then, they were subjected to extraction with chloroform (5:1). After centrifugation (12,000× g, 15 min, 12 °C), the upper (aqueous) phase was precipitated with isopropanol (1:1). The pellet was washed in $70\%$ ethanol, spun down (8000× g, 5 min, 8 °C), and dissolved in nuclease-free deionized water (Evrogen, Moscow, Russia). The isolated total RNA was treated with RNase-free DNase I (Qiagen, Hilden, Germany) to remove the traces of genomic DNA. The concentration of RNA was assessed spectrophotometrically with Nanodrop (Thermo Fisher Scientific) equipment according to the instructions provided by the manufacturer. If the A260/A280 ratio in one of the samples did not exceed 2.0, the samples were re-purified with Clean RNA standard kit (Evrogen). The quality of the preparations was verified by electrophoresis in $1.5\%$ agarose gel in non-denaturing conditions. ## 2.3. Real-Time PCR Before the experiment, total RNA was converted to complementary DNA (cDNA) using the MMLV RT reagent kit (Evrogen) according to the manufacturer’s protocol. The amplification of DNA was performed on a real-time DT prime 5M1 instrument (DNA-Technology, Moscow, Russia) using an SYBR Green master mix supplied by Evrogen, according to the manufacturer’s recommendations. The primers (Table 1) were designed using NCBI Primer BLAST [17] and checked with a multiple primer analyzer (Thermo Fisher Scientific) for the formation of potential secondary structures, such as loops and dimers, purchased from DNA-Synthes (Moscow, Russia). The following conditions were used to amplify the DNA: 3 min at 95 °C, followed by 40 cycles of incubations at 94 °C for 30 s and 57 °C for 15 s. Each sample served as input to prepare three sets of probes that would be sufficient to repeat the experiment three times. The results were analyzed using the standard 2−ΔΔCt method [18] to compare the levels of expressed genes. Each ΔCt value was calculated as ΔCt = Ct (tested gene) − Ct (housekeeping gene). ΔΔCt was calculated as ΔΔCt = ΔCt (treated skin) − ΔCt (healthy control). The experiments were repeated three times for each sample by a lab member unaware of the experimental design. The raw data were normalized to the expression level of the housekeeping gene (18S RNA) and processed using a built-in computer program distributed by the manufacturers. ## 2.4. Statistical Analysis The results were presented as mean value ± standard error (m ± SE). The comparison of two independent means was performed using Prism software (version 5.01; GraphPad Software Inc. San Diego, CA, USA) with the significance of the difference between the groups assessed by a one-way ANOVA followed by Tukey’s post hoc comparison of means test. The differences between the means were considered significant if the probability of accepting the null hypothesis did not exceed 0.05. The sample size was calculated using the “resource equation method” [19]. ## 3.1. Laser Therapy Normalizes the Histological Features of Skin Damaged with Bleomycin Histological examination did not reveal any signs of inflammation or fibrosis in the skin of the animals treated with saline (Figure 1a). Contrarily, we observed significant thickening and swollenness in the skin of bleomycin-treated mice (Figure 1b). Compared to the animals injected with saline, the skin of the animals injected with bleomycin exhibited a significant structural remodeling of the dermis. The dermis contained multiple bundles of collagen fibers traceable throughout the upper and lower dermis. Their presence reflected an increased deposition of collagen (Figure 1b). Subcutaneous fat was reduced and partially replaced by connective tissue. In the upper dermis, the blood vessels were dilated and characterized by pronounced thickening of the walls. In some of them, the endothelial cells distinctly bulged toward the lumen. Many of the blood vessels contained a fair number of immune (nucleated) cells. In addition, we observed almost complete atrophy of the skin appendages, such as hair follicles. The total thickness of the skin increased twofold (Figure 2a). We also saw evident signs of epidermal hyperplasia (Figure 2b). At the same time, we did not observe morphologically visible epidermal damage following laser irradiation. However, the therapy partially normalized the histological features of the skin. The subsequent treatment of mice with laser therapy significantly normalized the histological features of the affected skin (Figure 1c). In mice subjected to laser therapy (group 3), we noticed fever fibrotic foci compared to the bleomycin-only control (group 2). The diameters of blood vessels reduced. The skin thickness also decreased (Figure 2a). We also noticed a faster reappearance of hair follicles compared to group 2. In addition, the absence of significant differences in the epidermal thicknesses after laser therapy and healthy skin (group 1) suggests a cessation of hyperplasia (Figure 2b). ## 3.2. Analysis of Gene Expression Revealed Significant Changes in the Expression of Matrix Metalloproteinases Analyzing the expression of MMPs, we found that laser therapy of mice injected with bleomycin (group 3) significantly downregulated Mmp2 and -3. ( Figure 3) compared to sham and negative control (groups 1 and 2, respectively). We also discovered a similar trend in the expression of Mmp9 (Figure 3). However, the observed changes were not statistically significant ($$p \leq 0.065$$). Comparing the expression of MMPs in individual mice treated with a CO2 laser, we noticed that the expression levels of Mmp2, Mmp3, and Mmp9 were strongly correlated. The levels of Mmp3 were proportional to the levels of Mmp9 (Figure 4a), and the experimental data could be fit with a linear regression (r2 = 0.97). On the other hand, the correlation between Mmp2 and either Mmp3 or Mmp9 (Figure 4b,c) was non-linear and could be approximated with a polynomial function. ( r2 = 0.98 and 0.96 and, respectively). At the same time, a similar analysis of mice belonging to two other groups did not exhibit a strong correlation in the expression of any pair of MMPs. ## 4. Discussion In this study, we examined the skin samples of animals (group 3) treated with the SmartXide DOT® CO2 laser (DEKA) and animals of two control groups. Animals of group 1 (sham control) received injections of saline instead of bleomycin, and these mice did not receive the following treatment with laser therapy. Animals of group 2 (negative control) received injections of bleomycin. However, they did not receive laser therapy. We also analyzed changes in the expression of MMPs, namely Mmp2, Mmp3, and Mmp9, in all three groups of mice. The results of the histological analysis revealed that we successfully established the experimental model of bleomycin-induced skin fibrosis. In this regard, we found that the skin of animals injected with bleomycin was significantly thicker than that of the sham control animals (see groups 1 and 2 in Figure 1). Moreover, the skin of bleomycin-induced mice exhibited evident signs of epidermal hyperplasia (Figure 2a,b). The dermis contained multiple bundles of misarranged collagen fibers (Figure 1b). Subcutaneous fat partially replaced the connective tissue. In addition, the skin lost most of the appendages. We also showed the skin remodeling effect of fractional lasers through the histological study of the collected skin samples. We report that, after laser therapy, the thickness of the skin became comparable to that in the sham control (Figure 2a). Similar thicknesses of the epidermis between groups 1 and 3 indicated cessation of hyperplasia (Figure 2b). The irradiated dermis (group 3) also contained fewer fibrotic foci than the dermis of non-irradiated bleomycin-treated mice (group 2). The analysis of gene expression (Figure 3) indicated that laser therapy of bleomycin-injected animals (group 3) significantly reduced the expression of Mmp2 and -3 compared to the other groups of mice. The expression levels of MMPs in healthy skin are low according to [20]. After an injury, several MMPs, including Mmp2, Mmp3, and Mmp9, become upregulated [21]. In human patients, the level of MMP2 is significantly higher in collagen bundle regions of keloids compared to non-collagen bundle regions [22]. Earlier, Matuszczak et al. [ 23] reported that therapy with an ablative fractional CO2 laser decreased the plasma level of MMP2 and α1- type I collagen in human patients with hypertrophic burn scars. At the same time, the therapy improved the texture, color, and functioning of the affected areas of the skin. In addition, the authors did not observe any recurrence or worsening of scar appearance up to 4 years after the treatment. An obvious way to improve the results of laser therapy is to combine skin irradiation with a supplementary treatment. In this regard, Lee et al. [ 24] discovered that applying adipocyte-derived stem cell-containing medium supplemented with niacinamide, the precursor of the cofactors niacinamide adenine dinucleotide (NAD) and its phosphate derivatives (NADP), after irradiation of skin with an ablative fractional laser (AFL) improved skin texture and pigmentation. They also significantly suppressed the expression of MMP1, MMP2, and proinflammatory cytokines IL1α, -1β, and -6 in the following in vitro experiments. Using fractional lasers for scars has several advantages (rev. in [25]). Heating the dermis to 50–70 °C, irradiation with a laser produces ~3 mm deep microscopic skin lesions, initiating rapid wound healing in the affected area. Inducing collagenases reverses the accumulation of collagen fibers in the dermis. Furthermore, fractional lasers induce conformational changes in the collagen structure and the formation of new collagen fibers due to the produced thermal effect. Specifically, non-ablative fractional lasers (NAFLs) stimulate dermal remodeling and improve the functionality of the epidermal barrier. In addition, applying the therapy during the early phase of wound healing shrinks the microcapillaries, reducing the blood flow. These advantages are crucial, especially for burn scars that often penetrate the dermis. At the same time, using an AFL with incorrect settings may worsen scarring [26]. It can also cause postinflammatory hyperpigmentation (PIH) [27]. In a recent paper, Chung et al. [ 25] reported that the AFL and NAFL treatment of pigs significantly changed the expression of Mmp2 and Mmp9. In ELISA experiments, the authors showed that the level of Mmp2 significantly increased after low- and high-energy NAFL treatment compared to the sham control and AFL treatment. In turn, the differences between the sham control and AFL were insignificant. In qPCR experiments, Chung et al. [ 25] did not see significant differences between groups of hypertrophic scars subjected to AFL and NAFL treatment. Contrarily, the expression of Mmp2 in ALF-treated mice significantly exceeded that in the sham control. In this regard, we can propose that the observed changes between the two groups of scars treated with an NAFL likely reflected a progressive increase in Mmp2 expression. In our study (Figure 3), we show significant suppression of Mmp2 at the early stages of post-therapeutic recovery. Accordingly, the following increase in the expression of Mmp2 would explain the absence of statistical differences in Chung’s qPCR experiments [25] and the accumulation of secreted Mmp2 detected by ELISA. The same proposal can explain higher levels of Mmp2 in animals treated with low- and high-energy NAFLs. In addition, we would make a short comment on the unconventional experimental design used in Chung’s study that could influence their final results. The authors produced 40 hypertrophic burn scars on the abdomens of two animals. After epithelialization, they randomly subdivided scars into four groups and applied different treatments to each group. In this regard, we have to mention that it would be unusual to apply the same practice, i.e., simultaneously using a few treatments on the same human patient. Otherwise, controversial outbound signals released by inflamed tissue could originate the wrong type of immune response and cause an unexpected result. Forty-eight hours after the therapy, the expression of Mmp3 significantly decreases, similar to Mmp2 (Figure 3). As found earlier, stromelysin 1, encoded by Mmp3, contributes to the degradation of collagens, elastin, laminins, and other proteins of the ECM [28]. Furthermore, stromelysin 1 activates several matrix metalloproteinases: interstitial collagenase/MMP1, matrilysin/MMP7, and gelatinase B/MMP9. These enzymes are necessary for the degradation of proteins of the dermal ECM [29]. In cutaneous fibrosis, MMP3 is responsible for the contraction of fibroblasts and initiation of wound contraction [30]. Our data are in agreement with similar results obtained by others. For instance, Amann et al. reported that treatment of human three-dimensional (3D) organotypic skin models with a fractional erbium glass laser decreased the expression of MMP3 for five days after the therapy [31]. In our experiments, the expression of Mmp9 follows the same trend as two other MMPs (Figure 3). However, the changes between groups 2 and 3 are not statistically significant ($$p \leq 0.065$$). The data obtained by others [13] suggest that the expression level of Mmp9 after laser therapy reaches the baseline faster than that of Mmp3. For instance, the expression of MMP9 increased on day 5 in Amman’s qPCR experiments, although the authors did not find significant differences from the control [31]. Exploring the correlations of the expression levels, we found that the expression of MMPs in bleomycin-injected mice is well-coordinated. The levels of Mmp2, Mmp3, and Mmp9 were strongly correlated. We also noticed that changes in the expression level of Mmp3 were proportional to those of Mmp9, and the corresponding data points could be fit with a linear regression (Figure 4a). Contrarily, the correlation between Mmp2 and either Mmp3 or Mmp9 was non-linear and could be better approximated with a polynomial regression (Figure 4b and c, respectively). Non-linear correlations in gene expression of Mmp2 and two other MMPs may suggest that the biosynthesis of Mmp2 mRNA can be controlled by an enhancer. According to the published data [32], the proposed enhancer resides ~1500 base pairs upstream of the coding area. Its sequence contains a binding site for the transcription factor AP1, and its activation occurs during a traumatic injury of the tissue due to the binding to the enhancer of either Fosl1−JunB or FosB−JunB heterodimers of AP1 [33]. As we hypothesize, activation of the enhancer could produce an additional positive effect on the transcription of Mmp2 and accelerate the biosynthesis of Mmp2 mRNA at the site of damage compared to Mmp3 and Mmp9 mRNAs. According to another report, the enhancer turns to the active state following demethylation. During fibrogenesis, the enhancer becomes hypomethylated [34], and it increases the expression of Mmp2. Accordingly, transcriptional repression of the enhancer should produce a robust suppressive effect on the transcription of the Mmp2 gene compared to two other mRNAs in our experiments (Figure 4). At the same time, the absence of similar strong correlations within groups 1 and 2 suggest that, in mice not subjected to the laser therapy, the expression levels of MMPs remained close to their physiological values. To date, there is insufficient information on the molecular mechanism that regulates the expression of MMPs in laser-irradiated skin. According to Li et al. [ 35], the irradiation of the skin with a laser causes significant rearrangements in the 3D architecture of chromatin that produce changes in gene expression. Some of these changes occur due to the upregulation of IL-1β and the subsequent activation of the NFκB signaling pathways [36]. The others influence the availability of distant enhancers [37]. Summarizing the data cited in this paper, we presume that the regulatory mechanism controlling the expression of MMPs by laser irradiation is tissue-specific and scale-dependent since different devices and experimental conditions are likely to produce various outcomes. We have to admit that our study has limitations. First, we performed it in a mouse model, and, as we mentioned above, the wound-healing process in mice and humans is not the same. They are likely caused by differences in the immune systems and wound contraction mechanisms (rev. in [38]). Furthermore, mouse skin is much thinner and loosely attached to the underlying fascial tissue. Consequently, differences in skin tension cause different physiological responses to wounding. Third, human and murine skins differ in their density and size of hair follicles. The latter cannot be ignored because hair follicles contain stem cells that may contribute to wound healing. In addition, mice skin does not contain sweat glands, except on foot pads. On the other hand, we show that the experimental model of bleomycin-induced skin fibrosis could be used to explore the beneficial effects of laser therapy. Similarly to other animal models, this model can be used to perform as gene (protein) regulation as well as histological analysis. ## 5. Conclusions In conclusion, our histological analysis revealed that laser therapy partially normalized the structure of fibrotic murine skin in the experimental model of bleomycin-induced skin fibrosis. We also found significant changes in the expression of Mmp2 and -3 and statistically insignificant changes of Mmp9 ($$p \leq 0.065$$). In this regard, the obtained results demonstrated the efficiency of laser therapy and confirmed the efficiency of MMPs as biomarkers of skin fibrosis. In the paper, we speculate that the Mmp2, -3, and -9 can be responsible for the acceleration of the inflammatory response and the following progression of skin fibrosis. In addition, the named MMPs, especially Mmp2, can be used to monitor the course of skin damage. 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--- title: Elastic Textile Wristband for Bioimpedance Measurements authors: - Giuseppina Monti - Federica Raheli - Andrea Recupero - Luciano Tarricone journal: Sensors (Basel, Switzerland) year: 2023 pmcid: PMC10056993 doi: 10.3390/s23063351 license: CC BY 4.0 --- # Elastic Textile Wristband for Bioimpedance Measurements ## Abstract In this paper, wristband electrodes for hand-to-hand bioimpedance measurements are investigated. The proposed electrodes consist of a stretchable conductive knitted fabric. Different implementations have been developed and compared with Ag/AgCl commercial electrodes. Hand-to-hand measurements at 50 kHz on forty healthy subjects have been carried out and the Passing–Bablok regression method has been exploited to compare the proposed textile electrodes with commercial ones. It is demonstrated that the proposed designs guarantee reliable measurements and easy and comfortable use, thus representing an excellent solution for the development of a wearable bioimpedance measurement system. ## 1. Introduction Bio-impedance analysis (BIA) is a low-cost and non-invasive method widely adopted for body composition and clinical condition assessments [1,2,3,4]. The electrical properties of human tissues are exploited to estimate the fat mass and fat-free mass of an individual, starting with body impedance measurements. There are numerous applications that can benefit from BIA [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]: cardiovascular monitoring, monitoring dialysis and cancer patients, and monitoring the hydration of athletes during training and competitions. For some of these applications, particularly in the case of hydration monitoring, a real-time and continuous monitoring is useful. Accordingly, in the recent literature, some studies have investigated the possibility of replacing traditional BIA electrodes with textile electrodes. These studies take advantage of the enormous progress in the field of wearable electronics, from the use of conductive textile materials or materials that guarantee the same wearability as a common fabric to the development of extremely compact electronic modules [15,16,17,18]. The main problem that must be addressed in the development of wearable devices is the need to ensure comfortable and practical use. To this end, a device can be integrated into common wearable accessories (watches, bracelets, belts, etc.) or into clothing using non-conventional materials. Referring to the development of wearable electrodes for BIA, the main problem to overcome lies in the difficulty of developing textile solutions capable of providing reliable measurements without the use of liquid/gel moistures to reduce the contact resistance between the electrode and the skin [18,19,20,21,22,23,24]. In [12], a multi-segment and multi-frequency bio-impedance monitoring platform was presented. The proposed system was able to detect bio-impedance changes associated with fluid shifts due to physical exercises or postural changes. The focus was on the development of compact and wearable electronic circuitry that provided current excitation and voltage measurements, while the adopted electrodes were commercial disk electrodes similar to those used for electrocardiograms. In [20], two dry-textile electrodes for bioimpedance spectroscopy measurements were presented. The ankle-to-wrist whole body measurements of three subjects in the frequency range of 3 kHz to 500 kHz were analyzed. It was demonstrated that the proposed textile electrodes produced constant and reliable bioimpedance spectra. Regarding the agreement with the commercial electrodes, the error as function of the frequency was analyzed, and a certain deviation at high frequencies was observed. In [21], fully textile electrodes for hand-to-foot whole-body bioimpedance measurements were presented and compared with conventional Ag/AgCl electrodes. A configuration with four rectangular electrodes was investigated. The measurements of three subjects in the frequency range of 3–500 kHz were reported and discussed. The reported results showed stable and reliable measurements that were better than those reported in the reference literature due to the optimization of the straps. In [22], five different types of textiles as band electrodes for calf bioimpedance measurements were investigated and compared with conventional Ag/AgCl electrodes. The measurements of ten subjects in the frequency range of a few kHz up to 1 MHz were analyzed. The achieved results demonstrated that band electrodes allow for obtaining a more uniformly distributed current, and thus, they are preferable to spot electrodes for segmental measurements. The results presented in [20,21,22], even though few subjects were analyzed, not allowing for a complete statistical analysis, demonstrated the feasibility of using textile materials for the electrodes and the convenience of continuing with studies on the subject. In [23], textile electrodes fabricated by using the Silitex P130 fabric by Statex were presented and discussed. The Silitex P130 is a bi-elastic silver-plated knitted fabric with a layer of conductive silicone on one side. The proposed electrodes were rectangular electrodes with an area similar to that of a disposable commercial electrode, and the presence of the silicone layer allowed for reduced contact resistance, even in the absence of liquid moistures. However, the measurement campaign showed the need to pay great attention to the application of the electrodes, especially in the presence of hair. An interesting and new implementation of electrodes was proposed in [24], where bracelet-shaped electrodes fabricated by using a textile-conductive fabric were presented. In this paper, the preliminary results presented in [24] are further investigated and discussed. Various implementations of the proposed bracelet electrode are analyzed, and the results obtained from 40 subjects are discussed. It is shown that the proposed electrodes exhibit an excellent correlation with Ag/AgCl commercial electrodes and provide stable and reliable measurements, thus representing a good solution for developing a wearable system for bioimpedance measurements. The paper is structured as follows: In Section 2, after a brief introduction to BIA, the proposed textile electrodes are illustrated. Experimental results are presented and discussed in Section 3, and conclusions are drawn in Section 4. ## 2.1. BIA in Brief BIA is a non-invasive method for body composition evaluation from impedance measurements of the human body. A small AC (alternating current) current is applied, and the corresponding voltage drop is measured. As a result of the ratio of the measured voltage drop and the applied current, a complex impedance is obtained:Z=VI=R+jX. The resistive part of Z and R is mainly due to the amount of total body water, while the reactive part, X, is related to the capacitance of the cell membranes [3]. In most cases, the representations of Z in terms of module, \|Z\|, and phase angle, PA, are used. Bioimpedance measurements and, in particular, the phase angle, play key roles in the assessment of an individual’s clinical condition. In fact, biological tissues exhibit a complex electrical impedance that depends on tissue composition, health status, and the frequency of the applied AC current. The human body consists of biological tissues which, in turn, consist of cells that contain intracellular fluids and are suspended in extracellular fluids. Both intracellular and extracellular fluids have high conductivity (low resistance), being composed of ionic solution and other highly conducting materials. Intra- and extra-cellular fluids are separated by the cell membrane, which consists of a nonconducting layer sandwiched between two conducting layers. Accordingly, the cell membrane behaves as a capacitor, and therefore, it exhibits a reactance that decreases as the frequency of the excitation current increases. Consequently, the ability of the applied current to penetrate the cells depends on its frequency. At low frequencies (1–20 kHz), the current path is concentrated outside the cells and is more affected by extracellular fluids. For higher frequencies (20–200 kHz), the current path involves both extracellular and intracellular fluids. With the above considerations in mind, analytical formulas that exploit the measured values of R and X and anthropometric parameters to estimate the body composition have been proposed in the literature. In this regard, various models which differ in the number of compartments used to calculate the body composition are available. In the simplest one, the body is divided into only two compartments: fat and fat-free mass. In more sophisticated models (multi-compartment models), the following main compartments are adopted:-Total body water (TBW): This is the sum of intra-cellular water (ICW) and extra-cellular water (ECW).-Extra-cellular water (ECW): This is fluid localized outside cells. ECW is a very important parameter for evaluating a person’s hydration status since most of the fluids lost through sweating come from extracellular compartments. Given that changes in intracellular fluids are usually very small (typically less than $5\%$), most changes in TBW are due to changes in extracellular fluid. The evaluation of these compartments makes it possible to identify problems related to water retention, malnutrition, or local inflammation, which can lead to high ECW values.-Fat-free mass (FFM): This refers to all body mass that is not fat. With respect to fat mass, FFM has a higher conductivity due to its higher water content. Various formulas are available in the literature to evaluate FFM using bioimpedance measurements. The simplest formulas calculate the FFM starting with the height of an individual and the measured resistance R. More complicated formulas have recently been proposed, and these formulas also exploit other parameters, such as, for example, weight, age, sex, measured reactance, and anthropometric parameters.-Body cell mass (BCM): This represents the metabolically active tissues of the body. The BCM is an important index for assessing the physical condition of an individual, and it tends to decrease with age or due to poor nutrition. BCM is used to evaluate nutritional status in hemodialysis patients, and in general, it is a good reference value for the calculation of nutrient needs.-Fat mass (FM): This is the fat mass calculated as the difference between the total weight and the FFM. With respect to FFM, FM has a lower water content and thus a lower conductivity. ## 2.2. BIA Measurements Various instruments and methods are available for body composition assessments from impedance measurements. As already mentioned, a key role is played by the frequency of the applied AC currents and instruments for single-frequency or multi-frequency measurements. BIAs based on single-frequency measurements exploit empirical linear regression models for bioimpedance values measured at 50 kHz. Multi-frequency approaches use mathematical models and mixture equations based on bioimpedance measurements taken at different frequencies (0, 1, 5, 50, 100, and 200 to 500 kHz). Regarding the way measurements are performed, most of the commercially available instruments perform a whole-body impedance measurement by using four electrodes, where two electrodes are used for injecting the current and two are used for measuring the voltage drop. Different placements can be adopted as follows:-Hand-to-foot (H-F): In this case, two electrodes (one for the current and one for the voltage) are applied to the hand and two are applied to the foot.-Hand-to-hand (H-H) [1,2,3]: In this case, one pair of electrodes is applied to one hand (one for the current and one for the voltage) and the other pair is applied to the other hand. In both the H-F and H-H placements, the distance between the current electrode and the voltage electrode is a few centimeters, and starting from the end of the body, the current electrode precedes the voltage electrode. More recently, segmental-BIA approaches have been proposed. In these approaches, the impedance of a portion of the body is exploited for estimating abdominal fat, fluid accumulation in the pulmonary or abdominal region of the trunk, etc. However, for these approaches, standard protocols for the type and the placement of electrodes are not yet available. ## 2.3. Setup Adopted for the BIA Measurements The BIA 101 analyzer by Akern, compliant with Directive $\frac{93}{42}$ CEE and Standard CEI EN 60601-1 ($\frac{1998}{12}$), was used for measuring the bioimpedance [23,24,25]. The instrument performs single-frequency measurements at 50 kHz, and the output is the impedance in terms of real and imaginary parts. The BIA analyzer is equipped with a calibration kit that ensures a measurement accuracy equal to ±10 Ohms for \|Z\| and ±0.9 degrees for the phase angle. Total body H-H measurements were performed on participants (the persons under test, or PUTs) in a sitting position, as shown in Figure 1. Forty healthy PUTs volunteered to participate in the study. The volunteers were Italian men and women aged between 19 and 26 years. Each of them was informed and provided written and oral consent to participate. The characteristics of the PUTs (age, height, weight, and gender) are provided in Table 1. For each PUT, reference measurements were performed with commercial Ag/AgCl electrodes (with a contact area of 4.4 cm2) produced by EF Medica Ltd., Caldaro, Italy. ## 2.4. Proposed Textile Electrodes The electrodes analyzed in this paper are illustrated in Figure 2, and a photograph of them is shown in Figure 3. As can be seen, all the analyzed electrodes were designed to form bracelets with heights of 2 cm (Textile B) and 3 cm (Textile A and Textile C). As per the adopted materials, elastic conductive and non-conductive materials were adopted. All electrodes consisted of two elastic bracelets: an internal conductive bracelet attached to an external non-conductive bracelet. The outer bracelet consisted of an elastic band (Elastic Band 1, see Figure 2) which closed with adhesive Velcro. It acted as an insulator and allowed the electrode to be fixed firmly on the wrist by ensuring a good fit, regardless of the size of the wrist. The electrode referred to as Textile A was the one analyzed in [24] with an optimized implementation so as to improve its fit. In this case, the inner bracelet consisted of only the conductive fabric around which the non-conductive band with a Velcro closure was wrapped. The electrodes referred to as Textile B and Textile C differed for the conductive fabric bracelet. For the electrode of Textile B, the conductive fabric had an elastic textile core (Elastic Band 2, see Figure 2) and the conductive fabric covered the elastic but was not sewn to it. Finally, for the electrode of Textile C, the conductive fabric was sewn to an elastic band (Elastic Band 2, see Figure 2) so to cover the side of the elastic in contact with the skin. The basic idea behind the developed electrodes was the investigation of various designs with the aim of optimizing comfort and facilitating the application of the electrodes by non-expert users. The conductive fabric used was Shielded Technik-tex P180 + B by Statex [26]. It is a knitted fabric that is stretchable in one direction and has the following parameters:-raw material: $94\%$ polyamide +$6\%$ Dorlastan-total thickness: 0.57 mm ± $10\%$-metal plated: $99.9\%$ pure silver-surface resistivity (both sides): <2 Ω/☐-stretch: 095/$020\%$ OS (one stretch direction)-temperature range: −30 °C–90 °C This fabric is particularly suitable for fabricating electrodes for measuring vital parameters since it allows for obtaining a homogeneous current distribution; in this regard, the use of silver to make the fabric conductive makes it suitable for direct application to the skin. Furthermore, this fabric can be sewn, washed, and ironed, similar to common non-conductive fabrics. As can be seen from Figure 2 and Figure 3, all bracelets had a snap button which allowed them to be attached to a shirt. The button could be used to connect the electrodes with a conductive wire which could be used for measurements. Using a small bioimpedance meter, this solution would allow the system (electrodes and impedance meter) to be integrated into a shirt. ## 3. Results For each PUT, four measurements were performed: one with the Ag/AgCl disposable electrodes produced by EF Medica Ltd. and one for each textile electrode (Textile A, Textile B, and Textile C). The commercial electrodes had a solid adhesive gel with high conductivity, which allowed for minimizing the contact resistance without requiring specific treatments for the skin (i.e., application of additional gels). As per the textile electrodes, all measurements were performed without adding electrolytic gel or other substances to improve the adhesion/contact with the skin. Each PUT measurement with the different electrodes was performed consecutively, without any break between one measurement and the next except for the time needed to apply the electrodes (a few seconds). A comparison between the values obtained for the module (\|Z\|) and the phase angle (PA) of the impedance with the commercial and textile electrodes is provided in Figure 4. Measurements with textile electrodes refer to Textile C (similar results were obtained for Textile A and Textile B). As can be seen, a good agreement between the values measured with commercial and textile electrodes was obtained for all the PUTs. In order to validate the feasibility of using textile electrodes, the Passing–Bablok regression method [27] was adopted for comparing the data provided by the textile electrodes with those corresponding to the commercial electrodes. This method was first proposed in 1983 as a method for testing the agreement between two sets of measurements obtained via different systems [27]. In this paper, the Passing–Bablok regression analysis was exploited to investigate the presence of a bias (constant error) or/and a proportional error between the set of data provided by the commercial electrodes and that obtained with the textile electrodes. The achieved results are summarized in Figure 5 for \|Z\| and in Figure 6 for PA. According to the results obtained for the $95\%$ confidence intervals ($95\%$ CI) for both the intercept and slope, it could be concluded that there was no constant difference between the measured data obtained with commercial electrodes and those obtained with the textile electrodes. It can therefore be concluded that the measured data provided by the textile electrodes had a linear relationship with those provided by the commercial electrodes, and they did not present significant differences, i.e., the two types of electrodes were interchangeable. Additionally, it was important to observe that the random differences obtained for all electrodes were compatible with the accuracy of the measurement setup (see the results summarized in Figure 5 and Figure 6). It was also observed that the three solutions of textile electrodes provided very close results. The best performance in terms of variability of the measured values with respect to the commercial electrodes was provided by Textile B. Compared to previous solutions of textile electrodes that have been proposed in recent reference literature, the electrodes analyzed in this paper appeared to be an excellent alternative both for their ease of use and their performance and stability of the measurements. However, it was difficult to make an analytical comparison since, in most cases, the comparisons with commercial electrodes were limited to measurements from few subjects (and thus, they were not sufficient for drawing conclusions). In particular, according to the comparison with commercial electrodes, the electrodes presented in this paper provided results similar to those obtained with the electrodes proposed in [23] and fabricated by using the Silitex P130 fabric by Statex. In this regard, however, it is important to note that the comparison carried out in this paper exploited measurements from 40 PUTs while the one developed in [23] referred to measurements from only 15 PUTs, which was a decidedly lower number. However, some considerations could be made on the ease of use of the two electrodes. During the measurements performed using the electrodes proposed in [23], the authors experienced the need to pay attention to the application of the electrodes, trying to place them in a position with few hairs. In other words, it was verified that the presence of hair affected the measurements both in terms of R and X. The bracelet shape of the electrodes proposed in this paper and the elastic fabric (Shielded Technik-tex P180 + B) by Statex allowed us to overcome this problem. In fact, the measurements performed on the 40 subjects did not reveal a dependence of the measurements on the presence of hair (considering that most of the persons under test were men); rather, the electrodes could be worn as a simple bracelet, thus facilitating their application by non-expert users. Finally, it is worth observing that the forty volunteers who participated in the experimental tests were Italian men and women aged between 19 and 26 years. Although it was believed that this aspect (ethnicity and age of the participants) did not influence the behavior of the electrodes, it could be appropriate to extend the study to other volunteers of different ethnicities and age ranges. ## 3.1. Measurement Repeatability The variability of measurements over time was investigated through the measurements from PUT 1 (see Table 1). Ten sets of measurements were performed with a 5 min break between one set of measurements and the next. Each measurement set consisted of four measurements (one for each electrode). The person under test remained in a sitting position for the whole time of the measurement campaign. The achieved results are summarized in Figure 7, where the data obtained with the textile electrodes are compared with those obtained with the commercial ones. As can be seen, similar results were obtained for all the electrodes. When comparing the repeated measurements over time, small differences were observed for both \|Z\| and PA for all electrodes. ## 3.2. Performance after Washing The performance of the textile electrodes after washing was investigated, and measurements were performed on PUT 1. In particular, five washing cycles were performed, and measurements were taken after each washing cycle. The electrodes were hand-washed in cold water (30–37 °C) and left to dry for a day at room temperature. The obtained results are illustrated in Figure 8. Given the need to let the electrodes dry, the measurements were performed on different days. As can be seen, the electrodes appeared to keep working properly even after five washing cycles. ## 3.3. Comfort The comfort related to the use of the electrodes was investigated. Ten PUTs (five women and five men) were asked to wear each bracelet for one hour, with a half-hour break between one trial and the next. After wearing all the bracelets, the PUTs were asked to rate the bracelets on a scale from one to give with respect to: [1] ease of wearing of the bracelets, and [2] comfort. The results of the questionnaire are summarized in Table 2. As can be seen, the results obtained for Textiles B and C were very similar and were slightly better for Textile B, which was evaluated as being easier to wear and more comfortable. As for Textile A, almost all PUTs rated it as being uncomfortable to wear. As reported by the PUTs, in Textiles B and C, the presence of the Textile Elastic Band 2 (see Figure 2), which acted as a support for the conductive elastic fabric, made it easier to wear. The support helped the adherence of the conductive elastic fabric around the wrist, which, in Textile A, tended to roll up, forming a cord. ## 4. Conclusions Wristband electrodes for hand-to-hand bioimpedance measurements were investigated. Three different implementations of bracelet electrodes were analyzed through measurements from 40 healthy individuals. All the analyzed electrodes were fabricated using a combination of non-conductive elastic textile materials and a conductive fabric. The conductive fabric was Shielded Technik-tex P180 + B by Statex, and it allowed for obtaining a homogeneous current distribution. In particular, all the analyzed textile electrodes consisted of an internal conductive elastic bracelet fixed to an external non-conductive one, which served to stably fix the electrode to the wrist. The three implementations differed for the conductive bracelet, and different designs were investigated in order to maximize the comfort and ease of application of the electrodes. Measurements at 50 kHz were performed, and the data provided by the textile electrodes were compared with those obtained with the Ag/AgCl commercial electrodes. The results demonstrated a very high correlation for both the module and the phase angle measured with textile and commercial electrodes. In particular, the Passing–Bablok regression method was exploited to estimate the agreement between the data obtained with the textile electrodes and those provided by the commercial ones. The achieved results demonstrated that the measured data provided by the textile electrodes did not present significant differences with respect to those obtained with the commercial electrodes. In this regard, it is important to note that the applications that exploit bioimpedance measurements analyze the evolution of bioimpedance measurements over time rather than the value of a single measurement. In most cases, the objective is to analyze how the body composition evolves over time in order to evaluate, for example, the effects of a diet or a therapy. 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--- title: Prevalence of Postoperative Atrial Fibrillation and Impact to Nursing Practice—A Cross Sectional Study authors: - Ana Brčina - Kristian Civka - Renata Habeković - Sabina Krupa - Ana Ljubas - Wioletta Mędrzycka-Dąbrowska - Adriano Friganović journal: Medical Sciences year: 2023 pmcid: PMC10056994 doi: 10.3390/medsci11010022 license: CC BY 4.0 --- # Prevalence of Postoperative Atrial Fibrillation and Impact to Nursing Practice—A Cross Sectional Study ## Abstract Background: Atrial fibrillation is the most common clinically significant cardiac arrhythmia, and it might lead to heart failure, which prolongs the duration of hospitalization and consequently increases the cost of treatment. Thus, diagnosing and treating atrial fibrillation should be the first line of defense against further complications. This study aimed to determine the incidence rate of postoperative atrial fibrillation and correlation with cardiac surgery on heart valves. A specific aim was to determine the relationship between the prevalence of atrial fibrillation and socio-demographic features. Methods: The study has a prospective cross-sectional design. The questionnaire was anonymous, requesting socio-demographic information as inclusion criteria, and the data were analyzed using descriptive statistics methods. Results: The sample was 201 patients. χ2 test and t-test were performed where we found that the frequency of atrial fibrillation was higher in the groups that have had valve surgery compared to other cardiac surgeries (χ2 = 7.695, ss = 2, $$p \leq 0.021$$). Atrial fibrillation increased with the age of the patients, but the prevalence of atrial fibrillation was not correlated with body weight. Conclusion: The results of this this study show that atrial fibrillation was higher in the participants who had valve surgery compared to other cardiac surgeries. There was also an increase in atrial fibrillation in the older participants. The results of this study can help to improve nursing practice and the quality of care for cardiac surgery patients with regard to daily activities, or planning nursing care due to the patient’s condition. ## 1. Introduction Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia. It is thought that, by 2060, 17.9 million people in Europe will suffer from AF [1]. It is a major cause of morbidity, mortality, and prolonged healthcare in cardiac surgery patients. The onset of AF is influenced by numerous factors such as age, race, and gender; the prevalence of AF generally increases with age. Conditions such as heart valve disease, diabetes mellitus, arterial hypertension, obesity [2], sleep apnea, and inflammation [3] are risk factors for the occurrence of AF. Although uncommon, rheumatic arterial heart disease is significantly associated with the development of AF [4]. It is assumed that the prevalence of AF after cardiac surgery ranges from $15\%$ to $40\%$ in coronary revascularization surgical procedures; from $37\%$ to $60\%$ in valvular surgery interventions; and more than $60\%$ in combined interventions [5]. Of all cases of AF after surgery, $90\%$ of them occur within the first four days of the postoperative period [6,7]. Once a diagnosis of atrial fibrillation is confirmed, nurses together with doctors have an important role in explaining the condition to the patients in a way they can understand [8,9]. When assessing the patient’s condition, nurses collect data on the existence of palpitations, chest pain, shortness of breath, and dizziness [10]. Nurses had to assess other risk factors for developing AF such as excessive alcohol consumption, obesity, smoking, drug abuse, hyperlipidemia, previous rheumatic heart disease, and a history of valvular heart disease or ischemic heart disease [10,11,12,13,14,15]. Furthermore, identifying patients at high risk of developing of AF may allow for the modification of risk factors to reduce the postoperative atrial fibrillation (POAF) burden [16,17,18]. Nurses, as independent professionals, work together with other members of the healthcare team to meet a wide range of patient needs [19]. Since nurses are the first to be in contact with the patient, they must know the pathophysiology and symptoms of AF [20]. The early recognition of symptoms and a timely response to the occurrence of AF significantly reduces the possible complications of AF and increases patients’ qualities of life [21,22,23,24]. The main goals in the treatment of AF are to maintain adequate cardiac volume and tissue perfusion, and to prevent thromboembolism. The recognition of irregular rhythm and P-wave deficiency initiates the treatment of AF. Appropriate interventions such as measuring vital signs, properly positioning the patient in a semi-recumbent position, and the administration of oxygen can significantly reduce the risk of heart failure. The appropriate use of oral anticoagulation is an established pillar of AF management to reduce stroke risk [22]. With proper information about the occurrence of AF in the postoperative period, nurses can obtain valuable information and prepare specific plans for nursing care. An individual approach for specific group of patients may improve nursing care. The aim of this study was to show the association of the prevalence of post-operative atrial fibrillation with variables of gender, type of surgery, age, and body weight. A specific aim was to determine the relationship between the prevalence of atrial fibrillation and socio-demographic features. There are many other studies involving POAF in cardiac surgery, but there is no adequate research in Croatia. We believe that our study’s findings will produce a scientific contribution to nursing research and improve nursing practice and the quality of nursing care for patients [23]. This research will bring new knowledge to evidence-based nursing in Croatia, and for nursing in general [24]. ## 2.1. Study Design The research study “Prevalence of Postoperative Atrial Fibrillation and Impact to Nurses Practice—A Cross Sectional Study” was a prospective cross-sectional study conducted on all consecutive patients of the Department of Cardiac Surgery in the University Hospital Centre Zagreb May–September 2021. ## 2.2. Ethics and Approval Prior to conducting the study, on 12 May 2021, the principal investigator sought the approval of the Ethics Committee of the University Hospital Centre Zagreb (Class: 8.1-$\frac{21}{112}$-2; Number: $\frac{02}{21}$JG). The ethical principles and the principles of the Declaration of Helsinki were respected during the study [24]. The personal data, names, and surnames of the respondents were not used in the research. The data collected from the respondents were used only for the purposes of this research, and were stored in such a way that they were available only to the co-authors of this research. ## 2.3. Participants All the cardiac surgery patients in the observed period of five months were included in the study. The inclusion criteria were signed, informed consent by patients who are 18 years and older; inability or refusal to provide informed consent meant that the patient was excluded. The data on the occurrence of atrial fibrillation was collected for the analysis and confirmation of the correlation in all patients who underwent cardiac surgery at the Department for Cardiac Surgery in University Hospital Centre Zagreb. ## 2.4. Sociodemographic Features The study instrument was a data form in which the principal investigator collected information about the patients from a medical chart and nursing documentation. The sociodemographic features and clinical data were collected by the investigators of this study. The follow-up form included demographic data such as age, gender, and education level. Health-related information included the patient’s weight, height, and type of operation; the existence of pre- and postoperative AF; in the case of existing POAF, whether it had been treated and in what way, and whether it had been converted; and, finally, the existence of other diseases. ## 2.5. Statistics The collected data were entered into a Microsoft Excel file and analyzed using descriptive statistical methods (χ2 test and t-test); the significance level was $p \leq 0.05.$ For nominal (categorized) variables, the number and percentage of respondents in the corresponding categories were shown, and the statistical significance of the differences was calculated using the χ2 test. The SPSS program IBM SPSS Statistics for Windows, Version 25.0 was used for data analysis [25]. ## 3.1. Sociodemographic Features A total of 201 participants were included in this study. The data about the patients were collected from medical chart and nursing documentation. The socio-demographic data were age, gender, and education level, and the health data included body weight, height, and type of operation. The mean age of the participants was 75 ± 5 ($35.3\%$) years, and $69.8\%$ of the participants were men. Table 1 describes the baseline characteristics. Table 1 represents the sociodemographic features of the participants included in this study and the type of cardiac surgery they underwent. ## 3.2. Patient Data Table 2 presents the data on the prevalence of preoperative and postoperative atrial fibrillation. Table 2 represents the data regarding the prevalence of AF in patients included in this study and the treatment in the case of its existence. The research included data on the existence of diseases such as arterial hypertension in 153 patients ($76.2\%$), coronary heart disease in 95 patients ($47.5\%$), and rheumatic heart disease in 3 patients ($1.3\%$), and all the other conditions that were present in the participants; $97\%$ [195] of the participants had other diseases. Table 3 the represents prevalence of other diseases. Table 4 represents the data regarding the prevalence of postoperative AF in patients by gender. The χ2 test did not find any statistically significant differences between male and female patients in the prevalence of postoperative AF (χ2 = 0.405, ss = 1, Fisher’s exact $$p \leq 0.541$$). Table 5 represents the data regarding the prevalence of postoperative AF in patients by the type of cardiac surgery. The χ2 test showed that the prevalence of postoperative AF differs according to the type of surgery; the frequency of AF was higher in the groups that had had valve surgery compared to other cardiac surgeries (χ2 = 7.695, ss = 2, $$p \leq 0.021$$). Figure 1 represents the prevalence of POAF by age, whereas most participants between 70 and 79 years of age have had POAF, and most participants aged from 80 to 89 years of age did not develop POAF. The point—biserial correlation coefficient showed that the association of age with the prevalence of AF is low ($r = 0.22$, $p \leq 0.001$), which means that there is a greater likelihood of AF in older age groups (an increase in AF with the age of patients). Figure 2 represents prevalence of postoperative AF by body weight, whereas most participants between 81 and 90 kg had AF. They are followed by 26 participants who weighed between 71 and 80 kg but did not develop AF. The point—biserial correlation coefficient did not show the correlation of body weight with the AF prevalence (r = −0.08, $$p \leq 0.292$$). ## 4. Discussion This study aimed to determine the incidence rate of postoperative atrial fibrillation and its correlation with cardiac surgery on heart valves in nursing practice. We also aimed to show the association of the prevalence of postoperative atrial fibrillation with variables of gender, age, and body weight. The results of this study showed that the prevalence of postoperative AF differs according to the type of surgery; the frequency of AF was higher in groups that previously had valve surgery compared to other cardiac surgeries (χ2 = 7.695, ss = 2, $$p \leq 0.021$$). It is also confirmed that there is an increase in AF with the age of patients. However, there is no correlation of body weight with the AF prevalence. The prevention of AF should begin at the earliest possible age, considering that the prevalence of AF is known to increase with advancing age. The prevention of AF should be crucial at the primary and secondary levels in cardiovascular disease, but primordial and primary prevention are fundamental in young adults [26]. A study conducted in France showed that AF is more common in women and has a greater risk factor for cardiovascular diseases and death in women than in men [27]. However, the risk of developing cardiovascular disease increases with age in both genders. The increased risk of AF with old age appears to be greater in men, although not all studies have presented sex differences. However, although older men may be at a greater risk of developing AF, it is associated with worse outcomes in women, including stroke [28,29]. Our research included $69.8\%$ men and $30.2\%$ women (Table 1). Gleason et al. conducted a study examining the association of sex, age, and education level with patient-reported outcomes (AF-related quality of life, symptom severity, and emotional and functional status). The results of this study showed that women, younger adults, and individuals with lower levels of education reported comparatively poorer outcomes [30]. On the other hand, our results in Table 1 showed that many of the respondents in our study had high school education ($62.0\%$), while the smallest number of respondents had university education ($7.5\%$). A study conducted by Andersen et al. showed that body size is a very important indicator of the risk of developing AF. Greater height and weight are strongly associated with a higher risk of atrial fibrillation. The mechanisms remain unknown but may involve an increased atrial volume load with larger body size [31]. The average weight of our respondents was 81–90 kg ($27.7\%$), while the average height was 171–180 cm ($34.2\%$), which can be seen in Table 1. We did not find a correlation between the body weight and AF prevalence. According to the research of Baeza-Herreras et al., the prevalence of AF after cardiac surgery ranges from 15 to $40\%$ in coronary revascularization surgical procedures and from 37 to $60\%$ in valvular surgery interventions, and it is higher than $60\%$ in the combined interventions [6]. It occurs in $24\%$ of the patients undergoing a heart transplant. In this study, we particularly focused on the occurrence of AF after valve surgery, which was received by 69 subjects ($34.2\%$) (Table 1). A literature search found that the prevalence of POAF is 20–$40\%$ [32], but, in our study, as many as $46.8\%$ of the respondents developed AF in the postoperative period, as is shown in Table 2. Recent research suggests that the decision to treat AF is made by the physician in consultation with the patient, and it is primarily based on managing the patient’s symptoms and preferences. It has been reported in the literature that there are gender differences in these management strategies, i.e., which treatment is recommended and the response to therapy [33,34,35,36]. However, in $94.7\%$ of our subjects AF was treated when it occurred after surgery. In $5.3\%$ of the subjects, it was not treated because they had permanent AF that did not respond to drug treatment even before the surgery. In Table 2, we can see that most AF arising in the early postoperative period was converted to a sinus rhythm ($89.5\%$). This was most often achieved with the use of drugs ($83.2\%$). The prevalence of arterial hypertension can be up to $80\%$ in individuals older than 65 years, and $26\%$ in adults younger than 45 years old. Hypertension leads to cardiovascular complications, including coronary heart disease, and heart failure that consequently leads to atrial fibrillation and mortality [26,37,38,39]. Lee et al. investigated the relationship between the arterial hypertension burden and the development of incident AF. The results of their study showed that subjects with arterial hypertension burdens were associated with an increased risk of 8–$27\%$ for incident AF [40]. In our study, $76.2\%$ of the subjects had arterial hypertension. A strong correlation between AF and coronary heart disease has been reported [41]. In a study conducted by Ferreira et al., 241 patients ($4.8\%$) developed AF during the follow-up. Older age, LVEF > $35\%$, a history of PCI and CABG, white race, a SBP < 110, and a higher BMI were independently associated with the risk of new onset AF [42]. Coronary heart disease was present in $47.5\%$ of the participants in our study. In recent decades, there has been increasing interest in the association of AF with rheumatic heart disease. Recent studies have found that there is an association between rheumatic heart disease and high rates of disability and premature death across African and Asian countries [43]. Older age and the presence of mitral valve disease (special stenosis) are significantly associated with the development of atrial fibrillation [42]; 40–$75\%$ of individuals with mitral stenosis have AF [12]. Rheumatic heart disease is not a common disease, and only $1.3\%$ of our participants had it. Almost all our subjects, $97\%$ of them, had other diseases. Hyperlipidemia was as common as coronary heart disease, being present in $48\%$ of our subjects. Hyperlipidemia is in itself a risk factor for the development of heart disease. The next most common disease was diabetes mellitus, which was present in $32\%$ of our subjects. The pathophysiology of diabetic-related AF is not fully understood, but it is related to structural, electrical, electromechanical, and autonomic remodeling [44]. AF is very common in patients with severe aortic stenosis [45]; aortic valve stenosis was present in $20\%$ of our subjects. We aimed to show the difference in the incidence of AF in women and men. The results showed that there was no statistically significant difference in the incidence of POAF in women and men (Table 4). Numerous previous studies have indicated that there are significant gender differences in the epidemiology, pathophysiology, and therapeutic outcome of AF, which provided us with strong reasons for further research. A literature review showed that, in general, AF is more prevalent in men than in women in age-matched observations [46,47,48]. Our next goal was to determine which surgeries most commonly caused AF; the results listed in Table 5 showed that the incidence of AF was higher in the group of subjects who had heart valve surgery than in the groups who had other cardiac surgeries or a combination of heart valve surgery and other cardiac surgeries. A study entitled “Evaluation of the incidence of new atrial fibrillation after aortic valve replacement”, conducted over a period of three years, showed that fibrillation occurs in about $50\%$ of the patients hospitalized for transcatheter aortic valve implantation and aortic valve replacement. Hospitalizations due to newly developed atrial fibrillation are associated with increased in-hospital mortality [14]. Another study demonstrated that there are predictors that indicate whether new-onset AF is likely to occur after surgery. In a study involving 2261 subjects who underwent mitral valve surgery over a 10-year period, the prevalence of AF occurring more than 90 days after surgery in patients who did not have AF prior to surgery was examined. AF was found to occur in $14\%$ of the subjects after 5 years and in $23\%$ of subjects after 10 years. The patients who had degenerative mitral regurgitation were less likely to develop AF. Multivariable factors for the development of AF are tricuspid valve surgery, aortic valve surgery, and older age. New-onset AF did not affect overall survival [49]. Another aim of our research was to show the association of POAF with patient age. The results shown in Figure 1 show that the prevalence of POAF increases with advancing age. In AF, many factors work over the years. For example, a chronic subclinical inflammatory response, defined as continuous weak activation of the systemic immune response, is characterized by the biological aging of organ systems. Both age and AF are associated with elevated concentrations of reactive oxygen species. Furthermore, inflammation is associated with endothelial dysfunction and collagen catabolism, resulting in an increase in TGF-ß1 (transforming growth factor) activity [11]. Despite the fact that numerous recent studies have shown that there is a strong link between the development of AF and obesity, our results did not show that there was correlation between body weight and AF prevalence (Figure 2). The research conducted by the Framingham Heart Study states that each unit of increase in body mass index (BMI) is associated with an increase in the risk of AF by 4–$5\%$, independent of other comorbidities such as acute myocardial infection, diabetes, and other conditions [50]. Despite the significant impact of atrial fibrillation on health care and on the population, it has been generally considered a benign disorder in clinical practice and is often not seen as being as important as ventricular arrhythmias in clinical care nor in health care research [51]. By conducting this study, we wanted to emphasize the importance of the incidence of AF in patients and, accordingly, the impact on nursing practice during AF management. Nurses are integral to the care of patients with AF. It is essential for nurses to stay informed of current guidelines and new evidence so that the assessment, management, and education of the AF patients and their families can be optimized [52]. This study impacted nurses’ knowledge of atrial fibrillation and anticoagulation, and influenced their uptake and use of AF risk scales assessment tools in clinical practice. Future research should focus on whether a similar intervention might improve patient-centered outcomes such as patients’ knowledge of their condition and therapies, medication adherence, time in the therapeutic range, and quality of life. Many different studies suggested that nursing staffing practice in the postoperative period (adequate staffing level, adequate skills, and educational level) are connected with lower rates of surgical mortality and lower numbers of adverse events [53]. With the better assessment of potential complications, we can assure there is adequate nurses staffing to better monitor patients [53]. The estimation of staffing practice with POAF occurrence is important to help hospital managers determine the number and educational degree of nurses needed for the patient after cardiac surgery procedure with the aim to reduce postoperative complications [53,54]. Postoperative cardiac events are frequent complications of surgery, and their occurrence could be associated with suboptimal nurse staffing practices, but the existing evidence remains scattered [55]. Higher nurse staffing levels, higher registered nurse education (baccalaureate degree level), and more supportive work environments provide better patient safety [55]. The existing evidence regarding postoperative cardiac events is limited, which warrants further investigation. These facts also provide scientific potential for future research associated with nursing degrees and adverse events. ## 5. Study Limitations The biggest obstacle to this study was the ongoing SARS-CoV-2 pandemic that limited the number of hospitalized patients and their participation in this study. Additionally, this resulted in a reduced number of cardiac operations. Furthermore, there are not enough studies conducted in Croatia and worldwide regarding AF during the postoperative period of cardiac patients. Our study did not include children or infants. On the other hand, the patient data in this study are from 2021 and the sample size is small, suggesting that repeating the study with larger sample would be productive. ## 6. Conclusions This research paper summarizes the development of AF in the postoperative period of patients who had valve operations at the Department for Cardiac Surgery in the University Hospital Centre Zagreb. Our results showed that there was no correlation between body weight and AF prevalence but found that the frequency of AF was higher in the groups that had valve surgery compared to other cardiac surgeries. The results of this study can help to improve nursing practice and the quality of care for cardiac surgery patients with regard to daily activities for patients or planning nursing care due to the patient’s condition. The data regarding POFA can impact nursing staffing and assure the safety of care. ## References 1. Lippi G., Sanchis-Gomar F., Cervellin G.. 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--- title: The Association of Intravitreal Injections of Different Anti-Vascular Endothelial Growth Factor with Systemic Outcomes in Diabetic Patients authors: - Eugene Yu-Chuan Kang - Tzu-Yi Lin - Sunir J. Garg - Nan-Kai Wang - Lee-Jen Chen - Pei-Wei Huang - Ming-Jen Chan - Kuan-Jen Chen - Wei-Chi Wu - Chi-Chun Lai - Yih-Shiou Hwang journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10057023 doi: 10.3390/jpm13030544 license: CC BY 4.0 --- # The Association of Intravitreal Injections of Different Anti-Vascular Endothelial Growth Factor with Systemic Outcomes in Diabetic Patients ## Abstract This retrospective cohort study aimed to assess the systemic effects of three commonly available anti-vascular endothelial growth factor intravitreal injections in patients with diabetes, using data taken from a multi-institutional database in Taiwan. Patient data were sourced from the multi-institutional Chang Gung Research Database. Participants were divided into groups based on treatment with bevacizumab, ranibizumab, or aflibercept. Baseline characteristics were matched among the groups by the inverse probability of treatment weighting. The incidence rate of outcome events was calculated as the number of events divided by 100 person-years of follow-up. The cumulative incidence function was used to estimate the incidence rate of the outcome events among groups. The incidence of ischemic stroke was higher in the ranibizumab group than the bevacizumab and aflibercept groups (1.65, 0.92, and 0.61 per 100 person-years, respectively). The incidence of major adverse lower-limb events was higher in the bevacizumab group (2.95), followed by ranibizumab (2.00) and aflibercept (0.74). Major bleeding was relatively higher in bevacizumab (12.1) compared to ranibizumab (4.3) and aflibercept (3.8). All-cause death was higher for both bevacizumab (3.26) and aflibercept (2.61) when compared to ranibizumab (0.55), and all-cause admission was found to be highest with bevacizumab (58.6), followed by aflibercept (30.2), and ranibizumab (27.6). The bevacizumab group demonstrated a greater decrease in glycated hemoglobin compared to the baseline level (−$0.33\%$). However, a few differences in the clinical condition between the groups were still observed after matching. In conclusion, this study suggests that different anti-vascular endothelial growth factor agents may be associated with various and differing systemic adverse events. The differences might also be attributed to differences in patient characteristics and clinical status. ## 1. Introduction Diabetes mellitus is a widespread chronic disease, and its incidence continues to rise [1]. Beyond imposing a heavy burden on health infrastructure, it also leads to life-threatening macrovascular and microvascular complications [2]. In addition, a persistent hyperglycemic state can induce inflammation and angiogenesis in the retina [3]. Ocular disability resulting from diabetic retinopathy (DR) has a significant impact on patients’ daily lives. Diabetic macular edema (DME), proliferative diabetic retinopathy (PDR), and diabetic retinopathy (DR) in general are leading causes of vision loss in patients older than 40 years of age [4]. While retinal laser coagulation and the intravitreal injection (IVI) of corticosteroids still play essential roles in treating DME [5,6], intravitreal injection of anti-vascular endothelial growth factor (VEGF) agents has transformed the standard of care for retinal diseases [7]. VEGF increases capillary permeability and causes a breakdown of the blood–retinal barrier [8]. The resulting leakage of fluid into the retina can significantly affect vision. Anti-VEGF agents, including bevacizumab, ranibizumab, and aflibercept, have been extensively used for numerous retinovascular diseases. While ranibizumab and aflibercept are indicated only for ocular disease, the initial application of bevacizumab was as intravenous chemotherapy for colorectal, breast, and lung cancers. Intravenous bevacizumab has been associated with systemic adverse events, including hypertension, proteinuria, myocardial infarction, and stroke [9,10,11]. Furthermore, an elevated mortality rate has also been noted in patients treated with a combination of bevacizumab and chemotherapy [12]. Although only 0.05 mL of intravitreal anti-VEGF medications are injected in an eye, anti-VEGF agents still enter the systemic circulation [13,14]. Unilateral therapy has been shown to decrease VEGF levels in serum and regress the neovascularization of contralateral eyes [15]. Consequently, there has been interest in assessing the potential systemic effects noted after the IVI of anti-VEGF agents [16]. In several clinical trials, the various anti-VEGF agents demonstrated a low incidence of adverse events [17,18,19]. However, given the systemic comorbidities associated with diabetes and the relatively uncommon nature of these events, the registry studies are underpowered to assess systemic impact. Different inclusion and exclusion criteria used for the various studies can also impact the incidence of adverse events from anti-VEGF agents. In addition, findings derived from clinical trials may have limited applicability in populations that fall outside of the inclusion and exclusion criteria [20]. In contrast, real-world data provide robust evidence to investigate systemic adverse effects. Some studies have suggested that anti-VEGF agents increase thromboembolic events and associated death [21,22], whereas other studies have found anti-VEGFs to be safe and without an elevated risk of major cardiac adverse events [23,24]. Despite these conflicting reports, the available data are limited. This study aims to assess the systemic association of three commonly used anti-VEGF agents in patients with diabetes mellitus, using data taken from a multi-institutional database in Taiwan. ## 2.1. Data Source This retrospective cohort study was conducted using the Chang Gung Research Database (CGRD), a multi-institutional electronic medical record database of 1.3 million patients across Taiwan. The database has de-identified clinical information, including clinical diagnosis, medication use, interventions, laboratory data, and operation notes, as well as other data obtained during routine clinical care. In addition, the CGRD contains information on self-pay items that were not covered by the Taiwan National Health Insurance program. The database was queried using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes before 2015 and both ICD-9-CM and ICD-10-CM after 2016. The detailed information on CGRD has been described in previously published studies [25,26]. All procedures adhered to the principle of the Declaration of Helsinki. Written informed consent was waived due to the de-identification of the data. This study has been approved by Chang Gung Medical Foundation Institutional Review Board (IRB No. 202200606B1). ## 2.2. Patient Inclusion Patients receiving IVI with anti-VEGF between 1 January 2014 and 31 December 2019, were identified in the CGRD. The index date was defined as the day anti-VEGF treatment was initiated. The new user design was adopted to reduce the potential selection bias. Therefore, patients with previous IVI with any anti-VEGF agents before 2014 were excluded. Moreover, patients under the age of 20 years, patients without diabetes, those with no baseline glycated hemoglobin (HbA1c) data, patients with a history of having received IVI with steroids, and those with any preexisting malignancy were also excluded. Patients were then grouped according to the anti-VEGF treatments. In Taiwan, this includes bevacizumab, ranibizumab, and aflibercept. ## 2.3. Covariates The covariates were demographics (sex, age, and body mass index (BMI)), the severity of DM, systemic comorbidities, Charlson’s Comorbidity Index (CCI) score, medication use, ocular history, and the number of outpatient visits to ophthalmology in the previous year prior to treatment. The severity of DM was assessed by the HbA1c, duration of diabetes, DM complications (diabetic neuropathy and diabetic foot ulcers), and the type of DM. Comorbidities included metabolic syndrome (hypertension and dyslipidemia), cardiovascular disorders (ischemic heart disease, ischemic stroke, heart failure hospitalization, myocardial infarction, and atrial fibrillation), and other diseases (chronic kidney disease, chronic obstructive pulmonary disease, and obstructive sleep apnea). The Charlson Comorbidity Index score was calculated to evaluate the burden of disease [27]. Medication usage catalogued at the index date included anti-platelets, anti-coagulants, statins, and fibrates. Prior ocular history included glaucoma, age-related macular degeneration, diabetic macular edema, retinal vascular occlusion, vitreous hemorrhage, myopic choroidal neovascularization, all-grade diabetic retinopathy, and whether an eye received retinal laser and/or vitrectomy for any indication. ## 2.4. Outcomes Outcomes were clinical events and changes in the laboratory data. Clinical events included all-cause death, all-cause hospital admission, major adverse cardiac event (an one of myocardial infarction, ischemic stroke, and cardiovascular death), major adverse lower-limb event (MALE) outcomes (peripheral arterial disease, claudication, critical limb ischemia, percutaneous transluminal angioplasty, and amputation), composite thromboembolic events (myocardial infarction, ischemic stroke, transient ischemic attack, extremity thromboembolism, and systemic thromboembolism), and major bleeding requiring hospitalization. The date, place, and cause of death were identified using the Taiwan Death Registry, which is released by the Taiwan Ministry of Health and Welfare. The occurrence of the following events was assessed during hospitalization: myocardial infarction, ischemic stroke, percutaneous transluminal angioplasty, amputation, and transient ischemic attack. The occurrence of diseases was defined as having outpatient diagnoses from at least two visits or an inpatient diagnosis at least once. Patients were followed up until individual clinical events, the 180th day after the index date, the day of death, the day of a switch between the three study drugs, the last visit in the CGRD, or 31 December 2019, whichever came first. Laboratory data were extracted at baseline and at the sixth month after the index date. Laboratory data of interest were the systolic blood pressure (SBP), diastolic blood pressure (DBP), HbA1C, low-density lipoprotein (LDL), estimated glomerular filtration rate (eGFR), and alanine aminotransferase (ALT). The change of laboratory data from baseline to the sixth month after the index date was analyzed. ## 2.5. Statistical Analysis When comparing the risk of clinical events among multiple anti-VEGF agents (bevacizumab vs. ranibizumab vs. aflibercept), an additional adjustment cohort was created using an inverse probability of treatment weights (IPTW) based on propensity scores (PSs). As there were multiple treatment groups (>2 groups) in this study, we estimated the PSs using the generalized boosted model based on 50,000 regression trees [28]. The variables included in the PSs estimation are listed in Table 1. However, the number of injections during the 6 month follow-up was not included. The balance among the multiple anti-VEGF agents before and after IPTW was assessed using maximum absolute standardized differences (MASD) in which a value less than 0.1 indicated a negligible difference and a value larger than 0.2 indicated a substantial difference among the groups [28]. The covariates with a MASD value >0.1 (non-negligible difference) in the IPTW-adjusted cohort were further additionally adjusted in the subsequent multivariable analysis. The incidence of clinical events was expressed using incidence density, which denoted the number of events per 100 person-years. The incidence of clinical events and the subsequent survival analyses were estimated in the IPTW-adjusted cohort. The risk of fatal outcomes (i.e., all-cause mortality) among the multiple anti-VEGF agents was compared using the Cox proportional hazard model. The incidence of other non-fatal time-to-event outcomes (i.e., major bleeding requiring hospitalization) among the multiple anti-VEGF agents was compared using Fine and Gray sub-distributional hazard model, which considered all-cause mortality a competing risk. The changes in laboratory data from baseline to the sixth month among the multiple anti-VEGF agents were compared using a linear mixed model in which the baseline value (intercept) and slope were set as random effects. A two-sided p value <0.05 was considered statistically significant. Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA). ## 3. Results This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn. ## 3.1. Patient Enrollment A total of 12,762 patients receiving IVI with anti-VEGF were identified. Of these patients, 9722 were excluded, leaving 3040 diabetic patients receiving IVI with anti-VEGF (Figure 1). These patients received either IVI with bevacizumab (1477 patients), ranibizumab (1056 patients), or aflibercept (507 patients). ## 3.2. Baseline Characteristics The baseline demographic and clinical data gathered prior to IPTW were compared according to different anti-VEGFs, and substantial differences (MASD > 0.2) were noted for several characteristics (Supplemental Table S1). A greater proportion of patients receiving bevacizumab also had a chronic kidney disease comorbidity ($52.1\%$) when compared with patients treated with ranibizumab ($39.3\%$) and aflibercept ($34.5\%$). Regarding ocular history, a greater proportion of patients receiving aflibercept ($47.1\%$) had a history of age-related macular degeneration compared to patients treated with bevacizumab ($19.8\%$) and ranibizumab ($24.1\%$). A greater proportion of patients receiving bevacizumab had a greater incidence of vitreous hemorrhage ($30.7\%$) compared with patients treated with ranibizumab ($15.7\%$) and aflibercept ($11.4\%$). Fewer patients treated with aflibercept had a history of all-grade diabetic retinopathy ($63.9\%$) when compared to patients receiving bevacizumab ($71.6\%$) and ranibizumab ($83.0\%$). Similarly, fewer patients treated with aflibercept had received retinal laser therapy ($29.0\%$) compared to patients receiving bevacizumab ($41.3\%$) and ranibizumab ($46.9\%$). The distribution of baseline characteristics among the three study groups was more balanced after IPTW adjustment (Table 1). However, there were still substantial differences (MASD > 0.2) for several variables, including chronic kidney disease, age-related macular degeneration, vitreous hemorrhage, all-grade diabetic retinopathy, and retinal laser. The prevalence of chronic kidney disease was higher in the bevacizumab group than the aflibercept group ($47\%$ vs. $35.7\%$). In the aflibercept group, the prevalence of age-related macular degeneration was the highest, while the prevalence of diabetic retinopathy and retinal laser was the lowest. The prevalence of vitreous hemorrhage was the highest in the bevacizumab group. The prevalence of any diabetic retinopathy was the highest in the ranibizumab group. The variables with non-negligible differences after IPTW (MASD > 0.1) will be further adjusted in the subsequent outcome analyses (either clinical events or laboratory outcomes). ## 3.3. Clinical Events The results of comparing the risk of outcomes among different anti-VEGFs after IPTW adjustment are listed in Table 2. During the follow-up of major adverse cardiac events, the cumulative event rate was significantly lower in patients that received bevacizumab compared to ranibizumab (adjusted sub-distribution hazard ratio (aSHR) 0.52, $95\%$ CI: 0.29–0.94) (Figure 2A). This result was mainly driven by ischemic stroke (Figure 2B). The risk of a MALE outcome was significantly higher in patients receiving either bevacizumab or ranibizumab compared to aflibercept (bevacizumab vs. aflibercept—aSHR: 3.24, $95\%$ CI: 1.20–8.78; ranibizumab vs. aflibercept—aSHR: 2.67, $95\%$ CI: 1.05–6.79) (Figure 2C). Incidences of major bleeding requiring hospitalization were higher in patients receiving bevacizumab than for the other two agents (Figure 2D). A statistically increased risk of all-cause death was observed in the bevacizumab and aflibercept groups when compared to the ranibizumab group (bevacizumab vs. ranibizumab—adjusted hazard ratio (aHR): 5.53, $95\%$ confidence interval (CI): 2.34–13.08; ranibizumab vs. aflibercept—aHR: 0.17, $95\%$ CI: 0.07–0.41) (Figure 2F). The risk of all-cause admission was significantly higher in patients receiving bevacizumab, followed by aflibercept and then ranibizumab (Figure 2E). ## 3.4. Laboratory Outcomes Changes in baseline measurements after six months were also assessed (Figure 3). No statistically significant differences in the changes from baseline to the sixth month were observed for SBP, DBP, LDL, eGFR, and ALT among the three study groups. However, a significant drop in HbA1c was observed in patients receiving bevacizumab (mean ± standard deviation (SD): −0.33 ± $1.65\%$) than those receiving ranibizumab (−0.04 ± 1.38) or aflibercept (−0.04 ± 1.36) during the 6 month follow up (Figure 3C). ## 4. Discussion Clinical trials have demonstrated that anti-VEGF treatment carries a low adverse event incidence. This is despite bevacizumab having been associated with adverse events and an elevated mortality rate when used as an intravenous chemotherapeutic. While an anti-VEGF is administered at doses far lower than those used in oncology, adverse events may still occur but go undetected, possibly because clinical trials are underpowered for these outcomes or are impacted by the eligibility criteria used in trial enrollment. The current literature on the association between adverse events and anti-VEGF treatment remains divided. This study aimed to assess the systemic adverse events of three anti-VEGF treatments in real-world diabetic patient data collected from a multi-institutional database in Taiwan. In our study, the real-world patient data demonstrated differences in the incidence of systemic outcomes between different anti-VEGF therapies. A trend towards a significantly higher incidence of ischemic stroke was seen in patients receiving ranibizumab vs. those receiving bevacizumab or aflibercept. The MALE outcome was higher in patients receiving either bevacizumab or ranibizumab when compared to aflibercept. Major bleeding incidents requiring hospitalization were higher in patients receiving bevacizumab. All-cause admission was found to be significantly higher in patients receiving bevacizumab, followed by aflibercept and then ranibizumab. The time to event outcome analysis suggests that a significantly higher incidence of all-cause death was detected for both bevacizumab and aflibercept compared to ranibizumab. However, the composite thromboembolic events were comparable between the three groups. Several differences were noted between the anti-VEGF groups when baseline demographic and clinical data were compared. Although an IPTW adjustment was used to balance intergroup differences, there were still substantial differences in several baseline characteristics, including the number of injections in 6 months, underlying chronic kidney disease, diagnosis of age-related macular degeneration, diagnosis of any-grade diabetic retinopathy, diagnosis of vitreous hemorrhage, and history of receiving retinal laser therapy (Supplemental Table S1). Regardless of the number of intravitreal injections, all-cause hospital admission was found to be the highest in patients receiving bevacizumab, followed by aflibercept and then ranibizumab. Major bleeding incidents requiring hospitalization were higher in patients receiving bevacizumab. The fitted cumulative incidence for all-cause admission and major bleeding increased soon after follow-up day 0 for bevacizumab compared to ranibizumab and aflibercept. This increase possibly suggests that the general condition of the patients receiving bevacizumab was worse than in the other groups, and this assumption could be affirmed by the higher rate of chronic kidney disease in the bevacizumab group compared to the others. In addition to the baseline difference, it has been previously noted that systemic exposure is higher for bevacizumab when compared to ranibizumab and aflibercept, and that systemic use of anti-VEGFs is associated with adverse effects such as exacerbating renal failure and proteinuria [29,30]. While the literature suggests that IVI with bevacizumab does not affect diabetic patients, those with preexisting renal dysfunction may be at risk of worsening albuminuria, and a recent case study reported worsening renal function in a diabetic patient treated with IVI bevacizumab [29,30,31,32]. Although we have used an IPTW adjustment to balance the age effect between groups, a higher proportion of age-related macular degeneration was found in the aflibercept group. In the baseline characteristic, patients receiving aflibercept treatment were also older than the patients in the bevacizumab and ranibizumab groups (Table 1) before the IPTW adjustment. These results may indicate a more degenerative health condition in the aflibercept group. This potential imbalance may also lead to higher mortality in the aflibercept group than ranibizumab group. Patients receiving ranibizumab had a higher rate of diabetic retinopathy, and retinal laser therapy was more commonly performed in this group. Diabetic retinopathy is the most common microvascular complication in diabetes [33]. An association between diabetic retinopathy and thromboembolic events has been reported [34]. This association could explain the higher incidence of MALE events and ischemic stroke in the ranibizumab group. Additionally, all-cause mortality is a competing risk of the cardiovascular outcomes in our study [35,36]. This may also explain the significantly lower incidence of major cardiac adverse events in the bevacizumab group, which had a higher incidence of all-cause mortality and a possibly worse baseline medical condition than the ranibizumab group. It has previously been demonstrated that anti-VEGFs can enter the systemic circulation, and systemic levels of VEGF were significantly lower in patients receiving bevacizumab and aflibercept compared to ranibizumab [14,15]. The reported lower VEGF levels, in combination with the current findings, suggest that the ability of different anti-VEGFs to enter systemic circulation might also contribute to systemic adverse events. Although the anti-VEGF dose and the incidence of systemic adverse events in ophthalmology are much lower than those in oncology [37], and previous articles reported no significant association between IVI with anti-VEGF and all-cause admission and cardiovascular adverse events irrespective of diabetic status [38,39], the target study population in our study comprised vascular vulnerable patients (i.e., diabetic patients, $45\%$ with chronic kidney disease) and may have a higher complication rate even under a low dose of anti-VEGF therapy. From the fitted cumulative incidence analysis, the systemic adverse events are usually observed within the 90 day follow-up (Figure 2) when the patients may receive the most frequent IVI with anti-VEGF (i.e., three monthly loading doses) [40]. In a systemic review with a meta-analysis, patients with diabetic macular edema were previously noted to have a higher risk of mortality that was slightly associated with an increasing number of anti-VEGF injections at twenty-four months [41]. Therefore, we would suggest monitoring not only ocular outcomes but also the general health conditions, such as blood pressure and cardiovascular signs, in patients who receive frequent anti-VEGF therapy, especially those with advanced age or diabetic complications. Of the systemic lab parameters that were assessed, only HbA1C was demonstrated to be reduced in patients receiving bevacizumab (Figure 3). In Taiwan, the use of aflibercept and ranibizumab requires review by the national health insurance prior to administration [42,43]. For diabetic macular edema, patients must to fit the criteria, including an HbA1c level ≤$10\%$, to receive the reimbursement for ranibizumab or aflibercept. Intravitreal bevacizumab injection was not covered by the national health insurance, and there were no criteria for using bevacizumab either. Higher HbA1c levels may disqualify patients from receiving aflibercept and ranibizumab, as these do not meet the NIH requirement (i.e., >$10\%$). Additionally, the application for reimbursement may also be time-consuming. Given these hurdles, anti-VEGF therapy with bevacizumab may be chosen for the timely treatment of diabetic retinal complications. As visual disturbance is one of the first presentations of diabetes, and patients treated with bevacizumab often also receive a hypoglycemic agent with their diagnosis of diabetes [44]. Therefore, the HbA1c may be decreased after the bevacizumab treatment. Interestingly, no change in systolic or diastolic blood pressure was observed in this study, which corresponds to the research by Glassman et al. in which no treatment group differences in BP were detected between anti-VEGFs [31]. These results contrast with a previous study by Shah et al., which reported an association between elevated BP and anti-VEGF injections in diabetic patients with DR [45]. There were some limitations in this study. The retrospective design may have a possible selection bias present in the study population, posing a limitation. The baseline characteristics of the study groups could not be fully matched due to the wide variance in the factors within each group. While we employed IPTW to balance the variables, the different baseline conditions could still potentially impact the study results. Nevertheless, the findings of this study could still provide valuable information for real-world clinical practice regarding the use of anti- VEGF therapies for the treatment of eye diseases. Interim data may also miss if the patient did not have regular follow-up at the institutes. The patients visiting Chang Gung Memorial Hospitals, which are either secondary or tertiary medical institutes, and may have had more advanced disease conditions. In addition, patient characteristics available on the CGRD have previously differed from those found in Taiwan’s national database [25]. This study compared adverse events between anti-VEGFs and did not include other treatments such as dexamethasone implants. Furthermore, we did not compare patients with mixed anti-VEGF use. The identification of a control group without anti-VEGF therapy was challenging due to the use of a real-world, multi-institutional database, resulting in a lack of such a group in our study. To address this limitation, future research could explore alternative methods to provide a more comprehensive understanding of the effects of anti-VEGF therapy. It is worth noting that residual confounding may still be present despite the use of IPTW to balance the covariates. Further studies could explore alternative methods to minimize selection bias and address unmeasured confounding factors. Additionally, investigations on the long-term outcomes and safety of anti-VEGF treatments could provide a more comprehensive understanding of their clinical utility. ## 5. Conclusions The main findings of this study suggest that different anti-VEGF agents may be associated with different systemic adverse events. While the baseline characteristics could not be fully matched across the study groups, the use of IPTW to balance the variables may reduce selection bias in this study. It is possible that these findings are related to different baseline characteristics or to the differences in anti-VEGF entering the systemic circulation. Despite potential limitations, the study results can still provide useful insights into the real-world effectiveness of anti-VEGF therapies in treating eye diseases. Monitoring the systemic adverse events is suggested in patients with advanced age or multiple diabetic complications. ## References 1. Liu J., Ren Z.-H., Qiang H., Wu J., Shen M., Zhang L., Lyu J.. Trends in the incidence of diabetes mellitus: Results from the Global Burden of Disease Study 2017 and implications for diabetes mellitus prevention. BMC Public Health (2020) 20. DOI: 10.1186/s12889-020-09502-x 2. Williams R., Karuranga S., Malanda B., Saeedi P., Basit A., Besançon S., Bommer C., Esteghamati A., Ogurtsova K., Zhang P.. 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--- title: Trait Mapping of Phenolic Acids in an Interspecific (Vaccinium corymbosum var. caesariense × V. darrowii) Diploid Blueberry Population authors: - Ira A. Herniter - Yurah Kim - Yifei Wang - Joshua S. Havill - Jennifer Johnson-Cicalese - Gary J. Muehlbauer - Massimo Iorizzo - Nicholi Vorsa journal: Plants year: 2023 pmcid: PMC10057043 doi: 10.3390/plants12061346 license: CC BY 4.0 --- # Trait Mapping of Phenolic Acids in an Interspecific (Vaccinium corymbosum var. caesariense × V. darrowii) Diploid Blueberry Population ## Abstract Blueberries (Vaccinium sect. Cyanococcus) are a dietary source of phenolic acids, including chlorogenic acid (CGA) and related compounds such as acetylated caffeoylquinic acid (ACQA) and caffeoylarbutin (CA). These compounds are known to be potent antioxidants with potential health benefits. While the chemistry of these compounds has been extensively studied, the genetic analysis has lagged behind. Understanding the genetic basis for traits with potential health implications may be of great use in plant breeding. By characterizing genetic variation related to fruit chemistry, breeders can make more efficient use of plant diversity to develop new cultivars with higher concentrations of these potentially beneficial compounds. Using a large interspecific F1 population, developed from a cross between the temperate V. corymbosum var. ceasariense and the subtropical V. darrowii, with 1025 individuals genotyped using genotype-by-sequencing methods, of which 289 were phenotyped for phenolic acid content, with data collected across 2019 and 2020, we have identified loci associated with phenolic acid content. Loci for the compounds clustered on the proximal arm of Vc02, suggesting that a single gene or several closely associated genes are responsible for the biosynthesis of all four tested compounds. Within this region are multiple gene models similar to hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyltransferase (HCT) and UDP glucose:cinnamate glucosyl transferase (UGCT), genes known to be involved in the CGA biosynthesis pathway. Additional loci on Vc07 and Vc12 were associated with caffeoylarbutin content, suggesting a more complicated biosynthesis of that compound. ## 1. Introduction The species commonly referred to as blueberry (Vaccinium sect. Cyanococcus) are perennial flowering plants native to North America. In the last thirty years, blueberry production has increased by over $600\%$, to 858,886 tonnes of blueberry harvested worldwide in 2020, with over $70\%$ of that production occurring in the United States of America, Peru, and Canada [1]. This increase in production has occurred in the wake of the publication of a number of studies, beginning in the mid-1990s, examining the potential health benefits associated with blueberry consumption, especially in regard to antioxidant activity, for which blueberry has among the highest levels compared to other fruits and vegetables [2,3,4]. In addition to the use of the berries as fresh or processed foodstuff, the plants have also been used in traditional medicines [5]. Even today in the realm of natural medicine, blueberries are recommended for use in treating Type II diabetes [6]. Compounds with antioxidant activity, such as phenolic acids, anthocyanins, and anthocyanidins, have an important function in the plant. Plants regularly experience environmental stresses resulting in the production of reactive oxygen species (ROS), excessive levels of which disrupt normal metabolic function by damaging lipids, proteins, and nucleic acids, all of which negatively impact plant growth and development [7,8,9]. Compounds with antioxidant activity scavenge ROS, protecting normal metabolic function [10]. While the evidence for effect of antioxidant compounds obtained through food on human health is slim, the possibility of efficaciousness has encouraged substantial research on the topic. One class of naturally occurring compounds known to have significant antioxidant capacity are phenolic acids, compounds containing a phenol moiety highly suited for trapping free radicals [10]. Chlorogenic acids are a family of polyphenol esters formed between trans-cinnamic acids and quinic acids [11]. Chlorogenic acids are one of the most well-studied families of polyphenols, due to their abundance in plant-based food and drinks [12]. They are widespread in plants and can be found in nearly all plant species [13,14,15,16]. One important subgroup of chlorogenic acids is the caffeoylquinic acids, which consist of esterifications of caffeic acid (Figure 1A), of which there are several isomeric forms. The most abundant of these isomers present in plants is 5-O-caffeoylquinic acid (5-CQA) [12], and it is the most widely studied due to its commercial availability [14]. Generally, the term “chlorogenic acid” (CGA) refers to 5-CQA [12]. It should be noted, however, that the nomenclature of the isomers 5-CQA (Figure 1B) and 3-CQA (3-O-cafffeoylquinic acid) (Figure 1C) can create some confusion. In 1976, the International Union of Pure and Applied Chemistry (IUPAC) reversed the order of the numbering of atoms on the quinic acid ring [17]. Consequently, the previously identified 3-CQA was renamed as 5-CQA [17], and the current 3-CQA refers to neochlorogenic acid in accordance with the new numbering system [12,16]. However, due to the name change, there is still considerable confusion in the literature, and many papers do not specify, when they use the term “chlorogenic acid”, whether they mean to refer to 5-CQA or 3-CQA [16]. This paper follows Clifford et al. [ 16] and uses the current IUPAC numbering, with CGA as 5-CQA. Chlorogenic acids are the major hydroxycinnamic acids present in blueberries [18], likely accounting for a large proportion of their antioxidant activity [19,20], with CGA being the major component [21]. CGA constitutes 10–$16\%$ of total acids in the blueberry fruit [22,23,24]. It is present in concentrations of 98–208 mg per 100 g FW (fresh weight) in V. corymbosum cultivars [21,24,25]. Other phenolic acids such as caffeic acid, p-coumaric acid, and ferulic acid are present in concentrations under $1\%$ [21,22]. CGA is one of the most abundant beneficial polyphenols in the human diet and is well known as a nutritional antioxidant in plant-based foods [26,27]. Dietary consumption of CGA is associated with the prevention of certain oxidative and degenerative, age-related diseases [28,29,30]. Compelling evidence indicates that dietary CGA can promote a wide range of pharmacological effects and biological activities in various tissues and organs [12]. Numerous studies have demonstrated the antioxidant activities of CGA, which include inhibiting the formation or scavenging of ROS [31]. CGA is also negatively correlated with the risk of various harmful conditions, such as oxidative and inflammatory stresses [32], type 2 diabetes mellitus [33,34], cardiovascular disease [35], neurodegenerative disease [36], and cancer [37]. Compounds closely related to CGA are the acetylated caffeoylquinic acids (ACQA, C18H20O10), which have previously been identified in blueberry [38]. These compounds have not been well characterized and the configurations of the compounds are not known, but the acetylation is likely to be on the quinic acid moiety [38]. Figure 1D shows a potential chemical structure for ACQA proposed by Jaiswal et al. [ 38]. Note that the regiochemistry of the acetyl group in Figure 1D is an arbitrarily selected example; the compound identified in the present analysis may not be the 4-acetyl caffeoylquinic acid isomer. While CGA has been studied for its potential for improving human health, similar efforts have yet to be made to understand the potential health impacts of consumption of ACQA1 and ACQA2. Further research is required to understand the potential benefits of the compounds, as well as their bioavailability when consumed in food or drink. Another related compound to CGA is caffeoylarbutin (CA). As with CGA, CA is an ester of caffeic acid, though with an arbutin group instead of quinic acid (Figure 1E). CA has been previously identified in the leaves of blueberry [39], as well as in the leaves of other Vaccinium species, such as lingonberry (V. vitis-idaea) [40,41], V. dunalianum [42,43], and bilberry (V. myrtillus) [39,41]. Other minor and specialty crops have seen great advances in the availability of genetic resources over the past decade. However, blueberry has lagged behind, with only a few published genome sequences [44,45,46] and a limited number of genotyped mapping populations [46,47,48,49], all of which were constructed through crosses between tetraploid cultivars of V. corymbosum. The development of mapping populations in blueberry is complicated by long generation time and partial self-sterility but is of exceeding importance to crop improvement efforts, as the identification of genetic markers and trait loci can increase the speed at which new varieties can be developed, and aid in understanding the mechanisms of compound biosynthesis. The blueberry (Cyanoccocus) section of Vaccinium is highly diverse, including species of differing ploidy levels and adapted to different environments. The highly interfertile nature of the section offers great opportunities for geneticists and plant breeders alike to identify potentially valuable variations which could be used to develop new blueberry cultivars with improved nutritional value and increased resilience in the face of climate change. The most commercially important blueberries are tetraploid (4n = 48) highbush plants (V. corymbosum). Accordingly, most of the available evidence on the beneficial health effects of blueberry deals with compounds identified in tetraploids. However, many wild species exist throughout North America [50], naturally occurring as diploids, tetraploids, and hexaploids [20]. Among these are numerous diploid blueberry species which offer diverse germplasm [51], though most have limited analyses of their genetics. Two species likely to be of use in this effort are V. darrowii and V. corymbosum. V. darrowii is a subtropical lowbush diploid blueberry native to the American southeast which has been used as a source of variation in breeding programs [52,53], while V. corymbosum is a temperate highbush blueberry native to the mid-Atlantic region of the United States. The varying climates to which these two species are adapted, as well as genetic drift over evolutionary time, have resulted in wide differentiation on many traits including its fruit chemistry which is quite distinct from that of the highbush species [32,36]. This differentiation is exploited in this study by using hybrid populations of these two species for trait mapping. By identifying quantitative trait loci (QTL) in a population containing V. darrowii ancestry, greater insight can be obtained into the genetic architecture of blueberry. A cost-effective method to produce genetic information for newly developed populations is genotyping-by-sequencing (GBS) as it simultaneously performs single nucleotide polymorphism (SNP) discovery and genotyping, eliminating a step required by other methods [54]. Using the newly available genome sequences, we can create highly accurate maps based on the physical ordering of the observed SNP markers. To the authors’ knowledge, phenolic acid content in blueberry has only been genetically mapped once previously, by Mengist et al. [ 48], who identified a QTL on Vc02 for CGA in an F1 tetraploid V. corymbosum population. While CA has been identified in a number of different species, as yet no QTL for CA has been identified. The ACQA compounds have not been fully characterized, and as yet have not been identified outside of blueberry, let alone been mapped. In this paper, we present the first genotyped diploid interspecific mapping population derived from crosses between V. darrowii and V. corymbosum var. caesariense (a diploid variety of V. corymbosum), two divergent species. This large population, recently developed at the Marucci Blueberry and Cranberry Research Center in Chatsworth, NJ, segregates for many traits of interest to breeders, including fruit chemistry. We present here mapping of the genetic control of phenolic acid content in blueberry fruit. ## 2.1. Qualitative and Quantitative Analysis of Phenolics Figure 2 illustrates a selected HPLC chromatogram of blueberry phenolic compounds. Table 1 summarizes the retention times and MS spectra of the studied blueberry phenolic compounds. The exact isomer of caffeoylquinic acid could not be determined using the mass data. However, based on the literature, the peak identified was assumed to be CGA (5-CQA). Similarly, two of the peaks were identified as isomers of ACQA. To clarify which one is being referred to, these compounds have been labeled as ACQA1 and ACQA2 based on the order in which they were eluted. Table 2 summarizes the mean and standard deviation values of all phenolic compounds among the genotypes. There were significant observed differences between average concentrations for all phenolic compounds in fruit among two genotypes of V. corymbosum var. caesariense (OPB-8 and OPB-15), two genotypes of V. darrowii (NJ88-12-41 and NJ88-14-03), and two interspecific hybrids (BNJ05-218-9 and BNJ05-237-8) (Figure 3). ACQA1 and ACQA2 concentrations were higher in the V. corymbosum var. caesariense blueberries, while CGA and CA concentrations were higher in V. darrowii blueberries, with intermediate levels in the hybrid blueberries (Figure S1). In the BNJ16-4 population, the mean CGA concentration was 0.25 ± 0.15 mg/g FW in 2019 and 0.23 ± 0.17 mg/g FW in 2020. The mean ACQA1 concentration was 0.14 ± 0.07 mg/g FW in 2019 and 0.13 ± 0.07 mg/g FW in 2020. The mean ACQA2 concentration was 0.11 ± 0.04 mg/g FW in 2019 and 0.09 ± 0.04 mg/g FW in 2020. The mean CA concentration was 0.05 ± 0.02 mg/g FW in 2019 and 0.05 ± 0.02 mg/g FW in 2020. Results for parents can also be found in Table 2. Graphs showing the range of concentrations found in the population and the parents can be found in Figure 3. ## 2.2. Trait Variation and Correlation In the parents and grandparent plants there was no observed harvest year effect. The Kruskal–Wallis rank sum test did not identify significant differences between 2019 and 2020 in compound concentrations in the parents and grandparents considered together (Table 3). Despite this OPB-15 did show great variation, with observed phenolic compound concentrations reduced by about half. In the BNJ16-4 population, harvest year effects were observed for all the tested compounds except ACQA1. In the parents and grandparents, each tested compound showed statistically significant correlations between itself and the other tested compounds (Table 4). ACQA1 and ACQA2 showed a strong correlation in both 2019 (0.97, $p \leq 0.001$) and 2020 (0.98, $p \leq 0.001$). CGA and CA also showed a strong correlation in 2019 (0.87, $p \leq 0.001$) and 2020 (0.85, $p \leq 0.001$). In addition, CGA and CA both showed moderate negative correlation with ACQA1 and ACQA2. In the BNJ16-4 population, ACQA1 and ACQA2 showed a strong correlation both in 2019 (0.89, $p \leq 0.001$) and 2020 (0.98, $p \leq 0.001$). CGA and CA showed a weak correlation in 2019 (0.26, $p \leq 0.001$) and a moderate correlation in 2020 (0.37, $p \leq 0.001$). ## 2.3. Map Information The output VCF from the “population” function on STACKS produced a VCF with 47,594 markers, of which 47,199 were located on chromosome assemblies. After filtering to remove monomorphic markers in the parents (BNJ05-237-8 and BNJ05-218-9), or where one parent had a missing call, or where the minor allele frequency (MAF) > 0.05, 34,445 markers remained. Following processing using a custom R script to turn the data into the JoinMap format, 24,134 markers remained. Within JoinMap, the non-segregating markers were removed, leaving 17,633 markers. We further separated markers physical chromosomes, where markers on each chromosome were filtered to remove markers with >=0.95 similarity and significantly distorted markers (<1 × 10−4), leaving a total of 9952 markers for formation of linkage groups. The physical marker order was used to set the marker order and the Kosambi algorithm was used for map construction. During map construction, 7862 markers were removed for lack of information provided or were otherwise not mapped, leaving 2090 markers in the final map. Of the 2090 markers in the final map, 771 markers were heterozygous in both parents, while 640 markers were heterozygous only in BNJ05-237-8, and 679 markers were heterozygous in only BNJ05-218-9. The total length of the map was 1591.6 cM, with the chromosomes ranging in length from 113.9 cM (Vc07) to 152.6 cM (Vc02), with an average length of 132.6 cM. Markers were generally well distributed across the genome, with an average of 0.8 cM between markers on both male and female parent-derived markers, with the exceptions of a 14.7 cM gap on Vc10, and gaps greater than 8 cM on Vc01, Vc06, Vc08, Vc11, and Vc12 (Table S1). The distribution of markers and marker types across the genome is shown in Figure 4. Graphs showing the relationship between physical and genetic maps of each chromosome can be found in Figure S2. Collinearity between the physical and genetic maps was very strong, with the Spearman’ correlation rho value being >0.999 for each chromosome. Polynomial formulae describing the instantaneous recombination rate across each chromosome were calculated and can be found in Table S2. Most chromosomes displayed the characteristic “S” shape, with the ends of the chromosome demonstrating higher recombination rates than the centromeric regions. In most of the chromosomes, regions of lower recombination were located in or near the physical center of the chromosome, with the exception of Vc06, in which the region of reduced recombination was shifted towards the proximal end of the chromosome. ## 2.4. Trait Mapping and Heritability Following phenotypic characterization and calculation of BLUPs, QTL were mapped using BLUPs and individual year data in R using the package qtl [55]. Peaks were identified for each tested compound (Table 5, Figure 5 and Figure S3). The identified peaks for CGA, ACQA1, and ACQA2 BLUPs clustered together on Vc02. The peaks for ACQA1 and ACQA2 BLUPs were identical, with a peak at 8.8 Mb with LOD scores of 26.8 and 23.6, and explaining $48.7\%$ and $44.4\%$ of the observed variation, respectively. The peak for CGA BLUPs was identified on Vc02 at 7.8 Mb with a LOD score of 17.7 and explaining $35.6\%$ of the observed variation. Peaks for CA BLUPs were identified on Vc07 and Vc12, with LOD scores of 5.6 and 7.0, and explaining $12.9\%$ and $16.0\%$ of the observed variation, respectively. Mapping with phenotype data from individual years generally identified similar regions to the BLUPs, though sometimes with the peaks shifted up or downstream. One notable difference was that the 2020 CA data were associated with a peak on Vc02 with a LOD score of 5.15 explaining $9.7\%$ of observed variation. The identified region on Vc02 overlaps with the region identified for CGA by Mengist et al. [ 48]. Complete marker-trait association LOD score data can be found in Table S3. Broad sense heritability, calculated using the BLUPs, for the tested traits was strong: 0.894 for CGA, 0.945 for ACQA1, 0.948 for ACQA2, and 0.891 for CA. ## 2.5. Candidate Gene Identification The minimal significant region on Vc02 (CGA, ACQA1, ACQA2), determined by where the mapped BLUP values overlapped with LOD scores above the threshold determined by permutation testing, stretched from 252,772 to 24,241,116 bp (23,988,344 bp). This region contains 1074 gene models. The region identified on Vc07 (CA) stretched from 16,286,998 to 29,752,448 bp (13,465,450 bp) and contained 474 gene models. The region identified on Vc12 (CA) stretched from 21,658,456 to 36,900,682 bp (15,242,226 bp) and contained 704 gene models. Within the significant region on Vc02, nine gene models had annotations matching known CGA biosynthesis pathway genes (Table 6). Seven had hits on hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyltransferase (HCT), one from tea (Camellia sinensis) and six from coffee (Coffea arabica), while two had hits on UDP glucose:cinnamate glucosyl transferase (UGCT) from poplar (Populus spp.). Additionally, within the significant region on Vc02 were 15 gene models annotated as MYBs, a transcription factor family which has been associated with the phenylpropanoid biosynthesis pathway. The complete list of candidate gene models can be found in Table S4. ## 3. Discussion Phenolic acids are known to be potent antioxidants [10] with implications for human health. Breeding programs can make use of the marker-trait association presented in this study to develop marker-assisted breeding programs, potentially increasing the speed of the selection process and leading to the development of improved varieties with increased antioxidant potential. The phenylpropanoid biosynthesis pathways have been extensively studied, with the pathways well understood [16,56,57,58], including both the enzymes involved, such as various transferases and hydroxylases and potential transcriptional regulators, such as those in the R2R3 MYB transcription family. Some potential pathways for the biosynthesis of ACQA1, ACQA2, and CA can be found in Figure 6. Using the BLUPs, CGA, ACQA1, and ACQA2 had QTL located on the proximal arm of Vc02. Mapping for CA using BLUPs did not identify a peak on Vc02, though mapping with 2020 data did identify a QTL close to the others, and the 2019 data showed elevated correlation between CA and the ACQA compounds but one which did not rise to the level of significance. The overlapping QTL on Vc02 suggest that a single gene or several genes located in that region are involved in the biosynthesis of all four compounds. Notably, seven genes in the significant region on Vc02 had BLAST hits on HCT from tea (Camellia sinensis) and coffee (Coffea arabica). Previous studies have shown that HCT performs the final step in the main CGA biosynthesis pathway, producing caffeoyl-CoA, the penultimate compound in the path [16,56]. While this is the main pathway, others have been proposed [56]. Of relevance is the alternate pathway wherein cinnamic acid is bonded with glucose by UGCT. Two gene models in the significant region have BLAST hits on UGCT identified from poplar (Populus spp.). Unfortunately, while Mengist et al. [ 46] mapped CGA content and produced transcriptomes in tetraploid blueberry, they did not make a comparison between genotypes with varying levels of CGA and as yet, there is no searchable database with expression data in different tissues (J. L. Humann, personal communication). R2R3 MYBs have been associated with the phenylpropanoid biosynthesis pathway [58,59]. Within the significant region identified on Vc02 are fifteen gene models annotated as MYB-encoding (Table S4), offering additional avenues of potential exploration. Future studies should focus on the potential activity of such transcription factors in the biosynthesis of CGA, ACQA1, ACQA2, and CA in Vaccinium, perhaps identifying markers which can be used for breeding purposes. Considerably less is known regarding the biosynthesis of other caffeoylquinic acid isomers. Our current understanding is that other caffeoylquinic acid isomers are derived from CGA, but there is little data on the enzymes that would be involved in such conversions [16]. In addition to the QTL identified on Vc02, CA also mapped to regions on Vc07 and Vc12, whereas the other compounds did not. This suggests that while CA biosynthesis is related to biosynthesis of the remaining compounds, it is more complicated. Further research is required to elucidate the biosynthesis pathways of the studied phenolic acids. Comparing the results of trait mapping presented here with the previous findings of Mengist et al. [ 46] showcases the mapping power of the interspecific population. While Mengist et al. [ 46] used a population of 196 individuals and found QTL for CGA with LOD scores of 5.9–6.9, the present study used a comparable number of individuals and identified QTL for CGA with LOD scores of ~16.0. This difference is likely partially explained by the larger difference between the parents in the present study. While the parents in Mengist et al. [ 46] population, the cultivars Jewel and Draper-44392, differed by ~25mg/100g FW, BNJ05-237-8 and BNJ05-218-9 differed by ~110mg/100g FW. It is unlikely, however, that this would explain the 10-magnitude difference in probabilities. It is more likely that the diploid nature of the BNJ16-4 population allows for greater clarity. Amadeu et al. [ 60] demonstrates the increased accuracy of trait mapping in diploid as compared to tetraploid populations, suggesting that trait mapping efforts should be focused on diploid populations when possible. The BNJ16-4 population shows segregation for a wide range of traits, beyond those presented here. Future studies should make use of the population to map those traits with a similar degree of confidence. In addition to its interspecific nature, the population used in this study offers advantages over the tetraploid populations used in other studies. This primarily stems from the diploid nature of the population, which greatly simplifies the genetic analysis. With just two copies of the genome, constructing maps is far simpler, as well as determining inheritance, dosage effects, and performance of QTL mapping [60]. Additionally, due to the relatively common failure of diploid male Vaccinium gametes to complete meiosis, ~$13.5\%$ of pollen grains have unreduced gametes [61], meaning that pollen from a diploid plant can be directly crossed with a tetraploid plant to bring the desired trait into the breeding population. Further, diploid blueberry can be induced to undergo polyploidization using oryzalin or colchicine [62]. Consequently, the bulk of breeding could potentially occur at the diploid level and the findings of mapping studies done in diploid plants, such as the present one, could be directly applied to the breeding program. The nature of the map conformed to expectations from the literature. The centromeric regions, as defined by a region with large physical distances and minimal genetic distances, and hence a slope near zero, was generally found at or near the center of the physical map (Figure S2). This accords with the karyotype data from Hall and Galleta [63], who reported that centromeres in Vaccinium were, when observed, median to submedian. The one exception was Vc06, where the centromere was shifted toward the proximal arm of the chromosome. The present study identifies a strong QTL for the tested phenolic acids, and its overlap with the QTL region identified by Mengist et al. [ 46] lends credence to our finding. However, the lack of independent populations in which markers could be tested means that any genetic selection, such as in a breeding program, would require using an individual or parent from the BNJ16-4 population as the donor. Further analysis on phenolic acid content in independent populations, such as biparental, multiparental, or germplasm collections, is required to develop widely applicable markers which could be used in marker-assisted selection. The observed near 1:1 correlation between ACQA1 and ACQA2, in conjunction with both traits showing highly significant associations with the same region on Vc02, suggests the possibility that biosynthesis of both compounds is carried out by a single enzyme, and which of the two compounds is produced is random. The minor positive correlation between CGA and CA observed in the BNJ16-4 population (0.26 in 2019 and 0.37 in 2020, Table 4) and the minor negative correlation between CGA and the ACQA compounds (Table 4) suggest a relationship in the biosynthetic pathways of these compounds. Phenolic content in OPB-15 varied dramatically between 2019 and 2020, with nearly double the concentration or more of each compound observed in 2019 as in 2020 (Table 2). This pattern was not observed in any of the other parents or grandparents. Previous studies have found that blueberries collected later in the season show increased levels of phenols, anthocyanins, and antioxidants [3,64,65,66]. Indeed, harvesting began and ended later in 2019 than in 2020. Another possible cause is elevated UV radiation in 2019 compared to 2020, which may have affected the phenolic content, though UV radiation was not measured in the greenhouse in which the plants were grown. Previous studies in carrot [67] and blueberry [68] have indicated that CGA content increases in correlation with increased UV radiation. It should also be noted that OPB-15 had low fruit yields in 2020, perhaps contributing or relating to the observed difference in CGA content. Furthermore, it is worth noting that the BNJ16-4 population also showed a general drop in phenolic compound concentrations in 2020 compared to 2019, with the exception of ACQA1, though not to the same extent as observed in OPB-15 (Table 2). This general decrease was significant, as evident in the results from the Kruskal–Wallis test (Table 3). Environmental variation could be involved in the inconsistent identification of a peak on Vc02 for CA. Further investigation on the possible environmental factors affecting phenolic content in blueberry is required, such as additional years of data or trials in different locations. Similarly, CA is thought to have potentially beneficial effects for human health. CA levels are high in the leaves of bearberry and V. dunalium, and a tisane is made from the leaves of both in traditional medicine [39,69]. Arbutin and its derivatives, such as CA, are used as skin-whitening agents, due to their activity in inhibiting melanogenesis by inhibiting tyrosinase [39,70]; indeed, it has been shown to inhibit melanogenesis in zebrafish [71]. Arbutin has also been shown to have anticancer properties, likely due to its high level of antioxidant activity [72,73]. CA appears to be bioavailable, being present in urine following consumption [39]. This paper demonstrates the presence of CA in blueberry and suggests possible breeding avenues to increase CA content, with potential health benefits for consumers. Each of the tested phenolic compounds contains a caffeoyl moiety (Figure 1A) as a component, suggesting that the significantly associated region encodes a gene responsible for the esterification of caffeic acid with various R groups. Further investigation could be directed at identifying other esters of caffeic acid in blueberry and mapping them to see if they co-locate. Cultivated blueberry consists mostly of tetraploid V. corymbosum, while the population tested here was developed from crosses between diploids, V. corymbosum var. caesariense and V. darrowii plants. The observed difference in the grandparents, where OPB-8 and OPB-15 (V. corymbosum var. caesariense) showed elevated ACQA1 and ACQA2 concentrations and NJ88-14-03 and NJ88-12-41 (V. darrowii) showed elevated CGA and CA concentrations, could represent different strategies by the species to combat oxidative stress. In addition, CGA is known to be bioavailable in humans [14,74,75] while to the authors’ knowledge the bioavailability of ACQA1 and ACQA2 has not been tested. While further studies are required to determine the bioavailability of the ACQA compounds, should they be less bioavailable in humans or show lower antioxidant activity, breeding to increase CGA levels in blueberry cultivars could improve nutritional value of the berries. Wang et al. [ 51] compared various wild diploid species along with tetraploid cultivars of V. corymbosum, showing that the chemical composition of the tetraploids was distinct from that of the tested diploid species for anthocyanin and flavanol glycoside content. This could indicate that introgression from a variety collection, such as the grandparents of the BNJ16-4 population, would be of use in introducing variation to breeding programs. ## Population Development and Maintenance A large F1 population derived from crosses between V. corymbosum var. caesariense and V. darrowii was used for this research. Two wild V. corymbosum var. caesariense plants, OPB-15 and OPB-8, were collected from a native population in Burlington County, NJ, near the Phillip E. Marucci Center for Research and Extension (39.71° N, 74.51° W). Two wild V. darrowii plants, NJ88-12-41 and NJ88-14-03, were collected from native populations in Liberty County, Florida (30.24° N, 85.01° W) and along Route 98S in the Saint Joseph Bay area of Florida (29.78° N, 85.28° W), respectively. Crosses between these plants were made in 2005, NJ88-14-03 x OPB-15, and OPB-8 x NJ88-12-41. The resulting hybrid plants were BNJ05-218-9 and BNJ05-237-8, respectively. Reciprocal crosses of the F1 plants were made in 2016. Where BNJ05-218-9 was the female parent, plants were designated with the prefix “BNJ16-4”. Where BNJ05-237-8 was the female parent, plants were designated with the prefix “BNJ16-5”. The resulting F1 population consists of 1025 full-sib individuals, 949 BNJ16-4′s and 76 BNJ16-5′s. A scheme representing the pedigree of the population is presented in Figure S4. Plants were maintained in pots in a greenhouse located at the Philip E. Marucci Blueberry and Cranberry Center for Research and Extension in Chatsworth, New Jersey, USA (39.72° N, 74.51° W). The plants went through the normal cycle of seasonal growth and winter dormancy (greenhouse maintained in a “cold” state, allowing for winter chilling at minimum 0–4 °C). Bumblebees (Koppert Biological Systems, Howell, MI, USA) were brought into the greenhouse in late spring/early summer during flowering for open pollination for fruit set. ## 4.2. Fruit Collection A subset of genotypes was selected arbitrarily to be included in the phenotyping as it was not feasible to analyze every genotype. Berry samples were collected from 185 genotypes in both 2019 and 2020, 45 just in 2019, and 48 just in 2020, as not every plant produced fruit in both years of the study. Samples were also collected from the parents and F1 plants in both 2019 and 2020. Fully ripe, i.e., blue, fruit samples were harvested from each plant at 7–14-day intervals over the fruiting period. In 2019 fruit was harvested from the parents beginning on 9 April through 10 June, and from the BNJ16-4 subpopulation beginning on 4 June through 16 August, with a peak around 24 June. In 2020 fruit was harvested from the parents beginning on 1 May through 24 June, and from the 16-4 population beginning on 7 May through 2 July, with a peak around 4 June. Berry samples were placed in polyethylene bags and kept chilled until weight measurements were taken. After weighing, samples were stored at −80 °C until analysis. Information on the number of berries collected from each plant in both 2019 and 2020 can be found in Table S5. ## DNA Extraction and GBS Young leaf samples from each genotype (BNJ16-4 and BNJ16-5 progeny, their parents, and grandparents) were collected in spring 2020 and DNA was extracted using a modified cetyltrimethylammonium bromide (CTAB) solution following Daverdin et al. [ 2017]. DNA quantification was performed using a Qubit 3 fluorometer and the Qubit dsDNA BR assay kit (Invitrogen, Waltham, MA, USA). Genotyping-by-sequencing (GBS) libraries were developed using a protocol derived from [76]. In brief, 200 ng of DNA was double digested using the restriction enzymes MspI and PstI-HF (New England Biolabs, Ipswich, MA, USA) at 37 °C for 2 h. A common adaptor and a unique barcode adaptor for each accession were ligated to the digested genomic DNA. Following ligation, the solutions were cleaned using 0.7 volumes of Axyprep Mag PCR Clean-Up magnetic beads (Axygen, Union City, CA, USA). The cleaned fragments were then amplified using Taq 5X Master Mix (New England Biolabs) and PCR primers with specific sequences to allow Flowcell binding and Illumina sequencing. After amplification, DNA from each accession was quantified again using the Qubit 3 fluorometer, then diluted to 5 ng/ul. Several pools were made using the diluted DNA from 112 samples with different barcodes. These pools were then then cleaned again using the Axyprep magnetic beads and quantified to ensure a concentration greater than 5 ng/ul. Pools of purified and barcoded genomic DNA were sequenced by Genewiz (South Plainfield, NJ, USA) in a 2 × 150 bp configuration on an Illumina Hiseq to produce paired end reads. ## 4.4. Demultiplexing, Sequence Alignment, and Variant Calling GBS sequencing data were processed using STACKS v2 [77]. The process_radtags function was used to filter for quality, demultiplex, and trim the raw reads. The following options were used: -c to clean the data, removing any reads with uncalled bases; -q to remove low quality reads (phred ≥ 10); -r to rescue the barcodes and RAD-tags. Demultiplexed reads were then aligned to the reference genome of W85 [48], a wild diploid V. corymbosum var. caesariense accession collected in Ocean County, NJ, using SAMtools [78]. The obtained BAM files were processed in STACKS using the ref_map.pl command for SNP calling. To facilitate trait mapping, the outputs were formatted into VCF files using the populations command in STACKS with the following options: “--min-samples-per-pop 0.90” so that only SNPs present in at least $90\%$ of the population would be retained; “--min-maf 0.2” so that only SNPs with a minor allele frequency >$20\%$ would be retained; “--ordered-export” to order the markers by physical location. ## 4.5. Map Construction Map construction was performed using JoinMap v5 [79]. The VCF output file from STACKS was converted to JoinMap format in R. Prior to map construction, markers for which both parents were monomorphic or where either parent had missing data were filtered out. We examined progeny genotypes for patterns of coherent segregation and the presence of impossible genotypes, which were converted to missing data when aberrant genotypes were below a $5\%$ threshold, otherwise loci above this threshold were removed from downstream analysis. A final step included the removal of markers with greater than $10\%$ missing data in the progeny. The remaining markers were imported into JoinMap, with markers from each chromosome imported independently. In JoinMap, highly distorted (p-value ≤ 1 × 10−5) and highly similar (≥0.95) markers were removed. Maps were constructed using regression mapping with the Kosambi mapping function and with a fixed marker order based on the physical order within the reference genome. Default settings were used for ordering of markers within a linkage group, which include linkages with a recombination frequency <0.4000, a LOD of >1.00, a “goodness-of-fit” threshold of 5.00, followed by rippling after every added locus. The genotype data are available in Table S6, and the map information is available in Table S7. ## 4.6. Instantaneous Recombination Rate A polynomial curve fitting the cM position as a function of physical location was generated in R version 4.1.3 (The R Foundation for Statistical Computing) for each of the 12 chromosomes. The linear model lm from the base stats package was used to determine the linear regression for polynomials up to the 25th degree. Then the output polynomials were examined to find the lowest degree polynomial with an R2 value greater than 0.998. The recombination rate (first derivative) of this polynomial was calculated along the length of the chromosome using the deriv function, also from the base stats package. ## Chemicals and Reagents All solvents, including water, acetonitrile, methanol, and acetone, were purchased from EMD Millipore (Billercia, MA, USA) and were of HPLC grade. Acetic acid was purchased from Avantor Performance Materials (Center Valley, PA, USA), and formic acid was purchased from Mallinckrodt baker (Phillipsburg, NJ, USA). A commercial standard of chlorogenic acid (CGA) was purchased from Sigma-Aldrich, Inc. (St. Louis, MO, USA). ## 4.8. Extraction of Blueberry Phenolic Compounds For chlorogenic acid quantification, depending on sample availability, 2–8 g of fruits were weighed, average berry weight (AW) was recorded, and samples were ground with a Precellys Evolution homogenizer (Bertin Corp., Rockville, MD, USA) using 2.8 mm ceramic beads at 7200 rpm for 1.5 min. $80\%$ aqueous acetone with $0.1\%$ acetic acid (1:4 sample to solvent w/v) was added to suspend ground fruit, and samples were extracted overnight at 4 °C in a standard refrigerator. Liquid extracts were then centrifuged at 13,300 rpm for 2 min, and 1 mL aliquots of clear supernatant were taken. Aliquots were dried with a SpeedVac vacuum concentrator (Savant SPD2010-220, Thermo Scientific, Waltham, MA, USA) under no heat and re-dissolved in 500 uL of $100\%$ methanol by sonication for 15 min. Samples were then centrifuged at 11,000 rpm for 5 min, and clear supernatants were analyzed with high-performance liquid chromatography (HPLC). ## 4.9. HPLC Apparatus and Conditions Two HPLC systems were used for phenolic acid identification and quantification. The phenolic acids were analyzed in a Waters Alliance LC system composed of a Waters e2695 Separations Module and Waters 2998 PDA Detector (Waters Corp., Milford, MA, USA). A Gemini 150 × 4.6 mm 5 μm C18 110 Å LC column (Phenomenex, Torrance, CA, USA) was used for separation, and compounds were detected at 366 nm. The injection volume was 10 μL. For compound identification of phenolic acids, the samples were analyzed using the method described in Wang et al. [ 51] with a Waters ACQUITY® UPLC I-Class system coupled with a Waters Vion Ion Mobility Quadrupole Time of Flight (IMS QTof) mass spectrometer (MS) (Waters Corp., Milford, MA, USA). The same column, solvent system, and elution gradient as described by Wang et al. [ 51] were used with the system for compound identification. In addition, a 1:3 splitter was used to direct one-fourth of the flow (0.25 mL/min) into the MS. Compounds were identified by liquid chromatography tandem mass spectrometry (LC-MS-MS) based on accurate masses, retention times, and UV absorbance at 305 to 390 nm. All solvent systems and elution gradients are summarized in Table 7. ## 4.10. Compound Identification with MS Spectrometry of Samples Ion-Mobility High-Resolution Mass Spectrometry data were acquired in high-definition MSE mode, with the following parameters: ion source, ESI negative ion; analyzer type, sensitivity; source temperature, 100 °C; desolvation temperature, 400 °C; cone gas flow, 50 L/h; desolvation gas flow, 850 L/h; capillary voltage, 2.50 kV; low collision energy, 6.0 eV; high collision energy, 15.0–45.0 eV; mass range, 50–2000 m/z; scan rate, 0.25 s. Leucine encephalin (50 pg/mL, 10 μL/min) was used for lock mass correction at 0.25 min intervals. MS and ion mobility data were acquired and processed in UNIFI (Waters Corp., Milford, MA, USA). ## 4.11. Compound Characterization and Quantification Phenolic acid characterization was carried out by comparing LC retention times, UV spectra and/or MS/MS data with standards (Table 1). For quantification of phenolic compounds, chromatograms were viewed at absorbance 366 nm and quantified as equivalents of their available standard, chlorogenic acid. The concentration of each compound was expressed in milligrams of its equivalent external standard per gram of fresh weight sample. ## 4.12. Phenolic Acid Data Analysis Statistical analyses were performed using R version 4.1.1 (The R Foundation for Statistical Computing) and Microsoft Excel for Microsoft 365 MSO (New York, NY, USA). The lme4 [80], lmerTest [81], and emmeans [82] packages were used to fit a linear regression model to the data, determine whether a significant difference exists between genotypes with Satterthwaite’s method, and run post-hoc analyses on differences between pairs of genotypes using Tukey multiple comparison tests with Kenward-Roger’s degree of freedom method. The corrplot [83] package was used to generate a Pearson’s correlation matrix among the phenolic concentrations. Correlations were classified as strong if r > \|0.7\|, moderate if \|0.3 > r > 0.7\|, or weak if r < \|0.3\|. Kruskal–Wallis H tests were used to evaluate harvest year and localization effects on individual compound concentrations in the parents and F1 plants. Excel was used to generate tables using mean and standard deviation values derived from the data. Excel was also used to generate frequency distribution histograms and tables using F1 data. Best linear unbiased estimates (BLUPs) were calculated using the lme4 package in R. Only genotypes with phenotypic data from both 2019 and 2020 were used to calculate BLUPs, with harvest year considered as a fixed variable. Broad-sense heritability was calculated using the variance components as follows, modified from Mengist et al. [ 46]:[1]H2=∂g2∂g2+∂gy2y+∂e2s where ∂g2, ∂gy2, and ∂e2, are the variance components of genotypes, genotype-by-environment interactions, and environment, respectively; y is the number of years the plants were phenotyped (2 years of the study) and s is the total number of samples in the data set. ## 4.13. Trait Mapping Trait mapping was done using the R package qtl [55]. Data were imported using the “read.cross” function, treating the population as a four-way cross and incorporating information about marker phase, and the “jittermap” function was used to separate markers at the same cM location. The probability value of each SNP was determined with the function “calc.genoprob (data, step = 0).” Afterward, to map the QTL probabilities, both a standard interval mapping using the EM algorithm: “scanone (data)” and a Haley-Knott regression: “scanone (data, method = “hk”, n.cluster = 2)” were used. Both algorithms showed similar results. To test for significance, 1000 permutations were performed on the Haley–Knott regression: “scanone (data, method = “hk”, n.perm = 1000)”. Only SNPs with LOD scores greater than the significance threshold determined through the permuation test were considered significant. Trait mapping was performed on calculated BLUPs and individual year data. Phenotypic data used in this study are available in Table S8. SNPs with LOD scores above the significance threshold were considered to be significant. ## 4.14. Candidate Gene Identification The QTL mapping results for each trait and for each year were compared to identify the region containing overlap between significant regions as determined by the LOD scores. The list of annotated gene models from the W85 (V. corymbosum var. caesariense) sequence [48] was surveyed to identify lists of genes located in the overlapping significant regions as determined by the permutation test. The gene annotations on this list were then examined for similarity with genes known from the literature to be involved in the CGA biosynthesis pathway. ## 5. Conclusions This study analyzed phenolic acid content and identified marker-trait associations in an interspecific population developed from crosses between V. corymbosum var caesariense and V. darrowii. The QTL identified explained 9.7–$48.7\%$ of the observed variation. We identified overlapping peaks on Vc02 for all the tested compounds, with additional peaks for CA on Vc07 and Vc12. 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--- title: 'Cross Sectional and Case-Control Study to Assess Time Trend, Gender Differences and Factors Associated with Physical Activity among Adults with Diabetes: Analysis of the European Health Interview Surveys for Spain (2014 & 2020)' authors: - Carlos Llamas-Saez - Teresa Saez-Vaquero - Rodrigo Jiménez-García - Ana López-de-Andrés - David Carabantes-Alarcón - José J. Zamorano-León - Natividad Cuadrado-Corrales - Napoleón Pérez-Farinos - Julia Wärnberg journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10057052 doi: 10.3390/jcm12062443 license: CC BY 4.0 --- # Cross Sectional and Case-Control Study to Assess Time Trend, Gender Differences and Factors Associated with Physical Activity among Adults with Diabetes: Analysis of the European Health Interview Surveys for Spain (2014 & 2020) ## Abstract [1] Background: We aim to assess the time trend from 2014 to 2020 in the prevalence of physical activity (PA), identify gender differences and sociodemographic and health-related factors associated with PA among people with diabetes, and compare PA between people with and without diabetes. [ 2] Methods: We conducted a cross-sectional and a case–control study using as data source the European Health Interview Surveys for Spain (EHISS) conducted in years 2014 and 2020. The presence of diabetes and PA were self-reported. Covariates included socio-demographic characteristics, health-related variables, and lifestyles. To compare people with and without diabetes, we matched individuals by age and sex. [ 3] Results: The number of participants aged ≥18 years with self-reported diabetes were 1852 and 1889 in the EHISS2014 and EHISS2020, respectively. The proportion of people with diabetes that had a medium or high frequency of PA improved from $48.3\%$ in 2014 to $52.6\%$ in 2020 ($$p \leq 0.009$$), with $68.5\%$ in 2014 and $77.7\%$ in 2020 being engaged in two or more days of PA ($p \leq 0.001$). Males with diabetes reported more PA than females with diabetes in both surveys. After matching by age and gender, participants with diabetes showed significantly lower engagement in PA than those without diabetes. Among adults with diabetes, multivariable logistic regression showed confirmation that PA improved significantly from 2014 to 2020 and that male sex, higher educational level, and better self-rated health were variables associated to more PA. However, self-reported comorbidities, smoking, or BMI > 30 were associated to less PA. [ 4] Conclusions: The time trend of PA among Spanish adults with diabetes is favorable but insufficient. The prevalence of PA in this diabetes population is low and does not reach the levels of the general population. Gender differences were found with significantly more PA among males with diabetes. Our result could help to improve the design and implementation of public health strategies to improve PA among people with diabetes. ## 1. Introduction Diabetes is a prevalent chronic disease with a significant impact on quality of life, morbidity, mortality, and healthcare costs [1,2]. The 10th Edition of the Diabetes Atlas of the International Diabetes Federation estimates that 537 million adults are living with diabetes, with a continuous increase in prevalence, making this disease one of the main public health problems worldwide [3]. According to this atlas, the prevalence in Spain reaches $14.8\%$, affecting 5.1 million adults, and it is estimated that $30.3\%$ of them are not diagnosed [3]. The number of deaths in Spain caused by diabetes in 2020 was 11,297 [4]. The study DI@BET.ES estimated a cumulative incidence of $6.4\%$ in 7.5 years of follow-up among people aged 18 years or older in Spain. This incidence increased with age and was higher in males [5]. Diabetes is a chronic and complex disease that requires, in addition to glycemic control, multifactorial risk reduction strategies [2]. In many cases, diabetes is preceded by abdominal obesity, metabolic syndrome, and prediabetes. Early and strict intervention of diabetes and its preceding diseases are the key factors to delay the disease appearance and to succeed in its treatment and control [2]. Lifestyle modifications, mainly those related to treatments adherence, nutritional guidelines, and physical exercise, provide the greatest benefits to these patients and have proven to be cost-effective [2,6]. Scientific evidence collects numerous benefits of physical activity (PA) on physical and mental health in people of all ages: it prevents, controls, and helps in the treatment of diseases, improves body composition, favors proper development of young people, and increases the quality and life expectancy of adults [7]. Despite this fact, the WHO reports that $80\%$ of adolescents and 1 in 4 adults do not comply with the minimum recommended amount of PA [7]. Performing PA reduces the risk of premature death in at least 25 chronic conditions [8]. Regular PA is considered essential in the management of diabetes as it can prevent or delay the onset of diabetes, reduce other cardiovascular risk factors, contribute to weight loss, improve insulin sensitivity, reduce HbA1c levels, and increase the quality of life of these patients [2,9]. In the Record 2021 Guide, the Spanish Society of Endocrinology and Nutrition recommends aerobic and strength-resistance exercise guidelines for people with diabetes to improve their clinical status [10]. The Spanish Ministry of Health published in year 2013 the document Diabetes Strategy of the National Health System (Estrategia en Diabetes del Sistema Nacional de Salud) that includes strategies to improve diabetes patient care in our country, also providing indicators that should be used to assess changes overtime. The Strategic #1 named: “Promotion of healthy styles living and primary prevention” recommends the use of national health surveys to provide information and monitor engagement in PA among people with diabetes [11]. We have analyzed the last European Health Interview Surveys for Spain (EHISS,) conducted in Spain in year 2020 (EHISS2020) [12,13], to describe changes and factors associated with PA in people with diabetes. To our knowledge, this survey has not been analyzed so far for this purpose. The EHISS is a powerful source of information through the collection and analysis of demographic and socio-economic characteristics, self-reported clinical conditions, use of medications and health services, and lifestyles; many of these variables are usually difficult to obtain from clinical records. The EHISS is useful to monitor trends in illness and disability, to identify access barriers to appropriate healthcare, to evaluate the impact of health programs, and for tracking progress toward national health objectives [11,14,15,16,17]. Previous research studies in Spain on PA and diabetes have constantly shown a higher frequency of sedentary lifestyle among people with diabetes than in the general population and suggested a negative trend in the adherence to PA recommendations overtime [15,18,19,20,21,22,23,24,25,26]. Furthermore, several sociodemographic and clinical variables have shown an association with PA among people with diabetes [15,16,17,25,26,27,27,28,29,30,31]. These variables include among others, gender, obesity, age, lower educational level, current smoking, chronic conditions, mental disorders, and self-perceived health [15,16,17,25,26,27,27,28,29,30,31]. However, the results are not conclusive and seem to be changing over time. This may be due to differences in sampling methods, study variables, information collection, and control of confounding variables, among others [15,16,17,25,26,27,27,28,29,30,31]. Unlike most previous studies, in our investigation, we have matched people with and without diabetes by age, gender, and region of residence to improve study efficiency by increasing precision and therefore providing novel and more reliable results [32]. The results or our investigation will provide policymakers with relevant data on time trends in PA and valuable information to target promotion and educational interventions to improve PA for those population groups with diabetes that would benefit most. Beside the reductions in the morbidity and mortality that increasing PA would yield in people with diabetes, a recent Spanish investigation estimated that EUR 2151 per individual may be saved if a minimum level of PA is implemented, due to a decrease in absenteeism and a lower use of healthcare services) [2,6,9,16]. In our opinion, all the mentioned issues require more investigation. Using two EHISS conducted in years 2014 and 2020, the objectives of our investigation were to (i) assess the temporal trend in self-reported PA among people with diabetes from 2014 to 2020; (ii) identify gender differences in the frequency of PA among people with diabetes; (iii) compare the frequency of PA between people with diabetes and gender-age-matched non-diabetic subjects; and (iv) determine which sociodemographic and health-related variables were associated with reporting PA in people with diabetes. ## 2.1. Study Design and Data Source To reach the proposed objectives, we have conducted a cross-sectional and a case–control study. The data source was two EHISS corresponding to years 2014 (EHISS2014) and 2020 (EHISS2020). Details on the EHISS2014 and EHISS2020 are available online [12,13]. Both surveys have identical methodology and questions [12,13]. Briefly, the EHISS is a home-based personal interview conducted with a three-stage sampling method to obtain a national representative sample of people aged ≥15 years residing in households. The EHISS2014 was conducted from January to December 2014 and the EHISS2020 from July 2019 to July 2020. Due to the COVID-19 pandemic, during the last months (March to July) of the EHISS2020, the interviews could not be fulfilled at the person’s home, so they were conducted by telephone [13]. ## 2.2. Study Population and Matching Method The study populations included all adults (≥18 years) interviewed in the EHISS2014 and the EHISS2020. A participant was considered to have diabetes if answered affirmatively to the question: “Has your doctor told you that you are suffering from diabetes?”. Those who answered “no” were classified as non-diabetic subjects. For each person with diabetes “case”, we randomly matched a person without diabetes “control” interviewed in the same year and with identical age, gender, and region of residence. ## 2.3. Study Variables Two dependent variables have been created to assess how frequently participants engaged in PA. To do so, we used two questions. The first question used was “Which of these possibilities best describes how often you do some PA in your free time?”, with four possible answers: 1. “ I don’t exercise. I occupy my free time almost completely sedentary”; 2. “ I do some occasional physical or sports activity”; 3. “ I do PA several times a month”; and 4. “ I do sports or physical training several times a week”. With this question, we created the variable “Frequency of PA” and those participants who answered options 1 and 2 were classified as “sedentary or with a low frequency”, and those who answered options 3 and 4 were classified to have a “medium or high frequency” of PA. The second question used was “How many days, in a typical week, you do sports, gymnastics, bicycling, or walking fast for at least 10 min continuously?”. The possible answers (0 to 7 days) were categorized in “none or one day” and “two days or more”. This study variable was named “Number of days per week of PA”. For study purpose, we excluded those patients who did not complete these questions or answered “I don’t know”. Sociodemographic covariates included gender, age, educational level, and if the person interviewed lived with a partner or not. Details of the questions used and the covariates created are shown in Table S1. The health-related covariates analyzed were self-rated health over the last year and self-reported presence of physicians diagnosed chronic conditions. Conditions included chronic obstructive pulmonary disease (COPD), heart diseases, stroke, cancer, mental disease, and high blood pressure. Information regarding lifestyles such as alcohol consumption, active smoking, and body mass index (<25, 25–29.9, and ≥30) were also collected, as detailed in Table S1. ## 2.4. Statistical Analysis The frequency of PA and the number of days per week of PA was estimated according to study covariates for people with (cases) and without diabetes (controls). Absolute numbers with percentages are shown for qualitative variables and means with standard deviations for quantitative variables. To compare unmatched qualitative variables, we used the chi-square test. We checked the normality of continuous variables using the Kolmogorov–Smirnov test and found that our study variables followed a normal distribution, so the Student’s t-test was used for comparisons. For matched comparison, the corresponding tests applied were McNemar’s test and paired Student’s t-tests as required. Multivariable logistic regression models were constructed, following the recommendation of Hosmer et al. [ 33], to identify which study variables were independently associated with the frequency and the number of days per week of PA among participants with diabetes and to assess possible changes from year 2014 to 2020. Adjusted odds ratios (ORs) with $95\%$ confidence intervals ($95\%$ CIs) are provided as the measure of association of the multivariable models. The statistical software used was STATA 14.0 (Stata Statistical Software Version 14. StataCorp LP, College Station, TX, USA). ## 2.5. Sensitivity Analysis To assess if diabetes was associated with the dependent variables, and if this association can be explained by other sociodemographic or clinical covariates besides age and gender, we conducted a multivariable analysis using logistic regression with the entire study population and including all those covariates that showed a significant relationship with the PA. ## 2.6. Ethical Aspects Any investigator can freely download the databases of the EHISS2014 and the EHISS2020 from the Spanish Ministry of Health website [14]. According to Spanish legislation, for investigations conducted with public access anonymous data provided by the health authorities, the approval of an ethics committee is waived. ## 3. Results Shown in Figure S1 is the flowchart of participant’s selection. Before matching, people with diabetes were significantly older than those without this condition in both surveys, 68.61 ± 13.40 vs. 51.71 ± 17.85 in the EHISS2014 ($p \leq 0.001$) and 70.25 ± 12.83 vs. 53.84 ± 18.07 ($p \leq 0.001$) in the EHISS2020. Regarding gender, the proportion of females was higher among those without diabetes than among those with diabetes ($54.1\%$ vs. $51.9\%$; $$p \leq 0.045$$ in the in the EHISS2014 and $53.3\%$ vs. $49.6\%$ in the EHISS2020; $p \leq 0.001$). The number of participants aged 18 years or older with self-reported physicians diagnosed diabetes were 1945 in the EHISS2014 and 2150 in the EHISS2020. After excluding those with missing data, the total number of matched couples reached 1852 and 1889 in these surveys, respectively. The distribution of people with diabetes according to study variables for the two EHISS is shown in Table 1. The proportion of females decreased significantly from $52.5\%$ in 2014 to $48.8\%$ in 2020 ($$p \leq 0.023$$), whereas the mean age increased from 68.2 to 69.7 years ($p \leq 0.001$). The educational level improved overtime. Regarding clinical variables, people with diabetes had better self-rated health in year 2020 when compared to 2014 and a significant reduction was found for self-reported COPD, mental disease, and body mass index. On the other hand, alcohol consumption rose significantly from $37.3\%$ to $41.2\%$ ($$p \leq 0.014$$). The proportion of people with diabetes that had a medium or high frequency of PA improved from $48.3\%$ in 2014 to $52.6\%$ in 2020 ($$p \leq 0.009$$). A similar tendency was observed for the number of days per week of PA, with $68.5\%$ in 2014 and $77.7\%$ in 2020 being engaged in two or more days ($p \leq 0.001$). ## 3.1. Gender Differences in Self-Reported PA between Males and Females with Diabetes Shown in Figure 1 are the frequency of PA and number of days per week of PA according to gender among people with self-reported diabetes included in the EHISS conducted in years 2014 and 2020. As can be seen in the figure, males with diabetes had a significantly higher frequency of medium or high PA ($58.1\%$ vs. $39.5\%$ in 2014 and $74.0\%$ vs. $63.5\%$ in 2020) and two or more days per week of PA ($59.0\%$ vs. $45.8\%$ in 2014 and $81.6\%$ vs. $73.6\%$ in 2020) than females with diabetes in both surveys (all $p \leq 0.001$). For both genders, a significant increment in the frequency of PA and number of days per week was observed from the EHISS2014 to the EHISS2020 (all $p \leq 0.001$). ## 3.2. Differences in the Self-Reported PA between Participants with Diabetes and Age–Gender-Matched Non-Diabetic Subjects Once the participants were matched, and both surveys joined, the medium or high frequency of PA was found in $59.4\%$ of participants without diabetes and $50.5\%$ of those with diabetes ($p \leq 0.001$). The frequency of PA was significantly higher among controls without diabetes than among cases with diabetes when the analysis was stratified by any of the sociodemographic variables shown in Table 2. The proportion of people with diabetes that reported doing PA two or more days per week was significantly lower than for those without diabetes ($73.1\%$ vs. $78.3\%$; $p \leq 0.001$). As found for the frequency of PA, controls had higher number of days per week than cases after stratification by any sociodemographic variables. For both sub-populations (cases and controls), being a male, younger age, higher educational level, and living with a partner were associated to higher frequency and number of days of PA. The frequency of PA and the number of days of PA according to clinical variables and lifestyles among participants with diabetes and matched controls without diabetes is shown in Table 3. For most categories of the variables shown in Table 3, the proportions of matched controls without diabetes who reported medium or high frequency of PA or practicing two or more days per week were significantly higher than among those with diabetes. Among people with diabetes, “Very good/good” self-rated health and not reporting any of the chronic conditions analyzed were associated to higher frequency and number of days of PA. Regarding lifestyles, participants with no diabetes who had alcohol consumption and lower BMI reported more PA than those with diabetes. ## 3.3. Multivariable Analysis to Determine which Study Variables Were Associated with Reporting PA among People with Diabetes The results of the multivariable logistic regression model to identify, among participants with diabetes, which variables were independently associated with the frequency and the number of days per week of PA are shown in Table 4. After adjusting for all the variables shown in the table, being a male was significantly associated to reporting a medium or high PA (OR 1.52; $95\%$ CI 1.31–1.75) and to engaging in PA two or more day per week (OR 1.20; $95\%$ CI 1.02–1.41). All age groups under 75 years reported more PA than the elderly. The results of the multivariable model evidenced that having a higher educational level was associated to more frequency and greater number of days per week of PA. Furthermore, “Very good/good” self-rated health was also associated to more PA. On the other hand, self-reported COPD, heart diseases, stroke, mental disorders, active smoking, or a BMI over 30 were variables associated to lower frequency and number of days of PA. After adjusting for possible confounders, the proportion of participants with diabetes who reported medium or high PA increased by $18\%$ (OR 1.18; $95\%$ CI 1.03–1.35) from 2014 to 2020. Furthermore, the increment in those who engaged in ≥2 days per week of PA was $62\%$ (OR 1.62; $95\%$ CI 1.39–1.88). ## 3.4. Sensitivity Analysis Shown in Table S2 are the results of the multivariable analysis to assess if diabetes was associated with the frequency of PA and the number of days of PA, after controlling for all the sociodemographic or clinical covariates. The results found, with the entire study population, are very similar to those reported for participants with diabetes with any of the two dependent variables used. Therefore, being a male, younger age, higher educational level, good self-rated health, not suffering from concomitant chronic conditions or not being obese, and not consuming tobacco were factors associated to more PA. Finally, the sensitivity analysis confirmed that people with diabetes reported a medium or high PA (OR 0.87; $95\%$ CI 0.86–0.96) and being engaged in ≥2 days per week of PA (OR 0.91; $95\%$ CI 0.82–0.99) significantly less than participants without diabetes. ## 4. Discussion Our work showed an increase in the prevalence of self-reported PA from 2014 to 2020 in Spanish adults with diabetes, as well as a better perceived self-rated health. Studies of PA trends in the population with diabetes are limited [27], but until now, they have mostly shown stable or unfavorable trends [15,27,28]. Jimenez et al. found for Spaniards with diabetes aged older than 65 years using data from health surveys from 1995 to 2006 a greater proportion of a sedentary lifestyle over time [15]. Zhao et al. in the United Sates observed a stable trend in PA frequency in adult with diabetes from 1996 to 2005 [28]. The improvement observed in our study population is significant, but it is still insufficient: in $47\%$ of persons with diabetes, the frequency of PA was classified as sedentary or low, and $22.3\%$ dedicated one or no days per week to engage in any PA. Other Spanish studies obtained similar discouraging prevalence of PA in adults with diabetes, in all cases below the recommended levels [10,16,17,25,26]. Sarria Santamera et al., using the 2017 Spanish National Health Survey (SNHS), reported $36.3\%$ physical inactivity among 1496 adults with diabetes [16]. López-Sánchez et al. estimated a prevalence of physical inactivity, measured through the International Physical Activity Questionnaire, of $35.4\%$ in his diabetes population of 1014 persons in year 2020 [17]. These data are possibly even worse if we consider that previous studies have shown that people with diabetes frequently overestimate their levels of PA [34,35]. In a systematic review analyzing adherence to PA in individuals with type 2 diabetes, results ranged from $32\%$ to $100\%$, with a median of $58\%$, although in only one study, PA compliance was the primary outcome [36]. As expected, subjects with diabetes engaged less in PA than controls matched for age and gender, and this finding was confirmed in the sensitivity analysis. This lower PA has been reported in a sample of over 100,000 adults in Germany using data from national population health surveys between 1997 and 2018, with lower prevalence of PA among people with obesity and diabetes than among people with normal weight and no diabetes [29]. Similar results have been found in the US population in the years 2016 and 2017, where $44.2\%$ of 4860 persons over 65 years of age with diabetes or prediabetes reported PA two or three times a week compared to $48.1\%$ from a matched sample without diabetes [30]. A recent systematic review of the literature found that these results are independent of the measurement instrument or study location [27]. We also found marked gender differences in the practice of PA in subjects with diabetes in favor of males. These differences have been previously observed in Spanish and international studies [16,25,26,31]. In Spain, a multicenter population study on adherence to healthy lifestyles in type 2 diabetes patients found that male gender was the variable most strongly associated with adequate compliance with nutrition and PA recommendations [26]. A systematic review and meta-analysis on gender differences in PA in type 2 diabetes adults throughout life concluded that these do not occur among adolescents but do appear with a remarkable magnitude in the older population [31]. The identification of females as a target group in which to increase PA adherence is especially relevant if we consider that females with diabetes have higher risk of suffering coronary disease than males with diabetes, with greater sequelae and mortality [37]. This excess of cardiovascular risk has been associated with poorer control of risk factors in females, mainly in the prediabetic phase, associated with a higher percentage of body fat, and together with other sociodemographic variables [38]. In addition to being a female, we found that higher age, lower educational level, and current smoking were associated to lower PA in the multivariable analysis. These associations have been confirmed in studies conducted among people with diabetes and in the general population [25,26,29,30,36]. Older people with diabetes are especially vulnerable, since they have a greater risk of suffering the consequences of inactivity, such as frailty, sarcopenia, and other chronic diseases, compared to younger ones [39]. Yang et al. in a recently published work on participation in PA among American elderly adults with type 2 diabetes found that beyond sociodemographic variables, personal factors such as extroversion and low neuroticism in adherence to exercise were factors that should be considered to optimize the results of health improvement strategies based on lifestyle modifications [40]. The presence of comorbidities such as COPD, heart disease and stroke, mental disorders, and obesity was also associated with less PA in our population with diabetes. These findings are like those reported in the literature [16,30,41], and possibly have a two-way direction causality. Furthermore, related to this association with comorbidities, and as expected, better self-perceived health status was found as an independent predictor of more PA, agreeing with other studies [42]. The association between obesity and less PA among people with diabetes is especially relevant due to the benefit in glycemic control and the cardiovascular risk profile of weight loss that can be achieved with PA [2,9,29]. Intervention approaches in this subgroup of patients should be individualized, multidisciplinary, and always considering their specific barriers to PA [2,9,29,43]. More efforts are needed to promote greater adherence to PA recommendations in the Spanish population with diabetes. It has been estimated that the improvement in PA in these patients can be associated with global savings in direct and indirect costs that represent $35\%$ of the total healthcare expenditure in Spain [16]. Health policies should promote PA at the population level, through campaigns that publicize its benefits, the recommended levels of PA, and that “every movement count”, especially if it is combined with a reduction in sitting position [39]. Physical education should be encouraged from the school environment, as well as promote the creation and improve accessibility to spaces for the practice of exercise [39]. Efforts directed at the general population will result in an improvement in the population with diabetes, but it is important to implement lifestyle improvement programs specifically aimed at the most vulnerable groups of people with diabetes, such as females, the elderly, and obese individuals, to help them to initiate and maintain the benefits of an active lifestyle. Linking exercise to leisure and socialization can be a particularly positive strategy in these patients [2,42,43,44,45]. Health professionals should be aware of this patient profile and include in their ongoing training plans knowledge about PA, benefits, safety considerations, and practical management options [44]. Any informative action that is addressed to these groups from health centers will have a positive impact. The recommendation of exercise must be individualized and centered on the patient with diabetes, and a comprehensive and multidisciplinary approach, which can be optimized by including graduates in physical activity and sports sciences in therapeutic teams. Mobile health interventions (mHealth) can represent an alternative or complement to face-to-face programs, although their results still require further effectiveness studies [42,43,44,45,46]. ## Limitations Our study has limitations that should be mentioned. First, the causality direction cannot be addressed due to the study design. Second, the questions used for self-reported PA and diabetes have not been validated in the EHISS. However, a Spanish study showed a specificity of >$95\%$ and a sensitivity > $70\%$ for self-reported diabetes using medical records as the gold standard [47]. In epidemiological research, the use of self-reported PA and diabetes within population surveys has been previously reported [15,16,17,24,27,28,30,31,35,36,41,42]. Third, the EHISS lacks specific information on diabetes, such as type, complications, duration of the disease, and treatments. Forth, as for any interview survey, the existence of recall errors or socially desirable responses must be considered. Fifth, another relevant limitation of our investigation is that only two levels of frequency of PA (up to 1 day/week and >2 days/week) were available to the participants, so we cannot assess a possible dose–response relationship. Furthermore, as commented before, previous studies have reported that overestimation of PA is possible when it is self-reported [34,35]. However, our intention using this question was to identify those individuals that had a very severe degree of sedentarism, because they did not even walk for at least 10 min continuously in a week more than once. Sixth, important factors such as the patients’ area of living (rural vs. urban) are not collected by the EHISS, so they could not be analyzed. Previous works have found that this factor might be a factor linked to PA trends [48]. Finally, the response rates for the EHISS 2014 were $61\%$ and for the EHISS2020 it was $59\%$; therefore, a non-response bias could have affected our results [21,22]. As commented before, due to the COVID-19 pandemic, the collection method during the last months of the EHISS2020 was modified and the effect of this change or of the pandemic itself on our results cannot be ruled out [13]. ## 5. Conclusions In conclusion, the trend of adherence to self-reported PA in Spanish adults with diabetes is favorable but insufficient. The prevalence of PA in this diabetes population is low and does not reach the levels of PA in the general population. The main factors associated with lower adherence to exercise were female gender, being older, lower educational level, worse self-rated health, the presence of comorbidities, obesity, and smoking. Our result could help to improve the design and implementation of public health strategies to improve PA among people with diabetes. However, it is necessary to deepen the research with studies specifically designed to identify attitudes and barriers to PA in people with diabetes, as well as the role of new technologies to improve adherence to PA in this population. ## References 1. Tsilidis K.K., Kasimis J.C., Lopez D.S., Ntzani E.E., Ioannidis J.P.. Type 2 diabetes and cancer: Umbrella review of meta-analyses of observational studies. 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--- title: In Vivo Analysis of a Biodegradable Magnesium Alloy Implant in an Animal Model Using Near-Infrared Spectroscopy authors: - Anna Mathew - Hafiz Wajahat Hassan - Olga Korostynska - Frank Westad - Eduarda Mota-Silva - Luca Menichetti - Peyman Mirtaheri journal: Sensors (Basel, Switzerland) year: 2023 pmcid: PMC10057053 doi: 10.3390/s23063063 license: CC BY 4.0 --- # In Vivo Analysis of a Biodegradable Magnesium Alloy Implant in an Animal Model Using Near-Infrared Spectroscopy ## Abstract Biodegradable magnesium-based implants offer mechanical properties similar to natural bone, making them advantageous over nonbiodegradable metallic implants. However, monitoring the interaction between magnesium and tissue over time without interference is difficult. A noninvasive method, optical near-infrared spectroscopy, can be used to monitor tissue’s functional and structural properties. In this paper, we collected optical data from an in vitro cell culture medium and in vivo studies using a specialized optical probe. Spectroscopic data were acquired over two weeks to study the combined effect of biodegradable Mg-based implant disks on the cell culture medium in vivo. Principal component analysis (PCA) was used for data analysis. In the in vivo study, we evaluated the feasibility of using the near-infrared (NIR) spectra to understand physiological events in response to magnesium alloy implantation at specific time points (Day 0, 3, 7, and 14) after surgery. Our results show that the optical probe can detect variations in vivo from biological tissues of rats with biodegradable magnesium alloy “WE43” implants, and the analysis identified a trend in the optical data over two weeks. The primary challenge of in vivo data analysis is the complexity of the implant interaction near the interface with the biological medium. ## 1. Introduction Osteoporosis has been a concern in European countries for decades. Osteoporosis patients have a higher tendency for bone fractures. Worldwide, it is reported to have more than nine million fractures a year. It means somebody gets a fracture every three seconds [1]. Bone healing may take a few weeks and sometimes several months, impacting the quality of life. Infection post-surgery is challenging to handle, making patient life even harder [2]. Bone tissues are unique as they can heal themselves after damage. Depending on the nature of the fracture, at times, supporting frames such as plates and screws are inserted into the bone to avoid deformed healing. If supporting plates/screws are made of biodegradable materials, it would prevent further complications with additional surgical procedures to remove the implants in the same region [3,4]. When a biodegradable implant decays, the mechanical properties of the materials reduce with time through biochemical reactions. Magnesium inside the living body is balanced based on the dynamic equilibrium of absorption and excretion [5]. Elements within certain limits are permissible for the human body, favoring the use of such biodegradable magnesium implants [6], which also have good biocompatibility. Magnesium implants have properties such as an elastic modulus and density close to natural bone [3,7]. In addition, magnesium-based alloys can promote tissue regeneration [5]. About $60\%$ of the total magnesium is stored in the bones [8]. Thus, magnesium’s benefits make it one of the most promising biodegradable substitutes for biomedical applications with numerous clinical trials [9]. Pure magnesium degrades quickly and does not provide adequate mechanical strength until bone healing occurs. Therefore, magnesium is typically alloyed to produce magnesium-based implants that are suitable for use in implant applications. It not only helps to prevent the fast degradation of magnesium, but also extends the mechanical life of the implant [3,10]. Magnesium alloy as an implant material has been researched to improve its properties [3,7,9,11,12]. Alloying helps to reduce the fast degradation rate of pure magnesium implants, giving sufficient mechanical strength to the bone until it heals completely [5,13]. To sum up, biodegradable implant reaction rates are to be such that they provide sufficient time for bone healing to serve the purpose. There are chemical changes near the implant interface, as illustrated by the equations [7,9,12]. When magnesium reacts with water in the tissue, it produces hydrogen, as in Equation [4]. Gas bubbles occupy loose skin near the magnesium implants [14]. The gas gradually disappears within 2–3 weeks of the in vivo implant surgery [9]. [ 1]Mg→Mg2++2e− The anodic reaction [2]2H2O+2e−→ 2OH−+ H2 ↑ The cathodic reaction [3]Mg2++2OH−→Mg(OH)2 ↓ Then the overall reaction can be written:[4]Mg+2H2O→Mg(OH)2 ↓+ H2 ↑ Overall reaction These Mg(OH)2 hydroxide layers (based on Equation [4]) form a layer covering the implant surface. However, its instability makes it react with the chloride ions from body fluids to form highly soluble magnesium chloride, as in Equation [5] [7]. The hydroxide ions (OH−) that are formed near the interface of the implant influence the local pH. [5]Mg(OH)2+2Cl−→MgCl2+2OH− The pH of a medium is the concentration of hydrogen ions (H+). The exact pH can also be the OH− ion concentration value. A solution is alkaline or basic if the increase in hydroxide ions OH− is numerically the same as a decrease in (H+). The reaction presented in Equation [5] suggests that the local pH near the interface shall increase due to the expulsion of hydroxide ions OH− [12,15]. The biochemical reactions are highly complex and form precipitates [16]. [ 6]OH−+HCO3−→ CO32−+ H2 O [7]Mg2++CO32−→MgCO3 ↓ [8]HPO42−+ OH−→ PO43−+ H2 O [9]3Mg2++2PO43− →Mg3(PO4)2 ↓ Calcium also reacts to form precipitates:[10]Ca2++CO32−→CaCO3 ↓ [11]3Ca2++2PO43− →Ca3(PO4)2 ↓ This chemical analysis indicates a delicate pH balance near the implant surroundings. A medium is alkaline or basic if there is an increase in hydroxide ions. As in Equation [5], the local pH near the interface shall increase due to the expulsion of hydroxide ions. Nevertheless, as the reactions proceed (Equations [7] and [9]–[11]), these hydroxide ions react and form precipitates, such as MgCO3, Mg3(PO4)2,CaCO3,and Ca3(PO4)2. Such precipitates near the interface can form a further coating near the implant that helps reduce the degradation [7,12]. The literature mentions the formation of phosphorous precipitates after a few weeks of implantation of magnesium implants in an in vivo analysis [5]. The precipitate layer obstructs the interaction of the implant with water and helps control the formation of hydroxide ions that contribute to pH changes. The body’s ability to naturally balance body parameters helps to suppress pH changes and maintains pH homeostasis. When the pH shifts toward alkalosis due to the magnesium implant’s presence, it can affect the healing of the wound caused by surgery [15]. It is pH homeostasis that helps to balance the change. The reaction (from Equation [4]) happens with the support of water. In addition, the literature highlights that water is a byproduct of the succeeding reactions [7]. Thus, near the implant interface, there are changes related to the presence of water. In an in vivo system, water, blood, and other physiological fluids are naturally present. Thus, a magnesium implant in constant contact with fluids and tissues leads to a dynamic interface that constantly undergoes functional (metabolic) and structural (surface) changes in the implant–tissue interface. Currently, monitoring implants requires a complex and resource-demanding healthcare system [17]. Near-infrared (NIR)/IR spectroscopy provides a patient-friendly, nondestructive, and noninvasive technology. As part of a previous project (MgSafe), an optical probe (Figure 1) was developed to collect photons within NIR/IR spectroscopic data from tissue samples [18,19,20]. The construction and validation of the probe are reported in [21,22], respectively. In this paper, we utilize optical spectral analysis to investigate the effects of magnesium implants in vitro and in vivo. The in vitro experiment aims to examine the optical interaction of a magnesium implant in a cell culture medium over time. In the in vivo setup, we perform exploratory data analysis to identify a specific observation or trend for animals with magnesium implants. Moreover, we discuss possible reasons for the differences in trends between the in vivo and in vitro experiments. We hypothesize that we can detect a difference in the optical spectra in animals implanted with a biodegradable Mg implant over time. ## 2. Materials and Methods The optical probe emits light from 680 nm to 1100 nm. It can pass through tissues for measuring changes in biological tissues noninvasively [18,23]. The light from the source reaches the detector after passing through the medium of interest. Some photons in the light ray are absorbed in the medium while others are scattered. Hence, the light that reaches the detector indirectly indicates the changes in the medium through which it passes [24]. Experiments were performed as part of this publication, including in vivo and in vitro experiments. In vivo experiments were conducted on animals. Preparatory in vitro data collection and analysis were conducted to minimize uncertainties. The primary target of the experiments was to gain insights into the use of optical near-infrared spectroscopy to monitor the interaction of biodegradable Mg-based implants with tissue over time, enabling the possibility of grouping optical spectra on a particular day in the corresponding experiment. In vivo and in vitro experiments were studied in the first two weeks; however, two experiments were conducted in two different environments and were not compared directly day to day. In vitro experiments were designed to study the effects of magnesium degradation in simulated body fluid. In vivo experiments were conducted to evaluate the performance of magnesium pins implanted in rats. The time points were selected based on the goals and requirements of each experiment. The animals’ well-being was the priority; hence, day points varied slightly in this pilot study. The animals’ temperature was monitored during the experiments and ensured that it was stable and did not differ significantly for the animals. This paper investigates the observations or trends identified in the in vitro experiment in the context of the in vivo experiment. ## 2.1. Methodology For the reflection-type measurement, the light source and detector were on the same side of the thick medium, such as hard tissues. Figure 2a illustrates the experimental setup used in vivo. The light source and detector were on opposite sides of the medium for the transmission-type measurement, as shown in Figure 2b. The spectrometer (Avaspec-2048x14, Avantes, Apeldoorn, The Netherlands), used with Avantes software AvaSoft8, produced a spectrum ranging from 650 nm to 1100 nm. The instrument was calibrated every time the experiment was set up for the measurements. Based on the degradation of the Mg implant in the surrounding medium, the reflection or transmission of light was affected, resulting in changes in the scattering and absorption of the light [21]. One of the efficient exploratory model approaches is principal component analysis (PCA). Dimensionality reduction based on the principal components is PCA’s main advantage as it helps to plot the data into their principal components. In spectral analysis, the variables are wavelengths, and the multi-dimensional nature of the spectral data makes it challenging to visualize. Therefore, PCA was performed using python with the help of the scikit-learn library on the collected data after preprocessing. The explained variance and the score plot were further analyzed and interpreted based on the different experimental conditions [26,27]. Furthermore, a comparison study was conducted based on the in vivo progressions results of two weeks post-surgery. ## 2.2. In Vitro Experiment and the Trend The primary goal of the in vitro study was to check the feasibility of using variations in the optical spectrum as a response to changes in the surrounding medium caused by the degradation of magnesium implants with time. The experiment used the transmission-type optical data collection [25], as illustrated in Figure 2b. The experiment used three samples of biodegradable Mg alloy ZX00 disks [0.45 wt% Zn—0.45 wt% Ca. Tech (Bri. Tech, Graz, Austria)] in cell culture medium DMEM (Gibco Dulbecco’s Modified Eagle Medium). Optical data were collected on days 0, 2, 5, and 10. Day 0 refers to the spectrum collected before placing the ZX00 disk in the medium. Day 2 is the second day’s optical spectrum from the DMEM solution. Likewise, day 5 and day 10 give optical spectra for the corresponding days. Throughout the experiments, we observed a gradual change in the color of the DMEM solution as the pH levels varied with the degradation of the magnesium implants, although the change was not significant. We used DMEM with a composition of 1 g/L of glucose and sodium bicarbonate without L-glutamine for our experiments. Each day, ten measurements were performed on each Mg alloy disk. Data cleaning that involved removing noise, artifacts, and outliers that could affect the analysis results was followed by dimension reduction based on its principal components. ## 2.3.1. Ethical Considerations All in vivo experiments were carried out following the National Ethical Guidelines (Italian Ministry of Health, Rome, Italy; D.L.vo $\frac{26}{2014}$) and the guidelines from Directive $\frac{2010}{63}$/EU of the European Parliament. The protocol was approved by the Instituto Superiore di Sanità on behalf of the Italian Ministry of Health and Ethical Panel (Prot. no. $\frac{299}{2020}$-PR) and the local ethics committee. Additionally, the protocol conformed to the ARRIVE guidelines. ## 2.3.2. In Vivo Data Gathering This study included four 12-week-old female Wistar rats that were implanted with Mg alloy WE43 cylindrical pins inserted through the mid-diaphyseal region of both femurs. Briefly, the 12-week-old female Wistar rats were anesthetized, and a transcortical hole was drilled in the femur using a 1.55 mm diameter drill. A pin implant made of Mg alloy (WE43) was inserted, and the wound site was closed with resorbable sutures. Please refer to our previous publication [28] for more information on the surgical procedure. NIRS acquisitions were performed at predetermined time points. In vivo experiments were conducted to evaluate the performance of magnesium pins implanted in rats and based on physiological meaning. The animals’ well-being was the priority; hence, day points were also selected considering the postoperative recovery state of the animals. For example, on day 3, animals fully recovered from the surgical procedure and had normal hydration levels. Day 0 acquisition was made immediately after surgery, so animals were already anesthetized. We used a rectal thermometer in all animals to guarantee their temperature stabilized at a normal value. An optical reflective setup was used to measure the NIR data (referring to Figure 2a) for two weeks after surgery. The probe was placed in a consistent manner for each measurement using the femur as an anatomical reference on the lateral side of the leg over the implantation site and parallel with the femur. Acquisitions were made on day 0 (a few minutes after surgery), day 3, day 7, and day 14 after the animals were anesthetized, and the anesthesia was maintained throughout the measurements. The animals were anesthetized with $2.5\%$ isoflurane in pure oxygen and placed laterally on a stereo-foam platform to minimize heat loss. The animals woke up naturally 1–2 min after removing the anesthesia. We paid special attention to maintaining a controlled breathing rate. If necessary, any regrowth fur was removed with a depilatory cream (Veet) 2–3 min before acquisition to avoid any skin microcirculation changes due to friction. All acquisitions were made in the same room at a constant temperature of 25 °C and under low luminosity conditions to reduce background noise. More details on the acquisition procedure can be found in our previous publication [21]. The selected time points were chosen considering the critical pH variations in the surrounding region of the implant during the first two weeks [25] and physiological meaning. The first two weeks are determinant for the success of implant osseointegration, and it is when soft tissue surrounding the implant heals. Between day 0 and days 2–3, there is an ongoing process of acute inflammation, blood vessels ruptured during the surgery, leading to hematoma formation. Several complex biochemical interactions happen around the implantation site. At the same time, when the implant degradation rate is fastest, the fast release of hydrogen leads to gas bubble formation. From days 5 to 7, it is possible to find new tissue organization, i.e., revascularization, new blood vessels forming, bone tissue regenerating, and gas bubbles reducing. Other in vivo studies investigating biodegradable implant degradation and surrounding tissue regeneration use similar time points. Hence, the selected time points do not differ significantly as the degradation rate of the alloys in vitro is different from in vivo [29]. Throughout this study, the animals’ physiological parameters, such as temperature and blood oxygen saturation levels, were closely monitored for any signs of distress or discomfort, including during surgery and NIRS acquisitions. Although we did not use instruments to monitor breathing rates in real time, we manually checked the animals’ breathing rates to ensure their overall health. Since our focus was mainly on deep-tissue measurements, we anticipated that respiration’s impact on NIRS measurements would be minimal. Our findings are supported by the analysis of oxy and de-oxy data, which revealed that changes in respiration rate would result in rapid changes in de-oxy measurements instead of oxy values. Additionally, we closely monitored the animals’ rectal temperature, which can change rapidly and affect both cardiac and respiratory rates, to ensure that the temperature remained within normal levels throughout the experiments. In vivo data collection used a probe light source–detector separation of 8.0 mm. As a rule of thumb, the measurements were expected to be from a depth equal to around $\frac{1}{2}$ to $\frac{1}{3}$ of the source–detector distance [30,31]. The experiment with the optical probe showed a $100\%$ difference in the PCA score plot of the data collected from 6 mm and 8 mm source–detector distances [21]. The optical probe previously showed the capability to differentiate post-mortem pork tissues using the source and detector at an 8 mm separation in the PCA score plot [32]. In this experiment, the data collected include ten measurements by positioning the sensor on the animal’s leg above the implant. The optical data are specific to the location from where they were collected in the animal [33]. These biological differences across the body parts call for the model to be specific to the body location. ## 2.3.3. In Vivo Data Processing Only data collected from the animals’ right femur were considered for data processing. The first step of processing the data was to separate the required conditions while the spectrometer range with noise was avoided. Different layers, such as fat, skin, muscles, and blood flow, influence the in vivo optical data from animals. The fat scatters light, while hemoglobin in the blood absorbs NIR light [31,34,35]. Due to the degradation of magnesium-based implants, there are complex chemical reactions near the implants, including changes in pH [15,25]. The hydrogen gas formed as part of the implant reaction occupies the regions near it, and these gas bubbles near the implant can slowly increase in dimension as they move close [14]. Such bubbles influence the optical information due to light scattering if they fall in the light’s path. In addition, as the wound heals, there are changes in the skin tissue [30]. The body healing after the surgical procedure adds more changes at the cellular level as damaged cells start to heal in the upcoming days of the post-surgery period. Hence, the optical information gathered from the surface above the implant is the net effect of complex biological changes. Thus, in vivo data need further preprocessing to identify the information regarding the implant surface. The standard normal variate (SNV), which divides the mean-centered spectrum by its standard deviation, was used to preprocess the in vivo spectra [36,37]. A derivative approach can help to enhance the changes in the spectrum. The Savitsky–Golay filter derivative technique with the first derivative [34] was used after SNV. Following these steps for all datasets, a similar trend was observed in the in vitro data in the PC1–PC2 plot by separating the optical data into different day groups. ## 3. Results The in vitro experiment aims to study the interaction of implants with DMEM cell culture medium. Figure 3a shows the medium pH values over time. The medium tends to become alkaline over time, primarily due to the implants’ degradation in the medium. The PCA-based score plot from the optical NIR spectra from different days is shown in Figure 3b. The in vitro data are influenced significantly by PC1 as the explained variance is $96.9\%$, while PC2 is $2.5\%$. The graph shows that the measurements made on the three implant samples seem to be clustered by day (day 0, day 2, day 5, and day 10). However, on day 5, the sample distribution is drastically influenced. As previously shown by the author in [14], these changes on day 5 are not outliers; there is meaningful information related to the changes in the medium. The implant interacts with the medium, which leads to an expected change over days. The trend identified is that the samples can be grouped based on the day. The in vitro data analysis resulting in Figure 3a,b suggests that optical data on a particular day change proportionally to the internal changes in the surrounding medium through which the light passes. Here, we explored the hypothesis that the “implant interaction with the medium leads to an expected change over days, measurable with optical spectra”. The same hypothesis was further investigated in the in vivo experiment. The recovery progression due to the magnesium implant (WE43 type) was expected to be similar post-surgery. It has to be noted that the degradation rate of WE43 is different from ZX00 [38]. However, as these two alloys are used in two different settings, the conclusions would not contradict each other as the in vivo pH is controlled locally, and the structural changes would not be the only influencing cause of the detected signals [15]. Slight variations based on the difference in rats is acceptable. Among the four rats, three rats survived the first two weeks. Rat 1 died on the third day after implant surgery. The optical data for rat 1 differ, whose health was negatively progressing post-surgery. However, the variation in the optical data from rat 2 that survived the first two weeks (Figure 4a,b) challenges the trend of the in vitro experiment. Hence, the reason for the variation in this rat’s optical spectrum needs clarification to confirm the hypothesis of this paper. A loading plot in the PCA can relate to the features in the datasets [39]. In the spectral datasets, features are the wavelengths. Studying the wavelengths that influence the spectra can be meaningful. The literature illustrates that chromophores influence different wavelengths. The NIR range strongly influences absorption due to hemoglobin and cytochrome C oxidase. Oxyhemoglobin absorption rises, while deoxyhemoglobin reduces below 800 nm, and metabolism-related changes (cytochrome C oxidase) peak at around 800 nm [31,40,41,42]. The region above 940 nm hints at water content in the tissues. The in vivo preprocessing uses the derivative-one filter that helps to highlight the wavelengths that have changes in the raw spectra. The slow rise in the absorption curves is converted as the sharp changing peaks in the derivative curves of spectra. The amplitude of the sharp peaks is proportional to the changes in the actual spectra [40]. The derivative spectra of in vivo data from the rats are studied. The derivative spectra are plotted using measurement 7 (random selection) from a particular rat on a specific day. Figure 5 is the derivative spectra plotted for all four rats on day 3. It is interesting to note that two rats (rat 3 and rat 4) overlapped. Due to the $100\%$ overlap, only rat 4 is visible in the graph. Referring to the PCA plot from Figure 4 on the third day for rats 3 and 4, it is due to this $100\%$ overlap. It is to be noted that rat 2, which also survived for two weeks, had spectra different from the other surviving rats. It closely resembles the derivative spectra of rat 1 in specific wavelengths. ## 4. Discussion The findings of the in vivo experiment indicate that utilizing derivative spectra to identify wavelengths contributing to progress in Mg-based implant surgery is an effective approach. Score plots based on PCA can differentiate rats based on their internal changes. The optical data reflect a combined effect of internal changes. The first week after the implant is critical, as illustrated by the pH curve in Figure 3a from the in vitro experiment, and any deviation from the required progression in the optical spectra signals the need for medical attention. The derivative spectra from the in vivo experiment on day 3 (Figure 5) demonstrate that hemoglobin-based changes were opposite for the dead rat (rat 1) and rat 2 with skin rashes compared to the other rats without visible complications. This study confirms the feasibility of applying PCA to optical spectra data to identify deviations in the positive progression of Mg-based implant surgery. When analyzing the PCA-based score plots in Figure 4a for in vivo data, it is evident that there are two distinct clusters for day 3, separated by $100\%$. The derivative spectra for the same day in Figure 5 show that rat 3 and rat 4 exhibited similar optical spectra that overlapped. Thus, the optical spectra from rat 3 and rat 4 are one cluster on the positive axis of PC1, while the other cluster is from rat 1 and rat 2 (refer to Figure 4b). Hence, it can be considered a reference to compare the other two rats on day 3. In relation to the derivative spectra results presented in Figure 5, it is observed that rat 1 and rat 2 exhibit an inverse pattern when compared to rat 3 and rat 4 (the rats that survived for two weeks). Figure 5: In vivo results. Derivative spectra plotted from one measurement of day 3. It is plotted for all four rats. Rat 3 and rat 4 overlap 100%. Rat 1 and rat 2 are significantly different from rats 3 and 4. Later that same day, rat 1 died. This rat showed two predominant peaks that stand out from the normal rats (rat3 and rat4), one at 965–975 nm and the other at 680–800 nm. The one at 965–975 nm and the other at 680–800 nm correspond to water absorption and hemoglobin. Interestingly, rat 1, which survived only until day 3, showed two predominant peaks that stand out from the normal rats, one at 965–975 nm and the other at 680–800 nm. The one at 965–975 nm and other at 680–800 nm correspond to water absorption and hemoglobin, respectively [35]. It is evident that rat 2 also has a distinct pattern. For the derivative spectra from days 3 and 14 (Figure 5 and Figure 6), rat 2 shows significant peaks between the wavelengths 680 nm and 800 nm, suggesting issues related to blood flow on the implantation site highlighted in Figure 6. Observational analysis reported red rashes near the wound site, a possible allergic reaction to the shaving cream that led to a continuous inflammatory state of the skin. Hence, we believe this abnormal skin rash influenced the optical measurements and might justify the significant difference near 955 nm compared to rats 3 and 4, as highlighted in Figure 6. Therefore, we hypothesized that the derivative spectra, particularly in the 700–900 nm range collected from rat 1 and rat 2, indicated changes in hemoglobin and cell metabolism at the wound site that correlated with outcomes not associated with the successful progression of healing (reference to Figure 2 in [31]). Optical data depend on local tissue [33]. It is possible that wound healing combined with the effects of magnesium implant reactions in vivo contributed to the optical changes near the interface for rat 2. A previous in vivo study [14] suggests that by the fifth day, maximum internal changes occur due to wound and implant reactions. It is essential to separate days in the in vivo setup because each time point is associated with a stage of healing. If optical measurements along with PCA can distinguish between days after surgery, it would also mean that they could detect progress in the healing process of the tissue. The two rats with an abnormal response to surgery (rat 1 died, rat 2 developed an allergic skin reaction) presented a very different derivative spectra from the healthy rats (rats 3 and 4). In fact, rats 3 and 4, which presented a normal recovery process after implantation, had overlapping derivative spectra. The spectral analysis for rats 1 and 2 demonstrates the potential of spectral analysis to detect abnormal recovery processes. Although the limited number of samples is a constraint in this pilot study, it highlights the potential for a future NIRS device. ## 5. Conclusions This research uses exploratory data analysis to relate the optical changes near the in vivo magnesium implant interface. In vitro experiments facilitated the identification of the trend and the possibility of grouping optical data collected from a particular day. They helped to initiate this study from an optically less complicated medium due to the absence of blood flow and cellular metabolism. The in vivo optical data analysis gave meaning to the trend by comparing the health of the rats in the post-surgery days. The possibility of separating days is intriguing in the in vivo studies. The study of two particular cases of rats confirmed that it is possible to separate rats with variations in recovery progression post-surgery optically. The biochemical reactions occurring near the implant site over time led to differences in the optical spectra. The optical probe developed at the university can capture these combined changes in vivo. Hence, optical diagnosis seems to be a promising approach. The dynamic degradation of magnesium implants with simultaneous tissue regeneration requires medical follow-up support for extended periods. The optical probe we have developed offers a noninvasive approach that can be useful for monitoring the early healing stage. It gives information on oxygen saturation, hemoglobin concentration, [28,43] and water content, which may indicate physiological healing processes. Further measurements conducted with the optical probe may lead to a characteristic optical pattern associated with the healing process. In the future, it can become a user-friendly device and be used by clinicians and patients periodically during post-operative periods where physical movement is restricted. Furthermore, predictive models can be developed based on the knowledge of magnesium implant degradation behavior and the specific optical patterns associated with the process. A deviation from these expected optical patterns may be an indication of an abnormal healing process and an early sign for further medical analysis. On the other hand, patients showing an expected optical pattern from the knowledge base statistics can avoid unnecessary hospitals visits. 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--- title: Mechanochemical Synergism of Reactive Oxygen Species Influences on RBC Membrane authors: - Elena Kozlova - Viktoria Sergunova - Ekaterina Sherstyukova - Andrey Grechko - Snezhanna Lyapunova - Vladimir Inozemtsev - Aleksandr Kozlov - Olga Gudkova - Aleksandr Chernysh journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10057059 doi: 10.3390/ijms24065952 license: CC BY 4.0 --- # Mechanochemical Synergism of Reactive Oxygen Species Influences on RBC Membrane ## Abstract The influences of various factors on blood lead to the formation of extra reactive oxygen species (ROS), resulting in the disruption of morphology and functions of red blood cells (RBCs). This study considers the mechanisms of the mechanochemical synergism of OH• free radicals, which are most active in the initiation of lipid peroxidation (LPO) in RBC membranes, and H2O2 molecules, the largest typical diffusion path. Using kinetic models of differential equations describing CH2O2t and COH•t, we discuss two levels of mechanochemical synergism that occur simultaneously: [1] synergism that ensures the delivery of highly active free radicals OH• to RBC membranes and [2] a positive feedback system between H2O2 and OH•, resulting in the partial restoration of spent molecules. As a result of these ROS synergisms, the efficiency of LPO in RBC membranes sharply increases. In blood, the appearance of OH• free radicals is due to the interaction of H2O2 molecules with free iron ions (Fe2+) which arise as a result of heme degradation. We experimentally established the quantitative dependences of COH• CH2O2 using the methods of spectrophotometry and nonlinear curve fitting. This study extends the analysis of the influence of ROS mechanisms in RBC suspensions. ## 1. Introduction The most important processes in living organisms are redox processes, which regulate the state and morphology of red blood cell (RBC) membranes. The combination of oxidative and antioxidant processes helps to maintain metabolic processes in RBCs, tissues, and the body as a whole [1,2]. A wide range of reactive oxygen species (ROS) in aqueous solutions plays a major role in oxidative processes in RBCs. Some examples of active oxygen-containing compounds are OH•, O2•−, HO2•, H2O2, OH+, OH−, O2, O21, and HO2− [3,4,5]. ROS are formed under the action of various factors in the organism, in particular, in blood, such as toxins, radiation, etc. [ 6,7,8]. ROS production can also occur in various diseases [9,10,11] as a result of bacteria and viruses [12,13,14], such as COVID-19 [15,16], and during the long-term storage of packed red blood cells (pRBCs) [17,18,19]. Oxidative stress is frequently described as an imbalance between the production of reactive oxygen species in the biological system and their ability to defend through sophisticated antioxidant machinery [20,21]. Oxidant molecules act on the cell membrane, resulting in the distortion of RBC homeostasis. A major source of oxidative stress in blood is extracellular hemoglobin (Hb) originating from the hemolysis of RBCs [22]. Free Fe2+ arising from heme degradation is an active component of the Fenton reaction, which plays an important role in redox processes in the blood [22,23]. The consequence of oxidative stress development is the initiation of lipid peroxidation (LPO), which is a chain process of destructive reactions in RBC membranes. This leads to the formation of LPO products, in particular, hydroperoxides in RBC membrane lipids [4,24]. The most important product in LPO initiation is free radical OH•. Biophysical consequences of LPO in biological membranes include the formation of bonds between atoms with a large difference in their electronegativity and the consequent local appearance of polar covalent bonds between atoms in the tails of membrane lipids [25]. This is the biophysical basis for the formation of local dipoles in lipid tails, which leads to a change in the permeability of water molecules and ions through the biological membrane [24,25]. Thus, the passive transport of ions through the RBC membrane changes, and osmotic phenomena can occur. The development of oxidative processes also leads to the disturbance of protein structures and their aggregation [26]. As a result, the effects of ROS on membrane lipids and proteins contribute to the violation of RBC cell morphology. The surface membrane nanostructure and cytoskeleton configuration can also change [27]. Biological systems also use ROS to damage bacteria and viruses. The appearance of ROS and their interaction with biological objects is a multistage process. For instance, the influence of ionizing radiation on biological objects can be broken down into four consecutive stages [4]: physical, physicochemical, chemical, and biological stages. Their peculiarity is a very strong difference in the characteristic duration of each stage, from 10−13 s to several minutes or even years. Other influences in the first stage include a weakening of the immune system during various diseases, resulting in the initiation of other stages of development [15,28,29,30]. Multiple stages must be taken into account in the biophysical and mathematical analysis of the effects of ROS on biological membranes. The mutual transformation of ROS during various chemical reactions is a distinctive feature. Kinetic models such as the Malthus, Verhulst, and Lotka–Volterra models are usually used for the mathematical modeling of the change in the number of specimens during biological and chemical processes [31,32,33]. In the case of mutual interactions of ROS in biological systems, the complex spatiotemporal pattern must be taken into account. At the same time, the activities of ROS, as well as their typical lifetimes and diffusion paths, differ significantly. Analysis of the influence of ROS must also consider their wide range of influences on biological objects, in particular, the cell membrane. To study oxidative stress, RBCs are often used as a research model [34,35]. Moreover, such analyses must take into account that the most active ROS that influences lipids in RBC membranes is radical OH• [36]. At different levels of organization of a biological system, the synergism of the action of factors plays an important role [37,38,39,40]. This is especially important for the blood system. In this work, based on a biophysical approach and a mathematical model, we established synergism between ROS in RBC suspension. The effectiveness of the action of several ROS on RBCs in terms of their synergism is greater than their separate actions. Among ROS, we singled out those that demonstrate the most pronounced synergistic effect in RBC suspensions, leading to a significant biological effect. The biophysical approach and mathematical modelling of ROS synergism can expand our understanding of the oxidative influence on the membrane structure and morphology of RBCs, as well as their function. ## 2.1. ROS in Blood Quantitative Study of H2O2 to OH• Conversion Due to the Fenton Reaction in In Vitro Experiment Various reactions involving ROS occur in the blood (Figure 1). The presence of Fe2+ in hemoglobin is characteristic of RBCs during redox processes. The hemolysis of RBCs also produces free Fe2+ as a result of heme degradation, leading to the initiation of the Fenton reaction [22,23]:[1]Fe2++H2O2=>Fe3++OH−+OH•. In this important reaction, the H2O2 molecules are converted into OH• free radicals, which are specific ROS. They can initiate the chain reaction of lipid peroxidation in RBC membranes. The increase in the membrane permeability caused by lipid peroxidation has serious consequences for cells. In our experiments, the dependence of COH• on CH2O2 was determined quantitatively. To determine the kinetics of COH• t, we measured the kinetics CFe3+ t according to Equation [1]. The experiment was carried out according to the scheme shown in Figure 2A. The H2O2 solution at different concentrations was added to the working solution containing FeSO4. To quantify the concentration of Fe3+ in the solution at any time point, spectrophotometry and nonlinear curve fitting (NCF) methods were used. The technique is described in detail in Section 3: Materials and Methods. Alterations to the absorption spectrum of the experimental sample after adding H2O2 was demonstrated in the experiments (Figure 2A,B). Thus, peaks appear in the absorption spectrum at λ1 = 224 nm and λ2 = 304 nm, which are typical for ions Fe3+ [41]. With an increase in the concentration of CH2O2, the peaks increase in amplitude. This indicates the production of Fe3+ ions in the solution as a result of the Fenton reaction. To quantify the unknown concentration CFe3+, we used the nonlinear curve fitting method. For this, the optical spectrum Dlλlexper was approximated using the theoretical curve Dlλltheor:[2]Dlλltheory=FεFe3+,εFe2+,CFe3+,CFe2+. NCF is described in detail in the Materials and Methods. Figure 2B on the left shows the measured spectra Dlλlexper and the corresponding fitting curves Dlλltheor constructed for the parameters calculated by Equation [2]. CFe3+ is an important biophysical parameter since CFe3+=COH•. Thus, for the H2O20 (control) concentration, CFe3+=0.05 (SE=0.01); for H2O20.05, CFe3+=0.22 (SE=0.01); for H2O20.1, CFe3+=0.41 (SE=0.01); and for H2O20.2, CFe3+=0.79. All measurements are in arbitrary units (A.U.). Figure 2B on the right shows the quantitative dependence of CFe3+ (CH2O2) based on the subtraction of the control values. Thus, an increase in the concentration of H2O2 in the selected concentration range resulted in a corresponding linear increase in CFe3+ with the proportionality coefficient β≈4 (A.U.) (Figure 3B). Since, according to the Fenton reaction (Equation [1]), CFe3+=COH•, then the curve in Figure 2B corresponds to the kinetics of the change in COH• depending on CH2O2:[3]COH•=βCH2O2. That is, the dependence COH•CH2O2 is also linear. This reaction plays an important role in the functioning of RBCs. The interaction of H2O2 with the extracellular molecules of Hb resulting from RBC hemolysis (Figure 2C) ultimately leads to heme degradation and the appearance of free iron Fe2+ in the RBC suspension (Figure 1 and Figure 2C) [22]. The Fenton reaction (Equation [1]) occurs in the RBC suspension. The resulting OH• free radicals are the main free radicals that induce the lipid peroxidation of the cell membrane. The lipid peroxidation of RBC membranes causes a disruption to the nanostructure of the membranes and their cytoskeleton, an increase in the membrane’s rigidity and permeability, as well as a disruption to the cell morphology up to their hemolysis (Figure 2C). As a result of the interaction of H2O2 with hemoglobin, the oxidation Fe2+ → Fe3+ can also occur, resulting in the conversion of HbO2 to MetHb, which is detrimental for the functioning of RBCs. ## 2.2. Mechanochemical Synergism of Two Factors in Their Effect on RBCs Synergism is the interaction or cooperation of two factors to produce a combined effect trait which is greater than the sum of their individual effects. In our experiments, we examined the following possible mechanochemical synergism (Figure 3A) in the suspension of RBCs. Suppose the activity of the agent M on a biological object (BO) significantly exceeds the activity of the agent N on the same object:[4]ActM≫ActN, ActN=1, ActMActN=K, K≫1, for example, $K = 106.$ RBCs and their membranes can be considered BOs. The possible effects of two factors, M and N, are shown in Figure 3A,B. Due to the different activity of the agents, the typical lifetime τ and thus the typical diffusion path length of these agents, S, will differ significantly: τN≫τM, SN≫SM. The probability that agent M is immediately next to a BO, which is at a distance S≫SM, is very small. Therefore, the result of agent M’s action will be 0 (Figure 3): ResM=0. The probability that agent N is immediately next to a BO is high if SN≥S, but its impact activity is small, ResN=1. Then, the result (Res) of the total impact of agents M and N without their synergistic interaction will be: Res M+ResN~1. Agent N can cooperate with agent M, SynM+N, that is, deliver it in some way to the BO (RBC membrane). In this case, the total result of the action will approach the value of $K = 106$ (Equation [4]). Thus, the result of the action of factors M and N during their synergistic interaction will be many times greater than the sum of the results of the action of these agents individually:[5]ResSynM+N≫ResM+ResN ## 2.3. Biophysical Basis for Identification of a Pair of ROS That May Be Involved in the Synergistic Process in Blood As a result of various factors (decreased antioxidant activity, long-term storage of pRBCs, effects of ionizing radiation, massive blood loss, viruses, bacteria, etc.) on the blood, excited atoms and molecules, radicals, and ions appear. There is a swarm of ROS. The amount of ROS will be increased by the value of ΔROS (Figure 1). Excited molecules and radicals undergo all kinds of transformations. The molecules that burst out of the swarms can damage RBC membranes. This leads to changes in the structure of RBCs at various levels in terms of their properties and functions (Figure 1 and Figure 2C). Figure 1 shows some of the chemical reactions in which ROS are involved in in biological objects, especially in RBCs. Note that only ROS without active nitrogen forms and lipids are shown. Depending on the environment, ROS have different spatio-temporal characteristics of interactions with each other. A short lifetime τ of corresponding radicals is usually typical of the excited state: superoxide oxygen anion radical τO2•−∼10−6s, hydroperoxy radical τHO2•∼10−8s, singlet oxygen τO21∼10−6s, and hydroxyl radical τ OH•∼10−9−10−6s [4,42,43]. Therefore, the distances for their diffusion in a biological object are very short. This means that radicals and excited molecules are practically unable to escape from the swarm. That is why the radicals themselves can practically reach the membrane lipids, if the formation of ΔROS, for example, due to ionization, has taken place exactly in the lipid molecules or very close to them. In the ROS series, we distinguish two objects: hydroxyl radical OH• and hydrogen peroxide H2O2 (Figure 1 and Figure 3C). Reactions involving these ROS are highlighted in purple in Figure 1. The OH• radical is very active with respect to the lipid molecules of the RBC membranes. When it is located directly next to the lipid molecule LH, it is able to initiate a chain process of lipid oxidation, leading to the formation of lipid hydroperoxides LOOH. Accordingly, OH• is short-lived. It cannot travel long distances and therefore cannot damage molecules in the RBC membrane if the cell is far from the source of the radical. This is shown schematically in Figure 3C. The interaction of these radicals during their action on biological objects is also discussed in [44]. The activity of the H2O2 molecule is low, including a small effect on the RBC membrane lipid molecules. The H2O2 molecule is a long-lived ROS. Correspondingly, it can travel relatively long distances (Figure 3C). Relative activities with respect to lipids: AH2O2=1. AOH•=109. Thus, AOH•/AH2O2=109. Why did we choose OH• out of all the possible radicals? The fact is that OH• radical triggers a lipid peroxidation chain reaction (Figure 3C and Figure 4C). ## 2.4. Synergism of OH• and H2O2 When Acting on Biological RBC Membranes This raises the question of whether H2O2 molecules can assist free radicals OH• in damaging lipids. In other words, can these ROS act synergistically when targeting the RBC membrane lipids? How can synergism be achieved in the specific case of OH• and H2O2? Let us consider the mechanism of mechanochemical synergism according to the scheme shown in Figure 4C. As noted above, the free radical OH• cannot move from its point of origin to the point where the target (RBC membrane) is located at a distance S≫SOH• (Figure 4A). Let us also assume that SOH• ≪S<SH2O2. What could be the possible delivery route for highly active free radicals OH• using long-lived H2O2 molecules? The solution to this problem is schematically presented in Figure 4B using a mechanistic example. The H2O2 molecules play the role a vehicle for the delivery of OH•. As it moves, the vehicle delivers free radicals OH• to various points, including directly to the RBC membranes. This ensures the impact of highly active free radicals OH• on RBC membrane lipids. Obviously, this is only a schematic mechanistic representation of the mechanochemical synergism of the agents H2O2 and OH•. In reality, the free radicals OH• and molecules H2O2 actively participate in many types of chemical processes (Figure 1). At the same time, there are several reactions in which they change into each other. From all chemical reactions shown in Figure 1, we have selected and included in the model only those reactions where OH• and H2O2 participate together (Figure 4C). In solution, the H2O2 molecules participate in chemical reactions with e−+H+, Fe2+, and O2•−, resulting in an increase in free radicals OH•—stage 1 (Figure 4C). This happens as H2O2 diffuses in solution at a distance SI<SII<SIII. In turn, hydroxyl radicals OH• can interact with each other to form the molecules H2O2; this process occurs rapidly in a short time and at a short distance—stage 2 (Figure 4C). Thus, the concentration of H2O2 molecules can partially recover in points SI, SII, and SIII. This feedback is shown by the arrows in Figure 4C. The synergism of chemical processes involving OH• and H2O2 can occur in solution as H2O2 molecules diffuse. However, if OH• met the molecules of membrane lipids LH at a distance SIII, then it would also interact with them (Figure 4C, line III)—stage 3 (Figure 4C). This is a rapid process. As a result, at a distance of SIII, free radicals OH• will participate in two chemical processes, namely, their interaction with each other (with the reduction in H2O2 molecules) and their interaction with lipid molecules (without the reduction in H2O2 molecules)—corresponding to stages 2 and 3 (Figure 4C). Figure 4D schematically shows the synergism of OH• and H2O2. Free radicals OH• rapidly initiate the peroxidation chain reaction. H2O2 molecules themselves cannot oxidize lipids but they can participate in the delivery of radicals to the membrane of biological objects. This schematic representation shows the interconversion of OH• and H2O2 in space and time. This is the first level of the mechanochemical synergism of OH• and H2O2, namely, H2O2 molecules “deliver” free radicals OH• to cell membranes. In the work, a kinetic modeling of synergism based on biophysical principles has been proposed. The synergism of free radicals OH• and H2O2 molecules allows us to organize a positive feedback system in RBC suspension. In this case, the effective distance of OH• action on the RBC membrane will increase significantly due to synergism, and the diffusion distance of H2O2 molecules will also increase. Simultaneously with the first level, the second level of synergism also occurs, namely, free radicals OH• interacting with each other, generating H2O2 molecules, thus partially replenishing their loss (Figure 4C). The synergism of free radicals OH• and H2O2 molecules allows for organizing a positive feedback system in RBC suspension. As more H2O2 molecules are consumed, more free radicals OH• are produced (Figure 4C, stage 1), and in turn, more H2O2 molecules are produced (Figure 4C, stage 2), which compensates for the losses in stage 1. The process becomes cyclic. In this case, the effective distance of OH• action on the RBC membrane will increase significantly due to synergism, and the diffusion distance of H2O2 molecules will also increase. ## 2.5. Kinetic Model of the Mechanochemical Synergism of H2O2 and OH• Effects on RBC Membrane: The Role of Synergism in the Initiation of RBC Membrane LPO Let us consider the mathematical model of the synergism involving H2O2 and OH•. The first level of mechanochemical synergism. This synergism delivers OH• radicals to RBC membranes, where they initiate LPO, as shown schematically in Figure 3 and Figure 4. The molecule H2O2 is involved in chemical processes, some of which result in the formation of OH• (Figure 1):[6]H2O2+e−+H+→OH•+H2O,H2O2+Fe2+→OH•+OH++Fe3+,H2O2+O2•−→OH•+OH−+O2. As a result of these reactions, CH2O2t will decrease over time. Let us derive a differential equation to describe this process. Let us designate: xt—concentration of OH• radicals depending on time, COH•t; yt—concentration of H2O2 molecules depending on time, CH2O2t; z—concentration of all components with which H2O2 molecules interact. In the equations, consider a time t that which is much longer than the typical lifetime of the radicals OH•, but comparable to the lifetime of H2O2 molecules. A differential equation of the change in y, stage 1 (Figure 4C):[7]dydt=−kzy. Initial condition: suppose at some point that $t = 0$, y=y0. Suppose that:[8]y0=100, and k is the rate constant of the reaction. Note that in the calculations, all concentrations of chemical components are presented as arbitrary units (A.U.), which are not specified, including in the graphs. Then, we obtain:[9]y=y0e−kzt. Suppose that:[10]$k = 1.$ The graph of yt is shown in Figure 5A. The kinetics of the concentration change over time; CH2O2t is shown for the three concentrations of values z with which H2O2 interacts: $z = 2.5$, $z = 10$, $z = 40.$ As the concentration z increases, the typical time of the reduction in y decreases. The change in the value of y occurs due to the conversion of H2O2 into OH• (Figure 4C). Thus, the loss dyt is equal to the increase in hydroxyl radicals dxt over the time interval dt:[11]dx=−dy=kzydt. As a result, OH• radicals will be able to appear in the RBC suspension during the time τOH•syn when they are produced under synergism by long-lived H2O2 molecules; that is, during the time which is much longer than their own lifetime: τOH•syn≫τOH•. The second level of mechanochemical synergism. There is a positive feedback system between H2O2 and OH• and, therefore, a partial recovery of consumed H2O2 molecules. For different times, the values of dxt will be different, because the H2O2 concentration decreases with time according to Equation [9]. Consider a small interval where dy is not significant, so that dy/y is no more than $10\%$. For the parameters in Equation [9], the time interval is dt=10−2 s. Let us take the concentration of radicals OH• formed during this time interval dt=10−2 s as their initial concentration at $t = 0$ for stages 2 and 3:[12]xt=0=x0. These radicals will participate in further conversions, causing their reduction xt. Two main processes will be responsible for their reduction: The interaction of OH• with each other (stage 2, Figure 4C) is described by the differential equation:[13]dxdt=−ax2, where a is the rate constant of this chemical reaction. Initial condition:[14]xt=0=x0. When OH•(x) interacts with lipids LH (stage 3, Figure 4C), there is a decrease in the lipid concentration Lt and xt. This process is described by the differential equation: [15]dLdt=−bLx, accordingly, the change in xt in this case will be:[16]dxdt=−bLx, where b is the reaction rate constant. The kinetics of the change in xt during the simultaneous occurrence of both processes during stages 2 and 3 (Figure 4C) is described by a differential equation:[17]dxdt=−ax2−bLx. Initial condition:[18] xt=0=x0, Lt=0=L0. Then, the solution is:[19]xt=bL−a+ebLt+ ln(bL+ax0/x0)bL. Figure 6 shows the kinetic COH•t for different lipid concentrations L. Moreover, for each graph corresponding to a given concentration L, three curves are presented for three cases: only stage 3 (brown line), only stage 2 (blue line), and both stages (2 + 3) simultaneously (purple line). If $L = 0$, there would be no change in OH• if it will be only stage 3 (Figure 4C). In this case, xt decreases only due to the interaction of OH• among themselves (blue and purple lines coincide) (Figure 6A). Additionally, this would lead to the partial recovery of the reduced H2O2 with the replenishment coefficient q1. If $L = 10$, up to the time $t = 6$·10−9s, the loss of xt would be determined mainly by the interaction of x among themselves. This part of the radicals can turn into H2O2 and thereby partially replenish the loss of these molecules; replenishment coefficient q2. If $L = 100$, the interaction of x with L (stage 3) plays a major role and stage 2 hardly occurs. In this case, the replenishment coefficient q4 is small. Among replenishment coefficients, the following inequality is satisfied: q1>q2>q3>q4. The possibility of replenishing the loss of molecules H2O2 due to the interaction of OH• with each other leads to the decrease in the effective reaction rate constant k in Equations [7]–[11]. What does this lead to? Figure 7A,B shows the relations CH2O2t and COH• t for various k. Coefficient k1 corresponds to the process without the recovery of H2O2 due to stage 2; coefficient k2 corresponds to process with the recovery of H2O2 due to stage 2. In graphs 1 and 2 (Figure 7A), the lifetimes τH2O21 and τH2O22 are indicated. Lifetime τH2O2 is the time for the H2O2 concentration to halve. Correspondingly, the lifetimes τOH•syn1 and τOH• syn2, showing how long they can be generated during the synergetic process, are indicated in Figure 7B. According to the model, τOH•syn 2>τOH•syn1 (Figure 7A,B). The time τH2O22 is the effective lifetime, since it was obtained by taking into account the effect of the replenishment of H2O2 due to the interaction of hydroxyl radicals. This will lead to an increase in the duration of OH• generation. Figure 7B shows a red line near $t = 0$ corresponding to a decrease in hydroxyl radicals over time in the absence of first-level synergism between OH• and H2O2. As already mentioned, the lifetime of the radicals OH• is very short: τOH• ≪τOH•syn1. ROS interaction occurs along their diffusion distances (Figure 7C–F). The diffusion distance can be estimated by the formula S~Dt [45]. For the radicals OH•, it is very small. H2O2 plays the main role in the diffusion processes with ROS synergism: S~DH2O2τH2O2, where DH2O2 is the diffusion coefficient of H2O2 molecules. Considering that in water DH2O2∼10−5 cm2/s [45,46], we will obtain that the molecule H2O2 diffuses in RBC suspension over a distance of tens of microns in 1 s. Knowing the dependences yt and xt, and also considering that the distance over which the particle travels due to diffusion during time t can be estimated from the equation S~Dt, we obtain the dependences yS and xS. These dependencies are shown in Figure 7C–F. The increase in the effective lifetime τH2O2 due to the second level synergism of OH• and H2O2 leads to an increase in the diffusion distance. Under the conditions of ROS synergism, the H2O2 molecules (Figure 7E, green curve) will be able to diffuse and consequently generate OH• (Figure 7F, blue curve) over longer distances S2 considering the recovery of y than S1 without the recovery (Figure 7C,D). Thereby, OH• radicals will cover a larger area of membrane lipids. For comparison, Figure 7 below shows the illustration of the diffusion distance for H2O2 and OH• under synergism for two cases: without (C, D) and with (E, F) the recovery of y, respectively. The thickness of the RBC layer, which can be affected by free radicals OH•, S2>S1 (highlighted in pink). ## 3.1.1. Preparation of the Working Solution Fricke’s solution was used as the working solution [41]. In the following, we will refer to this solution as solution A. For its preparation, 2 mg of FeSO4·7H2O (Sigma-Aldrich, Saint Louis, MO, USA) and 0.240 mg of NaCl were weighted and then added to 4 mL of H2O. To the resulting solution, 88 µL of H2SO4 $99.999\%$ (Sigma-Aldrich, Saint Louis, MO, USA) were added. Thus, solution A was prepared. Next, different volumes of hydrogen peroxide (H2O2 $3\%$) (0, 0.1, 0.2, 0.4, and 0.8 µL) were added to solution A. Thus, solution B was prepared (Figure 2A). In this case, the Fenton reaction took place. The Fe2+ ions were converted into Fe3+ ions by oxidation, resulting in free radical OH• generation. This process is typical of RBCs. ## 3.1.2. Spectrophotometry for Quantitative Analysis of Fenton Reaction Results To determine the concentration of OH• in the studied solutions, we used the spectrophotometric method to measure the absorption spectrum of solution B. The optical absorption spectra of solution B were measured using a Unico 2800 digital spectrophotometer (United Products & Instruments, Dayton, FL, USA) (Figure 2A). The experimental spectra Dλexper were measured in the wavelength range of λ=200−400 nm with a step of 1 nm. The experimental spectra are shown in Figure 2A,B. The maximum characteristic of Fe3+ ions was observed at the wavelength λ=304 nm. We observed changes in the spectrum of solution B, namely, the absorption maximum amplitude increased with the rising concentration of H2O2. This indicated the conversion of Fe2+ to Fe3+. We need to determine the concentration of Fe3+ according to the chemical reaction (Equation [1]). Due to the fact that COH•=CFe3+, according to the Fenton reaction, we estimated the dynamics of COH• changes by analyzing the spectrum of solution B. The working solution B with different concentrations of hydrogen peroxide was designated as H2O20, H2O20.025, H2O20.05, H2O20.1, and H2O20.2 for CH2O2=0, 0.025, 0.05, 0.1, and 0.2 µL/mL, respectively. ## 3.1.3. Nonlinear Curve Fitting of Optical Spectra for Determination of the Unknown Concentration COH• To determine the unknown concentration COH•, it was necessary to perform nonlinear curve fitting of the wavelength dependences of the optical densities Dlλlexper obtained experimentally by spectrophotometry, where l is the number of wavelengths and λl is the set of the wavelengths. We used software of Origin Pro 2019 (OriginLab Corporation, Northampton, MA, USA) for NCF of the experimental data. We used the absorption law function as the theoretical function for NCF:[20]Dlλltheor= εFe2+CFe2+L+εFe3+CFe3+L. Equation [20] was created based on biophysical principles [47]. The fitting curve was chosen on the basis of the biophysical law of light absorption, the Bouguer–Beer–Lambert law, considering that absorption in a solution containing two ions (Fe2+ ions, Fe3+ ions) Dlλltheor is a nonlinear function. Here, εFe2+λl and εFe3+λl are molar absorptivity coefficients at given wavelengths λl. Molar absorptivity coefficients are the nonlinear functions of λl. We obtained these values based on the data of [41]. Thus, in the model, these parameters and layer thickness $L = 1$ cm can be considered as the known parameters. To determine concentrations of CFe2+, CFe3+ we made NCF in the whole range of experimental data 200–400 nm. The unknown parameters in Equation [20] are the concentrations CFe2+, CFe3+. In Equation [20], instead of the theoretical massive Dlλltheor, we used the experimental optical density data Dlλlexper. The calculation of the fitted values in NCF is an iterative procedure performed using the Levenberg–Marquardt algorithm. Iteration to adjust parameter values continues to bring the theoretical curve with calculated model parameters closer to the experimental data. A number of studies have considered that the R-squared is not the most significant factor in non-linear analysis [48]. In our study, we reported the fitted value, standard errors, and the residuals of the fitting in the legend (in Figure 2B). The model parameters which were obtained from NCF are used to plot the theoretical function according to Equation [20]. The CFe3+ was obtained by fitting. Since COH•=CFe3+ as a result of the Fenton reaction, we actually obtained the OH• concentration for different CH2O2. ## 3.2. Statistical Analysis The data were analyzed using software of Origin Pro 2019. Optical spectra were measured five times for a given concentration of H2O2, and the mean concentrations of Fe2+ ions, Fe3+ ions, and H2O2 molecules were calculated by NCF. The experiments were performed 15 times for each CH2O2. For each sample, the mean and standard error of CFe3+ were calculated. These data are reported in the Results and Discussion section. All data in Figure 2B on the right are presented as the mean ± SD. Thus, in the study, the fitted value, standard error of the fitting, and the residuals are reported as statistical parameters. The mean, SE, and SD of the sample parameters were also calculated. ## 3.3. Kinetic Model of the Synergistic Interaction between H2O2 and OH• Mathematical modeling was used to study the kinetics of change in the concentrations of H2O2 and OH•. Based on the law of mass action, ordinary differential equations describing the kinetics of chemical reactions were recorded. The kinetic equations were solved using the method of the separation of variables, considering the initial conditions. The basic assumptions of the kinetic model are as follows. As a result, the calculated kinetic curves CH2O2t and COH• t take into account the mutual transformation of H2O2 and OH•. The interaction of OH• with RBC membrane lipid molecules is also considered. ## 4. Conclusions Red blood cells are continuously exposed to both endogenous and exogenous sources of reactive oxygen species in the circulation. To minimize the effects of these ROS, RBCs possess an extensive antioxidant system. An imbalance between excessive ROS production and antioxidant defense results in oxidative stress. The study of the mechanisms of the occurrence of an excess number of ROS and their amplification during the functioning of the circulatory system remains the subject of intensive scientific research. This is due to the fact that oxidative stress plays a significant role in the damage of erythrocyte membranes, cell morphology, and deformability. The mechanisms of the processes involved in the appearance and development of these pathologies are still not fully understood. This is currently the subject of research and scientific discussion. ROS are characterized by a strong interrelation as they undergo chemical transformations. The same ROS can be both a consequence and a cause of chemical transformations of others. Such an overlapping of events between a large number of ROS makes the study and the interpretation of the results very difficult. In this work, based on the biophysical principles, a kinetic model of the mechanochemical synergism of the action of free radicals OH• and molecules H2O2 on the initiation of lipid peroxidation of RBC membranes is discussed. 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--- title: Development and Characterization of Drug Loaded PVA/PCL Fibres for Wound Dressing Applications authors: - Ali Afzal - Mohammed Jalalah - Abid Noor - Zubair Khaliq - Muhammad Bilal Qadir - Rashid Masood - Ahsan Nazir - Sheraz Ahmad - Faheem Ahmad - Muhammad Irfan - Munazza Afzal - Mohd Faisal - Saeed A. Alsareii - Farid A. Harraz journal: Polymers year: 2023 pmcid: PMC10057071 doi: 10.3390/polym15061355 license: CC BY 4.0 --- # Development and Characterization of Drug Loaded PVA/PCL Fibres for Wound Dressing Applications ## Abstract Nowadays, synthetic polymers are used in medical applications due to their special biodegradable, biocompatible, hydrophilic, and non-toxic properties. The materials, which can be used for wound dressing fabrication with controlled drug release profile, are the need of the time. The main aim of this study was to develop and characterize polyvinyl alcohol/polycaprolactone (PVA/PCL) fibres containing a model drug. A dope solution comprising PVA/PCL with the drug was extruded into a coagulation bath and became solidified. The developed PVA/PCL fibres were then rinsed and dried. These fibres were tested for Fourier transform infrared spectroscopy, linear density, topographic analysis, tensile properties, liquid absorption, swelling behaviour, degradation, antimicrobial activity, and drug release profile for improved and better healing of the wound. From the results, it was concluded that PVA/PCL fibres containing a model drug can be produced by using the wet spinning technique and have respectable tensile properties; adequate liquid absorption, swelling %, and degradation %; and good antimicrobial activity with the controlled drug release profile of the model drug for wound dressing applications. ## 1. Introduction Skin is the human body’s largest organ and has the functions of regulating fluid flow, body temperature, and enable sensation (e.g., cold, heat, pain, and touch). Skin also provides a semipermeable protective layer against pathogens [1]. In a chronic wound, tissue homeostasis is interrupted [2], while vasculature is partially damaged or fully damaged in the case of an acute wound, limiting the regeneration of cells [3]. The period to close the wound is associated with an increased risk of complications such as pain, infection, and the occurrence of scarring [4]. Wound infection is, therefore, the main reason for morbidity, mortality, and delay in wound healing [5,6]. More than 11 million people worldwide are affected by burn injuries annually. In burn units, the prevalence of infections is about $66\%$ [7,8]. There are several wound healing dressing products available to improve healing. The choice of material for a particular type of wound is therefore essential for proper healing [9]. Additionally, to operate, the dressings must be in contact with the wound [10,11]. Dressings also prevent trauma, reduce wound infection, keep matrix materials in contact with the wound, and improve the wound’s electrical gradient [6,12]. Synthetic polymers are being used in different areas including drug delivery applications [13,14]. In biodegradable polymers, PVA is one of the most used polymers and has a lot of good properties that make it suitable to use in medical applications, especially in wound healing properties in both in vivo and in vitro studies [15,16]. PVA has been reported to be used in tissue engineering, wound dressings, and ophthalmic applications [17,18]. PVA hydrogels have also been used in the drug delivery system due to their similarity with natural tissues and excellent biocompatibility [19,20,21]. PCL is another biodegradable polymer that has many applications in biomedical applications. PCL has been used in tissue engineering, drug delivery, and wound dressing [22,23,24,25,26,27]. Mehteroglu et. al. prepared and characterized electrospun PCL fibres as a potential dressing for wound healing applications [28]. PCL shows its exceptional properties when blending with different natural polymers. Prolonged biodegradation and hydrophobicity of PCL were modified by blending it with collagen, hyaluronic acid, gelatine, chitosan, etc. [ 29,30,31]. Drug delivery vehicles of different sizes (micro and nano) based on PCL are undergoing extensive research [32,33]. To develop fibres from different polymers, various techniques including wet spinning, gel spinning, melt spinning, and, most widely, electrospinning have been used to produce nanofibers [34,35]. Among these techniques, wet spinning is one of the most used techniques to develop medical fibres [36]. By using wet spinning technique, fibres of PVA [37], PCL [38], chitosan and alginate [39,40,41], psyllium husk [42,43], cellulose [44], polylactic acid [45], silk fibroin [46], polypyrrole [47], and collagen [48,49] have been prepared. Due to its low cost, scalability, and reproducibility, as well as the fact that it is a safe and clean technique, interest in using the wet spinning technique for pharmaceutical and biomedical applications has increased [50]. From previous studies, it was revealed that PVA has been used in the medical and textile industry due to its non-toxicity, biomedical compatibility, biodegradability, aqueous solubility, chemical resistance, and low environmental impact [51]. Like PVA, PCL is also one of the most important used polymers in medical applications for drug delivery, sutures, and tissue engineering [52,53]. From the literature review, it was found that both PVA and PCL have been used in many applications, but no significant data were found on the development of PVA and PCL fibres as a blend containing the drug using any scalable production process. The PVA/PCL hybrid fibre mats loaded with drugs are reported in the literature developed by the electrospinning process [53,54,55,56,57,58]. Such electrospun materials cannot be scalable for commercial production due to the low production rate and cost. Composite structures developed with electrospun PCL fibres with PVA hydrogels as hybrid composite structures are also reported [59]. A fibre having hydrophilic and hydrophobic parts will provide the best combination for the transdermal drug delivery system. Therefore, in this study, PVA/PCL fibres containing the drug were developed by using the wet spinning technique for wound dressing applications. ## 2.1. Materials Polyvinyl alcohol, having a molecular weight of 66,000 g/mol ($99\%$ hydrolysed), was purchased from Siheung, Republic of Korea. Sodium sulfadiazine (NaSD) (Mw 272.26 g/mol), acetic acid, acetone, and ethanol were purchased from Sigma Aldrich (St. Louis, MO, USA). Polycaprolactone (Mw 80,000 g/mol) was purchased from Haihang Industry Co., Ltd. (Jinan, China). Deionized and distilled water were purchased from the local market. ## Dope Solution Preparation and Fibre Development The dope solution of PVA and PCL, according to Table 1, was first prepared separately. PVA powder was dissolved in acetic acid and stirred at 800 rpm for 5 h at 90 °C until a homogenous mixture was obtained. Similarly, PCL pellets were also dissolved in acetic acid and stirred mechanically at 600 rpm for 6 h at 50 °C until a homogenous mixture was obtained. After that, both solutions were mixed and stirred to obtain a homogenous mixture. The model drug (NaSD), according to Table 2, was also inserted in this homogenous solution. This solution was poured into the dope tank of the wet spinning machine. The solution was kept overnight to remove air bubbles. The next day it was extruded at room temperature (22 °C ± 2 °C) via a spinneret with the help of a feeding pump at the speed of 5 rpm, the first roller speed at 8 rpm, and the second roller with the speed maintained at 20 rpm. The draw ratio was maintained at 2.2. Polymers dissolved in solvent were pushed through a spinneret (micron) and passed through the coagulation bath (first bath) comprising ethanol ($100\%$) at 4 °C. The dope solution became solidified in this bath. In the second bath, fibres were rinsed (in deionized water), drawn, and collected on collecting rollers. The wet spinning setup is illustrated in Figure 1a. Fibres were dried after rinsing in water followed by immersion in acetone solution of different concentrations in $25\%$, $50\%$, $75\%$, and finally in $100\%$ acetone. In each solution, fibres were kept for 30 min. Fibres in complete dry form were then stored in polythene zippers. Developed PVA/PCL fibres containing the drug are shown in Figure 1b. ## 2.3.1. Linear Density According to ASTM D1059-12, 40 specimens each having a length of 2 inches were prepared from each sample and weighed on a digital weighing balance up to 4 decimals. Length was converted into meters. Ten measurements of each sample were taken and averaged. Then the linear density calculation was made by using Equation [1]. [ 1]Linear density tex=Weight in grams1000 m ## 2.3.2. Tensile Properties Tensile properties of the fibres were measured by using the tensile strength tester by using the standard ASTM D3822 method. A Testometric 2.5 single fibre strength tester was used. A sample was placed between 2 clamps, one fixed and one moveable, 10 mm apart. The force applied on the clamp was 10 N and set at 12 mm/min CRE (constant rate of extension). Five measurements of each sample were performed and averaged. ## 2.3.3. Liquid Absorption The absorption values of developed fibres were tested by using three different solutions, i.e., distilled water (DW), saline solution (SS) ($0.9\%$ w/v NaCl), and solution A (SA) ($0.8298\%$ w/v NaCl + $0.0368\%$ w/v CaCl2). Developed fibres were soaked for 1 h in each solution and hung in the air to remove the excess liquid droplets and then weighed. Wet fibres were kept in the oven overnight at 105 °C to dry the fibres. Five measurements of each sample were made and averaged. By using Equation [2], liquid absorption was calculated. [ 2]Absorption gg=Ww−WdWd where Ww and Wd are the wet and dry weight of the fibre [60,61]. ## 2.3.4. Swelling Behaviour An optical microscope attached with a digital camera was used (at 40X) to examine the swelling behaviour of developed fibre in three different above-mentioned solutions. Single fibre poured in these solutions was kept for 4 min at 25 °C, and then the diameter was measured. Five readings of each sample were taken and averaged. The swelling percentage was calculated by using Equation [3]. [ 3]Swelling %=Dw−DdDd×100 where Dw and Dd refer to wet and dry fibre diameter, respectively. ## 2.3.5. Degradation Test A degradation test was performed to measure the degradation % of the developed fibres. The PVA and PCL wet-spun fibres were then cut into specific lengths and dried in an oven and weighed (W1). The dried specimens were placed in capped bottles containing 250 mL of phosphate buffer solution (PBS, pH 7.4) with 0.3 mg hydrolase and then placed in a shaking incubator (100 rpm) at 30 °C for 24 h. After degradation time, specimens were removed and rinsed with distilled water thoroughly. Specimens were then dried in a desiccator and weighed (W2). The weight loss was calculated to assess the degradation rate by using Equation [4] [62]. Five readings of each sample were taken and averaged. [ 4]Degradation %=W1−W2W1×100 ## 2.3.6. FTIR To study the chemical interaction between PVA, PCL, PVA/PCL, and drug-containing PVA/PCL fibres, Fourier transform infrared spectroscopy (FTIR) was performed. Infrared spectra of these fibres were measured by the attenuated total reflection (ATR) method using a Fourier transform infrared spectrometer connected to a PC with specific software analysis. All spectra were recorded between wave length 500 and 4000 cm−1. ## 2.3.7. Scanning Electron Microscopy (SEM) The morphology of PVA/PCL fibres containing the model drug was observed by using scanning electron microscopy (SEM Quanta 250). First, the specimen was brought into SEM holders and subsequently sputtered with gold. SEM uses incident electrons from an electron gun having a voltage of 10 kV under a high vacuum at a magnification range of 800x. ## 2.3.8. Antimicrobial Efficiency The standard testing method AATCC 147-1998 (American Association of Textile Chemists and Colorists) was used to check out the anti-microbial efficiency of developed fibres. An aliquot of approximately 25 μL of 10−5 dilution of the overnight incubated bacterial strains—Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)—was spread on the sterile agar plate. The specimens (drug-containing fibres) were placed on the agar surface and pressed gently to ensure intimate contact. The agar plates were then incubated at 37 °C for 24 h. After incubation, the agar plates were examined for microbial growth under and around the test specimens, and the zone of inhibition was observed. The affected zone was calculated by applying the formula given in Equation [5]. [ 5]W=T−D2 In the above formula, ‘W’ stands for the width of the zone, which is clear from inhibition in millimetres, the total diameter of the test sample and the clear zone is denoted by ‘T’, and the diameter of the test sample is mentioned as ‘D’ in the formula. ## 2.3.9. In Vitro Drug Release To measure the quantity of released NaSD (drug) from the developed PVA/PCL fibres with the function of time, an in vitro study of the released drug was performed. A standard absorption curve at different concentrations of the drug was developed to measure the release of the drug. A series of drug loaded standard solutions of were prepared in distilled water to obtain a calibration curve. With a slit width of 5.0 nm, a Shimadzu UV–visible spectrophotometer (UV-2450) was used to measure absorption at 37 °C; 0.5 g of drug-loaded PVA/PCL fibres were placed into a 100 mL volumetric flask. This was filled with distilled water at 25 °C. Samples of 3 mL were drawn at 1, 2, 3, 8, 12, 24, 48, and 72 h (after mixing the solution with a micropipette). After each sampling, the solution was replenished with a similar solution of fresh dissolution media. Solutions were scanned in triplicate to measure the absorbance. A standard absorption curve at a maximum absorbance of 289 nm was drawn for the drug. From the standard absorption curve, a linear regression equation (fitted line plot) was developed. The linear regression equation and the standard absorption curve are shown in Figure 2. ## 3.1. Optimization of the Wet Spinning Process Fibre surface characteristics are mainly dependent on its manufacturing process. In the preparation of wet spun fibres, different process parameters such as the non-solvent solution (coagulation solution), coagulation temperature, pump speed, and stretching and drawing rollers speed were optimized after many trials. Output was evaluated by linear density, SEM, tensile properties (tenacity and elongation), liquid absorption, swelling behaviour, degradation test, antimicrobial activity, and drug release profile test of the extruded fibres. Different concentrations of PVA and PCL (according to Table 1) were used. Acetic acid was used as a solvent for both PVA and PCL. Ethanol was used as a non-solvent solution in a coagulation bath at 4 °C (cold). A pump speed of 5 rpm and a draw ratio of 2.5 was used to obtain smooth and long fibres. All these optimized parameters of the wet spinning process to produce the fibres are given in Table 3. ## 3.2. Linear Density, Tenacity, and Elongation The linear density of the developed fibres was obtained in a range of 6.48 Tex to 9.98 Tex having an average value of 8.13 Tex. The comparative effect of PVA and PCL on the developed fibre linear density is shown in Figure 3a. From the results, it can be seen that both PVA and PCL have a direct effect on the developed fibres’ linear density. When the concentration of PVA increased, the fibres’ linear density also increased. The increase in fibre linear density might have been due to the increase in the viscosity of the solution, which is shown in Table 4. The addition of PCL also increased the fibre’s linear density, but the effect of PCL was less as compared to PVA. The concentration effect of PVA was more prominent than PCL concentration. This increase in linear density may have been due to the similar fact that the increase in PCL concentration resulted in an increase in viscosity, which resulted in an increased linear density to some extent. The tenacity of the developed fibres was in the range of 14.11 to 11.94 cN/tex, as shown in Figure 3b. The tenacity of a fibre indicates the load-bearing capacity of the fibre per linear density of the fibre material. The higher the tenacity, the stronger will be the fibre. From the results, it can be seen that the PVA and PCL had an indirect effect on the tenacity of the developed fibres, as tenacity decreased by increasing the concentration of PVA and PCL. The developed fibre sample no. F16 (PVA/PCL $\frac{20}{12}$ concentration) exhibited the lowest tenacity. This may have been due to the increase in viscosity because when the concentration of PVA and PCL increased, the viscosity of the solution also increased, which resulted in increases in the linear density. The reason might have been due to the irregular arrangement of polymeric chains by the increase in viscosity of the solution, as the polymeric chains might not have had sufficient time to arrange themselves in a regular manner with an increase in solution viscosity, hence reducing the load bearing capacity of the fibre. Elongation % is the elasticity of a fibre. It indicates the limit of fibre extension without any breakage. To determine the mechanical properties of the developed fibres, elongation was used. A higher elongation % is beneficial when the fibres have to face external forces. Elongation of the developed fibres was in the range of $102.73\%$ to $46.61\%$. Figure 3c represents the effect of PVA and PCL on the elongation % of the developed fibres. It was found that PVA had a negative effect on the elongation % of developed fibres. By increasing the concentration of PVA, elongation % decreased. This has maybe due to the crystalline structure of PVA. The regular placement of polymeric chains reduced the elongation %, while on the other hand, PCL had a positive effect on the elongation % of the developed fibres. When the concentration of PCL increased, elongation also increased to a significant level. This may have been due to the elastomeric nature of PCL. Furthermore, an increase in PCL concentration resulted in a reduced crystalline behaviour of developed fibres, which also increased the elongation % [63]. ## 3.3. Liquid Absorption (g/g) The absorption (g/g) of the developed fibres was tested in three different solutions, i.e., DW, SS, and SA, as shown in Figure 4. It was observed from the results that fibres showed higher absorption in DW. This may have been due to the easy accessibility of water molecules to highly absorbent fibres. Moreover, hydroxyl groups in the PVA enhanced the fibre’s water absorption properties. As compared to DW, fibres showed less absorption properties in SS due to the presence of Na+ ions, which reduced the ion exchange and resulted in less absorption. In comparison to SS and DW, the novel fibres exhibited very low absorption (g/g) in SA; this may have been due to the presence of calcium ions in SA, which created hindrance in ion-exchange, which resulted in less absorption. Liquid absorption (g/g) of the fibres increases by increasing the concentration of PVA. When the concentration of PVA increases, OH groups also increase, which attracts the liquid molecules and results in increased liquid absorption. PCL has an indirect impact on the absorption of developed fibres because PCL is hydrophobic, so when the concentration of PCL increases, liquid absorption decreases at a constant concentration of PVA. ## 3.4. Swelling % For an ideal wound dressing, excessive exudate management is a very important thing, which is usually examined by the swelling % of the fibres. The swelling % of developed fibres was analysed and calculated in three different above-mentioned solutions by using an optical microscope attached with a digital camera at 40X, which is shown in Figure 5. Figure 6 shows the fibre swelling % in three different solutions. From the results, it was observed that the developed fibres had the highest swelling % in DW due to the presence of hydroxyl groups in PVA. The presence of OH groups in PVA makes it a good hydrophilic polymer [64]. Therefore, when the concentration of PVA increases, the number of OH groups increase, which attracts water molecules to make the fibres swell more. The increase in linear density may also be due to the non-presence of sodium and calcium ions in water. Developed fibres showed less swelling % in SS as compared to DW. The presence of sodium ions in SS reduced the ion exchange, which resulted in less swelling %. SA had the lowest swelling % as compared to DW and SS. In SA, the Ca+ ion reduced the swelling % due to the creation of hindrance in ion exchange. The hindrance of ions in SA resulted in decreased swelling %. The concentration of PVA and PCL also affected the swelling % of the developed fibres. When the concentration of PVA increased, the swelling % increased due to the hydrophilic nature of PVA. Moreover, with the increase in PVA concentration, hydroxyl groups increased, which resulted in increased swelling % of the developed fibres. On the other hand, PCL had a negative effect on the swelling % of the developed fibres. When the concentration of PCL increased, the swelling % decreased due to the hydrophobic nature of PCL. Overall swelling % increased by increasing the PVA at a constant PCL concentration. ## 3.5. Degradation Test The degradation test was performed to measure the degradation of developed fibres. Figure 7 shows the degradation rate of PVA/PCL fibres. From the results, it was observed that PVA had a direct impact on the degradation of the developed fibres. By increasing the concentration of PVA, the degradation rate increased. PVA is highly soluble in water and has the ability to reduce weight when dissolved in liquid media for many hours. Therefore, when the concentration of PVA increased, the degradation % of the developed fibres also increased. On the other hand, PCL had less effect on degradation % as compared to PVA. When the concentration of PCL increased, degradation increased but to some extent because PCL degraded slowly as compared to PVA. ## 3.6. FTIR The FTIR spectra of PVA, PCL, PVA/PCL, and drug-containing PVA/PCL fibres were performed to identify the functional groups after the successful incorporation of PVA in PCL as well as drug in PVA/PCL solution. Figure 8 shows the FTIR spectra of PVA, PCL, PVA/PCL, and drug-containing PVA/PCL fibres. Confirmation of different materials used in the development of fibres was carried out by the detection of functional groups having specific peak areas. The characteristics peaks for PVA were depicted at 3267 cm−1, 2915 cm−1, 1708 cm−1, and 1220 cm−1, which confirmed the presence of O–H stretching, C–H stretching, carbonyl group (C=O) stretching, and ester group stretching, respectively [53]. In the case of PCL, characteristics peaks were depicted at 2942 cm−1, 2865 cm−1, 1720 cm−1, and 1366 cm−1, showing the presence of CH2-asymmetric stretching, CH2 symmetric stretching, carbonyl group stretching, and C–H2 bending vibrations, respectively [53]. A blend of PVA/PCL fibres showed peaks at 3260 cm−1, 2940 cm−1, 1720 cm−1, and 1117 cm−1, which confirmed the presence of O–H stretching, C–H2 stretching, and C=O carbonyl group stretching and C–H2 bending vibrations, respectively. The characteristic peaks of PVA and PCL in a blend of PVA/PCL fibre confirmed the successful co-extrusion of the two polymers. The peak depicted at 1117 cm−1 confirmed the presence of the C–O ester group in PCL. In the developed PVA/PCL fibre-containing drug, the peak at 1440 cm−1 was related to the asymmetric stretching vibration of the S=O sulphite functional group, which confirmed the presence of NaSD. The FTIR spectra confirmed the successful loading of NaSD (drug) in the developed PVA/PCL fibres. ## 3.7. Scanning Electron Microscopy The developed fibres were subjected using scanning electron microscopy to analyse their surface and porosity. Three optimized fibres were selected based on varying drug concentrations, which are given in Table 2, to check and verify the effect of drug concentration on the surface morphology of the developed fibres. From Figure 9, it is observed that with the change in drug concentration, the surface roughness increased. A smooth surface was obtained for the minimal drug concentration, which changed into a rough surface with an increase in drug concentration in the fibre. Slashes on the surface of the developed fibres could be observed in the longitudinal direction. This may have been due to the extrusion process of the developed fibres because the dope solution was extruded through a spinneret having circular holes directly into a coagulation bath containing ethanol (at 4 °C), which solidified the fibres quickly. The rough spinneret surface may have caused roughness in fibres after the coagulation process. This also supported a higher surface area, which facilitated better drug release per cross-sectional area. Images of developed optimized fibres containing drug in the longitudinal view were taken, which are shown in Figure 9. ## 3.8. Antimicrobial Activity To analyse the relative effect of the drug, antimicrobial activity was performed for the optimized fibre with three different concentrations of the drug according to Table 2. Antimicrobial activity was tested against two different bacterial strains, i.e., Staphylococcus aureus (Gram-positive), the most commonly studied in wound practices, and Escherichia coli (Gram-negative). Figure 10 shows the zone of inhibition resulting from the developed OF1, OF2, and OF3 fibres containing $1\%$, $1.5\%$, and $2\%$ drug, respectively. It is clearly seen from the photographs of agar discs that the optimized fibres were effective against S. aureus bacterial strains and showed no effect against E. coli bacterial strains. This may have been due to differences in their bacterial cell wall structures. Gram-negative bacteria have an outer cell membrane, which is not found in Gram-positive bacteria. Gram-positive bacteria’s cell wall is rich in peptidoglycan, which is responsible for preserving the Gram-staining dye and iodine solutions/liquids of Gram [41,65,66]. Table 5 shows the zone of inhibition against the S. aureus bacterium. Twenty readings of each sample were taken from the fixed centre of samples to different edges of fibres and zones of inhibition as well. The average radius of fibres and zone of inhibition was calculated from these readings. Through this radius, the average diameter of fibres and zone of inhibition was calculated. From Figure 10a, it is observed that the zone of inhibition against S. aureus bacterial strains was at its maximum at a $2\%$ drug concentration and a minimum at $1\%$ drug concentration, which showed the presence of the drug in the developed fibres, while no bacterial strains were found in the F13 sample, which was without a drug. OF1, OF2, and OF3 fibres showed a clear inhibition zone with a particular diameter, while the fibres without drug showed bacterial colonies. It may be considered that the drug-containing fibres exhibiting a higher release of the drug showed a bigger inhibition zone as compared to those without drug fibres (F13). As the developed fibres containing the drug exhibited good antibacterial activity against S. aureus that causes wound infections, the developed fibres can be used for preventing infection if used as a dressing either alone or with some supportive dressings. ## 3.9. In Vitro Drug Release Figure 11 shows the controlled release of the drug (NaSD) from OF1, OF2, and OF3 fibres at different time intervals. From the results, it was seen that the developed optimized fibres showed a maximum release of the drug in the first three hours. This may have been due to the burst release of the drug. The process by which the drug is released involves the dissolution of the drug followed by diffusion to enter the release medium via the swelling grooved structure, and this entire mechanism proceeds at the interphase. The experimentally measured initial rapid release of the drug is known as the burst effect, which occurs due to the large interphase (due to rough fibres’ surfaces) between developed fibres and dissolution medium, which contributes to the dissolution and migration of the drug onto the fibres’ surface and results in the burst release of drug in the first three hours. The slow-release rate of the drug is reached after this burst release of the drug. The non-covalent interaction between the hydrophilic polymer (PVA) and drug (dispersed in fibres’ rough structures) is greatly increased as a consequence of the large surface area. This phenomenon limits the drug’s diffusion and thus contributes to a drop in the rate of drug release. From Figure 11, it is also observed that the drug showed its maximum release in SS. SS has a high amount of sodium ions, which may have resulted in the production of water-soluble sodium ions and caused the higher release of the drug. The release of the drug was better in DW as compared to SA. However, this release was less than in SS. The higher release of the drug in DW may be due to the easy accessibility of water molecules to the drug. However, the release of drug in DW is only due to its soluble nature. In the case of SA, the presence of Ca+ in the solution resulted in the lowest release of the drug. Calcium ions in SA hinder ion exchange, which may result in less release of the drug. ## 4. Conclusions In this study, hydrophilic PVA/PCL fibres containing a model drug were prepared by using the wet spinning technique. Process parameters were set after initial trials. The novel developed hydrophilic fibres were used as a substrate to load drug for wound dressing applications. From the results, it is concluded that developed fibres can be produced by using PVA and PCL as a blend containing the drug by passing through a coagulation bath containing ethanol at 4 °C. The inclusion of PVA and PCL increases the linear density. Absorption (g/g) and swelling % of the developed fibres increase to a significant level by increasing the PVA concentration but decrease by increasing the PCL concentration. The degradation % of the developed fibres also depends on the PVA concentration. As PVA content increases, the degradation % of the developed fibres also increases. Degradation % decreases when the PCL concentration increases at a constant PVA. FTIR confirmed the presence of the drug in the developed fibres. Developed fibres also have good resistance against S. aureus bacteria with good drug release. Good mechanical properties, absorption (g/g), swelling %, degradation %, and enhanced antimicrobial activity with an appropriate drug release profile test nominate the developed PVA/PCL fibres containing the model drug as a promising wound dressing candidate for wound dressing applications. ## References 1. Wan W., Cai F., Huang J., Chen S., Liao Q.. 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--- title: 'It Runs in the Family: Testing for Longitudinal Family Flynn Effects' authors: - Linda Wänström - Patrick O’Keefe - Sean A. P. Clouston - Frank D. Mann - Graciela Muniz-Terrera - Stacey Voll - Yun Zhang - Scott M. Hofer - Joseph L. Rodgers journal: Journal of Intelligence year: 2023 pmcid: PMC10057072 doi: 10.3390/jintelligence11030050 license: CC BY 4.0 --- # It Runs in the Family: Testing for Longitudinal Family Flynn Effects ## Abstract The Flynn effect refers to increases over time in measured (particularly fluid) intelligence of approximately 3 IQ points per decade. We define the Flynn effect at the family level, using longitudinal data and two new family-level cohort definitions. Multilevel growth curve analyses of the National Longitudinal Survey of Youth 1979 data showed that children in families with later-born mothers had higher average PIAT math scores, and lower average reading comprehension scores and growth, in young and middle childhood. Children in families where the first child was born later had higher average PIAT math, reading recognition, and reading comprehension scores, as well as larger developmental growth. The latter family-level Flynn effects were of higher magnitudes than the usual individual-level Flynn effect found in previous studies. Our results, showing family level-intercept and slope Flynn effects for both maternal birthyear and first child birthyear, have implications for research aiming to explain the Flynn effect. ## 1. Introduction Almost 40 years ago, James Flynn [1984] revived interest in studying the secular increase in intelligence that was occurring in the U.S. (see Lynn 2013, for a history). Following this, thousands of research studies have documented and attempted to explain increasing IQ scores across countries, ages, and different IQ measures. Comprehensive meta-analyses are presented in Pietschnig and Voracek [2015] and Trahan et al. [ 2014]. In the first 20 years after Flynn’s first study, a number of Flynn effect discoveries were published, as well as a number of explanatory models that attempted (without full success) to provide explanations for the empirical patterns. A relatively recent Flynn effect discovery was the finding that the pace may be slowing down—or even reversing—in some parts of the world (see Teasdale and Owen 2005). However, recently, the pace of discovery has stagnated. This paper documents new Flynn effect patterns. Few past studies have questioned the “location” of the Flynn effect in relation to the family. Rather, past work has strongly focused on individual-level patterns to document the Flynn effect. In addition, few past studies have investigated the Flynn effect longitudinally, examining the presence of Flynn effects on not only differences in intelligence levels between cohorts, but also differences in intelligence development between cohorts. In the current study, we evaluate whether the Flynn effect exists at the family level, and whether it exists in both levels and in growth. A Flynn effect in levels means that later-born cohorts have higher IQ levels than earlier-born cohorts. A Flynn effect in growth means that later-born cohorts are increasing faster in IQ than earlier-born cohorts. In the remainder of the introduction, we review past Flynn effect studies, and present our empirical analysis. The Flynn effect refers to secular increases in measured intelligence—particularly fluid intelligence, the type of intelligence associated with problem-solving—that have been documented globally, and have occurred for more than a century. The average increase during the 20th century was approximately 3 IQ points per decade. Flynn [1984, 1987] noted that individuals belonging to later generations tended to score higher on the same IQ tests than those who took the tests years earlier, and individuals taking several tests, normed at different times, tended to score higher on tests that were normed earlier. In the past few decades, and in some locations, the pace of the Flynn effect has appeared to be slowing, or even reversing (Dutton and Lynn 2013; Sundet et al. 2004; Teasdale and Owen 2000; however, different patterns were found by Gonthier et al. 2021; O’Keefe and Rodgers 2020). The research community has reached no broad consensus on the causes of the Flynn effect, or for this potential slowing trend (Rindermann et al. 2016). Over a dozen face-valid, empirically defensible explanations exist; however, each explanation has empirical/theoretical weaknesses. Pietschnig and Voracek’s [2015] meta-analysis suggested that Dickens and Flynn’s [2001] social multiplier (niche-picking) theory, and the life history perspective (e.g., Woodley et al. 2013), had the most support. Other popular mechanisms have included nutrition (e.g., Lynn 2009), education (e.g., Baker et al. 2015; Williams 1998), technology (e.g., Neisser 1997), testing effects (e.g., Fuggle et al. 1992), health and health care services (e.g., Steen 2009), and heterosis (e.g., Mingroni 2007). A number of broad summaries of these theories have been published (e.g., Ang et al. 2010; Pietschnig and Voracek 2015). Rodgers [1998] suggested that research on the Flynn effect had moved forward more aggressively than the empirical understanding of the effect could justify. Almost two decades later, Rodgers [2015] was more sanguine about the knowledge base, because of a closer match between the empirical evidence and the theories that had been proposed. Nevertheless, he still raised important methodological and empirical issues, such as empirically separating within- and between-family variance, and using growth curve models, when studying the Flynn effect. The current paper addresses these concerns by studying the Flynn effect longitudinally and at the family level. Most previous studies have evaluated the Flynn effect at the individual level, ignoring family units, although some studies have distinguished between within- and between-family variance. Sundet [2014] and Bratsberg and Rogeberg [2018] found Flynn effect patterns inside families, across siblings, in Scandinavian data. Rodgers [2014] used data from Belmont and Marolla [1973] to show that the Flynn effect could be the cause of empirically observed, cross-sectional, within-family birth order effects, when measured in cross-sectional studies. O’Keefe and Rodgers [2017] used U.S. data from the National Longitudinal Survey of Youth (NLSY79), based on a household probability sample of U.S. adolescents in 1979, and found that the Flynn effect was most strongly linked to between-family patterns. When the child cohort variable in the NLSY-Children (NLSYC) data (the children born to NLSY79 females) was separated using multilevel models into between-family, within-family, and within-individual components, most of the effect was located in between-family patterns. Finding Flynn effects at the family level, i.e., with family as the unit of measurement, may have implications for theories of the causes of the Flynn effect. In the present study, we investigate the presence of a family-level Flynn effect, both on levels and slopes of growth curves, using children and mothers from the NLSY79 data. Importantly, in the current study, new measures of cohort at the family level are defined. When investigating the Flynn effect at the individual level, previous studies have used the individual’s birthyear (or birth decade, etc.) as the cohort measure. When family is the unit of interest, defining a cohort is not quite as straightforward. O’Keefe and Rodgers [2017] studied components of child and maternal birthyears and ages, including the age of the mother at the birth of her first child, as control variables (though not to define cohorts). In the current study, we use two definitions, both relevant to the idea of measuring the family cohort: the birthyear of the mother, and the birthyear of the firstborn child (note that the difference between these two measures is maternal age at first birth, a common and often-used variable in the psychological and demographic literature; e.g., Rodgers et al. 2008; Neiss et al. 2002). We use these family-level cohort measures because they are defined at the family level, and reflect between-family variance (which was identified as the primary location of the Flynn effect in the NLSY data by O’Keefe and Rodgers 2017). They are substantively interesting in relation to a family-level Flynn effect. The former variable defines the cohort of (one of) the parents. If it is parental characteristics that determine the family outcomes, this is a plausible cohort variable to capture such effects. The second variable defines the beginning of parenting for a family. If it is the timing of parents’ entry into parenthood that determines family outcomes, this cohort variable is more appropriate. Our design differs from O’Keefe and Rodgers’ [2017] in our explicit definitions of the two family cohort variables, and in our longitudinal focus, by studying the presence of family Flynn effects (using both family cohort variables) in both the levels and slopes of the family growth trajectories. Our analyses also incorporate more outcome measures (the Peabody Individual Achievement Test (PIAT) math, reading recognition, and reading comprehension subtests). Our approach is different from most earlier Flynn effect studies using the NLSY, where child-level birth cohorts defined the Flynn effect, rather than family birth cohorts. Because James Flynn argued in his early papers (e.g., Flynn 1984, 1987) for a cohort-based interpretation of the observed changes in intelligence, most theories have (implicitly or explicitly) searched for causes of the effect that are cohort-based. Whether the causes of the Flynn effect are predominantly cohort, period, or aging phenomena (or some combination of these) has seldom been addressed however (see critique relevant to this issue in Clouston et al. 2021; Rodgers 1998). The methodology for separating these three (confounded) processes with regard to the Flynn effect, or other processes in general, is limited in key ways (Bell and Jones 2013; Fienberg 2013; Fosse and Winship 2019a; O’Keefe et al. 2022), although models are estimable with constraints (Fosse and Winship 2019b; Keyes et al. 2010). Most past Flynn effect studies have used cross-sectional designs, in which cohort and aging effects are especially difficult to disentangle. Giangrande et al. [ 2022] noted several problems in cross-sectional studies of the Flynn effect. As an example, Dickinson and Hiscock [2010] used the WAIS in a U.S. sample to compare 20- and 70-year-olds in terms of verbal and performance IQ. The younger sample out-performed the older sample, providing support for an aging interpretation. However, in this (and other) samples, aging and cohort effects are confounded by the cross-sectional nature of the dataset. When they adjusted for a Flynn effect at the cohort level, they concluded that around $85\%$ of the apparent aging decline between ages 20 and 70 was attributable to the cohort change, whereas only $15\%$ was attributable to aging, suggesting that the Flynn effect can “stand in” for an aging effect. The Flynn effect may show different magnitudes depending on the ages of the individuals studied (e.g., Pietschnig and Voracek 2015). Rodgers and Wänström [2007] noted tendencies toward larger Flynn effects for children of older ages when they compared PIAT Math scores among different birth cohorts of 5-year-olds, different birth cohorts of 6-year-olds, and so on up to different birth cohorts of 13-year-olds. Kanaya et al. [ 2005], on the other hand, found smaller effects for older children when they studied WISC scores of students who tested for admittance to special education. The latter corresponds to the findings of Shakeel and Peterson [2022], who found larger effects for younger children when analyzing the results of math and reading tests. In contrast, Salthouse [2015], studying adults, did not find any differential Flynn effects at different ages. In order to separate cohort and aging effects, the Flynn effect should be studied longitudinally, which has been done in some studies. For example, Skirbekk et al. [ 2013] used data from the English Longitudinal Study of Ageing survey and found greater cognitive improvement among later cohorts, whereas Karlsson et al. [ 2015] found faster declines with aging for later Swedish cohorts. Giangrande et al. [ 2022] estimated latent growth factors (intercept and slope), and found Flynn effects in the levels (intercepts) and in the growth (slopes) for both fluid and crystallized measures, suggesting that the later cohorts scored higher on average, but also had steeper growth (for ages 7–15). They stressed the importance of studying the Flynn effect both between and within individuals in order not to miss effects on cognitive development, and noted that multilevel models are suitable for this purpose. In order to study longitudinal Flynn effects, we will use raw scores from PIAT measures, using multilevel growth curve models. This will enable us to study Flynn effects in both the score levels and in the slopes of trajectories, which enables us to detect differential growth effects for different family cohorts. Previous studies on Flynn effects in the NLSY (Ang et al. 2010; O’Keefe and Rodgers 2017) have used age-normed scores and have thus not been able to detect effects on development (Rodgers and Wänström 2007, analyzed both normed and raw scores, but did not analyze growth). To summarize, the past century has shown increases in measured intelligence across cohorts (Flynn effects); however, most previous research has studied the Flynn effect at the individual level, using cross-sectional data. In the present study we investigate the Flynn effect at the family level (using a different design and different definitions of cohort than those in O’Keefe and Rodgers 2017). Our design is longitudinal, using children belonging to the same family, examining both differences in levels and slopes of family trajectories. We use two definitions of family cohorts: maternal birthyear and first child birthyear. The significance of this research is to further expand the focus of the Flynn effect from the individual to the family, to develop new ways to operationalize what a cohort effect is in relation to the family, and to investigate the Flynn effect in developmental slopes in addition to levels. Each of these goals pushes research on the Flynn effect in new directions, methodologically and empirically. ## 2.1. Data The National Longitudinal Survey of Youth 1979 (NLSY79) (Bureau of Labor Statistics and U.S. Department of Labor 2019a) is an ongoing longitudinal survey based on a household probability sample of 12,686 adolescents and young adults in the US between the ages 14 and 21 at the end of 1978. The NLSY-Children (NLSYC) (Bureau of Labor Statistics et al. 2019b) are the biological children of the mothers in the NLSY79 and they have been surveyed every other year since 1986. The children ($51\%$ males, $49\%$ females; $53\%$ non-Black/non-Hispanic, $28\%$ Black, $19\%$ Hispanic or Latin) were cognitively assessed using the Peabody Individual Achievement Test (PIAT) mathematics, reading recognition, and reading comprehension subtests every other year starting at age 5 until they reached age 15, between 1986 and 2014. Thus, the children have repeated measurements on these subtests (assessments at ages 5, 7, 9, 11 and 13, or at ages 6, 8, 10, 12 and 14). The mean assessment age was 9.75. The NLSYC respondents were born between 1970 and 2009, with a majority ($50\%$) born between 1982 and 1991. Because our aim is to study the family as a unit, longitudinally, using the NLSY79 and NLSYC datasets serves our purposes well, because all children (of the appropriate assessment ages) of the mothers of the original NLSY79 sample were assessed up to five times for a long period of time (1986 until 2014), making most families complete with regards to child-rearing. Using child siblings instead of adult siblings is also preferable, with regard to our aims, because children share their environments with their siblings to a much greater extent than do adult siblings, making the definition of a family more appropriate. Approximately $25.7\%$ of the families had one cognitively assessed child, $39.9\%$ had two, $22.0\%$ had three, $8.4\%$ had four, $2.7\%$ had five, and the rest had six to ten assessed children. Because the mothers of the NLSYC children were born between 1957 and 1964, our maternal birthyear cohort variable ranges from 1957 to 1964. The first NLSYC firstborn child was born in 1970 and the last firstborn child was born in 2007, so our first child birthyear cohort variable ranges from 1970 to 2007. We used the PIAT measures math, reading recognition, and reading comprehension for our analyses in this study. Previous studies have found child Flynn effects using an individual-level cohort definition in the NLSYC for PIAT math (Ang et al. 2010; Rodgers and Wänström 2007) as well as between family effects for PIAT math (O’Keefe and Rodgers 2017). The child Flynn effects in PIAT reading recognition and reading comprehension were of smaller magnitudes, and typically reduced towards zero when controlling for maternal IQ (Rodgers and Wänström 2007). We include all three PIAT measures in the current study because our family analyses use family-level cohort definitions, a different design, and different scalings of the response variables as compared to the previous studies. There was a total of 11,530 children in the NLSYC dataset. Out of these, 9233 were assessed with the PIAT math test, resulting in a total of 4055 mothers with 9233 children and 34,498 measurements used in our PIAT math analyses. The corresponding numbers for PIAT reading recognition were 4051 mothers with 9220 children and 34,358 measurements, and for PIAT reading comprehension, 4046 mothers with 9199 children and 33,655 measurements. All the children’s PIAT math scores come from the same instrument of 84 items, increasing in difficulty, and used across all ages. The starting point on the instrument increases for each age; if a test-taker misses the first several items, they move back to the starting point for the previous age. Once a respondent correctly answers five items in a row, the first item is established as the “basal”. Respondents continue from the basal as long as they get a subset of items correct, and finish when they miss five out of seven items. Their PIAT math score is the item number of the final of the five correctly answered items, with the total number of incorrect items since the basal subtracted. The PIAT reading recognition score provides an indication of a child’s ability to silently read and pronounce words. This subtest includes 84 items that require children to read and pronounce individual letters and words out loud, which increase in difficulty as the child progresses through the subtest, beginning with simple words (e.g., “run”, “play”, “jump”) and ending with more advanced words (e.g., “credulily”, “disaccharide”, “apophthegm”). Related, the PIAT reading comprehension score provides an indication of a child’s ability to read and understand full sentences or passages. This subtest includes 66 items that ask children to read a sentence silently to themselves. After they have finished reading, they are asked to point to one of four pictures that best describes what they have read. The procedures for test administration and scoring decisions for the PIAT reading comprehension and recognition subtests are identical to those described for the PIAT math scores. Thus, the PIAT reading scores are the difference between the item number of the final of the five correctly answered items and the total number of incorrect items since the basal. However, for children with a reading recognition score less than 19, the reading comprehension subset was not administered. In such cases, the reading recognition score is equal to the reading comprehension score. Further details involving norming and slight adjustments in scoring procedures over time can be found on the NLSY website. As mentioned previously, we used the raw scores (as opposed to the normed scores, used in the previously mentioned NLSYC studies) in order to investigate growth in scores across age. ## 2.2. Statistical Models We estimated growth curves, using multilevel modeling, for the families in the NLSY using the child PIAT scores. A family growth curve thus consisted of repeated measurements for all children in the family. Each NLSYC child had up to five repeated measurements for PIAT math, reading recognition, and reading comprehension. Multilevel models with repeated measurements at the first level, children at the second level, and mothers at the third level were estimated. We estimated models separately for the three PIAT measures, instead of adding them together as total scores, in order to detect differential family Flynn effects (as was found in, e.g., Rodgers and Wänström 2007, with regard to individual Flynn effects) for the different measures. Because, after inspection, the developmental curves showed nonlinear, quadratic growth, a quadratic age component was included in model 1:PiatScoretij=αij+β1ijChildAge+β2ijChildAge2+ϵtij,αij=αj+vαij,β1ij=β1j+vβ1ij,β2ij=β2j+vβ2ij,αj=α+uαj,β1j=β1+uβ1j,β2j=β2+uβ2j, where PiatScoretij is the PIAT math, reading recognition, or reading comprehension score, respectively, at the tth age for the ith child of the jth mother, αij is the intercept of the growth curve for the ith child of the jth mother, β1ij is the linear slope, and β2ij is the quadratic slope of the growth curve of the ith child of the jth mother, ChildAge (centered around its grand mean) is the child age in months, ϵtij is a residual, αj is the intercept for the jth mother, β1j is the linear slope, and β2j is the quadratic slope, for the jth mother, α is an overall intercept, β1 and β2 are overall slopes, vαij, vβ1ij and vβ2ij are child residuals, and uαj, uβ1j and uβ2j are mother residuals. The residuals are assumed to be multivariate normally distributed within levels, and covariances between levels are assumed to be zero. Inserting the bottom equations into the top equation and collecting fixed effects at the beginning and random effects at the end yields one single equation to estimate:PiatScoretij=α+β1ChildAge+β2ChildAge2+uαj+uβ1jChildAge+uβ2jChildAge2+vαij +vβ1ijChildAge+vβ2ijChildAge2+ϵtij We then investigated the presence of the Flynn effect in the family growth curves using the two different cohort definitions: maternal birthyear and first child birthyear. We added these cohort variables to separate models, instead of including them together in a single model. This was done because our aim was to study the effects of family cohort on family intelligence. If they were to be included together in a single model, the meaning of the cohort variables would change. The meaning of the effect of the first child birthyear, net of the effect of maternal birthyear, is for example a version of the variable maternal age at first birth. Although this is a family-level variable, it does not define membership in a cohort. The maternal cohort variables were added at the third level (maternal birthyear—model 2; first child birthyear—model 3): PiatScoretij=αij+β1ijChildAge+β2ijChildAge2+ϵtij,αij=αj+vαij,β1ij=β1j+vβij,β2ij=β2j+vβ2ij,αj=α+γ1MaternalCohort+uαj,β1j=β1+γ2MaternalCohort+uβ1j,β2j=β2+γ3MaternalCohort+uβ2j A significant estimate of γ1 would indicate that the family intercepts differ depending on the maternal cohort, a significant estimate of γ2 would indicate that the linear part of the family slopes differs depending on the maternal cohort, and a significant estimate of γ3 would indicate that the quadratic part of the family slopes differs depending on the maternal cohort. Inserting the bottom equations into the top equation to get a single equation yields:PiatScoretij=α+β1ChildAge+β2ChildAge2+γ1MaternalCohort+γ2MaternalCohort·ChildAge+γ3MaternalCohort·ChildAge2+uαj+uβ1jChildAge+uβ2jChildAge2+vαij+vβ1ijChildAge+vβ2ijChildAge2+ϵtij As noted in the single equation above, two interaction terms are created from the model, and γ1 can therefore also be seen as an estimate of the main effect of maternal cohort, whereas γ2 and γ3 can be seen as interaction effects between maternal cohort and age and age squared (note, however, that our use of the word “effect” should not be interpreted to indicate a causal relationship). As mentioned previously, two definitions of family cohort were used: maternal birthyear (the year the mother was born, centered around its grand mean: 1960.53) and first child birthyear (the year at which the mother had her first child, centered around its grand mean: 1983.02). A positive main effect of maternal birthyear, in the equation above, would indicate a family Flynn effect in levels, i.e., that the average family scores, at the average age of all children, and as estimated from the child PIAT scores, were higher for later born mothers. A positive interaction effect between maternal birthyear and age, on the other hand, would indicate the presence of a family Flynn effect in the slopes, i.e., that the scores of mothers born later are increasing at a higher rate, i.e., that children of mothers born later have steeper PIAT slopes over time. An interaction between maternal birthyear and age squared would indicate that the non-linear part of the developmental trajectory differs between families with mothers of different maternal birthyear cohorts. Similarly, a positive main effect of first child birthyear would indicate that children of mothers who gave birth to their first child later in time had higher estimated average scores (family Flynn effect in levels), and a positive interaction effect between first child birthyear and age (or age squared) would indicate that children of mothers who had the first child later in time were increasing at a higher rate (family Flynn effect in the slopes). Our analyses were conducted in SAS (SAS Institute Inc. 2013) version 9.4 using the procedure MIXED. In past NLSY Flynn effect research, it has been important to adjust for an inherent selection bias in the NLSYC data. The bias is caused by older mothers with (on average) higher IQ, education, and income scores having (on average) later childbearing. Thus, children in later birth cohorts might have higher intelligence scores because of the Flynn effect, or because of this selection causing them to have higher-IQ mothers. Adjusting maternal IQ out of children’s IQ scores leaves the Flynn effect as the primary cause of any observed cohort changes in IQ. Rodgers and Wänström [2007] presented two sets of results: those for PIAT scores and those for PIAT scores adjusted for maternal cognitive ability scores on the Armed Forces Qualifying Test (AFQT), collected in the NLSY79 in 1980 when respondents were 15–22 years of age. The size of the Flynn effect reduced somewhat (but typically stayed significant) for PIAT math, and reduced to closer to zero in the PIAT reading recognition and PIAT reading comprehension scores. In the current study, this adjustment for maternal cognitive performance is not as logical for at least two reasons. First, when using maternal birthyear as the cohort measure, there is no selection bias of this type by definition (because mothers have obviously not yet given birth when they are born). Secondly, we are investigating possible effects of family cohort on family intelligence, as measured by child intelligence (PIAT scores). If we adjust for maternal intelligence, we are investigating the effects of family cohort on the part of child intelligence from which maternal intelligence is excluded. Although we do not find this model to be as interpretable as the models presented above, it is arguably of interest, especially when we use first child birthyear as the cohort measure. Thus, to portray these results for interested readers, we have added maternal IQ (AFQT) to Models 2 and 3 and present these results in detail in the Supplementary Materials, and more briefly here in the results section. ## 3.1. PIAT Math The parameter estimates of fixed effects, variances of random effects, and standard errors (S.E.) from estimating Models 1, 2 and 3 for PIAT math are presented in Table 1. The estimates from Model 1 show that the estimated PIAT math score at the average age (9.75 years old) is 42.362. The estimated coefficient for the linear age slope is 0.436 and the estimated coefficient for the quadratic age slope is negative, at −0.003. The family scores are thus found to be increasing across child ages; however, the increase starts to level off for older child ages. The average linear increase is 5.232 per year (0.436 ×12), which is not a surprising number, given that children are expected to grow in their mathematical ability as they get older. There is considerable variation in the intercepts of the growth curves between families (σuαj2=32.676), but also between the children within families (σvαij2=24.731). Approximately $35\%$ of the variance in PIAT math scores is thus between-family variance (32.676/(32.676 + 24.731 + 34.682)), and approximately $27\%$ is between children within families (24.731/(32.676 + 24.731 + 34.682)), at the average ages of the children. The estimates from Model 2 in Table 1 indicate a significant positive main effect of maternal birthyear. The average family score (at child age 9.75) for mothers born one year later was, on average, 0.158 higher, compared to other mothers. The usual Flynn effect is an increase of about 3 IQ points per decade (Flynn 2009). Although it is difficult to compare our estimate to the usual increase, because the PIAT tests are on a raw metric, we note that the standard deviation for PIAT scores in the NLSYC sample is approximately 10 per age in years (with lower values for younger ages, and higher values for older ages). The usual increase of 3 IQ points per decade corresponds to a $20\%$ standard deviation (SD) increase per decade ($\frac{3}{15}$), or a $2\%$ SD increase per year. Two percent of an SD per year corresponds to 0.2 PIAT points per year. The value 0.158 is thus a little lower than the usual individual-level Flynn effect of 3 IQ points per decade. Later-born mothers did not show a significantly steeper linear part of the slope; however, the significant quadratic component of the slope indicates that their children’s scores leveled off more with increasing ages. Figure 1 illustrates the family Flynn effects with regard to maternal birthyear. The blue line shows the model-estimated growth trajectories across child ages 5 to 14 for families of mothers born in 1964 (the latest cohort), and the red line shows the corresponding trajectory for families of mothers born in 1957 (the earliest cohort). As shown, the Flynn effect is only apparent for ages around 7–13. The model estimated difference between the cohorts is, e.g., 1.066 points for 9-year-olds, and 1.100 for 10-year-olds (which is somewhat lower than the 1.4 point difference, which would correspond to the usual Flynn effect of 3 IQ points difference per decade for these cohorts that are seven years apart). As shown from the estimates from Model 3, in Table 1, the main effect of first child birthyear is positive and significant. The average score for families who had their first child one year later is, on average, 0.543 points higher than other families, and the yearly linear increase is estimated to be 0.036 (0.003 × 12) points higher, and is estimated to level off for older ages. First child birthyear explains approximately $23.5\%$ of the variance between families ((32.676–24.989)/32.676). Figure 2 illustrates the family Flynn effects. The blue line shows the model-estimated family trajectory for the first child birth cohort of 2007 (the latest cohort) and the red line shows the corresponding line for the 1970 cohort (the earliest cohort). As shown, child scores in the later family cohort are higher across all ages, increase more across higher ages, and then start to level off. The model-estimated differences between cohorts are about 18.701 for 9-year-olds, and 20.397 for 10-year-olds. These differences are much higher than the 7.4 point difference that would correspond to the usual Flynn effect of 3 IQ points difference per cohort decade for these cohorts that are 37 years apart. ## 3.2. PIAT Reading Recognition The parameter estimates of fixed effects, variances of random effects, and standard errors from estimating Models 1, 2 and 3 for PIAT reading recognition are presented in Table 2. The estimated PIAT reading recognition score at the average age (9.75 years old) is 45.261, and the estimated linear increase per year is 5.652 (0.471 × 12), with a negative coefficient for the quadratic component (model 1). There is also a considerable variation in the intercepts of the growth curves, with approximately $37\%$ of the variance in PIAT reading recognition scores between families (45.205/(45.205 + 43.789 + 32.267)), and approximately $36\%$ between children within families (43.789/(45.205 + 43.789 + 32.267)), at the average ages of the children. There is no significant main effect or interaction effect of maternal birthyear (Model 2). The main effect of first child birthyear and the interaction effect with age are, however, positive and significant, and the interaction with age squared is negative and significant (Model 3). The average score, at the average child age, for families who had their first child one year later is, on average, 0.529 points higher than other families. First child birthyear explains approximately $17.0\%$ of the variance between families ((45.205–37.521)/45.205). Figure 3 illustrates these family Flynn effects. As shown, child scores in the later family cohort are higher across all ages, and increase across higher ages and then start to level off. The model-estimated differences between cohorts are about 17.980 for 9-year-olds and 19.959 for 10-year-olds, which are much higher than the 7.4 point difference that would correspond to the usual Flynn effect of 3 IQ points difference per cohort decade. ## 3.3. PIAT Reading Comprehension The parameter estimates of fixed effects, variances of random effects, and standard errors from estimating Models 1, 2 and 3 for PIAT reading comprehension are presented in Table 3. The estimated PIAT reading comprehension score at the average age (9.75 years old) is 41.213, and the estimated linear increase per year is 4.644 (0.387 × 12) while the quadratic part of the slope is negative. There is also a considerable variation in the intercepts of the growth curves; approximately $34\%$ of the variance in PIAT reading comprehension scores is between-family (31.751/(31.751 + 24.569 + 38.142)), and approximately $26\%$ is between children within families (24.569/(31.751 + 24.569 + 38.142)), at the average ages of the children. There is a significant negative main effect and there are significant negative interaction effects of maternal birthyear, which contradict the usual positive family Flynn effect (Model 2). These effects are illustrated in Figure 4. As shown, the scores start off at similar levels for younger ages, and then the scores of children in families in earlier maternal birthyear cohorts increase across older ages. The differences between cohorts are 0.687 for 9-year-olds and 0.926 for 10-year-olds, which increase to 2.483 for 14-year-olds. For older ages this negative Flynn effect is thus of a greater magnitude than the 1.4 difference (as expected for cohorts seven years apart). The observed standard deviations of PIAT scores for older ages were, however, larger than 10, so this latter estimate may be an overestimation. The main effect of first child birthyear is positive and significant, as is the interaction between first child birthyear and age, whereas the interaction with age squared is negative and significant (Model 3). The average score, at the average age, for families who had their first child one year later is, on average, 0.388 points higher than other families. First child birthyear explains approximately $12.9\%$ of the variance between families ((31.751–27.644)/31.751). Figure 5 illustrates these family Flynn effects. As shown, the child scores in the later family cohort are higher across all ages, and increase across higher ages and then start to level off. The model-estimated differences between cohorts are about 20.863 for 9-year-olds, and 21.964 for 10-year-olds, which are much higher than the 7.4 point difference that would correspond to the usual Flynn effect of 3 IQ points difference per cohort decade. ## 3.4. Adjusting for Maternal IQ When we added maternal IQ to Models 2 and 3, with PIAT math, reading recognition, and reading comprehension as response variables, the main effects of maternal birthyear and first child birthyear typically decreased, but stayed significant, in the models in which they were previously significant. None of the interaction effects changed much. The main effect of maternal birthyear decreased from 0.158 to 0.102 for PIAT math, and from −0.124 to −0.170 for PIAT reading comprehension. The main effect of first child birthyear decreased from 0.543 to 0.307 for PIAT math, from 0.529 to 0.227 for PIAT reading recognition, and from 0.388 to 0.153 for PIAT reading comprehension. The effect of maternal IQ was positive and significant in all models, as expected, and decreased the between-family variance (also as expected). A complete table with results adjusting for maternal IQ can be found in the Supplementary Materials. ## 3.5. Summary of the Results We found Flynn effects for both of our cohort definitions. With regard to the first cohort definition, maternal birthyear, we found family Flynn effects in PIAT math scores of a slightly smaller magnitude than the usual individual Flynn effect magnitude found in previous studies. Children in families in which the mother was born later started off (at age 5) at about the same level as children in families in which the mother was born earlier, then increased in terms of math scores at a higher rate, before leveling off to about the same levels at ages 14. We also found family cohort effects of maternal birthyear in PIAT reading comprehension; however, these were in the opposite direction (sometimes referred to as reverse Flynn effects). Children in families in which the mother was born later started off (at age 5) at about the same level as children in families in which the mother was born earlier; however, these then had a slower development, and differed by more than the usual individual Flynn effect magnitude by the age of 14. With regard to the second cohort definition, first child birthyear, we found family Flynn effects of higher magnitudes compared to the Flynn effects for maternal birthyear. Children in families that had their first child later had higher PIAT math, reading recognition, and reading comprehension scores than children in families that had their first child earlier overall, but they also increased in terms of scores at a higher rate. The differences in increases started leveling off somewhat for older ages. The patterns were similar for math, reading recognition, and reading comprehension scores, and these family cohort effects were much larger than the usual individual Flynn effect magnitude. ## 4. Discussion The aim of our study was to investigate the presence of family-level Flynn effects, in levels and in growth. We used child PIAT raw scores and multilevel growth models to obtain estimates of family-level intelligence scores in the NLSY79 and NLSYC data. Previous studies found child Flynn effect patterns, particularly in the NLSYC PIAT math measures (Rodgers and Wänström 2007; Ang et al. 2010). Following this, O’Keefe and Rodgers [2017] identified between-family variance as the primary source of the child Flynn effect in the NLSYC PIAT math scores. Our design differs from those because we evaluated the Flynn effect longitudinally using raw scores, and studied the effects on both family levels and family slopes, using family cohort measures (rather than child cohort measures). It is not as straightforward to define family cohorts as individual cohorts. Our first definition, the mother’s birthyear, assumes that the family entity is influenced by the mother’s own cohort. Our other definition, the birthyear of the first child, assumes that the family entity starts when the first child is born. Our choices of family cohort measures fit our data well, because the NLSYC data contain information that help us to easily construct these measures. Other definitions are possible and may fit other data sources better. The rate of development may be different for different cohorts, but it may also be different for different age intervals, and/or time intervals, making longitudinal assessments of the Flynn effect important. Our use of raw scores enabled us to estimate both the levels and slopes of growth curves. We found significant variance both within individuals (individual child scores increase as the children get older, due to an ageing/growth effect), between individuals within families (differences in scores of children in the same family), and between families (differences in scores of children of different families). Our design enabled us to find both Flynn effects in the average scores of families of different cohorts (levels) and differences in family growth (slopes). Mothers born later, on average, had children who increased more in terms of their math scores between the ages of 7 and 13, with children of mothers born earlier then catching up at later ages. Children of mothers born later, however, increased in terms of reading comprehension scores at a slightly lower rate compared to children of mothers born earlier. Mothers who started their families later had steeper increases in all three subtests, although the difference in increases leveled off at older ages. There is no consensus in the research community as to the causes of the Flynn effect. Most researchers consider multiple causes (see, for example, Pietschnig and Voracek 2015, among others). Flynn effects on growth, as found in our study, have implications for the search for explanations of the Flynn effect. Explanations should explain differences in levels, but also in growth. For example, parents with higher education levels may be able to continue to help their children with schoolwork as they grow older. Improved child education, or equal opportunity programs, could be other explanatory factors. Moreover, family Flynn effects may have different interpretations compared to individual Flynn effects. The presence of individual Flynn effects suggests that the individual is somehow affected by the time in which he/she is born and grows up, e.g., that time-linked improvements in nutrition, education, etc., are beneficial to individual cognition. Family Flynn effects, on the other hand, suggest that the overall family changes in a time-related context (because of the family providing better nutrition to the children, Lynn 2009; or the parents having better education, Cuartas 2022, etc.), and that this context is important to the cognition of the family members. Our results differ between family cohort definitions. Explanations of our maternal birthyear effect should be sought in factors that differ between mothers of different birth cohorts, e.g., education and income, nutrition, or maternal Flynn effects themselves. In other words, the usual theories used to explain individual-level Flynn effect findings from previous studies are relevant. Overall, our observed family Flynn effects were consistently larger for the first child birthyear cohort definition than for the maternal birthyear definition, suggesting that the family Flynn effect leverages off of both the mother and the children in the family. The timing of parents’ entry into parenthood appears to be more important for these family outcomes than parental cohort. Adding first child birthyear to the growth curve models also explained substantive parts of the between-family variance in PIAT scores. We found that mothers who had their first child later had children who increased faster in terms of their scores, although the difference between cohorts started to level off with older ages. The usual Flynn effect of 3-IQ points increase per decade corresponds to a $20\%$ SD increase. Our observed first child birthyear effect was around a $50\%$ SD increase per decade for 9-year-olds, and this became larger as children grew older (however, it leveled off around age 14). Explanations of this family Flynn effect should be sought in family-level factors/events that differ between time points after the first child is born, e.g., better education or parental caregiving systems for the children in families who start later, higher incomes, higher standards of living, etc. Ang et al. [ 2010] examined sub-group Flynn effects for the NLSYC PIAT math test, and found that children in higher-income families, and children with higher-educated mothers, had stronger Flynn effects. Shakeel and Peterson [2022] found larger Flynn effects for higher SES groups among older students; however, they found smaller effects for higher SES groups among younger students (which they note may have been due to equal opportunity programs). The relationship between SES and cognitive ability is well-known (e.g., McCulloch and Joshi 2001; Nash 2001; Neiss et al. 2002; Paterson 2021; Rodgers et al. 2008), and an obvious explanation of our results may therefore be that higher-IQ mothers (and higher SES families) tend to start their families later. Our family Flynn effects decreased somewhat, but still persisted after controlling for maternal IQ, suggesting that this is not the sole explanation, however. We feel that one of the under-appreciated explanations for the Flynn effect is quality of parenting, and this interpretation may help explain some of our findings regarding the increasing differences for children of different family cohorts, which start to level off at older ages. It seems virtually axiomatic that the effect of parenting matters the most for young children, and then reduces as children get older. O’Keefe and Rodgers [2022] evaluated secular changes in the quality of the home environment in the NLSY-Children data, and found results consistent with this interpretation. For children from infancy through age 9, the quality of the home environment had been improving between 1985 and 2010. However, as these children aged into adolescence, this secular change weakened and disappeared. We stress, however, that our purpose in the current study was foremost to test for family Flynn effects in our data, and not to search for possible causes of the effects. We call for future research to search for nuanced family-level explanations/causes (as detection necessarily preceeds the search for explanations). Future research may estimate family growth models and add factors at different levels (factors that change over time, factors that differ between individuals, and factors that differ between families). Several of the factors mentioned in the previous paragraph are available in the NLSY79 and the NLSYC data. As noted previously, our observed family Flynn effects were smaller in magnitude for maternal birthyear than for the first child birthyear cohort definition. They were also inconsistent between subtests for the maternal birthyear definition. We found family Flynn effects in PIAT math of about a $15.2\%$ SD increase for children around 9 years old. Although smaller than our other cohort definition, this magnitude is similar to those in Trahan et al. ’s [2014] meta-analysis, with an overall Flynn effect of 2.31 (=$15.4\%$ SD) when including all available Flynn effect studies, and an effect of 2.93 (=$19.5\%$ SD) when including recent studies using Wechsler or Binet tests. Our results show that mothers born later, on average, had children who showed greater increases in their PIAT math scores between the ages of 7 and 13, with children of mothers born earlier then catching up at later ages, suggesting that this PIAT math family Flynn effect in these data primarily occurred in young and middle childhood. This goes along with the results of Shakeel and Peterson [2022], who found larger child math Flynn effects at younger ages (around age 9) compared to early adolescence (around 13–15) and older ages (around 17). We found no Flynn effect for PIAT reading recognition, and a negative (reverse) Flynn effect for PIAT reading comprehension. The magnitude of this negative Flynn effect was about $9.8\%$ SD for 9-year-olds, increasing as the children grew older. Differential effects for math and reading scores, as well as negative Flynn effects, have also been found previously. Shakeel and Peterson [2022] found Flynn effects (differing by the agency administering the test) varying from a $10\%$ SD decrease to a $27\%$ SD increase for math tests, and from a $2\%$ SD decrease to a $12\%$ SD increase for reading tests. Rodgers and Wänström [2007] found positive child Flynn effects for PIAT math scores and reading comprehension scores; however, the positive child Flynn effects for raw reading comprehension scores decreased to a mean value of 0 for child ages 0–13 when maternal IQ was controlled. Individual Flynn effects have usually been larger for fluid than for crystallized intelligence (Pietschnig and Voracek 2015), and stronger correlations have been found between fluid tests and math tests than between fluid tests and reading tests (Peng et al. 2019), which may account for some of the differences between our observed effects. Some items in the PIAT math subtest are more related to problem-solving than the items in the reading recognition and reading comprehension subtests. Ang [2008] performed an item analysis of the PIAT math test and found that items that were more related to problem solving contained more of a Flynn effect than others. These were items such as “Two birds were on a fence. Two more landed on the fence. How many birds were now on the fence?” ( note that this is not a real PIAT math item, but rather simply an example). In contrast, almost equivalent items such as “what is 2+2?” did not contain as much of a Flynn effect. More studies are, however, needed to examine whether our negative Flynn effect result is specific for the test (PIAT reading comprehension), for the family cohort definition (maternal birthyear as opposed to first child birthyear), or for the dataset (NLSYC). We have not investigated the presence of Flynn effects at the individual child level in our study. We know that they exist (Ang et al. 2010; O’Keefe and Rodgers 2017; Rodgers and Wänström 2007); however, adding a child cohort variable into a model with a family cohort variable changes the interpretation of the family cohort variable. For example, the effect of first child birthyear, net of the effect of maternal birthyear, is a version of the variable maternal age. These types of analyses can, however, be found in O’Keefe and Rodgers [2017], who separated cohort variables into within-child, between-child, and between-family measures, and related them to PIAT math scores. We also were not able to examine Flynn effects on maternal cognition in the NLSY data, because cognition was only measured at one time point for the NLSY79 sample, and the mothers were of varying ages (see Rodgers and Wänström 2007). Future studies, using other data sources, may be able to disentangle parental Flynn effects and family Flynn effects, although such complex longitudinal datasets may be difficult to identify. In summary, several contributions are made to the Flynn effect literature within the current study. We have focused on Flynn effects in the family (motivated by previous NLSY studies, in particular O’Keefe and Rodgers 2017). In addition, we examined Flynn effects on raw score levels and on growth. Future research could focus on replicating our results in other datasets, expanding to different age intervals, disentangling individual and family Flynn effects, and examining possible explanatory factors at different levels (within-person, between persons, and between families). ## References 1. Ang Siew Ching. The Flynn Effect Within Demographic Subgroups and Within Items: Moving from the General to the Specific. Doctor dissertation (2008) 2. Ang Siew Ching, Rodgers Joseph Lee, Wänström Linda. 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--- title: Impact of a Digital Atopic Dermatitis Educational Intervention on Hispanic Patients and Family Members authors: - Luis Fernando Andrade - MaryJo Bekhash - Siri Choragudi - Juan M. Gonzalez - Rodrigo Valdes - Gil Yosipovitch journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10057075 doi: 10.3390/jcm12062130 license: CC BY 4.0 --- # Impact of a Digital Atopic Dermatitis Educational Intervention on Hispanic Patients and Family Members ## Abstract With the increasing incidence of atopic dermatitis (AD) in the U.S., the highest prevalence of AD being found in Hispanic countries, and the rising Hispanic-American population, educational resources on eczema focused on Spanish-speaking populations are needed more than ever. As such, the primary goal of this project was to assess the beneficial impact of an educational intervention conducted through a virtual platform for Hispanic individuals living with atopic dermatitis. Utilizing WhatsApp, the study enrolled 55 participants diagnosed with AD and/or parents of children diagnosed with AD. Participants were enrolled in a seven-day educational module with daily topics on AD health knowledge. A health knowledge survey was administered before the educational program, upon completion of the program, and one month after completion. The program found a $14\%$ increase in AD health knowledge upon completion of the program ($p \leq 0.001$). Most importantly, there was no significant difference found between the health knowledge survey submitted at program completion and one month after completion, signaling that health knowledge taught through the course was successfully retained by participants ($$p \leq 0.29$$). Qualitative themes involving atopic disease were additionally explored through group discussions, including mental health and peer stigma. This study is the first of its kind in dermatology utilizing the WhatsApp format. The success of retained health knowledge regarding AD demonstrates that future virtual endeavors can be effective and accessible methods of patient education overall for populations that might not have ease of access to major medical centers. ## 1. Introduction According to the United States Census Bureau, the Hispanic population of the United States (US) has been steadily increasing each year. In 2017, people of Hispanic origin became the largest ethnic or racial minority in the US, making up $18.1\%$ of the country’s total population. By the year 2050, the Hispanic population is predicted to reach nearly 100 million people, making up $26\%$ of the US population [1]. The prevalence of atopic dermatitis in Hispanic adults living in the United States has been reported at $6.0\%$ [2]. There has been a variation of AD prevalence reported among countries in Latin America, with countries such as Argentina having an overall prevalence of $9.7\%$ and countries such as Brazil having an upwards of $20.1\%$ prevalence [3] In the US, Hispanic people have faced several obstacles when accessing healthcare. For Hispanics with limited proficiency in English, communication is often limited, which makes it difficult for them to obtain and maintain longitudinal care with a healthcare provider [4]. Barriers to healthcare access, such as communication and language proficiency, are one of many factors driving health disparities in diseases prevalent in the Hispanic community, one of which is atopic dermatitis (AD). Atopic dermatitis has a negative impact on the quality of life of patients and their families. The associated psychosocial morbidity of AD includes sleep disruption, depression, agitation, anxiety, altered eating habits, reduced self-esteem, and difficulty concentrating [5]. Patients with AD also report feeling embarrassed and stigmatized due to their skin lesions and have a more negative body image compared to healthy controls [6]. Of note, a previous study showed that mothers of children with AD had the same levels of stress as mothers of children suffering from other severe disabilities (e.g., profound deafness and insulin-dependent diabetes) [7]. Visits to medical settings for acute symptoms associated with atopic dermatitis are highest in the Hispanic population, specifically in the costly urgent care and emergency room settings [8]. Barriers to health access such as overall lower socioeconomic status, disparities in educational level, and denial of medication by insurance companies further exacerbate lower quality of life conditions in Hispanic individuals living with atopic dermatitis [8]. Of several factors identified with poor disease control of AD, Hispanic ethnicity was significant [9]. Similarly, Hispanic children presented with more chronic and recurrent symptoms associated with atopic dermatitis, even when factoring in socioeconomic disparities [10]. Hispanic children at all levels of management for atopic dermatitis were almost three times more likely than white children to visit a medical office due to symptoms of atopic dermatitis, even in groups categorized as having good management of disease control [11]. The previously mentioned social determinants of health, including Hispanic ethnicity and socioeconomic status, impact both patients and the health system negatively by increasing health utilization and worsening health outcomes in general. As a public health intervention, much success has been seen in the usage of educational modules for positive quality-of-life outcomes in AD. A prior study using an educational website regarding AD health knowledge showed a significant improvement in the quality of life for participants both statistically and clinically [12]. Interventions that are culturally competent, cost-effective, easily accessed, and available in the Spanish language can provide a solution to the traditional barriers to health access found in the Hispanic community. WhatsApp is a social media platform that emphasizes direct and group messaging. Compared to other applications such as Facebook and Instagram, WhatsApp remains the dominating messaging application in Hispanic culture, with surveys from Brazil revealing that over $93\%$ of all smartphone users had the application downloaded on their phones [13]. Polling studies based in the US confirm this perspective as a recent National Tracking Poll (#201048) from New Morning Consult revealed that out of 999 Hispanic adults polled, nearly half admitted to having a WhatsApp account. In contrast, only $24\%$ of adults overall mentioned usage of the app. This is a significant social and cultural phenomenon in the Hispanic community both internationally and in the U.S., where social media platforms such as Facebook and Instagram report the highest overall usage in the general population. The social media application is both free and easy to access, with certain public health interventions taking advantage of this fact to develop educational interventions on the virtual platform. A recent educational intervention focused on breast cancer knowledge implemented the usage of WhatsApp to conduct lessons and found data in the post-survey showing significant increases in health literacy regarding risks, protective factors, and clinical manifestations of breast cancer [14]. Similarly, another study on diabetes control found a significant drop in HbA1c levels for the test group with the WhatsApp intervention compared to the control group that did not receive educational modules through the application [15]. No similar study in the current literature has been conducted for WhatsApp educational interventions targeting patients suffering from dermatological conditions. A virtual platform for these interventions is a modern yet underutilized innovation that can overcome significant barriers to resource access such as cost, convenience, cultural understanding, and translation to the Spanish language. As such, this study developed a 1-week long WhatsApp-based educational intervention in Spanish focused on atopic dermatitis as a much-needed innovation for both the local Hispanic community and long-term reproducibility in Latin American countries with a high prevalence of atopic dermatitis. ## 2. Materials and Methods Participants that identified as adults (aged 18 or older), living with a medical diagnosis of AD, and/or parents of children living with AD were recruited through a social media advertisement for the educational study, with additional inclusion criteria including access to a cellular device for the duration of the educational study, being Spanish speakers, and familiarity with the WhatsApp (Menlo Park, CA, USA) platform. Exclusion criteria included adults unable to consent, individuals under the age of 18, individuals unable to speak Spanish, individuals with severe hearing or visual impairment, and individuals unfamiliar with how to utilize the WhatsApp application. The main social media platform utilized for recruitment included virtual Facebook groups focused on patients living with atopic dermatitis sharing their lived experiences; IRB-approved text and flyer advertisements were posted in these virtual groups. A 25 USD gift card (Seattle, WA, USA) was offered as compensation for the completion of the program. Participants that expressed interest by commenting on the public post were contacted by the study team to obtain consent and enroll in the study. Enrolled participants completed a pre-survey on Qualtrics consisting of 37 true or false statements pertinent to atopic dermatitis health knowledge (Supplementary Materials). This questionnaire was developed by expert dermatologists in the field of atopic dermatitis and questions were made to evenly reflect and cover the material taught in each module. Questions were simplified to be able to be understood by participants at a middle school reading level. This same survey was administered to participants after the completion of the program and 1-month post-intervention completion. The educational intervention consisted of a weeklong module on a WhatsApp group chat led by a study moderator with daily educational modules consisting of educational text, audio messages, visual aids, and a summary video at the end of each day recapping the key takeaways from the daily module. Similar to the questionnaire, the educational module was created by experts in the field of atopic dermatitis and simplified to be accessible at a middle-school reading level. All documents were translated and back-translated by native Spanish speakers to provide accuracy in the translated format. New educational content was published each day in the group chat at a spread pace between 5:00 PM–10:00 PM EST. Discussion and questions were encouraged and moderated by the facilitator both during and outside of these hours. The study moderator was a research fellow on the study team with 3 years of experience in the clinical and research field of atopic dermatitis. Three separate educational groups were implemented to see if the group size has an impact on participation and outcome. Group 1 had 28 participants, Group 2 had 17 participants, and Group 3 had 10 participants join the study. This content was developed by experts in the field of atopic dermatitis. Participants were encouraged to ask questions and discuss among participants. Participants were prompted to reply to specific moderator questions throughout the daily lessons to track participation. Modules included an introduction to AD, up-to-date knowledge on the cause of AD, common triggers of AD, pharmacological and non-pharmacological treatments for AD, dietary considerations, and stress reduction/psychological impacts of AD. Descriptive statistics of the surveys were summarized using means and standard deviations for continuous variables and proportions for categorical variables. The differences between the mean pre-survey, post-survey, and 1-month post-survey were determined by utilizing a one-way ANOVA. Two sample t-testing was performed to evaluate the difference in means between pre-survey vs. post-survey, and post-survey vs. 1-month post-survey. Mean difference and $95\%$ confidence intervals were reported, and statistical analyses were conducted using SPSS version 27 (IBM, Chicago, IL, USA). The standard deviation of the response to individual survey questions was also examined to determine questions participants more frequently scored incorrectly. Descriptive qualitative methodology was implemented by using standardized questions meant for individual responses by study participants to promote discussion. Qualitative statistics of the intervention were further collected using thematic analysis. Once the WhatsApp modules were finalized, transcripts were read from beginning to end, coded with themes, and evaluated for common summary points. ## 3. Results Descriptive statistics are summarized in Table 1. Most participants in group 1 belonged to the 18–29 age category and the 30–39 age category. Most of the participants in the groups were females, had university-level education qualifications, and had a household income of less than 40,000 USD per year. A one-way ANOVA was performed to compare differences between the three groups on pre-survey and 1-month post-survey scoring. The analysis revealed there was no significant difference in health knowledge scoring between groups for both the initial survey (F[2, 52] = [2.932], $$p \leq 0.062$$) and the final survey (F[2, 31] = [0.022], $$p \leq 0.978$$). A one-way ANOVA was performed to compare the effect of the educational intervention on pre-survey, post-survey, and 1-month post-survey scoring. The analysis revealed there was a significant difference in health knowledge scoring between groups (F[2, 133] = [39.52], $p \leq 0.001$). A two-sample t-test was performed and revealed a significant increase ($p \leq 0.001$) in health knowledge between pre-survey ($M = 26.51$, SD = 3.27) and post-survey ($M = 31.57$, SD = 3.22). A two-sample t-test was performed and revealed no significant difference ($$p \leq 0.29$$) in health knowledge between post-survey ($M = 31.57$, SD = 3.22) and 1-month post-survey ($M = 30.46$, SD = 2.99). The percent increase in health knowledge from pre-intervention to post-intervention was calculated at $14\%$. The percent decrease in health knowledge from post-intervention to 1-month post-intervention was $3.2\%$. The average health knowledge scores obtained by participants on the pre-survey, post-survey, and post-one-month survey are summarized in Table 2 and Table 3. Standard deviations of individual survey questions were also examined to determine which questions were most frequently incorrectly answered to determine topics for future clinical education. The questions with the largest disparity in answer choices (SD = 0.45–0.50) were “Bleach and oatmeal baths are not helpful in treating eczema”, “Topical antihistamines work great for itch in atopic dermatitis”, “When applying the treatments on your skin, the back and front of your trunk require 6–7 fingertip units each of corticosteroid for eczema attacks”, “Children should eliminate foods considered ‘common allergens’ (eggs, wheat, soy) from their diets”, “Meditation and yoga are not helpful for symptoms of atopic dermatitis”, and “Wet wrap therapy can be used for periods longer than 2 weeks at a time”. When examining socioeconomic factors, reported yearly income demonstrated a significant difference ($$p \leq 0.05$$) in the baseline knowledge score. Individuals reporting a yearly income of less than 40,000 USD, 40,001–60,000 USD, 60,001–100,000 USD and >100,000 USD had an average pre-survey knowledge percent correct of $71.7\%$, $70.5\%$, $83.8\%$, and $89.2\%$. respectively. There was a significant difference ($$p \leq 0.001$$) in the percent health knowledge increase from pre-survey to one-month post-survey across education level reporting. Middle school education, high school education, university education, and post-graduate education had a percent health knowledge increase of $17.6\%$, $14.14\%$, $10.76\%$, and $7.6\%$, respectively. Qualitative data about the quality of living with atopic dermatitis were also collected through the module discussions. Topics and questions that garnished the most participation and discussion amongst the groups were coded by the facilitator to determine common themes. Common themes explored include symptoms of depression and reduced quality of mental health that were frequently reported by participants throughout the module. For parents of children living with eczema, bullying was a common theme throughout the schooling of children. Adults living with AD mentioned discrimination from co-workers, friends, and family in the form of ostracization through several assumptions about their disease status, including the misconception that the eczema flares were possibly contagious. Participants even reported stressful interactions with medical providers themselves, mentioning that often clinicians not well versed in the atopic dermatitis realm, underestimate the impact of pruritus on quality of life, and assume that the patients themselves are “exaggerating” or intentionally worsening their exacerbation through itching an otherwise mild exacerbation. Most participants in the intervention answered in the affirmative when inquired by the moderator about whether the material presented in each module was new to them. This revealed an additional common theme found throughout the educational module, which was the reported lack of exposure to educational AD health knowledge by prior health professionals, in particular for information regarding non-pharmacological treatment. During the educational lesson on non-pharmacological treatments, $70\%$ and $90\%$ of participant respondents mentioned they had never heard of wet-to-dry dressing therapy and bleach baths as treatment options for AD flares, respectively. A shared theme through both the anecdotes from parents of children with atopic disease and adults living with AD was a common misconception from peers that their disease has the potential to be contagious. The following is both exacerbated and potentially caused by the peer stigma surrounding the atopic rash: episodes of depression, anxiety, and low self-esteem. Participants reported incidents where they would selectively isolate themselves from social gatherings during episodes of exacerbations for fear of judgment from friends, families, and peers. Anecdotes of ostracization were reported both in school settings for children living with atopic dermatitis and the lack of employment opportunities in certain fields for adults living with the condition that was prone to frequent and visually evident exacerbations. ## 4. Discussion To our knowledge, this is the first study in the field of dermatology utilizing the WhatsApp virtual platform to administer an educational intervention. The program found a $14\%$ increase in AD health knowledge upon completion of the program ($p \leq 0.001$). Most importantly, there was no significant difference found between the health knowledge survey submitted at program completion and one month after completion, signaling that health knowledge taught through the course was successfully retained by participants ($$p \leq 0.29$$). As a whole, the results from our study demonstrate that the virtual WhatsApp platform can be an effective learning tool for patient education in addition to the standard of care offered to patients. In addition to the increase in health knowledge, it is important to also recognize that the material learned through this $14\%$ increase in health knowledge constitutes information that has the potential to cause a significant beneficial impact on the lives of people suffering from AD and their families. A theme of lack of knowledge regarding non-pharmacologic interventions was identified. While both bleach bath and wet-to-dry techniques are evidence-based practices that have been demonstrated to increase the quality of life and reduce itch exacerbation in patients living with atopic disease, our qualitative results showed that this was new material for a large proportion of participants. Not only were patients interested in learning more about at-home management of atopic dermatitis, but a portion of participants started actively implementing elements taught through the modules over the course of the week and providing feedback to their peers. The lack of familiarity with issues that otherwise might seem common knowledge by experts shows that there might be a lack of communication and patient education beyond medical management in the non-pharmacological treatment of eczema, or possibly even a lack of specialized education regarding atopic dermatitis and its treatment by health care providers not frequently exposed to the disease. Several participants who have their AD managed mainly by primary care providers (PCPs) commented that they, at times, feel the lack of clinician education and exposure to the more severe cases of AD can be impacting their overall outcomes. In addition to receiving the educational material, participants were encouraged to discuss in the group setting and actively utilized the group chat setting to discuss how each of the modules was pertinent to life with eczema, paralleling a support group for people to confide in others living with similar experiences. Through group discussions, participants confided in each other with a common feeling of isolation in having atopic disease, and feeling, at best, a lack of empathy and understanding from members of their community regarding life with AD, and at worst, ostracization and discrimination because of their disease. One of the positive takeaways from these discussions on themes of depression and social isolation was that through the group format of WhatsApp, the participants found comfort in having the ability to discuss, support, and confide in other individuals that have lived through shared realities, something that a majority of participants have reported not having access to in their daily life. Regarding WhatsApp functionality, patients commented that they had little to no difficulty accessing the educational modules through group chats, as WhatsApp is a common platform already utilized in South American countries and through Hispanic Americans living in the United States. Prior studies have shown that WhatsApp’s ease of access allows the utilization of the virtual platform for a large portion of the Spanish-speaking populace, especially elderly populations that might have difficulty utilizing newer applications such as Zoom [16]. Interestingly, the majority of study participants ($69\%$) were in the 18–40 range, hinting that WhatsApp can be a preferential method of communication for Hispanic young adults as well compared to other virtual applications. In addition to the widespread use of WhatsApp in the Hispanic population, the benefits of this application include its speed and multimedia sharing capabilities including video, images, and audio in groups while encouraging collaborative discussion [17]. The asynchronous nature of a WhatsApp group setting also allows individuals working throughout the day to be able to review the course work at their own pace and be able to recap prior modules, videos, and audio clips through the streamlined chat settings. While specialized patient education by experts in dermatitis at dedicated centers would be the gold standard of AD education, the availability, location of these centers, and language barriers can make this a difficult prospect for a lot of individuals, particularly Spanish speakers living in rural settings. With the advent of COVID-19, the pandemic brought to the forefront the realization that telehealth-based settings can be an effective method of health utilization both for individuals otherwise not able to physically access it and for ease of access to participants that would have otherwise had the means to attend in-person appointments. In addition to reaching a broader audience, a virtual intervention has the benefit of technically unlimited participant capacity that might otherwise be limited in physical settings. Additionally, as discussed earlier with the themes of peer ostracization for atopic disease, being able to discuss it in a private, virtual setting with peers might improve participation versus an in-person educational setting. While all individuals were identified as Hispanic through the inclusion portion of the consent and enrollment process, only $83.6\%$ of all participants selected ‘Hispanic’ in the social demographics of the survey, with $16\%$ opting to select ‘White’ and/or ‘Other’. As the term Hispanic can encompass a wide variety of ethnic cultures and discussions on race and identity continue to evolve in the U.S., further studies regarding Hispanic acculturation or assimilation into the host culture, and its impact on atopic burden should be explored. For discussion purposes, our study has focused on the definition of Hispanic as both individuals with Spanish-speaking capacity and through the adopted National Institutes of Health definition as ‘A person descended from Spanish-speaking populations. People who identify their origin as Hispanic, Latino, or Spanish may be of any race.’ This distinction is important to highlight, as along with the previously mentioned differences in acculturation, ethnicity and language are not necessarily mutually exclusive, as shown by new generations of Hispanic-identifying Americans with less exposure to the Spanish language compared to Hispanic individuals living in predominately Spanish-speaking countries. This is a sociodemographic factor to consider for future public health studies. One of the findings we discovered throughout the intervention was that the majority of respondents that expressed interest in the study (and eventually participated) were women. While statistical analysis revealed no significant difference between the pre-survey and post-survey health knowledge scores based on gender, the low power and skew towards female participants in this study may underestimate possible differences in educational intervention effectiveness across gender, as prior studies in the realm of atopic dermatitis have shown data supporting differences in AD treatment adherence among men and women [18]. ## 5. Conclusions Overall, this study demonstrated that the utilization of virtual interventions with elements embedded in a study population’s culture (e.g., common usage of WhatsApp in the Hispanic population) can be an effective public health intervention, particularly in the field of eczema education. Future studies can explore virtual interventions in other realms of dermatology, including other inflammatory skin diseases. Clinicians can utilize topics with the largest number of incorrect answers on the post-surveys, such as non-pharmacological interventions and technical application of topicals, to focus additional time on patient education. Furthermore, using this virtual intervention, we can extend beyond examining the knowledge base to assess the impact of educational interventions on quality-of-life measures, such as sleep, mood, and stress. ## References 1. Hispanic Heritage Month 2018. (2018.0) 2. Silverberg J.I., Hanifin J.M.. Adult eczema prevalence and associations with asthma and other health and demographic factors: A US population-based study. J. Allergy Clin. Immunol. (2013.0) 132 1132-1138. DOI: 10.1016/j.jaci.2013.08.031 3. Borzutzky A., Larco J.I., Luna P.C., McElwee E.M., Pires M.C., Restrepo M.R., Saez-De-Ocariz M., Sanchez J.. Atopic Dermatitis in Latin America: A Roadmap to Address Data Collection, Knowledge Gaps, and Challenges. Dermatitis (2022.0) 33 S83-S91. 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--- title: Impact Assessment of vB_KpnP_K1-ULIP33 Bacteriophage on the Human Gut Microbiota Using a Dynamic In Vitro Model authors: - Fanny Laforêt - Céline Antoine - Sarah Lebrun - Irma Gonza - Elizabeth Goya-Jorge - Caroline Douny - Jean-Noël Duprez - Marie-Louise Scippo - Bernard Taminiau - Georges Daube - Abdoulaye Fall - Damien Thiry - Véronique Delcenserie journal: Viruses year: 2023 pmcid: PMC10057081 doi: 10.3390/v15030719 license: CC BY 4.0 --- # Impact Assessment of vB_KpnP_K1-ULIP33 Bacteriophage on the Human Gut Microbiota Using a Dynamic In Vitro Model ## Abstract New control methods are needed to counter antimicrobial resistances and the use of bacteriophages as an alternative treatment seems promising. To that end, the effect of the phage vB_KpnP_K1-ULIP33, whose host is the hypervirulent Klebsiella pneumoniae SA12 (ST23 and capsular type K1), was assessed on intestinal microbiota, using an in vitro model: the SHIME® system (Simulator of the Human Intestinal Microbial Ecosystem). After stabilization of the system, the phage was inoculated for 7 days and its persistence in the different colons was studied until its disappearance from the system. The concentration of short chain fatty acids in the colons showed good colonization of the bioreactors by the microbiota and no significant effect related to the phage treatment. Diversity (α and β), the relative abundance of bacteria, and qPCR analysis targeting different genera of interest showed no significant variation following phage administration. Even if further in vitro studies are needed to assess the efficacy of this phage against its bacterial host within the human intestinal ecosystem, the phage ULIP33 exerted no significant change on the global colonic microbiota. ## 1. Introduction Even if antibiotic resistance is a natural adaptation of the bacterium, their overuse accelerated the process, thus new therapies are needed. In this context, phage therapy seems promising [1,2]. In the case of per os treatments with bacteriophages, these are directly exposed to the intestinal microbiota. Thence, even if lytic phages are well-known to be specific to their host and to be part of the intestinal microbiota [3], the use of phages targeting enterobacteria, a commensal bacterial family of the human gastro-intestinal tract [4], should be performed with caution. Currently, only a few works have studied the impact of bacteriophages on the intestinal microbiota. When one of them do not exert any impact [5], others, on gnotobiotic mice, bring out community changes in the microbiota, not only on target bacteria but also on others present in the microbiota [6,7,8]. This can be an issue if the new targeted bacterium is a beneficial bacterium for host health and if these changes disrupt the normobiosis of the gut microbiota. To assess the efficacy of new phages, and their effect on human intestinal microbiota, several in vitro, in vivo, or ex vivo models exist. In vivo and ex vivo models (using humans, animals or organs biopsy) are limited mainly because of ethical restrictions. Regarding in vitro colon fermentation models, 2 types of systems exist: the static and the dynamic models. The first, also called batch fermentation models, such as CoMiniGut [9], are mainly used for preliminary investigations, to simulate the fermentations usually observed in the distal part of the colon for a short period of time and using a consensus protocol (InfoGest) [10,11,12]. In contrast, dynamic models seek to mimic continuous fermentations by a constant addition of nutrients to the system [12]. From mini fermentation models such as Mini Bio with 32 bioreactors [13,14] or MiPro using 96-deep well plates [15] to bigger models similar to the mono-compartmental colonic ARCOL (Artificial Colon) [16,17] or the 4 fermenters TIM-2 (TNO intestinal model 2) [16,17,18] or the upper colonic PolyFermS [19], other more complex models exist. These complex models simulate the entire digestive system including the digestion and the fermentation processes such as the SIMulator of the Gastro-Intestinal tract (SIMGI) (often used for its in vitro stomach properties) [20] or the SHIME® (Simulator of Human Intestinal Microbial Ecosystem). The SHIME® model is a computer-controlled system that allows the simulation of the stomach, ileum, ascending colon, transverse colon, and descending colon [21]. Mucin beads can be added to the system (M-SHIME®) to maintain the mucin-associated ecosystem. Sometimes, dialysis membranes are also added to simulate the passive absorption of the metabolites. This model is, therefore, one of the most complete models to study the microbiota and is validated to study the impact of various treatments on the microbiota [11,22]. Microbiota profiling, α and β-diversities, semi-quantification of specific genera using qPCR, phage titrations, and short-chain fatty acid production can be deeply analyzed from SHIME® samples. Microbiota profiling (relative abundance of the different bacteria) and diversity parameters are obtained using 16S RNA gene sequencing. These are well-known tools to study the microbiota of different bacterial ecosystems such as food samples [23], skin [24], or intestinal microbiomes [25]. Semi-quantitative methods such as qPCR are used to follow specific genera involved, for example, in gut health [26,27,28,29,30,31] or diseases [30,31,32]. Short-chain fatty acids (SCFA) such as acetate, propionate, and butyrate are the main end products of the degradation of polysaccharides by the bacteria of the microbiota through fermentation and play important roles on hosts’ health [33,34]; hormones regulation (for example leptin or insulin), anti-inflammatory properties (through the inhibition of the histone deacetylase), antimicrobial peptides or intestinal mucus productions, energy source for the colonocytes or tight junction activation. The SHIME® model has previously been used to study phage therapy in intestinal conditions. Verthé et al., studied the persistence and impact of a phage targeting a strain of Klebsiella aerogenes (previously named Enterobacter aerogenes) after a single injection into the model [35]. A second study assessed the interaction of a phage cocktail with Salmonella Typhimurium and the microbiota [36]. Then, Federici et al. [ 37] studied the activity of 2 phages against Klebsiella pneumoniae after their passage through the proximal or distal colonic microbiota. The main aim of this work was to assess the impact and the persistence of the phage vB_KpnP_K1-ULIP33 in the gut microbiota after daily injections for a period of 7 days, using the SHIME® model. This phage was isolated from sewage water using a hypervirulent K. pneumoniae ST23 (SA12, SB4385) [38] and showed the capacity to increase the survival of Galleria mellonella larvae infected with this capsular type K1 strain [38]. Potential changes in the microbiota biocenosis due to the phage treatment were investigated using microbiota profiling, phage titrations, SCFA analysis and semi-quantification of specific genera by qPCR. In addition, the secondary goal of this study was to assess the repeatability of the model in the case of phage addition experiment using a technical triplicate. ## 2.1. Set-Up of the SHIME® Model (Simulator of Human Intestinal Microbiota Ecosystem) The SHIME® system was set up to mimic an adult gastrointestinal tract (Figure 1) and simulate the different conditions found in the stomach/duodenum (shortened as stomach), jejunum/ileum (shortened as ileum), and the 3 colons (ascending (AC), transverse (TC) and descending (DC)) as described previously [21,22]. The experiment was performed in triplicate. In brief, 5 bioreactors, each representing a part of the digestive tract, were airtightly sealed, continuously stirred using magnetic stirrers, maintained at 37 °C, connected to each other, and maintained in anaerobic conditions using a daily flow of nitrogen. The stomach bioreactor was connected to two different media. The first, called feed (Prodigest, Ghent, Belgium; ref PD-NM001B), was discharged into the system 3 times a day to mimic food intake. The second, a synthetic pancreatic juice, called PJ, a mix of NaHCO3-pancreatin-bile salts (Prodigest, Ghent, Belgium), was discharged into the system 3 times a day to mimic digestion. The colon bioreactors were inoculated with human feces and the pH of each colon was maintained in a specific range, close to in vivo conditions: AC: 5.6–5.9, TC: 6.15–6.4, and DC: 6.6–6.9. ## 2.2. Feces Collection A fresh inoculum was obtained from a healthy 34-year-old volunteer donor (female) with no history of antibiotic use for at least 6 months. This collection of feces and its use in the model was approved by the ethical committee of the University of Liège (ULiège, Liège, Belgium; file number $\frac{2022}{274}$). Directly after the sampling, the feces were stored in anaerobic jars and sent to the laboratory for immediate processing. The inoculum was diluted to $20\%$ (w/v) in phosphate buffer and then homogenized in a stomacher (VWR, Leicestershire, UK) for 10 min. The fecal suspension was then macro-filtered at 250 μm (VWR, Leicestershire, UK). The supernatant was collected and placed in glycerol ($15\%$ (v/v)) to be frozen (25 mL, 40 mL, and 30 mL of supernatant were necessary for the inoculation of the AC, TC, and DC, respectively). On the inoculation day, the defrosted fecal suspension was inoculated in the different colon bioreactors already filled with automatic pH adjusted culture media. Following inoculation, the systems were maintained for a period of 23 days to allow the stabilization of the introduced fecal community. ## 2.3. Bacteriophage Characteristics and Inoculation The bacteriophage studied in this experiment was vB_KpnP_K1-ULIP33 [38]. This phage was isolated in sewage from Rueil-Malmaison (France) using the bacterial strain Klebsiella pneumoniae SA12 (SB4385), a capsular type K1 ST23 bacterium. The phage was amplified using a classical amplification process: overnight contact of the phage with its host bacteria (SB4385) and then filtration of the solution using 0.22 µm microfilters. This amplified lysate was semi-purified with a $30\%$ sucrose cushion using an ultracentrifugation cycle (50,000× g for 4 h). The obtained pellet was then washed through another ultracentrifugation cycle (4 h at 50,000× g) in phosphate-buffered solution (PBS). Next, the phage was diluted to the desired concentration using PBS. After inoculation of the human feces in the ascending, transverse, and descending colons, 23 days of microbiota stabilization were necessary (Figure 2). Next, 10 mL of the phage were inoculated at 109 (PFU/mL) once a day, for 7 days (from day 1 till day 7), in the ascending colon, just before the transfer of the feed media in the AC. The persistence of the phage was then studied until its disappearance (until day 24 for repetition 1 and day 21 for repetition 2 and 3). To that end, titrations, on SA12 bacterial overlays, were carried out: on day 1 (4 h after the first injection), at days 2, 4, and 6 during the injection week, at day 8, and from day 11 until the end of the experiment. Based on Verthé et al. [ 35], a mathematical model was applied to compare the theoretical elimination of the phage by the transit when considered as an inert molecule. The theoretical persistence (concentration in PFU/mL) of the phage (meal after meal of 200 mL) in the different bioreactors was mathematically translated as follows:[AC]=[AC′] ×500−[AC′]×200500 [TC]=[TC′] ×800+[AC′]×200−[TC′]×200800 [DC]=[DC′]×600+[TC′]×200−[DC′]×200 600 With [AC], [TC], and [DC] being the concentrations (in PFU/mL) during the meal for AC, TC or DC colons; [AC′], [TC′], and [DC′] being the concentrations (in PFU/mL) during the previous meal; and 500, 600 and, 800 being the contents (in mL) in the bioreactors for AC, TC, and DC colon, respectively. The volume of 200 mL is the volume transferred from one bioreactor to another (140 mL of feed and 60 mL of PJ). An additional mathematical model was formulated to calculate the expected concentration of the phage in the total volume of 1900 mL at the end of the 7 days of phage inoculation. This model considered the dilutions of the phage due to the 3 meals, the loss toward the waste material, and the gain through the injections:PI=[LM]×1900+1010 Loss=200×PI1900 [AM]=PI−Loss1900 With “PI” being the PFU post-injection in the bioreactors; [LM] the concentration after the last meal; “Loss” being the PFU lost toward the waste material; and [AM] the final concentration after the meal. ## 2.4. Short-Chain Fatty Acid Analysis Samples from the colon bioreactors of the SHIME® were collected 3 times per week and were analyzed for their SCFA content. The studied short-chain fatty acids (SCFA) were acetic, propionic, and butyric acid. Solid phase micro-extraction (SPME) fiber (Thermo Scientific, Merelbeke, Belgium) was used to extract the components and the Focus CG gas chromatograph (GC) (Thermo Scientific, Merelbeke, Belgium) with a Supelcowax-10 column (Thermo Scientific, Merelbeke, Belgium) was used to separate them. Finally, the ion trap PolarisQ mass spectrometer (MS) (Thermo Scientific, Merelbeke, Belgium) allowed their analysis [39]. To prepare the samples, 25 µL of SHIME® sample, 40 µL of 2-methylvaleric acid (0.2 mg/mL) as internal standard, 15 µL of H2SO4 at 0.9 M and 920 µL of water were pipetted together into a 20 mL glass vial. The mixture was vortexed and then placed in the SPME-CG-MS for analysis. The lower limits of quantification (LLOQ) were 2, 1, and 1 mM for acetate, propionate, and butyrate, respectively, and the upper limits of quantification (ULOQ) were 120, 55, and 40 mM. ## 2.5. 16S rRNA Gene Sequencing Two mL of samples collected from the colon bioreactors were pelleted and analyzed for their microbiological content. The endpoints chosen for the microbiota profiling were day 1 as control, day 8, day 15, and day 24 or 21 (last day of the experiment). The DNA was extracted using the PSP spin Stool DNA Basic kit associated with the Stool DNA Stabilizer (Invitek Molecular, Berlin, Germany). After purification, the quality and the quantity of DNA were measured using the Nanodrop 2000 (Thermo Scientific, Merelbeke, Belgium). The V1–V3 hyper variable region of the 16S rDNA bacterial gene were amplified using the E9–E29 and E514-530 primers [40] and amplification products (amplicons) purified with a Wizard SV PCR purification kit (Promega, Alken, Belgium). After this step, the amplicons were submitted to a second PCR for indexing, using the Nextera XT index kit with dual 8-base indices (Illumina, San Diego, CA, USA). After cleaning with AMPure XP beads (Beckman Coulter, Indianapolis, IN, USA), the librairies were quantified, pooled and sequenced by 2 × 300 bp paired end sequencing on the MiSeq platform using MiSeq v3 Reagent Kit (Illumina, San Diego, CA, USA). Using Mothur package v 1.47, the sequences were trimmed, filtered, and cleaned for chimeras (https://www.mothur.org, accessed on 30 June 2021). The final reads were clustered into operational taxonomic units (OTUs) at a 0.01 distance unit cutoff. Using BLASTN algorithm, a representative sequence for each OUT was compared to SILVA dataset (SILVA v138) [41]. Each OTU was analyzed as a proportion of reads to deduce the relative abundance. ## 2.6. qPCR of Selected Taxa The qPCR were performed using the same DNA extracts as those used for 16S rRNA gene sequencing. The assays were performed in 96 well plates (Nippon Genetics Europe, Düren, Germany) in technical duplicates of the triplicate SHIME samples ($$n = 6$$). The wells were filled with 10 µL of Takyon™ No ROX SYBR MasterMix (Eurogentec S.A., Seraing, Belgium), reverse and forward primers of each selected sequence (from 0.3–0.5 µM) (Eurogentec S.A., Seraing, Belgium), 2.5 µL of the DNA sample (at 4 ng/µL) and diluted in molecular biology grade water to reach 20 µL per well. In all assays, a negative control was included with molecular biology grade water instead of the DNA product. The qPCR protocol included an initial denaturation step at 95 °C for 5 min, followed by 35 cycles of: denaturation at 95 °C for 15 s, annealing at optimal primer temperature for each targeting species (Table S1) for 15 s, elongation at 72 °C for 30 s followed by a final elongation step at 72 °C for 5 min [42,43]. An analysis of the melt curve (ranging from 65 °C to 95 °C) was made to evaluate the specificity of the amplified products. The 2−ΔΔCt method [44] was used to calculate the relative changes in the target populations normalized to total bacteria population after phage injections. Eleven taxa were followed by qPCR: Akkermansia muciniphila [45], Bifidobacterium [46], Faecalibacterium prausnitzii [47] and Oscillospira [48] as gut health biomarkers; Bacteroides/Prevotella [47], Phascolarctobacterium faecium [49] and Veillonella [50] as SCFA producer biomarkers (especially propionate production); Ruminococcus [51] and Mucispirillum schaedleri [52] as chronic inflammation biomarkers; and Escherichia/Shigella [47] as acute inflammation biomarker and Klebsiella pneumoniae complex [53] to test the specificity of ULIP33 phage. ## 2.7. Statistical Analysis Statistical analyses were performed using R with “vegan” and “Rcmdr” packages [54]. A significant threshold of 0.05 was applied for all the statistical tests and the Bonferroni correction was applied if needed. The graphical representations of the SCFA were performed using GraphPad Prism version 8.0.2 for Windows, GraphPad Software (San Diego, CA, USA). Regarding the metagenetic results, the β-diversity and the α-diversity were studied. The β-diversity (based on microbial diversity between the samples) was visualized using a “Non-metric Multidimensional Scaling” (NMDS). Then, the “Analysis of the MOlecular VAriance” (AMOVA) was calculated based on the Bray–Curtis dissimilarly matrix and the homogeneity of the groups were then tested with the “HOmogeneity of the MOlecular VAriance” (HOMOVA) and the multiple comparison with Tukey–Kramer method when needed. For α-diversity, the Shannon diversity index, the Piélou index, the Simpson index, and the chao1 estimator were studied [55,56]. SCFA concentrations, relative quantification by qPCR and α-diversity index were analyzed to highlight changes due to the phage addition in each colon bioreactor. The first step was to investigate the normality of the residues of each distribution using a histogram, a quantile-quantile plot (QQ-plot), a boxplot, a Shapiro–Wilk test, and the homoscedasticity of the distributions. If the residues were normally distributed, a repeated-measures ANOVA was performed with paired Student’s t-test. If not normally distributed, a Friedman test and a Pairwise Wilcoxon Rank Sum Test were performed. ## 3.1. Bacteriophage Titrations During the week of injections, the concentration of the phage remained stable in all the fermenters (Figure 3) with an observed average concentration of 6.6 × 105 PFU/mL in the total volume of the bioreactors (1.9 L). The theoretical expected average concentration during the week of injections was 9 × 106 PFU/mL with an expected final concentration of 1.2 × 107 PFU/mL on day 8. After the end of the 7 days of inoculation, the phage gradually disappeared from the system in each colon (Figure 3). It disappeared at a faster rate than the mathematical expectation. In the ascending colon (AC) (Figure 3a), the phages disappeared at day 13 and day 17 for, respectively, the first and the third repetition. In the second repetition, it disappeared at day 16, as expected through the mathematical model. In the transverse colon (TC) (Figure 3b), the phages disappeared at day 18 in the first and third repetitions and at day 19 for the second repetition. In contrast, the expected wash-out calculated by the mathematical model was at day 24. In the descending colon (DC) (Figure 3c), the phages disappeared at day 20 for repetition 3, at day 21 for repetition 2, at day 23 for repetition 1, and at day 25 for the expected wash-out obtained through the mathematical model. ## 3.2. Short-Chain Fatty Acid Profile In AC, the concentrations of the three mains different SCFA (acetate, propionate, and butyrate) were stable over time (Figure 4a). The means of the concentrations (in mM) at key day-point samples varying from 13 ± 4 to 21 ± 3 for acetate, 16 ± 4 to 18 ± 6 for propionate, and 11 ± 2 to 12 ± 1 for butyrate (Table 1). In TC, after stopping the phage inoculation, the microbiota increased its production of acetate (Figure 4b). However, the means concentrations (in mM) varied from 28 ± 2 to 35 ± 5 for acetate, 20 ± 3 to 26 ± 5 for propionate, and 18 ± 2 to 20 ± 3 for butyrate (Table 1). DC (Figure 4c) was stable with means concentrations (in mM) between 31 ± 12 and 37 ± 10 for acetate, 20 ± 4 and 27 ± 4 for propionate, and 17 ± 3 and 22 ± 2 for butyrate (Table 1). In the AC colons of the three repetitions a higher ratio of propionate to acetate was observed contrarily to the TC and DC (Table 1). The performed statistical analyses, either ANOVA tests (for parametric distributions) or Friedman tests (for non-parametric distributions), did not reveal any significant changes in colons following phage treatments, for they all tested SCFA. Friedman tests were calculated on acetate and propionate production in the TC and butyrate production in DC. ANOVA tests were performed for all the other distributions. ## 3.3.1. Microbiota Composition in the SHIME® Model Regarding the composition of the fecal inoculum from the donor, Faecalibacterium presented the higher relative abundance, followed by the Bacteroides, Lachnospiraceae_ge, Agathobacter, Oscillospirales_ge, Subdoligranulum, Lachnoclostridium, Roseburia, Parabacteroides, and Lachnospiraceae_NK4A136_groups (Figure S1). A total of 83 OTU was observed for this donor. Regarding the composition of the microbiota over time, at the phylum level, the Firmicutes and the Bacteroidota presented the highest relative abundance in all the colons for all the repetitions (Figure 5). The relative abundance of Firmicutes was higher than Bacteroidota, except for in the third repetition. The other phyla abundances were low except for the Proteobacteria in AC (Figure 5a). In the first repetition, the relative abundance of the Firmicutes and the Bacteroidota remained stable in AC (Figure 5a) and TC (Figure 5b) over time. In DC (Figure 5c), the Firmicutes increased over time when the Bacteroidota decreased. In the second and the third repetitions, the relative abundance of the Firmicutes and Bacteroidota stayed stable over time in all the colons (Figure 5). Nevertheless, in AC (Figure 5a), the relative abundance in Proteobacteria increased over time. In all colons, at genera level (Figure 6), Bacteroides and Lachnoclostridium covered more than $70\%$ of the relative abundance in the first and second repetitions. In the third repetition, Lachnoclostridium was found in a lower proportion in all the colons (compared to the other repetitions) while Bacteroides showed a higher proportion mainly in DC. In AC (Figure 6a), Klebsiella and Phascolarctobacterium were found in high proportion in all the samples of the experiment. The abundance of Lachnospiraceae_ge was important in the first and second repetition. Tyzzerella was abundant in the first and third repetition while Escherichia-Shigella was abundant in the second and third. In TC (Figure 6b), Subdoligranulum was found in high proportion in the second and the third repetitions. Faecalibacterium and Lachnospiraceae_ge were abundant in all the samples of all repetitions and Phascolarctobacterium in the first repetition. In DC (Figure 6c), the main genus found were Phascolarctobacterium (all repetitions), Lachnospiraceae_ge (all repetitions), Faecalibacterium (repetition 1), and Subdoligranulum (repetitions 2 and 3). ## 3.3.2. α-Diversity The results of α-diversity index and OTU numbers at key-point samples are summarized in Table 2. For the Shannon diversity index, the expected maxima predictions were 4, 4.6, and 4.9 for the AC, the TC, and the DC, respectively (with the maximum corresponding to ln(S), with S being the total number of species present in the bioreactor). In AC, the Shannon index varied between 1.44 ± 0.12 and 1.64 ± 0.11 while it varied between 1.67 ± 0.23 and 1.85 ± 0.20 in TC. Finally, DC the range was from 1.43 ± 0.17 until 1.68 ± 0.17. The Piélou equitability index varied between 0.39 ± 0.03 and 0.45 ± 0.06, 0.32 ± 0,04 and 0.38 ± 0.05, and 0.40 ± 0.05 and 0.43 ± 0.04 for, respectively, the AC, TC, and DC. Regarding the Simpson index, the obtained samples showed a variability from 0.65 ± 0.02 to 0.71 ± 0.02, from 0.68 ± 0.05 to 0.71 ± 0.04, and from 0.57 ± 0.12 to 0.66 ± 0.04 in AC, TC, and DC, respectively. Finally, the chao1 estimator had a range between 31 ± 5.83 and 42 ± 4.16 in AC, 76 ± 3.91 and 82 ± 4.77 in TC, and 80 ± 11.15 and 113 ± 38.07 in DC. For the inoculum, the values of the index were 2.58, 0.58, and 0.82 for the Shannon, Piélou, and Simpson index respectively, and 97 for the chao1 estimator. The number of OTU was 83. Statistical analysis was applied to highlight diversity index changes following treatment. Depending on the normality and homoscedasticity of the distributions, either ANOVA tests, for the Piélou index and Chao1 estimator in the TC, or Friedman tests, for all other distributions, were applied. Independently of the statistical test applied, no significant changes in the different index were observed in all the colons and for all the selected bacteria. ## 3.3.3. β-Diversity The Non-Metric multidimensional scaling, representing the β-diversity, showed that the bacterial diversity between the samples was close, regardless of the sampling day or repetition (Figure 7). In addition, the samples seemed to be strongly clustered by repetition. The groups clustering test (AMOVA) and the homogeneity test (HOMOVA) showed no significant results, even for the AC, TC, or DC (no significant genetic difference between the samples). ## 3.4. Evolution of Target Bacteria Followed by qPCR The results of the 11 taxa followed by qPCR are presented in Figure 8. Firstly, the results showed that 1 targeted specie was not detected at all during the experiments: Akkermansia muciniphila. Secondly, 1 targeted specie, Faecalibacterium prausnitzii, was only detected in TC and DC samples (Figure 8d). Thirdly, Bifidobacterium was not detected in all samples of the experiment. In AC (Figure 8b), it was not detected at day 1 and day 15 for repetition 1 and at day 8 for repetition 2. In TC and DC, it was only detected at the end of the experiment in repetitions 2 and 3. A statistical analysis was performed to highlight relative quantification changes following treatment. Either ANOVA tests or Friedman tests, depending on the normality and homoscedasticity of the distributions, were applied. In AC, Bacteroides/Prevotella, Escherichia/Shigella, Oscillospira, Phascolarctobacterium faecium, and Ruminococcus results presented non-parametric distributions while Klebsiella pneumoniae complex, Mucispirillum schaedleri, Bifidobacterium, and Veillonella showed parametric distributions. In TC, Escherichia/Shigella, Klebsiella pneumoniae complex, Mucispirillum schaedleri, Oscillospira, Phascolarctobacterium faecium and Ruminococcus presented non-parametric distributions while the other selected taxa showed parametric distribution. In DC, only Klebsiella pneumoniae complex and Ruminococcus presented parametric distributions. Independently from the applied statistical test, no significant changes in the relative quantification were observed in any colon and for any selected bacteria. ## 4. Discussion Phage therapy is a promising approach for fighting antimicrobial resistance. However, proofs of the safety of this antibiotics’ alternatives are needed, especially for the impact on the human intestinal microbiota. Indeed, whether to fight bacterial digestive diseases or to use the digestive tract as the route of medicine administration, the impact of phage on the bacterial ecosystem in the colon is an important factor that must be taken into account. In this context, the SHIME® model was a suitable in vitro model to simulate the in vivo conditions encountered in the ascending (AC), transverse (TC) and descending (DC) colons. The SHIME® system is a well-known model to highlight the effects of probiotics [57,58,59], prebiotics [60], both [25], or phytochemicals [61] on the gut microbiota. However, the impact of phage therapy on intestinal microbiota has not been investigated in detail using this model and, to the best of our knowledge, only three studies have been published so far. The first presented the effects of repeated injections of a phage cocktail against Salmonella Typhimurium in the proximal colon’s microbiota and study the phage’s impact and persistence in this complex microbial community [36]. The second underlined the stable activity of two phages against K. pneumoniae, phages 1.2–3 s and MCoc5c, after a short passage (21 h) in either the proximal either the distal colon microbiota [37]. The last investigated the persistence of the phage UZI (isolated against a Klebsiella aerogenes, previously named Enterobacter aerogenes) in the large intestine microbiota’s ecosystem after one injection (with or without an additional bacterial host injection) [35]. In this third study, a mathematical formula was applied to study the theoretical persistence of the phage into the gastrointestinal model and to check if the phage was mechanically washed out from the system. The mathematical model of our study was based on this last study [35]. Similarly to the phage UZI, phage ULIP33 disappeared faster than expected, if the phage is considered as an inert particle, irrespectively of the colon. However, it stayed detectable for 8 ± 2, 11 ± 1, and 14 ± 2 days after cutting off the treatment in AC, TC, and DC, respectively (versus 9, 17 and 18 days), indicating its persistence in intestinal conditions of the model. Different hypotheses can explain this washout phenomenon. Firstly, the absence of the bacterial host probably prevented the phage replication. Secondly, the experimental conditions could have decreased the lytic activity of ULIP33. Indeed, even if the phage was thermostable until 45 °C and showed a stable lytic activity over a pH between 6 and 10, its lytic activity was decreased after 1 h incubation at pH 4 [38]. Thirdly, a non-host material can interact with the phage and decrease its lytic activity, for example pancreatin or bile salt, as already seen in another study with an E. coli phage [62]. Testing the resistance of the phage ULIP33 in the PJ and feed for a long time would be interesting to perform. Fourthly, the automatic acid and base discharges can dilute the phage concentration. Finally, the titration method has also a limit of detection. These assumptions can explain why a lower-than-expected concentration was observed after the treatment in the total volume of the bioreactors. The production of SCFA, is a key step of bacterial colonization [12]. In healthy conditions, microbioma from fecal samples presents a ratio of acetate, propionate, and butyrate around $\frac{60}{20}$/20 [63]. In this study, propionate producer’s species such as Bacteroides, Phascolarctobacterium faecium, or Veillonella [28,29] were well represented and could have led to a higher propionate ratio. In addition, the high proportion of butyrate was probably due to a high proportion of butyrate-producer bacteria such as Faecalibacterium prausnitzii or Roseburia genus [28,29]. In vivo, SCFA concentration is almost the same in the different part of the colon [64]. In this model, the increase in SCFA concentration from proximal to distal parts of the colon can be explained by the lack of absorption in the model. Regarding the impact of phage addition, no significant SCFA production variation was observed over time after the treatment. This observation is similar to other studies and is promising for phage therapy. In a randomized, double-blind, placebo-controlled crossover intervention trial, a commercial cocktail of Escherichia coli-targeting phages was given to healthy volunteers [65]. Feces and blood were sampled to study the impact of the phages on the microbiota and on inflammatory markers, and no impact of the SCFA production was highlighted. In another more recent study, the impact of the phage vB_EcoS_Ace, targeting STEC E. coli, on the intestinal microbiota was evaluated in a 24 h in vitro fermentation model [66]. In that study as well, SCFA production showed no significant changes due to the phage addition. Microbiota profiling is also an important factor for host’ health. In healthy individuals, this complex ecosystem is in a steady state. However, in several situations, this normobiosis can switch to dysbiosis. Microbiota compositions changes are linked to different diseases such as obesity, inflammatory bowel disease, neurological degenerative disease, or diabetes [67,68,69,70,71]. Although phages are an integral part of the gut microbiota [72], phage treatment could disrupt the balance of this biocenosis in the same way as antibiotics [73] or xenobiotics [74]. In general, at the phyla level, $90\%$ of the intestinal microbiota is represented by the phyla Firmicutes and Bacteroidota [75]. In this model, the ratio was closer to $100\%$ in the TC and DC, as observed in the inoculum (data not shown). In the AC colon, the ratio was close to $90\%$ with a higher proportion of enterobacteria (especially Klebsiella and Esherichia-Shigella), belonging to the Proteobacteria phylum. These differences can be explained by different in vitro conditions in the AC colon compared to the end of the digestive tract, and the fecal inoculum. In this study, with a high representation of the phylum Firmicutes and the family Lachnospiracae in the stool sample, the donor’s microbiota was characterized as enterotype 3, the most common enterotype [76,77]. At the genus level, 14 genera were found with a relative abundance higher than $1\%$ in the model: Anaeroglobus, Bacteroides, Eisenbergiella, Escherichia-Shigella, Faecalibacterium, Klebsiella, Lachnoclostridium, Lachnospiraceae_ge, Parabacteroides, Phascolarctobacterium, Selenomonadaceae_ge, Sudoligranulum, Tyzzerella, and Veillonella. Only some of them were found in higher proportion in the fecal inoculum: Bacteroides, Faecalibacterium, Lachnoclostridium, Lachnospiraceae_ge, Parabacteroides, and Subdoligranulum. The bacterial composition of the TC and DC was more comparable to the fecal inoculum than the AC community. Indeed, the in vitro conditions of these parts are close to the in vivo conditions encountered at the end of the gastro-intestinal tract. Moreover, the TC and DC’s in vitro conditions are close to each other and can sometimes be associated as a distal colon [78]. Biological communities can be characterized by diversity index: α and β [79]. In this study, three indexes—Shannon, Simpson, and Piélou—and 1 estimator—Chao 1- were calculated. These indexes are often calculated in studies about the phage’s impact on the gut microbiota and the most commonly used ones are the Shannon index [65,66,80,81,82,83] and the Chao1 estimator [65,81,83]. No significant results were observed after the statistical analysis performed to highlight any changes in these indexes after the phage treatment. Nevertheless, the Piélou index was higher and the Chao1 lower in the AC colon than in the other colons, probably due to the saccharolytic substrate availability which promotes specific bacterial genus growth such as Veillonella, a trend already seen in other SHIME® experiments [22,25,84,85]. Regarding the non-significant statistical results, previous in vivo and in vitro studies concluded the same. The in vivo models were based on a human stool sample analysis that was testing phage cocktails against E. coli (called PreforPro) [65,80], on infected mice with a E. coli O157:H7 and a phage cocktail against E. coli, Salmonella spp. and Listeria monocytogenes (named F.O.P. for Foodborne Outbreak Pill) [81], or on a peritonitis mouse infection model with Enterococcus faecalis and a cocktail of the phages EFDG1 and EFLK1 against Enterococcus species [82]. The in vitro model tested the phage vB_EcoS_Ace [66] or the FOP phage cocktail in the case of Listeria monocytogenes infection [86]. However, in two rodent model studies, the researchers highlighted an increase in the α-diversity in parallel to a hyper-permeability of the gut barrier, which is a signature of intestinal inflammation [83,87]. Then, to compare the diversity between different samples, the Bray–Curtis dissimilarity was measured and showed a high clustering of all the samples, which was even more important in the same repetition samples. The same strong clustering of the samples with or without the phage treatment was highlighted in in vivo studies on human stool analysis and in a peritonitis mouse infection model with Enterococcus faecalis or in in vitro model with Listeria monocytogenes targeted by cocktail phages [65,80,82,86]. Finally, to mathematically assess the genetic diversity between the samples over time in the same colon, AMOVA and HOMOVA tests were calculated, based on the Bray–Curtis dissimilarity matrix. This showed no significant results. All these parameters lead to the same conclusion: the phage did not impact the β-diversity of intestinal microbiota. To further investigate the microbiota composition, semi-quantitative analyses were obtained for six genera and five species using qPCR and the delta–delta Ct method [44]. Some of those species are important health promoters such as Akkermansia muciniphila and Bifidobacterium. The first degrades the intestinal mucus and produces substrates locally, such as monosaccharides and acetate, used by other intestinal bacteria through a cross-feeding phenomenon. Akkermansia also plays a role in the gut barrier, immune-modulation, and obesity [26], but was not detected in this donor. The Bifidobacterium genus is present in a higher proportion in babies due to their bifidogenic alimentation through milk [88], and nowadays has an use as probiotic for human health [27]. This genus was found in a higher proportion in AC compared to the other colons. This is probably due to the saccharolytic substrate availability in this part of the in vitro model, which can be used by these bacteria [89]. No significant quantification variations were shown after the phage treatment in AC and some bacteria were found at the end of the experiment in TC and DC. The propionate-producers Phascolarctobacterium faecium, Veillonella, and Bacteroides/Prevotella showed no significant quantification variations after the phage treatment. However, the quantification of Veillonella slightly increased over time. The decrease in the quantification of Faecalibacterium prausnitzii, a butyrate producer species, in parallel to an increase in Escherichia/Shigella is a signature of inflammation and inflammatory bowel diseases [30,31]. In this study, the decrease of F. prausnitzii was not observed. However, even if no significant changes were obtained after the treatment, the quantification of Escherichia/Shigella varied greatly between the repetitions. In the second repetition especially, the quantification of E. coli/Shigella was increased but not associated with a decrease of F. prausnitzii. No F. prausnitzii were found in the AC probably due to its sensitivity to acidic pH and bile salts [90]. Oscillospira, Ruminococcus, and Mucispirillum schaedleri are three genera related to chronic inflammatory bowel disease [32]; Oscillospira is negatively correlated and the others are positively correlated. However, all their quantifications remained stable over time after the phage treatment. The results obtained after the qPCR quantification of Klebsiella pneumoniae complex (including, among others, the pneumoniae subspecies) showed that this group remained stable even in the presence of the phage. Additionally, no lytic activity on Petri dishes of the phage ULIP33 was observed against the Klebsiella pneumoniae found in the microbiota of the donor (data not shown). These results, closely interlinked with the wash out of the phage and the absence of its replication, are supporting arguments for non-off target replication of the phage ULIP33. Lastly, even if the parameters studied through this work did not reveal any impact of the phage on the gut microbiota, it is important to underline some limitations and characteristics of this in vitro model. Firstly, the in vitro microbial diversity was less diverse than the inoculum with lower OTU numbers due to a specialization of the microbiota to the in vitro conditions, as already seen in other SHIME experiments [22]. On one hand, this specialization is a feature of the model to distinguish the three parts of the colon. On the other hand, this loss of diversity could be due to the sampling, storage, and culture method of the stool samples. Furthermore, as a classical technical replicate, the three repetitions of the experiment were practiced with the same feces inoculum to avoid differences between repetition as already highlighted [25]. However, the bacterial populations implanted in the system were different from one repetition to another. Therefore, even if the results obtained through this study provide interesting information on the restricted effect of the phage ULIP33 on the gut microbiota using the SHIME dynamic in vitro model, further studies are needed for drawing general conclusions. For example, testing the phage effect on other donors’ microbiotas could give more validation to these results. Another example would be to compare the microbiota parameters of a SHIME experiment without phage injection during the whole period of the experiment. The use of an inert tracer to follow the transit in parallel could also be useful to compare the observed persistence of the phage with the tracer. Additionally, the next important step, would be to test the ability of the ULIP33 phage to interact with its host, the K. pneumoniae SB4385, in this same model. ## 5. 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--- title: Chitosan/Albumin Coating Factorial Optimization of Alginate/Dextran Sulfate Cores for Oral Delivery of Insulin authors: - Bruno Pessoa - Mar Collado-Gonzalez - Giuseppina Sandri - António Ribeiro journal: Marine Drugs year: 2023 pmcid: PMC10057083 doi: 10.3390/md21030179 license: CC BY 4.0 --- # Chitosan/Albumin Coating Factorial Optimization of Alginate/Dextran Sulfate Cores for Oral Delivery of Insulin ## Abstract The design of nanoparticle formulations composed of biopolymers, that govern the physicochemical properties of orally delivered insulin, relies on improving insulin stability and absorption through the intestinal mucosa while protecting it from harsh conditions in the gastrointestinal (GI) tract. Chitosan/polyethylene glycol (PEG) and albumin coating of alginate/dextran sulfate hydrogel cores are presented as a multilayer complex protecting insulin within the nanoparticle. This study aims to optimize a nanoparticle formulation by assessing the relationship between design parameters and experimental data using response surface methodology through a 3-factor 3-level optimization Box–Behnken design. While the selected independent variables were the concentrations of PEG, chitosan and albumin, the dependent variables were particle size, polydispersity index (PDI), zeta potential, and insulin release. Experimental results showed a nanoparticle size ranging from 313 to 585 nm, with PDI from 0.17 to 0.39 and zeta potential ranging from −29 to −44 mV. Insulin bioactivity was maintained in simulated GI media with over $45\%$ cumulative release after 180 min in a simulated intestinal medium. Based on the experimental responses and according to the criteria of desirability on the experimental region’s constraints, solutions of $0.03\%$ PEG, $0.047\%$ chitosan and $1.20\%$ albumin provide an optimum nanoparticle formulation for insulin oral delivery. ## 1. Introduction The feasibility of innovative insulin delivery systems, which is viewed as a valid alternative to enable reduction of the number of injections [1], has been investigated in clinical studies, and more recently, the results of a phase 3a study revealed the superiority of a weekly insulin injection (insulin icodec) in decreasing HbA1c when compared with once daily insulin glargine in people with type 2 diabetes [2]. An oral long-acting acylated insulin analogue co-formulated with an absorption enhancer (I338) assessed in an 8-week trial in people with type 2 diabetes treated with oral glucose-lowering drugs, showed no difference in the magnitude of hypoglycemia or rates of adverse events detected in people randomized to I338 vs. insulin glargine [3]. The possibility of developing oral insulin formulations for the treatment of type 1 and type 2 diabetes continues to be explored [4]. It is currently in phase 3 studies, but according to the press release on the clinical trial sponsor’s website, the endpoints were not met [5]. Nanoparticles have been used widely for the oral delivery of biopharmaceuticals such as insulin. Nanoparticles stabilize active biomolecules of interest against harsh gastrointestinal (GI) conditions and ensure biological activity during manufacturing processes and transit through the GI tract [6]. Developing and optimizing nanoparticle-based formulations based on physicochemical and physiological parameters involves several and often connected processes [7]. Oral insulin delivery relies on nanoparticle properties. Therefore, nanoparticles, including biopolymers that show favorable characteristics for insulin retention, protection, absorption across the GI tract and targeted delivery, are a promising approach [6,8]. The ideal techniques to develop nanoparticles for protein and peptide drugs should avoid using solvents and harsh chemical conditions to ensure insulin activity after manufacturing [9,10]. Biopolymer blend-based nanoparticles have been used with success for oral delivery of insulin. Examples include alginate and chitosan [11], sterculia gum and chitosan [12], and dextran sulfate and chitosan [13]. Among their advantages are biocompatibility, biodegradability and multipurpose functions assigned to biopolymers [14]. Nanoparticle formulations prepared by ionotropic pregelation and containing insulin consist of an internal multilayer complex where insulin is protected within the nanoparticle and an outer coat consisting of protein with protease protection properties. The internal particle core consists of the polysaccharides alginate and dextran sulfate. Insulin is retained thanks to complexation with polysaccharide chitosan and further coating with bovine serum albumin [6]. Alginate consists of anionic polymeric chains of mannuronic acid and guluronic acid with biodegradable and biocompatible properties. It forms stable hydrogels in the presence of multivalent cations such as calcium and zinc due to intramolecular and intermolecular crosslinking of polymer chains [15,16]. Chitosan consists of unbranched polymer glucosamine and N-acetyl glucosamine chains. Additionally, chitosan shows biodegradable and biocompatible properties. Chitosan stabilizes alginate-based hydrogels during the formulation of nanoparticles and enhances insulin absorption through the paracellular route [17]. Chitosan coating is essential to strengthen the alginate/dextran sulfate core [18] and to incorporate other polyanionic biopolymers that increase insulin stability against GI enzymes and pH and to modulate insulin release in the GI tract [19,20]. Dextran sulfate is a branched polysaccharide consisting of α-1–6 linked glucose residues in the main chain and α-1–3 linked glucose in ramifications. This polysaccharide has 2.3 negative charges per monomer [21] and thus interacts easily with polycations such as chitosan. Stabilizers such as polyethylene glycol (PEG) have been shown to maintain the structural properties of nanoparticle-based formulations [22] during manufacturing or storage by providing stability in aqueous suspension and impacting the interaction between particles and other biological environmental components, including enzymes, cells and membranes. Albumin coating has been shown to minimize insulin degradation under GI conditions. This is because albumin could act as a protein sacrificial target for local enzymatic degradation [23,24]. Premature insulin release and degradation stand out among the factors that counteract the pharmacological action of orally delivered insulin [13]. We have optimized the properties of multilayer complex nanoparticles for specific purposes, such as nanoparticle size and stability. Among the several components, chitosan has significantly impacted nanoparticle physicochemical properties [13], which is especially interesting given that chitosan has been one of the most challenging components to standardize in biopolymer-based nanoparticle formulations [25]. The amount of chitosan used in biopolymer-based nanoparticles has been shown to play a principal role in drug release from chitosan complexes with alginate [15] and zein [26,27]. On the other hand, as nanotechnology characterization tools have been improved in recent years, major concerns have been raised regarding the stability of nanoparticles, thus justifying a complete and comprehensive characterization of nanoparticles during experiments [28]. Chitosan stabilizes polyanionic nanoparticles due to its polycationic nature [29] and its conformation in solution [30]. Suspensions of biopolymer-based nanoparticles remain stable because of the electrostatic repulsion forces between negative charges on their surface [31]. Furthermore, a prominent role played by the steric hindrance of polymers such as PEG [32,33] has been described in biopolymer-based formulations prepared by ionotropic gelation. As nanoparticle formulations comprising biopolymers, such as chitosan and albumin, are associated with a high number of process and formulation factors, multiparametric design can be an excellent approach to assess the impact of several factors on nanoparticle features related to physicochemical properties and biological performance [34]. Experimental design has been applied to optimize nanoparticle-based formulations considering several advantages outcomes, including a reduction in the number of experimental runs, development of models to evaluate the relevance and statistical significance of studied factor effects, and evaluation of eventual interaction effect between factors [35,36]. Box–Behnken designs are 3-level factorial designs that have shown success as tools for optimization of formulations following response surface methodology because they permit assessment of the parameters, design of sequential designs and detection of eventual lack of model fit [37]. In the present study, a design of 15 experimental runs is proposed, for which a quadratic model is generated as follows:[1]Y=b0+b1X1+b2X2+b3X3+b12X1X2+b13X1X3+b23X2X3+b11X12+b22X22+b33X32 where Y is the measured dependent variable related to each factor level combination, b0 is an intercept, b1 to b33 are regression coefficients computed from experimental runs, and X1, X2 and X3 are the coded levels of independent variables. The terms Xi, Xi2 and XiXj (i and $j = 1$, 2 or 3) correspond to a linear effect, quadratic effect and interactions, respectively [38]. Chitosan/albumin coating of alginate/dextran sulfate cores led to adequate nanoparticles for the oral delivery of insulin [39]. However, the reduction in the previously standardized chitosan amount of nanoparticles showed opposite effects on the size and stability of the nanoparticles [13]. The novelty of this study lies in the optimization of the chitosan/albumin coating step of alginate/dextran sulfate cores for oral delivery of insulin and the investigation of the eventual relationship between studied design factors and obtained experimental responses through the combination of response surface methodology with Box–Behnken design. The experimental design is based on previous knowledge related to the effect of PEG, chitosan and albumin on the nanoparticle structure and release properties, not neglecting the monitoring of insulin bioactivity during nanoparticle development and release studies. The optimum nanoparticle formulation is developed based on the effect of PEG, chitosan and albumin on minimizing nanoparticle size to increase particle uptake, minimizing granulometric size distribution to predict enhanced drug transport, reducing zeta potential to values lower than −30 mV to increase particle stability in suspension, minimizing insulin release in gastric conditions for insulin protection against gastric pH and enzymes, and maximizing insulin release in simulated intestinal conditions to improve insulin absorption across the GI epithelium. ## 2.1. Preparation and Characterization of Nanoparticles The Box–Behnken design was performed to assess any relationship between formulation components PEG, chitosan and albumin on physicochemical properties of nanoparticles for the optimization of formulation for oral insulin delivery. Insulin nanoparticles were prepared by ionotropic gelation followed by complexation [13,40]. The selection of dependent variables is aimed at a comprehensive characterization of nanoparticles, including size, PDI, zeta potential and insulin release behavior, which are considered critical for improving the oral bioavailability of proteins [41]. The responses for particle size, PDI, zeta potential and insulin release in simulated gastric medium after 120 min and intestinal medium after 180 min are shown in Table 1. The nanoparticle diameter was chitosan- and albumin dependent. The minimum size (within 313–317 nm) corresponded to the highest chitosan ($0.075\%$) and higher albumin (1.0 and $1.5\%$) concentrations independently of the PEG concentration, according to Table 1. Interestingly, keeping PEG and albumin concentrations fixed at 0.02 and $1.5\%$, respectively, the size of the nanoparticles varied as a function of the chitosan content. The same trend was obtained at prefixed PEG and albumin concentrations of 0.02 and $0.5\%$, respectively. In both cases, the nanoparticle size was reduced by more than 240 nm when the chitosan concentration was increased from 0.025 to $0.075\%$. The effect of albumin and chitosan concentrations on nanoparticle diameter can be attributed to a reduction in the electrical repulsion within nanoparticle polymer networks, since modifications in their electrical state may lead to nanoparticle swelling or shrinking [42]. Particle size distribution is a relevant characterization parameter of nanoparticles, as significant variations have been observed in the drug bioavailability and efficacy of nanoparticle formulations with broad particle size distributions [35]. Comparing formulations 9 to 11 or 10 to 12, nanoparticle size decreased in both cases, while the change in zeta potential between both formulations was │4│ and │3│ mV, respectively. It is important to note that both changes occurred in the stability region of zeta potential. The effect of PEG concentration on nanoparticle size distribution resulted in less particle aggregation, leading to a narrower size distribution. PDI was lower (0.17) when the PEG concentration was higher ($0.30\%$), but it also depended on the chitosan and albumin concentrations. This low PDI is relevant, considering that the mean particle size and the particle size distribution can be critical factors for the evaluation of the performance of nanoparticle formulations [25,34]. Surprisingly, the PEG concentration increased the PDI at a chitosan concentration equal to $0.050\%$ (formulations 5 and 6). Notably, in all cases, the albumin concentration was kept unchanged. The results in Table 1 show that the zeta potential of the nanoparticles for all formulations was strongly negative. The zeta potential was lower than −29 mV for all formulations. The zeta potential values were mainly dependent on chitosan due to the protonated amino group, where the higher the concentration of chitosan was, the higher the zeta potential value. Alginate/dextran sulfate cores produced by the same protocol revealed a zeta potential of −36 mV [40], confirming the prevalence of predominantly negatively charged groups in the biopolymers alginate and dextran sulfate. The resulting nanoparticles upon coating with chitosan and PEG still did not reverse the zeta potential to a positive value, contrary to what was observed for similar nanoparticle formulations coated with more chitosan [43]. The effect of chitosan coating on the zeta potential of nanoparticles depends on the experimental conditions, including pH and the type and amount of chitosan [44,45]. Further coating of nanoparticles with albumin at pH 4.6 did not show an effect on zeta potential because at this pH, close to its isoelectric albumin is less negatively charged compared to a coating step at pH 5.1 [44]. Although albumin presented a zeta potential close to 0 mV at pH 4.6 [13], it still has protonated groups that may interact through a balance of repulsive electrostatic forces, H bonds and hydrophobic forces with nanoparticle components such as chitosan [46,47]. Lower zeta potential can be interpreted as a higher electrostatic stabilizing effect of nanoparticles in aqueous suspension, which suggests a low aggregation of nanoparticles in most of the conditions to which the nanoparticles have been exposed in this work. Zeta potential, which depends on the surface charge of nanoparticles, is essential for the stability of nanoparticles in an aqueous suspension [48] as well as a significant factor in the adsorption of nanoparticles onto the cell membrane [49]. A negative zeta potential reveals a predominance of negatively charged groups, thereby suggesting the presence of an albumin coating on the nanoparticle surface that interacts with predominantly positively charged chitosan [35]. High stability of nanoparticles in aqueous suspension is relevant, and their maintenance during manufacturing can predict better insulin physicochemical and biological stability in drug delivery systems such as nanoparticles [50]. Insulin release in simulated GI media was assessed. First, insulin retention within nanoparticles and consequent protection against acidic degradation. Later, for insulin release in simulated intestinal medium, insulin can be absorbed through the intestinal epithelium. Surprisingly, when fixing chitosan and albumin concentrations, a change in the PEG concentration did not result in the variation of insulin release in simulated gastric medium. No insulin escaped from nanoparticles in simulated gastric medium after 120 min for formulations 3, 4, 8, and 12 with higher albumin concentrations (1.0 and $1.5\%$). Insulin release from nanoparticles was primarily observed for formulations with lower concentrations of albumin ($0.5\%$) and chitosan ($0.025\%$), where the insulin release was up to $60\%$, depending on the mentioned factors at the lowest level. High concentrations of albumin coatings led to lower insulin release, likely due to a strengthening of the electrostatic interaction between the positively charged albumin/chitosan network and the negatively charged alginate/dextran core reliant on the pH conditions. Insulin release from nanoparticle formulations containing a lower concentration of chitosan occurs because, at low pH, the ionic interaction between encapsulated insulin and the alginate/dextran core is weakened due to a destabilization effect by ions present in simulated gastric medium [19]. When compared to similar nanoparticle formulations, the formulation reported herein due to the chitosan/albumin coating showed high retention of insulin. Many of the previously studied nanoparticle formulations for oral delivery of insulin have not retained the peptide drug under simulated gastric conditions [13,51,52], not providing the highest amount of insulin initially encapsulated to be absorbed in the intestinal tract and thus not contributing to the highest insulin oral bioavailability. After incubation in gastric medium, nanoparticles were transferred to simulated intestinal medium. A cumulative insulin release, between 50 and $78\%$, was observed after 180 min for all formulations in the simulated intestinal medium. Among those, a lower value of cumulative release was observed for formulations with the highest concentration of chitosan ($0.075\%$). Incomplete release probably occurs due to insulin–polysaccharide and insulin–albumin interactions. Therefore, the amount of insulin retained within nanoparticles under intestinal simulation may be tightly bound to the alginate nucleus, requiring more extensive dissolution for additional release. The pH triggered insulin release when incubated nanoparticles in the acidic gastric medium reached the intestinal medium. This three-hour insulin release would result in its availability close to the absorption site, which constitutes an excellent benefit for oral insulin bioavailability [19]. CD, the technique to monitor the integrity of insulin against harsh conditions [18], as seen in Figure 1, showed that the spectrum of unprocessed standard insulin (I) in PBS at pH 7.4 has bands with two minima at approximately 209 and 224 nm, indicating the presence of a significant α-helix structure with some β–sheets. Insulin released from nanoparticles (II) showed a similar spectrum. Nevertheless, minima were attenuated with respect to the standard solution. The interpretation of this result is that the secondary insulin structure may have slightly changed upon encapsulation into the nanoparticle. The simplest explanation is that the peptide drug could be linked to the biopolymers, resulting in the modification of the protein structure, although not representing denaturation or loss of insulin activity. Thus, the use of nanoparticles allows the preservation of the secondary structure of insulin after being released as a consequence of media exposure [53]. ## 2.2. Fitting Data of Dependent Variables to Model Statistics An inverse relationship depending on chitosan and albumin concentration was found in the nanoparticle formulation. The mean particle size of the nanoparticles varied from 314 to 585 nm depending on the chitosan and albumin concentrations, as shown in Table 1. The effect of levels of the independent variables on particle size is shown in Figure 2. In Table 1, an inverse relationship between the PDI and the PEG concentration was observed in formulations prepared with chitosan and albumin concentrations of at least $0.05\%$ and $1.0\%$, respectively, where the PDI decreased by increasing the PEG concentration from 0.01 to $0.03\%$ while keeping the concentration of chitosan and albumin solutions constant, as observed in formulations 3 and 4. As nanoparticle dispersions were submitted to dialysis, the escape of PEG from the nanoparticle structure cannot be excluded. The PEG density on nanoparticles is hardly achieved since continuous and complete separation of excess polymers from nanoparticle dispersions may lead to particle aggregation and precipitation [54]. In this experimental work, the presence of PEG in the nanoparticle structure was assessed using FTIR analysis. As shown in Figure 3, nanoparticles prepared with chitosan/PEG exhibit FTIR spectra similar to those of nanoparticles prepared without PEG. Although no evidence for developing new bands or disappearance of characteristic bands considered relevant to PEG was observed, changes in the shift in the absorption bands assigned to PEG located at 962, 1278 and particularly 2885 cm−1 [33] were observed. This indicates the presence of an interaction between chitosan and PEG, which could proceed from the intermolecular hydrogen interactions between chitosan and PEG [55]. The zeta potential of the nanoparticle suspension varied in the range of −29 to −44 mV, as presented in Table 1. The zeta potential values were mainly dependent on chitosan due to the protonated amino group, where the higher the concentration of chitosan was, the higher the zeta potential value. The zeta potential interval may indicate the nanoparticles’ aqueous stability, with values higher than 30 mV in absolute modulus representative of stable nanoparticle formulations in suspension [48]. Insulin release in both simulated gastric medium and simulated intestinal medium depended on chitosan and albumin concentrations. Notably, insulin release from nanoparticles in simulated intestinal medium was higher for formulations with low and medium levels of chitosan, as seen in Table 1. Experimental data were statistically analyzed, searching for the models best fitting the independent variables. Therefore, a quadratic model was established for the dependent variables’ particle size, PDI, zeta potential and insulin release in simulated gastric and intestinal media with high fitting coefficients (above 0.95). A linear model was established for the zeta potential with a fitting coefficient of 0.81. The regression equations of each model were plotted. Then, a polynomial equation comprising the individual main effect as well as the effect derived from the interaction between components was selected based on the determination of statistical parameters to optimize the nanoparticle formulation. Table 2 shows the coefficients of all the independent variables related to their effect and their comparative significance on the responses observed in the dependent variables. In the regression equation, a positive value represents a beneficial effect on the optimization as a synergistic effect occurs, whereas a negative value represents an inverse relationship as an antagonistic effect between the independent factor and the response is likely to occur [56]. The independent variable corresponding to chitosan concentration (X2) negatively affected particle size (Y1) and PDI (Y2) responses and released insulin from nanoparticles in simulated GI media (Y4 and Y5), whereas a positive effect on zeta potential (Y3) was observed. Albumin (X3) negatively minimized insulin release from nanoparticles in simulated gastric medium (Y4). In contrast, the PEG (X1) concentration negatively affected the PDI (Y3). The PDI was lower at a higher PEG level, possibly due to a lower tendency of multilayer complexes to form aggregates. Regression equation of the fitted model:[2]Y=b0+b1X1+b2X2+b3X3+b12X1X2+b13X1X3+b23X2X3+b11X12+b22X22+b33X32 Higher-order terms or coefficients with more than one factor in the obtained regression equation correlate to a quadratic relationship or an interaction between terms, respectively, suggesting a nonlinear relationship between independent and dependent variables [31]. In this way, independent variables can originate different degrees of response when compared to that predicted by regression equations upon their variation at different levels or in case of simultaneous changes of more than one factor. Except for zeta potential, for which independent variables presented a linear relationship, responses in Y1, Y2, Y4 and Y5 were affected by the interactions between factors, demonstrating a quadratic relationship. The interaction effect between X1 and X3 showed a negative effect on PDI and was twofold higher than the effect of X1. The interaction effect between X2 and X3 was favorable for response in Y4 but did not affect response in Y5. The quadratic effects of X2 and X3 were observed for responses in Y1, Y2, Y4 and Y5, whereas most positive quadratic effects for X2 and X3 were observed for Y1 and a negative quadratic effect for X2 was observed for Y5. ## 2.3. Response Surface Analysis Graphs of three-dimensional models are plotted in Figure 4, Figure 5, Figure 6 and Figure 7, in which response analyses have been plotted toward optimization of the critical dependent variables of nanoparticles for oral insulin delivery. Response surface plots can be used to interpret the interaction effects of two independent variables on the dependent variables when a third factor is kept at a constant level. Except for the zeta potential, where the interaction effects of PEG and chitosan were linear, the relationships among the three independent variables were nonlinear. Small particle sizes are most likely to increase intimate contact with the intestinal mucosa, as their higher surface area-to-volume ratio increases nanoparticle uptake in GI mucosa. As seen in Figure 4, a more pronounced effect of chitosan concentration on nanoparticle size is observed for chitosan concentration values lower than $0.05\%$, whereas, lower PDI values were obtained with a higher concentration of PEG and an intermediate and higher concentration of chitosan, as seen in Figure 5. Insulin release in enzyme-free simulated digestive media depended on chitosan and albumin concentrations, as shown in Figure 6 and Figure 7. The protection of insulin against adverse conditions in gastric simulation through its retention within the nanoparticles is obtained by a higher concentration of chitosan and albumin at a constant level of PEG. Upon transferring nanoparticle formulations into the intestinal medium, the insulin release from nanoparticles increases when the chitosan concentration is lower than $0.05\%$, regardless of the albumin concentration tested, as shown in Figure 6. ## 2.4. Optimization and Model Validation The optimum nanoparticle formulation can be set by analyzing various dependent variables and monitoring the constraints by a mathematical approach. Following the constraints of the parameters established in the Box–Behnken design, the optimum nanoparticle formulation comprising biopolymers for insulin delivery by the oral route was selected. It is formulated with solutions of $0.03\%$ PEG, $0.047\%$ chitosan and $1.20\%$ bovine serum albumin and has predictive values of particle size of 357 nm, PDI of 0.19, zeta potential of −35.0 mV, total retention of insulin in gastric conditions and $76\%$ release in the simulated intestinal medium, as presented in Table 3. The formulation of nanoparticles according to the composition stated in Section 2.4 validates the Box–Behnken design obtained in this work since dependent variables showed experimental values with an error equal to or lower than $5\%$ with respect to predicted ones, as seen in Table 3. The optimized nanoparticle formulation has a mean particle size of 357 nm, PDI of 0.19, zeta potential of −35 mV, full insulin retention within nanoparticles in the enzyme-free simulated gastric medium for 120 min, and insulin release equals $76\%$ in enzyme-free intestinal simulation after 180 min. ## 3. Conclusions The Box–Behnken design was applied to optimize the formulation of nanoparticles and to evaluate the main interaction of the factors that influence the formulation obtained. The quadratic effects of these factors on particle size, PDI, zeta potential, and insulin release from nanoparticles in simulated gastric and intestinal media were also studied. Experimental designs allowed the multiparametric optimization of the nanoparticle formulation by selecting physicochemical parameters critical for oral delivery of insulin, evaluating the most relevant factors on responses, and investigating any relationship existing between factors upon response surface methodology. A 3-factor, 3-level design based on 15 experiments allowed for exploring the linear and quadratic response surfaces and establishing a second-order polynomial model. Chitosan and albumin, as coating biopolymers, were revealed to be the main formulation factors regarding the desired physicochemical properties of the nanoparticles, except for PDI and insulin release under simulated GI conditions. Based on the experimentally obtained values and according to desirability, solutions of $0.03\%$ PEG, $0.047\%$ chitosan and $1.2\%$ albumin led to the optimum nanoparticle formulation for oral insulin delivery. Compared to previous nanoparticle-based formulations prepared using the same protocol, the factorial optimized formulation resulting from this work showed a narrow size distribution induced by the PEG/chitosan ratio. In addition, the model developed in this work increases nanoparticle characterization robustness, thereby making it easier to predict nanoparticle properties such as drug release, blood circulation time, bioavailability and cellular uptake. ## 4.1. Materials Alginic acid sodium salt (200 kDa with a mannuronic/guluronic ratio of 1.56, Ref A2158), low molecular weight chitosan (50 kDa with a deacetylation degree >$75\%$, Ref. 448869), bovine serum albumin (66.5 kDa Ref A1933) and trifluoroacetic acid (TFA) $99\%$ (v/v) were purchased from Sigma–Aldrich (Madrid, Spain), dextran sulfate sodium salt (5 kDa) and polyvinylpyrrolidone (PVP) K 30 were purchased from Fluka (Buchs, Switzerland), poloxamer 188 (Lutrol® F68) was kindly supplied by BASF (Hürth, Germany), calcium chloride and sodium chloride were purchased from Riedel-de-Haën (Lower Saxony, Germany), lactic acid $90\%$ was purchased from VWR BDH Prolabo (Rosny-sous-Bois, France), polyethylene glycol 4000 (PEG 4000) was acquired from Fisher Scientific® (Loughborough, UK), acetonitrile LiChrosolv®, hydrochloric acid $37\%$, potassium dihydrogen phosphate and sodium hydroxide were purchased from Merck KGaA (Darmstadt, Germany), and Actrapid® 100 IU/mL (Novo Nordisk A/S, Bagsværd, Denmark) was supplied by a local pharmacy. Biopolymer solutions were prepared in ultrapure water. Chitosan was dissolved in an aqueous solution containing lactic acid at $0.5\%$ (v/v), and otherwise stated solutions were under-vacuum filtered using a Millipore#2 paper filter. ## 4.2.1. Preparation of Nanoparticles Nanoparticles were prepared using ionotropic pregelation [57] of alginate/dextran sulfate solution containing poloxamer 188 and insulin with calcium ions, following polyelectrolyte complexation with both oppositely charged chitosan and albumin. Ionotropic pregelation involved dropwise extrusion of 7.5 mL of a calcium chloride solution into 117.5 mL of pH 4.9 $0.06\%$ (w/v) alginic sodium salt, $0.04\%$ (w/v) dextran sulfate, $0.04\%$ (w/v) poloxamer 188 and $0.006\%$ (w/v) insulin at constant stirring. A two-step complexation involved dropwise addition of 25 mL of chitosan and polyethylene glycol 4000 solution at pH 4.6 for stabilization of the pregel core into nanoparticles, followed by dropwise addition of 25 mL bovine serum albumin solution at pH 4.6. The concentration for each of the last three components varied between formulations, as indicated in Table 4. Nanoparticles were concentrated after pregelation and coating steps by dialysis [39] using a regenerated cellulose membrane with a tubing nominal dry thickness of 10 kDa molecular weight cutoff (MWCO) (SnakeSkin Pleated Dialysis Tubing, Thermo Fisher Inc., Waltham, MA, USA) and a dialysis solution of $20\%$ (w/v) PVP K 30 for 24 h at 4 °C. The pH of the suspension was set at 4.9, and KNO3 as an ionic agent was added at $0.075\%$ (w/v) [13]. ## 4.2.2. Particle Size Analysis Nanoparticle size was characterized by using dynamic light scattering (DLS) (NanoZetasizer, Malvern, UK) at 25 °C with a detector angle of 173°, setting the nanoparticle concentration based on a suitable operating procedure (SOP) of the instrument. Measurements were made in hexaplicate. The number of runs was established by the software to reach the quality criteria. Each run lasted 10 s with no delay between measurements. Nanoparticle formulations were screened for one-week size stability of samples upon refrigeration of samples between 2–8 °C [13]. For this study, size measurements were carried out after preparation and refrigeration of samples between 2–8 °C. Each curve in a plot shows the average of the measurements using a protocol validated for reproducible intensity and number distributions. Distribution by intensity allowed the characterization of nanoparticle size. In contrast, distribution by number was obtained by the software assuming the particles to be spherical, the homogeneity of the sample, and the accuracy of the distribution by intensity, allowing the relative populations of the particles to be estimated. ## 4.2.3. Zeta Potential Analysis The potential ζ, an electrical charge-related measurement on the surface of a nanoparticle, was performed by using the same apparatus. For each assay, three automated measurements were made. ## 4.2.4. Insulin Release Studies Insulin release from the nanoparticles was determined in simulated enzyme-free digestive media. A sample of 3 mL was added into dialysis diffusion bags with an MWCO of 100 kDa (Spectra/Por®, Biotech CE, Spectrum Laboratories Inc., Piscataway, CA, USA) and then immersed in 100 mL of simulated pepsin-free gastric medium [58] at 37 °C (120 min/100 rpm), followed by incubation in a simulated pancreatin-free intestinal medium [58] for 180 min after recovering nanoparticles by centrifugation (20,000× g/15 min). Sample aliquots were withdrawn after 120 min in gastric medium and 180 min after transferring nanoparticle formulations to intestinal medium conditions. Release studies were carried out in enzyme-free media to determine the pH-responsive properties of nanoparticles, minimizing interference of enzymes that may not reveal changes toward the pH shift from the stomach to the small intestine. The nanoparticles analyzed varied in the concentration-tested ranges of PEG, chitosan and albumin. Collected samples were submitted to centrifugation (20,000× g/15 min), and the supernatant was assayed for insulin by HPLC. The cumulative percentage release of insulin from nanoparticles refers to the insulin content in the nanoparticles. Studies were carried out in triplicate. ## 4.2.5. Insulin Determination The determination of insulin was performed using an LC-2010 HT HPLC system (Shimadzu Co., Kyoto, Japan) equipped with a quaternary pump, an HP 1050 programmable multiple wavelength detector set at 214 nm, a reversed-phase X-Terra® RP 18 column, 5 lm, 4.6 mm × 250 mm (Waters Co., Milford, MA, USA) and a Purospher STAR® RP-18 precolumn 5 µm (Merck KGa, Darmstadt, Germany). A gradient-operated mobile phase consisting of acetonitrile (A) and $0.1\%$ trifluoroacetic acid (TFA) aqueous solution (B) at a flow rate of 1.0 mL/min set to 30:70 (A:B), changed to 40:60 (A:B) in 5 min for elution over 5 min, and changed to 30:70 (A:B) in 1 min for elution over 1 min. Peak area responses of the chromatograms were measured with an automatic integrator. The method was validated and was linear in the range of 2.1–108 µg/mL (R2 = 0.9996). ## 4.2.6. Conformational Stability of Insulin By using circular dichroism (CD) spectroscopy, the secondary structure of insulin released from the nanoparticles was evaluated. The CD spectra were collected using a Jasco J-815 spectropolarimeter (Tokyo, Japan) with a temperature controller. Spectra were collected at 25 °C using a 0.1 cm cell over a 200–260 nm wavelength range. A resolution of 0.2 nm and scanning speed (50 nm/min) with a 4-s response time was employed. Each spectrum acquired is an average of five consecutive scans. Blank buffer subtraction, noise reduction and data analysis were performed using Jasco’s standard and temperature/wavelength analysis software. The spectra of insulin samples extracted from nanoparticles with concentrations of approximately 10 µM in phosphate-buffered saline (PBS) were compared with those of unprocessed insulin in the same medium. ## 4.2.7. Fourier Transform Infrared (FTIR) Spectroscopy FTIR analysis was used to ascertain the presence of PEG in the nanoparticle structure. Infrared spectra of freeze-dried nanoparticle formulations and PEG were recorded in the range of 650 to 4000 cm−1 with a 400 N FT-NIR Imaging System (Perkin-Elmer, Tampa, FL, USA). Each sample was read in 64 scans at a resolution of 4 cm−1. The formulations were frozen overnight at −80 °C and dried in a chamber at 0 °C for 48 h at 0.133 mbar, corresponding to a condenser temperature of −50 °C, using a Lyph-lock 6 apparatus (HETO LyoPro 3000, Heto/Holten A&S, Allerød, Denmark). ## 4.2.8. Experimental Design The Box–Behnken design was selected because it requires a low number of runs in the case of three variables. As seen in Table 4, a 3-factor, 3-level design was used to optimize nanoparticle formulation with PEG and chitosan and albumin concentrations, which were defined as the independent variables or formulation factors with three levels of concentration values, low, medium and high. The points located at the median values of the edges of the experimental design were evaluated in triplicate [59]. These center points are useful to determine if there is curvature in the relationship between independent and dependent factors. In addition, using several center points enables an estimate of pure error. The range of concentrations was established based on previous studies of similar nanoparticles containing insulin [13,39], where PEG was determined to be appropriate for promoting particle stability, and the chitosan amount strengthened the alginate/dextran sulfate while interacting with another polyelectrolyte polymer, albumin, which proved critical as a sacrificial target, thus protecting insulin within the nanoparticle. The dependent variables are nanoparticle size, PDI, zeta potential, and insulin release in simulated GI media after 120 min and 180 min, with disclosure of constraints applied, as described in Table 4. Design-Expert® software (v.13 Stat Ease Inc., Minneapolis, MN, USA) was used to generate and evaluate the statistical experimental design. The values of formulation factors and the corresponding responses for these dependent variables are shown in Table 4. ## 4.2.9. Analysis of Experimental Data and Model Validation Polynomial equations for disclosure of the main effect and interaction among factors were determined upon estimating statistical parameters, including multiple correlation coefficients, adjusted multiple correlation coefficients and the predicted residual sum of squares generated by the software. To determine the optimized formulation, three-dimensional surface plots were drawn. All responses were fitted to linear or quadratic models. The polynomial equations’ validation was established by analysis of variance (ANOVA) provision available in the software. Accordingly, the optimum values of the dependent variables were determined graphically and numerically using Design-Expert® and based on the criterion of desirability [60]. Following the preparation of nanoparticles according to the optimum formulation, the resultant experimental responses were compared with the predicted responses to determine the percentage of the predicted error [59]. The optimization protocol was validated for predicted error values lower than $5\%$. ## 4.2.10. Statistical Analysis Measured values are represented as the mean ± standard deviation (SD) of at least three independent experiments. One-way ANOVA with Bonferroni post hoc test (SPSS 20.0, Chicago, IL, USA) was used to statistically analyze the data. The level of significance was set at probabilities of * $p \leq 0.05$, $p \leq 0.01$, and * $p \leq 0.001.$ ## References 1. Drucker D.J.. Transforming Type 1 Diabetes: The next Wave of Innovation. Diabetologia (2021.0) 64 1059-1065. DOI: 10.1007/s00125-021-05396-5 2. Singh A.K., Singh A., Singh R., Misra A.. Once-Weekly Basal Insulin Icodec: Looking ONWARDS from Pharmacology to Clinical Trials. Diabetes Metab. Syndr. Clin. Res. 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--- title: Cetyl Alcohol Polyethoxylates Disrupt Metabolic Health in Developmentally Exposed Zebrafish authors: - Matthew K. LeFauve - Roxanne Bérubé - Samantha Heldman - Yu-Ting Tiffany Chiang - Christopher D. Kassotis journal: Metabolites year: 2023 pmcid: PMC10057089 doi: 10.3390/metabo13030359 license: CC BY 4.0 --- # Cetyl Alcohol Polyethoxylates Disrupt Metabolic Health in Developmentally Exposed Zebrafish ## Abstract Alcohol polyethoxylates (AEOs), such as cetyl alcohol ethoxylates (CetAEOs), are high-production-volume surfactants used in laundry detergents, hard-surface cleaners, pesticide formulations, textile production, oils, paints, and other products. AEOs have been suggested as lower toxicity replacements for alkylphenol polyethoxylates (APEOs), such as the nonylphenol and octylphenol polyethoxylates. We previously demonstrated that nonylphenol polyethoxylates induced triglyceride accumulation in several in vitro adipogenesis models and promoted adiposity and increased body weights in developmentally exposed zebrafish. We also demonstrated that diverse APEOs and AEOs were able to increase triglyceride accumulation and/or pre-adipocyte proliferation in a murine pre-adipocyte model. As such, the goals of this study were to assess the potential of CetAEOs to promote adiposity and alter growth and/or development (toxicity, length, weight, behavior, energy expenditure) of developmentally exposed zebrafish (Danio rerio). We also sought to expand our understanding of ethoxylate chain-length dependent effects through interrogation of varying chain-length CetAEOs. We demonstrated consistent adipogenic effects in two separate human bone-marrow-derived mesenchymal stem cell models as well as murine pre-adipocytes. Immediately following chemical exposures in zebrafish, we reported disrupted neurodevelopment and aberrant behavior in light/dark activity testing, with medium chain-length CetAEO-exposed fish exhibiting hyperactivity across both light and dark phases. By day 30, we demonstrated that cetyl alcohol and CetAEOs disrupted adipose deposition in developmentally exposed zebrafish, despite no apparent impacts on standard length or gross body weight. This research suggests metabolic health concerns for these common environmental contaminants, suggesting further need to assess molecular mechanisms and better characterize environmental concentrations for human health risk assessments. ## 1. Introduction Alcohol polyethoxylates (AEOs) are high-production-volume nonionic surfactants used in laundry detergents ($39\%$ of total use), hard surface cleaners ($13\%$), dishwashing detergents ($12\%$), personal care products ($23\%$ of total use), and in a variety of other industrial and/or household applications [1]. The high efficiency and low costs of polyethoxylated surfactants have supported global annual production of >13 million metric tons in 2008 [2] and >33 billion USD in global revenues in 2014 [3]. Alkylphenol polyethoxylates (APEOs), such as nonylphenol ethoxylates (NPEOs), are widely used nonionic surfactants with growing evidence for toxicity. This encouraged a 2010 US Environmental Protection Agency (EPA) report: the Nonylphenol and Nonylphenol Ethoxylates Action Plan, which was designed to address concerns over ecological and other effects associated with the use of NPEOs. As part of this process, EPA’s Design for the Environment Program prepared an “Alternatives for Nonylphenol Ethoxylates” report in 2012 to help industry choose lower-toxicity replacement chemicals [4]. Despite the high reported aquatic toxicity, AEOs have been presented as some of the recommended alternatives, given their presumed lower persistence and lower metabolite toxicity [4,5]. Alcohol ethoxylates are thus the most widely used alternatives for APEOs in Europe and likely elsewhere [6]. As such, diverse AEOs have been widely reported globally at a range of concentrations in influent, effluent, surface and groundwater, and even in treated drinking water. Alcohol ethoxylates were measured in surface water near US wastewater treatment plants at mean concentrations from 116–184 μg/L in influent, from 286 to 506 μg/L in effluent and from 105 to 315 μg/L in outfall [7]. AEOs were measured in sewage treatment plants and receiving waters in NE Spain, with C12 ethoxylates ranging from <LOD to 12.3 μg/L ($90\%$ detection frequency), C14 ethoxylates ranging from <LOD to 19.4 μg/L ($84\%$ detection frequency), and C16 ethoxylates detected only in influent samples [5]. Concentrations of AEOs were measured in effluent from activated sludge wastewater treatment plants in Europe and Canada, with an overall mean concentration of 5.7 μg/L [8]. Specifically, median concentrations of C12, C13, C14, C15, C16, and C18 ethoxylates were 0.8 μg/L ($14\%$ of total AEO burden), 1.1 μg/L ($19\%$), 1.0 μg/L ($18\%$), 0.9 μg/L ($17\%$), 1.0 μg/L ($17\%$), and 0.9 μg/L ($16\%$), respectively [8]. C12 AEOs were also reported in Polish sewage effluent at concentrations that decreased with increasing ethoxylation: 0.6–2.0 μg/L for the base alcohol, 0.2–0.5 μg/L for one ethoxylate chain, and down to 50–150 ng/L for C12 alcohols with a six ethoxylate chain [9]. Similarly, river water samples were reported from 300 to 850 ng/L for the base alcohol and 50 to 500 ng/L for the one-to-six ethoxylate chain length C12 alcohols [9]. C12 AEOs (3–9 ethoxylate chains) were reported in the groundwater of farming areas in Denmark at concentrations from 60 to 190 ng/L and in soil interstitial water from 30 to 70 ng/L [6]. Colorado River and Colorado drinking water samples were analyzed for a series of AEOs, reporting C12, C13, C14, C15, C16, and C18 AEOs with ethoxylate chains ranging from 2 to 20 at low ng/L concentrations [10]. Overall, these studies suggest widespread contamination with high ng/L to μg/L concentrations of diverse alkyl chain length and ethoxylate chain length AEOs across water types and regions. Studies have generally described AEOs as readily degraded in the environment, though some have described only partial degradation, potentially due to the toxicity of the AEOs to the bacteria responsible for their degradation [11]. This research also demonstrated that AEOs were the most toxic surfactants tested across a range of species [11]. Comprehensive assessment of diverse C12–18 and EO0–18 AEOs and degradation through wastewater treatment plants (WWTPs) across the United States reported $95\%$+ removal. However, the retention of diverse AEOs in effluent samples was approximately 3.64 μg/L, suggesting massive influent concentrations of diverse AEOs [12]. While limited research has evaluated the in vitro biotransformation of shorter alkyl chain length AEOs in fish, the reported bioconcentration factors have indicated the potential for long-term adverse effects in aquatic environments [13]. An even higher bioconcentration factor was identified in follow-up research for an AEO with a longer alkyl chain length (equivalent to the one studied here), suggesting potential for bioaccumulation of AEOs [14]. Unlike APEOs, limited research has been conducted to investigate the in vivo health impacts of AEOs as they are presumed to be lower toxicity replacements [15,16,17,18,19,20,21,22,23]. We previously examined the adipogenic activity of a range of AEOs and APEOs in the 3T3-L1 adipogenesis model [24]. We demonstrated that both AEOs (including cetyl, lauryl, and tridecyl alcohol) and APEOs promoted significant adipogenic activity, with NPEO and cetyl alcohol ethoxylates (CetAEOs) inducing the greatest magnitude of effects via both triglyceride accumulation and pre-adipocyte proliferation. The adipogenic effects appeared to be specific to the ethoxylate chain length, with the base hydrophobes inducing limited or no adipogenic activity themselves and medium (4–10 ethoxylate chain lengths) size inducing maximal adipogenic activity [24]. Just recently, we confirmed these findings in vivo for the NPEOs [25], reporting consistent obesogenic effects on growth and development, particularly for the medium-chain-length NPEOs. However, we have yet to confirm these findings for the AEOs, such as the CetAEOs. Given the increasing use of zebrafish for metabolic health research [26,27,28,29,30,31,32,33,34,35], similarities to humans [36,37,38,39], and calls for reducing mammalian vertebrate animal use, there is a strong impetus for the further utilization of the zebrafish model to conduct metabolic health assessments. Specifically, zebrafish have emerged as a validated model for metabolic health research [26]. They develop quickly and have morphologically similar adipose to humans, storing neutral triglycerides in lipid droplets within white adipocytes, similar to mammals, and have similar gene expressions associated with adipocyte differentiation, lipolysis, and endocrine function [36,38,39,40,41]. Zebrafish are transparent and are thus readily amenable to fluorescent staining and full-body imaging to characterize and quantify their 34 anatomically, physiologically, and molecularly distinct adipose depots [37,38,39], with a comprehensive developmental timeline available to assess perturbations in adipose deposition [38,39]. Given these factors, a growing body of literature has utilized zebrafish to demonstrate altered growth, adipose development, and metabolic dysfunction following exposure to diverse environmental contaminants [26,27,28,29,30,31,32,33,34,35]. Moreover, there is an expanding body of work identifying diverse environmental contaminants as metabolism disruptors able to directly modulate metabolic health endpoints in vitro and/or in vivo [42,43,44,45,46]. With metabolic disorders, such as obesity, vastly increasing in incidence (obesity affecting >$42\%$ of US adults, >$70\%$ obese and overweight [47]), it is imperative to characterize how metabolism-disrupting environmental contaminants may be exacerbating this pandemic. The goals of this study were to assess the potential of CetAEOs to disrupt metabolic health in vivo. We hypothesized that medium ethoxylate chain lengths (6–10 ethoxymers) would promote the greatest adipogenic/obesogenic effects, consistent with what we previously observed for the NPEOs [25]. While we previously demonstrated adipogenic activity for a single CetAEO [24], the influence of varying ethoxylate chain lengths on adipogenic and/or obesogenic effects has yet to be systematically assessed. We sought to expand our understanding through the evaluation of CetAEOs in two separate human mesenchymal stem cell (hMSC) models and also in the murine 3T3-L1 pre-adipocyte model. We further evaluated CetAEOs through comprehensive metabolic health evaluation in a developmental exposure zebrafish model to assess in vivo metabolic health disruption. ## 2.1. Chemicals The chemicals used are described in detail in Table 1. Stock solutions were prepared in $100\%$ DMSO (Sigma, St. Louis, MO, cat # D2650) using the molecular weight (control chemicals) or average molecular weight (ethoxylated surfactants). Since none of the ethoxylates included here have commercially available pure standards, we instead utilized commercial mixtures with average ethoxylate chain lengths (Table 1). All of the stock and working solution vials were stored at −20 °C between uses. All of the chemicals were tested in vitro and in vivo at concentrations ranging from 10 μM to 1 nM, though 10 μM concentrations were toxic in vivo. Therefore, 1 μM is the highest test concentration used in zebrafish. ( TBT: 1 nM–1 pM and MEHP: 1 and 0.1 μM for in vivo testing; Table 1). ## 2.2. Cell Care 3T3-L1 cells (Zenbio cat# SP-L1-F, lot# 3T3062104; Research Triangle Park, NC; passage 8) were maintained in pre-adipocyte media (Dulbecco’s Modified Eagle Medium–High Glucose; DMEM-HG; Gibco # 11995, Thermo Fisher, Waltham, MA, with $10\%$ bovine calf serum and $1\%$ penicillin/streptomycin; Gibco # 15140) at a subconfluent state, as described previously [48,49,50,51,52], and utilized between passages 8 and 12. 3T3-L1 cells were seeded at ~30,000 cells per well into 96-well tissue culture plates, grown to confluency, and then allowed 48 h for growth arrest and clonal expansion before initiating differentiation (Figure S1A). Differentiation was induced by replacing media with test chemicals and/or controls (Table 1) using a DMSO vehicle (at $0.1\%$) in differentiation media (DMEM-HG with $10\%$ fetal bovine serum, $1\%$ penicillin/streptomycin, 1.0 μg/mL human insulin, and 0.5 mM 3-isobutyl-1-methylxanthine, IBMX). After 48 h of differentiation induction, the media were replaced with fresh dilutions of the test chemicals and/or control chemicals in adipocyte maintenance media (differentiation media without IBMX), and these media were refreshed every 2–3 days until assay, ten days after induction. Zenbio (cat# HBMMSC-F, lot# HBMMSC071819A, female, Caucasian, age 35) and Lonza (cat# PT-2501, lot# 19TL155677, male, Black, age 31; Lonza, Basel, Switzerland) hMSCs were induced to differentiate according to manufacturer’s instructions, as described previously [25]; differences in differentiation timelines reflect the differences in recommended protocols by the cell line providers (Lonza and Zenbio). Briefly, the cells were seeded in provider-specific basal media into 96-well plates at 10–15,000 cells per well and grown to confluence. Once confluent, differentiation was induced using the cell line providers’ commercially available differentiation media (Figure S1B,C). Briefly, the media were replaced with the test chemicals in differentiation media, as above (Zenbio catalog # DM-2-500; Lonza catalog # PT-3102B). For Zenbio-sourced cells, the differentiation media were left undisturbed for three days and then removed and replaced with fresh dilutions in adipocyte maintenance media (Zenbio catalog # AM-1); these were refreshed every 3–5 days for a further 18 days until assay at day 21 (Figure S1B). Lonza cells were treated with differentiation media and test chemicals for three days, then switched to adipocyte maintenance media (Lonza catalog # PT-3102A) for three days (Figure S1C). This cycle was repeated twice more (three days chemicals/differentiation media, then three days chemicals/adipocyte maintenance media) and then maintained in adipocyte maintenance media (media/chemical changes every 3–4 days) until assay at day 21. ## 2.3. Adipogenic Differentiation and Outcome Measurements The plates were processed for measurements of triglyceride accumulation and DNA content, as described previously [48,49,50,51,52]. Briefly, the cells were rinsed with Dulbecco’s phosphate-buffered saline (DPBS) and then treated with 200 μL/well of a dye mixture: ~19 mL DPBS, 20 drops/mL NucBlue® Live ReadyProbes® Reagent (DNA content; Thermo cat # R37605) and 500 μL Nile Red solution (40 μg/mL solution; Sigma cat #72485-100MG). After the addition, the plates were protected from light and incubated for forty minutes, and then the fluorescence was measured using a Molecular Devices SpectraMax iD5 (San Jose, CA) at $\frac{485}{572}$ nm excitation/emission for Nile Red and $\frac{360}{460}$ for NucBlue. Triglyceride accumulation was reported as percent activity, corrected for intra-assay differentiated vehicle control responses and relative to the rosiglitazone-induced maximum. The DNA content was reported as percent activity relative to the differentiated vehicle control responses. Normalized triglyceride content was calculated as total triglycerides per well per unit DNA content (used as a proxy for triglycerides per cell). Four technical replicates (wells within each assay plate) and three biological replicates (separate cell passages/assays) were utilized for every test chemical and concentration. ## 2.4. Zebrafish Husbandry Wildtype (AB) zebrafish (Danio rerio) were maintained according to protocols approved by the Wayne State University Institutional Animal Care and Use Committee, IACUC-20-06-2408. Breeding for embryo generation occurred following standard procedures [53]. Briefly, adult AB zebrafish were paired in breeding chambers overnight with gates pulled to initiate spawning the next morning at the time of lights on. Embryos were collected, cleaned, checked for viability, and stored overnight in embryo media (EM) with methylene blue. Embryos were fed beginning at 6 days post fertilization (dpf) with GEMMA Micro 75 (Skretting) twice daily until 15 dpf. At 15 dpf embryos were switched to GEMMA Micro 150 until 30 dpf. ## 2.5. Zebrafish Exposures At 24 h post-fertilization (hpf), the embryos were staged, and viable embryos were separated into 50 mL glass crystallizing dishes in 10 mL EM for chemical exposures ($$n = 15$$ individual embryos/chemical test concentration). All exposures were performed in 10 mL EM using chemical stocks at $0.1\%$ DMSO vehicle. Embryos were exposed from 24 hpf through 6 dpf with complete EM and tested for the chemical changes occurring daily. At 6 dpf, the media were replaced with fresh EM without test chemicals, and the embryos were aged to 30 dpf for morphometric measurements. Embryos were maintained in glass dishes in 15–30 mL of EM, until the time of sacrifice, with media changes occurring daily throughout the 30 days. ## 2.6. Zebrafish Metabolic Health The alamar blue assay was used to measure zebrafish metabolic rate. The assay was performed at 6 dpf, according to previously published protocols [25,54]. Following chemical exposures, the zebrafish were transferred into fresh EM with no added chemicals. For metabolic testing, wells of $$n = 3$$ embryos were set up in 24-well black clear-bottom microtiter plates. For each test, chemical, control, and concentration, $$n = 3$$ wells were used per plate ($$n = 9$$ fish per exposure group, with two separate exposure experiments/biological replicates). Briefly, EM was removed from all wells and replaced with 1 mL of alamar blue working dye solution ($99\%$ embryo media, $1\%$ alamarBlue Cell Viability reagent (Thermo cat# DAL1100)). Fluorescence was immediately measured using an iD5 Molecular Devices plate reader under $\frac{530}{590}$ nm excitation/emission wavelengths, and then plates were incubated at 28 °C and protected from light. The fish were incubated overnight (approximately 16 h), and the fluorescence was then measured again; the change in fluorescence was calculated by the difference of the values at 16 h from those at the immediate read. The data are presented as the relative change in arbitrary fluorescence units normalized to the DMSO control animals. ## 2.7. Larvae Locomotion At 6 dpf, the larval activity was assessed by using the swim distance in light and dark cycles, which was automatically quantified using Noldus Ethovision (version XT 16; Leesburg, VA, USA, [55]) during a 20 min period. Briefly, the larvae from the control and exposure groups were placed into a 24-well plate and were allowed to acclimate to a sound-insulated, temperature-controlled (26 °C), and light-controlled testing chamber, away from home tanks. All of the larvae were subjected to a 10 min period of light followed by a 10 min period of dark [56,57]. The movements of 24 individual larvae were simultaneously measured using an auto-detect feature of Ethovision, with all of the movement data then being binned into 60-s intervals. Manual observation of tracking success was conducted on at least two wells in each plate before any analysis. To reduce the potential for outlier observations, the data were smoothed using Ethovision before analysis. The raw data were exported into Microsoft Excel, and the average total distance moved (cm) per minute was analyzed in GraphPad Prism 9.0 (Boston, MA). The assay was replicated at least three times for each chemical and ethoxylate chain length, with each repetition performed on a different day with different larvae. ## 2.8. Morphology and Adipose Quantification Following locomotor and alamar blue analyses, the fish were returned to glass dishes and maintained until 30 dpf. At 30 dpf, the fish were stained with a 0.5 μg/mL concentration of Nile Red for 30 min and protected from light. The fish were then euthanized with 150 mg/L tricaine (MS-222), mounted onto depression slides, and imaged using a Leica Thunder M205FA stereoscope. The fish were imaged under brightfield and a yellow fluorescent protein (YFP) filter at 2× magnification for full-body imaging and standard-length measurements. The fish were then imaged at 16× magnification for higher-resolution adipocyte fluorescence quantification. After imaging, the fish were blotted and weighed on a microbalance, then snap-frozen in liquid nitrogen. Body mass indices were calculated by converting weights to grams and dividing by the squared lengths in millimeters for each individual fish, as performed previously [25,58]. For standard length and adiposity quantifications, the files were imported into Fiji (version 2.1.0). The standard length of the fish was obtained from the images taken at 2× magnification by using the segmented line tool to trace the contour of the fish from the frontmost part of the mouth to the beginning of the caudal fin. Fiji was able to calculate the length of the line based on the internal scale of each individual image. For images taken at 16x magnification, an individual image thresholding was used to select a pixel intensity range that outlined Nile-Red-stained, fluorescent adipose tissue. Regions of interest were drawn around the adipose tissue if the pixel intensity range was unable to outline adipose tissue without also outlining confounding high-intensity pixels, such as eye shines or fluorescence reflected off of the swim bladder. The total adipose tissue (AT) area was calculated for every individual in each treatment group and concentration. Image thresholding based on pixel intensity was performed to delineate AT area. For ATs that did not touch, the threshold was manually set using the slider until the area approximated the lipid dye [39]. Following quantification, the 34 defined adipose depots [37,39] were scored as present or absent and then compared to controls to determine the potential dysregulation of specific adipose depots. ## 2.9. Statistical Analysis The cell data are presented as means ± SEM from four technical replicates of three independent biological replicates. Zebrafish growth and metabolic data are presented as means ± SEM from 10–15 replicates (technical replicates from four independent spawning events/biological replicates). Non-normality was confirmed, and Kruskal–Wallis with Dunn’s multiple comparisons test was performed to determine significant differences across concentrations and relative to DMSO control fish ($p \leq 0.05$ considered significant). Statistical comparisons were made using GraphPad Prism 9.0. To address whether the outliers may influence the take-home findings from these experiments, a sensitivity analysis was also performed by removing the outliers with values greater than or equal to three standard deviations from the mean. These results are provided in the supplemental information and discussed in greater detail below. ## 3. Results CetAEOs were assessed for adipogenic activity in vitro by utilizing one murine pre-adipocyte model and two hMSC models and for obesogenic activity in vivo by utilizing developmental exposures and growth measurements in zebrafish. ## 3.1. Adipogenic Activity of CetAEOs Cetyl alcohol failed to induce any triglyceride accumulation in murine pre-adipocytes, though each of the varying chain length ethoxylates did (Figure 1A). Specifically, CetAEO-6 induced $85\%$ triglyceride accumulation relative to the maximal rosiglitazone-induced response, with lower (35–$40\%$) activity for CetAEO-2, 10, and 20. CetAEO-4 induced the lowest effects on triglyceride accumulation (~$16\%$). Neither the CetAEOs nor the base alcohol were able to induce pre-adipocyte proliferation in 3T3-L1 cells (Figure 1B). In Zenbio-sourced hMSCs, cetyl alcohol induced $12\%$ triglyceride accumulation relative to the maximal rosiglitazone-induced response at 10 mM (Figure 1A). In this model, CetAEO-6 and 10 induced the greatest degree of triglyceride accumulation ($19\%$ and $21\%$, respectively), with CetAEO-2 and 20 inducing approximately $11\%$ (Figure 1C). CetAEO-6 also promoted significant proliferation ($11\%$) relative to the differentiated solvent control responses (Figure 1D), equivalent to the rosiglitazone-induced response via this metric. In Lonza-sourced hMSCs, more potent and efficacious responses were observed relative to the Zenbio-sourced hMSCs. CetAEO-10 promoted the strongest effect ($48\%$ at 0.1 μM), with CetAEO-6 promoting $11\%$ triglyceride accumulation at 0.1 μM as well (Figure 1E). At 1 mM, CetAEO-20, the base cetyl alcohol, CetAEO-2, and -4 promoted significant triglyceride accumulation ($39\%$, $22\%$, $17\%$, and $14\%$, respectively). As with Zenbio-sourced hMSCs, CetAEO-6 was the only compound able to promote significant proliferation (Figure 1F). Maximal responses for each test chemical were further compared across each of the three adipogenesis models for triglyceride accumulation and proliferation. For triglyceride accumulation, responses were generally consistent (Figure 1G), with maximal responses occurring in medium-chain-length compounds (CetAEO-6 or -10) and with generally lower efficacies for the hMSCs relative to the 3T3-L1 model (with the exception of the longer chain length CetAEOs). This was generally consistent for proliferation responses (Figure 1H), though most of these responses were not significantly different from the baseline; as noted previously, only CetAEO-6 promoted significant effects in the hMSC models. ## 3.2. Lethality of NPEOs on Zebrafish Zebrafish were exposed to test chemicals at a range of concentrations from approximately 24 h post-fertilization through to 6 days post-fertilization (dpf). Throughout and following the exposures, the zebrafish were checked daily for the lethality of the test chemicals (dead embryos removed to protect remaining live zebrafish) and to determine non-toxic concentrations for subsequent analyses. Both the vehicle (DMSO)-treated and control (embryo media, no exposure) fish had average survival rates of approximately $70\%$ throughout the 30 days, with no appreciable mortality observed during the chemical exposure window and limited mortality in the weeks following. Chemical exposures were calculated relative to the DMSO-treated fish survival to account for this baseline mortality observed in our system. Each of the CetAEOs at 10 μM induced >$75\%$ mortality relative to the DMSO-treated fish and were thus removed from the study following preliminary dose-finding. The TBT positive control was significantly more toxic than CetAEOs, with complete lethality noted for concentrations of 0.01 μM and above (and thus excluded). At 1 μM, MEHP induced approximately $60\%$ mortality ($p \leq 0.05$), CetAEO-4 promoted $40\%$ mortality ($p \leq 0.10$), and CetAEO-6 promoted approximately $50\%$ mortality ($p \leq 0.05$; Figure 2). Interestingly, cetyl alcohol had significant mortality in the 0.001 μM and 0.1 μM exposure groups ($50\%$ and $60\%$ mortality, respectively; $p \leq 0.05$), despite having no significant effects at 0.01 μM and 1 μM. A similar pattern was observed for CetAEO-10, which had non-significant increases in mortality at 0.001 and 0.1 μM ($40\%$ and $30\%$, respectively; $p \leq 0.10$). CetAEO-6 also tended to exhibit mortality at the 0.1 μM dose (~$30\%$ mortality, $p \leq 0.10$). Lastly, CetAEO-20 tended to increase mortality at the 0.01 and 0.1 μM doses ($28\%$ and $20\%$, respectively; $p \leq 0.10$). Energy Expenditure and Activity at 6 Days. Energy expenditure was determined via the alamar blue assay as an approximate measure of zebrafish oxidative metabolism and cellular metabolic respiration (e.g., NADH2 production) [54,59]. The 0.00001 μM TBT control had significantly lower energy expenditure than the DMSO control animals ($p \leq 0.05$, Figure 3), and the 1 μM MEHP animals tended to be reduced as well ($p \leq 0.10$). The two lowest doses of cetyl alcohol (0.001 and 0.01 μM) also had lower energy expenditure, along with the 0.1 μM CetAEO-2 group, the 1 μM CetAEO-4 group, the 0.01 μM CetAEO-6 group, the 0.01 μM CetAEO-10 group, and the 1 μM CetAEO-20 group ($p \leq 0.05$ all). The 0.1 μM CetAEO-2 and 0.1 μM CetAEO-6 groups tended to be reduced as well ($p \leq 0.10$). While no groups were significantly elevated relative to the DMSO control animals, a number of groups had increased variance, with a subset of animals demonstrating much greater energy expenditure relative to the DMSO control animals. This was particularly apparent in the 0.000001 μM and 0.001 μM TBT groups and across many of the medium- and long-chain-length CetAEOs. Removing outliers resulted in similar results for energy expenditure testing. The groups retained significance, and increased significance was noted for the CetAEO-2 exposure groups and the 1 μM cetyl alcohol group (Figure S2). A separate set of fish was examined for the total activity at 6 dpf through light/dark photoperiod activity tracking. Following acclimation, activity in the DMSO-exposed fish remained low (~2 cm per minute) during the ten-minute light period and then sharply increased to approximately 10 cm/min during the subsequent ten-minute dark period (Figure 4). At 1 μM, cetyl alcohol, CetAEO-2, CetAEO-4, CetAEO-6, and CetAEO-10 displayed significantly greater activity throughout the light period relative to the DMSO control fish ($p \leq 0.01$), while TBT and CetAEO-20 showed no significant difference. During the dark period, CetAEO-4 and -6 displayed significantly greater activity relative to the control fish ($p \leq 0.01$; Figure 4). Interestingly, responses to CetAEO-4 and -6 were strikingly distinct from the other chemicals, maintaining a high degree of activity regardless of the light versus dark cycles. Testing was also performed across the full range of concentrations for each test chemical (Figure S3). While every other compound had effects for at least one concentration, no disruption of activity was observed for any concentration of TBT or CetAEO-20 tested (Figure S3A,G). Cetyl alcohol had significantly increased activity during the light period at 1 μM, 0.1 μM, and 0.001 μM ($p \leq 0.01$), and during the dark period only at 0.001 μM ($p \leq 0.01$; Figure S3B). CetAEO-2 had significantly increased activity at each concentration during the light period and only at 0.01 μM during the dark period ($p \leq 0.01$; Figure S3C). CetAEO-4 had significantly increased activity at each concentration during the light period and also for 0.001 μM, 0.1 μM, and 1 μM during the dark period ($p \leq 0.01$; Figure S3D). CetAEO-6 had significantly increased activity for 0.001 μM, 0.01 μM, and 1 μM during the light period and for 0.01 μM, 0.1 μM, and 1 μM during the dark period ($p \leq 0.01$; Figure S3E). Lastly, CetAEO-10 had significantly increased activity only at 1 μM during the light period, as noted above, with no effects observed during the dark period ($p \leq 0.01$; Figure S3F). ## 3.3. Growth Trajectory, Weights, and Adipose Deposition Developmentally exposed zebrafish were aged to 30 dpf, stained with Nile Red, and then imaged, measured, and weighed. The zebrafish did not exhibit any significant changes in standard length across the treatment groups (Figure 5A), suggesting no gross impacts on body size due to the chemical treatments. There were also no significant differences in blotted weights at 30 dpf between the groups (Figure 5B). Despite this, there were appreciable increases in zebrafish body mass index (BMI; g/mm2) in relation to some control chemicals (Figure 5C). Specifically, TBT-exposed fish had increased BMI relative to the DMSO control fish ($15\%$ increase for 0.00001 μM TBT, $p \leq 0.10$; $31\%$ increase for 0.0001 μM TBT, $p \leq 0.05$; $12\%$ increase for 1 μM MEHP, $p \leq 0.10$). A quantitative assessment of the total body lipid accumulation across the test chemicals and concentrations in vivo was performed via fluorescent microscopy following Nile Red staining. The area of fluorescent adipose tissue was measured using ImageJ as pixels (Figure 6A) across the whole fish to determine the differences between the exposure groups. Manual scoring of the presence/absence of visible fluorescing adipocytes in each of the 34 characterized zebrafish adipose depots [37,39] was also performed to assess the location of any shifts in adipose deposition. The complete breakdown of adipose depot occurrence is provided in Figure 6B, with the relative occurrence (relative increase or decrease in treatments relative to DMSO control animals) provided in Figure 6C. The DMSO control fish had few apparent adipocytes by 30 days, primarily focused in the pancreatic and abdominal visceral adipose depots (~$60\%$ had visible adipocytes in these depots), though ~$10\%$ of the DMSO-exposed fish had visible adipocytes in some subcutaneous cranial adipose regions. Several significant differences were noted by fluorescent adipose tissue (FAT) area quantification. Specifically, 0.0001 μM TBT fish had increased total adipose relative to the control fish ($p \leq 0.05$; Figure 6A), and this appeared to be focused on the pancreatic, abdominal, and renal visceral (PVAT, AVAR, and RVAT, respectively) depots; the oesophageal non-visceral (OES) depot; the basihyal hyoid, ceratohyal hyoid, and urihyal (BHD, CHD, and UHD, respectively) depots; and the lateral truncal (LSAT) depot (Figure 6C). The 0.1 and 1 μM MEHP groups had increased adipose relative to the controls ($p \leq 0.05$), and this appeared to be mostly constrained to the PVAT, AVAT, and RVAT depots. Each concentration of cetyl alcohol (0.001–1 μM) had greater adipose relative to the controls, and this appeared to be spread across the PVAT, AVAT, RVAT, OES, BHD, CHD, and LSAT, as well as the posterior ocular (pOCU) and dorsal opercular (dOPC) depots. The 0.001 and 0.01 μM concentrations of CetAEO-2 had significantly increased FAT area relative to control fish, and this appeared to be concentrated in the PVAT, AVAT, RVAT, BHD, CHD, and dOPC depots. The 0.001–0.1 μM concentrations of CetAEO-4 had significantly increased FAT area, apparently constrained to the PVAT, AVAT, RVAT and LSAT depots. Lastly, the 0.01 and 1 μM CetAEO-10 fish had increased FAT area relative to the control fish, with the increased adipose seemingly focused in PVAT, AVAT, RVAT, BHD, CHD, and ventral opercular (vOPC) depots. Apparent shifts in adipose presence were observed across other groups (most notably for 0.001 μM CetAEO-10) and might reflect the re-distribution of adipose rather than increased deposition, as the FAT area was not significantly different for some of these other groups. Removing outliers resulted in similar results for adipose area quantification, with all exposure groups retaining the significant differences noted in the primary analysis of all exposed zebrafish (Figure S4). ## 4. Discussion We previously published data using the 3T3-L1 murine pre-adipocyte model demonstrating a high magnitude of adipogenic activity for various alkylphenols, alcohols, and their polyethoxylates [24]. Further assessment of the NPEOs demonstrated consistent adipogenic effects in two separate human mesenchymal stem cell models [25] as well as pro-obesogenic effects following developmental exposures in zebrafish [25]. A purportedly lower toxicity replacement to NPEOs are AEOs, which have been suggested to degrade faster and into less toxic metabolites. Despite this, many of these AEOs demonstrate moderate to high aquatic toxicity. To determine whether the previous 3T3-L1 testing was robust, we herein characterized CetAEOs with a range of ethoxylation in three in vitro models and also in zebrafish. Herein, we report consistent pro-adipogenic effects in hMSCs and 3T3-L1 cells, which vary in terms of ethoxylate chain length, as well as metabolic disruption in vivo in the vertebrate zebrafish model (altered energy expenditure, total activity, and disrupted adipose development). Several differences were noted between adipogenic activity testing across the models. Significant effects on triglyceride accumulation were noted for each CetAEO, with the maximal effects (~$90\%$ activity relative to the rosiglitazone-induced maximum) observed for CetAEO-6. No effects were observed for the base cetyl alcohol or for any of the compounds for the pre-adipocyte proliferation metric. Each of the CetAEOs induced significant triglyceride accumulation in both hMSC models, though to different maximal activities and at different concentrations than in 3T3-L1 cells. The effects were often higher potency (significant effects at lower concentrations) in hMSCs but with considerably lower magnitudes than in 3T3-L1 cells. Interestingly, consistent effects were observed in the hMSC models on the proliferative response, with CetAEO-6 promoting significant proliferation relative to the differentiated vehicle control cells. Consistency was also observed in patterning, with medium-chain-length AEOs inducing the highest magnitude effects across test chemicals, similar to what was observed for the NPEOs [25]. We report for the first time that cetyl alcohol and its ethoxylates induced metabolic health disruption in developmentally exposed zebrafish. Interestingly, we observed no significant changes in either standard length or total body weight. We did observe significant increases in zebrafish BMI (weight/length squared) for the positive control compounds TBT and MEHP, though still not for any of the ethoxylates examined. Our effects appeared to be more restricted to adipose deposition. Adipose area (as determined via Nile Red fluorescence) was significantly increased in TBT and MEHP control fish, as well as in multiple concentrations of the cetyl alcohol, CetAEO-2, CetAEO-4, and CetAEO-10 groups. There was increased variability in the CetAEO-6 group, with some fish showing greatly enhanced adipose, but these differences were not significantly different relative to controls. This is appreciably distinct from our previous study, where we saw more significant effects in our TBT-exposed animals [25]; though importantly, this was with a different source of fish, in a different facility, and on a different larval diet. While this could be that our assay for this set of chemicals was less sensitive than our previous testing, we retained significant effects in our control chemicals and thus suspect we would have observed effects for the CetAEOs if they were apparent. In addition, the increased activity we observed for many of our test compounds, even during the light cycles, supports the lack of increases observed in weights. In the case of the cetyl alcohols and ethoxylates, we seemed to see the most enhanced adipose deposition in subcutaneous cranial adipose depots. There was certainly enhanced visceral adiposity in the pancreatic, abdominal, and renal visceral depots, though this diversity was lower relative to the NPEOs we previously assessed. Instead, we saw a greater assortment of non-visceral depots occurring in the test chemical exposures in depots where no adipose was observed in our control animals. Interestingly, this increased adiposity, independent of changes in standard length or total body weight, suggests that there could be impacts on the recruitment of other cell lineages. This should be assessed further in future research. We observed a particularly robust response to the light/dark testing performed in the exposed zebrafish at 6 dpf. Greatly elevated activity was observed in both the light and dark cycles across chemicals and concentrations, with the most striking effects observed for CetAEO-4 and CetAEO-6, where the larvae had no apparent response to light/dark changes and maintained a large increase in activity throughout the testing period. Increased activity was also observed for the base cetyl alcohol, CetAEO-2, and CetAO-10 during the light phase only, whereas CetAEO-20 did not impact activity in either phase at any concentration tested. Neurotoxicity has also been observed previously following exposure to a commercial AEO mixture, with the 0.8 and 3.2 μg/L concentrations reducing the distance traveled and total activity of the exposed zebrafish [60]. This was also demonstrated in another study examining commercial AEO mixtures used in lubricant emulsions [61]. A recent study examined a set of detergents (known to contain AEOs) and several linear AEOs and found that both the detergents and the AEOs were able to significantly reduce the mean swimming speed of zebrafish following exposures to 150 ppm detergents or 50 ppm AEOs [62]. In stark contrast, we observed significantly increased activity; this difference could be due to a number of factors. First, the timing of exposure in each of these three studies varied from ours, spanning embryonic to adult exposures, and varying stages of brain development. Second, the compositions and concentrations of the test chemicals varied between studies, with us testing more analytical mixtures as compared to more technical industrial mixtures employed in these other studies. We focused on a single alkyl chain length (cetyl, 16 carbons) and a range of ethoxylation (average of 0, 2, 4, 6, 10, and 20 ethoxylate chains), while these other studies assessed varying alkyl and ethoxylate chains. Third, large differences in the behavioral testing paradigm used between studies may have contributed to differences as well. Interestingly, one-third to one-half of the animals in the medium-to-long-chain CetAEO groups demonstrated a drastic increase in activity under the light/dark neurodevelopmental testing. This same trend was observed in the alamar blue energy expenditure testing, with a subset of animals demonstrating a large increase in energy expenditure relative to the DMSO control animals. It could be that these increased energy expenditure and increased activity animals were a subset of animals demonstrating a differential response to the contaminants relative to the rest of the exposure groups. This should be examined further in future studies, as well as potential mechanisms for the variance in these responses. Greater gross toxicity was observed for the CetAEOs relative to the NPEOs that we examined previously [25]. For the NPEOs, the 10 μM concentration was toxic for all but NPEO-20, with the 1 μM concentration exhibiting 20–$30\%$ mortality for the base nonylphenol as well as NPEO-2 and NPEO-4. The 10 μM concentration was overtly toxic for all CetAEOs and was thus excluded from our study. Significant toxicity of up to $50\%$ relative to the DMSO control animals was observed for CetAEO-4 and -6 at 1 μM and for CetAEOs-6, -10, -20, and for the base cetyl alcohol at nM concentrations. A number of studies have previously reported a high degree of aquatic toxicity following CetAEO exposures in various organisms. Studies examining commercial AEO mixtures in Xenopus reported 72 h LC50 values of ~5 mg/L, various malformations (edema, loss of pigmentation, and microcephaly), and the collapse of the mitochondrial electrochemical gradient [63]. Various alcohol-based surfactants induced 96 h LC50 values of ~8 mg/L in bluegill sunfish [64] and ~3.0 mg/L 28-day toxicity in fathead minnows [65] (with more sensitive effects on growth than survival). A variety of AEOs induced no observed effect concentrations on survival and reproduction from 0.8 to 2.8 mg/L with varying carbon chain lengths of 10–14.5 (cetyl alcohol, examined here, has a backbone of 14 carbons) and average ethoxylate chains of ~6.5 in Daphnia [66]; 21-day LC50s were 1.2–5.9 mg/L for these surfactants [66]. To provide context, our observed effects generally occurred at 1 and 10 μM concentrations, which are approximately equivalent to concentrations of 0.33–3.3 mg/L for CetAEO-2 and up to 1.12–11.23 mg/L for CetAEO-20. Given the presumed lower toxicity of the AEOs and their metabolites, this increased toxicity should be evaluated further in future studies. Studies have generally reported that the toxicity of AEOs increased with increasing alkyl chain length [67,68,69] across species, which we also found to be true for adipogenic responses previously [24]. They have also generally supported that average alkyl chain length had a greater impact on toxicity than average ethoxylate chain length [67,69], though this was species-specific. Interestingly, another study confirmed that toxicity increased with increasing alkyl chain length and showed a parabolic relationship with ethoxylate chain length (with a maximum at eight ethoxylate units) [68]; this is very similar to the results obtained in our study, where our maximum effects occurred around an ethoxylate chain length of six. Some reports have also suggested synergistic effects of AEOs on pesticide-induced toxicity against several pests and Daphnia [70,71], with synergism observed across multiple pesticide combinations, though antagonistic relationships observed for several [71], to an appreciably greater degree than the APEO alternatives. There has also been some toxicity testing in zebrafish previously. Specifically, AEOs were found to be quite toxic to zebrafish, with 48 h embryo and 96 h adult LC50 values of 5–6 mg/L [72]. Another study examining a commercial AEO mixture in zebrafish embryos reported an LC50 of ~15 μg/L, with detrimental effects on organ development at concentrations as low as ~3 μg/L (increased heart rate, reduced hemoglobin, increased liver size, increased total lipid retention) [60]. Another study of the AEO mixtures used in lubricants found that decreasing ethoxylation resulted in increased toxicity to zebrafish [61]. A recent study examined a set of detergents (known to contain AEOs) and several linear AEOs and found that both the detergents and the AEOs were able to significantly induce lethality in zebrafish larvae, with the AEO mixture demonstrating robust lethality even at the lowest concentration tested (50 ppm) [62]. These results raise concerns over the use of these surfactants to replace the APEOs. Our preliminary assessment of cetyl alcohol and its ethoxylates suggests metabolic disruption potential that does not appreciably decrease with decreasing ethoxylate chain length. Similar to NPEOs, there was greater reported toxicity for the base cetyl alcohol, which the ethoxylates eventually degraded into. The CetAEOs are purported to have lower toxicity and degrade into less toxic metabolites, though our results suggest that this may not be entirely true. Interestingly, we saw perhaps the most striking effects on adipose deposition for the base cetyl alcohol and across the full range of concentrations, with significantly increased adipose even in the 0.01 μM exposure group. While we previously reported that internal visceral depots were more disrupted with the NPEO exposures relative to subcutaneous depots, this same trend was not necessarily observed for this set of contaminants. We saw greater diversity in the subcutaneous depots, though the visceral depots were certainly enhanced in the chemically exposed animals relative to the controls. Consistent with the NPEOs, we again observed the development of adipose depots long before their normal developmental timing. PVAT and AVAT are the first depots to develop in the zebrafish [37,38,39]. Other impacted depots (BHD, CHD, RVAT) are also earlier developing depots, though here we observed their development often before their standard developmental length (as a measure of developmental timepoint). Other depots, such as UHD and dOPC, developed in some exposed fish long before they should normally have developed. These results describe a need for future assessments that further elucidate depot-specific effects following exposure to adipogenic chemicals. Interestingly, we observed different impacts on endpoints comparing these AEOs to APEOs [25]. In vitro, we observed the same pattern of adipogenic activity as we had observed for the NPEOs, with maximal activity observed in the medium-chain-length ethoxylates. We have also reported extremely high adipogenic activity and toxicity for a novel set of fluorotelomer ethoxylates found in commercial products [73], which should also be explored further in vivo. The medium-chain-length AEOs had the greatest impacts in the neurodevelopmental testing (based on activity during the light/dark testing) and in the alamar blue energy expenditure testing. However, the greatest impacts on adipose deposition were observed in the base cetyl alcohols and the lower-chain-length ethoxylates (CetAEO-2, 4). The mechanisms underlying these effects and underlying the differences between the NPEOs and the CetAEOs should be assessed further in future studies [24]. ## 5. Conclusions In summation, we report obesogenic effects in our zebrafish model and in multiple in vitro models for these cetyl alcohol polyethoxylates. These compounds are NPEO alternatives, suggesting that they may be a “regrettable substitution”. There is high conservation across vertebrates for adipose morphology, energy storage and lipid depot development, and associated/underlying gene signaling [37,38,39,74]; as such, these results, coupled with testing in human cell models, suggest a potential underexplored human health risk that necessitates further investigation into whether these chemicals may exacerbate obesity or contribute to the ongoing metabolic disorder pandemic [44,45,46]. Importantly, none of the ethoxylates included here have commercially available pure standards; instead, we have utilized commercial mixtures with average ethoxylate chain lengths. 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--- title: 'Remnant cholesterol and mild cognitive impairment: A cross-sectional study' authors: - Qiaoyang Zhang - Shan Huang - Yin Cao - Guanzhong Dong - Yun Chen - Xuanyan Zhu - Wenwei Yun - Min Zhang journal: Frontiers in Aging Neuroscience year: 2023 pmcid: PMC10057110 doi: 10.3389/fnagi.2023.1069076 license: CC BY 4.0 --- # Remnant cholesterol and mild cognitive impairment: A cross-sectional study ## Abstract ### Objective Emerging evidence suggests that elevated remnant cholesterol (RC) correlates with several health conditions. To explore the association of plasma RC with MCI incidence and the relationship between plasma RC and different domains of cognition in MCI patients. ### Methods Thirty-six MCI patients and 38 cognitively healthy controls (HC) were enrolled in the present cross-sectional study. Using total cholesterol (TC) minus high-density lipoprotein cholesterol (HDL-C) minus low-density lipoprotein cholesterol (LDL-C) as the formula for calculating fasting RC. Cognition was assessed using the Chinese version of the Montreal cognitive assessment (MoCA), Auditory Verbal Learning Test (AVLT), Digit Symbol Substitution Test (DSST), Trail Making Test (TMT), and Rey-Osterrieth Complex Figure Test (ROCF). ### Results Compared to healthy controls, MCI patients had a higher level of RC, the median difference in RC levels between these two groups was 8.13 mg/dl ($95.0\%$CI: 0.97–16.1). Concurrently, plasma RC level was positively associated with MCI risk (OR = 1.05, $95\%$CI: 1.01–1.10). Notably, elevated RC level was correlated with impaired cognition in MCI patients, such as DSST (pr = −0.45, $$p \leq 0.008$$), ROCF- Long Delayed Recall (pr = −0.45, $$p \leq 0.008$$), AVLT-Immediate Recall (pr = −0.38, $$p \leq 0.028$$), and TMT-A (pr = 0.44, $$p \leq 0.009$$). Conversely, no significant correlation was found between RC and the AVLT-Long Delayed Recall test. ### Conclusion This study found that plasma remnant cholesterol was associated with MCI. Further large longitudinal studies are needed in the future to confirm the results and clarify the cause-and-effect relationship. ## Introduction Dementia is defined as a significant decline in cognition that interferes with independence and daily functioning. China accounts for approximately $25\%$ of the world’s population with dementia (Jia et al., 2020). Mild cognitive impairment (MCI) is a transitional state between normal aging and dementia disorders, particularly Alzheimer’s disease (AD). Each year, approximately 10–$15\%$ of individuals with MCI develop dementia (Giau et al., 2019). As elevated cholesterol in plasma has been linked to several health conditions, it may involve in the pathogenesis of MCI. For example, A large Chinese population-based study ($$n = 46$$,011) suggested hyperlipidemia as a risk factor for MCI (Jia et al., 2020). Another meta-analysis reported that elevated cholesterol levels in mid-life may increase the risk of cognitive impairment in late life, whereas higher levels of cholesterol in late life were not associated with dementia or cognitive impairment (Anstey et al., 2017). Thus, investigators believe that the relationship between cholesterol and MCI is age-dependent and mid-life hyperlipidemia is a risk factor for developing dementia or cognitive impairment at a later age. In recent years, accumulating evidence suggests that remnant cholesterol (RC) in triglyceride-rich lipoproteins promotes residual atherosclerotic cardiovascular disease (ASCVD) risk after lowering low-density lipid cholesterol (LDL-C) to the recommended target (Saeed et al., 2018; Burnett et al., 2020; Langsted et al., 2020; Bruemmer and Cho, 2021; Chevli et al., 2022; Hao et al., 2022; Zheng et al., 2022). Remarkably, a large prevention study ($$n = 17$$,532) reported that RC predicts cardiovascular disease beyond LDL-C and apolipoprotein B in patients without known ASCVD (Quispe et al., 2021). RC is defined as total cholesterol (TC) minus LDL-C minus high-density lipid cholesterol (HDL-C). In the fasting state, RC includes very low-density lipoproteins (VLDL) and intermediate-density lipoproteins (IDL), and RC in the non-fasting state is composed of these two lipoproteins plus chylomicron remnants. Furthermore, except for cardiovascular disease, several studies reported that RC could predict stroke, hypertension, nonalcoholic fatty liver disease, diabetes mellitus, and aortic valve stenosis (Kaltoft et al., 2020; Jansson Sigfrids et al., 2021; Qian et al., 2021; Chen et al., 2022; Hu et al., 2022; Huang et al., 2022; Li et al., 2022). Considering the wide connection between the diseases above and MCI, we hypothesized that plasma RC levels were also related to MCI. Therefore, in this study, we examined the association between plasma RC and MCI incidence and the relationship between plasma RC and different domains of cognitive performance in MCI patients. ## Ethics statement This study was approved by the Institutional Review Board (IRB) of The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University. All participants completed an informed consent form. The studies involving human participants were reviewed and approved by This study was approved by the Institutional Review Board (IRB) of The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University. All participants completed an informed consent form. The patients/participants provided their written informed consent to participate in this study. ## Participants A total of 38 MCI patients and 40 cognitively healthy controls (HC) participated in this cross-sectional study. However, 2 MCI patients and 2 HC subjects were excluded because of their negative RC levels calculated by formula. As a result, 36 MCI patients and 38 HC subjects were enrolled in our final analyses. The MCI patients were recruited in the memory clinic of Affiliated Changzhou Second People’s Hospital of Nanjing Medical University in February–December 2021. The inclusion criteria included: [1] aged 50–70 years; [2] meeting the MCI criteria, based on the 2011 guidelines of the National Institute of Aging-Alzheimer’s Association workgroups (NIA/AA) (Albert et al., 2011). The exclusion criteria included: [1] having a substance use disorder except for nicotine; [2] personal or family history of severe psychiatric disorders; [3] a history of serious chronic medical conditions that may affect cognitive function, including liver and renal failure, hypothyroidism, cerebral infarction, cerebral hemorrhage; [4] a history of coronary heart disease or taking the prescribed lipid-lowering drug. Community-dwelling volunteers aged 50–70 years who had a Montreal Cognitive Assessment (MoCA) score of 26 or higher were recruited as HC subjects. The exclusion criteria for HC subjects were the same as for MCI patients. ## Data collection All participants provided sociodemographic data, health-related information, cognitive assessments, and blood sample for cholesterol analysis. All the data were collected in one day. Current smoking status was defined as at least 10 cigarettes/d for more than 3 years. Hypertension was defined as having a self-reported history of hypertension, SBP ≥ 140 mmHg, or DBP ≥ 90 mmHg, or taking any anti-hypertensive drugs. Diabetes was defined as any self-reported history, receiving hypoglycemic medication, fasting blood glucose ≥7.0 mmol/L, and/or OGTT ≥11.1 mmol/L. ## Cognitive assessment Global cognition was assessed using the Chinese version of the Montreal cognitive assessment (MoCA). Meanwhile, different domains of cognition were measured as follows: [1] Memory: the Auditory Verbal Learning Test (AVLT), which includes immediate recall (AVLT-IR) and 20-min long-delayed recall (AVLT-LR); [2] Sustained Attention: the Digit Symbol Substitution Test (DSST); [3] Executive Function: the Trail Making Test (TMT), which includes part A (TMT-A) and part B (TMT-B); [4] Visuospatial Skill: the Rey-Osterrieth Complex Figure Test (ROCF), including immediate recall (ROCF-IR) and long-delayed recall (ROCF-LR). All scales were conducted by experienced investigators following the guidelines. ## Lipid measurement Overnight fasting blood samples were drawn at 8 am during the medical check. Plasma lipids including TC, triglycerides (TG), HDL-C, and LDL-C were immediately enzymatically measured at the clinical laboratory on Roche Cobas 8,000 automatic biochemical analyzer with commercial reagents (Roche Diagnostics, Shanghai). RC was calculated by subtracting high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) from total cholesterol (TC): We categorized the lipid measurements according to the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Cholesterol in Adults (ATP-III) (NCEP, 2002). ## Statistical analysis Variables with a normal distribution were expressed as mean ± SD, while variables with a skewed distribution were expressed as median (interquartile range [IQR]). Categorical variables were expressed as frequencies (%). Demographic characteristics of the MCI and HC group were analyzed using independent samples t-test (normal distribution), Mann–Whitney U-test (non-normal distribution), and chi-square test (categorical variables). Data Analyses with Bootstrap-coupled ESTimation (DABEST) were used to compare the differences in RC levels (Ho et al., 2019). By plotting the data as the median difference in RC levels between MCI patients and cognitively healthy controls, the Gardner-Altman estimation plots can help visualize the effect size. Based on prior studies and theoretical considerations, we selected established risk factors for MCI (Liu et al., 2021; Wang M. et al., 2021). Thus, age, gender, education level, smoking status, hypertension, diabetes, and RC were entered into binary logistic regression analyses (Forward: LR). To examine whether impaired cognitive performance correlated with elevated RC in MCI patients, we tested their associations using partial correlation analyses controlling for age and education level. All analyses were performed with the statistical package R 4.2.01 (R Foundation). The significance level was defined as $p \leq 0.05$ (two-sided). ## Demographic and clinical parameters of MCI patients and healthy controls As shown in Table 1, there were significant differences between MCI patients and healthy controls in demographics, and cognitive performance, including age, education level, TMT-A, AVLT-IR, AVLT-LR, DSST, ROCF-LR, and MoCA (all $p \leq 0.05$). MCI patients had a worse performance at the cognitive subtests above. Table 1 \| Variable \| Overall (n = 74) \| HC (n = 38) \| MCI (n = 36) \| P \| \| --- \| --- \| --- \| --- \| --- \| \| Sociodemographic and health-related characteristics \| \| \| \| \| \| Age \| 60.0 (56.0, 66.0) \| 58.5 (54.3, 63.0) \| 61.5 (57.0, 67.3) \| 0.022 b \| \| Gender, n (%) \| \| \| \| 1a \| \| Male \| 60 (81.1) \| 31 (81.6) \| 29 (80.6) \| \| \| Female \| 14 (18.9) \| 7 (18.4) \| 7 (19.4) \| \| \| Education, years \| 9.0 (8.0, 9.8) \| 9.0 (9.0, 12.0) \| 9.0 (7.0, 9.0) \| 0.003 b \| \| Current smoker, n (%) \| \| \| \| 1a \| \| No \| 40 (54.1) \| 21 (55.3) \| 19 (52.8) \| \| \| Yes \| 34 (45.9) \| 17 (44.7) \| 17 (47.2) \| \| \| History of hypertension, n (%) \| \| \| \| 0.889a \| \| No \| 18 (24.3) \| 10 (26.3) \| 8 (22.2) \| \| \| Yes \| 56 (75.7) \| 28 (73.7) \| 28 (77.8) \| \| \| History of diabetes, (%) \| \| \| \| 0.876a \| \| No \| 51 (68.9) \| 27 (71.1) \| 24 (66.7) \| \| \| Yes \| 23 (31.1) \| 11 (28.9) \| 12 (33.3) \| \| \| Lipid profiles \| \| \| \| \| \| Triglycerides, mg/dL \| 131.6 (100.1, 162.1) \| 127.6 (97.0, 162.1) \| 133.8 (111.9, 163.9) \| 0.36b \| \| TC, mg/dL \| 157.4 ± 36.9 \| 153.4 ± 36.1 \| 161.7 ± 37.8 \| 0.334c \| \| LDL-C, mg/dL \| 85.8 ± 29.5 \| 87.8 ± 28.8 \| 83.7 ± 30.4 \| 0.554c \| \| HDL-C, mg/dL \| 39.9 ± 8.7 \| 39.6 ± 9.2 \| 40.2 ± 8.3 \| 0.744c \| \| RC, mg/dL \| 30.4 (20.1, 38.1) \| 26.3 (17.0, 33.4) \| 34.4 (26.0, 45.0) \| 0.003 c \| \| Cognitive assessment \| \| \| \| \| \| TMT-A \| 50.7 ± 7.1 \| 48.8 ± 6.7 \| 52.7 ± 7.2 \| 0.02 c \| \| TMT-B \| 185.5 ± 23.8 \| 180.4 ± 20.1 \| 190.9 ± 26.3 \| 0.058c \| \| AVLT-IR \| 4.0 (3.0, 4.0) \| 4.0 (4.0, 5.0) \| 3.50 (3.0, 4.0) \| 0.008 b \| \| AVLT-LR \| 5.0 (5.0, 6.0) \| 6.0 (5.0, 6.0) \| 5.0 (4.0, 5.0) \| <0.001 b \| \| DSST \| 26.7 ± 5.4 \| 27.9 ± 5.9 \| 25.3 ± 4.6 \| 0.042 c \| \| ROCF-IR \| 34.0 (33.0, 36.0) \| 35.0 (34.0, 36.0) \| 34.0 (32.8, 35.3) \| 0.182b \| \| ROCF-LR \| 19.0 (16.3, 21.0) \| 19.0 (17.3, 22.0) \| 17.5 (15.0, 20.0) \| 0.024 b \| \| MoCA \| 27.0 (25.0, 29.0) \| 29.0 (28.0, 29.0) \| 25.0 (25.0, 25.0) \| <0.001 b \| ## Lipid profiles of MCI patients and healthy controls RC levels were significantly higher in MCI patients than in cognitively healthy controls ($p \leq 0.05$) (Table 1). However, these two groups had no significant difference in Triglycerides, TC, LDL-C, and HDL-C levels. In addition, the estimation plot of differences in RC levels was shown in Figure 1, the median difference in RC levels between MCI patients and cognitively healthy controls was 8.13 mg/dl ($95.0\%$CI: 0.97–16.1). Figure 1: Median Differences of RC levels in MCI patients and cognitively healthy controls. Raw data points of both groups are plotted on the left panel, and the median differences are plotted on the right panel by using 5,000 bootstrapped resamples. The black dot in the right panel represents the median difference, the vertical error bar indicates 95% confidence interval, and the shaded area represents bootstrapped sampling error distribution. ## Affecting factors of MCI patients As shown in Table 2, the related factors for MCI patients were as follows: age (OR = 1.18, $95\%$CI: 1.06–1.33), education level (OR 0.64, $95\%$CI: 0.45–0.84), and RC (OR = 1.05, $95\%$CI: 1.01–1.10). Specifically, RC was positively associated with the incidence of MCI, for every 1-unit (mg/dL) increase in RC, the incidence of MCI increased by 0.05 ($95\%$CI: 1.01–1.10). Table 2 \| Variable \| B \| SE \| Wald \| OR \| 95% CI \| p \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Age \| 0.16 \| 0.06 \| 8.21 \| 1.18 \| 1.06–1.33 \| 0.004 \| \| Education \| −0.45 \| 0.16 \| 8.42 \| 0.64 \| 0.45–0.84 \| 0.004 \| \| RC \| 0.05 \| 0.02 \| 6.15 \| 1.05 \| 1.01–1.10 \| 0.013 \| ## Associations between RC and cognitive performance in MCI patients The partial correlation analyses in MCI patients were provided in Figure 2. Regarding the associations between RC levels and cognitive performance in MCI patients, we found significant negative correlations of RC levels with DSST (pr = −0.45, $$p \leq 0.008$$), ROCF-LR (pr = −0.45, $$p \leq 0.008$$), AVLT-IR (pr = −0.38, $$p \leq 0.028$$), and a positive correlation with TMT-A (pr = 0.44, $$p \leq 0.009$$). However, no significant correlation was found between RC and the AVLT-LR subtest. Figure 2: (A–D) Scatter plots show correlations between RC levels and cognitive performance in MCI patients. pr, partial correlation coefficient. ## Discussion In this study, we examined the relationship between plasma RC and MCI. The main results were [1] MCI patients had a higher level of RC than cognitively healthy controls; [2] Plasma RC level was positively associated with MCI risk; [3] RC level was related to impaired cognitive performance among MCI patients. The mechanism of the association between RC and MCI remains unknown but biologically plausible. Vascular cognitive impairment (VCI), caused by cerebrovascular or cardiovascular diseases, is the second most common neuropathology of MCI (Aronow and Ahn, 2002; Smith, 2017; Gu et al., 2019). Specifically, cerebrovascular and cardiovascular diseases contribute to VCI via multiple types of vascular brain injury (e.g., infarcts, hemorrhages, white matter lesions, enlarged perivascular spaces, altered white matter microarchitecture, and disrupted network connectivity) (Smith, 2017). RC could also take part in the pathology of amyloid-positive MCI (Sagare et al., 2012). Experiments on cell cultures and animal studies suggested that the accumulation of cholesterol in neurons contributes to amyloid deposition in the brain by accelerating the cleavage of amyloid precursor proteins into amyloidogenic components, whereas cholesterol is kept low in neurons may inhibit Aβ accumulation (Toro et al., 2014; Wang H. et al., 2021). Furthermore, Reed B and colleagues (Reed et al., 2014) reported an association between persons’ serum cholesterol levels and cerebral β-amyloid (Aβ), with Aβ quantified using carbon C11-labeled Pittsburgh Compound B positron emission tomography. We also found a wide range of relationships of RC with different domains of cognition, including executive function, visuospatial skill, immediate memory, and sustained attention, except for delayed memory. The small sample size might cause the lack of association between RC and delayed memory. Other possibilities could be the predominantly frontal impairment due to microvascular pathology, which needs to be confirmed in future studies. Besides, in the present study, we did not find differences in TG, TC, HDL-C, and LDL-C between MCI patients and healthy controls. Similarly, in a Chinese population-based study of older adults($$n = 184$$), investigators reported that serum HDL-C was negatively related to the likelihood of MCI, without finding differences in serum TG, TC, and LDL-C between MCI patients and healthy controls (Wang M. et al., 2021). Conversely, in another Chinese case–control study ($$n = 227$$), plasma TC, TG, and HDL-C levels were reported to be associated with the risk of MCI, whereas LDL-C was not significantly different between the MCI group and controls (He et al., 2016). The inconsistent results may be influenced by different inclusion/exclusion criteria, and sample sizes. Several limitations need to be acknowledged in the present study. First, the small sample size may impact the robustness of the study, thus further larger studies are needed to provide robust evidence for the relationship between plasma RC and MCI. Second, since the study design was cross-sectional, any causality of RC with MCI could not be explored. Third, residual confounders may exist due to several unmeasured factors such as marital status, BMI, physical activity, and depression. Lastly, the findings should be generalized with caution when considering our participants were only recruited from the memory clinic, population-based studies are needed in the future to verify the findings. ## Conclusion In conclusion, to our best knowledge, this study is the first to identify a relationship between plasma remnant cholesterol and MCI. In addition, remnant cholesterol was associated with different domains of cognitive function in MCI patients. Larger longitudinal studies are needed in the future to confirm the results due to the small sample size. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Author contributions QZ: conceptualization, methodology, software, investigation, formal analysis, and writing–original draft. XZ and YuC: data curation. GD: visualization and investigation. YiC and WY: resources and supervision. 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--- title: Lipocalin-2 and neutrophil activation in pancreatic cancer cachexia authors: - Min Deng - Merel R. Aberle - Annemarie A. J. H. M. van Bijnen - Gregory van der Kroft - Kaatje Lenaerts - Ulf P. Neumann - Georg Wiltberger - Frank G. Schaap - Steven W. M. Olde Damink - Sander S. Rensen journal: Frontiers in Immunology year: 2023 pmcid: PMC10057111 doi: 10.3389/fimmu.2023.1159411 license: CC BY 4.0 --- # Lipocalin-2 and neutrophil activation in pancreatic cancer cachexia ## Abstract ### Background Cancer cachexia is a multifactorial syndrome characterized by body weight loss and systemic inflammation. The characterization of the inflammatory response in patients with cachexia is still limited. Lipocalin-2, a protein abundant in neutrophils, has recently been implicated in appetite suppression in preclinical models of pancreatic cancer cachexia. We hypothesized that lipocalin-2 levels could be associated with neutrophil activation and nutritional status of pancreatic ductal adenocarcinoma (PDAC) patients. ### Methods Plasma levels of neutrophil activation markers calprotectin, myeloperoxidase, elastase, and bactericidal/permeability-increasing protein (BPI) were compared between non-cachectic PDAC patients ($$n = 13$$) and cachectic PDAC patients with high (≥26.9 ng/mL, $$n = 34$$) or low (<26.9 ng/mL, $$n = 34$$) circulating lipocalin-2 levels. Patients’ nutritional status was assessed by the patient-generated subjective global assessment (PG-SGA) and through body composition analysis using CT-scan slices at the L3 level. ### Results Circulating lipocalin-2 levels did not differ between cachectic and non-cachectic PDAC patients (median 26.7 (IQR 19.7-34.8) vs. 24.8 (16.6-29.4) ng/mL, $$p \leq 0.141$$). Cachectic patients with high systemic lipocalin-2 levels had higher concentrations of calprotectin, myeloperoxidase, and elastase than non-cachectic patients or cachectic patients with low lipocalin-2 levels (calprotectin: 542.3 (355.8-724.9) vs. 457.5 (213.3-606.9), $$p \leq 0.448$$ vs. 366.5 (294.5-478.5) ng/mL, $$p \leq 0.009$$; myeloperoxidase: 30.3 (22.1-37.9) vs. 16.3 (12.0-27.5), $$p \leq 0.021$$ vs. 20.2 (15.0-29.2) ng/mL, $$p \leq 0.011$$; elastase: 137.1 (90.8-253.2) vs. 97.2 (28.8-215.7), $$p \leq 0.410$$ vs. 95.0 (72.2-113.6) ng/mL, $$p \leq 0.006$$; respectively). The CRP/albumin ratio was also higher in cachectic patients with high lipocalin-2 levels (2.3 (1.3-6.0) as compared to non-cachectic patients (1.0 (0.7-4.2), $$p \leq 0.041$$). Lipocalin-2 concentrations correlated with those of calprotectin (rs =0.36, $p \leq 0.001$), myeloperoxidase (rs =0.48, $p \leq 0.001$), elastase (rs =0.50, $p \leq 0.001$), and BPI (rs =0.22, $$p \leq 0.048$$). Whereas no significant correlations with weight loss, BMI, or L3 skeletal muscle index were observed, lipocalin-2 concentrations were associated with subcutaneous adipose tissue index (rs =-0.25, $$p \leq 0.034$$). Moreover, lipocalin-2 tended to be elevated in severely malnourished patients compared with well-nourished patients (27.2 (20.3-37.2) vs. 19.9 (13.4-26.4) ng/mL, $$p \leq 0.058$$). ### Conclusions These data suggest that lipocalin-2 levels are associated with neutrophil activation in patients with pancreatic cancer cachexia and that it may contribute to their poor nutritional status. ## Introduction Cancer cachexia is a multifactorial syndrome characterized by ongoing body weight loss that results in reduced quality of life, low tolerance for anti-cancer treatment, and poor survival [1]. It is highly prevalent in many types of cancers but is most common in pancreatic cancer, where it affects up to $80\%$ of patients with frequently more than $10\%$ body weight loss [2]. The molecular mechanisms underlying the development of cancer cachexia remain poorly defined, although tumor-derived catabolic factors such as activins, myostatin, and pro-inflammatory cytokines arising from tumor-immune system crosstalk are thought to contribute to its progression [3, 4]. For example, elevated circulating TNF-α, IL-6, and GDF-15 have been reported to be associated with the severity of cachexia in cancer patients and mouse models [5, 6]. Furthermore, functional data support the participation of pro-inflammatory factors in tumor progression and cachectic features such as adipose tissue lipolysis and muscle wasting [7, 8]. Neutrophils are the most abundant immune cells in the circulation of humans (up to $70\%$ of the total white blood cell count) and form an essential part of the innate immune response against infection and various other inflammatory cues. They have also been implicated in pancreatic cancer. For instance, Pratt et al. have shown that gene signatures associated with neutrophil recruitment are increased in pancreatic ductal adenocarcinoma (PDAC) tissue as compared to normal pancreatic tissue [9]. Furthermore, high levels of circulating and intratumoral neutrophils have been shown to correlate with poor survival in patients with pancreatic cancer [10]. Additionally, neutrophils can promote pancreatic tumor metastasis by the formation of so-called neutrophil extracellular traps (NETs). Pancreatic cancer cells can induce the release of NETs in vitro [11], and NETs are elevated in the blood of mice and patients with PDAC [12, 13]. In the context of cancer cachexia, emerging investigations revealed increased circulating neutrophils both in patients with cancer cachexia and in different mouse models of cancer cachexia [1, 14, 15]. Furthermore, blocking C-C motif chemokine receptor 2 (CCR2) signaling by neutrophils infiltrated in the velum interpositum region of the brain has been shown to ameliorate cachexia-associated metabolic alterations in mouse models of pancreatic cancer cachexia [16]. Upon activation by inflammatory stimuli, neutrophils can secrete a plethora of cytotoxic proteins, including neutrophil elastase (NE), myeloperoxidase (MPO), calprotectin, bactericidal/permeability-increasing protein (BPI), and lipocalin 2 (LCN-2, also known as neutrophil gelatinase-associated lipocalin or NGAL). LCN-2 can also be released by other cell types including macrophages, adipocytes, and hepatocytes [17]. This protein has been associated with several diseases such as obesity, type 2 diabetes, breast cancer, and pancreatic cancer [17, 18]. The biological functions of LCN-2 are diverse and include antibacterial, anti-inflammatory, as well as pro-metastatic actions [17]. Recently, LCN-2 was identified as a bone-derived hormone with central metabolic regulatory effects which suppresses appetite by binding to the melanocortin 4 receptor (MC4R) [19]. Furthermore, a study in a mouse model of pancreatic cancer cachexia revealed that circulating LCN-2 levels were increased in cachectic mice and correlated with anorexia and muscle loss; genetic deletion of LCN-2 ameliorated cachexia-associated anorexia [20]. In the same study, a significant increase in LCN-2 mRNA was found in circulating neutrophils of cachectic mice and it was suggested that together with the bone marrow compartment, neutrophils are the predominant source of circulating LCN-2 during cancer cachexia development. Using IL6- and Myd88- knockout mice, it was shown that LCN-2 is an inflammation-induced factor in cancer cachexia [20]. Although it is clear that neutrophil and bone marrow derived-LCN-2 contributes to cancer cachexia development by suppressing appetite in mice, whether the same mechanism applies in PDAC patients with cachexia remains unknown. Given that systemic inflammation is a hallmark of cancer cachexia, and since neutrophils release cytotoxic proteins and LCN-2 upon activation by inflammatory stimuli, we hypothesized that neutrophils contribute to systemic inflammation and the release of LCN-2 in cachectic patients with pancreatic cancer. We aimed to 1) investigate the association between circulating levels of LCN-2 and neutrophil activation markers as well as features of cachexia in PDAC patients; 2) determine whether there is a link between LCN-2 levels and appetite in pancreatic cancer patients with cachexia. ## Patients 81 patients undergoing pancreaticoduodenectomy for suspected adenocarcinoma of the pancreas at the Maastricht University Medical Centre (MUMC+) or University Hospital RWTH Aachen were enrolled in this study. Exclusion criteria were neoadjuvant chemo- and/or radiotherapy and the presence of another malignancy. This study was approved by the Medical Ethical Board of the MUMC+ in line with the ethical guidelines of the 1975 Declaration of Helsinki, and written informed consent was obtained from each subject (METC 13-4-107 and 2019-0977 for patients from MUMC+, EK $\frac{172}{17}$ for patients from Uniklinik Aachen). ## Diagnosis of cancer cachexia and screening of cachexia status Cachexia was defined according to the international consensus definition as 1) weight loss >$5\%$ over the past 6 months in the absence of starvation, and/or 2) BMI <20 kg/m2 and >$2\%$ ongoing weight loss, and/or 3) sarcopenia and >$2\%$ ongoing weight loss. Patients were diagnosed with cancer cachexia if ≥1 of the criteria were met [1]. Body weight loss was reported by the patient and body weight data were retrieved from the medical record. Sarcopenia was defined using established cut-offs in patients with pancreatic cancer as L3-skeletal muscle index (L3-SMI) <45.1 cm2/m2 in males and <36.9 cm2/m2 in females [21]. Body composition parameters were quantified by analyzing a cross-sectional CT image at the third lumbar (L3) vertebra that was acquired preoperatively for diagnostic purposes, using sliceOmatic 5.0 software (TomoVision, Magog, Canada) for Windows. Using predefined Hounsfield Unit (HU) ranges, the total cross-sectional area (cm2) of skeletal muscle (SM) tissue (-29 to 150 HU) was determined. In addition, the total areas of visceral adipose tissue (VAT, -150 to -50 HU) and subcutaneous adipose tissue (SAT) as well as intramuscular adipose tissue (IMAT) (-190 to -30 HU) were assessed. Tissue areas (cm2) were adjusted for patient height to calculate the respective L3-indices (L3-SMI, L3-VATI, L3-SATI) in cm2/m2, which correspond well with total body muscle and adipose tissue mass [22]. Previously published validated sex-specific cut-off values (SMI, 45.1 cm2/m2 for men and 36.9 cm2/m2 for women) that were established from a local MUMC+ cohort including pancreatic cancer patients [21] were used for the CT-derived body composition analysis. In addition, the skeletal muscle radiation attenuation (SMRA), which reflects the extent of lipid accumulation in the muscle, was calculated as the average HU value of the total tissue area for muscle (i.e. within the specified range of -29 to 150 HU). A total of 80 patients were included for body composition analysis (one patient had no CT-scan available). L3-SATI could not be accurately assessed in 8 patients because of incomplete CT-scans not showing all tissue. ## Assessment of patient’s nutritional status and appetite Patients’ nutritional status was assessed by using the patient-generated subjective global assessment, a validated nutritional screening tool [23] (PG-SGA, category A: well-nourished, category B: moderate malnutrition, category C: severe malnutrition). Patient’s appetite was assessed according to the question in box 2 of the PG-SGA questionnaire and rated as normal food intake (unchanged or more than usual) or less than usual food intake. ## Plasma preparation A venous blood sample was obtained preoperatively for measuring clinical laboratory data and levels of neutrophil activation markers. To avoid artefactual neutrophil activation during plasma preparation, venous blood was collected in EDTA tubes and gently centrifuged at 1500xg at 4°C for 15 min without brake, after which plasma aliquots were stored at -80°C until analysis. ## ELISAs Levels of circulating neutrophil activation markers calprotectin, myeloperoxidase (MPO), elastase, bactericidal permeability increasing protein (BPI), and LCN-2 were measured by solid-phase enzyme-linked immunosorbent assays (ELISA) based on the sandwich principle, according to the manufacturer’s instructions (Hycult Biotech, Uden, The Netherlands; Human calprotectin, Catalog #HK379; Human MPO, Catalog # HK324; Human elastase, Catalog # HK319; Human BPI, Catalog # HK314; Human LCN-2, Catalog # HK330). All plasma samples were analyzed in duplicate in the same run. The intra- and inter-assay coefficients of variance of the various assays were <$10\%$. Clinical laboratory data including circulating C-reactive protein (CRP), albumin, neutrophils (%), and lymphocytes (%) were measured in the clinical setting. For some of the patients, these clinical data were not available, the exact number of the studied patients for these data is indicated in each figure legend. ## Statistical analysis Statistical analysis was performed using Prism 7.0 for Windows (GraphPad Software Inc., San Diego, CA) and R (R-4.2.0 for Windows). Data are presented as the median with interquartile range (IQR). Non-parametric tests were used for statistical analysis (Mann-Whitney U test for analysis of two groups; Kruskal-Wallis test followed by Dunn’s post-testing for analysis of multiple groups). Correlations were calculated using Spearman’s correlation coefficient (rs), and Spearman’s correlation matrix was generated by the Corrplot R package [24]. P values <0.05 were considered statistically significant. ## Characteristics of the study cohort A total of 81 patients with PDAC were enrolled in this study (31 females and 50 males). The median age of patients was 69.0 years. CT scan-based body composition analysis showed that $63.7\%$ ($$n = 51$$) of patients were sarcopenic, with a median L3-SMI of 47.5 (42.8-51.2) cm2/m2 for males and 37.5 (35.1-40.5) cm2/m2 for females. The median L3-VAT and L3-SAT indices were 40.5 (25.3-74.5) cm2/m2 and 46.7 (34.7-58.5) cm2/m2. Given that LCN-2 levels have been reported to correlate with fat and lean mass wasting (two key features of cachexia) in patients with pancreatic cancer [20], we subdivided cachectic patients into groups with high or low LCN-2 using a median cut-off value of 26.9 ng/mL (see Table 1). The median weight loss of the non-cachectic patients was 3.1 (0.7-3.6) %, which was significantly less than the weight loss of the cachectic patients with high LCN-2 (median 11.5 (7.8-14.1) %, $p \leq 0.001$) and the cachectic patients with low LCN-2 (median 8.4 (6.5-14.2) %, $p \leq 0.001$). According to the PG-SGA, $95\%$ ($$n = 18$$) of patients with cachexia and high LCN-2 were malnourished ($42\%$ moderate malnutrition (category B) + $53\%$ severe malnutrition (category C)), which was higher than the prevalence of malnutrition in patients without cachexia ($50\%$; $40\%$ category B + $10\%$ category C) and in patients with cachexia with low LCN-2 ($74\%$; $53\%$ category B + $21\%$ category C). Further patient characteristics are presented in Table 1, and Spearman correlations between studied variables are shown in Figure 1. ## Circulating LCN-2 is higher in males and correlates with systemic inflammation To assess whether LCN-2 levels are altered in patients with cancer cachexia, we determined circulating LCN-2 concentrations by ELISA. Whereas higher LCN-2 levels were observed in cachectic patients (median 26.7 (19.7-34.8) ng/mL) as compared to non-cachectic patients (median 24.8 (16.6-29.4) ng/mL), the difference was not significant ($$p \leq 0.141$$, Figure 2A). In line with other studies (25–27), circulating LCN-2 levels showed a sex-specific difference, being higher in males than in females (median 27.8 (23.8-37.8) ng/mL vs. 21.6 (15.9-29.0) ng/mL, $$p \leq 0.002$$, Figure 2B). Figure 2: Circulating LCN-2 levels of PDAC patients differ according to sex and correlate with systemic inflammation. Comparison of circulating LCN-2 levels in PDAC patients with and without cachexia (A). Comparison of circulating LCN-2 levels between male and female PDAC patients (B). CRP/Albumin ratio in PDAC patients within the indicated study groups (n=44) (C). Relationship between circulating LCN-2 levels and CRP/Albumin ratio in PDAC patients (n=44) (D). Scatter plots (A–C) show the median + IQR and individual data points in each group. Mann-Whitney U test for analysis of two groups; Kruskal-Wallis test followed by Dunn’s post-testing for analysis of multiple groups. Spearman’s rank correlation coefficient (rs ) was used to test for the relationship between variables. Significant differences among the groups are signified by asterisks (p < 0.05, *p < 0.01). Since systemic inflammation is a hallmark of cancer cachexia, and because LCN-2 release is associated with inflammation, we determined the degree of systemic inflammation as expressed by the CRP to albumin ratio in the study groups. As expected, a significantly higher CRP/Albumin ratio was observed in cachectic PDAC patients with high LCN-2 levels as compared with patients without cachexia (2.3 (1.3-6.0) vs. 1.0 (0.4-1.4), $$p \leq 0.041$$, Figure 2C). Furthermore, circulating LCN-2 levels correlated positively with the CRP/Albumin ratio (rs =0.30, $$p \leq 0.047$$, Figure 2D). ## Systemic lipocalin 2 levels correlate with levels of neutrophil activation markers LCN-2 can be produced by many different cell types, including cells relevant to cachexia such as adipocytes and hepatocytes [28, 29]. However, it was previously shown that in experimental cachexia in mice, neutrophils were the main source of LCN-2 [20]. To investigate the contribution of neutrophils to the systemic LCN-2 pool in pancreatic cancer patients with and without cachexia, we quantified circulating levels of reliable neutrophil activation markers calprotectin, MPO, elastase, and BPI in relation to levels of LCN-2. We observed consistent significant positive correlations between the concentrations of LCN-2 and all tested neutrophil activation markers (calprotectin: rs =0.36, $p \leq 0.001$; Figure 3A; MPO: rs =0.48, $p \leq 0.001$; Figure 3B; neutrophil elastase: rs =0.50, $p \leq 0.001$; Figure 3C; BPI: rs =0.22, $$p \leq 0.048$$; Figure 3D). Moreover, levels of calprotectin, MPO, elastase, and BPI were also strongly positively correlated to each other (Figure 1). Figure 3: Correlation analysis of circulating LCN-2 and neutrophil activation markers in PDAC patients. Systemic levels of LCN-2 were positively correlated with levels of calprotectin (A), MPO (B), elastase (C), and BPI (D). Spearman’s rank correlation coefficient (rs ) and level of significance are indicated in the respective plots. N=81. In addition, when comparing cachectic patients with high versus low levels of LCN-2, the median MPO levels of cachectic patients in the high LCN-2 group were significantly higher than those of cachectic patients with low LCN-2 or those found in patients without cachexia (median 30.3 (22.1-37.9) ng/mL vs. 20.2 (15.0-29.2) ng/mL, $$p \leq 0.011$$; vs. 16.3 (12.0-27.5) ng/mL, $$p \leq 0.021$$, respectively) (Figure 4A). Similarly, cachectic patients with high LCN-2 levels had significantly higher concentrations of calprotectin and elastase than cachectic patients with low LCN-2 levels (calprotectin: median 542.3 (355.8-724.9) ng/mL vs. 366.5 (294.5-478.5) ng/mL, $$p \leq 0.009$$, elastase: 137.1 (90.8-253.2) ng/mL vs. 95.0 (72.2-113.6) ng/mL, $$p \leq 0.006$$) (Figures 4B, C). However, no significant differences in calprotectin and elastase levels were observed between patients without cachexia and patients with cachexia with either high or low LCN-2 levels (Figures 4B, C). For BPI, no significant differences were observed between cachectic patients with high LCN-2 (median 3.6 (0.4-8.9) ng/mL) and patients without cachexia (5.4 (2.1-7.3) ng/mL, $$p \leq 0.584$$) or cachectic patients with low LCN-2 levels (2.9 (0.4-8.6) ng/mL, $$p \leq 0.931$$) (Figure 4D). Taken together, these data strongly indicate that LCN-2 levels in PDAC patients are associated with neutrophil activation. Figure 4: Circulating levels of neutrophil activation markers in PDAC patients without cachexia and in cachectic patients with high or low LCN-2 levels. Comparison of systemic levels of calprotectin (A), MPO (B), elastase (C), and BPI (D) in PDAC patients without cachexia and in cachectic patients with high or low LCN-2 levels. Scatter plots show the median + IQR and individual data points in each group. For statistical analysis, the Kruskal-Wallis test followed by Dunn’s multiple comparisons test was used. Significant differences among the groups are signified by asterisks (p < 0.05, *p < 0.01). ## Circulating LCN-2 does not correlate with cachexia features To investigate whether circulating LCN-2 levels are associated with specific cachexia features in PDAC patients, we performed correlation analyses. As shown in Figures 5A–D, no correlations between circulating LCN-2 levels and cachexia features such as weight loss (%) (rs =0.17, $$p \leq 0.120$$, Figure 5A), body mass index (rs =0.04, $$p \leq 0.748$$, Figure 5B), or skeletal muscle index (rs =0.22, $$p \leq 0.054$$, Figure 5C) were observed. However, a negative correlation between plasma LCN-2 and subcutaneous fat index (SATI) was found (rs =-0.25, $$p \leq 0.034$$) (Figure 5D). Figure 5: Circulating LCN-2 levels do not correlate with cachexia features in pancreatic cancer patients. Correlation analysis between circulating LCN-2 levels and cachexia features weight loss (%) (A), body mass index (B), skeletal muscle index (n=80) (C), as well as subcutaneous fat index (n=72) (D). Spearman’s rank correlation coefficient (rs ) was used for the relationship between variables. ## Neutrophil activation is associated with complement system activation in PDAC patients Next, we focused on potential causes of the neutrophil activation. Since it is well-known that complement factors promote neutrophil activation [30] and because we previously reported complement system activation in patients with cancer cachexia [31], we investigated whether neutrophil activation was associated with complement system activation in a subgroup of patients in the current cohort that overlapped with the cohort reported on in [31] ($$n = 16$$). Patient characteristics of this subgroup are shown in Supplementary Table S1. Interestingly, both C3a, a cleavage product of the central complement C3 component, and terminal complement complex (TCC), an end product of complement activation, were strongly positively correlated with the studied neutrophil activation markers calprotectin (C3a: rs =0.51, $$p \leq 0.046$$; TCC: rs =0.47, $$p \leq 0.066$$; Figures 6A, B), MPO (C3a: rs =0.80, p=<0.001; TCC: rs =0.52, $$p \leq 0.041$$; Figures 6A, B), elastase (C3a: rs =0.52, $$p \leq 0.040$$; TCC: rs =0.53, $$p \leq 0.036$$; Figures 6A, B), and BPI (C3a: rs =0.43, $$p \leq 0.095$$; TCC: rs =0.28, $$p \leq 0.292$$; Figures 6A, B). This suggests that complement activation may contribute to neutrophil activation in patients with pancreatic cancer. Figure 6: Correlation analysis of neutrophil activation markers and complement system activation markers. Relationship between systemic levels of C3a and calprotectin, MPO, elastase, BPI, and LCN-2 (A). Relationship between systemic levels of TCC and calprotectin, MPO, elastase, BPI, and LCN-2 (B). n=16 for each graph. Spearman’s rank correlation coefficient (rs ) and level of significance are indicated in the respective plots. ## LCN-2 levels in severely malnourished patients with pancreatic cancer Given that administration of LCN-2 has been shown to suppress appetite in mouse models of pancreatic cancer cachexia [20], we next examined the link between LCN-2 levels and the nutritional status of patients using the validated PG-SGA questionnaire, which contains questions about food intake. Whereas patients with cachexia had a higher prevalence of poor appetite than non-cachectic patients, the difference was not significant ($65.3\%$ ($\frac{32}{49}$) vs. $46.2\%$ ($\frac{6}{13}$), Table 1, $$p \leq 0.448$$). Moreover, no significant difference was observed between PDAC patients with normal food intake and PDAC patients with less food intake in terms of circulating LCN-2 (median 26.1 (24.1-32.7) ng/mL vs. 25.7 (16.7-31.0) ng/mL, $$p \leq 0.320$$, Figure 7A). However, we found that LCN-2 levels tended to be higher in patients with poor nutritional status (PG-SGA category A vs. category B vs. category C, median 19.9 (13.4-26.4) ng/mL vs. 27.2 (19.3-32.1) ng/mL vs. 27.2 (20.3-37.2) ng/mL, $$p \leq 0.058$$, Figure 7B). Figure 7: Circulating LCN-2 levels in PDAC patients according to food intake and nutritional status. Comparison of systemic levels of LCN-2 in PDAC patients with normal versus reduced food intake (A). LCN-2 levels in plasma from well-nourished (category A), moderately malnourished (category B), and severely malnourished (category C) PDAC patients (B). Scatter plots showing the median + IQR and individual data points in each group. For statistical analysis, the Mann–Whitney U test was used for two groups and the Kruskal-Wallis test followed by Dunn’s post-testing for analysis of multiple groups. ## Discussion It was previously reported that increases in LCN-2 levels in pancreatic cancer patients correlate with loss of fat and muscle, two key features of cachexia [20]. Based on relatively weak correlations to neutrophil abundance and the neutrophil/lymphocyte ratio, the LCN-2 elevations were attributed to neutrophil expansion [20]. The current study provides several additional lines of evidence for a contribution of neutrophil activation to the elevated LCN-2 levels in patients with pancreatic cancer. We showed strong correlations between circulating levels of LCN-2 and the degree of systemic inflammation (CRP/albumin ratio) as well as a set of four different neutrophil activation markers, and demonstrated that cachectic patients with high systemic LCN-2 levels have significantly higher levels of the neutrophil activation markers calprotectin, MPO, and elastase than patients with low LCN-2 levels. Furthermore, consistent correlations between these neutrophil activation markers and activated complement factors C3a and TCC were observed in these patients, suggesting that systemic complement activation may contribute to neutrophil activation in pancreatic cancer. Of note, although circulating LCN-2 levels were not related to cachexia and food intake, higher LCN-2 levels were associated with worse nutritional status of patients, providing some support for the concept that LCN-2 contributes to cancer cachexia by suppressing patients’ appetite. Taken together, these results suggest that LCN-2 levels in cachectic patients with pancreatic cancer are related to neutrophil activation and complement activation. LCN-2 is a polypeptide released by several cell types including adipocytes, hepatocytes, epithelial cells, and neutrophils. Elevated circulating LCN-2 has been found in many types of cancer and promotes malignant development in cancer patients [18, 32]. The functional roles of LCN-2 include regulating body fat mass and lipid metabolism as well as immune responses to inflammatory stimuli. As a biomarker of inflammation, LCN-2 has been associated with chronic inflammatory disorders such as inflammatory bowel disease, obesity, and pancreatic cancer [18, 33, 34]. In line with this, we found a positive correlation between circulating LCN-2 levels and systemic inflammation. However, LCN-2 levels were not associated with cachexia status of the patients. Of note, the mean plasma concentrations of LCN-2 in our cohort were considerably lower than the serum levels reported by others [20, 35]. It is well-known that neutrophils become rapidly activated by many common preparation methods [36]. In particular, activation of neutrophils during serum preparation is common, and it is therefore advisable to analyze neutrophil products in plasma instead of serum. Moreover, delays in blood processing have been shown to be associated with neutrophil death leading to artefactual increases in neutrophil products [37]. To avoid neutrophil activation and death during plasma preparation, we applied careful centrifugation using freshly obtained blood, which is unlikely to be performed in retrospective studies where blood was routinely collected and processed at clinical chemistry departments. Recently, emerging evidence revealed that LCN-2 suppresses appetite in mice. For example, Mosialou and colleagues demonstrated that LCN-2 suppresses food intake in mice by crossing the blood-brain barrier and binding to its receptor MC4R in the hypothalamic paraventricular nucleus [19]. Similar appetite suppression by LCN-2 was observed in primates who received daily administration of recombinant human LCN-2 which resulted in a $21\%$ decrease in food intake [38]. In the context of cancer cachexia, a more recent study showed that administration of LCN-2 to mice reduced food intake and decreased body weight, while deletion of LCN-2 restored appetite in a mouse model of pancreatic cancer cachexia [20]. To explore the relevance of this finding in human pancreatic cancer cachexia, we compared LCN-2 levels between PDAC patients with normal or reduced food intake and investigated the relationship between circulating LCN-2 and several features of cachexia including weight loss, body composition, and nutritional status. While LCN-2 is able to suppress appetite in mice with pancreatic cancer cachexia, we did not observe a relationship between food intake and LCN-2 levels in our patient cohort. Moreover, circulating LCN-2 levels did not correlate with weight loss and body mass index, although a significant negative association between circulating LCN-2 and the subcutaneous adipose tissue volume was observed. Also, LCN-2 levels were higher in patients that were malnourished according to the PG-SGA. Thus, although our data do not provide evidence for a direct link between LCN-2 and appetite in the context of human pancreatic cancer cachexia, LCN-2 may still indirectly affect cachexia-related nutritional factors. Previously, we reported complement system activation in pancreatic cancer patients with cachexia [31]. To gain an understanding of the potential relationship between neutrophil activation and complement activation in PDAC patients with cachexia, we performed correlation analyses. Intriguingly, we found strong correlations between all neutrophil activation markers studied and the central complement system activation markers C3a and TCC. A previous in vitro study showed that neutrophils can activate the alternative pathway of complement and release C5 fragments that further enhance neutrophil activation [30], which is in line with our current observations. Furthermore, several studies have shown that the treatment of human neutrophils with C3a leads to neutrophil degranulation, aggregation, and chemotaxis [39, 40]. Thus, complement and neutrophil activation in these patients may result from a positive feedback loop. In obesity and type 2 diabetes, neutrophilic inflammation has been shown to be involved in the development of insulin resistance and other metabolic aberrations [41, 42]. In line with this, we found a correlation between SMRA, which reflects the extent of lipid accumulation in skeletal muscle, and levels of the neutrophil activation markers elastase and BPI (see Figure 1), which could suggest that neutrophil activation also promotes inflammation in skeletal muscle tissue of pancreatic cancer patients leading to insulin resistance and ectopic fat accumulation. In addition, we could corroborate the previously described differences in LCN-2 levels between males and females (25–27), with higher levels in males. It would be interesting to explore if this contributes to recently reported sex differences on cancer cachexia progression [43, 44], also given that we identified a correlation between LCN-2 and SATI, which is higher in females. Certain limitations of this first clinical study on circulating LCN-2 levels in association with neutrophil activation in pancreatic cancer patients should be acknowledged. First, the study population was relatively small, and our results should be validated in a larger patient cohort. Second, the applied cut-off value for LCN-2 was based on the median value in cachectic patients which should be optimized by generating ROC curves in future large cohort studies. Third, although sarcopenia (defined by low SMI) is usually strongly associated with cachexia, cachectic patients in our study did not have a lower SMI than non-cachectic patients. Since self-reported unintentional weight loss of >$5\%$ is the central diagnostic criterion for cachexia, this “subjective” value could obscure actual differences in the SMI of each group. Fourth, given the reported role of LCN-2 in regulating appetite, a more detailed and objective assessment of nutritional intake using validated questionnaires would be desirable in follow up studies. Finally, although a strong positive correlation between circulating LCN-2 and neutrophil activation markers was observed which is in line with activated neutrophils as the main source of LCN-2, we cannot exclude the contribution of bone-derived LCN-2 to circulating levels in the patients studied. In conclusion, the present study shows that circulating LCN-2 is associated with neutrophil activation in pancreatic cancer patients, irrespective of their cachexia status. Generalized activation of the innate immune system seems to contribute to the production of circulating LCN-2 as indicated by the correlations between neutrophil activation markers and activated complement components. Follow-up studies investigating the potential of LCN-2 as a therapeutic target in cancer cachexia are warranted given the association between its levels and the nutritional status of PDAC patients. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Medical Ethical Board of the MUMC+. The patients/participants provided their written informed consent to participate in this study. ## Author contributions Conceptualization: SR. Methodology: SR and MD. Formal analysis: MD and AB. Resources: SO and SR. Data curation: SR. Writing—original draft preparation: MD and SR. Writing—review and editing: MA, GK, KL, UN, GW, FS, AB, and SO. Supervision: SO and SR. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1159411/full#supplementary-material ## References 1. Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL. Definition and classification of cancer cachexia: an international consensus. 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--- title: '11- to 13-Year-Old Children’s Rejection and Acceptance of Unfamiliar Food: The Role of Food Play and Animalness' authors: - Rikke Højer - Margit Dall Aaslyng journal: Nutrients year: 2023 pmcid: PMC10057112 doi: 10.3390/nu15061326 license: CC BY 4.0 --- # 11- to 13-Year-Old Children’s Rejection and Acceptance of Unfamiliar Food: The Role of Food Play and Animalness ## Abstract Promoting children’s healthy food behavior is important in reducing the risk of developing obesity; it is therefore relevant to investigate methods to promote healthy food choices. This study’s aim was to investigate differences in rejection–acceptance mechanisms related to unfamiliar foods depending on the inclusion of tactile exercises prior to cooking and food origin. Participant observation was applied in a school setting. Eight fifth and sixth grade classes were recruited from four Danish schools ($$n = 129$$). The classes were divided into two groups: animal (AG; quail) and nonanimal (NAG; bladderwrack). AG and NAG were subdivided into two groups: food print (FP) and no food print (NFP). Applied thematic analysis was applied. During preparation/cooking, NFP displayed disgust-related rejection, whereas FP displayed inappropriateness-related rejection. FP exhibited more playful behavior. Inappropriateness and animalness drove AG rejection. NAG rejection was driven by the slimy texture of the food and the perception of it ‘not being food’. Acceptance was driven by taste and familiarity. In conclusion, the inclusion of tactile exercises could increase children’s exploratory food behavior, and the promotion of children’s healthy food behavior should not solely focus on choosing foods deemed safe and familiar, since, despite rejection during cooking, acceptance is ultimately possible. ## 1.1. Background Promoting healthy food behavior from childhood is an important target area. In 2016, $18\%$ of the world’s children and adolescents aged between 5 and 19 years were either overweight or obese [1]. In Denmark, $18\%$ of 9- to 13-year-old children are overweight [2]. Childhood obesity is associated with an increased risk of developing, for example, low self-esteem, type 2 diabetes (T2D), and cardiovascular disease (CVD) [3,4]. Breastfeeding during infancy and exposure to a variety of new foods during the complementary feeding period has been shown to moderately reduce the risk of childhood obesity and promote self-regulation of intake and preference for healthy foods later in life [1,5]. As children become older and more independent, the family arena is partly replaced with the school arena together with influence from peers on food behavior. The World Health Organization [1,6] recommends that schools implement programs that promote healthy school environments, health, nutrition, food literacy, and physical activity among school-age children and adolescents in order to reduce childhood obesity and promote life-long healthy food behavior. The school is a relevant arena for the promotion of culinary skills since a survey of Danish children’s participation in home cooking activities has shown that children’s participation in cooking the family evening meal decreased from 2020 to 2022. In 2020, $68\%$ of Danish children participated in the cooking of the evening meal once a week. In 2022, only $58\%$ participated [7,8]. Furthermore, the school arena is relevant since previous studies have found that children who participate in cooking activities have an increased enjoyment of cooking in later life, a greater willingness to taste novel foods, an increase in self-esteem regarding the choice of healthy foods, and increased preference for healthy food [9,10,11,12,13,14,15]. Culinary skills are embedded in the concept of food literacy, which Vidgen and Gallegos [16] define as follows: ‘[…] a collection of inter-related knowledge, skills and behaviours required to plan, manage, select, prepare and eat food to meet needs and determine intake. This can simply be interpreted as the tools needed for a healthy lifelong relationship with food’ [16] (p. 54). The concept of food literacy can be applied to the promotion of healthy food behavior since it includes functional (knowledge), interactive (skills), and critical components (transformation and empowerment). Food literacy is not only associated with knowledge and skills related to cooking. It is also about promoting a person’s ability to make critical and reflective health and food choices (for example, knowing what foods to eat and why, how to read food label information, etc.) whilst recognizing that individual, social, cultural, and environmental experiences with food affect one’s ability to navigate the food system [16,17,18]. According to Pendergast, Garvis, and Kanasa [19], the ability to navigate the complex food system can be achieved through the development and promotion of self-efficacy, which is a belief in one’s ability to cope with and take required action when engaging in a given task and/or situation [20,21]. The concept of food literacy has been applied as a framework in prior health promotion interventions [22,23,24]. According to Rozin and Fallon [25,26], the taxonomy of food acceptance and rejection is driven by three main motivations: sensory-affective factors (e.g., liking/disliking taste or smell), anticipated consequences (e.g., negative/positive physiological or social), and ideational factors (e.g., knowledge of the nature or origin of the food). Motivations for rejecting food are distaste (all sensory perceptions, real or imagined [27,28]), danger, inappropriateness, and disgust. Motivations for accepting food are good taste, beneficial, appropriate, and transvalued [27,29]. Rozin and colleagues’ [25,29,30] taxonomy of rejection and acceptance has been applied in a variety of food behavior studies (e.g., [24,27,28,31,32,33]). Furthermore, Sick, Højer, and Olsen [32] found that dislike of taste and appearance and bad smell were among the most common reasons for children rejecting food, whereas curiosity was a driver of acceptance. Højer, Wistoft, and Frøst ([33], Figure 8, p. 12) suggested a rejection–acceptance continuum, which illustrates the movement between rejection and acceptance based on how children categorize food (exemplified by fish: from first exposure to the fresh fish until the fish has become a meal through cooking). The continuum categories were animal, nonanimal, animal, and food, where the categories ‘animal’ promoted rejection (animal 1: seeing the whole fresh fish, touching it, washing it; animal 2: filleting the fish), whereas the categories ‘non-animal’ (gyotaku exercise/fish printing) and ‘food’ (cooking and eating) promoted acceptance. They concluded that tactile play could be a relevant tool in promoting children’s acceptance of food. In previous studies, tactility or tactile play have also been suggested as drivers in promoting acceptance of healthy food [34,35,36,37]. This study focuses on children’s acceptance and rejection of unfamiliar food in order to shed light on tools that could be used in promoting children’s healthy food behavior with particular reference to official Danish dietary guidelines, for example, eating a varied diet and eating less meat from four-legged animals (max. 350 g of meat in total per week) [38]. ## 1.2. Aim of the Study The aim of this study was to investigate if there is a difference in the rejection–acceptance continuum mechanisms in relation to unfamiliar food items for children aged 11 to 13 years depending on [1] whether or not a tactile exercise is included prior to cooking and [2] whether the food is of animal or nonanimal origin. ## 2.1. Study Design The study design was an intervention with multiple cases [39]. Eight different classes (fifth and sixth grade) from four different schools were included in the study, as shown in Figure 1. Two schools each had two classes working with a food item categorized as ‘animal’: quail (Coturnix coturnix), and two schools each had two classes working with a food item categorized as ‘non-animal’: bladderwrack (Fucus vesiculosus Linnaeus [1753]). At each school, one class completed a food printing exercise prior to cooking, whereas the other class did not. ## 2.2. Participants Eight classes from fifth and sixth grades (11 to 13 years of age) were recruited from four different Danish public schools ($$n = 129$$; boys: $$n = 57$$; girls: $$n = 72$$). The schools were situated in the region of Zealand. The recruitment was conducted through an existing network by sending out information letters via e-mail to schools in the eastern part of Denmark addressed to the schools’ food knowledge teachers. Before the study started, each participant’s legal guardian provided written informed consent. The participating children were also asked to provide written informed consent even though this was not legally necessary. This was done due to ethical considerations related to the inclusion of the participating children as recommended by the Danish National Council for Children [40]. ## 2.3.1. Setting The intervention took place in a natural setting at the children’s schools in the school teaching kitchen as part of the subject of food knowledge. Classes carried out the exercises based on the same food-specific exercise guide. Six trained research assistants conducted the exercises in two teams. Each team conducted all the exercises within the same category (animal/nonanimal). In each case, the class teacher was present during the exercise. At the school, the children were already divided into four kitchen groups, and therefore, since the exercise took place as part of a formal subject, the existing groups were not altered. The exercise was carried out over two consecutive lessons (2 × 45 min.). ## 2.3.2. Materials Four exercise manuals (two for animal and two for nonanimal: quail or bladderwrack; food print/no food print) were developed and tested internally by the research team prior to the intervention to ensure feasibility of recipes, level of difficulty, time frame, and uniformity in communication. The food printing exercises for both animal and nonanimal categories were modified from the traditional Japanese fish printing technique Gyotaku [41]. The following materials were used: squid ink diluted with tap water in a cup, a small sponge, five A4 pieces of paper cut into eight equal parts, paper towels, and printing paper (Chinese rice paper). The animal category subject was European quail (Coturnix coturnix) from breeding stock (see Figure 2a). In both exercises (food print/no food print) in the animal category, the children had to wash the quail and debone it in preparation for a dish: roasted quail stuffed with apples, cinnamon, and butter. The children who did the food printing exercise prior to deboning and cooking would start by washing the quail, drying it with paper towels, placing cut paper squares around and slightly under the quail (to avoid getting excess squid ink on the print later in the process), and covering the quail with squid ink using the sponge. Then, the paper squares were removed, and the squid ink-covered quail was covered with the printing paper. Using gentle strokes, the squid ink was transferred to the paper, and when the paper was lifted, a mirror print of the quail would appear (see Figure 2b). All children in the food printing groups were given the opportunity to create their own print. The nonanimal category subject was bladderwrack (Fucus vesiculosus) Linnaeus [1753], which is an edible brown macroalga (Ochrophyta, Phaeophyceae) (seaweed) [42] (see Figure 3a). In both exercises (food print/no food print) in the nonanimal category, the children had to wash the bladderwrack in preparation for a dish: pasta with bladderwrack pesto and bladderwrack chips. The children who did the food printing exercise prior to cooking would follow the same printing exercise procedure as in the animal category (see Figure 3b for result). ## 2.4. Data Collection The following qualitative data collection methods were applied in this study: participant observation [43] and situational photography as a supporting method [44]. A loosely constructed participant observation guide was drawn up, allowing observations to follow the rejection–acceptance continuum categorizations [33] and the phases in the exercise (see Table 1). Furthermore, the participant observation guide was developed based on the taxonomy of food rejection and acceptance [25,29]. It was primarily concept-driven [45], but the structure of the guide also left room for exploratory inquiry. Documentation methods used during the participant observation comprised written field notes and situational photos to document various situations and child–food interactions. The field note strategy was inscription and transcription [45]: Descriptions of behaviors (inscriptions) and informants’ own words and dialogues (transcriptions) were documented in an observational journal. If the children asked what had been written in the journal, they were given the opportunity to read it and comment on it. Furthermore, if children refused to touch, handle, and/or taste the bladderwrack or quail, this was respected by the researchers. Data were collected in October 2021. ## 2.5. Data Analysis Data were analyzed using applied thematic analysis (ATA) developed by Guest, MacQueen, and Namey [46]. Thematic analysis has been applied as a data analytical method in previous food behavior studies (e.g., [47,48,49]). The concept-driven [45] data processing (see Figure 4 for an overview of the ATA process) was based on four metathemes related to the exercise flow and model frame of the acceptance–rejection continuum [33]: animal/seaweed, nonanimal/not seaweed, animal/seaweed-food, and food. Precoded text was organized in a matrix based on the frequency of observed behaviors and conversations related to rejection and acceptance. A thematic scheme was developed to investigate possible themes and subthemes across cases (see Appendix A Table A1). A rereading of the data set and a reconsideration of subthemes and themes were performed to ensure accurate representation and relevance in accordance with the study aim [46,50]. The ATA process resulted in two metathemes, rejection and acceptance, and seven themes, inappropriate, disgust, distaste, curiosity, person/pet, familiarity, and liking. Furthermore, thirteen subthemes were identified (see Figure 5 for a presentation of the ATA frame). Data without relevance to the study aim were excluded from the analysis. Furthermore, the ATA frame (analysis, results, and discussion thereof) was discussed within the research group (the essence of metathemes and themes is available in Table A2). ## 3. Results The data are presented according to the ATA frame (Figure 5) and study aim. The following abbreviations are applied in the presentation of the results: QP = quail—food print group; QNP = quail—no food print group; BP = bladderwrack—food print group; BNP = bladderwrack—no food print group; AG = animal-origin group (QP + QNP); and NAG = non-animal-origin group (BP + BNP). ## 3.1.1. Metatheme Rejection: Disgust, Distaste, and Inappropriateness Thematically, no differences between the QP and QNP were observed in relation to rejection and the categorization of the quail as an animal throughout the experiment. Nevertheless, differences were observed in how the children handled the quail during the preparation phase (animal categorization according to the acceptance–rejection continuum). Children who had not engaged in the food printing exercise displayed their rejection through disgust related to a contamination dimension to a greater extent than the children who had participated in the printing exercise. The latter was more driven by inappropriateness in the form of feeling sorry for the quail:QNP:Before starting the deboning, they have to remove the remaining feathers from the quail. Several only touch the quail with their fingertips or only with tweezers. Many of the children do not want to touch the quail with their other hand (to hold the quail steady on the cutting board) (School 2D, 21).QP:As they are about to cut off the quail’s head, a girl holds her hands over her quail’s eyes so it will not see it (School 1A, 60). Similarly, no thematic differences were observed between the BP group and BNP group in relation to rejection and the categorization of the bladderwrack as a nonanimal/raw food throughout the experiment. Nevertheless, differences were observed in how the children handled the bladderwrack during the preparation phase (resembling the animal categorization according to the acceptance–rejection continuum). The BNP group displayed a higher degree of disgust based on the contamination factor during the preparation and cooking phase compared to the BP group. For example, children in the BNP group were very concerned about getting too close to the bladderwrack and would carry it between two fingertips, holding it away from the body. The BNP group also displayed a more pronounced distaste, particularly with regard to smell:BNP:A boy says to a girl: ‘just f****** touch it’. The girl replies: ‘it smells’ (School 3F, 100). In addition, the subtheme inappropriateness related to the ideational theme “it is not food” was evident in the BNP group. It was particularly related to the bladders on the bladderwrack. Several groups chose to remove them before making pesto or chips. This did not seem to be an issue in the BP group. ## 3.1.2. Metatheme Acceptance: Curiosity, Person/Pet, Familiarity, Liking Even though no differences in acceptance and categorization between the QP and QNP were observed throughout the experiment, differences were observed in how the children reacted in the (pre-) preparation phase and the meal/tasting phase. The QP displayed a higher degree of food play after the printing. They would touch the quail all over and, for example, make it fly and make up small stories about the quail. This was also observed in the QNP, though to a lesser extent. Children who had participated in the food printing exercise were highly driven by curiosity related to the exploration of the taste of the quail dish, and references to familiar food were made (e.g., ‘tastes just like chicken’, ‘best cutlet ever’). However, children from the QNP tasted the quail but were more concerned with the animal dilemma: QNP:‘If you didn’t know what you were eating, you would eat it quickly’. Response: ‘WE ARE EATING!’ ( said in an accusing manner) (School 2D, 59). Furthermore, observations indicated that the QP was generally faster and better at deboning the quail than the QNP. Thematically, no differences between the BP and BNP groups were observed in relation to acceptance and categorization of the bladderwrack throughout the experiment. Nevertheless, differences were observed in how the children in the two groups reacted in the meal/tasting phase (food categorization according to the acceptance–rejection continuum). In the BP group, the children displayed a more curious exploratory approach to tasting the dishes with bladderwrack than the BNP group:BP:Tasting the pesto: A girl tastes the pesto while another girl reminds her that it has seaweed in it. The girl who is tasting covers her mouth with her hand as if in surprise and replies: ‘the aftertaste is actually very good’ (School 4G, 184–187). In the BNP group, there seemed to be concern related to gathering the courage to taste. For example, one child in the group would taste before the rest of the group. ## 3.2.1. Metatheme Rejection: Inappropriateness, Disgust, and Distaste Both the AG and NAG categorized their food item as inappropriate, though the way in which the item was inappropriate differed between the two groups. Inappropriateness in the AG was based on two approaches to the ideational dimension: ‘feeling sorry for’ and ‘it’s an animal’, whereas the NAG focused ideationally on the bladderwrack not being food. Feeling sorry for the quail in the AG was particularly displayed in the preparation phase during deboning:AG:‘I really feel sorry for it’ (School 1B, 20).AG:‘If you cut off its head, you are an animal abuser’ (School 2D, 4). Furthermore, observations also found that feeling sorry for the quail was closely related to the perception that, even though the quail was dead before they got it, they were the ones killing it during the deboning phase. In the AG, the element of the inappropriateness of it being an animal was present in the introduction, picking up, and deboning phases. During the introduction and picking-up phases, the inappropriate animalness was related to visual cues, and some children said that they could not kill a small chicken or that they did not slaughter animals, but just ate them. During the deboning phase, the dimension of killing drove the perception of animalness together with the knowledge that the quail was an animal and/or had had a life: AG:A girl looks at the quail before starting to debone: ‘YUCK! It is a little bird’ (School 1A, 71). In the NAG, inappropriateness was linked to bladderwrack being perceived as not being food, especially during the preparation phase. The bladders in particular were regarded as not being food, and they were removed. In addition, before the bladderwrack was added to the pesto mix and blended in a food processor and during the blending, it was also not regarded as food:NAG:Preparation of pesto: Group 2 begins to blend their pesto. They scream when the bladderwrack makes noises: ‘YUCK!’, ‘ what is this?’ ( School 4H, 76). Furthermore, a display of mixed inappropriateness (not food) and disgust and a fear of contamination was observed in the NAG since the group did not want to mix their pesto with the pasta even though this was the serving style recommended in the recipe. Both the AG and NAG displayed disgust in handling their food item, particularly during initial physical contact, picking up the food item, and the preparation phase. The reactions of disgust displayed in both groups were characterized as a fear of contamination driven by the tactile properties of the food items. The bladderwrack was slightly slimy, which resulted in excessive rinsing, with some groups rinsing several times followed by a thorough drying of every single bladderwrack. In addition, the bladders led to contamination-related observations, with the children arguing about the content of the bladders (e.g., puss, pimples, “do not want to know”). In the case of the quail, a fear of contamination was particularly linked to coming into contact with waste products:AG:‘I do not want dead bird on me’ (School 2C, 47). During the preparation phase, disgust was displayed through reactions related to sensory properties. In the AG, particularly, the sound of cutting through the skin and bones of the quail resulted in a display of disgust, but also touch, closely related to contamination, and smell were factors that promoted reactions of disgust (e.g., turning away, mimicking vomiting). Disgust-related sensory properties in the NAG were displayed as reactions to smell (by mimicking vomiting) and touch, closely related to contamination, which was displayed in the way the bladderwrack was handled (e.g., carrying it away from the body, holding it with only two fingertips, not touching during rinsing but turning the bowl around to move the bladderwrack). Rejection related to distaste sensory properties were displayed in both the AG and NAG. However, only visual and touch-related cues were present in the AG, though the NAG also displayed distaste related to smell and touch. For the AG, distaste related to either visual cues or touch was primarily connected to inappropriateness in the introduction, picking up, and preparation phases. For example, during the introduction phase, the children said that they were not going to eat THAT (the quail), and during the preparation phase, touching the quail was avoided by not holding it while cutting (even though they had had no problem touching it when printing). In the NAG, distaste was also displayed as touch avoidance (but without outbursts and mimicking vomiting), and references to bad smell were made, particularly during the preparation of chips and pesto:NAG:Group opens the oven and agree that it is smelly (from the bladderwrack chips). They really do not like the smell (School 3F, 73). The NAG displayed distaste in the meal/tasting phase related to the taste of the food, which was not the case for AG. In particular, the taste of the chips was not liked, which resulted in several children spitting them out after tasting them. ## 3.2.2. Metatheme Acceptance: Curiosity, Pet/Person, Familiarity, Liking Both the AG and NAG displayed curiosity when handling their food item. For example, general exploration was displayed in the AG at the beginning of the preparation phase before deboning. The children explored the quail’s beak by opening it up and checking for teeth, lifting the wings, etc., and, during the deboning process, they explored whether the quail contained an egg (several did). In the NAG, general exploration was primarily done after initial contact (after rinsing) and as part of the beginning of preparation. In particular, the children would explore by smelling the bladderwrack after rinsing, and they were observed to be speculating on what the bladders were. Exploration through food play was also observed in both the AG and NAG, though food play was much more pronounced in the AG than in the NAG. In the AG, examples of food play included lifting the quail up and pretending that it was flying (preparation phase, before deboning), playing with the viscera, and playing with the carcass (preparation phase, during/after deboning). Food play in the NAG was isolated to try to poke holes in the bladders during preparation. Curiosity related to exploring taste was primarily related to the meal/tasting phase in both the AG and NAG, but the way in which the exploration of taste was handled differed between the two groups. In the NAG, there was a more cautious approach since the pesto was not mixed with the pasta but served separately, so if the pesto (an unfamiliar ingredient in a familiar food) was not liked, it was still possible to eat the pasta (known food). In the AG, the animal dilemma was a factor, with several children commenting that it had been a live animal. The theme of personification/petification was observed to be present only in the AG, and it was very much displayed during food printing and during deboning in the preparation phase. The children would make up small stories about their quails and give them names: AG:During food printing: ‘This is Jens, and this is Birgitte. They are married’ (School 1A, 31).AG:During the deboning, a girl finds an egg in her quail. She takes the head of the quail, which has been cut off earlier, and shows the egg to the head, saying: ‘congratulations, you have become a father!’ ( School 1A, 83). Familiarity was observed to be a theme related to the promotion of acceptance in both the AG and NAG with no difference between the groups. However, the phases differed: In the AG, at the end of deboning, a girl looks at the breast fillets and says: ‘yummy, yummy, yummy’ (visually they resembled small chicken breasts), and several children also commented on how good the quail looked in the oven and how good it smelled (visually and in terms of smell, it resembled a small oven-roasted chicken). During eating, the taste was referred to as ‘like chicken’. In the NAG, familiarity was primarily displayed during the meal/tasting phase, for example, by referring to sushi, salad, and pesto in general. Displays of liking sensory properties differed in two ways between the AG and NAG. Firstly, in the NAG, the children were focused on how different the bladderwrack felt before and after rinsing, which was not the case in the AG (rinsing of quail):NAG:After washing the bladderwrack, a boy says: ‘it is not that gross anymore’ (School 3E, 43). Secondly, in the AG, liking of taste and willingness to taste/eat were influenced by the ideational theme related to the animal dilemma/animalness. What they were eating had been an animal, and, in their mind, they had killed it: AG:While eating, a child says: ‘You don’t think about what you have done’ (School 2D, 56). ## 4. Discussion This paper investigated differences in the rejection–acceptance continuum mechanisms in relation to unfamiliar food items for children depending on whether or not a tactile exercise was included prior to cooking and whether or not the food was of animal origin. The applicability of the study results relates to the potential use of relevant tools in promoting children’s healthy food behavior. ## 4.1. Tactile Exercise and Food Play With regard to the inclusion of a tactile exercise prior to cooking, our results showed that the absence of tactile exercise tended to lead to disgust-related rejection behavior. However, the inclusion of a tactile exercise to a greater extent resulted in an inappropriateness-related rejection behavior during the preparation phase (food item categorized as animal or seaweed according to the acceptance–rejection continuum). Furthermore, the tactile exercise group displayed a more playful and exploratory behavior compared to the no tactile exercise group. The playful and exploratory behavior related to the inclusion of tactile food exercises in children’s cooking classes was also found by Højer et al. [ 33]. Tactile food play has previously been suggested as being a successful method for promoting healthy food behavior in children. Coulthard and Sealy [36] found that, when food play was included before tasting, the number of varieties of fruit and vegetables (FV) tasted increased compared to when children did not participate in a food play session prior to tasting and when children were only visually exposed to FV prior to tasting. Nederkoorn, Theißen, Tummers, and Roefs [37] found that children who had played with jelly before eating a jelly dessert ate more than children who had only played a board game prior to eating the jelly dessert. To the knowledge of the authors, the difference between the inclusion and absence of a tactile exercise as part of a cooking session has not been investigated in previous studies. Therefore, particularly, the finding that rejection behavior seems to be driven by different mechanisms depending on the inclusion or absence of tactile play is of interest and should be investigated further in future studies aimed at promoting children’s healthy food behavior. ## 4.2. The Inappropriate (Unfamiliar) Meal: Animalness and Not Food This study also sought to investigate whether the origin of an unfamiliar food item (animal/nonanimal) had an impact on children’s display of rejection and acceptance–related behavior throughout the cooking exercise, including tasting/eating. Quail was chosen to represent the unfamiliar food item of animal origin since it was possible to buy it with head and feet (increased animal reference), and it is small in size, which matched the frame for preparation time. Bladderwrack was chosen as a nonanimal food item for its sensory properties: slightly slimy to the touch. Moreover, neither quail nor bladderwrack is commonly consumed in Denmark, and they were therefore deemed to be unfamiliar food items for the children. According to Angyal [51] and Rozin and Fallon [26], disgust is closely linked to the perception of a food item being spoiled (for example, because of a slimy texture and/or inappropriate smell for that food), possessing the power to contaminate other (food) objects, or having animal-like properties. Martins and Pliner [27] found that slimy texture was a promoting factor in disgust-related rejection, but they did not find support for the animal-induced disgust perception as put forward by Rozin and Fallon [26]. In our study, disgust-related rejection behavior was particularly displayed during initial physical contact and preparation, and the contamination factor was pronounced in both the animal and nonanimal groups. In the animal group, animalness (quail waste products) was the primary reason for displaying disgust, whereas the slimy surface of the bladderwrack led to disgust in the nonanimal group. The animal origin of food items has been found to affect perceived disgustingness by Traynor, Moreo, Cain, Burke, and Barry-Ryan [52]. They found that odor evaluation differed depending on the knowledge of the origin of the food item (animal origin was deemed to be more disgusting when knowledge of the origin was presented). In another study, Martins and Pliner [53] investigated the difference between familiar and unfamiliar foods of both animal and nonanimal origin. The results indicated that novel animal foods were considered more disgusting than novel nonanimal foods, and participants displayed less willingness to try novel foods of animal origin. In our study, we did not find a difference in willingness to taste between the animal and the nonanimal group. Most participants in both groups displayed a willingness to taste their dishes, even though tasting in the nonanimal group was characterized by slightly more caution (trying the bladderwrack pesto alone before mixing it with the pasta). Furthermore, Martins and Pliner [53] also concluded that perceptions related to a novel food’s disgusting properties may be a predictor of people’s willingness to try it. This indicates that familiarity is a relevant factor in reducing the perception of disgust, for example, by exposing children to a broad variety of foods in terms of both origin and sensory properties. The school is an arena with the potential to do just that through, for example, cooking classes aimed at increasing food literacy and food acceptance, an approach also supported by, for example, Muzaffar, Metcalfe, and Fiese [14], Utter, Fay, and Denny [15], and Højer et al. [ 24]. In both the AG and NAG, inappropriateness was a driver of rejection-related behavior, but what promoted the perception of inappropriateness varied across groups. In the AG, it was promoted by a sense of feeling sorry for the quail and the animal origin parameter, whereas in the NAG, it was closely related to bladderwrack not being perceived as food. According to Rozin and Fallon [26], the perception of inappropriateness is deeply embedded in culture, but it is also related to the foodscape in which an individual moves [54]. In the AG, the feelings of sorrow for the quail and the animal theme could be closely related to the acceptance theme of personification/petification. By giving their quails names and making up small stories about their quail, the children had anthropomorphized the quail, which promoted feelings of sorrow for it when they had to debone it. This could be an expression of what Stanton [55] refers to as the Disneyfication of the animal. She argues that it is implied in Walt Disney productions (WDP) (cartoons) that animals depicted as heavily anthropomorphized should not be harmed. In addition, she underlines that the animals in WDPs are rarely depicted as dying due to the most common cause of death (slaughterhouses), and therefore WDPs are disconnected from reality. This could explain why participants in the animal group found it wrong and disgusting to ‘kill’ their quail. Furthermore, the animal dilemma was also present during eating, with the children not wanting to recall what they had just done in the deboning process (in their minds they were killing the quail). This is supported by Stewart and Cole’s [56] findings in an examination of the role of children’s movies featuring ‘talking animals’ with regard to how meat and a toy (representing a movie figure, here, ‘Babe’ the pig) are viewed by the child. Throughout Babe’s journey, the pig is subjectified, whereas the pig as meat is objectified, and the child does not connect the two. As such, animals can belong to different domains due to their perceived use by humans. Our findings also correlate with those of McGuire, Palmer, and Faber [57], who found that children (9–11 years old) will more often categorize farm animals as pets than food and find it less morally acceptable to eat meat and animal products compared to young adults and adults. In the NAG, inappropriateness could be related to the lack of familiarity with bladderwrack as food. The most common form of seaweed eaten in Denmark is sushi wrapped in nori (dried seaweed typically made from the red algae Pyropi yezonesis and P. tenera). The two seaweeds are very different in appearance, and the nori is dried and pressed in sheets and as such, bears little resemblance to fresh seaweed. The inappropriateness, together with a perceived risk of contamination, resulted in the bladderwrack pesto being served on the side without being mixed with the pasta. In this way, the ‘safe’ familiar food (the pasta) was not contaminated with an unfamiliar food item. Since the children tasted the pesto alone, most of them concluded that it actually tasted good, and then they would mix the two ingredients. A study of children’s preferred food serving styles showed that young children (7 to 8 years old) preferred dish components to be served separately on the plate, whereas older children (12 to 14 years old) preferred a partly mixed and mixed serving style [58]. However, the study only included traditional familiar dishes. According to Fallon, Rozin, and Pliner [59], it is an expected behavior to separate foods to avoid contamination, especially if one of the food items has an anticipated distaste. At the end of the exercise, most children across cases (AG, NAG) chose to taste and eat the food they had cooked except for the bladderwrack chips, which were highly disliked due to the taste and smell. This finding corresponds to that of Sick et al. [ 32], who found that children’s two main reasons for rejecting unfamiliar foods were taste and smell. Acceptance was promoted by exploration, curiosity, and liking of taste, which corresponds to findings by Sick et al. [ 32] who found that curiosity and good taste were the two reasons most often given by children for acceptance of unfamiliar foods. Furthermore, acceptance was promoted when the food items were transformed into recognizable dishes, which was also found by Højer et al. [ 24]. Furthermore, a willingness to taste and eat the dishes could also stem from a combination of having made the food themselves and the context (school and cooking together). The effect of ‘I cooked it myself’ (pride) on the willingness to taste was also found in previous studies of children’s participation in cooking classes [12,33,60], as was the effect of context [12,33,61]. To the knowledge of the authors, no studies have tested children’s rejection and acceptance mechanisms in relation to food items of animal and nonanimal origin within the same study. In particular, our findings that inappropriateness is closely linked to how the food item is categorized (AG: animal, petification; NAG: not food), to the perceived domain depending on human use, the degree of anthropomorphization, and familiarity are interesting since they show that children perceive and react to food items of animal and nonanimal origin differently when it comes to rejection-related behavior. Nonetheless, most children chose to taste and eat their dishes despite prior rejection-related behavior. This could indicate that, even though rejection took two separate paths depending on the origin of the food item, acceptance was promoted by exploratory behavior, food transformation, ‘I cooked it myself’, and, ultimately, context. ## 5.1. Conclusions With regard to the effect on the rejection–acceptance continuum mechanisms depending on the inclusion or absence of a tactile exercise prior to cooking, we found that rejection-related behavior differed. The no tactile exercise group displayed a disgust-related rejection behavior during the preparation/cooking phase, whereas the other group who did a tactile exercise displayed an inappropriateness-related rejection behavior during the preparation/cooking phase. In addition, the tactile exercise group exhibited a more playful and exploratory behavior during preparation/cooking than the no tactile exercise group. In our investigation of the effect of the origin of the food item (animal vs. nonanimal) in relation to rejection–acceptance continuum mechanisms, we found that rejection-related behavior in both the AG and NAG was driven by disgust related to a fear of contamination, but in the NAG, the slimy tactile attribute was also a factor. However, inappropriateness was conditioned by the origin of the food item. In the AG, feeling sorry for the quail and the fact that it was an animal (they were killing the quail according to the children’s perception) were prominent factors, whereas in the NAG, the main factor was the bladderwrack not being perceived as food, probably due to lack of familiarity. In the NAG, the source of rejection was bad taste and smell with regard to the bladderwrack chips. Acceptance mechanisms present in both the AG and NAG were exploration and familiarity, though the children’s behavior differed between groups: The AG displayed a more playful exploratory behavior than the NAG, and in relation to familiarity, the AG made familiar references during deboning and eating, whereas the NAG did not do so until eating. Petification was only present in the AG. Liking generally did not differ between groups and was driven by good taste and familiarity. ## 5.2. Practical Application: Food Literacy and Health Promotion The results from this study emphasize the importance of incorporating tactile exercises in cooking classes and food-related exercises with children in kindergarten, school, and leisure-time cooking activities since it seems to change the rejection reaction from disgust to inappropriateness. Inappropriateness could be reduced through exposure and increased familiarity. 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--- title: Detection of antibiotic-resistant canine origin Escherichia coli and the synergistic effect of magnolol in reducing the resistance of multidrug-resistant Escherichia coli authors: - Yin-Chao Tong - Yi-Ning Zhang - Peng-Cheng Li - Ya-Li Cao - Dong-Zhao Ding - Yang Yang - Qing-Yi Lin - Yi-Nuo Gao - Shao-Qiang Sun - Yun-Peng Fan - Ying-Qiu Liu - Su-Zhu Qing - Wu-Ren Ma - Wei-Min Zhang journal: Frontiers in Veterinary Science year: 2023 pmcid: PMC10057116 doi: 10.3389/fvets.2023.1104812 license: CC BY 4.0 --- # Detection of antibiotic-resistant canine origin Escherichia coli and the synergistic effect of magnolol in reducing the resistance of multidrug-resistant Escherichia coli ## Abstract ### Background The development of antimicrobial resistance in the opportunistic pathogen Escherichia coli has become a global public health concern. Due to daily close contact, dogs kept as pets share the same E. coli with their owners. Therefore, the detection of antimicrobial resistance in canine E. coli is important, as the results could provide guidance for the future use of antibiotics. This study aimed to detect the prevalence of antibiotic-resistance of canine origin E. coli in Shaanxi province and to explore the inhibition effect of magnolol combined with cefquinome on MDR E. coli, so as to provide evidence for the use of antibiotics. ### Methods Canine fecal samples were collected from animal hospitals. The E. coli isolates were separated and purified using various indicator media and polymerase chain reaction (PCR). Drug-resistance genes [aacC2, ant(3')-I, aph(3')-II, aac(6')-Ib-cr, aac(3')-IIe, blaKPC, blaIMP−4, blaOXA, blaCMY, blaTEM−1, blaSHV, blaCTX−M−1, blaCTX−M−9, Qnra, Qnrb, Qnrs, TetA, TetB, TetM, Ermb] were also detected by PCR. The minimum inhibitory concentration (MIC) was determined for 10 antibiotics using the broth-microdilution method. Synergistic activity of magnolol and cefquinome against multidrug-resistant (MDR) E. coli strains was investigated using checkerboard assays, time-kill curves, and drug-resistance curves. ### Results A total of 101 E. coli strains were isolated from 158 fecal samples collected from animal hospitals. MIC determinations showed that $75.25\%$ ($\frac{76}{101}$) of the E. coli strains were MDR. A total of 22 drug-resistance genes were detected among the 101 strains. The blaTEM−1gene exhibited the highest detection rate ($89.77\%$). The TetA and Sul gene also exhibited high detection rate (66.34 and $53.47\%$, respectively). Carbapenem-resistant E. coli strains were found in Shangluo and Yan'an. Additionally, in MDR E. coli initially resistant to cefquinome, magnolol increased the susceptibility to cefquinome, with an FICI (Fractional Inhibitory Concentration Index) between 0.125 and 0.5, indicating stable synergy. Furthermore, magnolol enhanced the killing effect of cefquinome against MDR E. coli. Resistance of MDR E. coli to cefquinome decreased markedly after treatment with magnolol for 15 generations. ### Conclusion Our study indicates that antibiotic-resistance E. coli has been found in domestic dogs. After treatment with magnolol extracted from the Chinese herb Houpo (Magnolia officinalis), the sensitivity of MDR E. coli to cefquinome was enhanced, indicating that magnolol reverses the resistance of MDR E. coli. The results of this study thus provide reference for the control of E. coli resistance. ## 1. Introduction Escherichia coli is one of the most important and common Gram-negative bacteria (GNB) living in the gut of humans and animals which can lead to severe diarrhea. As an opportunistic pathogen, E. coli can be transmitted between humans and animals, especially between pets and their owners (1–4). It is therefore important to monitor antimicrobial resistance in E. coli derived from pet animals in order to prevent the further development of resistance. Research to identify ways to eliminate bacterial antimicrobial resistance has thus become high priority worldwide [5]. Several systematic reviews have described the complex mechanisms leading to antibiotic resistance, which can be mediated by plasmids, changes in target sites, modifications of antibiotic-degrading enzymes, cell adaptation, and efflux pumps, all of which have been linked to the inappropriate use of antibiotics [6, 7]. Thus, the identification of antibiotic alternatives or synergistic approaches to reduce resistance is of clinical importance. Some studies have indicated that combinations of Chinese herb extracts and antibiotics show synergistic effects against E. coli via different mechanisms. A range of volatile oils from Cukangchai [Mallotus philippensis (Lam.) Muell. Arg.] inhibit conjugal transfer of drug-resistance plasmids, which reduces the lateral transmission of drug-resistance [8]. Quercetin has the ability to cause MDR E. coli to regain susceptibility to tetracycline by increasing cell permeability and the intracellular drug concentration [9]. Two studies found that baicalin from Huangqin (Scutellaria baicalensis Georgi) inhibits the activity of NDM-1 and decreases the expression of fimB, which is a major bacterial adhesion factor [10, 11]. Resveratrol from Lilu (Veratrum nigrum L.) reduces the expression of the efflux pump protein AcrAB-TolC in E. coli to inhibit drug-resistance [12]. Magnolol is an extractive from Houpo (Magnolia officinalis), which was first recorded in the Shennong Herbal Classic. As shown in previous studies, magnolol has multiple biological activities, including the prevention and amelioration of diseases such as cancer [13], anti-depressant [14] and anti-diabetes [15] effects, and improvement of growth performance [16]. However, the Ben Cao Gang Mu (Compendium of Materia medica) indicates gastrointestinal tract diseases such as dysentery and cholera as indications for Houpo, which suggests that Houpo interacts with the intestinal flora. Another study suggested that magnolol affects E. coli [17]. However, the potential effectiveness of magnolol in the treatment of bacterial infection–related diseases has not been fully explored. In this study, based on a previous synergistic study of magnolol and meropenem [18], magnolol and cefquinome were hypothesized to inhibit E. coli synergistically at a safe dose [19]. Canine fecal samples were collected from veterinary hospitals to test the antimicrobial resistance of E. coli isolates and the potential for magnolol to reverse drug resistance. This study aimed to detect the prevalence of antibiotic-resistance of canine origin E. coli in Shaanxi province and to explore the inhibition effect of magnolol combined with cefquinome on MDR E. coli, so as to provide evidence for the use of antibiotics. ## 2.1. Sample collection In this cross-sectional study, 158 canine fecal samples were collected (Supplementary Table 1) from 12 animal hospitals in eight prefecture-level cities (Yulin, Yan'an, Xi'an, Xianyang, Baoji, Ankang, Shangluo, Hanzhong and Weinan) in Shaanxi Province during November 2021 to August 2022, which were numbered by the first letter of the sampling city with patient number. ## 2.2. Bacterial isolation and identification All collected samples were enriched in trypticase soy broth for 10~12 h at 37°C until reaching logarithmic phase and then transferred onto MacConkey agar and then eosin-methylene blue agar and incubated aerobically for 16~18 h at 37°C. The red isolates on MacConkey agar an the black with metallic luster isolates were chosen and saved from next steps. The E. coli isolates were subjected to Gram stain followed by primary identification. All media were purchased from Qingdao Hope Biotechnology Co., Qingdao, China. The E. coli strain ATCC® 25922™ preserved in our laboratory was used as a control. ## 2.3. Molecular confirmation of E. coli isolates Single, pure isolates were enriched for a second time in Mueller-Hinton broth (MHB) for 24 h at 37°C. Thereafter, 1 mL of bacterial culture was centrifuged at 14,000 rpm for 15 min. After decanting the supernatant, the pelleted cells were washed with sterile ultrapure water, and the centrifugation and wash steps were repeated twice. To extract genomic DNA, washed bacteria were boiled in sterile ultrapure water for 10 min. After centrifugation at 14,000 rpm for 15 min, the resulting supernatant was used as the DNA template for polymerase chain reaction (PCR) assays [4, 20] using primers specific for 16S rDNA to identify the isolates, as described previously [21, 22]. PCR assays were performed in a final volume of 20 μL, consisting of 10 μL of master mix (Dining, China), 1 μL of each forward and reverse primer, 1 μL of DNA template, and 7 μL of nuclease-free water. PCR assays were performed in a thermocycler (Bioer TC-XP-G, China) using the following proGram: initial denaturation at 94°C for 5 min, followed by 30 cycles of denaturation at 94°C for 30 s, annealing (Supplementary Table 2) for 30 s, and extension at 72°C for 30 s, followed by a final extension at 72°C for 7 min. The positive and negative controls were E. coli ATCC® 25922™ and nuclease-free water, respectively. Electrophoresis was performed on a $1.5\%$ agarose gel stained with DiRed Safe DNA DYE (Dining, China) to determine the size of PCR products compared to a 2,000-bp DNA ladder. The gel was scanned using a UV-light transilluminator (72/BR04467, Bio-Rad, USA). Confirmed isolates were stored at −80°C in MHB containing $35\%$ glycerol until further analysis. ## 2.4. Antibiotic susceptibility testing Broth-microdilution assays were performed to determine the antibiotic susceptibility and minimum inhibitory concentrations (MICs) for 10 antibiotics, including ceftiofur, cefquinome, ceftazidime, ceftriaxone, meropenem, norfloxacin, ciprofloxacin, gentamycin, kanamycin and amikacin (Shanghai Macklin Biochemical Co., Ltd, China) as recommended by the Clinical and Laboratory Standards Institute and the European Committee on Antimicrobial Susceptibility Testing. The above antibiotics are commonly used in veterinary clinical treatment of E. coli infection. Susceptibility to ceftiofur and cefquinome was determined in reference to previous research [23]. All drugs were diluted 2-fold in MHB and mixed with an equal volume of bacterial suspension in a 96-well microtiter plate. Each test was repeated three times. Escherichia coli ATCC® 25922™ was used as the quality-control strain (without magnolol). According to a previous report, bacteria resistant to ≥3 different classes of antibiotics were considered MDR [24]. ## 2.5. Molecular detection of antibiotic resistance genes All primers were reflected in Table 1. PCR assays were performed in a final volume of 20 μL, consisting of 10 μL of master mix (Dining, China), 1 μL of each forward and reverse primer, 1 μL of DNA template, and 7 μL of nuclease-free water. PCR assays were performed in a thermocycler (Bioer TC-XP-G, China) using the following proGram: initial denaturation at 94°C for 5 min, followed by 30 cycles of denaturation at 94°C for 30 s, annealing (Supplementary Table 2) for 30 s, and extension at 72°C for 30 s, followed by a final extension at 72°C for 7 min. The negative control was nuclease-free water. Electrophoresis was performed on a $1.5\%$ agarose gel stained with DiRed Safe DNA DYE (Dining, China) to determine the size of PCR products compared to a 2,000-bp DNA ladder. The gel was scanned using a UV-light transilluminator (72/BR04467, Bio-Rad, USA). Confirmed isolates were stored at −80°C in MHB containing $35\%$ glycerol until further analysis. Table 1 \| Gene \| Detection rate (%) \| Gene.1 \| Detection rate (%).1 \| \| --- \| --- \| --- \| --- \| \| blaTEM−1 \| 89.77 ± 0.22 \| aac(6')-Ib-cr \| 22.77 ± 0.00 \| \| TetA \| 66.34 ± 0.00 \| TetB \| 17.82 ± 0.00 \| \| Sul \| 53.47 ± 0.00 \| aph(3')-II \| 15.84 ± 0.00 \| \| aac(3)-IIe \| 41.91 ± 0.22 \| QnrB \| 8.91 ± 0.00 \| \| blaCTX−M−1 \| 40.59 ± 0.00 \| ErmB \| 6.93 ± 0.00 \| \| TetM \| 30.69 ± 0.00 \| QnrA \| 6.27 ± 0.22 \| \| blaCTX−M−9 \| 30.03 ± 0.22 \| blaSHV \| 4.95 ± 0.00 \| \| QnrS \| 27.72 ± 0.00 \| CMY \| 4.95 ± 0.00 \| \| aph(6)-Id \| 25.74 ± 0.00 \| OXA \| 3.96 ± 0.00 \| \| ant(3”)-I \| 25.74 ± 0.00 \| IMP-4 \| 3.96 ± 0.00 \| \| aacC2 \| 23.76 ± 0.00 \| blaKPC \| 0.99 ± 0.00 \| ## 2.6. Checkerboard assay The combined antibacterial effect of magnolol and cefquinome was assessed using a checkerboard assay, as previously described [25]. Briefly, both magnolol (≥$98\%$ HPLC, Shanghai Aladdin Bio-Chem Technology Co., China) and cefquinome were diluted to prepare seven gradient concentrations ranging from $\frac{1}{16}$ MIC to 2 MIC. Each longitudinal column of tubes contained the same concentration of drug A, and each horizontal row of tubes contained the same concentration of drug B. Each tube was inoculated with bacterial suspension to a final density of approximately 1 × 106 CFU/mL. Single-drug control tubes and blank control tubes were also prepared, and E. coli ATCC® 25922™ was used as a sensitivity control strain. Six isolates with the highest number of antibiotic-resistance genes were used as experimental bacteria. All tubes were incubated at 37°C for 16 h under aerobic conditions. The experiment was repeated in triplicate. Fractional inhibitory concentration index (FICI) was calculated according to the following formula: FICI = MIC of magnolol in combination/MIC of magnolol alone + MIC of cefquinome in combination/MIC of cefquinome alone. An FICI value ≤ 0.5 indicated synergy; 0.5 < FICI ≤ 0.75 indicated partial synergy; 0.76 < FICI ≤ 1 indicated additive effect; 1 < FICI ≤ 4 indicated indifferent effect; and FICI > 4 indicated antagonism. In this study, synergy and partial synergy were defined as a synergistic relationship, whereas additive, indifferent, and antagonistic results were regarded as a non-synergistic relationship [26]. ## 2.7. Time-kill curves Time-kill assays were used to evaluate the antibacterial effects of the combination of magnolol and cefquinome against MDR E. coli by measuring the reduction in the calculated population in CFU/mL within 24 h. Magnolol and cefquinome were incubated with an equal volume of E. coli culture at different levels of magnolol and cefquinome [27]. As a control, MHB was added instead of magnolol or cefquinome. All samples were cultivated at 37°C. After 0, 2, 4, 6, 8, and 24 h of incubation, 100-μL samples were removed. After dilution to proper levels, 100 μL of each sample was spread onto Mueller-Hinton agar for colony counting. Each assay was repeated in triplicate. ## 2.8. Drug-resistance curves Drug-resistance curves were used to evaluate the effects of magnolol in reducing the resistance of MDR E. coli to cefquinome by determining the MIC after magnolol treatment within 15 generations. Magnolol (0.25 MIC) was incubated with an equal volume of each E. coli culture in MHB at 37°C for 16 h. An inoculating loop of each MHB culture was then streaked onto Mueller-Hinton agar and incubated at 37°C for 16 h. After 0, 1, 2, 3, 6, 9, and 15 generations, a single, pure-colony of each isolate was removed and placed in MHB and incubated at 37°C for 16 h, after which the MIC was determined. Each assay was repeated in triplicate. ## 2.9. Statistical Analysis Data are expressed as the mean ± standard deviation. The statistical significance of differences was determined using t-tests with SPSS 27.0 software. For all comparisons, $P \leq 0.01$ and $P \leq 0.05$ were considered indicative of statistical significance. All figures were made by GraphPad Prism 8.0.1. The maps were downloaded from Standard Map Service (http://bzdt.ch.mnr.gov.cn/) and edited by Photoshop 2021. ## 3.1. Samples and E. coli isolates A total of 101 E. coli strains were isolated from 158 canine fecal samples (Supplementary Table 1) obtained using anal swabs, for a rate of $63.92\%$ (Figure 1A). As shown in Supplementary Figure 1A, 22 drug-resistance genes, including 6 aminoglycoside-resistance genes, 8 β-lactam–resistance genes, 3 quinolone-resistance genes, 3 tetracycline-resistance genes, 1 sulfa drug-resistance gene and 1 macrolide-resistance gene were detected. Figure 1: (A) Geographical distribution of the sampling and detection rates of E. coli isolates. Sources of 158 samples and rates of E. coli detected in 8 cities in Shaanxi Province. (B) Detection rates and regional distribution of E. coli isolates carrying different numbers of antibiotical genes. (C) Detection rates and regional distribution of MDR E. coli isolates. In general, as shown in Figure 2A, the detection rate of sulfa drug–resistance genes was the highest of six types of tested genes, at $53.47\%$. The detection rate of tetracycline-resistance genes was $38.28\%$, which was the second highest. The detection rates of aminoglycoside-resistance genes and β-lactam–resistance genes were similar, at $25.91\%$ and 22.28, respectively. The detection rates of quinolone-resistance and macrolide-resistance genes were $14.19\%$ and 6.93, respectively. Figure 2: (A) Detection rate of 6 classes of antibiotical genes among 101 E. coli strains identified. (B) Resistance rates of E. coli strains to four different classes of antibiotics. In terms of single genes, as shown in Table 1, all tested genes were detected, with blaTEM−1 exhibiting the highest detection rate, at 89.77 ± $0.22\%$. The detection rates of TetA and sul were both >$50.00\%$, at 66.34 and $53.47\%$, respectively. The detection rates of aac[3]-Iie and blaCTX−M−1were similar, at 41.91 ± $0.22\%$ and $40.59\%$, respectively. The detection rates of aacC2, ant(3')-I, aph(3')-II, aac(6')-Ib-cr, aph[6]-Id, blaCTX−M−9, QnrS, and TetM were similar ($22.77\%$~$30.69\%$), followed by TetB and aph(3')-II (17.82 and $15.84\%$, respectively). The detection rates of KPC, IMP-4, OXA, CMY, blaSHV, QnrA, QnrB and ErmB were all < $10\%$ (0.99~$8.91\%$), and the detection rate of blaKPCwas the lowest, at $0.99\%$. As the result of the high detection rate of blaTEM−1, the resistance rates for extended-spectrum β-lactamases were high. E. coli strains with more than 10 resistance genes accounted for $13.86\%$ ($\frac{14}{101}$) of isolates in this study (Shaanxi Province). As shown in Figure 1B, the geographical rate was $25.00\%$ in Yulin, $18.75\%$ in Xi'an, $9.682\%$ in Xianyang, and $7.14\%$ in Baoji. No isolates with more than 10 resistance genes were detected in Shangluo, Weinan, and Hanzhong. The rates of E. coli strains with 5~9 resistance genes were $46.53\%$ in Shaanxi Province, and geographically, $75.00\%$ in Hanzhong, $73.33\%$ in Yan'an, $71.43\%$ in Baoji, $50\%$ in Yulin, $50.00\%$ in Weinan, $38.71\%$ in Xianyang, $35.71\%$ in Shangluo, and $25\%$ in Xi'an. ## 3.2. Antibiotic susceptibility testing The 10 tested antibiotics were grouped into 4 classes (Supplementary Figure 1B), including cephalosporins (ceftiofur, cefquinome, ceftazidime, ceftriaxone), carbapenems (meropenem), quinolones (norfloxacin, ciprofloxacin), and aminoglycosides (gentamycin, kanamycin, and amikacin). As shown in Figure 2B, the carbapenem class of antibiotics had the lowest resistance rate ($10.89\%$). In contrast, the resistance rate of the cephalosporin class of antibiotics ($71.29\%$) was highest. The resistance rates of aminoglycosides and quinolones were 42.24 and $28.22\%$, respectively. In general, the rate of MDR E. coli was $75.25\%$ ($\frac{76}{101}$). The highest rate of MDR E. coli occurred in Yulin, at $100\%$. However, no MDR E. coli were detected in Weinan. The detection rate of MDR E. coli in other cities was between 56.25 and $80.00\%$, as shown in Figure 1C. ## 3.3. Synergistic effect of magnolol in combination with cefquinome To evaluate the potential synergistic effect of magnolol combined with cefquinome, checkerboard dilution assays were performed against 6 MDR E. coli strains with the highest number of antibiotic-resistance genes in this study (XA4, XA50, XA 59, XY1-4, XY1-14, and XY1-17). The MIC values of magnolol monotherapy against isolates was shown in Table 2. Notably, as shown in Figure 3 and Table 2, the FICI of ATCC® 25922TM was <0.75, indicating the partial synergistic effect. The FICI of XA4, XA 59, XY1-4 and XY1-14 was <0.5, indicating the synergistic effect. The FICI of XA50 and XY1-17 was <0.75, indicating the partial synergistic effect. What's more, as shown in Figure 3, the use of cefquinome in combined treatment decreased 8- to 32-fold compared to monotherapy, which suggesting that magnolol decreases the resistance of MDR E. coli to cefquinome. ## 3.4. Time-kill analyses Based on the results of MIC assays, time-kill curves were prepared to evaluate the bactericidal effect of cefquinome against MDR E. coli treated with magnolol. As shown in Figure 4, compared with either the single cefquinome group or the single magnolol group, at all concentrations tested, the combination of magnolol and cefquinome exhibited an enhanced bactericidal effect against the three tested MDR E. coli strains within 24 h. During 0–8 h, the population of E. coli strains in MAG + CEF group decreased 102- to 103- fold compared to CEF group. Until about 24 h later, the differences of the population in MAG+CEF group and CEF group reached 104- to 106- fold. Moreover, the bactericidal effect appeared to be dose-dependent, as demonstrated by the phenomenon that the group with equal level of cefquinome but with a high level of magnolol exhibited clearly stronger bactericidal activity than the low level of magnolol group, which indicated that magnolol may be a potential antibiotic activator. The above results suggest that magnolol exerts an effective and rapid bactericidal effect on MDR E. coli. Figure 4: Bactericidal effect of magnolol (MAG) and cefquinome (CEF) in different combinations or levels against MDR E. coli. All data are expressed as mean ± SD determined from three independent experiments performed in triplicate. ## 3.5. Drug-resistance curve analyses Based on the results of MICs determined in generations 0, 1, 2, 3, 6, 9, and 15, drug-resistance curves were prepared to evaluate the changes in drug resistance of MDR E. coli treated with magnolol over the course of 15 generations. As shown in Figure 5, compared with the negative control group, the MICs of MDR E. coli strains in the magnolol group decreased more quickly in the first two generations (4- to 8- fold compared to the negative group). Starting with generation three, the MICs in the magnolol (MAG) group continued to decrease, and the changes in MICs were more stable than those of the negative control group, suggesting that magnolol is a strong and stable synergistic activator of cefquinome. After 15 generations, the MIC values in the magnolol group were 16 times lower than the negative control group in average. Regardless of the generation, except the generation 1 of the E. coli XA4, there were statistically significant differences between the negative control group and the MAG group, as the standard that: $P \leq 0.05$ (difference), $P \leq 0.01$ (significant difference), $P \leq 0.001$ (significant difference), $P \leq 0.0001$ (significant difference). These results collectively suggested that magnolol increases the sensitivity of MDR E. coli to cefquinome. Figure 5:** Effect of magnolol (MAG) on changing the sensitivity of MDR E. coli to cefquinome. All data are expressed as mean ± SD determined from three independent experiments performed in triplicate. nsP > 0.05; P < 0.01; P < 0.001; *P < 0.0001. ## 4. Discussion The emergence of MDR E. coli has become a worldwide public health concern [28]. Numerous surveillance studies of MDR bacteria in clinical and veterinary medicine have demonstrated that MDR E. coli is associated with an increased risk of transmission and poses a significant threat to the sale of food products and public health (29–31). To understand the current situation of MDR E. coli, we examined the presence of 22 drug-resistance genes in 101 E. coli strains isolated from eight cities in China, including Yulin, Yan'an, Shangluo, Xianyang, Xi'an, Hanzhong, Weinan, and Baoji. The results of this study showed that the rates of MDR E. coli and the numbers and kinds of antibiotic-resistance genes carried by the isolates varied in the different cities. For example, the MDR rate in Weinan was $0.00\%$, but the rates in other cities were over $50\%$. Moreover, no strains carrying more than 10 antibiotic-resistance genes were found in Shangluo and Weinan. However, the rates in Yulin and Yan'an were over $20\%$. The question then arises: what could cause such a phenomenon? After communicating with local veterinarians, we hypothesized that the variations may be related to local and individual medication habits. For instance, veterinarians in Weinan preferred natural extracts to antibiotics when treating bacterial infections; thus, the rate of MDR E. coli was lowest in Weinan. This factor is related not only to the MDR E. coli rate but also the prevalence of antibiotic-resistance genes. Veterinarians in Xianyang and Xi'an used more aminoglycosides than β-lactams; therefore, the number of aminoglycoside-resistance genes carried by the isolates was greater than the number of β-lactam–resistance genes, and the same case can be found in the MIC results. However, the prevalence of antibiotic resistance cannot be related to just local and individual medication habits. To examine the issue further, the results of this study were compared with those of previous studies in China. In the case of extended-spectrum β-lactamase (ESBL)-resistant E. coli, the rate of blaCTX−Min Yangzhou city was higher than the rates in the previous study [32]. Furthermore, the differences were not only in rates but also gene types. In our study, the blaCTX−M genes detected included blaCTX−M−1and blaCTX−M−9, but blaCTX−M−14, blaCTX−M−15, and blaCTX−M−55 were detected in Yangzhou city. Although blaCTX−M−1 and blaCTX−M−15, blaCTX−M−9, and blaCTX−M−14 belong to the same gene group, some differences still exists in the molecular structures, which led us to a second hypothesis, that the prevalence of antibiotic-resistance genes may be related to geographic factors that affect the molecular characteristics of the genes. This hypothesis can be verified in northeastern China [33]. Public policy is another factor that could affect the prevalence of antibiotic resistance. In 2015, the European Union published guidelines on the prudent use of antimicrobial veterinary medicines, the implementation of which led to lower rates of MDR strains in Europe (34–37) compared with China, where appeals to reduce and replace antibiotics with other agents in veterinary medicine did not occur until 2020. The degree of social and economic development can also affect the prevalence of antibiotic resistance. Compared with previous studies in Africa and west Asia (38–40), the rates of antibiotic-resistance genes in this study were lower, except for blaTEM−1. The impact of social and economic development can also be verified through comparisons with wealthier regions. The rates of ESBL-resistance genes in this study were slightly higher than the rates in United States [41, 42]. In summary, four factors can affect the prevalence of canine-origin antibiotic-resistant E. coli: 1. Local and individual medication habits; 2. Geographic factors that impact molecular characteristics of the genes; 3. Differences in public policies; and 4. The degree of social and economic development. With the development of high-grade cephalosporins, resistance to these agents has also developed, which in turn has led to the emergence of ESBL-resistant E. coli. Resistance to cefquinome, the highest grade of cephalosporin used in veterinary medicine, has also emerged. Therefore, the identification of potent adjuvants to rescue cefquinome activity is of high priority. Currently, most studies examining the synergistic effects of combinations of natural compounds and antibiotics focus on inhibiting bacterial growth. A previous study found that resveratrol combined with colistin showed synergistic effects against E. coli [43]. Another study found that the combination of salicylate and curcumin can inhibit colistin-resistant E. coli by inhibiting efflux pumps [44]. Zhou et al. [ 45] identified pterostilbene as a potential MCR-1 inhibitor, which when combined with polymyxin B showed synergistic effects against E. coli. Studies of the efficacy of combinations of natural extracts and antibiotics against bacterial infections are not limited to E. coli. Cai et al. [ 46] reported that baicalin inhibits the CTX-M-1 gene and that the combination of baicalin and cefotaxime shows synergistic effects against Klebsiella pneumoniae. Yi et al. [ 47] reported the synergistic antibacterial activity of tetrandrine combined with colistin against MCR-mediated colistin-resistant Salmonella. Although magnolol has multiple biological activities, including the prevention and amelioration of diseases such as cancer [13], anti-depressant [14] and anti-diabetes [15] effects, and improvement of growth performance [16], its potential activity against bacterial diseases has not been fully explored. In this study, we unexpectedly found that magnolol exhibits potent potentiation (8- to 64-fold) of the effectiveness of cefquinome against resistant bacteria, even at high magnolol concentrations. To our knowledge, this study is the first to report the effect of the combination of magnolol and cefquinome in inhibiting cefquinome-resistant bacteria. Importantly, based on previous studies and the reported safety and toxicology of magnolol [19], we evaluated high levels of magnolol with cefquinome to determine if the bactericidal effect is related to magnolol dose. As indicated by time-kill curve analyses, the bactericidal effect was magnolol dose dependent. The above results clearly indicate that continuous combination therapy in relatively low doses ($\frac{1}{4}$ MIC to $\frac{1}{2}$ MIC) is needed to effectively reduce resistance to cefquinome, suggesting that it is possible to reduce antibiotic resistance through continuous use of combinations of natural extracts and antibiotics. To predict the possibility of the use of magnolol in veterinary clinic against bacterial infection, several previous studies were referred. First of all, the blood concentration of magnolol can reach 0.74 μg/mL (ppm) in rats [19, 48] indicating that it's hard to achieve bactericidal concentrations in vivo. What's more, the literature and studies about the effects of magnolol against bacterial infection in vivo are very rare, including studies of effective physiological concentrations of magnolol and the mechanism of magnolol against bacterial infection in vivo. However, it's a common sense that the internal environment is complex and changeable. No matter whether cells or body is infected with germs, the system of innate immune response (TLR4/NF κB p65 pathway) will be activated promptly which will contribute to inflammation to remove the pathogens [49, 50]. Therefore, the effects of inflammation and oxidative damage should be taken into account while evaluating the anti-bacterial effects of magnolol in vivo. As a result, relevant studies remain to be conducted. As previous studies have indicated, antibacterial drug combinations can exhibit synergism in four ways: elimination of drug-resistance plasmids [51], inhibition of biofilm formation [52], inhibition of the activity of drug-resistance enzymes [45], and inhibition of drug efflux pumps [53]. Based on previous research [18], we speculate that the mechanism of the synergy observed in this study is related to the inhibition of β-lactamases. This study has some limitations. First, the sample size in the different cities was relatively small; thus, larger samples will be needed to verify the four above-mentioned hypotheses. Second, despite the observed synergism of the combination of magnolol and cefquinome, the mechanism of the synergy remains to be explored. Transcriptome sequencing has been used in drug-resistance reduction studies [27, 54] to identify changes in the expression of drug-resistance genes, data which can in turn be used in further studies to elucidate the effect of magnolol on the expression of drug-resistance genes. Furthermore, molecular docking and Western blotting have been used to evaluate the inhibitory effects of drugs on the activity and expression of drug-resistance enzymes [55, 56]. These techniques can also be used to determine the inhibitory effect of magnolol on the activity and expression of ESBLs. In conclusion, our data indicate that MDR E. coli strains are present in high rates in dogs kept as pets in China, thus raising public health concerns. Our study also demonstrated the synergistic effects of magnolol combined with cefquinome against MDR E. coli. However, the mechanism of this synergistic activity remains to be elucidated in our further studies. The discovery of magnolol as a novel cefquinome adjuvant highlights the enormous antibacterial potential of compounds extracted from plants. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors. ## Ethics statement The animal study was reviewed and approved by the Institutional Animal Care and Use Committee and Ethics Committee of Northwest A&F University (approval number: NWLA-2021-063). Written informed consent was obtained from the owners for the use of their animals in this study. ## Author contributions Y-CT and Y-NZ conceived and designed the experiments, analyzed the data, and wrote the manuscript. Y-CT, Y-NZ, P-CL, Y-LC, D-ZD, YY, Q-YL, and Y-NG performed the experiments. S-ZQ interpreted the study results. W-RM and W-MZ revised the manuscript. All authors contributed to the manuscript and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2023.1104812/full#supplementary-material ## References 1. Carvalho AC, Barbosa AV, Arais LR, Ribeiro PF, Carneiro VC, Cerqueira AM. Resistance patterns, ESBL genes, and genetic relatedness of. Braz J Microbiol. 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--- title: Exogenous abscisic acid and sodium nitroprusside regulate flavonoid biosynthesis and photosynthesis of Nitraria tangutorum Bobr in alkali stress authors: - Jie Zhang - Kai Cheng - Xinyue Liu - Zhichao Dai - Lingling Zheng - Yingchun Wang journal: Frontiers in Plant Science year: 2023 pmcid: PMC10057120 doi: 10.3389/fpls.2023.1118984 license: CC BY 4.0 --- # Exogenous abscisic acid and sodium nitroprusside regulate flavonoid biosynthesis and photosynthesis of Nitraria tangutorum Bobr in alkali stress ## Abstract Abscisic acid (ABA) and nitric oxide (NO) are involved in mediating abiotic stress-induced plant physiological responses. Nitraria tangutorum Bobr is a typical salinized desert plant growing in an arid environment. In this study, we investigated the effects of ABA and NO on N.tangutorum seedlings under alkaline stress. Alkali stress treatment caused cell membrane damage, increased electrolyte leakage, and induced higher production of reactive oxygen species (ROS), which caused growth inhibition and oxidative stress in N.tangutorum seedlings. Exogenous application of ABA (15μm) and Sodium nitroprusside (50μm) significantly increased the plant height, fresh weight, relative water content, and degree of succulency in N.tangutorum seedlings under alkali stress. Meanwhile, the contents of ABA and NO in plant leaves were significantly increased. ABA and SNP can promote stomatal closure, decrease the water loss rate, increase leaf surface temperature and the contents of osmotic regulator proline, soluble protein, and betaine under alkali stress. Meanwhile, SNP more significantly promoted the accumulation of chlorophyll a/b and carotenoids, increased quantum yield of photosystem II (φPSII) and electron transport rate (ETRII) than ABA, and decreased photochemical quenching (qP), which improved photosynthetic efficiency and accelerated the accumulation of soluble sugar, glucose, fructose, sucrose, starch, and total sugar. However, compared with exogenous application of SNP in the alkaline stress, ABA significantly promoted the transcription of NtFLS/NtF3H/NtF3H/NtANR genes and the accumulation of naringin, quercetin, isorhamnetin, kaempferol, and catechin in the synthesis pathway of flavonoid metabolites, and isorhamnetin content was the highest. These results indicate that both ABA and SNP can reduce the growth inhibition and physiological damage caused by alkali stress. Among them, SNP has a better effect on the improvement of photosynthetic efficiency and the regulation of carbohydrate accumulation than ABA, while ABA has a more significant effect on the regulation of flavonoid and anthocyanin secondary metabolite accumulation. Exogenous application of ABA and SNP also improved the antioxidant capacity and the ability to maintain Na+/K+ balance of N. tangutorum seedlings under alkali stress. These results demonstrate the beneficial effects of ABA and NO as stress hormones and signaling molecules that positively regulate the defensive response of N. tangutorum to alkaline stress. ## Introduction Soil salinization is an important factor restricting agricultural development (Delang, 2018). The harmful salts in salinized soil include NaCl and Na2SO4, as well as alkali salts, mainly Na2CO3 and NaHCO3 (Li et al., 2016). Alkali stress is highly destructive to the ecology, affecting the growth of plants, seriously affecting the soil structure, and increasing soil nitrification (Schroeder et al., 2013; Fancy et al., 2017). Currently, most studies focus on salt stress, and there are few studies on alkali stress and wild plants with strong tolerance to this environment. In general, plants produce a series of defensive responses under adverse conditions (Zhu, 2002; Zhu, 2016). Stress hormones [e.g., abscisic acid (ABA), jasmonic acid (JA), and methyl jasmonate (MeJA)] and signaling molecules [e.g., NO, Ca2+, and H2S] play important regulatory roles in this process(Huot et al., 2014; Petrov et al., 2015; Zhu, 2016; Fancy et al., 2017; Guo et al., 2018). ABA is an important plant stress hormone. Multiple studies have shown that plants can enhance their tolerance to stress by regulating the content of ABA in their bodies and then regulating a series of physiological reactions (Gai et al., 2020). For example, it can promote stomatal closure, improve photosynthesis and antioxidant enzyme activity, and accelerate the accumulation of secondary metabolites (Wang, C et al., 2020). Tomatoes and wheat can participate in stomatal movement under drought stress through ABA, thus controlling transpiration and reducing water loss (Aroca et al., 2008; Du et al., 2013). ABA can improve the photosynthetic efficiency of plants under stress by regulating a series of photosynthetic indexes, such as chlorophyll content and photosynthetic parameters, to adapt to or tolerate the adverse effects of environmental stress (Wang, C et al., 2020). Studies have shown that exogenous application of ABA can alleviate the chlorophyll loss of plants under drought stress, maintain high maximum photochemical efficiency PSII (Fv/Fm), Fm/Fo, φPSII, qP, and net photosynthetic rate (Pn) and low non-photochemical quenching (NPQ) and alleviate the decrease of leaf Pn and intercellular CO2 concentration (Ci). Thus, the photosynthetic capacity of plants under adverse conditions can be improved (Huang et al., 2017; Wang et al., 2020). ABA also plays an active role in antioxidant and osmotic regulation. For example, exogenous ABA is involved in the response of poplar to salt stress by inducing the accumulation of osmotic regulators and improving the activity of antioxidant enzymes to reduce the accumulation of Na+ under salt stress (Li et al., 2004). Flavonoids play an important role in plant resistance to adversity, and ABA can regulate the accumulation of flavonoids’ secondary metabolites in harsh environments (Lang et al., 2021). For example, flavonols are increased in Ligustrum vulgare under salinity or ultraviolet (UV) radiation stress and have a significant antioxidant function in plant photoprotection (Agati et al., 2011). Gonzalez‐Villagra and Gai et al. found that ABA treatment increased drought tolerance of Aristotelia chilensis by promoting the accumulation of anthocyanins in leaves and flavonoids in tea trees under drought stress (González-Villagra et al., 2019; Gai et al., 2020). Naringin, like most flavonoids, has antioxidant properties(Hasanein and Fazeli, 2014). In addition, the combined transcriptional and metabolic analysis showed that exogenous ABA significantly induced the expression of genes related to sucrose synthesis and flavonoid biosynthesis in tea under drought stress, such as UDP-glycosyltransferase (UDPGase), Sucrose phosphate synthetase (SPS), Chalcone isomerase (CHI), dihydroflavonol 4-reductase (DFR), Flavonoid 3’- hydroxylase (F3’H)and Flavonol synthase (FLS), and increased the contents of sucrose, glucose, fructose and soluble sugar, thereby enhancing drought tolerance (Thalmann and Santelia, 2017). The exogenous application of ABA also promoted the high expression of Chalcone synthetase (CHS), a key enzyme in flavonoid synthesis, and the accumulation of flavonoid metabolites, improving the drought tolerance of pigeon pea(Yang et al., 2021). NO is an important gas signaling molecule. Many studies have shown that NO is involved in regulating various physiological processes of plants, including germination, root formation, seedling growth, stomatal movement, maturation, leaf senescence, and biological and abiotic stress responses (Corpas and Barroso, 2013; Groß et al., 2013; Fancy et al., 2017). Many studies have proved that the exogenous application of NO donor SNP can effectively improve plant resistance (Corpas and Barroso, 2013; Fancy et al., 2017). SNP can regulate photosynthesis under stress. For example, in chrysanthemum and wheat, SNP application alleviates the decline of transpiration rate and stomatal conductance under heat and cold stress and improves tolerance (Yang et al., 2011; Ozfidan-Konakci et al., 2020). SNP can significantly induce the increase of Chl content, Pn, Ci, and stomatal conductance (Gs) in leaves under low temperature and alkali stress and reduce PSII and NPQ, thus improving the cold tolerance and alkali resistance of cucumber and ryegrass (Liu et al., 2012; Zhang et al., 2020). NO can also regulate ions, antioxidant enzymes, and osmotic regulators in plants under stress conditions. It has been reported that NO improves ion homeostasis under plant stress by reducing oxidative damage and enhancing K+ and Ca2+ ion absorption (Procházková et al., 2013). SNP can also enhance the accumulation of antioxidants and osmotic regulators to promote cold tolerance in wheat. NO can also regulate the accumulation of secondary metabolites of flavonoids under stress, thus alleviating the growth inhibition of plants (Prakash et al., 2021). For example, SNP can improve the accumulation of phenolic substances, including flavonoids, such as vinblastine, quercetin, and chlorogenic acid, in different tissues of periwinkle seedlings under salt stress, thus reducing their oxidative damage (Yang et al., 2014). Exogenous application of NO can also regulate the anabolism of flavonoids in the tea plant (Camellia sinensis L.) by activating the activity of phenylalaninase (PAL) (Li et al., 2019). However, most studies have focused on model plants and cultivated crops to explore the role of ABA and NO in promoting abiotic stress tolerance of plants, and few studies have addressed the process of ABA and NO tolerance to wild plants, such as N. tangutorum. N. tangutorum belongs to the Nitraria L. of Zygophyllaceae and is endemic to China. It is an important ecological group plant in the western desert area of China. It is highly adaptable to arid and saline-alkali desert environments. As a characteristic resource plant in central and western China and a typical representative of high-resistant and high-quality forage plants in the desert steppe, N. tangutorum is of great research value (Wang, B et al., 2020).Previous studies by our group have shown that external application of MeJA and SNP can improve the salt stress tolerance of N. tangutorum seedlings by reducing oxidative stress and ionic toxicity (Gao et al., 2021; Gao et al., 2022). However, few studies have been conducted on the tolerance of N. tangutorum to alkaline soil environments, especially the roles of ABA and SNP in regulating the adaptability to alkaline soil environments. In this study, we evaluated the alkaline resistance of N. tangutorum seedlings. We measured the activity and gene expression of anti-stress-related proteases under alkali stress and comprehensively analyzed the effects of the external application of ABA and SNP on the growth and metabolic activities of N. tangutorum seedlings under alkali stress from the aspects of growth status, ion homeostasis, stomatal movement, photosynthetic regulation, osmotic regulation, accumulation of flavonoids, and reactive oxygen species balance. Among these, we mainly discussed the effects of exogenous ABA and SNP on growth inhibition and physiological damage relief caused by alkali stress from the perspective of maintenance of photosynthetic efficiency, accumulation of photosynthates and flavonoids, and compared the different effects of ABA and SNP. This study will contribute to a better understanding of the role of ABA/NO signaling in regulating plant physiological responses to stress. ## Plant materials and growth conditions In July, N. tangutorum seeds were collected in Alxa Left Banner, Inner Mongolia. After the seeds are collected, they are cleaned and stored in paper bags at 4°C. The seed must first remove the outer coat, sterilize with $5\%$ sodium hypochlorite for 15 min, and then be sown in standard Murashige and Skoog medium that has been sterilized. The seeded bottles were placed at a temperature of 25°C, $70\%$ humidity, and a photoperiod of (Light/Dark = 16h/8h). After 21 days, consistent seedlings were transplanted into glass tubes containing sterile modified Hoagland solution and cultured for 19 days for future experiments. ## Plant stress treatment Forty-day-old seedlings were treated with a modified Hoagland solution containing Alkaline stress (NaHCO3: Na2CO3 = 9:1; 0, 10, 30, 50, 100, and 150 mM) for 12 days, and the maximum Alkaline stress concentration tolerated by N. tangutorum was selected. The fluid was changed every 2 days. Ten seedlings were analyzed for every treatment, repeated five times. We selected 100 mM alkaline stress as the follow-up experiment (Wang et al., 2022). We treated 40-day-old N. tangutorum seedlings with different concentrations of ABA(Coolaber Biology Co., Ltd., purity $98\%$) (0, 10, 15, and 30 μM) and SNP(Solarbio Biology Co., Ltd., purity $98.5\%$) (0, 30, 50, and 70 μM) for 6 hours in darkness in 100 mM alkaline stress to determine the effects of ABA and SNP on plants and the optimal concentration for subsequent experiments. These seedlings were then transferred to a Hogland solution containing 100 mM alkaline and grown under normal photoperiod conditions. In order to eliminate the factors of light decomposition of SNP and cPTIO and the factors of bacteria contamination in indoor operation and maintain the consistency of experimental treatment, we use the light cycle of plants (Light/Dark = 16h/8h) to spray in the ultra-clean platform at 10:00 pm every night. For 12 days, treatments were given every two days. The experiment was repeated three times with thirty seedlings. The optimal ABA and SNP concentrations were respectively determined to be 15 μM and 50 μM, and were used in subsequent experiments. The same method was used to determine the concentration of ABA inhibitor fluridone (Flu) and NO scavenger 2-(4-carboxy-2-phenyl)-4,4,5,5-tetramethy limidazoline-1-oxyl-3-oxide (cPTIO) as 5 μM and 70 μM. 40 days after planting, collect samples before treatment. The stress treatment was started on the 41st day for 12 days, and the samples after treatment were collected at 10:00 pm on the 12th day. Seedling growth parameters were measured at the end of these treatments, and samples, such as leaf, shoot and root, were collected and stored at -80°C for further analysis. A ruler was used to measure plant height elongation. An electronic balance was used to determine the fresh weight of whole seedlings. ## Physiological and biochemical index detection After the stress is over, measure the plant height and fresh weight. Measure the relative water content (RWC). At the end of the treatment, determine the fresh weight (W0) of the leaves (0.2 g) on the middle branch of each seedling. Then, they were soaked in distilled water for 24 hours, and the saturated fresh weight was weighed and absorbed (W1). Clean the surface water with absorbent paper, dry at 105°C for 15 minutes, then dry at -80°C to a constant weight, and record (W2). Then RWC is (W0-W2)/(W1-W2)×$100\%$. The electrolyte permeability was measured using a conductivity meter. Take the middle new leaves (3–4 pieces) of each seedling and put them in a test tube with 1 ml of distilled water at 37°C for 3 hours. Measure and record the conductivity E1. Then, for 20 minutes, boil 100 degrees Fahrenheit water; measure and record the conductivity E2. The calculation formula is E1/E2×$100\%$. The degree of leaf succulentism was calculated based on the fresh weight of 0.1 g of leaf. According to the formula: FW/DW to get the degree of leaf succulentism. The content of chlorophyll was extracted by the acetone extraction method, treated in the dark for 12 hours, and calculated by measuring the absorption values at 663nm, 645nm, and 445nm wavelengths, respectively. Chia = 12.72•D663 - 2.59•D645 for chlorophyll pigment concentration; Chlorophyll b pigment concentration: Chib = 22.88•D645-4.67•D663; Carotenoid pigment concentration: Car=4.7•D440-0.27 (Ca+Cb); Concentration of total chlorophyll pigment: Total = Ca + Cb. For all the parameters measured above, 3 samples of each treatment were analyzed and repeated 3 times. The collected frozen samples are used to detect the accumulation levels of malondialdehyde (MDA), Hydrogen peroxide(H2O2) and Superoxide anion(O2 -), the content of proline and soluble sugars and proteins, and the enzyme activities of SOD, APX, GR and CAT. Antioxidant content such as GSH, GSSG, The measurement was carried out according to the instructions given by Suzhou Keming. For all the parameters measured above, 3 samples of each treatment were analyzed and repeated 3 times. ## Diaminobenzidine and nitro blue tetrazolium staining Diaminobenzidine (DAB) and nitro blue tetrazolium(NBT) staining are as described in the Soleibao instructions, using DAB and NBT chloride staining to detect H2O2 and O2 -. We took the middle taproot of each seedling for DAB staining, immersed it in 1 mg·ml-1 DAB solution, and kept it in the dark at room temperature for 12 h before observing the staining. For NBT staining, the taproot (3–4 pieces) of each seedling was immersed in 1 mg·ml-1 NBT and kept in the dark at room temperature for 8 h before observation. Ten seedlings of each treatment were analyzed for all the parameters measured above and repeated three times (Gao et al., 2022). ## Infrared thermal imaging and water loss rate As previously mentioned, thermal imaging is used to monitor the temperature of plant leaves under CK and different stress treatments (Nguyen et al., 2018). We first let plants grow under normal conditions and then select seedlings with the same growth momentum to simultaneously start alkali stress and other treatments. We used a thermal imaging camera to detect and record the temperature of plant leaves after 12 days of stress. We used the method described previously and made some modifications (Leung et al., 1997). Before and after the stress treatment, the water content of plant leaves was initially weighed once, and the weight was weighed every 10 min. The water loss rate is the ratio of the weight dropped to the weight of the detached blade at 0 min. Eight leaves were selected from each strain, and the experiment was repeated three times. At the same time, take the lower epidermis of leaves after 14 days of treatment, observe the stomata and take photos. ## Chlorophyll fluorescence imaging and determination of photosynthetic index As previously mentioned, a plant in vivo imaging system was used to monitor the chlorophyll content of plants under CK and different stress treatments. After 12 days of stress, the seedlings with the same growth vigor were placed into the chlorophyll fluorescence imager, and the observation records were taken at 50 s intervals under sufficient light, following the method of (Wang and Kinoshita, 2017), with minor modifications. An LI-6400 device (LI-COR Inc, Lincoln, USA) was used to measure the Pn, Ci, and transpiration rate (Tr) of leaves of N. tangutorum seedlings at the same leaf position in different treatment groups. We followed the protocol described by Lu et al. ( Lu et al., 2003). At room temperature (25°C) and relative humidity of $60\%$, the chlorophyll fluorescence parameters Fv/Fm, φPSII, qP, and ETRII were measured using a portable chlorophyll fluorescence meter (PAM-2500) of leaves in different treatment groups. Before determination, the plant must adapt to dark conditions for 30 min (we used the 3rd to 6th fully expanded leaf from the top of the plant). ## Sugar and starch content measurement The sugar substances and starch were determined according to the instructions of the Suzhou Keming Reagent Kit. Take 0.1g of tissue and add 1 ml of distilled water to grind it into homogenate, and take a water bath at 95°C for 10 minutes; 8000 g, centrifuged at 25°C for 10 minutes, and 20 ul of the supernatant are taken to be tested. Reagent II and Reagent III are mixed in 1:1 equal volume, mixed with the supernatant, and kept at 25°C for 15 minutes. The absorbance value was measured at 505 nm with the enzyme marker. The absorbance values of a blank tube, a standard tube, and a measuring tube are recorded as A1, A2, and A3, respectively. Calculate the content according to the formula: glucose content=0.5 × (A3-A1)÷(A2-A1)÷W. Similarly, the extraction and determination of fructose, sucrose, total sugar, starch, and soluble sugar were carried out step by step according to the instructions of the Suzhou Keming Reagent Kit. According to different formulas, the sequence is: fructose content = (A3-A1) ÷ (A2-A1) ÷ W; Sucrose=(A3-A1) ÷ (A2-A1) ÷ W; soluble sugar=2.34 × [(A3-A1)+0.07] ÷W; Starch content=0.289 × (A3+0.0295) ÷W; Total sugar=33.311 × [(A3-A1)+0.0507] ÷W × Dilution ratio. ## Determination of flavonoids by LC-MS To determine total flavonoids and flavonol, we diluted rutin standard solution in gradient, added 0.5 ml $5\%$ NaNO2, mixed, and placed at room temperature for 6 min. Next, we added 0.5 ml of $10\%$ Al (NO3) 2 solution, mixed well, and left for 6 min. We then added 4 ml of $4\%$ NaOH solution and $60\%$ ethanol to a constant volume of 10 ml. A microplate reader measured the absorbance at 510 nm (standard curve: $y = 0.0059$x+0.0406, R2 = 0.9994). One gram of fresh N. tangutorum seedlings was added to liquid nitrogen and ground into a powder with a mortar. Ethanol ($60\%$) was then refluxed and extracted three times. The light absorption value was determined according to the above steps (OD = 510 nm). The content of total flavonoids was calculated according to the standard curve. One gram of sample powder was added into a 100 ml conical flask with 50 ml of methanol and extracted by ultrasonic wave (500W, 40kHz) three times, 30 min each time. The filtrates were then filtered, combined, and extracted with petroleum ether at a ratio of 1:1 three times, concentrated by reducing pressure, then adding methanol to dissolve it by ultrasonic wave. Finally, this was diluted to 25 ml in a brown volumetric flask and mixed to obtain the test solution. We then determined the light absorption value at OD = 440 nm and calculated the total flavonol content relative to a standard. Anthocyanin and proanthocyanidin content measurement: we first accurately weighed 2 mg of cyanidin-3-O- glucoside and dissolved this in 1 ml of extract (methanol:HCl:H2O = 80:5:15, v:v:v), which was diluted with the extract in equal gradient. Of this, 200 μl was used to determine the OD value at 520 nm and generate a standard curve ($y = 0.00306$x+0.00042, R2 = 0.9999). One gram of fresh sample was added into 3 ml of extraction solution. Ten minutes later, draw 200 µl of this was added to the enzyme standard plate, and the OD value was measured at 520 nm. We calculated the anthocyanin content according to the standard curve. The sample was treated with the same method, adding 385 µl methanol and 192 µl DMACA reagent [$2\%$ (w/v) DMACA was dissolved in methanol, and 6 M concentrated hydrochloric acid (1:1) (v/v)]. The absorbance value was detected at A643 by microplate, and proanthocyanidins were calculated. The quantification of Catechin;Quercetin; Isoamnetinrh; Kaempferol; Naringenin using an HPLC–MS/MS platform as described Plant materials (50 mg FW) were frozen in liquid nitrogen, ground into powder, and extracted with 0.5 mL methanol, water, or formic acid (15:4:1, V/V/V) at 4 °C. The extract was vortexed for 10 minutes before being centrifuged at 14,000 rpm for 5 minutes at 4°C. The supernatant was collected, and the extraction steps were repeated. The combined extracts were evaporated to dryness under a stream of nitrogen gas, reconstituted in $80\%$ methanol (V/V), ultraphoniced (1 min), and redissolved in 10 µL of methanol and passed through a 0.22 µm filter before HPLC–MS/MS analysis. The sample extracts were analyzed with an UHPLC (Agilent 1290 Infinity, Santa Clara, CA)-triple quadropole (Agilent 6430). The analytical conditions were as following, HPLC: The analysis column was an Agilent ZORBAX Eclipse Plus rapid resolution high definition C18 (octadecyl silane) column (2.1 × 50 mm, 1.8 µm); solvent system, water ($0.1\%$ aqueous formic acid), methanol; and Needle (ND) injection volume: 2 µL. as follows: (0 to 4 min, 40 to $40\%$; 4 to 7.5 min, 40 to $45\%$; 7.5 to 8 min, 45 to $48\%$; 8 to 9 min, 48 to $100\%$; 9 to 11 min, $100\%$). ESI-MS in negative ionization mode was used for detection with capil-lary 3 kV, gas temperature at 325°C, gas flow at 12 L/min, and nebulizer at 40 psi in multiple-reaction-monitory (MRM) mode. Chromatographic peaks were identified and con-firmed by comparison with standards of their mass-charge ratio (m/z) values, product ions, and retention times. Standard curves consisting of 3.1, 12.2, 48.8, 195.3, 781.3, 3125.0, 12500.0, 50000.0, and 200000.0 ng/mL of catechin, quercetin, Isoamnetin rhcontent; Kaempferol; Naringeninusing were used to quantify each compound. Ten fully expanded leaves from 10 independent plants subjected to each of the stress treatments and the control were pooled as one biological replicate for flavonoids quantification, and three biological replicates were performed. ## Na+/K+ content detection The plant leaves and roots were washed with clean water and dried to a constant weight, and 0.1 g was used for subsequent experiments. The sample was nitrated with $10\%$ (v/v) HNO3 for 8 h and centrifuged at 12000 rpm for 1 min. The supernatant was analyzed using an Optima 8000 ICP-OES DV spectrometer (PerkinElmer, Inc.) following the manufacturer’s instructions. Three replicates were measured for each group and repeated three times. ## RT-PCR and qRT-PCR TransStart Tip Green qPCR SuperMix and Rotor-Gene Q 5plex HRM Priority Package were used for real-time PCR and melting curve analysis. The NtACTIN genes were used as internal controls. The primers are listed in Table S1. Different cDNAs were prepared from three independent RNA samples and repeated three times. The 2-ΔΔCT method was used to determine the relative expression level of the target gene. For each sample, three biological replicates and three technical replicates were measured. The sequence of each gene was obtained from our previous N. tangutorum transcriptome data (accession no. PRJNA273347). ## ABA and NO content measurement Fresh leaves (1 g) were taken and crushed, and an extract solution containing 3 ml 50 mM cool acetic acid buffer (pH 3.6) and $4\%$ zinc diacetate was added. After centrifugation at 1000 prm at 4°C for 10 min, the supernatant was taken and added with Griess reagent ($1\%$ sulfameic acid, $1\%$ N-1-naphthalene ethylenediamine dihydrochloride, $5\%$ phosphoric acid),incubated at room temperature for 30 min. The absorbance was determined at 540 nm for the determination of NO. ABA was quantified on frozen leaves (1 g). The extraction and fractionation processes have previously been described (Pan et al., 2010). The phytohormones were detected via ultra-high performance liquid chromatography (Agilent) and triple quadrupole mass spectrometry (Agilent) using an SPE column (Strata X-C 33 mm,30 mg/ml−1, Phenomenex). All experiments were repeated three times. ## Data analysis All experimental data are expressed as mean ± standard deviation ($$n = 3$$–5). Statistical differences were evaluated by Duncan’s multiple range test using one-way analysis of variance (ANOVA). A significant difference is guaranteed at $P \leq 0.05.$ All analyses were performed using SPSS 18.0 (IBM, Armonk, NY, USA). All data statistics are graphed with Gragh8. ## ABA and SNP alleviated the growth inhibition of N. tangutorum seedlings under alkali stress To study the effects of an alkaline salt soil on the growth and development of N. tangutorum seedlings, NaHCO3: Na2CO3 = 9:1 was used to simulate alkali stress, and related phenotypes and physiological indicators of plants were detected. We found that a low concentration of 10 mM alkali stress (NaHCO3: Na2CO3 = 9:1, pH = 9.5) did not affect the normal growth of plants. But 30 mM alkali stress and above significantly inhibited the growth of N. tangutorum plants, and the inhibition increased with the increase in concentration (Figure 1A). The main manifestations were decreased plant height, fresh weight (FW), relative water content (RWC) and chlorophyll (ChI) (Figures 1B, C, F), and increased electrolyte leakage (EL) and MDA content (Figure 1D). Under 100 mM alkali stress, plant damage was serious; plant height, FW, RWC, succulent degree (DS), and ChI were significantly decreased compared with CK. FW and DS were significantly decreased by two times, and MDA and EL significantly increased by two times and three times, respectively. It should be noted that the contents of total flavonoids and total anthocyanins in plants were gradually increased in a concentration-dependent manner under alkali stress of 30 mM and above, and reached 1.86 times and 2.05 times, respectively, of CK at 100 mM alkali stress (Figure 1E). Because 150 mM alkali stress caused the death of the plants, the correlation values were not shown. The above studies show that alkali stress severely inhibited the normal growth of N. tangutorum seedlings, and 100 mM alikali stress (NaHCO3: Na2CO3 = 9:1) was the most significant, which was used for the subsequent experiments. Figure 1: Phenotypes and indicators of N. tangutorum treated with different concentrations of alkaline salt(AS). (A): Phenotype under different alkali treatments(AS: NaHCO3: Na2CO3 = 9:1, pH=9.5); (B): Fresh weight and plant height; (C): Relative water content and degree of succulent; (D): Eletrical leakage and MDAcontent; (E): Total flavonoids and total anthocyanin content; (F): Chlorophyll content. Selected 40-day-old plants were treated with different concentrations of alkaline salt(AS)(10/30/50/100/150 mM). Plants grew in Hoagland solution served as controls(CK). Each group was treated with 25 plants and repeated 3 times. Data presented are the mean ± SDs (n=3). Different letters next to the number indicate significant difference (Duncan multiple range test; P<0.05). Many reports have shown that exogenous ABA and SNP can improve plant adaptability to abiotic stress. To investigate the effect of exogenous ABA and SNP on alleviating the adverse effects of alkali stress on the growth of N. tangutorum seedlings, different concentrations of ABA (10, 15, and 30 μM) and SNP (30, 50, and 70 μM) were combined based on 100 mM alkali stress. Seedling growth and associated physiological indicators were measured (Figure S1 and Table 1). The results showed that low concentrations of ABA (10 μM) and SNP (30 μM) had some alleviating effects on RWC and DS of seedlings under alkali stress but had little effect on plant height and FW. Exogenous application of 15 μM ABA and 50 μM SNP significantly improved plant growth inhibition. Compared with the single alkali stress, the RWC and DS respectively increased by more than $30\%$ and $40\%$, and the FW increased by $50\%$ and $20\%$. At the same time, exogenous ABA treatment increased plant height by $25\%$, but SNP treatment had no significant change. Compared with 50 μM SNP treatment, 15 μM ABA had more significant effects on physiological indexes. When ABA and SNP concentrations reached 30 μM and 70 μM, the growth state and physiological indexes of seedlings were not significantly different from those of 15 μM ABA and 50 μM SNP, respectively. Exogenous application of No-specific scavher cPTIO (70 μM) or ABA synthesis inhibitor Fluorine (5 μM) reversed the effects of ABA and SNP on plant growth under alkali stress. Therefore, 15 μM ABA and 50 μM SNP were considered to be the optimal treatment concentrations for subsequent experiments (Figure S1 and Table 1). Table 1 \| Physiological Indicators \| Fresh Weight (g) \| Plant Height (cm) \| Relative Water Content (%) \| Degree of Succulency(FW/DW) \| \| --- \| --- \| --- \| --- \| --- \| \| CK \| 1.56 ± 0.12a \| 25.3 ± 0.02a \| 93 ± 0.01a \| 26.9 ± 0.14a \| \| 100 mM aline-alkali(AS) \| 0.85 ± 0.08c \| 18.3 ± 0.04c \| 39 ± 0.04d \| 9.7 ± 0.13d \| \| AS+10 μM ABA \| 0.91 ± 0.08c \| 18.2 ± 0.03c \| 47 ± 0.02c \| 11.4 ± 0.04c \| \| AS+15 μM ABA \| 1.24 ± 0.03b \| 22.8 ± 0.03b \| 51 ± 0.04b \| 16.1 ± 0.08b \| \| AS+30 μM ABA \| 1.13 ± 0.01b \| 23.5 ± 0.09b \| 53 ± 0.05b \| 16.5 ± 0.015b \| \| AS+30 μM SNP \| 0.96 ± 0.05c \| 18.1 ± 0.07c \| 45 ± 0.06d \| 11.9 ± 0.026c \| \| AS+50 μM SNP \| 1.06 ± 0.08b \| 18.7 ± 0.05c \| 52 ± 0.13c \| 13.5 ± 0.14c \| \| AS+70 μM SNP \| 1.05 ± 0.04b \| 19.5 ± 0.04c \| 59 ± 0.09c \| 14.6 ± 0.02c \| \| AS+70 μM cPTIO \| 0.65 ± 0.02d \| 17.5 ± 0.05d \| 25 ± 0.08e \| 8.3 ± 0.05e \| \| AS+5 μM Fluridone \| 0.58 ± 0.01de \| 17.2 ± 0.01d \| 14 ± 0.16f \| 4.2 ± 0.12f \| ## Exogenous ABA and SNP further increased the content of endogenous ABA/NO and the expression levels of related genes in N. tangutorum seedlings under alkali stress To further evaluate the effects of alkali stress and combination treatment on ABA and NO levels and signaling pathways in N. tangutorum seedlings, the content of ABA and NO and the expression levels of related genes were detected. We found that compared with CK, the endogenous ABA content of N. tangutorum seedlings was significantly increased by 5.84 times under single alkali stress and further increased to 8.21 times that of the CK group under combined ABA (Figure S2C), but there was no significant difference in ABA content under combined SNP or single alkali stress. The expression of related genes also showed the same trend. Alkali stress alone or combined application of ABA and SNP could significantly increase the NO content in plants, which was 1.87 times, 4.28 times, and 3.41 times that in the CK group, respectively (Figure S2B). The application of fluorine and cPTIO has the opposite effect. Real-time fluorescence quantitative PCR was used to detect the expression levels of genes related to NO and ABA biosynthesis and key elements of ABA signaling pathways. The results showed that they participated in the biosynthesis of NO (NtNOA1, NtNR2) and ABA (NtNCED$\frac{1}{3}$/$\frac{4}{5}$, NtAAO, NtSDR), and the expression levels of genes related to key elements of ABA signaling pathway (NtPYL$\frac{2}{6}$, NtPP2C, NtABF$\frac{1}{3}$, NtSnRK$\frac{2.2}{2.3}$) were significantly induced under single alkali stress (Figure S2A). The combined application of ABA and SNP under alkali stress further induced the expression of these genes to varying degrees, and the effect of ABA was more obvious than SNP. These results indicate that alkali stress induced the accumulation of endogenous NO and ABA in plants, and the combination of exogenous NO and ABA could further enhance the accumulation of ABA and SNP in plants and alleviate the growth inhibition of plants under alkali stress. It was speculated that ABA and NO might be involved in regulating the response of N. tangutorum seedlings to alkali stress. ## ABA and SNP enhanced the oxidative balance of N. tangutorum seedlings under alkali stress by improving the efficiency of the antioxidant system ABA and SNP not only alleviated plant growth inhibition but also reduced oxidative damage suffered by plants. NBT and DAB staining intuitively showed the degree of oxidative damage to leaves. The accumulation of ROS in leaves induced by alkali stress significantly reduced the accumulation of blue and brown precipitates in leaves (Figure 2A). The content of ROS in leaves was determined by the microplate method. The results showed that alkali stress significantly aggravated MDA and EL and significantly induced the production of H2O2, and O2 -, which were 3.92, 6.62, 5.93, and 3.49 times greater than for the CK group, respectively (Figures 2B–E). External application of 15 μM ABA and 50 μM SNP significantly decreased MDA, EL and H2O2 by 20–$40\%$ (Figures 2B–D). Among them, ABA and SNP had the strongest regulatory effects on O2 -, with significant decreases of $35\%$ and $49\%$, respectively (Figure 2E). The application of Fluorine and cPTIO has the opposite effect (Figure 2). Figure 2: ABA and SNP alleviate ROS accumulation and accelerates antioxidant defenses under 100mM alkali stress in leaves (AS: NaHCO3: Na2CO3 = 9:1, pH=9.5). Growth status of N. tangutorum 40-day-old seedlings treated with different treatments (100 mM alkali, 100 mM alkali +15 μm ABA/5 μm Flu, 100 mM alkali +50 μm SNP/70 μm cPTIO) for 12 days. (A): DAB and NBT staining of the leaves; (B): Electrical leakage (EL); (C): MDA; (D): Hydrogen peroxide content (H2O2); (E): Superoxide anion content (O2 -); (F): GSH content; (G): GR activity; (H):CAT activity; (I): GSSG content;(J): Total antioxidant capacity; (K): SOD activity; (L): GSH/GSSG ratio; (M): GST activity; (N): APX activity. Bars represent the means ± SDs of three replicates. Significant differences among treatments are indicated by different letters within a panel based on Duncan’s multiple range test (P < 0.05). Plants usually turn on the oxidative defense system to resist oxidative damage caused by stress. To evaluate the effects of antioxidant enzymes and antioxidants on alleviating oxidative damage in N. tangutorum seedlings under alkali stress and the role of ABA and NO in regulating the antioxidant system, we examined the content of antioxidant enzymes under different treatments. Compared with untreated plants, alkali stress significantly induced the activities of GR, GST, CAT, APX, and SOD, decreased the GSSG content and the GSH/GSSH ratio, and increased the GSH content and total antioxidant capacity (Figures 2F–N). Compared with the seedlings treated with alkali stress alone, the activities of the above antioxidant enzymes and antioxidant content were further significantly increased by 1.21-1.62 times under the treatment of ABA and SNP (Figures 2F–H, J–L). The application of Fluorine and cPTIO has the opposite effect. These results suggest that exogenous application of ABA and SNP can further activate antioxidant defense in seedlings under alkali stress and reduce ROS accumulation (Figures 2F–N). ## ABA and SNP promoted Na+/K+ balance in N. tangutorum seedlings under alkali stress Alkali stress usually causes plants to accumulate a large amount of Na+, resulting in the imbalance of Na+/K+homeostasis, thus affecting the normal development of plants. In this study, we also measured the Na+ and K+contents in the shoot and roots of N. tangutorum seedlings under alkali stress. The results showed that after 100 mM alkali stress, the Na+content and Na+/K+ratio in the shoot and root of seedlings increased significantly, while the K+content decreased significantly, resulting in significant Na+ toxicity. After ABA and SNP treatment, the Na+content and Na+/K+ratio in the shoot and root decreased significantly, alleviating the accumulation of Na+ caused by alkali stress. In addition, ABA and SNP also significantly promoted the accumulation of K+ in roots and less excessive absorption of Na+ in roots; At the same time, the K+content of leaves remained unchanged (Figures 3A–C). Real-time quantitative PCR analysis showed that different treatments significantly affected the expression of ion transporter-related genes. Alkali stress significantly increased the expression of plasma membrane-localized Na+/H+ antiporter (NtSOS1) and vacuolar Na+/H+ antiporter (NtNHX$\frac{1}{2}$/3) in leaves and roots. At the same time, the K+/H+ reverse transporter 5 (NtKEA5) located by trans Golgi was significantly increased in leaves and significantly decreased in roots, while the transcription level of the high-affinity K+ transporter (NtHKT1) located by plasma membrane was decreased in leaves and roots (Figures 3D, E). Compared with alkali stress alone, ABA and SNP significantly induced the expression of NtSOS1, NtNHX$\frac{1}{2}$/3, NtKEA$\frac{3}{5}$ in leaves, and NtSOS1, NtNHX$\frac{1}{3}$, NtKUP4, NtKCO, NtHAK$\frac{6}{12}$ in roots. In addition, ABA and SNP also decreased the expression of NtHKT1, NtHAK$\frac{6}{12}$, NtKCO, and NtKUP4 in leaves, which was consistent with the change of K+ content in leaves (Figure 3). The application of Fluorine and cPTIO has the opposite effect. The results showed that both ABA and SNP could significantly promote the accumulation of K+ in plant roots, reduce the Na+/K+ in aboveground and underground parts, and reduce the Na+ toxicity induced by alkali stress. Figure 3: Ion content and quantification of related genes in N. tangutorum seedlings under different treament. 40-day-old seedlings were treated with different treatments (100 mM alkali, 100 mM alkali +15 μm ABA/5 μm Flu, 100 mM alkali +50 μm SNP/70 μm cPTIO) for 12 days(AS : NaHCO3: Na2CO3 = 9:1, PH=9.5). Plants grew in Hoagland solution served as controls (CK). (A) Na+ content, (B) K+ content, (C) the Na+/K+ ratios, (D, E) The expression level of ion transporter genes in leaf and root, including NtSOS1, NtNHX1/2/3, NtKUP4, NtKCO, NtHAK6/12, NtKEA2/3/5 and NtHKT1. Bars represent the means ± SDs of three replicates. Significant differences among treatments are indicated by different letters within a panel based on Duncan’s multiple range test (P < 0.05). ## Exogenous ABA and SNP can regulate stomatal opening and reduce water loss of N. tangutorum seedlings under alkali stress Leaf surface temperature can indicate the degree of water loss caused by transpiration. The higher the stomatal opening, the stronger transpiration, the lower the leaf surface temperature, and the more serious the water loss. Therefore, we measured the stomatal opening, leaf surface temperature, and water loss rate of N. tangutorum seedlings under 100 mM alkali stress with combined ABA and SNP for 12 days. The results showed that under normal growth conditions, the leaf surface temperature was about 23.1°C, and the water loss rate was the lowest. Under alkali stress, the stomatal opening of N. tangutorum seedlings decreased significantly, but the stress still caused an increase in water loss. The water loss rate reached the maximum within 0–3 h in vitro, and the leaf temperature decreased significantly to 17.2°C. After the combined application of ABA and SNP, stomata were further closed, water loss was significantly reduced compared with single alkali stress, and leaf surface temperature increased to 20.8°C and 21.1°C (Figures 4A–E). The contents of osmotic regulatory substances such as betaine, soluble protein and proline were significantly increased under alkali stress, indicating that N. tangutorum seedlings reduce water loss under stress by regulating the accumulation of osmotic substances. In addition, compared with alkali stress, the combined application of SNP induced a more significant accumulation of the above substances than ABA (Figures 4G–I), and the application of Fluorine and cPTIO had the opposite effect. Applying SNP can promote stomatal closure and the accumulation of osmoregulation substances of N. tangutorum seedlings more than ABA, thus reducing the water loss caused by alkali stress. Figure 4: Effects of different treatments on leaf transpiration and osmomodulatory substances of N. tangutorum seedlings. 40-day-old seedlings were treated with different treatments (100 mM alkali, 100 mM alkali +15 μm ABA/5 μm Flu, 100 mM alkali +50 μm SNP/70 μm cPTIO) for 12 days(AS : NaHCO3: Na2CO3 = 9:1, pH=9.5), and subsequent experiments are the same. Plants grew in Hoagland solution served as controls(CK). (A, B, D): Infrared imaging, leaf water loss rate and leaf temperature of leaves of N. tangutorum seedlings under different stress treatments. (C) and (E): stomatal phenotype and stomatal aperture of N. tangutorum seedlings under different treatments. (F): Intercellular CO2 concentration; (G): Betaine content; (H): Soluble protein content; (I): Proline content. Bars represent the means ± SDs of three replicates. Significant differences among treatments are indicated by different letters within a panel based on Duncan’s multiple range test (P < 0.05). In addition, we also measured the intercellular CO2 concentration. The intercellular CO2 concentration increased significantly when plants were subjected to stress, but the exogenous application of ABA and SNP could significantly reduce the intercellular CO2 concentration, and the effect of SNP was also significantly higher than that of ABA. These results indicate that alkali stress can reduce the utilization of CO2 by plants, and the external application of ABA and SNP can promote the use of CO2 by N. tangutorum seedlings under alkali stress (Figure 4F). ## ABA and SNP increased photosynthetic efficiency and accumulation of photosynthates in N. tangutorum seedlings under alkali stress Plants use photosynthesis to ensure an adequate energy supply to cope with adverse environmental damage. In this study, we not only examined the CO2 utilization efficiency of plants but also examined the changes in chlorophyll content and paid attention to a series of photosynthetic-related indicators and the accumulation of photosynthates to evaluate the damage of alkali stress on the photosynthesis of N. tangutorum seedlings, and the positive effect of ABA and SNP on alleviating this photosynthetic damage. In this study, the yellowing phenotype occurred in the mature and young engagement of N. tangutorum seedlings under alkali stress, and the contents of chlorophyll a, chlorophyll b, carotene and total chlorophyll decreased significantly. After the combined application of ABA and SNP, the yellowing of leaves was significantly alleviated, and chlorophyll content was significantly increased (Figures 5A, B). Chlorophyll fluorescence also showed a similar pattern. Compared with single alkali stress, leaf chlorophyll fluorescence partially recovered after ABA and SNP application. Combined SNP application had a higher fluorescence recovery and a more obvious effect than ABA (Figure 5C). The application of Fluorine and cPTIO has the opposite effect. This indicated that SNP could significantly reduce chlorophyll loss and reduce plant senescence than ABA. Alkali stress not only significantly inhibited the synthesis of chlorophyll in plants but also significantly reduced the Fv/Fm, ETRII, PSII, and Pn, and increased qP, thus reducing the photosynthetic efficiency of plants. Importantly, the reduction of the above indexes could be alleviated after the combination of ABA and SNP. Compared with ABA, external SNP application significantly improved Fv/Fm, ETRII, Pn and PSII and NPQ, thus more effectively alleviating the photosynthetic inhibition caused by alkali stress (Figures 5D–I). The application of Fluorine and cPTIO has the opposite effect. Figure 5: Effects of different treatments on chlorophyll content and photosynthesis in leaves of N. tangutorum seedlings. 40-day-old seedlings were treated with different treatments (100 mM alkali, 100 mM alkali +15 μm ABA/5 μm Flu, 100 mM alkali +50 μm SNP/70 μm cPTIO) for 12 days (AS : NaHCO3: Na2CO3 = 9:1, pH=9.5), and subsequent experiments are the same. Plants grew in Hoagland solution served as controls(CK). (A): Phenotypes of old and new leaves under different stress treatments; (B): determination of chlorophyll content; (C): Fluorescence imaging of plant chlorophyll in vivo; (D–I): Determination of photosynthetic indexes under different treatments, including: Photochemical efficiency of photosystem II (Fv/Fm); photochemical quenching (qP); photosystem II apparent photosynthetic electron transfer efficiency (ETRII); non photochemical quenching (NPQ); quantum yield of photosystem II (φPSII); net photosynthetic rate(Pn). Bars represent the means ± SDs of three replicates. Significant differences among treatments are indicated by different letters within a panel based on Duncan’s multiple range test (P < 0.05). Alkali stress seriously affects the photosynthesis of plants, which may lead to changes in carbohydrate products of photosynthesis. We detected the changes in related carbohydrate substances under different treatments. The results showed that alkali stress significantly reduced the contents of glucose, starch, and total sugar but had no significant effect on fructose and sucrose. Compared with single alkali stress, combined ABA and SNP treatment significantly increased the contents of glucose, starch, and total sugar, especially sucrose content significantly increased by two times but did not affect fructose content (Figures 6A–F). In addition, alkali stress significantly induced soluble sugar accumulation compared with normal conditions, which increased by 1.78 times compared with control. External application of ABA and SNP increased soluble sugar content to 2.73 and 3.34 times (Figure 6E). In general, SNP application was better than ABA in promoting the accumulation of sugars under alkali stress, especially for sucrose, starch, and soluble sugars (Figure 6). The application of Fluorine and cPTIO has the opposite effect. In conclusion, the combined application of ABA and SNP effectively alleviated the photosynthetic inhibition caused by alkali stress and promoted the accumulation of photosynthate sugars, and the promotion effect of SNP was better than ABA. Figure 6: Effects of different treatments on sugar accumulation in N. tangutorum seedlings. 40-day-old seedlings were treated with different treatments (100 mM alkali, 100 mM alkali +15 μm ABA/5μm Flu, 100 mM alkali +50 μm SNP/70 μm cPTIO) for 12 days(AS: NaHCO3: Na2CO3 = 9:1, pH=9.5). Plants grew in Hoagland solution served as controls (CK). (A–F): Changes in the accumulation of carbohydrates in leaves of N. tangutorum seedlings under different stress treatments, in order: glucose, fructose, sucrose, starch, soluble sugar and total sugar. Bars represent the means ± SDs of three replicates. Significant differences among treatments are indicated by different letters within a panel based on Duncan’s multiple range test (P < 0.05). ## ABA and SNP promoted the accumulation of flavonoids in N. tangutorum seedlings under alkali stress Flavonoids are a class of powerful antioxidants widely recognized to play a role in plant resistance to adversity. To evaluate the role of secondary metabolites of flavonoids in response to alkali stress in N. tangutorum seedlings and the regulatory role of ABA and SNP in the accumulation of flavonoids, we examined the content of flavonoids under different treatments and the expression levels of key genes in the metabolic pathway. We found that alkali stress significantly induced the accumulation of total flavonoids, total anthocyanins and flavonols. Combined application of ABA further promoted the accumulation of these compounds, but SNP had no significant effect (Figures 7A–C). Compared with CK, proanthocyanidins accumulated obviously under alkali stress, but the external application of ABA and SNP had no further promoting effect on the proanthocyanidins (Figure 7D). The application of Fluorine and cPTIO has the opposite effect. The contents of several flavonoids were further detected, and the results were consistent with the above results. Alkali stress significantly induced the accumulation of flavonoids such as naringin, quercetin, isorhamnetin, kaempferol, and catechin, which increased significantly to more than 2 times CK, and among them, catechin even reached three times CK. Exogenous application of ABA could further promote the induction effect, and the contents of several flavonoids were further increased to 3.25 times, 3.74 times, 2.01 times, 2.44 times and 6.47 times of CK, among which the content of isorhamnetin was the highest, and the increase of catechin was the largest. However, SNP also had little effect on the accumulation of several flavonoids (Figures 7E–I). Figure 7: ABA and SNP promote the accumulation of flavonoid and anthocyanin secondary metabolites under alkaline stress. 40-day-old seedlings were treated with different treatments (100 mM alkali, 100 mM alkali +15 μm ABA/5 μm Flu, 100 mM alkali +50 μm SNP/70 μm cPTIO) for 12 days (AS : NaHCO3: Na2CO3 = 9:1, pH=9.5). Plants grew in Hoagland solution served as controls(CK). (A): Total flavonoid content; (B): Flavanol content; (C): Total anthocyanin content; (D): Proanthocyanidin content; (E): Catechin content; (F): Quercetin content; (G): Isoamnetin rhcontent; (H): Kaempferol content; (I): Naringenin content. Bars represent the means ± SDs of three replicates. Significant differences among treatments are indicated by different letters within a panel based on Duncan’s multiple range test (P < 0.05). At the same time, we detected the expression levels of key genes in the pathway of flavonoid synthesis under different treatments, including five phenylalanine lyases (PALs), one cinnamate hydroxylase (C4H), six coumadany-CoA ligases (4CLs), one Chalcone synthase (CHS), four Chalcone isomerases (CHI), one Fatty ω-hydroxy acid/fatty alcohol hydroxycinnamoyl transferase (FHT), seven flavonol synthases (FLS), one flavanone-3-hydroxylase (F3H), five flavonoid-3 ‘-hydroxylases (F3’H), one flavonoid-3’5’-hydroxylase (F3’5’H), four dihydroflavonol reductases (DFR), two anthocyanin synthases (ANS/ANR), one colorless anthocyanin dioxygenase (LDOX), four flavonoidyltransferases (UFGT), five flavonoid $\frac{3}{5}$/7-O-glycosyltransferases (3GT/5GT/7GT), twelve glutathione S-transferases (GST), and two O-methyltransferases (OMT). The results showed that alkali stress significantly induced the expression of genes related to the synthesis of flavonoids, and the combined application of ABA further significantly promoted the transcription of these genes. Compared to the controls, alkali stress increased NtCHS/NtFLS/NtF3’H/NtF3H/NtANR/NtDFR/NtUFGT/NtGST expression 3–4 times. Combined application of ABA further promoted the expression of the above genes, and the expression levels of NtDFR/NtUFGT/NtGST increased to 4–5 times that of alkali stress, indicating that exogenous application of ABA can significantly promote the transcription level of flavonoids synthesis genes under alkali stress. However, the expression of the above genes was almost consistent with the single alkali stress when SNP was applied, which was consistent with its content change (Figure 8). Figure 8: RT-PCR analysis of the effects of exogenous ABA and SNP on the expression of related genes in the synthesis pathways of phenylpropane, flavonoids and anthocyanins under alkaline stress. 40-day-old seedlings were treated with different treatments (100 mM alkali, 100 mM alkali +15 μm ABA, 100 mM alkali +50 μm SNP) for 12 days (AS : NaHCO3: Na2CO3 = 9:1, pH=9.5). Plants grew in Hoagland solution served as controls (CK). The detected genes include: NtPAL(DN22899/DN30845/DN32316/DN27795/DN28855),NtC4H(DN22630),Nt4CL(DN27635/DN26161/DN26165/DN26520/DN22466/DN24012), NtCHS(DN26129), NtCHI(DN25681/DN30553/DN26750/DN23554), NtFHT(DN24264), NtFLS(DN27463/DN25711/DN36475/DN26381/DN28412/DN31474/DN11103), NtF3’H (DN40094/DN33376/DN34723/DN11734/DN26220), NtF3H(DN12356), NtF3’5’H (DN26145), NtDFR(DN29583/DN20811/DN29793/DN2454 0), NtLDOX(DN27002), NtANS(DN28028), NtANR(DN27423), NtUFGT(DN30699/DN26126/DN29030/DN23964/), Nt3/5/7GT(DN22396/DN28282/DN25447/DN30437/DN22901),NtGST(DN30723/DN46257/DN18263/DN27066/DN30434/DN22619/DN23788/DN33175/DN8177/DN25640/DN25885/DN16942),NtOMT(DN 13998/DN37666). Under alkaline salt stress, N. tangutorum seedlings can reduce oxidative damage and osmotic stress by regulating the expression level of genes related to the accumulation of flavonoids’ secondary metabolites and accumulating anthocyanins and flavonols in large quantities. The external application of ABA had an important regulatory effect on the accumulation of flavonoids and anthocyanins, while SNP was no involved in the relevant regulation. ## Discussion Plants have developed complex regulatory mechanisms to cope with environmental stress and accumulate nutrients during long-term evolution. Some gas signaling molecules (e.g., NO and CO) and hormones (e.g., ABA, ethylene, and Brassinosteroids) have played important roles in this process. Similar to ROS such as H2O2, NO, and its derived molecules (NO2, ONOO−, GSNO) are collectively referred to as active nitrogen (RNS), which act as signaling molecules to regulate various physiological processes in plants (Fancy et al., 2017). ABA is considered one of the most important anti-stress hormones in plants, regulating the expression of multiple stress response genes, inducing SnRK family protein kinase activity and MAPK signaling cascade defense response (Huang et al., 2017). During a plant stress response, NO plays an important role in regulating the production of reactive oxygen species (ROS) induced by ABA in regulating the activities of plant antioxidant systems such as catalase (CATs), superoxide dismutase (SODs) and ascorbic acid-glutathione cycle. ( Garcia-Mata and Lamattina, 2007). For example, in tall fescue seedlings, NO participated in ABA-mediated plant tolerance response to low light stress by regulating the antioxidant defense system and high photosynthetic rate. However, there are few studies on the regulation of ABA and SNP in response to alkaline stress at high pH (Zhang et al., 2018). This study found that 100 mM alkaline salt (pH= 9.5) treatment significantly inhibited the growth of N. tangutorum seedlings, resulting in decreased photosynthesis, oxidative damage, osmotic regulation imbalance, and ion toxicity. The combined application of 15 μM ABA and 50 μM SNP could partially restore the photosynthetic efficiency and ion homeostasis of N. tangutorum seedlings, further improve the reactive oxygen scavenging capacity of the antioxidant system and the accumulation of flavonoids secondary metabolites, and alleviate the growth inhibition and physiological damage caused by alkali stress (Figure S1 and Table 1). Among them, SNP significantly improved the photosynthetic efficiency and the regulation of carbohydrate accumulation more than ABA; ABA had a more positive effect on regulating the accumulation of flavonoids’ secondary metabolites and improving the efficiency of the antioxidant system. Our results suggest that ABA and SNP play a positive role in alleviating the adverse effects of alkali stress on N. tangutorum seedlings from different angles. ## Exogenous ABA and SNP increase ABA and NO content in plants under alkali stress, reduce oxidative damage and Na+ toxicity Under environmental stress, plants activate complex signal transmission networks, causing physiological changes in response to adverse external conditions. Oxidative damage is a major harm to plants caused by alkali stress, severely restricting plant growth and development and reducing plant biomass (Klay et al., 2018; Sun et al., 2019). In this study, alkali stress of 30–100 mM resulted in significant increases in the contents of H2O2, O2-, MDA, and EL, which caused serious oxidative damage to N. tangutorum seedlings and significantly reduced the growth indicators, such as plant height, fresh weight(FW), relative water content(RWC), and degree of succulent(DS) (Figure 1). It has been found that multiple environmental stresses stimulate the generation of endogenous ABA and NO in plants, such as drought and alkali stress that induce significant ABA accumulation in wheat and maize, respectively (Ma et al., 2018; Wang et al., 2022), which promoted their adaptability to the adverse environment through regulating photosynthesis, antioxidant pathways, ion balance and other pathways. It has been reported that exogenous application of ABA or SNP can effectively promote the production of ABA or NO in plants and reduce the damage caused by stress, such as Brassica pekinensis (Ren et al., 2020), Sorghum bicolor (Sun et al., 2019), and Medicago sativa (Li, X et al., 2020). In this study, alkali stress also induced a significant accumulation of ABA and NO, and the expression of ABA and NO synthesis and signal transduction-related genes, suggesting that ABA and NO may be involved in the regulation of N. tangutorum seedlings’ response to alkali stress (Figure S2). Exogenous application of 15 μM ABA and 50 μM SNP under alkali stress further promoted the accumulation of ABA and NO in plants, effectively alleviated the burst of ROS caused by stress, and reduced the oxidative damage and growth inhibition. The H2O2, O2 -, MDA, and EL levels were significantly reduced. Oxidative stress is plants’ main problem when they encounter harsh environments. The stronger the scavenging ability of the antioxidant system to the excess accumulation of ROS, the smaller the oxidative damage caused to it. Therefore, plants activate antioxidant systems through various regulatory pathways(Gupta et al., 2020), including ABA and NO signaling pathways. For example, exogenous SNP can increase SOD activity in cucumber seedlings, thus promoting the transformation of H2O2 into H2O and O2; meanwhile, it can enhance the activity of H2O2 scavenging enzymes (CAT, APX and GPX) and reduce the accumulation of ROS in root cell mitochondria, thus alleviating oxidative damage caused by salt stress (Shi et al., 2007). Exogenous ABA can significantly improve the activities of antioxidant enzymes, including POD, SOD, CAT, and APX, and promote the ability of tall fescue to clear newly generated ROS, thus reducing oxidative damage (Zhang et al., 2018). In this study, the ROS outbreak occurred in N. tangutorum seedlings under alkali stress, and the antioxidant system of the plants was also significantly induced, with the activity of antioxidant enzymes/GR/GST/CAT/APX/SOD and the content of antioxidant GSH significantly increased. After exogenous application of 15 μM ABA and 50 μM SNP, the above antioxidant enzyme activity and antioxidant content were further enhanced, which significantly inhibited the excess accumulation of ROS and oxidative damage, thus improving the physiological activity of plants from many aspects and alleviating the damage caused by alkali stress to plant growth. Interestingly, external ABA administration had a more significant positive effect than SNP (Figure 2). Saline-alkali stress usually keeps a high Na+ and Na+/K+ ratio in plant cytoplasm, leading to Na+ toxicity. High Na+ and pH values in the external environment under alkali stress can lead to the increase of Na+ and H+ concentration gradient inside and outside the cell, resulting in excessive accumulation of Na+ in the cell and affecting the growth and development of plants. For example, high pH will destroy the inhibiting ability of wheat seedlings to absorb Na+, resulting in excessive accumulation of Na+ induced by alkali stress(Li et al., 2020). Under high pH stress, Na+ content in the leaf and stem of Hippophae rhamnoides L. accumulated significantly, resulting in significant ion toxicity (Chen et al., 2009). Studies have shown that the exogenous application of ABA and SNP can regulate the pattern of ion accumulation in plants and maintain ion homeostasis (Chen et al., 2013; Chen et al., 2020). For example, SNP pretreatment can significantly reduce the content of Na+ and increase the content of K+ in the radicle and germ of Brassica napus under salt stress, thus reducing the ratio of Na+ to K+(Hasanuzzaman et al., 2018). In rice under salt stress, exogenous application of ABA regulates sodium and potassium ion homeostasis by promoting Na+ outflow and K+ inflow, thus enhancing the tolerance to saline-alkali stress (Li et al., 2020). In this study, alkali stress significantly reduced the K+ content in the roots of N. tangutorum seedlings and significantly increased the Na+ and Na+/K+ in the shoots and roots. From the results of quantitative gene analysis, the expression of plasma membrane-located Na+/H+ reverse transporter (NtSOS1) and vacuole-located Na+/H+ reverse transporter (NtNHX$\frac{1}{2}$/3) in leaves and roots was significantly up-regulated, while the transcription level of plasma membrane-located high-affinity K+ transporter (NtHKT1) was significantly down-regulated. The expression level of K+/H+ reverse transporter 5 (NtKEA5) located in the Golgi apparatus reverse mask capsule was significantly increased in the shoots (Figures 3D, E). The N. tangutorum seedlings mobilized the ion transport system to transport Na+/K+ ions to a certain extent, but it still could not effectively prevent the excessive accumulation of Na+ and the blocked absorption of K+ in the shoots and roots, resulting in obvious ion toxicity and growth inhibition. When 15 μM ABA and 50 μM SNP were applied under alkali stress, the accumulation of K+ in the roots of N. tangutorum seedlings was effectively promoted, and the content of Na+ and the ratio of Na+/K+ in the shoots and roots were significantly reduced, which played a positive role in maintaining the homeostasis of Na+/K+ ions in plants. ABA and SNP significantly induced the transcription of the Na+ transporter gene (NtSOS1/NtNHX$\frac{1}{3}$) in the shoots and roots, and the K+ influx transporter gene (NtHAK$\frac{3}{6}$/12/KUP4/KEA3) in the root (Figures 3A–D). The synergistic effect of these ion transporters made the outflow of Na+ in root and abovemonial cells greater than the inflow, which significantly reduced the content of Na+ in cells compared with the single alkali stress. At the same time, the content of K+ in the root was significantly increased, effectively reducing the Na+/K+ imbalance caused by alkali stress. In conclusion, ABA and SNP further regulate the ion accumulation pattern in plants by regulating the expression of Na+ and K+ transporter-related genes, thus alleviating the ion toxicity caused by alkali stress on N. tangutorum seedlings. ## Stomatal movement and water loss in leaves of N. tangutorum seedlings under alkali stress regulated by ABA and SNP In response to adverse environments, plants usually take a series of self-protection measures, such as regulating stomatal movement and reducing transpiration (Wang, Z et al., 2020). The regulation of stomatal opening is not only regulated by plant genetic genes but also influenced by various external environmental factors such as atmospheric CO2 concentration (Gray et al., 2000), heavy metals (Gonçalves et al., 2020), salt damage (Hu et al., 2013), light(Wei et al., 2020), hormones, and other signaling molecules (Huang et al., 2022). Studies have shown that the external application of ABA and SNP can induce stomatal closure and reduce stomatal density and conductance (Li et al., 2000; García-Mata and Lamattina, 2002). Salt and alkali stress can lead to the irregular spatial distribution of stomata in plants, decrease stomatal density, openness and conductance (Pompelli et al., 2021), reduce CO2 concentration in leaves, and ultimately lead to a decrease in net photosynthetic rate (Shabala et al., 2012). In this study, the leaf stomatal width aperture of N. tangutorum seedlings decreased significantly under alkali stress. In contrast, leaf surface temperature and water content decreased significantly. Water loss increased (Figure 4). Under alkali stress, applying ABA and SNP further reduced stomatal opening and water loss and recovered the decrease of leaf surface temperature and water content caused by transpiration, which effectively alleviated the water loss and physiological inhibition caused by alkali stress. The maintenance of the water retention capacity of plants is also closely related to the accumulation of osmotic regulatory substances. Studies have shown that the external application of ABA and SNP can effectively activate the osmotic protection system of plants and improve the water retention capacity of plants. For example, under salt stress, ABA can promote the accumulation of osmoprotective substances, such as proline in plants, and prevent the separation of cytoplasmic walls due to water loss in cells (Hewage et al., 2020). Exogenous SNP effectively improved the activity of antioxidant enzymes and the content of osmotic regulatory substances in pepper seedlings under salt stress to alleviating the inhibition caused by salt stress (Shams et al., 2019). In this study, the exogenous addition of 15 μM ABA and 50 μM SNP significantly induced the accumulation of osmotic regulating substances, such as betaine, soluble protein and proline in plants under alkali stress, which played a positive role in maintaining the osmotic pressure stability of plants and improving the water retention ability of plants. When SNP was applied externally, the accumulation of osmoregulatory substances in plants and water content in leaves were significantly higher than that of exogenous addition of ABA, indicating that NO had a better regulating effect on osmotic balance and reduced water loss in N. tangutorum seedlings under alkali stress (Figure 4). ## Exogenous ABA and SNP regulate photosynthetic efficiency and accumulation of photosynthate in N. tangutorum seedlings under alkali stress Photosynthesis provides material and energy for plant growth and is an important life process for plants. The factors leading to decreased plant photosynthesis can be divided into stomatal and non-stomatal restrictions (Tsai et al., 2019). Under environmental stress, the decrease of stomatal conductance leads to the decrease of CO2 entering stomata and the failure to meet the requirements of normal photosynthesis, which is called the stomatal restriction of photosynthesis. Non-stomatal limiting factors mainly refer to the destruction of chloroplast structure, the decrease of photosynthetic pigment content and photosynthase activity, and the destruction of reactive oxygen metabolism (Wu et al., 2014). Studies have found that environmental stress decreases chlorophyll content and plants’ photosynthetic rate (Hazrati et al., 2016). In this experiment, it was found that the stomata of N. tangutorum seedlings were partially closed under alkali stress, resulting in a significant increase in intercellular CO2 concentration compared with CK. At the same time, chlorophyll content, Pn rate and other non-stomatal limiting factors were significantly decreased, which indicated that non-stomatal limiting factors might be the main factors affecting the photosynthesis of N. tangutorum seedlings. 100 mmol/L alkali stress significantly reduced Fv/Fm in the leaves, indicating that the PSII reaction center was damaged by photoinhibition and the photosynthetic activity decreased (Figure 5). This is consistent with the conclusion of Conceicao Vieira Santos.etal in sunflower, indicated that the decrease of φPSII and qP further confirmed the photosynthetic electron transfer was inhibited, PSII light energy conversion efficiency was reduced, and excess excitation energy was increased (Scartazza et al., 2020). NPQ decreased to the lowest level under alkali stress, indicating that excess excitation energy may cause further damage to the chloroplast structure. Studies in cucumber and maize have shown that the addition of exogenous ABA and SNP can alleviate the decrease of chlorophyll content under salt stress, enhance the absorption and utilization of light energy by chloroplasts, promote the assembly of thylakoid membrane pigment-protein complex under iron deficiency conditions, and improve the photosynthetic rate (Hao et al., 2021; Rehaman et al., 2022). Many studies have shown that SNP can not only directly remove reactive oxygen species but also activate the activity of the antioxidant system (Corpas et al., 2019), protect the integrity of chloroplast structure and function, and improve photosynthetic efficiency (Pucciariello and Perata, 2017). In this study, exogenous application of 15 μm ABA and 50 μm SNP could effectively alleviate the decrease of chlorophyll content caused by alkali stress and significantly increase the Fv/Fm, qP, φPSII, and NPQ. These results indicate that exogenous ABA and NO could reduce the damage to chloroplast structure and function caused by oxidative damage by improving the scavenging capacity of ROS in the antioxidant system, preventing chlorophyll degradation, and partially restoring the photosynthetic efficiency of N. tangutorum seedings, thus alleviating the inhibition of plant photosynthesis in alkali stress. Meanwhile, the recovery of photosynthetic efficiency may be achieved by increasing the utilization rate of light energy rather than the heat dissipation of excitation energy. In addition, our physiological indicators showed that SNP had a more significant effect on the restoration of the photosynthetic system than ABA (Figure 5). However, the involvement of ABA and SNP in signal transduction and their molecular mechanisms in photosynthetic regulation under alkali stress remain to be further explored. As autotrophs, plants can assimilate inorganic carbon into sugars through photosynthesis to meet their own energy requirements (Hennion et al., 2019). Under a given abiotic stress condition, sugar plays the role of osmoprotectant and is part of the reactive oxygen scavenging system (Hennion et al., 2019; Salmon et al., 2020). In this study, the accumulation of photosynthates under various treatment conditions was detected, and it was found that alkali stress significantly reduced the contents of glucose, starch and total sugar but had no significant effect on fructose and glucose. But soluble sugar content was significantly up-regulated. This may be because N. tangutorum is a resistant plant, and soluble sugar plays an important role in osmotic regulation (Salmon et al., 2020). The contents of glucose, fructose, starch, soluble sugar and total sugar were significantly increased after ABA and SNP were applied under alkali stress. This may be because ABA and NO can improve the photosynthetic efficiency of plants, stimulate the metabolism of carbohydrates, and promote the accumulation of photosynthates under alkali stress. Similarly, the promotion effect of SNP on carbohydrate accumulation was significantly better than ABA, which was consistent with the alleviating ability of SNP and ABA on photosynthetic inhibition under alkali stress (Figure 6). ## Exogenous ABA and SNP promoted the accumulation of flavonoids in N. tangutorum seedlings under alkali stress Flavonoids are common secondary metabolites, including flavonols, anthocyanins, and others. It has many biological functions, including defense against biological and abiotic stresses. As important secondary metabolites in plants, flavonoids play an important role in pollen fertility (Wang, L et al., 2020), polar auxin transport (Zhang et al., 2021), and plant resistance to stress injury (Shomali et al., 2022). Previous reports have shown that the accumulation of flavonoids can remove stress-reactive elements from cells, such as free radicals, monopolistic oxygen molecules and peroxides (Wang et al., 2016)and reduce their levels after ROS formation, thus exerting antioxidant functions and enhancing plant tolerance to abiotic and biological stresses (Agati et al., 2012). The study found that under salt stress, drought stress, and MEJA treatment, the content of flavonoids in plants increased significantly to improve their tolerance to abiotic stress (Li, Y et al., 2020; Ahmed et al., 2021). In our study, alkali stress caused a large amount of flavonoid accumulation in N. tangutorum seedlings, indicating that flavonoids played a positive role in the removal of excessive ROS and osmotic regulation of N. tangutorum seedings. Exogenous application of ABA under alkali stress further increased the accumulation levels of total flavonoids, flavonol, and total anthocyanins, indicating that the ABA signaling pathway plays an important regulatory role in the accumulation of flavonoids, while SNP has little effect on the accumulation of flavonoids (Figure 7). Phenylalanine ammonia-lyase (PAL) is a synthase in the phenylpropane-like metabolic pathway, which can catalyze the deamination of L-phenylalanine (L-Phe) to trans-cinnamic acid and is the direct or indirect precursor of many secondary metabolites (Jun et al., 2018). Flavonoids are also synthesized through the phenylpropanoid pathway. Phenylalanine acts as the precursor molecule for flavonoid biosynthesis, which is transformed to cinnamic acid by phenylalanine ammonia lyase (PAL) (Anwar et al., 2019). A large number of studies have shown that PAL activity is significantly positively correlated with the accumulation of lignin, anthocyanins, and flavonoids play an important role in regulating the generation of secondary metabolites, maintaining normal plant growth and development, and improving plant resilience (Anwar et al., 2021; Peng et al., 2022). It was found that the wild-type Arabidopsis thaliana could accumulate anthocyanins to resist the stress under UV-B stress, but the double mutant pal1-pal4 could not synthesize anthocyanins normally, resulting in greater growth inhibition of the stress than the wild-type (Huang et al., 2010).In this study, alkali stress significantly up-regulated the PAL gene of phenylpropane, and SNP application did not increase its expression level and accumulation of flavonoids. After the exogenous application of ABA, the expression of PAL-related genes was significantly up-regulated, and the accumulation of flavonoids in the phenylpropane metabolic pathway was more significant (Figures 7A–D). The accumulation of these substances played a crucial role in the response of N. tangutorum seedlings to alkali stress. Flavonols and anthocyanins are the most important and abundant flavonoids, which have antioxidant properties in plants(Martens et al., 2010; An et al., 2016). As a key enzyme in Flavonol synthesis, Flavonol synthase (FLS) can control flavonol entry into the branch pathway of flavonol synthesis and form various flavonol compounds, mainly in the form of quercetin and kaempferol(Falcone et al., 2012). Some studies have found that exogenous ABA can significantly induce the accumulation of plant flavonoids under stress. For example, exogenous ABA can significantly induce the accumulation of flavonols such as kaverol in tea leaves and the high expression of genes related to flavonol synthesis, such as CHI, DFR, F3’H and FLS under drought stress (Gai et al., 2020). Exogenous application of ABA-induced large accumulation of CHS and flavonoid metabolites, which were key enzymes in flavonoid synthesis, and improved drought tolerance of pigeon pea (Yang et al., 2021). In this study, we found that external application of ABA significantly increased the accumulation of flavonoids such as naringin, quercetin, isorhamnetin, kaempferol and catechin in N. tangutorum seedlings under alkali stress, among which the increases of flavonols such as quercetin, kaempferol, and isorhamnetin were the most significant (Figures 7E-I). At the same time, the transcription levels of CHS, CHI, FLS and other enzyme synthesis genes that regulate the synthesis of these flavonols were also significantly up-regulated compared with alkali stress alone (Figure 8). Anthocyanins are a kind of water-soluble natural pigments belonging to flavonoids, which are widely found in angiosperms and are important components formed in the process of plant growth. Anthocyanins play an important role in improving the ability of plants to withstand stress and stress and are of great significance for plant growth, reproduction and environmental adaptation (Fan et al., 2016; Liang and He, 2018). The structural genes of the anthocyanin synthesis pathway can be divided into prophase biosynthetic genes (e.g., CHS, CHI, and F3H) and late biosynthetic genes (e.g., DFR, ANS and UF3GT). It was found that PAL, C4H, 4CL, CHS, CHI, F3H, DFR, LDOX, and UFGT are key structural genes involved in anthocyanin synthesis and are important enzyme reactions involved in the anthocyanin synthesis pathway (Zhang and Schrader, 2017). The increased expression of these genes can lead to the activation of the anthocyanin synthesis pathway of Arabidopsis thaliana, thus increasing the anthocyanin content in plants (Petroni and Tonelli, 2011). Some genes can regulate these anthocyanin synthesis genes, such as MYB. Overexpression NtMYB2 down-regulates both early and late key anthocyanin biosynthesis pathway genes. In addition, NtMYB2 down-regulated the proanthocyanidin (PA) biosynthetic pathway gene NtANR in tobacco flowers (Anwar et al., 2018). This study found that total anthocyanins and proanthocyanidins were significantly accumulated in N. tangutorum seedlings under alkali stress (Figures 7C, D), and their expression of their related synthetic genes were up-regulated (Figure 8). After exogenous application of ABA, the total anthocyanin content was significantly increased compared with that under alkali stress (Figure 7C), and the expressions of PAL, C4H, 4CL, CHS, CHI, F3H, DFR, ANS, LDOX and UFGT were all increased to varying degrees. The accumulation of anthocyanins and the expression of related genes were not significantly affected by SNP application. As excellent antioxidants, the increased flavonoid content plays a positive role in effectively removing ROS (An et al., 2016). In this study, the exogenous application of ABA was more effective than SNP in preventing the accumulation of ROS caused by alkali stress (Figure 2). On the one hand, ABA is better than SNP in inducing the increase of antioxidant enzyme activity in plants (Figure 2). On the other hand, exogenous application of ABA can significantly promote the accumulation of secondary metabolites of flavonoids under alkali stress than SNP and further improve the scavenging capacity of ROS in the antioxidant system, thus alleviating the physiological damage and growth inhibition of plants (Figures 2, 7). ## Conclusions In conclusion, N. tangutorum seedlings had strong alkaline resistance and could tolerate 100 mM alkali stress (NaHCO3:Na2CO3 = 9:1) for 12 days. The accumulation of flavonoids and anthocyanins was higher during the period. Exogenous application of ABA and SNP could significantly reduce oxidative damage and Na+ ionic toxicity, and alleviate the growth inhibition of N. tangutorum seedlings under alkali stress. They can also improve photosynthetic efficiency and promote the accumulation of flavonoids. Compared with ABA, SNP significantly promoted the accumulation of chlorophyll under alkali stress, increased the photosynthetic indexes such as Pn, Fv/Fm, φPSII and ETRII, thus improved the photosynthesis and accelerated the accumulation of glucose/fructose/sucrose/starch and total sugar. Compared with SNP, ABA can significantly promote the contents of total flavonoids, total anthocyanins and flavonols, improve the transcription level of the synthesis pathway of flavonoid metabolites such as NtFLS/NtF3’H/NtF3H etc genes, and the accumulation of naringin, quercetin, isorhamnetin, kaempferol and catechin. In a word, the effects of SNP on the improvement of photosynthetic efficiency and the regulation of carbohydrate accumulation under alkali stress were significantly better than ABA, and the effects of ABA on the regulation of flavonoids and anthocyanin secondary metabolites accumulation were more significant. At the same time, both of them can play a positive role in the defense response of N. tangutorum seedlings to alkali stress. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material, further inquiries can be directed to the corresponding author. ## Author contributions JZ and KC contributed to the design and performance of experiments, analyses, data interpretation, and manuscript drafting. YW contributed to study conception and design, revision, and final approval of submission. XL, ZD and LZ contributed to plant cultivation and measurement of physiological indicators. All of the authors have read and approved the final manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1118984/full#supplementary-material ## References 1. Agati G., Azzarello E., Pollastri S., Tattini M.. Flavonoids as antioxidants in plants: Location and functional significance. Plant Sci. (2012) 196 67-76. 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--- title: 'Effective Dose Range of Intrathecal Isobaric Bupivacaine to Achieve T5–T10 Sensory Block Heights for Elderly and Overweight Patients: An Observational Study' authors: - Ornwara Visavakul - Prangmalee Leurcharusmee - Tanyong Pipanmekaporn - Jiraporn Khorana - Jayanton Patumanond - Phichayut Phinyo journal: Medicina year: 2023 pmcid: PMC10057130 doi: 10.3390/medicina59030484 license: CC BY 4.0 --- # Effective Dose Range of Intrathecal Isobaric Bupivacaine to Achieve T5–T10 Sensory Block Heights for Elderly and Overweight Patients: An Observational Study ## Abstract Background and Objectives: The dose selection for isobaric bupivacaine determines the success of spinal anesthesia (SA). A dose higher than the optimal dose causes high SA, whereas an underdose leads to inadequate spread of cephalad. As it involves anatomical and physiological alterations, the dosing should be reduced with advancing age and body mass index values. Therefore, this study aimed to demonstrate the association between the isobaric bupivacaine dose and block height, and to determine the dose intervals of bupivacaine to achieve the T5–T10 sensory block with a low probability of high SA in elderly and overweight patients. Material and Methods: This retrospective observational study recruited 1079 adult patients who underwent SA with $0.5\%$ isobaric bupivacaine from 2018 to 2021. The patients were divided into four categories: category 1 (age < 60, BMI < 25), category 2 (age < 60, BMI ≥ 25), category 3 (age ≥ 60, BMI < 25), and category 4 (age ≥ 60, BMI ≥ 25). The bupivacaine dose and sensory block height (classified into three levels: high (T1–T4), favorable (T5–T10), and low (T11–L2)) were recorded. Results: The sensory block level increased significantly with increasing doses of bupivacaine for patients in categories 1 and 2. The suggested dose ranges for the favorable block heights were 15–17 and 10.5–16 mg in patient categories 1–2 and 3–4, respectively. In these dose ranges, the probability range of high SA was 10–$15\%$. Conclusions: The sensory block height following SA was associated with the bupivacaine dose in patients aged <60 years. Regardless of the BMI, the suggested dose ranges of $0.5\%$ isobaric bupivacaine are 15–17 mg (3.0–3.4 mL) and 10.5–16 mg (2.1–3.2 mL) for patients aged <60 and ≥60 years, respectively. ## 1. Introduction Spinal anesthesia (SA) is routinely performed in surgical procedures that require anesthetic coverage below the T4 spinal level [1,2]. One of the commonly used local anesthetics for SA is bupivacaine hydrochloride. ‘ Hyperbaric’ and ‘isobaric’ solutions are two forms of commercial bupivacaine. In contrast to its hyperbaric counterpart, isobaric bupivacaine is not influenced by gravitational forces, meaning its intrathecal spread or block height after a single-shot injection depends substantially on the dose of the administered drug [3,4]. The dose selection for isobaric bupivacaine determines the success of the SA. The recommended doses to reach the spinal block height at T4 and T10 in adults are 12–20 and 10–15 mg, respectively [1]. As it involves anatomical and physiological alterations, the dosing should be reduced with advancing age and body mass index (BMI) values [5,6]. With the aging process, there are configurational changes in the spinal column and a decreased total cerebrospinal fluid (CSF) volume [7,8]. With obesity, increases in abdominal and epidural fat reduce the lumbosacral CSF volume [8,9,10,11,12]. These variations are attributed to a greater degree of cephalad spread of SA. An intrathecal isobaric bupivacaine dose higher than the optimal dose causes a high spinal block, whereas an underdose leads to inadequate cephalad spread (i.e., failed spinal block) [13,14]. High and failed blocks are problematic, especially in elderly and overweight patients. A sensory block height higher than T6 is an independent risk factor for hypotension, bradycardia, and reduced stroke volume after SA [15,16,17]. Accordingly, a dosage guide of isobaric bupivacaine for SA allows the potential prevention of these undesired events. This retrospective study aimed to demonstrate the association between the dose of intrathecal isobaric bupivacaine and the sensory block height and to determine the dose ranges of isobaric bupivacaine for a single-shot intrathecal injection in general adult, overweight, elderly, and elderly overweight patients. We hypothesized that the doses of isobaric bupivacaine to achieve the T5–T10 sensory block, which is a ‘favorable’ block height for frequently performed operations, in patients aged ≥60 years or BMI ≥ 25 kg/m2 are lower than that of normal adult patients. ## 2.1. Study Population After ethical approval by the Research Ethics Committee of the Faculty of Medicine, Chiang Mai University on 15 December 2021 (study code: ANE-2564-09649) and clinical trial registration (thaiclinicaltrials.org: TCTR20220508002) on 8 May 2022, the data for adult patients undergoing SA with $0.5\%$ isobaric bupivacaine for any surgical procedure from January 2018 to December 2021 were collected retrospectively. Informed consent was waived by the Research Ethics Committee owing to the retrospective nature of the data collection. All collected data were kept confidential and accessible only to investigators. The exclusion criteria for data collection included parturient, failed, or repeated SA; the co-administration of intrathecal adjuvants; and incomplete records of the bupivacaine dose, sensory block height, operative site, and lumbar puncture level. Patients were divided into four categories according to age and BMI. These two parameters are both independent predictors of an exaggerated block height after SA because the lumbosacral CSF volume is smaller in those who have increased BMI or spinal stenosis [8]. In overweight and obese patients (BMI ≥ 25 kg/m2), the peak sensory block level following SA was higher than in normal adults (BMI 18.5–24.9 kg/m2) [18]. Moreover, the prevalence of acquired lumbar spinal stenosis is higher at ages ≥ 60 years [19]. Therefore, in our study, the pre-selected cut-off point for age was 60 years, and that for BMI was 25 kg/m2. ## 2.2. Spinal Block Technique As per the traditional technique used in our institute, spinal blocks were performed quite uniformly. Standard anesthetic monitoring was applied, and intravenous fluid pre-loading or co-loading was administered. A patient was placed in the lateral decubitus position, while an anesthesia resident performed the procedure under the supervision of an attending anesthesiologist. The size of the Quincke spinal needle (25G or 27G), injection level estimated from the Tuffier’s line (at the L2–S1 levels), needle approach (midline or paramedian), and dose (or volume) of $0.5\%$ isobaric bupivacaine were chosen at the discretion of the attending anesthesiologists. To ensure a full dose for the intrathecal drug administration, the bupivacaine was incrementally injected, and the free aspirated CSF flow was confirmed every 1 mL of injected bupivacaine. If the CSF did not flow freely, the spinal needle was rotated or repositioned. Generally, the rate of injection was approximately 0.1–0.2 mL/s. No intentional aspiration or reinjection of CSF (i.e., barbotage technique) was performed. At the end of the intrathecal injection, the patient lay supine, and the level of the sensory block on the midclavicular line was assessed using a cold pack or pinprick within 30 min. The highest dermatome at which there was a decreased sensation of cold or pain was recorded as the block height. ## 2.3. Outcome Measurements The characteristic data included the sex, age, weight, height, BMI, and type of operation. The procedural data included the dose of isobaric bupivacaine, level of lumbar puncture, and peak sensory block level. The sensory block levels were classified into three anesthetic outcomes: ‘high’, ‘favorable’, and ‘low’ dermatomal levels of sensory blocks. A high block level represented sensory block heights as high as T1–T4, which are typically related to hemodynamic instability [15,17]. The favorable sensory block level represented an anesthetic coverage up to the T5–T10 dermatomal levels, which are adequate anesthetic levels for abdominal (e.g., intestinal, urologic, gynecologic surgery), inguinal hernia, pelvic, and hip surgeries [1,2]. The low block height represented peak sensory block levels at T11–L2, which are sufficient for ankle, foot, perineal, and perianal surgeries [1,2]. This study focused on the association between the dose intervals of isobaric bupivacaine given to achieve the ‘favorable’ block height to avoid high SA or an inadequate spinal block for frequently performed operations. The suggested dose interval of isobaric bupivacaine to increase the probability of achieving a favorable block height while reducing the probability of a high sensory block was determined based on the average and half of the average incidence rate of high SA. ## 2.4. Statistical Analysis All statistical analyses were conducted using Stata 17 software (StataCorp, College Station, TX, USA). Continuous variables were described using mean and standard deviation (SD) values and compared using a one-way analysis of variance (ANOVA). Categorical variables were described using the frequency and percentage and compared using Fisher’s exact probability test or a Chi-square test, as appropriate. To examine the association between the isobaric bupivacaine dose and sensory block levels, a multivariable polytomous logistic regression was performed separately for each of the four subcategories of patients. The potential determinants incorporated within the model were the dose of isobaric bupivacaine, sex, age, height, weight, and level of lumbar puncture. Instead of incorporating the BMI as a single anthropometric measurement within our model, we separately modeled both the height and weight as predictors of block levels to preserve detailed information. A favorable sensory block level was defined as the base outcome in the polytomous logistic model. The linearity assumption of the association between the continuous determinants and outcome was inspected using a multivariable fractional polynomial (MFP) algorithm. In our analysis, the MFP algorithm revealed that all continuous determinants could be adequately fitted in their linear forms. We predicted the probabilities of achieving low, favorable, or high sensory block levels for each 0.5-mg increment in intrathecal isobaric bupivacaine from 5 to 20 mg, and illustrated these probabilities using marginal prediction curves. As the primary goal was to reduce the likelihood of achieving a high sensory block level, we aimed to determine the recommended dose interval of isobaric bupivacaine based on the following pre-specified criteria. First, the lower boundary of the suggested dose was located at the dose with a predicted incidence rate of high sensory blocks closes to but less than one-half of the average incidence rate. Second, the upper boundary of the suggested dose was located at the dose with a predicted incidence rate of high sensory blocks close to but less than the average incidence rate. The models’ performances were evaluated in terms of discrimination and calibration. All analyses were performed separately for each patient category. For discrimination, pairwise C-indexes using the conditional-risk method were estimated and reported [20]. For calibration, pairwise calibration plots contrasting the expected and observed probabilities were illustrated. To examine the potential utility of the suggested dose ranges in practice, we performed an apparent validation by comparing the incidence rates of achieving favorable sensory block levels and high SA between all patients and a group of patients who were administered with isobaric bupivacaine within the suggested dose ranges. ## 3. Results During the study period, 6843 patients underwent SA. Of these, 5628 patients received intrathecal hyperbaric bupivacaine; 66 patients had failed SA (no sensory block tested); and 70 patients had missing data on the anesthetic dose, site of surgery, level of lumbar puncture, or sensory block level. Finally, 1079 patients were included in the analysis (Figure 1). Overall, 762 ($70.6\%$) patients achieved a favorable block height, whereas the remaining 146 ($13.5\%$) and 171 ($15.9\%$) patients received low and high levels of sensory blocks, respectively. The mean age of the patients was 54.4 ± 20.6 years and the male/female ratio was almost equally distributed ($47\%$:$53\%$). There were significant differences in terms of the sex, age, weight, height, BMI, location of operation, and isobaric bupivacaine dose among the three groups of sensory block levels (Table 1). Table 1 shows the overall clinical characteristics of the study participants according to their sensory block height. The distribution of the peak sensory nerve block levels is also reported in Table 1. All patients were divided into four categories according to age and BMI. The category with the highest proportion of patients was category 1 (age < 60 years and BMI < 25 kg/m2 ($36.5\%$)), followed by category 3 (age ≥ 60 years and BMI < 25 kg/m2 ($31.9\%$)), category 4 (age ≥ 60 years and BMI ≥ 25 kg/m2 ($17.5\%$)), and category 2 (age < 60 years and BMI ≥ 25 kg/m2 ($14.1\%$)). The incidence rate of high sensory blocks was the highest in category 4 ($24.3\%$). Table 2 shows and compares the clinical characteristics of the study patients in each category across the three different groups of sensory block heights. The association between the isobaric bupivacaine dose and peak sensory block level was significant only in categories 1 and 2 (Table 2). However, a trend of increasing doses and higher sensory block levels was also observed in categories 3 and 4. Four multivariable polytomous logistic regression models were performed separately for each patient category (Supplementary Tables S1–S4), and marginal prediction curves were subsequently illustrated (Figure 2). The associations between the isobaric bupivacaine dose and the probability of achieving each group of sensory block height were similar for patients within the same age category, regardless of their BMI. Patients aged ≥60 years (categories 3 and 4) seemed to have a higher probability of achieving a favorable sensory block level with a lower bupivacaine dose, whereas the probability in patients aged <60 years (categories 1 and 2) was highest when a higher range of isobaric bupivacaine doses, at approximately 15 to 17 mg, was injected (Figure 2). Based on our pre-specified criteria, we provided recommendations for the suggested dose interval to achieve a favorable sensory block level while minimizing the incidence rate of high sensory blocks (Table 3). All four derived polytomous logistic models showed fair to excellent performance in discriminating between the three different groups of peak sensory block levels (Table 3), with acceptable calibration (Supplementary Figures S1–S4). When the overall patient data were compared with the selected data of patients who received isobaric bupivacaine within the suggested effective dose ranges, the overall incidence rate of achieving favorable sensory block levels increased from $70.6\%$ to $73.9\%$, whereas the incidence rate of achieving high SA decreased from $15.9\%$ to $12.1\%$. The improvement was more prominent in patients aged <60 years (categories 1 and 2) than in the older groups (categories 3 and 4). Table 4 presents the apparent validation results for each patient category. ## 4. Discussion This study demonstrated that the sensory block height, whether low, favorable, or high, was significantly associated with an increasing dose of isobaric bupivacaine in patients aged <60 years, and this trend was observed in patients aged ≥60 years. Regardless of their BMI, the suggested dose ranges of isobaric bupivacaine to achieve a T5–T10 sensory block level with the potential to reduce the probability of high SA were approximately 15–17 and 10.5–16 mg for patients aged <60 and ≥60 years, respectively. At these dose ranges, the probability of achieving the T5–T10 sensory block level was approximately $74\%$. Therefore, these suggested doses are considered the ‘effective’ dose ranges for intrathecal isobaric bupivacaine. Several factors influence the spread of an intrathecal local anesthetic. The patient characteristics, including the age, weight, height, BMI, sex, pregnancy, lumbosacral CSF volume, and abnormal spinal anatomy, are uncontrollable determinants, whereas the factors involving the spinal block technique, such as the site and speed of injection, orientation of the spinal needle tip, baricity and dose of local anesthetics, intrathecal adjuvant administration, and patient position, are adjustable and dependent on the anesthesiologist’s decision [3]. Without or with a minor impact from the gravitational force on an isobaric solution, the dosage of the local anesthetic is an important and controllable determinant of anesthetic spread [3,4]. The effective dose range of local anesthetics for successful and safe SA is wide in normal-sized adults and markedly narrower in pregnant women, the elderly, and obese patients, because these patient populations have a reduced lumbosacral CSF volume, which is a key patient-related determinant [8,9]. While effective doses (EDs) of intrathecal hyperbaric and isobaric bupivacaine for cesarean delivery have been comprehensively investigated by several research methods, dose–response studies specific to aging and obese populations have been reported scantly. From the limited evidence, the EDs of intrathecal isobaric bupivacaine for elderly and increased BMI patients vary depending on the definition of success in each study. Van Egmond et al. demonstrated the median ED (ED50) and calculated ED95 of $0.5\%$ isobaric bupivacaine in patients with an average age of 70 years to be 3.5 and 5 mg, respectively [21]. These EDs were adequate for unilateral total knee arthroplasty without a tourniquet and provided a duration of sensory and motor blockade of approximately 2 h. Chen et al. reported that the ED50s for $0.75\%$ isobaric bupivacaine were 6.6 and 5.8 mg for 61- to 70- and 71- to 80-year-old patients, respectively [5]. These doses provided an analgesic level to T10-L1 at 10 min after spinal block and a duration of motor blockade of around 3–4 h. These previous studies reported noticeably smaller doses than ours, which suggested doses of 11–16 mg for patients aged ≥60 years. We opine that these discrepancies stemmed mainly from different research methodologies. While others studies were experimental studies aiming to find the median ED for restricted levels and durations of anesthesia, we took advantage of our observational data to identify the ED for more generalized circumstances that require a sensory block up to the T5–T10 spinal nerve levels. To achieve this favorable block height, our suggested dose interval was relatively consistent with the ED50 reported by Michalek-Sauberer et al. [ 22]. In this previous study, the ED50 of $0.5\%$ isobaric bupivacaine was 11.2 mg for patients undergoing interstitial brachytherapy of the lower abdomen. Regarding the association between the BMI and dose of intrathecal isobaric bupivacaine, previous studies showed that an increased BMI was a determinant of the higher extent of SA and the dosing of isobaric bupivacaine was suggested to be inversely related to the BMI [11,12]. No dose–response study focusing on determining the ED of isobaric bupivacaine in overweight or obese non-pregnant patients has been reported. However, our study found that a dose reduction was not necessary for patients with BMI ≥ 25 kg/m2, regardless of their age. This finding indicates that when compared with the BMI, age is a dominant predictor of the intrathecal spread of isobaric bupivacaine. The dose is a parameter dependent on the concentration and volume. Previous studies have reported conflicting effects of the dose and volume of intrathecal local anesthetics [3]. Some studies demonstrated greater intrathecal spread with a higher dose or volume of local anesthetics, and some showed no difference. With a fixed concentration of $0.5\%$ isobaric bupivacaine in our study, a change in dose was accompanied by a proportionate change in the volume of administered bupivacaine. Therefore, whether the dose or volume plays more role in determining the sensory block height could not be differentiated in our study. The methodology of our study deserves discussion. We introduced a logical approach to investigate the ED of intrathecal isobaric bupivacaine. The estimation of the suggested dose intervals in our study was based on the rationale that generously accepted sensory blocks as high as the T5 level but strictly avoided sensory block levels ≥ T4 (i.e., high SA). In clinical practice, even though the trend towards ambulatory surgery and early ambulation is currently widespread, several ordinary operations increase surgical complexities, such as a revision hip replacement, which require extended operation times [23]. This situation is challenging for anesthesiologists to decide how much isobaric bupivacaine should be administered to provide successful and safe SA without retaining the spinal or epidural catheter. We, therefore, set the pre-specified criteria particularly related to the predicted incidence rate of high SA in each patient category. In addition, an observational design provides benefits in terms of generalizability in comparison to conventional dose-finding experimental studies. For instance, the level of the lumbar puncture, which has been reported as a determinant affecting the block height, was selected at the anesthesiologists’ discretion and could not be limited to one level in our study [24,25]. This made our results valid irrespective of the level of local anesthetic injection that might be incorrectly identified by palpation [26]. ## Limitations This study has some limitations. First, in addition to the extent of the sensory block, an assessment of the motor block levels and durations of anesthesia should also be incorporated to demonstrate the clinically effective doses of intrathecal local anesthetics because the dose directly affects the anesthetic duration, particularly in aging and obese patients [27,28]. Second, clinically relevant outcomes following high SA or inadequate SA, such as hypotension, bradycardia, or conversion to general anesthesia, were not recorded in this study. The association between these unfavorable events and the anesthetic height, as well as the dose of bupivacaine, should be further specified. Third, it is the nature of a retrospective observational study that could not control for potential confounding factors (e.g., level of lumbar puncture, and size of spinal needle) that might mask the actual association between the dose of isobaric bupivacaine and the anesthetic level. However, this study design reflects pragmatic practice and has good generalizability. Finally, when the suggested dose ranges were applied to the dataset, the proportion of achieving the favorable sensory block levels increased minimally and that of the high block levels reduced modestly. This point may raise concerns regarding the clinical utility of our findings. However, this could be explained by the distribution of the bupivacaine dose, which leaned toward the upper boundary of the suggested dose ranges in all patient subcategories. If the average bupivacaine doses were lower and close to the lower boundary, we would observe a significant reduction in high SA while preserving the incidence rate of favorable block levels > $70\%$. Further studies are warranted to validate the clinical utility of our suggested effective bupivacaine dose range. ## 5. Conclusions The effective dose ranges of $0.5\%$ isobaric bupivacaine to achieve T5–T10 sensory block levels were 15–17 and 10.5–16 mg for patients aged <60 and ≥60 years, respectively. In other words, the effective volumes of $0.5\%$ isobaric bupivacaine were 3.0–3.4 and 2.1–3.2 mL for patients aged <60 and ≥60 years, respectively. Within these dose (or volume) intervals, a higher dose (or volume) increased the probability of high SA, but not at levels greater than 10 to $15\%$. Conversely, a smaller dose (or volume) increased the probability of a lower sensory block level. Therefore, the suggested doses could guide anesthesiologists to select a safe and effective dose of intrathecal isobaric bupivacaine for aging and increased BMI patients. Further prospective dose–response studies are encouraged to determine the ED50 and ED95 values of isobaric bupivacaine in these specific populations. ## References 1. Gropper M.A.. Miller’s Anesthesia (2019) 1423-1425 2. Barash P.G.. 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--- title: H2O2-PLA-(Alg)2Ca Hydrogel Enriched in Matrigel® Promotes Diabetic Wound Healing authors: - Alexandra Cătălina Bîrcă - Cristina Chircov - Adelina Gabriela Niculescu - Herman Hildegard - Cornel Baltă - Marcel Roșu - Bianca Mladin - Oana Gherasim - Dan Eduard Mihaiescu - Bogdan Ștefan Vasile - Alexandru Mihai Grumezescu - Ecaterina Andronescu - Anca Oana Hermenean journal: Pharmaceutics year: 2023 pmcid: PMC10057140 doi: 10.3390/pharmaceutics15030857 license: CC BY 4.0 --- # H2O2-PLA-(Alg)2Ca Hydrogel Enriched in Matrigel® Promotes Diabetic Wound Healing ## Abstract Hydrogel-based dressings exhibit suitable features for successful wound healing, including flexibility, high water-vapor permeability and moisture retention, and exudate absorption capacity. Moreover, enriching the hydrogel matrix with additional therapeutic components has the potential to generate synergistic results. Thus, the present study centered on diabetic wound healing using a Matrigel-enriched alginate hydrogel embedded with polylactic acid (PLA) microspheres containing hydrogen peroxide (H2O2). The synthesis and physicochemical characterization of the samples, performed to evidence their compositional and microstructural features, swelling, and oxygen-entrapping capacity, were reported. For investigating the three-fold goal of the designed dressings (i.e., releasing oxygen at the wound site and maintaining a moist environment for faster healing, ensuring the absorption of a significant amount of exudate, and providing biocompatibility), in vivo biological tests on wounds of diabetic mice were approached. Evaluating multiple aspects during the healing process, the obtained composite material proved its efficiency for wound dressing applications by accelerating wound healing and promoting angiogenesis in diabetic skin injuries. ## 1. Introduction As hydrogels exhibit similar hydration with skin tissue and stimulate the epithelization process, they represent an attractive choice for treating wounds of any type [1,2,3]. Moreover, their biomimicking microstructure, composition-related loading efficiency of the therapeutic cargo, and circumstantial stimuli-responsive capacity provide indisputable characteristics for fabricating advanced topical formulations [4,5]. In particular, hydrogels as dressings could be a viable option to control the water permeability in injured skin [6]. This is an essential aspect that must be addressed because injured skin causes considerable losses of water and fluids, about 20 times higher than those caused by healthy skin. Regularly, the daily amount of water that is usually lost through healthy skin is ~250 gm−2/day at 35 °C, while when the skin is affected, it can lose up to 5000 gm−2/day, depending on the wound type. These processes must be considered when designing a wound dressing toward endowing it with high water-vapor permeability and the ability to absorb exudate fluid without causing other symptoms or conditions to patients. Thus, a wound dressing must act as an adherent protective membrane and simultaneously provide the conditions for optimal healing, with this balance being a challenge for research in the field [7,8,9,10,11,12]. The naturally originating alginates are leading representatives of polysaccharide-based hydrogels [13]. The reaction occurring during the formation of alginate-based hydrogels is either ionic cross-linking mediated by COO- groups or chemical cross-linking between additional side chains. Alginates exhibit promising features for the fabrication of performance-enhanced wound dressings. Specifically, given the intrinsic hydrophilicity, alginate-based hydrogels can absorb significant amounts of exudate released by the wound. Moreover, the alginate exerts a hemostatic effect when in contact with bleeding wounds and stimulates angiogenesis while promoting cell proliferation and collagen production. It also decreases the concentration of proinflammatory molecules (reactive species) and down-regulates the secretion of proinflammatory cytokines in chronic wounds. Additionally, the flexibility and easy removal make it a comfortable alternative to conventional wound dressings. However, alginates present one main drawback, namely the lack of adhesivity, therefore requiring the use of a secondary dressing or combination with other polymers to improve the mechanical properties [14,15,16,17,18]. Polylactic acid (PLA) is a synthetic polyester recognized for good biocompatibility and characterized by thermoplasticity and suitable mechanical properties, including strength [19]. This material has attractive medical applications, reportedly being used in biodegradable sutures, scaffolds, and a wide range of drug delivery systems [20,21]. The primary polylactic acid monomeric block is Lactide, characterized by L-lactide or D-lactide type. The chirality type differentiates between the properties of the polymer, such as mechanical properties, semi-crystalline/amorphous characteristics, and biodegradability. For instance, it has been shown that D and mixed L/D forms have a higher degradability rate than the L form. Compared to other polymers, the chirality-based chemical capacity of polylactic acid (L, D, or L/D) leads to semi-permeable properties to water and oxygen, thus being more susceptible to biodegradation compared to other polymers used in the medical field [22,23,24]. Moreover, the degradation of PLA can be enhanced by enlarging the surface area ratio or by increasing the porosity of the polymer. This is due to the fact that the degradation process is active both outside the surface and within the core of the polymer when the material shows porosity [25,26,27,28]. Another polymer of extensive use in biomedicine is polyvinyl alcohol (PVA), which shows promising outcomes in the field owing to its favorable properties, such as biocompatibility, non-toxicity, easy film-forming ability, high hydrophilicity, and mechanical and chemical resistance [29,30]. Given its versatile features, PVA is currently one of the oldest and most used synthetic polymers in biomedical applications, such as wound dressings, contact lenses, artificial organs, and drug delivery systems [31,32]. However, when blended with other polymers, such as PLA, synergistic benefits of the PVA-based platforms have been evidenced [33,34,35]. Besides choosing the optimum wound dressing substrate materials, the composite can be further enriched with bioactive molecules that augment the restorative potential and provide extensive therapeutic effects. For instance, the significant role of oxygen in modulating the wound healing process is well known, whether acute or chronic. Specifically, proper wound oxygenation contributes to the formation of granulation tissues and enhances collagen production, and also activates tissue-repairing pathways by providing sufficient energy levels to support cellular events [35,36]. Moreover, the local incidence of bacterial infection is significantly reduced, contributing to accelerated healing. In addition, oxygen supply stimulates the angiogenesis in the wound sites and determines bacteriostatic effects, being an essential factor for the physiological wound healing process [37,38,39,40]. Thus, fabricating a dressing with the ability to provide oxygen to the wounded site represents an appealing strategy to boost the healing process. One of the challenges in wound healing is vascularization, which can be sustained or induced by a cocktail of immunomodulatory molecules and growth factors that play a role in various steps of angiogenesis. Matrigel, a sterile and soluble extract from the basal membrane matrix derived from the Englebreth–Holm–Swarm tumor, represents a potential choice to overcome the limitations that arise during the wound healing process. What is very important is that Matrigel contains a mixture of structural proteins and proteoglycans, and angiogenic growth factors, thus leading to the support and generation of new blood vessels [41,42,43,44]. Therefore, its incorporation into the wound dressing will result in improved vascularization of the affected tissue. Out of the wide range of wound types, the diabetic foot ulcer represents a critical issue in wound care management. It occurs in 20–$30\%$ of diabetic patients and culminates in most cases with amputation surgery [45,46,47,48]. In diabetic patients, the inadequate glycemic level significantly affects the wound healing process by the impossibility of closing the ulceration. However, controlling glycemic levels is a serious challenge. Still, if this activity is maintained correctly, the benefits will appear in the patient’s quality of life regarding diabetic disease and chronic wound healing [49,50,51,52,53,54]. In this context, creating advanced wound dressings for diabetic foot ulcers is of tremendous importance, being a current focus of the scientific community and resulting in the elaboration of new and promising therapeutic approaches [55,56,57,58]. Taking everything into account, this study aims to develop performant polymer-based dressings that serve as platforms to treat diabetic ulceration with the help of oxygen activity. The struggle to evolve in the way of developing new materials and methods for improving the healing process of diabetic wounds led to obtaining some dressings that fulfill the crucial needs to have conclusive results. In this respect, three hydrogel formulations were developed herein (i.e., alginate hydrogel—HG, alginate hydrogel loaded with H2O2-PLA microspheres—HG_OMs, and alginate hydrogel loaded with H2O2-PLA microspheres and Matrigel—HG_OMs_MG) and characterized from the physicochemical and biological points of view. ## 2.1. Materials In order to obtain the alginate-based hydrogels, which additionally include PLA microspheres that encapsulate H2O2, the following materials were used: polylactic acid (PLA), polyvinyl alcohol (PVA), chloroform (CHCl3), hydrogen peroxide (H2O2), sodium alginate, calcium chloride (CaCl2), and Matrigel Matrix Growth Factor Reduced (code BD 356321). All reagents were purchased from Sigma–Aldrich (Merck Group, Darmstadt, Germany) and used as-received, without additional purification. The same supplier provided most of the reagents used during the in vivo assays (otherwise, the provider was properly mentioned). To use the developed dressings as an effective remedy for skin defects, H2O2 was embedded in PLA microspheres that were further loaded in a multifunctional hydrogel matrix that could control the oxygen release and prevent primary infection of the wound surface while accelerating the healing process. ## 2.2. Synthesis of H2O2-PLA Microspheres (OMs) A $2\%$ (w/v) PVA solution in distilled water at 80 °C under continuous stirring was first prepared. Then, the H2O2 solution and $2\%$ PVA solution were mixed at room temperature (1:1 volume ratio) to obtain the hydrophilic part of the microemulsion preparation. Secondly, a 50 mg/mL PLA/CHCl3 mixture, representing the hydrophobic part, was prepared. Both solutions were mixed and subjected to ultrasound, according to the following parameters: amplitude—$50\%$, time of action—3 min (successive 3 s ON/3 s OFF steps). The sonicated solution was washed and centrifuged for 15 min, under 6000 rpm, finally obtaining a precipitate (coded as OMs) for further use and physicochemical analyses. ## 2.3. Preparation of OMs-Loaded Alginate Hydrogels (HG_OMs and HG_OMs_MG) All hydrogel formulations were prepared starting from a 5 mg/mL alginate solution. Blank alginate solution resulted in the formation of HG hydrogel after CaCl2-mediated cross-linking. Further, H2O2-PLA microspheres dispersed in water were mixed with the polysaccharide solution (1:7 volume ratio) to obtain the physically cross-linked HG_OMs hydrogel. Finally, the HG_OMs_MG hydrogel was obtained using a similar protocol, but 2 mg of Matrigel was added. Hydrogels were obtained in duplicate, one part being lyophilized and investigated from a physicochemical point of view and another part being tested from a biological point of view. ## 2.4.1. Fourier Transform Infrared Spectroscopy (FTIR) To investigate the compositional integrity of the prepared formulations, sequential infrared studies were performed. A small amount of each sample was analyzed using a ZnSe crystal of the Nicolet 6700 FT-IR spectrometer purchased from Thermo Fischer Scientific (Madison, WI, USA). Measurements were performed at room temperature, with 32 sample scans/sample, between 4000 and 1000 cm−1 at a resolution of 4 cm−1. Recording and processing the information thus acquired was possible by using the OmnicPicta program (version 8.2, Thermo Fischer Scientific, Madison, WI, USA). ## 2.4.2. Scanning Electron Microscopy (SEM) Relevant information on the surface morphology and porosity of samples was obtained by SEM analysis using an FEI Quanta Inspect F50 electron microscope from Thermo Fischer Scientific (Hilsboro, OR, USA). The investigations were carried out in a high vacuum, using the secondary electron mode at 20 and 30 keV acceleration voltages. To reduce electrical charging through analysis, samples were capped with a thin gold film. ## 2.4.3. Swelling Rate A biopsy punch was used to obtain cylindrical sections (5 mm diameter) from the dressings, which were immersed in 2 mL of simulated body fluid (SBF, prepared according to Kokubo’s protocol) at a temperature of 37 °C. Samples were weighed before and after different time points of immersion in SBF, and their swelling rate was estimated using the formula:[1]Swelling ratio=Wt−WiWi×$100\%$ where Wi and Wt are the samples’ mass before (initial) and after (time-point) SBF immersion. ## 2.4.4. Degradation Rate Weighed HG and HG_OMs samples were immersed in SBF for 24 and 48 h, then dried and weighed again. Mass changes in dried hydrogels were used to estimate the incipient degradation of materials using the formula:[2]Degradation=1−W0−WtW0×$100\%$ where W0 and Wt represent the mass of each hydrogel dressing before immersion in SBF and after immersion/drying at different intervals. ## 2.4.5. Hydrogen Peroxide Quantification Regarding the qualitative and quantitative analysis of H2O2, an HR-MS (FT-ICR) protocol was developed using the well-known salicylic acid color reaction of FeIII ions after FeII oxidation by H2O2 [59]. The FeIII(Salyc)2 complex was identified (327.9867, 329.9820, and 330.9854 amu peaks) using as a reference the molecular cluster prediction from a simulation tool for high-resolution molecular clusters (Data Analysis software tool of the SolariX system). In addition, the fine isotopic structure of the 330.9854 amu peak was used as confirmation for the qualitative analysis approach (330.9825 amu—12C141H1057Fe16O6, 330.9845 amu—12C1313C1H1056Fe16O6, 330.9884 amu—12C141H92H56Fe16O6). For mass calibration purposes, a NaTFA solution was used in positive ESI ionization mode). After the sample preparation step, the sample and all calibration samples were analyzed using FT–ICR MS, using the direct infusion sample introduction device and positive ESI ionization mode. For the quantitative analysis approach, the 329.9820 amu mass peak was used, and calibration was performed using different H2O2 concentrations in the 35–150 mg/L range, where a R2 = 0.9905 was obtained using the $y = 0.0015$x + 0.5054 regression trendline. The average mass resolution of the 329.9820 amu mass peak was 1,063,690 (sd 0.431) FWHM. All HR-MS analyses were performed by an FT–ICR MS with a 15 T superconducting magnet (solar X–XR, QqqFT–ICR, Bruker Daltonics) with electrospray ionization (ESI) device and direct infusion for sample introduction. For the positive ESI ionization experiments, the sample was introduced at a sample flow rate of 120 µL/h, a nebulizing gas pressure (N2) of 2.5 bar at 200 °C, and a flow rate of 1 L/min. The spectra were recorded over a mass range between 250 and 480 uam at a source voltage of 3700 V [60]. ## 2.5.1. In Vivo Experimental Design The present study followed the ethics criteria of the Animal Facility regulation and Ethical Research Committee at Vasile Goldis Western University of Arad, Romania. Male CD1 mice (weight 25~30 g, 8 weeks of age) were intraperitoneally injected with streptozotocin (STZ, Santa Cruz Biotechnology, the Netherlands; 102 mg/kg body weight) in 50 mM citrate buffer (pH 4.5) to induce diabetes. Four days after injection, blood glucose levels were measured using an Accu-check Blood Glucose Meter (Roche Diagnostics, Indianapolis, IN, USA). A diabetic phenotype in the animals was confirmed with a blood glucose level of over 400 mg/dL. Diabetic mice were anesthetized with intramuscular ketamine hydrochloride and xylazine injection, and the surgical area was shaved. Full-thickness dermal wounds with a diameter of 3 mm were removed at four places on each side of the back created using a 3 mm biopsy punch, and fixed with a silicone ring to avoid wound contraction. Four types of skin wounds per animal were performed (Figure 1): 1. only the thick dermis was removed (control, C); 2. the wound was transplanted with alginate hydrogel (HG); 3. the wound was transplanted with oxygen-releasing alginate hydrogel, containing H2O2-PLA microspheres (HG_OMs); 4. the wound was transplanted with an oxygen-releasing hydrogel, containing H2O2-PLA microspheres and 4.44 µg/mg of Matrigel (HG_OMs_MG). The dressing covered just the area of the skin defect. After applying the primary dressing, the wound area was covered with gauze and secured by the adhesive bandage. On days 3 and 7, tissue samples were collected for histopathological analysis. ## 2.5.2. Histopathology The skin explants were washed with phosphate-buffered saline (PBS) and fixed in $4\%$ paraformaldehyde (PFA) for 24 h. After fixation, samples were dehydrated after sequential immersion in increasing concentrations of alcohols, cleared, and further embedded in paraffin blocks. Histological sections (5 μm) were prepared using a microtome and subsequently stained with hematoxylin & eosin (H&E, BioOptica, Italy) for morphological analysis and Dane stain (Titolchimica, Italy, TC 20822) to highlight pre-keratin and keratin on wound sites. The differentiation of pre-keratin and keratin, obtained through the Orange G solution, is evidenced by color variations (orange and red-orange, respectively). The stained sections were examined under light microscopy using an Olympus BX43 microscope (Olympus Life Science, Japan) and photographed using a digital camera (Olympus XC30). ## 3.1. Physicochemical Characterization Figure 2 presents the FTIR spectra of OMs, HG, and HG_OMs samples. All spectra show a broad vibration band between 3100 and 3400 cm−1, characteristic of abundant OH groups originating from the hydrophilic polymers used in our study [61,62]. Vibrational bands between 1500 and 1750 cm−1 are characteristic of C=O groups specifically shown in the PLA structure (OMs sample) and COO– moieties within the alginate (HG and HG_OMs samples). Overlapped infrared vibrations can be noticed at ~1400 cm−1 (symmetric deformation of PLA methyl and symmetric stretching of alginate-originating carboxyl) and between 1000 and 1100 cm−1 (C–O–C vibrations from both biopolymers and PVA-specific C–O stretching) [63,64]. The absorption bands in the 2800–3000 cm−1 wavenumber range are attributed to characteristic vibrations of the C–H groups. The SEM micrographs of PLA-based systems (Figure 3) show their spherical morphology and comparable dimensions. Polymeric spheres of micronic dimensions (1–3 µm), with smooth surfaces and no clear signs of structural damage, are obtained using an adapted emulsification protocol. At the same time, microsphere aggregates, consisting of interconnected particles, can be noticed. The SEM analysis of the hydrogel dressings reveals that HG (Figure 4) and HG_OMs (Figure 5) samples show a highly porous structure, which is beneficial for stimulating the cell adhesion process and promoting cellular proliferation and migration. However, the main difference between these samples relies on the pore structure, which is significantly modified following the addition of PLA-based microspheres. The collected micrographs show the uniform macroporosity and interconnected pore structure of the HG control sample (Figure 5), with a measured average pore size of 79.72 ± 2.12 µm. The smooth surface of pore walls can also be noticed. As previously mentioned, a distinctive pore structure of alginate-based hydrogels is evidenced after the addition of OMs (Figure 5), resulting in an increased and more heterogenous porosity. Wider pores, having an average dimension of 110.25 ± 6.15 µm, have been estimated for the HG_OMs hydrogel. It can be seen that the spheres are evenly distributed on the surface of the hydrogel but also within the hydrogel matrix (evidenced by the presence of an irregular and textured surface of the pore walls). Correlated with the SEM data obtained for the hydrogel-free spheres, one can notice that the OMs spheres retain their micronic dimensions after loading in the hydrogel matrix, with values ranging from ~1 μm (spheres distributed within the hydrogel) to ~3 μm (spheres distributed on the surface of the hydrogel). Mass variations of HG and HG_OMs soaked in SBF, directly correlated with the swelling rate in hydrogel formulations, are represented in Figure 6a. A time-dependent ability for progressive swelling is noticed for both samples, with a maximal swelling degree of ~$140\%$ (HG) and ~$160\%$ (HG_OMs) after 2 days. Complementary results evidence the time-dependent degradation of both hydrogels, with a reduced rate in the case of the HG_OMs dressing (Figure 6b). ## 3.2. Gross Morphological Analysis of the Wounds Figure 7 shows the gross aspects of the hydrogel-dressing-treated wounds compared to full-thickness skin defects (control) at 3 and 7 days post-injury. The size of wounds treated with HG_OMs_MG is smaller than those treated with HG_OMs and HG, and the gross aspect of the skin is improved at both time intervals post-surgery. Moreover, the wound dressing with the alginate hydrogel containing oxygen-enriched microspheres and Matrigel is almost the same, the re-epithelization is near completion, and no edema, erythema, or other clinical signs of inflammation are observed. ## 3.3. Histological Aspect of the Wounds 3 Days after Injury (H&E Stain) Histological analysis of the full-thickness skin defect on day 3 post-surgery shows a large and diffuse area of granulation tissue (GT) and proliferated keratin. Under GT, extended edema and inflammatory cells, mainly neutrophils, have been identified (Figure 8). The hydrogel-treated wound histology shows a reduction in the granulation tissue and less keratin on the surface. The edema area is reduced, and the presence of inflammatory cells is lower compared to the control (Figure 9). When treated with oxygen-entrapping PLA microspheres embedded into a hydrogel dressing, an improved histopathological aspect of the skin is observed, with the collected micrographs showing a progressive healing process characterized by a good delimitation of the granulation tissue. Moreover, the edema is not present on the wound site, demonstrating the efficient activity of the dressing (Figure 10). Compared to the control and the previous wound dressings (HG and HG_OMs), the addition of Matrigel significantly improves the morphological aspect of the injured skin (Figure 11). The granulation tissue is well-defined and much thinner, in contrast to the explant results without Matrigel. Few inflammatory cells are observed, but sufficient to support the healing process. The presence of Matrigel within the hydrogel dressing has a clear influence on the healing of skin tissue, with the angiogenesis process being activated and wound healing being well promoted. In the first 3 days, regeneration processes that activate the acute inflammatory events are represented by neutrophils and granular tissue presence. In the case of Matrigel-enriched hydrogel, the process of forming new blood vessels is also observed. ## 3.4. Histological Aspect of the Wounds 7 Days after Injury (H&E Stain) The histological analysis of the un-dressing full thick skin 7 days after the surgery shows no epidermal formation. The area of the dermis formed by connective tissue is very weak, which is unusual for normal healing after 7 days, as it should form the skin. The arrow illustrates the vascularization that occurs after 7 days from the defect (Figure 12). The tissue of the affected area treated with alginate hydrogel shows the formation of a new thin epidermis (NE), while the presence of isolated epithelial cells is also observed at the characteristic dermis area. The dermis shows a disturbed organization populated with collagen fibers, which makes the connection between the epidermis and dermis very fragile, with bleeding even occurring (Figure 13). After 7 days of treatment with the OMs-loaded hydrogel dressing, the formation of a new epidermis that tightly continues with a dermis can be noticed. Several collagen fibers are observed at the dermal level compared to the blank hydrogel, but the development of normal skin is not achieved. The presence of more capillaries, contrary to the control group, is evidenced by the arrow (Figure 14). The influence of Matrigel on the healing ability of the H2O2–PLA microspheres-loaded hydrogel dressing is evident after 7 days of defect occurrence (Figure 15). One can easily see the appearance of the new epidermis, which is thicker and better defined than in the case of HG-/HG_OMs-treated wounds. Its structure is characteristic of normal skin, so after 7 days, a normal epidermis is completely formed. ## 3.5. Histological Aspect of the Keratinization Process (Dane Stain) Dane coloring with orange/red-orange highlights the formation of keratin and pre-keratin, whereas the green color highlights the collagen in the skin. The collected micrographs of as-stained tissue specimens are included in Figure 16. Three days after the surgery, all wounds begin a keratinization process. However, there is a notable difference in keratinization in uncovered skin defects compared to the skin treated with synthesized hydrogels. The difference is in defining keratinization more precisely in the epidermis, so the healing process is more advanced than untreated defects. Moreover, in the case of the explant treated with the Matrigel-enriched hydrogel, it is noteworthy that collagen production (stained in green) is more intense than in the other experimental groups. At 7 days, the histopathological aspects highlight the successful keratinization process. Cells that produce keratin and pre-keratin appear deeply in the epidermis and find themselves even in the papillary dermis, which is not shown in the other groups. In the case of HG_OMs_MG-treated wounds, keratin is observed only in the epidermis, which correlates with the normal appearance of mature skin. In addition, the keratin layer is thinner and better defined. These effects demonstrate similar characteristics with normal skin, so even after 7 days, the same HG_OMs_MG hydrogel is proven the most effective in treating wounds. The collagen distribution differs depending on the material used, so after 7 days of the defect, collagen exhibits a normal appearance only for HG_Oms and HG_Oms_MG explants. ## 4. Discussion Hydrogels have been extensively used in recent studies as promising materials for wound dressings suitable for different injuries [17], including diabetic foot ulcers. Polymers such as chitosan [65,66,67,68], alginate [65,68,69], gelatin [69], bacterial cellulose [70], and polyvinyl alcohol [66,67,71] have been reported as useful substrate materials for healing cutaneous lesions. Given the attractive properties of polymeric materials in general and alginate in particular, this study explored the use of alginate-based hydrogels to enhance the healing of wounds in diabetic mice. The metabolic imbalance occurring in diabetic patients, in conjunction with diabetes-associated vascular conditions, causes a delayed and impaired healing process in skin injuries, generally resulting in chronic and complicated wounds [72,73]. As they undergo hypoxia, which alters the healing process through immune and cellular events, the fabrication of oxygen-releasing platforms emerges as a promising strategy for the local management of diabetic wounds [74,75]. Therefore, to improve the restorative potential of the herein-proposed hydrogels, oxygen-entrapping PLA microspheres were fabricated and incorporated into the base materials. Following their sequential investigation, FT-IR analysis demonstrated the efficient fabrication of composite formulations (Figure 2). For instance, the signature ester of PLA was identified at ~1680 cm−1, a slightly shifted position when compared to other PLA-based formulations [76,77,78]. This was particularly assigned to the vibrations of the hydrogen-bounded carbonyl, which resulted in entrapping the H2O2 within the polymer matrix. Consistent with this observation, OMs had the most intense band in the hydroxyl region due to their dual origin (PVA and hydrogen peroxide). The formation of oxygen-enriched PLA-based microspheres was supported by the infrared data of HG_OMs hydrogels, which evidenced a decrease in the hydroxyl band but still a more intense signal compared to the pristine HG sample. In the case of HG_OMs hydrogels, cumulative overlapped signals for moieties in the 1000–1650 cm−1 region were evidenced, confirming their composite formulation. Moreover, the embedding of OMs within the hydrogel matrix was evidenced by the modifications that occurred in C-H vibrations, which switched from a sharper double-humped aspect (due to intense symmetric and asymmetric vibrations of PLA-originating methyl side groups) [79,80] to a reduced-in-intensity broader band (due to predominant vibrational modes of aliphatic C–H from the hydrogel matrix) [81,82]. Hydrogel formation was confirmed for HG and HG_OMs samples through the presence of alginate-originating asymmetric vibrations of COO– (~1600 cm−1) involved in ionic bonding [83,84]. PLA-based spheres have been extensively investigated as bioactive, non-immunogenic, and biodegradable carriers for controlled and targeted therapeutic formulations [85,86]. PVA addition is usually preferred to properly tune the hydrophobic nature, stability, and degradation rate of PLA-based systems, but also the loading efficiency of the therapeutic payload [87,88]. Using an adapted microemulsion protocol, hydrogen peroxide was easily and successfully embedded within polyester formulations. The H2O2-loaded PLA-based systems (OMs) displayed smooth surfaces, a spherical morphology, and micrometric size, with diameters ranging from 1 to 3 μm, as evidenced through SEM micrographs (Figure 3). Moreover, when added to the alginate hydrogel, the spheres maintained their dimensions and were evenly distributed both on the surface and within the hydrogel matrix (Figure 5). The size order of the H2O2-PLA microspheres is consistent with other findings on the fabrication of oxygen-generating polymeric particles, with our spheres reaching even smaller dimensions than previously described. For instance, Nejati et al. [ 89] obtained PLA microparticles loaded with polyvinylpyrrolidone/hydrogen peroxide with average diameters between ~20 μm and ~90 μm. In comparison, Zhang and colleagues [90] synthesized oxygen-releasing polycaprolactone/calcium peroxide microspheres with sizes ranging from ~6 μm to ~30 μm. Porosity represents an essential aspect that must be addressed when designing and fabricating functional wound dressings. The intrinsic porosity of polymer-based hydrogels is beneficial for providing biomechanical support, nutrient transport, and moisture retention while mimicking the natural local microenvironment and promoting cell ingrowth and migration, and later reparative/regenerative events, finally leading to structural and functional restoration [91,92]. The alginate hydrogels developed herein exhibited a favorable interconnected porous structure, with pores in the micrometric range (average pore size of 79.72 ± 2.12 and 110.25 ± 6.15 µm for HG and HG_OMs, respectively) (Figure 4 and Figure 5). These findings are in accordance with similar studies [93,94,95], as previously reported structures presented comparable architecture and pore sizes (i.e., 50–150 μm and 5–15 μm, respectively) [94,95]. SEM analysis revealed the interconnected and uniform macroporosity of HG hydrogel, but also the smooth surface of the pore walls. By contrast, the HG_OMs hydrogel exhibited increased heterogeneous porosity, with H2O2-PLA microspheres being distributed both onto and within the hydrogel. Assessing the swelling behavior of dressing materials under biomimicking fluids provides essential information on their moisture absorption capacity, which is a relevant aspect for predicting their ability to absorb local exudate and edema. As SBF has similar ionic concentrations to human blood plasma, it was used in our study to evaluate the moisture uptake in hydrogel dressings (Figure 6a). It has been evidenced that both formulations had an important capacity to retain the fluid, starting from the first minutes of interaction and following a similar pattern. However, the addition of OMs determined a delayed dissolution of the hydrogel’s network, further resulting in increasing the SBF retention. This observation was better evidenced after 8 h of immersion due to the cumulative roles of OMs blocking the fluid from reaching the HG network and larger pores within the HG_OMs sample. Both hydrogels reached their maximal uptake capacity after 24 h, with ~$160\%$ and ~$200\%$ swelling rates being observed for HG and HG_OMs, respectively. After this time point, a decrease in their capacity to absorb the liquid occurred, finally resulting in ~$140\%$ (HG) and ~$160\%$ (HG_OMs) swelling rates. This decrease resulted from the degradation of both formulations (Figure 6b). A time-dependent loss in their mass was evidenced for HG and HG_OMs hydrogels, with no significant differences regarding the mass loss variation for samples of the same type. Though their porosity and fluid uptake rate were higher for HG_OMs, a reduced degradation was noticed for this hydrogel after the considered time points. This particular outcome was associated with the presence of PLA-based microspheres, which increased the stability of HG. Taking all into account, HG_OMs hydrogels showed increased interconnected microporosity, enhanced fluid retention capacity, and reduced degradation rate, being suitable for wound dressing applications. The amount of H2O2, quantified using FT-ICR MS, was estimated as 1.97 ppm. During the inflammation of the tissue, the concentration of the H2O2 is predicted to reach millimolar levels. H2O2 levels are an important aspect of cellular functions and proliferation, but they can also cause cell death, which is necessary to generate at reduced levels in wounds [96,97]. In diabetic wounds, the impaired inflammation and proliferation phases of the healing process, local hypoxia, and inefficient immune and cellular responses lead to microenvironment-induced chronic hypoxia. Exacerbated and prolonged hypoxic conditions drastically intercept the angiogenesis process, further causing delayed and complicated healing [98,99]. In this context, wound oxygenation by means of topical oxygen therapy represents an emerging strategy for the healing of diabetic wounds, with promising therapeutic outcomes being reported for oxygen-releasing/-generating platforms [100,101] and oxygen-delivery/antioxidant formulations [102,103]. Herein, oxygen-releasing hydrogel dressings were proposed to promote angiogenesis for faster and more effective wound healing. Our study evaluated the healing ability of hydrogel dressings on full-thickness dermal wounds of streptozotocin-induced diabetic mice. Following a 3-day and 7-day treatment, histopathological analysis was performed to collect information on the healing process. In comparison with untreated wounds, a reduction in local edema and inflammatory cells (3 days), but also the formation of the new epidermis and the presence of more blood vessels (7 days) were noticed after the treatment with HG dressings. These results are consistent with previous studies, validating alginate-based hydrogel’s high moisture absorption and pro-angiogenic effects [104,105]. Wounds treated with hydrogels embedding H2O2-PLA microspheres (HG_OMs group) showed no edema and significantly reduced inflammation after 3 days, while the formation of more and uniformly distributed capillaries and the presence of collagen fibers were evidenced after the longer treatment. As these findings were superior to the case of HG-treated wounds, we concluded that the oxygen released from polymeric spheres encouraged such beneficial outcomes, given the importance of oxygen in stimulating collagen production [106]. For what concerns the wounds treated with the HG_OMs_MG hydrogel dressing and in addition to the previous treatment group, well-defined granulation tissue and new capillaries were noticed after 3 days due to the structural support and biomolecule input provided by Matrigel [107] respectively. Moreover, a completely normal epidermis layer was observed, and a denser collagen network was observed after 7 days. Complementarily, a successful keratinization process occurred only in the epidermis of the HG_OMs_MG-treated wounds, representing a clear sign of mature skin. The oxygen-releasing ability of the hydrogels incorporating PLA microspheres improved the skin’s histopathological aspect, allowed a progressive healing process, and supported angiogenesis. Even though both oxygen-enriched hydrogel formulations led to better wound healing results than the untreated skin defect and the pristine alginate-treated wound, the best outcomes were noted for the Matrigel-containing hydrogel dressing. These results were predictable, as Matrigel has been successfully employed in the design of wound dressings for skin repair and regeneration, owing to the synergistic action of constituent macromolecules and growth factors [43]. Our findings revealed that the HG_OMs_MG formulation offers the best healing outcomes, being able to almost close the skin defect 7 days post-injury. In comparison with other tested hydrogels, the HG_OMs_MG showed remarkable results, counting the lack of edema, erythema, or other clinical signs of inflammation, the presence of much thinner and better-defined granulation tissue, the normal aspect of mature skin, and the appearance of a new thicker and well-defined epidermis. In addition, the HG_OMs_MG dressings proved, stimulated (3 days), and promoted the angiogenesis process, as evidenced at 7 days post-injury by the abundance of newly formed capillaries. This result validated the faster wound healing ability of HG_OMs_MG hydrogel, as angiogenesis is known to play an important role during the healing of injured skin. Thus, the developed hydrogel holds great promise in wound healing applications, being a desirable candidate for the proper treatment of skin defects in diabetic patients. ## 5. Conclusions This study aimed to prepare a new oxygen-releasing dressing to promote angiogenesis for faster and more effective wound healing. The goal was achieved by embedding hydrogen-peroxide-containing PLA microspheres in an alginate-based hydrogel matrix (HG_OMs). Moreover, the addition of Matrigel in the hydrogel dressing (HG_OMs_MG) improved the therapeutic outcomes, as revealed by in vivo tests. FT-IR analysis confirmed the formation of composite hydrogels, while SEM characterization evidenced their advantageous architecture. Specifically, H2O2-PLA microspheres were evenly distributed onto the surface and within the HG matrix, resulting in the formation of HG_OMs hydrogel, which exhibited a heterogenous interconnected porosity. This particular morphology is fitting for oxygen diffusion while allowing cell interaction and adhesion. The suitability of the composite dressings in wound healing applications was confirmed by in vivo tests on diabetic mice. After 3 days from a skin injury, the wound healing starts with granulation tissue and acute inflammation progression, mainly characterized by neutrophil extravasation. The healing process was observed to be more advanced in the order HG_OMsMG > HG_OMs > HG > empty defect. After 7 days from a skin injury, the new epidermis was formed in all experimental groups, except the empty defect. 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--- title: Sulfoxide-Containing Bisabolane Sesquiterpenoids with Antimicrobial and Nematicidal Activities from the Marine-Derived Fungus Aspergillus sydowii LW09 authors: - Xiao Yang - Hongjia Yu - Jinwei Ren - Lei Cai - Lijian Xu - Ling Liu journal: Journal of Fungi year: 2023 pmcid: PMC10057145 doi: 10.3390/jof9030347 license: CC BY 4.0 --- # Sulfoxide-Containing Bisabolane Sesquiterpenoids with Antimicrobial and Nematicidal Activities from the Marine-Derived Fungus Aspergillus sydowii LW09 ## Abstract Phytopathogens, such as phytopathogenic bacteria, fungi, and nematodes, have caused great losses of crops every year, seriously threatening human health and agricultural production. Moreover, marine-derived fungi are abundant sources of structurally unique and bioactive secondary metabolites that could be potential candidates for anti-phytopathogenic drugs. One new sulfoxide-containing bisabolane sesquiterpenoid aspersydosulfoxide A [1] and nine known analogues (2–10) were isolated from the marine-derived A. sydowii LW09. The absolute configuration of the sulfur stereogenic center in 1 was determined by electronic circular dichroism (ECD) calculations. Compound 5 showed inhibition activity against Pseudomonas syringae, with a minimum inhibitory concentration (MIC) value of 32 μg/mL, whereas, compounds 2, 7, and 8 showed antibacterial activities toward Ralstonia solanacarum, with the same MIC value at 32 μg/mL. Meanwhile, compounds 3, 7, and 8 inhibited the fungal spore germination of Fusarium oxysporum, with the half maximal effective concentration (EC50) values of 54.55, 77.16, and 1.85 μg/mL, respectively, while compounds 2, 3, 7, and 8 inhibited the fungal spore germination of Alternaria alternata, which could be induced by vacuolization of germ tubes, with EC50 values of 34.04, 44.44, 26.02, and 46.15 μg/mL, respectively. In addition, compounds 3, 7, and 8 exhibited nematicidal activities against Meloidogyne incognita second-stage juveniles (J2s). In addition, compound 8 possessed the strongest nematicidal activity of nearly $80\%$ mortality at 60 h with the half lethal concentration (LC50) values of 192.40 μg/mL. Furthermore, compounds 3, 7, and 8 could paralyze the nematodes and then impair their pathogenicity. ## 1. Introduction Phytopathogens have caused great losses of crops every year, seriously threatening human health and agroindustry [1]. As the major part of phytopathogens, bacteria, fungi, and nematodes can infect many important economic crops, such as potato, soybean, wheat, and rice [2], leading to significant economic and production losses. Although traditional synthetic drugs are quite effective in managing pathogens, the residue accumulation and chemical resistance problems severely polluted the ecosystem and decreased the drug potency [3,4]. Therefore, searching for alternative molecules for anti-phytopathogenic drugs is urgent. Fungi have been identified as a prolific source of secondary metabolites with effective properties of applications in pharmacy, food, and agriculture [5]. Specifically, in agronomy, many secondary metabolites exhibited significant antimicrobial activities against phytopathogens [6]. As clear examples, bioactive secondary metabolites produced by Metarhizium, Beauveria, and Trichoderma spp. have great potential in controlling phytopathogenic fungi, bacteria, and pests [7,8]. Marine-derived fungi, inhabiting special habitats such as high salinity, high pressure, absence of sunlight, and deficiency of nutrients, have been proved to be abundant sources of structurally unique and bioactive secondary metabolites for countering the biotic and abiotic stress [9,10]. The genus Aspergillus is prolific and ubiquitous in marine habitats, some of them could produce a variety of secondary metabolites with different structures, including polyketides, alkaloids, sterols, terpenoids, and peptides [11]. Many of these metabolites showed a wide range of bioactivities, such as antimicrobial, cytotoxic, insecticidal, and antioxidant activities [12]. During our ongoing efforts to search for new bioactive compounds from marine-derived fungi [13,14,15,16], a strain of A. sydowii LW09 isolated from a deep-sea sediment of the Southwest Indian Ridge was screened out for investigations. The EtOAc crude extract from the fermentation of this fungus showed antibacterial and antifungal activities. Bioassay-guided fractionation of this extract was performed, leading to the isolation of one new sulfoxide-containing phenolic bisabolane sesquiterpenoid aspersydosulfoxide A [1] and nine known analogues aspergillusene B [2], (−)-(R)-cyclo-hydroxysydonic acid [3], penicibisabolane G [4], (7S,11S)-(+)-12-hydroxysydonic acid [5], 11,12-dihydroxysydonic acid [6], expansol G [7], (S)-sydonic acid [8], aspergoterpenin C [9], and aspergillusene A [10]. All the isolated compounds were tested for antibacterial activities against P. syringae and R. solanacarum, spore germination inhibition of F. oxysporum and A. alternata, and nematicidal activities against M. incognita J2s. Details of the isolation, structural elucidation, and bioactivities of these compounds are described herein. ## 2.1. Molecular Identification Fungal genomic DNA of the strain LW09 was extracted from mycelia on a potato dextrose agar medium (PDA) using a previously published method [17]. For molecular analysis, the internal transcribed spacer (ITS), beta-tubulin (BenA), and calmodulin (CaM) regions were amplified using primer pairs ITS1 and ITS4, Bt2a and Bt2b, and cmd5 and cmd6, respectively [17]. PCR reactions were prepared following the published method [17]. Sequencing reactions were performed by Tsingke Biotechnology Co., Ltd., Beijing, China. All the sequences generated in this study were deposited in GenBank (ITS OP250138.1, BenA OP584347, and CaM OP584348). To determine the phylogenetic relationships of LW09, analysis was performed based on the three loci. Alignments were generated and manually edited by MEGA X (MUSCLE). Alignments of each locus were concatenated and used in the subsequent phylogenetic analysis. Maximum likelihood (ML) analysis was performed using RAxML v.7.4.2 Black Box (the code was released by Alexandros Stamatakis and Wayne Pfeiffer, US) in the CIPRES Science Gateway platform (https://www.phylo.org, accessed on 25 September 2022) with 1000 bootstrap replicates; Aspergillus aurantiobrunneus NRRL 4545 was used as an outgroup (Table S1). ## 2.2. General Experimental Procedure IR spectra were recorded using a Nicolet IS5 FT-IR spectrophotometer (Thermo Scientific, Madison, WI, USA). UV/vis spectra were recorded using a Thermo Scientific Genesys 10S spectrophotometer (Thermo Scientific, Madison, WI, USA). Optical rotations were obtained by an Anton Paar MCP 200 Automatic Polarimeter (Anton Paar GmbH, Graz, Austria). ECD spectra were obtained with an Applied Photophysics Chirascan spectropolarimeter (Applied Photophysics Ltd., Leatherhead, UK). The NMR data were recorded with a Bruker Avance-500 MHz spectrometer (Bruker, Rheinstetten, Germany) using solvent signals (Acetone-d6: δH 2.05/δC 29.8, 206.1) as references. The mass data were acquired with an Agilent Accurate-Mass-Q-TOF LC/MS 6520 instrument (Agilent Technologies, Santa Clara, CA, USA). PCR reactions were conducted using a T20D PCR instrument (LongGene, Hangzhou, China). Column chromatography (CC) was accomplished on silica gel (Qingdao Haiyang Chemical Co., Ltd., Qingdao, China), octadecylsilyl (ODS, 50 μm, YMC Co., Ltd., Kyoto, Japan), and Sephadex LH-20 (Amersham Biosciences, Uppsala, Sweden). Preparative HPLC was conducted with an Agilent 1200 HPLC using a C18 column (Reprosil-Pur Basic C18 column; 5 μm; 10 × 250 mm) at a flow rate of 2.0 mL/min. The absorbance of compounds in the 96-well plates was detected by a SpectraMax Paradigm microplate reader (Molecular Devices, Sunnyvale, CA, USA). Aureomycin (Coolaber Science & Technology Co., Ltd., Beijing, China), chlorothalonil (Shanghai yuanye Bio-Technology Co., Ltd., Shanghai, China), ivermectin (Shanghai yuanye Bio-Technology Co., Ltd., Shanghai, China), and Abamectin (Shanghai yuanye Bio-Technology Co., Ltd., Shanghai, China) were used as positive controls. ## 2.3. Fungal Materials, Cultivation, Fermentation, and Isolation The fungus sample A. sydowii LW09, deposited in the Institute of Microbiology, Chinese Academy of Sciences, Beijing, was isolated from a deep-sea sediment of the Southwest Indian Ridge (37°48′36” S; 49°39′36” E) at a depth of 2395 m. The strain was cultured on a PDA medium at 28 °C for 5 d. Afterward, four 0.5 cm3 plugs of agar with mycelia were inoculated in a 500 mL Erlenmeyer flask containing 250 mL liquid medium ($0.4\%$ glucose, $1\%$ malt extract, and $0.4\%$ yeast extract), then cultivated at 28 °C for 5 d on a rotary shaker at 200 rpm; subsequently, seed liquid culture was obtained and 5–10 mL spore suspension was transferred directly into a 500 mL Erlenmeyer flask with rice medium (80 g rice and 120 mL water) and fermented at 28 °C for 30 d in the dark. Afterward, a total of 10 kg fermentation sample was extracted three times with EtOAc (3 × 12 L); then, the filtrated organic solvent was evaporated in vacuo to obtain dryness extract (70.0 g). The dryness crude extract was fractionated by silica gel CC eluted with a gradient of petroleum ether (PE)/EtOAc (from 20:1 to 1:2, v/v) to give 10 fractions (Fr.1–Fr.10). The Fr.8 (0.6 g, eluted with PE/EtOAc 2:1) was repeatedly chromatographed by octadecylsilyl column chromatography (ODS CC), eluting with MeOH/H2O to yield four subfractions (Fr.8.1–Fr.8.4). The subfraction Fr.8.1 (48.0 mg, eluted with $50\%$ MeOH) was further purified by RP-HPLC (Agilent Zorbax SB-C18 column; 5 μm; 10 × 250 mm; $60\%$ CH3CN/H2O for 23.0 min; 2.0 mL/min) to give compounds 1 (2.5 mg, tR = 17.0 min) and 5 (2.2 mg, tR = 19.7 min). The Fr.4 (0.6 g, eluted with PE/EtOAc 8:1) was separated by ODS column eluting with MeOH/H2O and obtained five subfractions (Fr.4.1–Fr.4.5). The Fr.4.2 (42.3 mg, eluted with $60\%$ MeOH) was purified by RP-HPLC ($40\%$ CH3CN/H2O for 32.0 min; 2.0 mL/min) to yield compounds 2 (3.0 mg, tR = 30.0 min) and 9 (3.0 mg, tR = 21.1 min). Compounds 3 (1.5 mg, tR = 10.8 min) and 7 (1.7 mg, tR = 19.0 min) were isolated by RP-HPLC ($35\%$ CH3CN/H2O for 20.0 min; 2.0 mL/min) from the Fr.4.5 (35.0 mg, eluted with $100\%$ MeOH). The Fr.8.4 (55.0 mg, eluted with $100\%$ MeOH) was further purified by RP-HPLC ($67\%$ MeOH/H2O for 35.0 min; 2.0 mL/min) to yield compounds 4 (2.4 mg, tR = 30.0 min), 6 (3.0 mg, tR = 24.5 min), and 8 (1.9 mg, tR = 23.0 min). The Fr.3 (0.9 g, eluted with PE/EtOAc 12:1) was separated by ODS column eluting with MeOH/H2O, yielding three fractions (Fr.3.1–Fr.3.3). The Fr.3.2 (0.3 g, eluted with $80\%$ MeOH) was further purified by RP-HPLC ($73\%$ MeOH/H2O for 21.0 min; 2.0 mL/min) to yield compound 10 (2.8 mg, tR = 19.0 min). The flowchart of the compounds is given in Figure S11. Aspersydosulfoxide A [1]: colorless oil, [α]D25 = −2.0 (c 0.05, MeOH); UV (MeOH) λmax (log ε) 228 (3.70), 244 (3.66), 288 (3.32) nm; IR (MeOH) νmax 3178, 2954, 2926, 1643, 1610, 1577, 1466, 1424, 1294, 1167, 1036, 1024, 979, 817 cm−1; ECD (0.36 mM, MeOH) λmax (Δε) 230 (+1.25) nm, 253 (+0.38) nm; HRESIMS m/z 281.1571 [M+H]+ (calcd for C16H25O2S, 281.1575). For 1H and 13C NMR data, see Table 1. ## 2.4. ECD Calculation Methods Conformational analysis within an energy window of 3.0 kJ/mol was performed by using the OPLS3 molecular mechanics force field. The conformers were then further optimized with the software package Gaussian 09 at the B3LYP/6-311G (d,p) level, and the harmonic vibrational frequencies were also calculated to confirm their stability. Then, the 60 lowest electronic transitions for the obtained conformers in vacuum were calculated using time-dependent density functional theory (TDDFT) methods at the CAM-B3LYP/6-311G (d,p) level. ECD spectra of the conformers were simulated using a Gaussian function. The overall theoretical ECD spectra were obtained according to the Boltzmann weighting of each conformer [18]. ## 2.5. The Antibacterial Assay The antibacterial assay was performed according to the previous method [19]. Two typical phytopathogens (P. syringae BLY016 and R. solanacarum BLY014) were cultured in LB broth ($1\%$ peptone, $0.5\%$ yeast extract, and $1\%$ NaCl) at 38 °C for 24 h under static condition, and then the concentration of bacterial cells was adjusted to approximately 1 × 106 CFU/mL and 100 μL aliquots were inoculated in 96-well microtiter plates containing 100 μL of the isolated compounds diluted serially two-fold. The concentrations of tested compounds were ranged from 0.5 to 64 μg/mL. After incubation at 38 °C for 24 h, MIC was determined by microplate spectrophotometer. All experiments were performed in triplicate. ## 2.6. The Inhibition of Spore Germination Assay The inhibition of pathogen spore germination assay was performed according to the previous study [20] with some modifications. The fungal pathogens (F. oxysporum FLY001 and A. alternata LW37) were cultivated on PDA media at 28 °C for 7 d. The $0.5\%$ sterilized glucose buffer was used to rinse pathogen culture media and then filtrated through a syringe with four layers of sterilized gauze to yield spore suspensions. The working solution of the suspensions was adjusted to approximately 1 × 105–1 × 106 spores/mL. Spore suspensions of 100 μL were inoculated in 96-well microtiter plates containing 100 μL of the isolated compounds diluted serially two-fold, and the ranges were from 1 to 128 μg/mL. After cultivation at 28 °C for 24 h, 60 spores were observed by microscope and the germinated ones were counted. The spore germination rate was calculated with the formula given below. DMSO and chlorothalonil were used as negative and positive controls. All experiments were operated in triplicate and the data were presented as mean ± SD of three replicates. Afterwards, the EC50 values were generated by GraphPad prism 7.0 with different concentrations of the tested compounds and their spore germination rates. [ 1]Spore germination rate (%)=Treated germinated − positive germinatedTotal observed spores × $100\%$ ## 2.7. The Nematode Toxic Assay The nematode toxic assay was designed following the method [21] with some modifications. Eggs of M. incongnita were collected from the root of Ipomoea aquatica Forsk pot cultures. Afterward, the M. incongnita J2s hatched in the dark in sterile water for 24 h at 28 °C. Newly emerged J2s were then washed three times in sterile water before being used in the assays. To evaluate the toxicity of the compounds, J2s were diluted to about 1000 individuals/mL and the stock solution of the compounds was prepared at 10 mg/mL for further dilution to the required concentrations. Firstly, all the compounds were screened for their nematicidal activities against J2s at 800 μg/mL with a 48 h duration. Then, the active ones were set to 50, 100, 200, and 400 μg/mL in 96-well plates and incubated under 28 °C, with the death number of J2s recorded at set intervals. Before counting, 0.5 M NaOH was added to wells, which allowed the dead and alive nematodes to be clearly distinguished, as the living ones huddled when contacted and straight and immobile ones were defined as dead; then, the corrected mortalities were calculated by the formula given below. The mobility observations were determined by the wiggly frequency of nematodes; motionless ones were recognized as “−”, under 5 times per 10 s were identified as “+”, and upper ones were “++”. DMSO was used as a negative control; abamectin and ivermectin were used as positive control. The experiment was performed with three replicates and the LC50 values were calculated by GraphPad prism 7.0 with different concentrations of the tested compounds and the corrected mortality. [ 2]The corrected mortality (%)=(Treated mortality − Control mortality)(1 − Control mortality) × $100\%$ ## 2.8. Data Analysis and Process The NMR data analysis used MestReNova 14. The sequences were processed by MEGA X, BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed on 23 September 2022), and CIPRES (https://www.phylo.org, accessed on 26 February 2023) for identifying the isolated fungus. The corrected mortalities of the compounds were compared by the Tukey test with Origin 2021. All analyses and plots were conducted using Origin 2021 (Northampton, MA, USA), GraphPad prism 7.0 (San Diego, CA, USA), Adobe Illustrator 2021 (San Jose, CA, USA), and Office Excel 2016 (Redmond, DC, USA). ## 3.1. Phylogenetic Analysis The phylogenetic analysis based on three loci (ITS, BenA, and CaM) was constructed using ML analysis (Figure 1). Phylogenetically, the strain LW09 was clustered in the known strains of A. sydowii (Bootstrap values = $100\%$). Thus, the strain LW09 was identified as A. sydowii. ## 3.2. Structure Elucidation Aspersydosulfoxide A [1] was initially obtained as deep yellow oil. Its molecular formula was determined as C16H24O2S by HRESIMS (m/z 281.1571 [M + H]+, calcd for C16H25O2S 281.1575), indicating five degrees of unsaturation. The IR spectrum indicated the presence of a hydroxy group (3178 cm−1), an aromatic ring (1643, 1610, and 1577 cm−1) and a sulfoxide functional group (1036 cm−1) (Figure S2). The 1H NMR spectrum (Figure S3) of 1 exhibited resonances for three aromatic protons at δH 7.05 (d, $J = 7.7$ Hz, H-3), 6.84 (s, H-6), and 6.77 (dd, $J = 7.7$ Hz, 1.4 Hz, H-4), an olefinic proton at δH 5.46 (tq, $J = 7.4$ Hz, 1.2 Hz, H-8), one sp3 methine proton at δH 1.63 (m, H-11), six methylene protons at δH 3.94 (d, $J = 13.0$ Hz, H-15a), 3.85 (d, $J = 13.0$ Hz, H-15b), 2.18 (q, $J = 7.4$ Hz, H2-9), and 1.33 (m, H2-10), and four methyls at δH 2.46 (s, H3-16), 1.99 (s, H3-14), 0.92 (d, $J = 6.5$ Hz, H3-13), and 0.92 (d, $J = 6.5$ Hz, H3-12). The 13C NMR and HSQC data (Figures S4 and S5) of 1 showed 16 carbon signals, including four methyls (δC 38.1, 22.9, 22.9, and 17.1), three sp3 methylenes (δC 60.3, 39.5, and 26.9), one sp3 methine (δC 28.4), four sp2 methines (δC 130.9, 130.3, 122.2, and 118.1), and four quaternary carbons (δC 155.0, 134.7, 133.3, and 131.7) (Table 1). These data accounted for all 1H and 13C NMR resonances of 1, except for one unobserved hydroxyl group, one oxygen atom, and one sulfur atom. In the HMBC spectrum (Figure S7), the correlations from the aromatic protons H-6 to C-1, C-2, C-4, and C-15, from H-4 to C-2, C-3, C-5, C-6, and C-15, from H-3 to C-1, C-2, C-4, C-5, and C-7, and from the methylene protons H2-15 to C-4, C-5, and C-6 together with the 1H-1H COSY correlations (Figure S6) of H-3/H-4 (Figure 2) constructed the 1,2,5-trisubstituted benzene ring with the methylene carbon C-15 substituted at C-5. Other HMBC correlations from H-3 and H-9 to the quaternary carbon C-7 (δC 134.7), H-8 to C-2 and C-14, and from H3-14 to C-2, C-7, and C-8, combined with 1H-1H COSY correlations of H-8/H2-9/H2-10/H-11/H3-12/H3-13, indicated the presence of methylhept-2-en-2-yl group located at C-2 position of the benzene ring. The hydroxyl group was located at C-1 by default, which was supported by the chemical shift of C-1 (δC 155.0). The chemical shifts of the methyl group CH3-16 (δH/C $\frac{2.46}{38.1}$) and the methylene group CH2-15 (δH/C 3.94, $\frac{3.85}{60.3}$) indicated that both carbons were attached to a hetero atom. Considering the molecular formula of 1, the sulfinyl group deriving from the remaining one oxygen atom and one sulfur atom should be inserted between two carbons, C-15 and C-16, to form a methylsulfinyl substituent, which was further confirmed by the key HMBC correlations from H2-15 to C-16 and from H3-16 to C-15. Thus, the planar structure of 1 was established as shown (Figure 2). The E geometry of the olefin C-7/C-8 was assigned by the NOESY correlation (Figure S8) of H2-9 with H3-14 (Figure 2). The absolute configuration of the sulfur stereogenic center in 1 was determined by comparison of the experimental ECD spectrum of 1 with those of the time-dependent density functional theory (TDDFT) calculations at the B3LYP/6-311G (d,p) level performed on (SS)-1 and (RS)-1 (Figure S9). As a result, the trend of the experimental ECD spectrum of 1 was identical to that of the calculated curve for (SS)-1 (Figure 3), which indicated S configuration for the chiral sulfoxide of 1. The known compounds were identified to be aspergillusene B [2] [22], (−)-(R)-cyclo-hydroxysydonic acid [3] [23], penicibisabolane G [4] [24], (7S,11S)-(+)-12-hydroxysydonic acid [5] [25], 11,12-dihydroxysydonic acid [6] [26], expansol G [7] [27], (S)-sydonic acid [8] [28], aspergoterpenin C [9] [29], and aspergillusene A [10] [22], respectively, by comparison with corresponding data in the literature (Figure 4). ## 3.3. Antibacterial Activities of the Isolated Compounds Compounds 1–10 were evaluated for antibacterial activities against two phytopathogenic bacteria P. syringae and R. solanacarum using the broth microdilution method [30,31]. The MIC values of these compounds are shown in Table 2. Compound 5 showed modest antibacterial activity against P. syringae, with an MIC value of 32 μg/mL. Compounds 2, 7, and 8 showed modest antibacterial activity against R. solanacarum, with the same MIC value of 32 μg/mL. ## 3.4. Inhibition of Spore Germination of the Isolated Compounds Phytopathogenic fungi F. oxysporum and A. alternata are typical soil-borne and air-borne pathogens which caused huge losses of crops annually [32,33], and spore germination is an essential part of their disease cycle. The isolated compounds 1–10 were evaluated for their antifungal activities against the phytopathogenic fungi F. oxysporum and A. alternata using spore germination tests. Compounds 1, 2, 3, 5, 7, and 8 could inhibit the spore germination of F. oxysporum in the concentration range of 128–32 μg/mL (Figure 5A). Compounds 3 and 7 inhibited the spore germination of F. oxysporum, with EC50 values 54.55 and 77.16 μg/mL, respectively (Table 3). Among the test compounds, compound 8 strongly inhibited the spore germination of F. oxysporum, with an EC50 value of 1.85 μg/mL (Table 3). Furthermore, compound 8 was not going into a plateau phase at 1 μg/mL according to the curve (Figure 5A), revealing that it could inhibit the spore germination of F. oxysporum at a lower concentration (<1 μg/mL). In addition, compounds 2, 3, 7, and 8 also showed good inhibition against A. alternata spore germination in the range of 128–32 μg/mL (Figure 5B), with EC50 values of 34.04, 44.44, 26.02, and 46.15 μg/mL, respectively (Table 3). Further investigations showed that the spore germ tubes of A. alternata were vacuolated with the treatment of compounds 2, 3, 7, and 8 (Figure 5C and Figure S10). ## 3.5. Nematicidal Activity of the Isolated Compounds The nematicidal activity of the isolated compounds was assessed against the soil nematode M. incongnita J2s. Compounds 1–10 were firstly evaluated for toxic effects against the M. incongnita J2s at 24 h and 48 h with 800 μg/mL. Compounds 3, 7, and 8 exhibited significant nematicidal activity, and compound 3 was the most effective one (Figure 6). Then, we tested the time–concentration dependency of M. incongnita treated with target compounds 3, 7, and 8, which recorded the mortality at 12 h intervals from 24 to 60 h at the concentration range of 50–400 μg/mL (Figure 7B). Intriguingly, the nematicidal activity of compound 3 in these treatments was reduced, which might be caused by the treated concentration being close to its minimum lethal concentration. Additionally, the nematicidal activity of 8 became better than those of 3, 7, and ivermectin, with an LC50 value of 192.40 μg/mL, which was close to the abamectin positive control of 146.10 μg/mL at 60 h (Table 4). At the same time, nearly $80\%$ of the M. incongnita J2s treated with 400 μg/mL of compound 8 were dead (Figure 7B), whereas compounds 3 and 7 showed modest toxic effects at 200–400 μg/mL with 20–$60\%$ corrected mortality, and the toxic effects at 400 μg/mL were significantly higher than those of 200 μg/mL (Figure 7B). These results revealed that the minimum lethal concentration of compound 8 was much lower than those of 3 and 7, and the effective compounds exhibited time/concentration-dependent inhibition toward M. incongnita J2s. Furthermore, we observed that living nematodes showed different mobility, as they kept wiggling or stayed still during the observation time treated with different concentrations of compounds. This phenomenon revealed that the target compounds 3, 7, and 8 might possess paralytic ability toward nematodes. Compounds 3 and 7 could paralyze nematodes at 400–200 μg/mL, while compound 8 could paralyze nematodes at 50 μg/mL. Additionally, the appearance of the reversible paralytic processes (Table 5) could be caused by the drug resistance of different observed individuals. ## 3.6. Plausible Biosynthetic Pathways of the Phenolic Bisabolane Sesquiterpenoids Biogenetically, the phenolic bisabolane skeleton (a) of compounds 1–10 could be derived from mevalonate-farnesyl diphophate-bisabolane pathway, as proposed in Scheme 1. The key intermediate a underwent an oxidation reaction to give 10. Oxidation of C-15 of 10 afforded benzoic acid derivative b, which further underwent a series of oxidation, cyclization, reduction, and esterification to yield compounds 2–10. In addition, the methylthio radical (CH3S•), possibly formed during the in vivo sulfur metabolism pathways, could be trapped by the methyl radical (H3C•), which could be generated by oxidation of benzyl of intermediate a, resulting in the production of methyl thioether d. The oxidation of methyl thioether d finally generated the methylsulfinyl bisabolane sesquiterpenoid 1. ## 4. Discussion Fungal secondary metabolites have played a vital role in drug discovery [34]. With the tremendous chemical diversities and potent biological activities, some of the metabolites have great potential in agricultural applications, such as beauvericin and trichodermin [35,36]. Phenolic bisabolane sesquiterpenoids are a rare cluster of natural products, and most of them were obtained from marine fungi [26,27,29]. Structurally, phenolic bisabolane sesquiterpenoids were characterized by a para-alkylated benzene ring system and the structural variability of them was mainly caused by reduction, oxidation, and cyclization reactions of the side chain [37]. Meanwhile, the presence of the sulfoxide group was quite rare among phenolic bisabolane sesquiterpenoids, with only three compounds having been reported [37,38]. In this study, a rare sulfoxide containing phenolic bisabolane aspersydosulfoxide A [1] was isolated and identified. However, it did not show obvious activities in these bioassays. Compounds 2, 5, 7, and 8 showed selective antibacterial activities against the phytopathogenic bacteria P. syringae and R. solanacarum, and the difference in antibacterial activities might be determined by their different functional groups on their side chains. Spores are special forms in the fungal life cycle, and they possessed strong tolerance that can promote dissemination and keep long-term survival. Thus, compounds inhibiting spore germination could be developed into high-efficiency and low-toxicity drugs for preventing fungal diseases [39]. In our study, most of the phenolic bisabolane sesquiterpenoids inhibited the spore germination of F. oxysporum and A. alternata. Interestingly, the spore germ tubes of A. alternata were vacuolated with the treatment of compounds 2, 3, 7, and 8. Vacuolization could delay the spore germination progress of fungi [20]. Thus, we assumed that the phenolic bisabolane sesquiterpenoids 2, 3, 7, and 8 could inhibit the spore germination procedure of A. alternata by vacuolization of germ tubes. The root-knot nematode M. incognita is the main pest in tropical and subtropical regions that has caused great harm to many crops [40]. Second-stage juveniles are the most infective stage of M. incognita. They penetrated the root of the host and moved to the vascular cylinder through cortical tissue, then became sedentary [41]. Thus, controlling J2s might be an efficient management method for nematode disease. In previous studies, non-phenolic bisabolane sesquiterpenoids cheimonophyllons A–D and cheimonophyllal showed nematicidal activity [42], whereas the phenolic ones have not been investigated previously. In this study, the phenolic bisabolane sesquiterpenoids 3, 7, and 8 also exhibited nematicidal activity and limited the mobility of nematodes, indicating that the nematicidal activities of these sesquiterpenoids might depend on the groups of the aliphatic sidechain. ## 5. Conclusions In summary, one new sulfoxide-containing phenolic bisabolane sesquiterpenoid aspersydosulfoxide A [1] and nine known congeners (2–10) were isolated from the marine-derived fungus A. sydowii LW09. The absolute configuration of the sulfur stereogenic center in 1 was determined by ECD calculations. The biosynthetic pathways of compounds 1–10 were proposed. Some of the isolated compounds showed selective antibacterial activities against the phytopathogenic bacteria P. syringae and R. solanacarum, and inhibited the spore germination of the phytopathogenic fungi F. oxysporum and A. alternata. Meanwhile, it is possible that compounds 2, 3, 7, and 8 inhibited the spore germination procedure of A. alternata by vacuolization of germ tubes. The nematicidal activities of the phenolic bisabolane sesquiterpenoids 3, 7, and 8 were first reported. Our study not only expanded the chemical diversity of the phenolic bisabolane sesquiterpenoids, but also provided the potential lead compounds for anti-phytopathogenic drugs. ## Figures, Scheme and Tables Figure 1: Maximum likelihood analysis of several Aspergillus species based on ITS, BenA, and CaM sequences. Bootstrap values ≥$75\%$ were indicated at the nodes. The tree was rooted to A. aurantiobrunneus NRRL 4545. Figure 2: Key 1H-1H COSY, HMBC, and NOESY correlations of compound 1. Figure 3: Experimental ECD spectrum of 1 and the calculated ECD spectra of (SS)-1 and (RS)-1. Figure 4: Structures of compounds 1–10. Figure 5: Germination rates of F. oxysporum (A) and A. alternata (B) treated with the tested compounds. Error bars indicate ± standard deviations, calculated from three replicates. DMSO was used as negative control and chlorothalonil was used as positive control. (C) Microscopy images of A. alternata spores with different treatments: (I) negative control at 128 μg/mL DMSO; (II) positive control at 1 μg/mL chlorothalonil; (III) compound 8 at 128 μg/mL; (IV) compound 8 at 32 μg/mL. VA means germ tube vacuolization. Figure 6: Corrected mortality of M. incongnita J2s treated with the tested compounds at 800 μg/mL. DMSO was used as negative control with the same concentration. Plotted are mean + standard deviations; each treatment set three replicates. Compounds 3, 7, and 8 represented statistically significant differences (the Tukey test, p ≤ 0.001) between active ones and inactive ones, and 8 represented significant differences between 24 h and 48 h treated procedures (the Tukey test, p ≤ 0.05). Figure 7: (A) Microscopy images of nematodes treated with 50–400 μg/mL compound 8 at 60 h (I–IV); (V) negative control at 400 μg/mL DMSO; (VI) positive control at 400 μg/mL abamectin. (B) Corrected mortality of M. incongnita treated with different concentrations of the tested compounds in different observation times. Graphs were presented with mean + standard deviations. Asterisk above the bars indicated significant differences (the Tukey test, p ≤ 0.05; p ≤ 0.01; * p ≤ 0.001).* Scheme 1: The plausible biosynthetic pathways of compounds 1–10. TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 TABLE_PLACEHOLDER:Table 4 TABLE_PLACEHOLDER:Table 5 ## References 1. Brown J.K., Hovmøller M.S.. Aerial dispersal of pathogens on the global and continental scales and its impact on plant disease. Science (2002) 297 537-541. DOI: 10.1126/science.1072678 2. Strange R.N., Scott P.R.. Plant disease: A threat to global food security. Annu. Rev. Phytopathol. 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--- title: Association of Bioelectrical Impedance Phase Angle with Physical Performance and Nutrient Intake of Older Adults authors: - Sandra Unterberger - Rudolf Aschauer - Patrick A. Zöhrer - Agnes Draxler - Mirjam Aschauer - Benno Kager - Bernhard Franzke - Eva-Maria Strasser - Karl-Heinz Wagner - Barbara Wessner journal: Nutrients year: 2023 pmcid: PMC10057147 doi: 10.3390/nu15061458 license: CC BY 4.0 --- # Association of Bioelectrical Impedance Phase Angle with Physical Performance and Nutrient Intake of Older Adults ## Abstract In recent years, the phase angle (PhA) as a raw bioelectrical impedance analysis variable has gained attention to assess cell integrity and its association to physical performance in either sports-related or clinical settings. However, data on healthy older adults are scarce. Therefore, data on body composition, physical performance and macronutrient intake from older adults ($$n = 326$$, $59.2\%$ women, 75.2 ± 7.2 years) were retrospectively analyzed. Physical performance was evaluated by the Senior Fitness Test battery, gait speed, timed up and go and handgrip strength. Body composition was determined by the BIA and dual-energy X-ray absorptiometry (from a subgroup of $$n = 51$$). The PhA was negatively associated with the timed up and go test and age (r = −0.312 and −0.537, $p \leq 0.001$), and positively associated with the 6 min walk test, 30 s chair stand, handgrip strength, gait speed and physical performance score ($r = 0.170$–0.554, $p \leq 0.05$), but not protein intake ($r = 0.050$, $$p \leq 0.386$$). Hierarchical multiple regression analysis showed that especially age, sex, BMI, but also the PhA predicted the performance test outcomes. In conclusion, the PhA seems to be an interesting contributor to physical performance, but sex- and age-specific norm values still need to be determined. ## 1. Introduction Body composition plays a significant role in determining overall health, fitness and nutritional status. It is closely associated with various diseases, including cardiovascular diseases, diabetes, osteoporosis and various types of cancer [1,2,3]. As we age, physiological changes occur that impact body composition by lowering muscle mass and increasing body fat, whereby especially fat accumulation within muscles has been shown to be related to muscle weakness and poor function [4]. While sarcopenia is defined by the European Working Group on Sarcopenia in Older People as the loss of muscle mass, strength and function with ageing [5], frailty is a more general concept that goes beyond sarcopenia by including a range of physical, cognitive and social factors [6]. Several clinical scales and assessment methods exist for the diagnosis of sarcopenia and frailty [7]. Thereby, the assessment of physical function can be time-consuming or difficult, especially in the case of frailty, which is often complicated by comorbidities. In contrast, body composition is routinely determined by various methods [8] and, although debated, dual-energy X-ray absorptiometry (DXA) is widely considered as a gold standard for body composition assessment [9]. However, bioelectrical impedance analysis (BIA) is a promising non-invasive tool that can reliably estimate body fat mass, muscle mass, and hydration status in various clinical and non-clinical populations [10,11,12]. It has to be mentioned that BIA and DXA are based on different theoretical models that categorize the body into various compartments, and the interchangeable use of terms to describe these components often leads to confusion and misunderstandings when comparing them. The DXA method breaks down body composition into three components: fat mass, lean soft tissue and bone mineral content, while the BIA method provides only a two-component analysis of body composition, divided into lean body mass and fat mass. To compare these two methods, it is important to consider the difference in fat-free mass and compare the sum of lean soft tissue and bone mineral content for DXA with the lean body mass for BIA [13]. Most interestingly, a direct measure derived from the BIA, the phase angle (PhA), reflects the resistance and reactance of the body in response to the application of an external current. It is dependent on lean body mass and hydration status and, therefore, a decrease in muscle mass tends to decrease the PhA. The PhA can be quickly assessed, even at the bedside, and contrary to other BIA markers it is measured directly, avoiding errors attributable to regression equations [14]. Moreover, it has been shown that the PhA is associated with sarcopenia [15] and frailty [16,17], but data on the associations between raw BIA values and physical performance in an ageing population are scarce and inconclusive [18,19]. Furthermore, it has been shown that the PhA might be a useful screening tool to assess nutritional risk [20]. Interestingly, also nutritional behavior, such as the adherence to a Mediterranean diet [21] or higher dietary quality [22], was associated with the PhA in healthy populations. A very recent study suggests using the PhA assessment as a diagnostic tool to detect early changes in inflammatory parameters in response to a very low-calorie ketogenic diet [23]. It has been shown that protein intake is associated with muscle mass [24], but there is no information on whether protein (or macronutrient) intake is related to the PhA. Therefore, we aimed (a) to describe and validate sex-specific raw BIA values in an older population with DXA parameters, and (b) to determine the association between the PhA and physical performance, also taking macronutrient intake into consideration. ## 2. Materials and Methods This study is a secondary analysis of baseline data from three different randomized controlled trials, the Vienna Active Ageing Study [25], the NutriAging Protein Study [26] and the NutriAging Vitamin D Study [27], from which BIA and performance data are derived and which will be described in further detail in the next section. ## 2.1.1. Vienna Active Ageing Study The Vienna Active Ageing Study (VAAS) was conducted between 2011 and 2013 as a randomized controlled intervention study, with the aim being to investigate whether progressive resistance training with elastic bands over six weeks would influence physical performance in combination with and without nutrient supplementation. The trial was authorized by the Ethics Committee of the City of Vienna (EK-11-151-0811) and registered at ClinicalTrials.gov (NCT01775111) [25]. Participants comprised untrained, institutionalized older men and women over 65 years of age without severe health problems. The exclusion criteria were cognitive impairment (Mini-Mental State Examination score, MMSE < 23), chronic diseases that would not have allowed participation in sporting activities, serious cardiovascular diseases, diabetic retinopathy and regular use of cortisone-containing drugs. Baseline BIA data were available from 99 participants. Physical performance parameters included data from 30-s chair stand test and 6-min walk test ($$n = 95$$), handgrip strength ($$n = 88$$) and gait speed ($$n = 94$$). The 30-s arm curl and the timed up and go test were not assessed in this study (Figure 1). ## 2.1.2. NutriAging Protein Study The NutriAging Protein Study used a randomized, controlled, observer-blind trial design to investigate the impact of protein intake, with or without resistance training, on physical performance. The study population was randomly divided into the following groups: control group (observation only), recommended or high-protein intake plus resistance training groups. After the initial assessment, there were two phases, a six week nutritional observation/counselling phase, followed by an eight week phase in which resistance training was added to the nutritional intervention. Data were collected at the baseline, after 8 and 17 weeks. The trial was been approved by the Ethics Committee of the University of Vienna [00322] and registered at ClinicalTrials.gov (NCT04023513). The study duration was from July to December 2018 [26]. Included subjects were untrained community-dwelling women and men between 65 and 85 years. The following criteria led to exclusion: cognitive impairment (MMSE < 23), acute and chronic illnesses prohibiting resistance training, severe cardiovascular disease, diabetic retinopathy, manifest osteoporosis, anticoagulant or cortisone medications, a frailty index ≥ 3, or the need for walking aids. The BIA data were available for 129 persons and all physical performance parameters were also available for these participants (Figure 1). ## 2.1.3. NutriAging Vitamin D Study This trial was designed as a randomized, placebo-controlled, double-blind trial and examined the effect of vitamin D3 supplementation, with and without resistance training, on physical function. Participants were randomly assigned to a control group, a vitamin D3 daily group, or vitamin D3 monthly group. This study was also divided into two phases. The first phase included vitamin D3 or placebo supplementation, followed by the second phase, which included supplementation extended with resistance training. At the baseline, after the first phase and after the second phase (supplementation plus resistance training), physical performance parameters were collected. The Ethics Committee of the University of Vienna approved the study protocol [00390]. The study was registered at ClinicalTrials.gov (NCT04341818) and was conducted from February to July 2019 [27]. The inclusion criteria for the participants were as follows: women or men between the ages of 65 and 85 years, with an independent lifestyle and no cognitive impairment (MMSE > 23). Individuals were excluded from the study if they had: a 25(OH)D level >30 ng/mL before study entry, the need for walking aids, chronic medical conditions that contraindicate resistance training, serious cardiovascular disease, diabetic retinopathy, osteoporosis or osteopenia with vitamin D and/or calcium supplementation, renal disease, renal stones, parathyroid hormone level disorder, cardiac glycoside use, diuretic (thiazide) use, calcium level disorder, a frailty index ≥ 3, regular intake of cortisone or antibiotics (in the last six months) and regular resistance training (>1x/week) in the last six months before study entry. For 98 participants, the BIA data were available. Apart from the 30 s arm curl test ($$n = 97$$), all physical performance measures were available for the study population (Figure 1). ## 2.1.4. Participants’ Flow A total of 353 subjects, from all three studies, were included in these analyses. BIA data was not available for 27 people who were, therefore, excluded from the analyses, which finally included data from 326 subjects (Figure 1). ## 2.2. Outcomes For this secondary analysis, the PhA was considered as a primary outcome. Further outcomes were BIA resistance and reactance, as well as body composition estimates and physical performance tests, such as a 30 s chair stand, handgrip strength, 30 s arm curl test, timed up and go, gait speed and 6 min walk test. ## 2.3. Body Composition Body mass was measured to the nearest 0.1 kg and standing height to the nearest 0.1 cm in light clothes. The BMI (kg/m²) was calculated from body mass (kg) and height (m) measurements. A multifrequency (5, 50, 100 kHz) BIA system (Nutriguard-MS, Data Input, GmbH, Germany) was used to measure the PhA, resistance and reactance (standard placement of surface electrodes). To avoid any bias due to diet, all participants were evaluated in the morning after an overnight fast. A more detailed description is provided in the respective original studies from our research group [25,26,27]. However, resistance, reactance and the resultant PhA values were only derived from data using a frequency of 50 kHz, while estimates for lean body mass (extracellular plus body cell mass), total body water and body fat were derived from the multifrequency device software (NutriPlus software, Version 5.1, Data Input, GmbH, Germany). In addition, skeletal muscle mass (SM) was calculated using a population specific Equation [1]:SM (kg) = [(Ht²/R * 0.401) + (sex * 3.825) + (age * 0.071)] + 5.102[1] where Ht is height in cm, R is resistance in Ohm, sex (1 = for men and 0 = for women) and age is indicated in years [28]. For a subset of the population, whole body scans were performed using dual-energy X-ray absorptiometry (DXA, Hologic®, Hologic Inc., Bedford, MA, USA). Total and segmental fat-free mass (FFM, lean soft tissue plus bone mineral content) and fat mass (FM) were measured based on their X-ray attenuation properties [29]. To compare the FFM measured by the BIA and DXA, the lean body mass (including the extracellular and body cell mass) was used as the estimate for the BIA, while the estimate for the DXA was lean soft tissue plus bone mineral content. ## 2.4. Physical Performance Physical performance was assessed by the 30 s chair stand, 30 s arm curl and handgrip strength tests to measure the lower and upper extremity muscle strength, a 6 min walk test to assess aerobic endurance, as well as gait speed and timed up and go tests to measure agility and dynamic balance. The results from the 30 s chair stand, handgrip strength, gait speed and 6 min walk tests were used to calculate the physical performance score based on the weighted sum method. After scaling the test results into sex-specific Z-scores, a principal component analysis was conducted to obtain the loading value for each test. The performance score of each subject was calculated by multiplying the Z-scores by the corresponding loading value before summation [30]. ## 2.5. Assessment of Nutrient Intake To assess the macronutrient intake, the participants’ nutritional data were recorded via 24 h dietary recalls in a personal interview. The dietary intake data were recorded and analyzed using GloboDiet® software. The total energy intake (kcal), as well as the absolute (g/day) and relative per body weight (g/kg BW/d) intake of protein, carbohydrates and fat were used in this study [31]. ## 2.6. Assessment of Comorbidities During the medical examination, the following comorbidities were assessed: hyperlipidaemia, type 2 diabetes mellitus, osteoporosis, and a history of heart disease and cancer. According to World Health Organization criteria, obesity was defined as BMI ≥ 30 kg/m² and hypertension as systolic and diastolic blood pressure above $\frac{140}{90}$ mm Hg [32]. ## 2.7. Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics 27 (IBM Corporation, Armonk, New York, NY, USA) and R software (v4.1.1, R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were reported as mean ± standard deviation and categorical variables were presented as frequencies and percentages. The independent samples t-test and chi-square test were used to compare the sex differences. The relationships between the variables were determined using the Pearson’s product-moment coefficient (r). According to Pearson’s coefficient, the correlations were graded as weak (r < 0.3), moderate ($r = 0.3$–0.5), or strong (r > 0.5) [33]. Correlation matrix visualizations were conducted using the corrplot package in R. Bland–Altman plots were created to further examine the agreement between the BIA and DXA. Hierarchical multiple regression analysis was conducted using the 30 s chair stand, handgrip strength, 30 s arm curl, timed up and go, gait speed, 6 min walk test and physical performance score as dependent variables. Age, sex (female = 0, male = 1) and BMI were previously selected for the first regression model, based on their effects on performance, as described in the literature. The independent variables were inserted into the different regression models: model 1 consisted of age, sex and BMI (forced entry); model 2 and model 3 were derived from model 1 and the stepwise inserted variables resistance, reactance and/or PhA. All assumptions, error independence (Durbin–Watson values between 1–3), non-multicollinearity (tolerance values > 0.1; VIF values < 10), homoscedasticity (standardized residual values between −3 and +3) and non-influential cases (Cook’s distance values < 1) for multiple linear regression were tested for all models. p-values less than 0.050 were considered as significant. ## 3.1. Participants’ Characteristics The participants were 193 females and 133 males with a mean age of 75.2 ± 7.2 years. Compared to the male group, the female group was older, had lower weight, height, waist, arm and calf circumference. On the other hand, the male group had a smaller waist circumference and lower prevalence of obesity, hyperlipidemia and osteoporosis, when compared with the female group. Across the study, $47.5\%$ persons were overweight and $25.2\%$ were obese. As summarized in Table 1, women and men were comparable for BMI and incidence of hypertension, type 2 diabetes mellitus, and a history of heart disease and cancer. Absolute energy and macronutrient intake was higher in men than in women, although no relative difference in dietary intake was observed, except for fat intake. The BIA and physical performance parameters of the total study population, as well as the differences between the male and female participants, are shown in Table 2. For the raw values of the BIA, it was observed that men showed higher values for the PhA compared to women, while women had higher resistance and reactance ($p \leq 0.001$) values. Females were characterized by lower total body water, lean body mass, extracellular mass, body cell mass and SM and by higher body fat mass/percentage, as compared to males ($p \leq 0.001$). The physical performance parameters also showed differences between the male and female participants, except for the sex-specific physical performance score. Men completed more repetitions in the 30 s chair stand and 30 s arm curl, achieved higher values in handgrip strength, walking speed, distance covered in the 6 min walk test and were faster at the timed up and go in comparison to women ($p \leq 0.001$). ## 3.2. Agreement of Body Composition Parameters Measured by BIA and DXA In a subsample of 51 participants (46 females and 5 males) from the Vienna Active Ageing Study, body composition parameters were compared for agreement between the BIA and DXA. Pearson correlations for the BIA and DXA parameters are graphically displayed in Figure 2, whereas the detailed correlation coefficients and significance levels are shown in Table 3. The FFM (DXA) showed strong positive correlations with the device-derived BIA parameters: lean body mass ($r = 0.871$–0.917, $p \leq 0.001$), total body water ($r = 0.871$–0.917, $p \leq 0.001$), extracellular mass ($r = 0.690$–0.778, $p \leq 0.001$) and body cell mass ($r = 0.824$–0.890, $p \leq 0.001$). Body fat mass correlated weakly to moderately and positively with the FFM ($r = 0.276$–0.422 $p \leq 0.050$), while body fat percentage did not. In addition, the PhA showed significant moderate positive correlations with the FFM parameters: total FFM ($r = 0.425$, $$p \leq 0.002$$), FFM arm (left: $r = 0.$ 408, $$p \leq 0.003$$; right: $r = 0.415$, $$p \leq 0.002$$), FFM trunk ($r = 0.453$, $$p \leq 0.001$$) and FFM leg (left: $r = 0.344$, $$p \leq 0.013$$; right: $r = 0.350$, $$p \leq 0.012$$). The FFM head was not associated with the PhA ($$p \leq 0.198$$). In contrast to the PhA, resistance showed strong negative correlation with the total FFM (r = −0.665, $p \leq 0.001$) and segment specific FFM (r = −0.613 to −0.680, $p \leq 0.001$). The total FM (DXA), body fat mass and percentage (BIA) demonstrated significant strong positive correlations, $r = 0.907$ and $r = 0.816$, $p \leq 0.001$, respectively. However, there were no significant correlations between the total FM and the device-derived BIA parameters (all $p \leq 0.050$), with the exception of body cell mass ($r = 0.336$, $$p \leq 0.016$$). When using a population-specific equation to determine skeletal muscle mass, similar strong correlations were found in comparison to device-derived body cell mass: total FFM ($r = 0.877$, $p \leq 0.001$), FFM arms (left: $r = 0.852$, $p \leq 0.001$; right: $r = 0.877$, $p \leq 0.001$), FFM trunk ($r = 0.842$, $p \leq 0.001$) and FFM legs (left: $r = 0.802$, $p \leq 0.001$; right: $r = 0.845$, $p \leq 0.001$). Additionally, the agreement between the two methods was evaluated using Bland–Altman plots (Figure 3). The BIA results displayed good absolute agreement with the DXA for the assessment of either the FFM or FM. There was a significant mean difference between the FFM and lean body mass of −1.9 kg ($95\%$ CI −2.65, −1.06 kg). The difference between the FM and body fat mass accounted for +2.6 kg ($95\%$ CI 1.67, 3.59 kg). The limits of agreement were narrower for the FFM (−7.4 to +3.7 kg) as compared to the FM (−4.0 to +9.3 kg). The BIA tended to overestimate the FFM and to underestimate the FM in comparison to DXA. ## 3.3. Association between BIA Raw Parameters, Physical Performance, Age, BMI and Nutrient Intake In order to determine the relationships between the raw BIA parameters, physical function and other influencing factors, such as age, BMI and nutrient intake, a Pearson’s product-moment correlation matrix was created (Figure 4, Table 4). Across the total population, the PhA was negatively correlated with resistance (r = −0.293, $p \leq 0.001$) and positively correlated with reactance ($r = 0.524$, $p \leq 0.001$). A strong positive correlation was found between the PhA and the 6 min walk test ($r = 0.554$, $p \leq 0.001$), but a negative correlation was found between the PhA and age (r = −0.537, $p \leq 0.001$). The PhA moderately correlated with the 30 s chair stand ($r = 0.302$, $p \leq 0.001$), handgrip strength ($r = 0.488$, $p \leq 0.001$), gait speed ($r = 0.470$, $p \leq 0.001$), physical performance score ($r = 0.408$, $p \leq 0.001$) and timed up and go (r = −0.312, $p \leq 0.001$). Weak correlations were observed between the PhA and 30 s arm curl ($r = 0.170$, $$p \leq 0.011$$) and BMI (r = −0.122, $$p \leq 0.028$$), whereas protein intake did not correlate with the PhA ($p \leq 0.050$). Male population: A strong positive relationship was found for the phase angle and reactance ($r = 0.700$, $p \leq 0.001$). Age (r = −0.450, $p \leq 0.001$) and timed up and go (r = −0.324, $p \leq 0.001$) showed moderate negative correlations, while the 30 s chair stand ($r = 0.344$, $p \leq 0.001$), gait speed ($r = 0.317$, $p \leq 0.001$), 6 min walk test ($r = 0.482$, $p \leq 0.001$) and physical performance score ($r = 0.443$, $p \leq 0.001$) were positively correlated. Female population: For the female group, similar results were found. The phase angle had a strong positive correlation with resistance ($r = 0.678$, $p \leq 0.001$) and a negative correlation with age (r = −0.507, $p \leq 0.001$). Moderate associations were found with the handgrip strength ($r = 0.453$, $p \leq 0.001$), gait speed ($r = 0.379$, $p \leq 0.001$), 6 min walk test ($r = 0.449$, $p \leq 0.001$) and physical performance score ($r = 0.442$, $p \leq 0.001$). A weak correlation was reported between the phase angle and 30 s chair stand ($r = 0.217$, $$p \leq 0.003$$). Furthermore, in contrast to men, there was no significant correlation with the timed up and go among women ($p \leq 0.050$). The resistance, 30 s arm curl, BMI and protein intake showed no significant association with the phase angle for women and men ($p \leq 0.050$). ## 3.4. Hierarchical Multiple Regression Analyses Due to the absence of a significant correlation between the nutrient intake parameters and functional tests, they were not included in the multiple regression analyses. Hierarchical multiple regression analyses were performed to examine the impact on physical performance by entering independent variables as blocks into the models. Including sex, age and BMI as the first block significantly explained: $10.8\%$ of the 30 s chair stand, $77.5\%$ of the handgrip strength, $13.6\%$ of 30 s arm curl, $30.7\%$ of the timed up and go, $57.5\%$ of the gait speed, $68.2\%$ of 6 min walk test and $47.1\%$ of the physical performance score. As the second block, resistance, reactance and/or the PhA were stepwise included in further models. The addition of the PhA to the multiple regression model was statistically significant in the 30 s chair stand and resulted in an increase in R2 from 0.108 to 0.142, F[4, 306] = 12.607, $p \leq 0.001.$ When the PhA was added to the handgrip strength, the R2 increased significantly to 0.780 F[5, 305] = 220.890, $p \leq 0.001.$ In the timed up and go, the addition of the PhA led to a significant increase in the R2 to 0.320, F[4, 220] = 25.868, $p \leq 0.001.$ The gait speed also showed a significant increase in the R2 of 0.585, F[4, 312] = 109.820, $p \leq 0.001$, when the PhA was added to the regression model. The addition of the PhA in the physical performance score increased the R2 to 0.500, F[4, 309] = 77.334, $p \leq 0.001.$ Adding the PhA in the 6 min walk test resulted in a significant increase in the R2 of 0.701, F[4, 312] = 183.220, $p \leq 0.001$ and adding resistance increased the R2 to 0.714, F[5, 311] = 155.229, $p \leq 0.001.$ With the addition of resistance in the 30 s arm curl resulted in a significant increase in the R2 of 0.155, F[4, 220] = 10.110, $p \leq 0.001.$ Adding resistance in the handgrip strength resulted in a statistically significant increase in the R2 of 0.778, F[4, 306] = 272.191, $p \leq 0.001.$ Including reactance in the physical performance score led to a significant increase in the R2 to 0.509, F[5, 308] = 63.850, $p \leq 0.001$ (Table 5). The analysis resulted in the following equations for the physical performance tests and score (Equations [2]–[7]), where age is indicated in years, sex (1 = for men and 0 = for women), BMI is body mass index in kg/m2, PhA is phase angle in degree, R is resistance in ohm and Xc is reactance in ohm. 30 s chair stand (reps) = [(age * −0.016) + (sex * 0.351) + (BMI * −0.132) + (PhA * 0.885)] + 12.827[2] Handgrip strength (kg) = [(age * −0.508) + (sex * 13.702) + (BMI * −0.138) + (R * −0.015) + (PhA * 1.031)] + 69.060[3] Timed up and go (s) = [(age * 0.071) + (sex * −0.597) + (BMI * 0.065) + (PhA * −0.196)] + −0.122[4] Gait speed (s) = [(age * −0.036) + (sex * 0.358) + (BMI * −0.028) + (PhA * 0.091)] + 44.851[5] 6 min walk test (m) = [(age * −9.849) + (sex * 40.679) + (BMI * −11.397) + (PhA * 57.426) + (R * −3.704)] + 1443.476[6] PPscore [-] = [(age * −0.187) + (sex * −1.687) + (BMI * −0.196) + (PhA * 1.076) + (Xc * −0.054] + 17.116[7] The PhA did not contribute significantly to the regression model for the 30 s arm curl, but resistance did, which led to the following Equation [8]:30 s arm curl (reps) = [(age * −0.200) + (sex * 0.993) + (BMI * −0.151) + (R * −0.012)] + 40.573[8] ## 4. Discussion The aim of this study was to describe the sex-specific raw BIA parameters in an older population and to validate the agreement between the BIA and DXA parameters, as well as assess the associations between the PhA, physical performance and macronutrient intake. In agreement with the literature, the PhA of males was higher as compared to females, as a result of the lower reactance and resistance values of males [18,34,35,36]. It is known that the higher FFM and fluid volume of males are associated with a decrease in resistance, whereas their lower body fat mass results in lower reactance values [35]. When comparing the BIA-derived parameters to DXA, which is considered as a gold standard for measuring body composition [13], the PhA correlated moderately with the FFM and weakly with the FM. Interestingly, resistance correlated even stronger with the FFM, while reactance was neither correlated with the FM nor with the FFM. In addition to examining the relationship between the PhA, FFM and FM, the agreement between the BIA and DXA measurements for the FFM and FM was assessed using Bland–Altman plots. Our results showed that the BIA overestimated the FFM and underestimated the FM compared with DXA, which is in line with studies on middle-aged persons [37,38] and COPD patients [39]. For the practical application of the PhA, reference values and cut-off points are needed to assess the individual deviations from the population-based average. The reference values for healthy older adults (BMI of 19–25 kg/m²) range from 4.7 to 6.4° for women and 4.7 to 6.6° for men [34]. In our study, the average PhA value for women was at the lower end of the reference range, while that for men was in the lower third. This could be due to the slightly higher BMI in both sexes [40,41,42]. However, due to different sample characteristics and the variety of devices being used, various cut-off values are under discussion [18,34]. In a study of older intensive care patients, the cut-off value for low PhA was set at <4.8° [43]. For community-dwelling older persons with increased risk of incident disability, the cut-off value was set to ≤4.95° for men and ≤4.35° for women [44]. In another study, low PhA values of ≤4.1° were found to be determinants of frailty and sarcopenia in older adults [16]. Lower PhA values may indicate decreased cell integrity or cell death, whereas higher values can be attributed to greater cellularity (higher body cell mass relative to FFM), cellular integrity and cellular function [40,41]. Consequently, the PhA might reflect not only muscle mass, but also the muscles’ functional quality, explaining the relationship between the PhA and physical performance. This study confirmed the associations between the PhA and the 6 min walk test, 30 s chair stand, 30 s arm curl, timed up and go, gait speed, handgrip strength and physical performance score. These findings support the suggestion that the PhA could be a useful biomarker to estimate physical performance [18,45,46]. Besides functional performance, the PhA has been shown to correlate with muscle strength or power [47,48], knee extensor strength [49] and maximal torque of plantar and dorsal flexion [50], in middle-aged or older populations [51]. In order to prove whether the raw BIA parameters, the PhA and resistance and reactance, contribute to the prediction of physical performance, multiple regressions were computed. The PhA was identified as a predictor of the 6 min walk test, gait speed, timed up and go, 30 s chair stand, handgrip strength and physical performance score, whereas it did not contribute to the prediction of the 30 s arm curl test. It could be argued that there is a difference between the lower and upper body function, as muscle mass also differs between the lower and upper body [52]. Interestingly, age, sex and BMI with or without the addition of the PhA, resistance or reactance, explained a relatively high amount of the variability in the handgrip strength, gait speed and 6 min walk test, which was visibly lower for the 30 s chair stand, 30 s arm curl and timed up and go tests. A study conducted on elderly Koreans investigated the ability to predict functional test outcomes using independent variables, such as sex, age, BMI and body fat percentage instead of PhA. The results of the study showed similar coefficients of determination for hand grip strength (R² = 0.773) and timed up and go (R² = 0.384), while the value for the 30 s chair stand test (R² = 0.296) was higher as compared to our developed equations [53]. One possible reason for higher values in the linear regression model could be that a higher body fat percentage can limit mobility and flexibility, making it more difficult to stand up from a seated position. Furthermore, repeated standing up and sitting down during the 30 s chair stand test is biomechanically more demanding and requires more torque and range of motion in the lower limbs than walking [54]. This means that standing up and sitting down requires coordination of the trunk and lower limb movements and control of balance and stability, in addition to the muscular strength associated with the PhA [55,56]. Furthermore, this could also apply to the 30 s arm curl test, as it requires a high degree of coordination compared to handgrip strength. Although further validation studies may be required, predicting and not measuring physical performance could be useful in settings where it is difficult or time-consuming to conduct a variety of functional performance tests, and the inclusion of raw BIA values could improve the predictions. In addition to physical function, nutritional patterns are also an important factor in the health status of older people. However, in our study, no correlations were found between the PhA and the macronutrient intake parameter. The consensus statement from the European Society for Clinical Nutrition and Metabolism (ESPEN) recommends BMI to characterise malnutrition [57]. It has been shown that the standardized PhA is considerably lower in surgical patients [58] or advanced colorectal cancer patients [59] with impaired nutritional status, but the high proportion of overweight and obese participants, probably without severe malnutrition, could have masked this association. Regardless of the encouraging results, the limitations to this study must be considered. We have analysed the PhA at a single frequency (50 kHz) in this study. A multi-frequency BIA measurement allows for more accurate measurement and differentiation of the lean body mass, total body water, intracellular water and extracellular water, based on their different tissue penetration than a single frequency measurement [60]. Therefore, it would be interesting to measure/analyze the PhA at different frequencies in future studies to gain a more comprehensive understanding of the prognostic utility of the PhA. This approach could potentially contribute to a more accurate estimate of the PhA. In further studies or secondary analyses, it would be interesting to include persons at risk of being underweight and/or sarcopenia to obtain a more precise understanding of the impact of the phase angle in relation to both physical performance and nutritional status. Nevertheless, we suggest the PhA as an interesting parameter as it can provide information about body composition and cell integrity, both factors of which are important for muscle function. ## 5. Conclusions Higher PhA values are associated with better physical performance but are unrelated to macronutrient intake. The PhA seems to be an interesting parameter in the context of physical performance, as it is independent from the process of finding a suitable regression equation and might be useful for a broad range of populations and settings. In addition, the aspect of cell integrity is of particular interest, as the muscle cell and its contractile properties play an essential role in the context of physical fitness. Factors that can affect muscle cell integrity and, thus, physical performance include a lack of physical activity, age, certain diseases and probably, poor nutrition. In this study frail participants were excluded, but the PhA could be useful in situations where physical performance tests cannot be conducted. ## References 1. Després J.-P.. Body Fat Distribution and Risk of Cardiovascular Disease: An update. Circulation (2012) 126 1301-1313. DOI: 10.1161/CIRCULATIONAHA.111.067264 2. Pi-Sunyer X.. Changes in body composition and metabolic disease risk. Eur. J. Clin. Nutr. 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--- title: Purification and Structure Characterization of the Crude Polysaccharide from the Fruiting Bodies of Butyriboletus pseudospeciosus and Its Modulation Effects on Gut Microbiota authors: - Run Tian - Lu-Ling Wu - Hong-Fu Li - Zhi-Qun Liang - Pei-Hu Li - Yong Wang - Nian-Kai Zeng journal: Molecules year: 2023 pmcid: PMC10057200 doi: 10.3390/molecules28062679 license: CC BY 4.0 --- # Purification and Structure Characterization of the Crude Polysaccharide from the Fruiting Bodies of Butyriboletus pseudospeciosus and Its Modulation Effects on Gut Microbiota ## Abstract Polysaccharides from the species of Boletaceae (Boletales, Agaricomycetes, Basidiomycota) are economically significant to both functional foods and medicinal industries. The crude polysaccharide from Butyriboletus pseudospeciosus (BPP) was prepared, and its physicochemical properties were characterized through the use of consecutive experimental apparatus, and its impact on the gut microbiota of Kunming mice was evaluated. Analyses of the structure characteristics revealed that BPP was mainly composed of Man, Glc, and Gal, possessing the pyranose ring and β/α-glycosidic linkages. TG analysis exhibited that BPP had great heat stability. The SEM observation performed demonstrated that BPP appeared with a rough, dense, and porous shape. Through the BPP intervention, the serum and fecal biochemical index in mice can be improved obviously ($p \leq 0.05$). The abundance of beneficial microbiota in the BPP-treated group was significantly increased, while the abundance of harmful microbiota was significantly decreased ($p \leq 0.05$). Based on the Tax4Fun, we also revealed the relationship between the species of gut microbiota and showed that the high dose of BPP has significantly changed the functional diversities compared with those in other groups ($p \leq 0.05$). The results suggest that B. pseudospeciosus could serve as potential functional food or medicine. ## 1. Introduction Boletaceae (Boletales, Agaricomycetes, Basidiomycota) is a large family characterized by an umbrella-shaped fruiting body with dense tubes, occasionally with lamellae. It is distributed widely in the temperate, subtropical to tropical regions of the world [1,2,3,4]. In China, some species of Boletaceae were treated as medicinal fungi in “South Yunnan Medica”, and their efficacies included expelling pathogenic heat, enriching weakness, refreshing the brain, and improving blood circulation. Pharmacological studies have revealed that Boletaceae has antidiabetic, anticancer, immunoregulatory, and antioxidative activities [5,6,7]. Currently, the species of Boletaceae are regarded as promising functional food, and they have underwent an increasing number of investigations due to their delicious taste and versatile nutrients [8]. A variety of bioactive components of Boletaceae have been documented, such as polysaccharides, phenols, terpenoids, alkaloids, and sterols. Polysaccharide, as an aplenty substance in fungi of Boletaceae, has been recognized to generate pharmacological effects in several experimental studies. For example, Boletus edulis was proved to have antitumor effects since the polysaccharide restrains the proliferation of breast cancer cells (MDA-MB-231 and Ca761) and promotes apoptosis induced by mitochondrial pathways [9]. The polysaccharide of Leccinum duriusculum, in turn, exerts the immunoregulation ability by reducing the secretion of NO and immune factors of RAW 264.7 (IL-6 and TNF-α) [10]. Meanwhile, the polysaccharide from B. aereus also exhibits great antitumor activity and protects the immune organs [11]. Although other polysaccharides from fungi such as ganoderan [12], lentinan [13], and grifolan [14] have beneficial effects on gut microbiota, the effect of polysaccharides from boletes on regulating gut microbiota remains to be elucidated. Butyriboletus pseudospeciosus is a species of Boletaceae originally described from Yunnan Province, China [15], which has drawn increasing attention due to its edibility. Herein, the crude polysaccharide from the fruiting bodies of B. pseudospeciosus (BPP) is isolated, and its physicochemical properties are characterized. Moreover, the effects of BPP on the gut microbiota were studied. ## 2.1. Species Identification The voucher specimen (FHMU1391) was discovered to be clustered with the holotype of B. pseudospeciosus, with strong support (BS = $97\%$, PP = 1.00) (Figure S1). Moreover, our collections also matched well with the protologue of B. pseudospeciosus [3]. The morphological and phylogenetic evidence confirmed that our sample is B. pseudospeciosus. ## 2.2. Chemical Composition of BPP The crude polysaccharide from B. pseudospeciosus was prepared, and it was $4.20\%$ relative to the dried BPP samples (100 g). The contents of carbohydrates, protein, and uronic acid in BPP were $64.00\%$, $1.95\%$, and $1.19\%$, respectively (Table 1). The HPLC detection suggested that glucose (Glc), mannose (Man), and galactose (Gal) were the most abundant components in the compositions of BPP (Figure 1A), which is in line with the previous studies [7]. The results manifest that the polysaccharides from the fungi of the family Boletaceae could be composed of Glc, Man, and Gal mostly. ## 2.3. FTIR Analysis of BPP Fourier-transform infrared spectroscopy (FTIR) was used to analyze the glycosidic bond and functional groups of BPP [16]. BPP shared a strong absorption peak at 3412 cm−1 and a weak absorption peak at 2912 cm−1, which were assigned to the stretching peaks of O-H and C-H, respectively (Figure 1B). The intense absorption peak approximately at 1635 cm−1 could arise from the carbonyl group of the polysaccharide [7]. The absorption peak detected at 1413 cm−1 was due to the C-H variable angle vibration. Additionally, the absorptions at 1130 cm−1, 1080 cm−1, and 1051 cm−1 may be correlated to C-O-C and C-O-H stretching peaks, which are consistent with the pyranose ring structure [17]. The absorption peaks at 890 cm−1 and 804 cm−1 suggested the presence of β- and α-glycosidic bonds in BPP, respectively [18], whereas the peaks observed at 610 cm−1 could relate to the skeletal modes of pyranose [19]. ## 2.4. NMR Spectroscopy Analysis The 1H-NMR spectrum of BPP is shown in Figure S2A. Previous reports have reported that the crowded and narrow regions in the range of 3.0–5.5 were typical signals of polysaccharides [7,20], which are in line with our spectrum. As shown in Figure S2A, the anomeric hydrogen signals at 5.11 and 4.93 can be observed, respectively. Then, the anomeric hydrogen signals lower than 5 ppm corresponded to the β-pyranose unit mostly, while greater than 5 ppm corresponded to the α-pyranose unit [17,21], which suggests that BPP contains both α-pyranose and β-pyranose sugar units. The resulting spectrum is consistent with previous studies relating to the polysaccharides from the species of Boletaceae [7]. As shown in Figure S2B, we can find three carbon signals in the anomeric region (δ 103.51, 103.02, and 97.77) in the 13C NMR spectrum. In the previous documents [22,23], the chemical shifts in the pyranose residues in the 13C NMR spectrum were in the range of δ 100–106 for the β-configuration and δ 93–100 for the α-configuration. Thus, the results revealed that BPP contains both β-pyranose and α- pyranose. In addition, given that the uronic acid content is low, the absorption peak at δ 179 could relate to the presence of acetyl and ester group within polysaccharide by combining with Figure S3. The HSQC spectrum demonstrated the correlations between anomeric 13C signals and 1H signals. As shown in Figure S4, three cross peaks of δH/C $\frac{4.93}{98.1}$, $\frac{4.84}{103.3}$, and $\frac{4.61}{104.1}$ were observed in the HSQC spectrum, which confirmed the presence of β-configuration and α-configuration in the BPP [24]. ## 2.5. Thermal Stability of BPP The thermal stability of the crude polysaccharide was detected using thermogravimetric (TG) analysis. BPP underwent a slight weight loss ($14.62\%$) around 131.5 °C (Figure S5), probably owing to the evaporation of free water and bound water. The initial thermal degradation temperature of BPP was up to around 200 °C, suggesting that BPP maintains good thermal stability. When the temperature increased from 200 °C to 562 °C, the weight loss reached a maximum of $83.39\%$. ## 2.6. Scanning Electron Microscope (SEM) Analysis SEM was extensively used to detect the surface morphological characters of BPP [25]. The surface of BPP was rough, and it exhibited a dense and porous shape, as observed by SEM (Figure 2), indicating it may benefit the application of BPP as drug carriers [26,27]. The microstructure of the BPP was different from those of other mushrooms, such as Ganoderma lucidum, Hericium erinaceus, and Calocybe indica. The polysaccharide of G. lucidum has entirely intact and compact structures in a spongy form, H. erinaceus polysaccharide exhibited flakiness with smooth surfaces, and the C. indica polysaccharide has a smooth surface and no discernible network [28,29,30]. ## 2.7. BPP Influences Viscera Weight Compared with the K group (spleen index: 0.19 ± 0.01; thymus index: 0.86 ± 0.10), the spleen index and thymus index of the other groups increased, but these indexes in the PH (0.26 ± $0.03\%$, 1.11 ± 0.08), G (0.26 ± 0.03, 1.11± 0.22), and PHG (0.27 ± 0.04, 1.12 ± 0.19) were significantly higher than those in the K group ($p \leq 0.05$). There was no significance in the liver index among the groups except for the PHG group, and the liver index of all mice increased across all times (Table 2). ## 2.8. Biochemical Analyses of Feces and Serum The pH value of the treatment groups decreased gradually from 0 to 2 weeks. Meanwhile, a significant pH decrease was observed in PL, PH, G, PLG, and PHG ($p \leq 0.01$) (Table 3). Therefore, BPP exhibited a great power to decrease the pH value in both low and high doses groups, which is in accordance with the previous literature that plantain polysaccharide could reduce the pH value when in vitro fermented [31]. A significant decrease in the ammonia content in the PL and PH groups was found compared with the K group (Table 3), which indicates that BPP can reduce the ammonia content in the gut. The biochemical analyses are presented in Table 4. In the treatment groups, the total content of diamine oxidase (DAO) decreased significantly ($p \leq 0.01$) compared with the K group. Thus, we can speculate that BPP improves gut health by reducing the gut pH value. BPP can significantly decrease the DAO and ET content in the serum. Traditionally, it has been argued that ammonia, as a microbial metabolite in the gut, had potential poisonous effects on host health [32], and the amount of the DAO and ET in the serum inversely correlates with the permeability and integrity of the intestine [33,34]. ## 2.9.1. BPP Influences Gut Microbiota Diversity In the present study, the influence of BPP on the gut microbiota was examined using the bacterial 16S rRNA sequence. A total of 4,695,001 pairs of reads were obtained from the sequences of the fecal samples, with an average of 104,333 reads per sample. The average length of all sample sequences was mainly between 410 and 430 reads, which indicates that our results meet the requirements. The sample dilution curve (Figure S6A) and rank abundance curve (Figure S6B) tended to be gentle, indicating that the sampling is sufficient and our data volume maintains large enough for subsequent analysis. The flower diagram of OTUs analysis indicated that a total of 318 OTUs were contained in all experimental groups. Twenty-four unique OTUs were in the K group, 11 in the G group, 28 in the PL group, 11 in the PH group, 23 in the PLG group, and 3 in the PHG group, respectively (Figure S7). Therefore, the OTUs value of PL, PLG, and K groups are similar, indicating the microbiota compositions of these three groups are similar. The OTUs values of the G, PH, and PHG groups are similar and lower than that of the K group, which suggests that high-dose administration of BPP could reduce the number of species of gut microbiota. The alpha diversity was presented in our studies (Figure 3). The gut microbiota diversity could be assessed by alpha diversity analysis, including the Chao one, ACE, Shannon, and Simpson index [35]. The results demonstrated that the Chao one and ACE indexes of PH, PLG, PHG, and G are higher than those of the K group, but only PH showed a significant difference from the K group ($p \leq 0.05$). Therefore, it can be inferred that the diversity of the gut microbiota was significantly changed under the high dose of BPP treatment. Principal coordinated analysis (PCoA) was used to study the distinctions of microbiota for each group. The two principal axes demonstrated a $92.34\%$ variation (PC1: $62.73\%$ and PC2: $29.61\%$) (Figure 4A). The microbiota of the PH and PLG groups exhibited a longer distance from the PL, PHG, and G groups, suggesting that the PH and PLG groups have a greater effect on the gut microbiota diversity. The gut microbiota of the PL and K groups are similar, suggesting that the low dose of BPP does not markedly change the diversity of the gut microbiota. In addition, the Unweighted Pair–group Method with Arithmetic Mean (UPGMA) analysis demonstrated that the PH and PLG groups clustered together, farther away from the K group (Figure 4B). The UPGMA analysis is in accordance with the result of the PCoA analysis, indicating that the PH and PLG groups could change gut microbiota diversity. ## 2.9.2. BPP Influences Gut Microbiota Composition The influences of BPP on the gut microbiota composition at the phylum, class, order, family, genus, and species levels were studied. The gut microbiota composition of the PH group was obviously changed compared with the K group. At the phylum level (Figure 5A), Firmicutes, Bacteroidota, Proteobacteria, and Verrucomicrobiota were the most abundant types of gut microbiota in the K group, which are consistent with the previous study [36]. After 2 weeks of high dose of BPP treatment, the relative abundance of Firmicutes increased significantly from $33.75\%$ to $56.20\%$ ($p \leq 0.01$), while the relative abundance of Verrucomicrobiota decreased significantly from $23.93\%$ to $2.35\%$ ($p \leq 0.01$) (Figure 5B). The ratio of Bacteroidota/Firmicutes decreased obviously compared with the K group. Previous reports have shown that the polysaccharides could ameliorate chronic pancreatitis by reducing the ratio of Bacteroidota/Firmicutes and improve DSS-induced ulcerative colitis by enhancing the abundance of Firmicutes [37,38]. At the class level (Figure 5C), the abundance of Bacilli in the PH group ($38.19\%$) was significantly higher than that of the K group ($12.69\%$) ($p \leq 0.01$), while the abundance of Verrucomicroiae in the PH group ($2.35\%$) was significantly lower than that of the K group ($23.93\%$) ($p \leq 0.05$) (Figure 5D). Additionally, at the order level (Figure 5E), the gut microbiota of the PH group consisted of Bacteroidales, Lactobacillales, Clostridia UCG-014, and Verrucomicrobiales. Lactobacillales, which accounted for $12.41\%$ of the K group, drastically increased to $37.48\%$ in the PH group ($p \leq 0.01$) (Figure 5F). Verrucomicrobiales dramatically decreased from $23.93\%$ (in the K group) to $2.35\%$ (in the PH group) ($p \leq 0.05$). Additionally, Li et al. [ 39] also found that the TC (total cholesterol), TG (triglyceride), and LDL-C (low-density lipoprotein cholesterol) in HFD-induced SD rats could be reduced by inhibiting some microbiota, such as Verrucomicrobiales. At the family level (Figure 6A), the gut microbiota of the PH group mainly consisted of Lactobacillaceae, Muribaculanceae, and Akkermansiaceae. As can be seen in Figure 6B, the abundance of Lactobacillaceaein in the PH group ($37.42\%$) was significantly higher than that of the K group ($12.40\%$) ($p \leq 0.01$), while the abundance of Akkermansiaceae in the PH group ($2.35\%$) was significantly lower than that of the K group ($23.93\%$) ($p \leq 0.05$). It was noted that the proliferation of Lactobacillaceae in the gut could be associated with the treatment of obese diabetics, DSS-induced colitis, and colorectal cancer in mice [40,41,42]. At the genus level (Figure 6C), the abundance of Lactobacillus and Acinetobacter in the PH group increased significantly ($p \leq 0.01$), while the abundance of Akkermansia decreased significantly ($p \leq 0.05$) (Figure 6D) compared with the K group. Increasing evidence has revealed that Lactobacillus, a kind of key probiotic, exerts benefits to ulcerative colitis and protects the cardiovascular system by resisting pathogens or modulating cytokine secretion [43,44]. At the species level (Figure 6E), the gut microbiota of the treatment groups is mainly composed of Akkermansia muciniphila and Lactobacillus murinus. It can be seen from Figure 6F that the abundance of L. murinus increased significantly from $12.10\%$ (in the K group) to $36.94\%$ (in the PH group) ($p \leq 0.01$), while the abundance of A. muciniphila decreased significantly from $23.93\%$ (in the K group) to $2.35\%$ (in PH group) ($p \leq 0.05$). The previous report demonstrated that Lactobacillus murinus could be used as a useful probiotic to improve animal health and reduce the risk of gastrointestinal disorders [45], indicating that BPP could maintain intestinal health by improving the proliferation of L. murinus. In conclusion, we have analyzed the influences of all treatments on the gut microbiota at different levels, and the high dose of BPP has significantly changed the microbial communities in the gut compared with other treatment groups ($p \leq 0.05$). Therefore, BPP could have a potential prebiotic potency by modulating the composition and abundance of the gut microbiota. ## 2.9.3. BPP Influences Gut Microbiota Functions Tax4Fun was implemented to predict the gene content of the gut microbiota to demonstrate the varied functional diversities [46]. A total of 35 metabolic pathways were annotated, of which signal transduction, metabolism of co-factors and vitamins, glycan biosynthesis and metabolism, energy metabolism, nucleotide metabolism, and amino acid metabolism were the six most abundant pathways (Figure 7). Moreover, in the functional analyses (Figure 8), the high dose of BPP significantly changed the functional diversities and metabolism pathways more than that of the other groups ($p \leq 0.05$). The BPP treatment regulated gut microbiota functions, indicating that a high dose of BPP may impose potential and beneficial effects on human diseases by regulating the gut microbiota. In addition, the high dose of BPP obviously upregulated the genes that were responsible for lipid metabolism, nucleotide metabolism, membrane transport, translation, and xenobiotics biodegradation and metabolism ($p \leq 0.05$), which is similar to previous reports [47,48], suggesting that BPP could regulate the metabolism of the gut microbiota. Collectively, functional predictions via Tax4Fun demonstrate that BPP may modulate the gut micrology by regulating those bacteria to change their metabolites and cellular processes. ## 3.1. Materials and Chemicals The fruiting bodies of the mushroom used in this study were purchased from the market of Kunming, Yunnan Province of China. The voucher specimen (FHMU1391) was preserved in the Fungal Herbarium of Hainan Medical University (FHMU), Haikou City, Hainan Province, China. Techniques of specie identification, including morphological and molecular phylogenetic analyses, followed the methods of Zeng et al. [ 2], Chai et al. [ 3], and Chai et al. [ 49]. Three DNA sequences [nuc 28S rDNA D1-D2 domains (28S) (MH879687), nuc translation elongation factor 1-α gene (TEF1) (MH879716), and internal transcribed spacer 1 and 5.8S ribosomal RNA gene (ITS) (MH885349)] from the voucher specimen were deposited in GenBank. Diamine oxidase (DAO) and endotoxin (ET) assay kits were purchased from Mlbio Technology Co., Ltd. (Shanghai, China). Monosaccharide standard samples were all bought from Sigma-Aldrich (Steinheim, Germany); other chemicals were of analytical grade and mostly manufactured by Xilong Scientific Co., Ltd. (Guangdong, China). ## 3.2. Preparation of the Crude Polysaccharide The dried fruiting bodies of B. pseudospeciosus were ground into powder (40 meshes). Then, 100 g of powder was extracted with boiling water at a ratio of 1:25, followed by an ultrasonic-assisted extraction at 50 °C for 15 min and extracted with hot water at 85 °C for another 3 h. The above procedure was repeated twice. After extraction, the sample solution was centrifuged for 15 min at 4000 r/min; the combined filtrate was concentrated to one-fourth of its initial volume with a rotary evaporator under decompressed pressure at 50 °C and then deproteinized with Sevag reagent. Finally, the crude polysaccharide of B. pseudospeciosus was obtained by a four-fold volume of $95\%$ ethanol precipitation, dialysis (molecular weight cutoff at 3.5 kDa) in distilled water, and vacuum lyophilization. The total carbohydrate, protein, and uronic acid content of the polysaccharide were determined by Tian et al. [ 7] and Yang et al. [ 50]. ## 3.3. Monosaccharide Composition Analysis The monosaccharide composition was measured using high-performance liquid chromatography (HPLC) equipped with a DAD detector. The column was C18 column (250 mm × 4.6 mm, 5 µm, Agilent 1100, Santa Clara, CA, USA), the injection volume was 5 µL, and the flow rate was 1 mL/min. Mobile phase A was 100 Mm PBS buffer (pH = 6.7), and mobile phase B was acetonitrile. The detection wavelength was set at 250 nm, and the column temperature was maintained at 30 °C. ## 3.4. Infrared Spectrum (IR) Analysis Dried BPP (1 mg) and KBr powder (100 mg) were mixed and ground to make the pellet. FT-IR was used to assess the absorption spectrum of the sample with the wavenumber range of 4000–400 cm−1. ## 3.5. Thermal Stability of BPP The thermal gravimetric (TG) analyses of BPP were conducted using a TGA/DSC3 + instrument (Mettler Toledo, Zurich, Switzerland). The BPP samples were placed on the Al2O3 crucible under an air atmosphere. Additionally, the temperature was increased from 25 °C to 800 °C at 10 K/min. ## 3.6. NMR Spectroscopy Analysis BPP (60 mg) was dissolved in deuterium oxide (D2O) and frozen at −20 °C for 24 h, followed by thawing at room temperature, and the above procedure was performed three times. Then, the samples were re-dissolved with D2O and placed in an ECZ400S 400 MHz NMR instrument (JEOL, Tokyo, Japan) to measure the 1H NMR,13C NMR, DEPT-135, and HSQC spectra at room temperature. ## 3.7. Scanning Electron Microscope (SEM) Analysis The dried BPP was coated with gold, and its morphological features were recorded by a Quanta FEG 250 SEM system (FEI, Columbia, SC, USA) at the accelerating voltage of 10.0 kV. ## 3.8. Animal Study All of the experimental procedures were followed in accordance with the guidelines for the Care and Use of Laboratory Animals of the National Institutes of Health. Kunming mice were purchased from Skbex Biotechnology Co., Ltd. (Anyang, China), license number SCXK (Yu) 2020-0005, all of which were male, 6–8 weeks old, and weighing 20–30 g. The mice were housed in a temperature-controlled environment (20–25 °C) with a 12 h daylight circle. The mice were adapted to the environment for one week and could freely take in standard chow and sterilized water. After acclimation for one week, the mice were randomly divided into six groups ($$n = 7$$ per group): the normal group (K), the low dose (370 mg/kg) of the BPP group (PL), the high dose (740 mg/kg) of BPP group (PH), the positive (800 mg/kg spore powder of G. lingzhi) group (G), the (370 mg/kg of BPP + 800 mg/kg spore powder of G. lingzhi) group (PLG), and the (740 mg/kg of BPP + 800 mg/kg spore powder of G. lingzhi) group (PHG). BPP and the spore powder of G. lingzhi were dissolved in distilled water, and each group was administered via intragastric administration (0.3 mL). The mice in the normal group were supplied with the same volume of distilled water. After two weeks of feeding, all of the animals fasted for 8–12 h. On the 15th day, the serum samples were prepared from mice blood collected from the eye sockets, followed by centrifuge at 4000 r/min for 8 min. The fresh feces in the colon were collected and snap-frozen with liquid nitrogen until use. The liver, spleen, and thymus were excised and weighed, and the organ index was measured by the following formula: organ index (%) = organ weight/body weight × $100\%$. The schematic graph of the experimental procedure is shown in Figure 9. ## 3.9. Serum and Feces Biochemical Analysis ELISA kits (Mlbio Technology Co., Ltd., Shanghai, China) were used to quantitate the diamine oxidase (DAO) and endotoxin (ET) levels in the serum. Additionally, the ammonia concentration of feces was measured according to the description given by Joseph et al. [ 51]. Additionally, the pH values were determined by Jin et al. [ 52]. ## 3.10. Fecal Flora Genomic DNA Extraction and Amplicon Sequencing The genomic DNA from the fecal flora of each group was extracted following the instructions of the IMBS DNA Extraction Kit (TIANGEN, Beijing, China), and the purity and concentration of the extracted DNA were detected by $2\%$ (w/v) agarose gel electrophoresis. Total genomic DNA was processed by the Servicebio Technology Co., Ltd. (Wuhan, China), and it was followed as described by Sang et al. [ 12]. In brief, V3–V4 variable regions of the bacterial 16S rRNA gene were amplified by PCR with specific primers (515F and 806R). Subsequently, the PCR amplification products were homogenized and quantified, and they were purified by Qiagen Gel Extraction Kit (Germantown, MD, USA). Sequencing libraries were constructed and loaded using TruSeq DNA PCR-Free Sample Preparation Kit from Illumina (San Diego, CA, USA) according to the manufacturer’s instructions and sequenced with Illumina NovaSeq 6000 platforms. ## 3.11. Data Analysis and Bioinformatics The reads of each sample were merged and assembled by Fast Length Adjustment of SHort reads (FLASH) software [V1.2.7, http://ccb.jhu.edu/software/FLASH (accessed on 18 November 2020)], and the given sequence was called Raw Tags. Then, the high-quality tag data (Clean Tags) was obtained by using the Quantitative Insights Into Microbial Ecology (QIIME) software (V 1.9.1) to filter the spliced Raw Tags. These clean tags were compared with the reference database to detect the chimera sequences that were removed, and then effective tags were obtained for the subsequent bioinformatics analysis. Sequence analyses were displayed by Uparse software (V7.01001), and sequences with ≥ $97\%$ similarity were assigned to the same operational taxonomic units (OTUs), in which representative sequence was screened to obtain homologous species information and abundance distribution. The abundant group of each sample at all levels, including phylum, class, order, and family, were analyzed. Both QIIME software (1.9.1) and R software (2.15.3) were used to analyze the sample diversity (α and β-diversity). Tax4fun was used to predict the functions of the microbiota, and the predicted genes and their functions were aligned to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. ## 3.12. Statistical Analysis All of the statistical analyses were conducted using SPSS 16.0 software. The data were obtained as mean ± SEM (standard error of the mean), and the one-way ANOVA was used to compare the groups to each other. Significance was recorded at $p \leq 0.05$ and $p \leq 0.01$ levels. Additionally, other diagrams were drawn by Origin 2021 V9.8.0. ## 4. Conclusions The crude polysaccharide of B. pseudospeciosus (BPP) was prepared and characterized in this study. BPP is a dense, porous polysaccharide with a pyranose ring that exhibits good heat stability. BPP could increase the thymus and spleen index, indicating that BPP could be used as a potential immunoregulation food. BPP can reduce the intestinal pH value, which may be due to the production of short-chain fatty acids (SCFAs). 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--- title: Relationships between Easily Available Biomarkers and Non-Dipper Blood Pressure Pattern in Patients with Stable Coronary Artery Disease authors: - Andrei Drugescu - Mihai Roca - Ioana Mădălina Zota - Alexandru-Dan Costache - Maria-Magdalena Leon-Constantin - Oana Irina Gavril - Radu Sebastian Gavril - Teodor Flaviu Vasilcu - Ovidiu Mitu - Cristina Mihaela Ghiciuc - Florin Mitu journal: Life year: 2023 pmcid: PMC10057299 doi: 10.3390/life13030640 license: CC BY 4.0 --- # Relationships between Easily Available Biomarkers and Non-Dipper Blood Pressure Pattern in Patients with Stable Coronary Artery Disease ## Abstract Introduction. Chronic inflammation plays an essential role in the pathophysiology of both arterial hypertension (HTN) and coronary artery disease (CAD), and is more pronounced in individuals with a non-dipper circadian blood pressure (BP) pattern. A non-dipping BP pattern is in turn is associated with increased cardiovascular morbi-mortality, and a higher risk of atherosclerotic events. Neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR) and platelet to lymphocyte ratio (PLR) are readily available predictors of systemic inflammation and cardiovascular risk. The purpose of our study is to evaluate whether NLR, MLR and PLR can be used as cost-effective predictors of a non-dipping blood pressure pattern in hypertensive patients with stable CAD. Materials and Methods: We performed a cross-sectional retrospective analysis that included 80 patients with hypertension and stable CAD (mean age 55.51 ± 11.83 years, $71.3\%$ male) referred to a cardiovascular rehabilitation center. All patients underwent clinical examination, 24 h ambulatory blood pressure monitoring (ABPM) and standard blood analysis. Results: Baseline demographic characteristics were similar in both groups. Patients with non-dipper pattern had significantly higher NLR (median = 2, IR (2–3), $p \leq 0.001$), MLR (median = 0.31, IR (0.23–0.39), $p \leq 0.001$) and PLR (median = 175, IR (144–215), $p \leq 0.001$) compared to dippers. Conclusion: Our results suggest that MLR and PLR are inexpensive and easily accessible biomarkers that predict a non-dipping pattern in hypertensive patients with stable CAD. ## 1. Introduction The global burden of hypertension (HTN) is increasing worldwide, partly due to the current obesity pandemic and to the increased prevalence of sedentarism and unhealthy lifestyle choices. Apart from it being a disease per se, HTN is also considered a major modifiable risk factor for cardiovascular morbidity and mortality [1]. Medical progress has facilitated the diagnosis of chronic HTN and has developed a wide range of pharmacological and non-pharmacological instruments that are effective in controlling blood pressure (BP) values. However, treatment adherence and BP control are below optimal in Romania and in most of Eastern Europe, as shown by recent studies [2]. The most recent European Society of Cardiology (ESC) guidelines have revised the standard HTN diagnostic protocol. Current standard is that the patient should undergo repeated office BP measurements, as well as home-based blood pressure monitoring (HBPM) or ambulatory blood pressure monitoring (ABPM). ABPM is a readily available, noninvasive instrument that assesses circadian BP variability and characterizes the individual dipping status. Current guidelines recommend an increased use of ambulatory devices, since ABPM is a more reliable tool for HTN diagnosis and control assessment, but also for BP profile characterization. This is useful in predicting HTN-mediated cardiovascular risk and target organs damage [3]. BP values follow a circadian pattern, being higher during daytime and normally decreasing by 10–$20\%$ at nighttime [4]. Individuals who exhibit this physiological decline are known as “dippers”, while those who present a blunted decrease in nighttime systolic BP values (0–$10\%$) are considered “non-dippers”. Approximately $25\%$ of hypertensive patients exhibit a non-dipping BP profile, which is favored by the presence of diabetes, renal disease, autonomic neuropathies and old age [5]. BP values during the night are also more important in the assessment of cardiovascular risk, compared to daytime BP, as is decreased blood pressure variability registered on ABPM measurement [6,7,8]. Interestingly, an extreme dipping pattern (a reduction in SBP above $20\%$ during nighttime) does not impact cardiovascular risk until the age of 70. However, above this age, an extreme dipping pattern is associated with a four-time increase in cardiovascular risk [9]. Similarly to the ESC guidelines, the most recent American College of Cardiology and American Heart Association (ACC/AHA) guidelines on hypertension also recommend an increased use of ambulatory blood pressure monitoring in order to accurately characterize circadian BP profile [10,11,12]. Current research supports the fact that HTN and coronary artery disease (CAD) both have inflammatory components [13,14,15]. Persistently elevated cytokine concentrations promote endothelial dysfunction and impaired vasodilation, explaining the association between chronic inflammation, HTN and accelerated atherosclerosis [16]. Moreover, previous studies have documented a more pronounced inflammatory response in both hypertensive and normotensive individuals that present a non-dipping BP pattern [17,18,19]. In order to further investigate the inflammatory pathways in cardiovascular diseases, one of the most facile methods is the platelet to lymphocyte ratio (PLR). Though its initial use was that of a prognostic biomarker in oncological patients [20,21], its role has since extended in the field of cardiology, in pathologies such as heart failure [22,23,24], acute coronary syndromes [25,26,27,28,29], atrial fibrillation [30], deep venous thrombosis [31] or in patients undergoing cardiovascular rehabilitation programs [32]. MLR has recently emerged as a sensitive inflammatory marker with prognostic valences in oncology [33,34], preeclampsia [35], COVID-19 [36] and CAD [37]. Apart from PLR and MLR, the neutrophil to lymphocyte ratio (NLR) is a parameter with ease of determination and use in cardiovascular diseases [38,39,40]. Two previous studies showed that elevated NLR and PLR can predict a non-dipper status in hypertensive patients. As such, we hypothesized that NLR, MLR and PLR could be a useful tool in distinguishing dipper versus non-dipper BP profile in hypertensive patients with stable CAD [41,42]. NLR, MLR and PLR, are all accessible composite ratios that combine different inflammatory parameters, which may be able to provide additional information regarding the immunological pathogenesis of BP variability [43]. ## 2. Materials and Methods We performed a retrospective cross-sectional study of all hypertensive patients with stable CAD referred for cardiac rehabilitation between January 2020 and June 2021 in the Cardiovascular Unit of the Clinical Rehabilitation Hospital (Iasi, Romania), a nationally accredited clinic specialized in phase II-III cardiovascular rehabilitation [44]. Our study sample included hypertensive patients aged 18 or older, previously diagnosed with stable CAD and who underwent ABPM upon admission. Patients with ACS during the prior 12 months, anemia, paroxysmal, persistent or permanent atrial fibrillation, moderate or severe valvulopathy, decompensated congestive heart failure, or any other severe chronic disorder except CAD were excluded from our study. Furthermore, in line with the scope of our study, patients with acute or recent (past 30 days) infections were excluded from the analysis. All patients tested negative for COVID-19 (PCR) upon hospital admission. ABPM results and demographic, clinical, and biological data were obtained from official medical records. All patients enrolled in the study were under optimal therapy, according to current European treatment guidelines [3,45]. High blood pressure (HTN) was defined as current BP lowering therapy, resting systolic blood pressure (SBP) ≥ 140 and diastolic blood pressure (DBP) ≥ 90 mmHg, average BP/24 h ≥ $\frac{130}{80}$ mmHg, daytime BP average ≥ $\frac{135}{85}$ mmHg and nighttime BP average ≥ $\frac{120}{70}$ mmHg [3]. HTN was classified in grade 1 (SBP 140–159 mmHg and/or DBP 90–99 mmHg), grade 2 (SBP 160–179 mmHg and/or DBP 100–109 mmHg) and grade 3 (SBP ≥ 180 mmHg and/or DBP ≥ 110 mmHg) according to current guidelines [3]. Stable CAD was defined as the presence of typical angina pectoris and/or positive stress test, previous coronary revascularization or history of an acute coronary syndrome more than 1 year prior to current hospital admission [45]. Obesity was defined as a body mass index (BMI) ≥30 kg/m2. Diabetes was defined as a previous diagnosis of diabetes, current antidiabetic therapy, fasting glucose ≥ 126 mg/dL obtained on two separate occasions or glycosylated hemoglobin (HbA1c) ≥ $6.5\%$ [46].In line with hospital internal protocol, all blood samples were fasting blood samples collected in the morning upon admission, by qualified medical providers, and were processed in the same day in the hospital’s internal laboratory. Complete blood count was processed using the Pentra DF Nexus Hematology System® (Horiba Healthcare, Kyoto, Japan). Biochemistry was processed using the Transasia XL 1000 Fully Automated Biochemistry Analyzer (Transasia Bio-Medicals Ltd., Mumbai, India). We collected the following biomarkers: platelet, neutrophil, lymphocyte and monocyte count, low-density lipoprotein (LDL), high-density lipoprotein (HDL), HbA1c, erythrocyte sedimentation rate (ESR) and C reactive protein (CRP). We calculated NLR using the absolute neutrophil (N) and lymphocyte (L) values by the following formula: NLR = N/L. We calculated PLR using the absolute platelets (P) and lymphocyte (L) values, by the following formula: PLR = P/L. We calculated MLR using the absolute monocyte (M) and lymphocyte (L) values, by the following formula: MLR = M/L. All patients underwent 24 h ambulatory BP monitoring using DMS-300 ABP (DM Software, Stateline, NV, USA). Automatic BP measurement were obtained every 30 min during daytime (07:00—23:00), and every 60 min during nighttime (23:00—07:00). Patients were instructed to remain silent and still during each automatic BP measurement. An ABPM recording was regarded valid and included in the analysis if it encompassed at least $70\%$ successful BP recordings. Average 24 h, daytime and nighttime SBP and DBP were extracted from the ABPM report. BP dipping was computed by the following equation: (%) 100 × [(daytime SBP—nighttime SBP /daytime SBP]. A normal BP dipping index was defined as a 10–$20\%$ decrease in average nocturnal SBP compared to the average diurnal SBP. A non-dipping pattern was defined by a 0–$0.9\%$ decrease in average nocturnal SBP pattern compared to the average diurnal SBP. After assessing BP dipping index, we divided our initial study population into two subgroups: patients with a normal dipping pattern versus patients with a non-dipping pattern. ## Statistical Analysis We analyzed the normality of distribution of continuous data using the Shapiro–Wilk test. Continuous variables with normal distribution are presented as mean ± standard deviation (SD). Non-normally distributed continuous variables are presented as median with interquartile range. Categorical variables are listed as number of cases (N) with percent frequency (%). An independent samples T-test was applied to compare continuous variables with normal distribution. A non-parametric Mann–Whitney’s U test was used to compare non-normally distributed continuous variables. A p value < 0.05 was considered the threshold for statistical significance. Variables with $p \leq 0.05$ in the descriptive analysis were included in the multivariate logistic regression model, to assess the independent predictors of non-dipper pattern. The results are presented as odds ratio (OR) with $95\%$ confidence intervals (CIs). Statistical analysis was performed in SPSS 20.0 (Statistical Package for the Social Sciences, Chicago, IL, USA). The study received approval from the Review Board/Ethics Committee of the Clinical Rehabilitation Hospital (Iasi, Romania) ($\frac{28567}{21}$ December 2020) and of University of Medicine and Pharmacy “Gr. T. Popa” Iasi and complied with the Declaration of Helsinki. Informed consent was considered unnecessary, due to the retrospective design of this research (retrospective database analysis). ## 3. Results Our study included 80 patients (57 males, 23 females) with a higher prevalence of grade 3 HTN ($53\%$). Table 1 shows clinico-demographic characteristics and laboratory findings of the 80 analyzed patients and univariate analysis of the two subgroups according to dipping status. Age, the distribution of the 3 HTN grades and the prevalence of cardiometabolic comorbidities (body mass index (BMI), diabetes, LDL level, HDL level and HDL to LDL ratio) were similar among the two subgroups. Our analysis included 36 patients with dipping pattern and 44 patients with non-dipping pattern. Among the hematological parameters, the PLR, NLR and MLR were significantly higher in the non-dipping subgroup compared to the dipping subgroup ($p \leq 0.001$, Figure 1, Figure 2 and Figure 3). CRP and ESR were also higher in patients with non-dipper pattern; however, the difference was not statistically significant. All patients were under lipid-lowering and antiplatelet therapy. One third of patients ($34\%$) were under nitrate treatment. All patients used previous prescribed antihypertensive medication during ABPM. Angiotensin-converting enzyme inhibitor (ACEi)/ angiotensin receptor blocker (ARB), beta-blockers and thiazide-like diuretics were most frequent and their use was balanced between the two subgroups. Only $12\%$ of patients were treated with central alpha antagonists. Calcium antagonist use was significantly more frequent in patients with a dipper circadian profile, compared to non-dippers ($$p \leq 0.001$$). The prevalence of monotherapy and dual antihypertensive therapy was slightly increased in the non-dipper pattern subgroup, but the difference between groups did not reach statistical significance. Details are shown in Table 2. In a logistic multivariate model, PLR, MLR and calcium antagonist use remained significant predictors of non-dipper pattern (Table 3). Although NLR and lymphocyte count were significant predictors of non-dipper pattern in univariate analysis, their statistical significance was lost after inclusion in the multivariable regression model. ## 4. Discussion The primary result of this analysis was that MLR and PLR were elevated in non-dippers compared to dippers in patients with HTN and associated CAD. Both ESC and AHA HTN guidelines promote a more extensive use of ABPM in hypertensive patients in order to assess their circadian BP pattern. A non-dipping BP pattern is associated with elevated risk of atherosclerotic events and HTN-mediated target organ damage, accelerated CAD progression and an increased likelihood of obstructive sleep apnea [47,48,49]. Previous studies have documented increased inflammatory markers in both hypertensive and normotensive patients with a non-dipping BP profile [17]. Atherosclerosis is a dynamic low-grade inflammatory process [50,51,52,53] and routine inflammatory biomarkers are used for both acute and long-term cardiovascular risk stratification in CAD patients [10,13,54]. Neutrophils and monocytes are innate immune cells that are involved in the initiation and later activation of adaptive immunity, which plays a pivotal role in the pathology of hypertension and vascular injury [55]. While genetic studies have demonstrated that monocyte/macrophages are essential for angiotensin II-induced BP elevation and subsequent vascular dysfunction, neutrophils seem to play a more indirect role, promoting cardiovascular injury by activation of B and T lymphocytes [55]. In this context, the current study showed that the relationship between two inexpensive, routinely used inflammatory biomarkers and a non-dipper circadian BP pattern is maintained in the context of stable CAD association. Endothelial dysfunction, a cause and effect of HTN, is preceded by alterations in the expression of cytokines and of endothelial cell receptors, as well as dysregulation in vascular smooth muscle, platelet and monocyte function [56]. Lymphocyte to monocyte ratio (LMR) was negatively associated with the prevalence of HBP in a recent cross-sectional study [57]. MLR was also a mortality predictor in COVID-19 patients [36]. Furthermore, MLR and PLR were associated with coronary artery ectasia severity in CAD patients [37]. More importantly, it was recently postulated that LMR is a novel marker for BP variability and HTN-mediated target organ damage in primary and secondary HTN in children [58]. NLR reflects vascular parietal inflammation with cardiovascular prognostic implications. NLR is a predictor of cardiovascular events and mortality and was associated with the extent of coronary atherosclerotic lesions in patients with stable CAD [51,52]. NLR levels are significantly correlated with an increased risk of developing hypertension [59,60] and is increased up to $72\%$ in non-dipper hypertensive patients compared to dippers [41]. Kılıçaslan et al. showed that NLR is correlated with BP variability in both hypertensive and normotensive patients and suggested that elevated NLR is a predictor of increased HTN-mediated adverse cardiovascular events [61]. In a recent retrospective cohort, NLR and PLR were proposed as easily accessible markers of a non-dipper circadian profile in hypertensive patients [42]. Moreover, NLR was also associated with a reverse dipper pattern and exhibited a negative correlation with the decline rate of nocturnal systolic BP and diastolic BP [61]. Another study showed that NLR is elevated in subjects with resistant HTN compared to patients with controlled HTN [62]. Increased platelet activation plays a major role in initiation and progression of atherosclerotic lesions, and is associated with an elevated risk of plaque thrombosis. As such, PLR has been studied as a promising dual marker that reflects both inflammatory status and the extent of atherosclerosis [28,63]. Some studies showed that PLR is elevated in subjects with a non-dipping circadian profile [42,64,65,66]. Furthermore, PLR, but not NLR, was demonstrated to be an independent predictor of non-dipper circadian profile in a previous cohort of 166 hypertensive patients [41]. In another cross-sectional study, normotensive non-dipper patients had elevated PLR, similar to that of dipper hypertensive individuals, both higher than in dipper normotensive controls [67]. Although elevated CRP values have been previously associated with resistant and non-dipping essential hypertension [68], in our study the values of CRP and ESR were similar in patients with dipping and non-dipping circadian profile. High-sensitive C-reactive protein and carotid artery intima–media thickness are more sensitive short- and long-term prognostic cardiovascular biomarkers, but are too expensive to be routinely used in clinical practice [69]. Some classes of antihypertensive drugs (especially ACE inhibitors, ARB, b-blockers, and, to a lesser extent, calcium channel blockers [70]) possess anti-inflammatory effects shown by their ability to lower CRP levels. Several studies have focused on the “anti-inflammatory capacity” of the beforementioned antihypertensive classes. A study conducted by Fulop and colleagues showed that renin–angiotensin–aldosterone system inhibitors were more effective in reducing CRP levels than other antihypertensive drugs [71]. Nebivolol, a selective ß1-blocker, was studied by Fici et al. in a double-blind randomized trial. The authors reported that nebivolol not only decreases blood pressure values, but also modulates vascular microinflammation amelioration and reduces NLR in a manner independent and different from metoprolol [72]. Karaman et al. reported that valsartan (ARB) and amlodipine (calcium antagonists—dihydropyridines) were efficient in reducing NLR after 12 weeks of treatment in patients with newly diagnosed HTN [73]. A fixed dose combination of valsartan and amlodipine administered in a non-dipper hypertensive cohort, and compared to the same two antihypertensive drugs administered separately, showed a significant improvement in circadian blood pressure variation pattern in the polypill subgroup [74]. Besides careful medication selection, nighttime dosing of long-acting antihypertensive preparations demonstrated similar effects on nocturnal BP reduction and dipping rhythm restoration [75]. Despite emerging studies, the clinical impact of the anti-inflammatory properties of different classes of antihypertensive drugs is still insufficiently understood. Beyond medication, modification of traditional risk factors (BMI, smoking, and sedentary lifestyle) that affect inflammation becomes more of an issue in not only the control of HTN but also in decreasing comorbidities (CAD) and total cardiovascular risk. Obesity, diabetes and the related autonomic dysfunction are associated with a higher prevalence of non-dipping status. Furthermore, an impaired glucose metabolism also favors systemic inflammation, explaining why NLR and PLR were previously demonstrated to be useful predictors of prediabetes and diabetes mellitus [5,76]. However, both BMI and the prevalence of diabetes had similar values in the two analyzed subgroups. In our study, both NLR, MLR and PLR values were found to be higher in patients with a non-dipper pattern. On the other hand, CRP did not significantly vary between subgroups. ACEi/ARBs were the most frequently prescribed antihypertensive classes, and had a similar distribution between our two subgroups. However, calcium antagonist use was significantly lower in the non-dipper HTN. To summarize, NLR, MLR and PLR are simple, readily available, inexpensive, and noninvasive parameters, which lately emerged as potent inflammatory and oxidative stress biomarkers. Because NLR, MLR and PLR are ratios, they are less prone to bias/variations with dehydration, over-hydration and blood specimen handling than other individual blood parameters taken separately [41,55]. Although current hypertension guidelines recommend an increased use of ABPM, the investigation has its own limitations [77]: nighttime discomfort, patient reluctance, potential movement artifacts, as well as relatively limited availability of the device in general practice. On the other hand, NLR, MLR and PLR can be obtained from a simple blood count, which is routinely performed in all medical and surgical specialties. As such, the value of these inflammatory biomarkers could provide an additional argument for ABPM referral. To the best of our knowledge, our study is the first to analyze NLR, MLR and PLR levels in hypertensive patients with stable CAD. As inflammation plays a pivotal role in both hypertension and atherosclerosis, previous studies that analyzed the relationship between NLR, MLR, PLR and circadian BP variation excluded patients with CAD. Further studies should compare inflammatory biomarkers in hypertensive CAD patients with at least two different control groups: normotensive CAD patients and hypertensive patients without CAD. In our study MLR and PLR values, but not NLR values, were independent predictors for BP variability. The present study has some limitations. Principally, it is a single-center retrospective analysis that included a limited number of subjects and lacked the aforementioned control groups. Other important limitations included the use of a single determination of NLR, MLR and PLR and the lack of consideration for hsCRP, carotid intima–media thickness and other inflammatory biomarkers. Furthermore, some antihypertensive drugs possess anti-inflammatory properties and could therefore influence CRP, MLR and PLR. Although the use of different antihypertensive classes was generally balanced between subgroups, heterogeneity remained regarding the dose and the particular agent used in each patient, as well as regarding BP optimal control. For this reason, we chose not to include hemodynamic data in our study and to limit our statistical analysis to dipping versus non-dipping pattern. Although all patients tested negative for COVID-19 upon hospital admission, we did not perform COVID-19 antibody tests to accurately exclude any prior COVID-19 infection. ## 5. 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--- title: Repair and Mechanism of Oligopeptide SEP-3 on Oxidative Stress Liver Injury Induced by Sleep Deprivation in Mice authors: - Xin Hou - Chong Yi - Zekun Zhang - Hui Wen - Yufeng Sun - Jiaxin Xu - Hongyu Luo - Tao Yang journal: Marine Drugs year: 2023 pmcid: PMC10057301 doi: 10.3390/md21030139 license: CC BY 4.0 --- # Repair and Mechanism of Oligopeptide SEP-3 on Oxidative Stress Liver Injury Induced by Sleep Deprivation in Mice ## Abstract To investigate the effects of bonito oligopeptide SEP-3 on the repair of liver damage and regulation of liver biorhythm in sleep-deprived mice (SDM), C57BL/6 male mice were subjected to sleep deprivation by modified multi-platform water environment method, and were given different doses of bonito oligopeptide SEP-3 in groups. To determine the liver organ index, liver tissue-related apoptotic protein levels, Wnt/β-Catenin pathway-related protein expression levels, serum alanine transaminase (ALT), glutamicum transaminase (AST), glucocorticoid (GC), and adrenocorticotropin (ACTH) content in each group of mice, four time points were selected to examine the mRNA expression levels of circadian clock-related genes in mouse liver tissue. The results showed that low, medium, and high doses of SEP-3 significantly increased SDM, ALT, and AST ($p \leq 0.05$), and medium and high doses of SEP-3 significantly reduced SDM liver index and GC and ACTH. As SEP-3 increased the apoptotic protein and Wnt/β-Catenin pathway, mRNA expression gradually tended to normal ($p \leq 0.05$). This suggests that sleep deprivation can cause excessive oxidative stress in mice, which can lead to liver damage. Additionally, oligopeptide SEP-3 achieves the repair of liver damage by inhibiting SDM hepatocyte apoptosis, activating liver Wnt/β-Catenin pathway, and promoting hepatocyte proliferation and migration, and suggests that oligopeptide SEP-3 is closely related to repair of liver damage by regulating the biological rhythm of SDM disorder. ## 1. Introduction The high pressure of modern society, rich nightlife, and Internet addiction had led to an increasing incidence of sleep disorders. It is reported that about $38.5\%$ of Chinese people suffer from sleep disorders, and the number of women with sleep disorders is 1.5~2.0 times that of men [1]. Studies have shown that sleep deprivation can significantly increase the expression of 8-OHDG in the liver of the body, cause DNA disruption in hepatocytes, and then lead to apoptosis of hepatocytes, abnormal biochemical indicators such as alanine aminotransferase (ALT) and total bilirubin (TBIL), and impair cognitive and physical functions [2,3,4]. Li et al. [ 5] found that sleep deprivation not only induced oxidative stress response in rats, produced a large number of reactive oxygen species (ROS) to attack hepatocytes through mitochondrial consumption of oxygen, resulting in damage to hepatocytes, but also enhanced autophagy of hepatocytes by inhibiting the AKT/mTOR signaling pathway, thus leading to apoptosis of hepatocytes. Xing et al. [ 6] found that 72 h sleep deprivation can induce abnormal expression of various core clock genes in rat liver at both transcription and translation levels, and then lead to disorder of liver biological rhythm. Some studies have shown that His-Gly-Pro-Hyp-Gly-Glu (TGP5), Asp-Gly-Pro- Lys-Gly-His (TGP7), and Met-Leu-Gly-Pro-Phe were obtained by enzymatic hydrolysis of bonito scale gelatin Gly-Pro-Ser (TGP9), and other oligopeptides showed a high scavenging ability of the DPPH radical, hydroxyl radical, and superoxide anion radical, indicating that the skipjack enzymatic peptide had strong antioxidant capacity in vitro [7]. Previous studies in our group showed that many peptides extracted from aquatic by-products have good antioxidant activity, such as GEYGFE, PSVSLT and IELFPGLP extracted from Siberian sturgeon, which reduce the content of reactive oxygen species (ROS) and malondialdehyde (MDA) by regulating the endogenous antioxidant enzymes of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px), showing significant cytoprotective effects on HUVECs [8]; YEGDP and WF extracted from blue mussels can protect HUVECs from H2O2 damage by increasing SOD, GSH-Px and NO levels, reducing ROS and malondialdehyde content [9]; Wang et al. [ 10] hydrolyzed oligopeptides such as GHHAAA, PHPR, SVTEV, VRDQY, and SMDV obtained from bonito spleen by enzymatic hydrolysis technology and acted on H2O2-induced HUVECs, and found that they could improve the activities of antioxidant enzymes (such as SOD) and GSH-Px in mice. The content of ROS and MDA was decreased. The oligopeptide SEP-3 prepared by our research group using bonito waste showed that it could significantly improve the oxidative stress damage caused by sleep deprivation [8]. The repair level of liver injury is related to various physiological and biochemical indexes of liver. Therefore, in this paper, mice were deprived of sleep for 72 h by the modified multi-platform water environment method, and then SEP-3 was used for intervention. The viscera index, the contents of ALTAST, GC, and CTH in serum, the expression levels of apoptosis-related proteins and Wnt/β-Catenin pathway-related proteins in liver tissue, and the mRNA expression levels of clock-related genes in liver tissue were determined. The aim was to explore the repair effect and mechanism of skipjack oligopeptide SEP-3 on liver injury in sleep-deprived C57BL male mice. ## 2.1.1. Effects of SEP-3 on Liver Index in Sleep-Deprived Mice To explore the effect of SEP-3 on liver damage caused by sleep deprivation, we measured the level of liver index after 72 h of sleep deprivation, as shown in Figure 1. Compared with the normal group, the liver index of SD group was significantly decreased ($p \leq 0.05$). The liver index of the three groups treated with SEP-3 was higher than that of the SD group, and increased in a dose-dependent manner, but there was no significant difference between the LSEP-3 (Low SEP-3, 20 mg/kg) group and the SD group ($p \leq 0.05$), and the LSEP-3 group was significantly different from the MSEP-3 (Middle SEP-3, 40 mg/kg) and HSEP-3 (High SEP-3, 80 mg/kg) groups ($p \leq 0.05$). However, there was no significant difference between the MSEP-3 group and HSEP-3 group ($p \leq 0.05$). Although the liver index of mice in each administration group was lower than that in normal group, there was no significant difference between the normal group and HSEP-3 group ($p \leq 0.05$). The above results showed that sleep deprivation for 72 h induced liver atrophy in mice, impaired liver function, and led to a significant reduction in liver index. Guo Xiaolei et al. [ 11] found that liver index was significantly reduced and liver atrophy occurred in rats with sleep deprivation, which was similar to the results of this study. However, high-dose SEP-3 administration can significantly improve the liver index of SDM and has a strong effect on liver tissue repair. ## 2.1.2. Effect of SEP-3 on the Expression Levels of Serum ALT and AST in Sleep-Deprived Mice The effects of SEP-3 on SDM liver function indexes were investigated by detecting the activities of ALT and AST in mouse serum, and the results were shown in Figure 2. Compared with the normal group, the activities of ALT and ASL in serum of the SD group were significantly increased ($p \leq 0.05$). The activities of serum ALT and AST in the LSEP-3, MSEP-3, and HSEP-3 groups were 19.19, 18.87, 18.06 U/L and 59.21, 47.43, 36.87 U/L, respectively, which were significantly lower than those in the SD group (78.39 and 25.31 U/L, $p \leq 0.05$). There was no significant difference in serum ALT activity among all groups ($p \leq 0.05$), but it was significantly higher than that of the normal group ($p \leq 0.05$). The serum AST activity of mice in each administration group showed a dose-dependent decreasing trend, but the difference was significant only between the LSEP-3 group and the HSEP-3 group ($p \leq 0.05$). Although the AST activity of the three administration groups was higher than that of the normal group, there was no significant difference in serum AST between the HSEP-3 group and the normal group ($p \leq 0.05$). Chu et al. [ 12] found that after sleep deprivation, the macroscopic weight reduction, gross morphological changes, and the significant increase of ALT and AST activities in the liver of rats indicated that sleep deprivation could lead to liver injury, which was similar to the results of our study. These results indicated that SEP-3 administration could effectively inhibit the abnormal changes of serum ALT and AST levels in mice caused by sleep deprivation, suggesting that SEP-3 had a repair effect on liver injury; HSEP-3 especially had a strong repair ability. ## 2.1.3. Effects of SEP-3 on Serum GC and ACTH Levels in Sleep-Deprived Mice To explore whether liver injury caused by sleep deprivation causes abnormal expression of GC and ACTH, we detected the serum GC and ACTH contents of mice at ZT6, ZT12, ZT18, and ZT24 time points, and the results are shown in Figure 3. At all time points, the serum GC and ACTH expression levels of SD group were the highest, the normal group was the lowest (except for HSEP-3 group in ZT6-ZT12), and the rest of the groups were between the two groups. The difference between the SD group and the normal group was significant ($p \leq 0.05$). Between ZT6-ZT12, the contents of GC and ACTH in the serum of mice in the normal group showed an increasing trend, but the contents of GC and ACTH in the serum of mice in the model group showed a decreasing trend. The contents of GC and ACTH in the serum of mice in the LSEP-3 and MSEP-3 groups increased slowly, which was significantly different from that in the normal group ($p \leq 0.05$). The content of ACTH in HSEP-3 group showed a downward trend, contrary to that in normal group, but the contents of GC and ACTH in serum of the HSEP-3 group were similar to that in the normal group, and there was no significant difference ($p \leq 0.05$). In the other time periods, the changes of GC and ACTH contents in serum of mice in each group were basically consistent. Compared with the SD group, the contents of GC and ACTH in serum of the administration group were decreased in a dose-dependent manner, and there was a significant difference between the HSEP-3 group and the SD group ($p \leq 0.05$), but no significant difference between the HSEP-3 group and the normal group ($p \leq 0.05$). ## 2.2.1. Effect of SEP-3 on Expression of Apoptosis-Related Proteins in Liver Tissue of Sleep-Deprived Mice To explore the repair mechanism of SEP-3 on SDM liver injury, the expression levels of liver apoptosis-related proteins Bax, Cleaved caspase-3, and Bcl-2 were detected in this study, and the results were shown in Figure 4. Compared with the normal group, the expression levels of Bax and Cleaved caspase-3 protein in liver tissue of the SD group and each administration group were significantly increased ($p \leq 0.05$), and the expression level of Bcl-2 protein was significantly decreased ($p \leq 0.05$). Compared with the SD group, the expression levels of Bax and Cleaved caspase-3 protein in the livers of mice in the administration group were decreased in a dose-dependent manner, the expression level of Bcl-2 protein was increased in a dose-dependent manner, and the differences were significant ($p \leq 0.05$). The expression of Bax and Cleaved caspase-3 protein in mouse liver was negatively correlated with the dose ($p \leq 0.05$), and the expression of Bcl-2 protein in mouse liver was positively correlated with the dose. However, Bcl-2 protein expression was significantly different between the LSEP-3 group and the HSEP-3 group ($p \leq 0.05$). Bcl-2 is an anti-apoptotic protein, mainly distributed on both sides of the inner and outer membrane of mitochondria, and plays a key role in promoting cell survival and inhibiting cell apoptosis [13]. Bax is a pro-apoptotic protein, which is mainly distributed in the cytoplasm and partially adsorbed on the surface of mitochondria. It can not only change the structure of the mitochondrial membrane and affect the permeability of the mitochondrial membrane, but also induce the release of cytochrome C and activate Caspase-3 protease, thereby causing apoptosis [14]. These results indicate that SEP-3 can inhibit the apoptosis of mouse hepatocytes induced by sleep deprivation by upregulating the level of while Bcl-2, downregulating the level of Bax, and inhibiting the activity of Caspase-3 protease. ## 2.2.2. Effect of SEP-3 on Wnt/β-Catenin Pathway-Related Protein Expression in Liver Tissue of Sleep-Deprived Mice To further explore the repair mechanism of SEP-3 on SDM hepatocyte injury, the expression levels of Wnt/β-Catenin-pathway-related proteins β-Catenin and c-Myc in liver tissue were detected in this paper, and the results are shown in Figure 5. Compared with the normal group, the expression levels of β-Catenin and c-Myc protein in liver tissue of the SD group and each administration group were significantly decreased ($p \leq 0.05$), and the differences among groups were also significant ($p \leq 0.05$). The protein expressions of β-Catenin and c-Myc in the liver of mice in all administration groups were increased in a dose-dependent manner, and were significantly higher than those in the SD group ($p \leq 0.05$). β-Catenin is a biomarker to detect whether the Wnt/β-Catenin pathway is activated. Classical Wnt pathway signaling molecules can make β-catenin accumulate in the cytoplasm and enter the nucleus, regulate the expression of Wnt target genes, and promote cell proliferation and migration by blocking the degradation pathway of β-catenin [15]. The expression of c-Myc is regulated by the Wnt/β-Catenin pathway, which can be involved in regulating the expression of related genes in physiological processes such as cell growth and cell metabolism to promote cell growth and proliferation [16]. These results suggest that SEP-3 can promote the proliferation and migration of hepatocytes by activating β-Catenin and c-Myc proteins of the Wnt/β-Catenin pathway in SDM liver, thus achieving the repair of the liver injury. It has been reported that blue-mussel-derived peptides PIISVYWK and FSVVPSPK can promote the osteogenic proliferation and differentiation of hBMMSCs by activating the classical Wnt/β-Catenin signaling pathway [17]. ## 2.2.3. Regulation of SEP-3 on Core Genes of Liver Clock in Sleep-Deprived Mice In order to explore the effect of sleep deprivation on biological rhythm and the regulatory effect of SEP-3 on biological rhythm, RT-PCR was used to detect the relative expression levels of Clock genes Bmal1, clock, Per1, Cry1, Rev-erbα, and Rorα in mouse liver at ZT6, ZT12, ZT18, and ZT24 time points. The results are as shown in Figure 6, Figure 7 and Figure 8. mRNA transcription levels of each clock gene in liver tissue of normal mice tested in this study were different at four time points, and the differences were significant ($p \leq 0.05$). In conclusion, the mRNA expressions of Bmal1, Clock, Per1, Cry1, Rev-erbα, and Rorα showed their own rhythm; that is, the expressions of Bmal1 mRNA, Cry1 mRNA, and Rorα mRNA showed a trend of increasing first and then decreasing. The expressions of Clock mRNA, Per1 mRNA, and Rev-erbα mRNA decreased first, then increased, and then decreased. The expressions of Bmal1, Clock, Per1, Cry1, Rev-erbα, and Rorα mRNA in SDM liver tissue were also rhythmic. The expressions of Bmal1 mRNA, Clock mRNA, Rev-erbα mRNA, and Rorα mRNA increased first, then decreased, and then increased, while the expression of Per1 mRNA increased first, then decreased, and then slowly increased. The change trend of Cry1 mRNA expression over time was the same as that of the normal group. However, the normal group reached the peak at ZT18, while the SD group reached the peak at ZT12. These results indicate that the mRNA transcription levels of bell genes in liver tissues of the normal group and SD mice show different rhythmicity or different time phases of rhythmic expression. Under normal conditions, the transcription levels of Clock genes Bmal1, Clock, Per1, Cry1, Rev-erbα, and Rorα are rhythmically stable in phase. Once the clock is disturbed, the phase and oscillation period of clock genes and their proteins will change [18]. In this study, it was found that sleep deprivation caused significant changes in the phase and oscillation period of the mRNA expressions of circadian Clock genes Bmal1, Clock, Per1, Cry1, Rev-erbα, and Rorα in the liver of mice, indicating that sleep deprivation indeed caused the disturbance of biological rhythm in mice. Zhang et al. found that the expression of liver clock gene Cry1 was significantly up-regulated in mice with insulin resistance [19]. Machicao F et al. found that Cry2 can promote the storage of triglycerides and limit the production of glucose in liver energy metabolism [20]. Doi et al. confirmed that when the CLOCK gene was mutated in mouse liver, liver glycogen synthesis was affected and insulin resistance was shown to some extent [21]. Zhou et al. found that the knockdown CLOCK or BMAL1 gene in mouse liver cells by siRNAs technology can also induce insulin resistance in liver [22]. Some scholars have found that specific knockout of BMAL1 gene in mouse liver can cause fatty liver and insulin resistance [23]. It can be seen that the normal expression of CLOCK, BMAL1, and other clock genes in the liver is an important link in regulating liver glycogen synthesis and liver sensitivity to insulin. Abnormal expression of liver clock genes may be associated with liver damage. After SEP-3 intervention, the phase and oscillation period of each clock gene expression in the liver of mice gradually tended to the normal group with the increase of drug concentration, indicating that SEP-3 can regulate the disturbance of liver biorhythm caused by sleep deprivation in mice, suggesting that SEP-3 may repair the liver injury caused by sleep deprivation by regulating the disturbance of biorhythm. ## 3. Discussion The effects of skipjack oligopeptide SEP-3 on liver injury in SDM and its mechanism were studied. Liver index, serum ALT and AST contents can reflect the degree of liver injury [24]. The above indexes of mice in each group showed a dose-dependent trend toward the normal group, and there was no significant difference in liver index and serum AST content between the HSEP-3 group and the normal group ($p \leq 0.05$). According to modern medicine, GC and ACTH in vivo are mainly inactivated, degraded, and excreted by the liver [25], so the contents of GC and ACTH in serum can also reflect the degree of liver injury to a certain extent. The concentrations of GC and ACTH in serum of mice in four time periods were measured, and it was found that they also tended to be normal in a dose-dependent manner. Some studies have shown that the hypothalamic-pituitary-adrenal (HPA) axis can be excessively hyperactive under continuous stress, resulting in excessive ACTH and GC [26]. The experimental results in this paper support this conclusion. It has been found that stress can lead to HPA axis disorder, which in turn leads to increased GC and catecholamine release. The increase of GC level will promote gluconeogenesis in liver, inhibit glucose metabolism, inhibit glucose uptake in adipocytes and skeletal muscle, promote lipolysis in adipocytes, inhibit insulin secretion, cause insulin resistance and inflammation to affect glucose metabolism and guide the occurrence of metabolic disease diabetes [27]. Diabetes may promote inflammation and fibrosis through the increase of mitochondrial oxidative stress mediated by adipokines, thereby leading to liver injury [28]. These results indicated that SEP-3 could reduce the elevation of serum GC and ACTH induced by sleep deprivation in mice, suggesting that SEP-3 had a reversal effect on liver injury, especially at high dose. Dinel et al. [ 29] found that fish hydrolytic peptide reduced the plasma corticosterone level in mice under acute stress, which was similar to our study. The above results indicate that SEP-3 can repair liver injury in SDM. In this paper, SEP-3 regulates hepatocyte apoptosis and the Wnt/β-Catenin pathway to explore the repair mechanism of SDM liver injury. The results showed that the expression levels of Bax and Cleaved caspase-3 were significantly increased and the expression level of Bcd-2 was significantly decreased in liver tissue after sleep deprivation. These results indicated that sleep deprivation induced hepatocyte apoptosis by increasing the level of pro-apoptotic protein Bax, and then caused liver injury. After SEP-3 intervention, the expression of Bax and Cleaved caspase-3 protein in liver tissue of mice decreased in a dose-dependent manner, while the expression of Bcl-2 protein increased in a dose-dependent manner. SEP-3 can inhibit the apoptosis of mouse hepatocytes induced by sleep deprivation by upregulating the level of BcI-2, downregulating the level of Bax, and inhibiting the activity of Caspase-3 protease. Krajewsik [30] found that the increase of anti-apoptotic protein Bcd-2 was conducive to cell survival under adverse conditions, while the increase of pro-apoptotic protein Bax could induce cell death, which was consistent with our results. Meanwhile, the expression levels of Wnt/β-catenin-pathway-related proteins tended to be normal in a dose-dependent manner. β-Catenin is a biomarker to detect whether the Wnt/β-Catenin pathway is activated. Classical signaling molecules of the Wnt pathway accumulate in the cytoplasm and enter the nucleus by blocking the degradation pathway of -catenin to regulate the expression of Wnt target genes, promoting cell proliferation and migration [12]. The expression of c-Myc is regulated by the Wnt/β-catenin pathway and can be involved in regulating the expression of related genes in physiological processes such as cell growth and cell metabolism to promote cell growth and proliferation [17]. These results indicate that SEP-3 can promote the proliferation and migration of hepatocytes by activating β-Catenin and c-Myc proteins of the Wnt/β-catenin pathway in SDM liver, so as to realize the repair of liver injury. The relationship between sleep deprivation and liver injury, biological rhythm regulation, and liver injury repair in mice was also explored. Biological rhythm is a physiological phenomenon formed by organisms to adapt to the changes of their external environment with a cycle of about 24 h. Functions such as liver detoxification, expression and activity regulation of nuclear receptors, apoptosis, and division of hepatocytes, DNA repair, and metabolism of lipids, amino acids, and carbohydrates are all regulated by biological rhythms [31]. Sleep deprivation can lead to significant abnormal changes in the expression levels of multiple core clock genes in the liver [6,22], which may affect various physiological functions of the liver. Therefore, to explore the regulatory effect of SEP-3 on liver biorhythm in mice is of great significance for the repair of liver injury in sleep-deprived mice. The mRNA expression levels of clock-related genes in mouse liver tissues at four time points were measured. The results showed that with the increase of drug concentration, the phase and oscillation period of the mRNA expressions of Bal1, Clock, Per1, Cry1, Rev-erba, and Rora in the liver of mice gradually tended to the normal group, indicating that SEP-3 could regulate the disturbance of circadian rhythm in the liver of mice caused by sleep deprivation. These results suggest that SEP-3 may repair liver injury induced by sleep deprivation in mice by regulating the circadian rhythm of hormone disorder. ## 4.1. Animals, Materials and Reagents SPF C57BL/6 male mice (aged 8 weeks, weighing 20–25 g, the number of samples was 120) were purchased from Hangzhou Ziyuan Laboratory Animal Technology Co., Ltd., Hangzhou, China. The horizontal platform used for sleep deprivation modeling was customized by the research group according to the experimental requirements [32]. Specific design scheme: 18 platforms with a diameter of 2.5 cm and a height of 6 cm (fixed with threaded knobs) were placed in the rat cage, and the platform spacing was 3.5 cm. The water injection height in the cage was about 1 cm lower than the top of the platform, and the water temperature was kept at 24 ± 1 °C. The water inside the cage was always clean. The mice were free to move, eat, and drink on the platform during the experiment. Bonito oligopeptide SEP-3 sequence Leu-Leu-Phe-Thr-Thr-Gln, purity ≥ $95\%$ (synthesized by Shanghai Botai Biotechnology Co., LTD., Shanghai, China); ALT and AST kits (Jiancheng Bioengineering Institute); eosin dyeing solution and hematoxylin dyeing solution (Soleippo); ACTH and GC (Shanghai Enzyme-Linked Biology Co., LTD., Shanghai, China); reverse transcription kit, AceQ Qpcr SYBR Green Master Mix (Yuanxin Biological Company, Shanghai, China); Sds-page gel preparation kit, RIPA total protein lysate, BCA protein concentration determination kit, ECL chemiluminescence detection kit (ASPEN, South Africa); 0.45 μm PVDF membrane (Millipore, Burlington, MA, USA); Kodak Medical X-ray Glue (Kodak, Rochester, NY, USA) were all used in the experiment; the other reagents were imported or domestic analysis of pure. ## 4.2. Instruments and Equipment ASP300S automatic tissue dehydrator, HistoCore Arcadia H Leica ASP Leica embedding machine, NANOCUT automatic semi-thin paraffin slicer, all from Leica, Germany; Dyy-6c electrophoresis apparatus, Beijing Liuyi Instrument Factory; Tgl-16 refrigerated centrifuge, Hunan Xiangyi Laboratory Instrument Development Co., LTD. ( Changsha, China); Spark Multi-function microplate reader, Tecan, Switzerland; 5810R table top high speed refrigerated centrifuge, Eppendorf, Germany were all used in the experiment. ## 4.3.1. Mouse Feeding The mice were kept in the SPF animal room of Zhejiang Ocean University, with a temperature of about 24 °C, humidity of 55 ± $5\%$, and a 12 h light/dark cycle (light time from 8:00 to 20:00). All experiments were in accordance with the ethical standards of the Laboratory Animal Ethics Committee of Zhejiang Ocean University (Experimental Ethics Approval No. 2021028). ## 4.3.2. Establishment of Sleep Deprivation Mouse Model and Administration Method After adaptive feeding for one week, mice were randomly divided into 5 groups, with 24 mice in each group: LSEP-3, MSEP-3, and HSEP-3 groups were administered with 20 mg/kg, 40 mg/kg, and 80 mg/kg oligopeptide SEP-3 by gavage, respectively. The normal group (routine feeding, no model) and the sleep deprivation group (SD group) were administered with the same volume of normal saline. The time of gavage in each group was 8 am every day for 10 days. From the 7th day of drug administration, mice in the normal group were still routinely fed, while mice in the other four groups were fed in the modified multi-platform water environment and subjected to sleep deprivation for 72 h [33]. The time of turning on the light at 8:00 am on the 10th day was taken as the Zeitgeber time, denoted as ZT0. At the time points of ZT6 (14:00), ZT12 (20:00), ZT18 (2:00), and ZT24 (8:00), 6 mice in each group were randomly selected by eyeball harvesting and blood sampling. Centrifugation was performed at 4300× g for 10 min, and the supernatant was removed. Immediately after blood collection, the mice were sacrificed by cervical dislocation method and dissected immediately. The liver was quickly removed, rinsed with PBS buffer, and then dried with filter paper. The liver organ index was successively weighed and recorded. Organ index = (organ weight mg/mouse weight g) × 10 Serum and liver samples of all mice were separated and stored in −80 °C refrigerator for later use. Each mouse was weighed and recorded before being sacrificed. ## 4.3.3. Detection of ALT, AST, GC, and ACTH in Mouse Serum Mouse serum frozen at −80 °C was thawed at 4 °C. The contents of ALT and AST in serum of ZT6 mice and the contents of GC and ACTH in serum of ZT6, ZT12, ZT18, and ZT24 were determined by kit. ## Extraction and Concentration Determination of Total Protein from Liver Tissue An appropriate amount of liver tissue was weighed and rinsed with pre-cooled PBS buffer for 2–3 times to remove blood stains, then cut into small pieces and placed in a homogenizer. An amount of 10 times the tissue volume of histone extraction reagent (a protease inhibitor was added within a few minutes before use, so that its working concentration was 1); the ice bath was thoroughly homogenized. The homogenate was transferred to the centrifuge tube and oscillate. The ice bath was set for 30 min, during which the pipette was used to blow repeatedly to ensure that the homogenate was completely cracked. This was centrifuged at 4 °C at 12,000 r/min for 5 min and the supernatant was collected, which was the total protein solution. A BCA protein concentration assay kit was used to detect the protein concentration of samples [34]. ## SDS-PAGE Electrophoresis and Transmembrane The protein loading concentration of the sample was adjusted to 4 μg/μL, and appropriate amount of 5× protein loading buffer was added and heated in a boiling water bath for 5 min to denature the protein. Separate glue of $8\%$, $10\%$, $12\%$, and concentrate glue of $5\%$ were prepared, respectively. After adding TEMED, the glue was shaken well immediately. These were then placed into the electrophoresis tank, and the electrophoresis buffer was added, with 10 μL of the sample put into the sample hole. The concentrated glue voltage was 80 V, the separation glue voltage was 120 V, and the constant pressure electrophoresis was carried out until the bromophenol blue reached the lower edge of the glue plate. According to the molecular weight of the reference protein and the target protein, the required separation glue was cut for the transmembrane. The transfer filter paper and PVDF membrane were prepared. The PVDF membrane was activated with methanol for 3 min before use. The film transfer “sandwich” structure was placed in accordance with the direction of the positive and negative terminals. From the positive terminal to the negative terminal, the film transfer sponge, 3 layers of filter paper, PVDF film, glue, 3 layers of filter paper, and the film transfer sponge were successively placed. The bubbles in each layer should be removed during the placement process. Then, the membrane was transferred at a constant flow of 300 mA, and the transfer time was adjusted according to the molecular weight of the protein. Table 1 shows the electrophoresis and membrane transfer conditions of each target protein. ## Antibody Incubation After the membrane transfer was completed, the membrane was washed with TBST to remove the transfer liquid on the membrane, and the sealing liquid ($5\%$ skim milk) was added and closed at room temperature for 1 h. TBST washed off the blocking solution and the primary antibody diluted with the primary antibody diluent was added and incubated overnight by shock at 4 °C. The second antibody, diluted by the second antibody diluent, was added and incubated at room temperature for 30 min. Then, the TBST was added and washed by shock at room temperature for four times for 5 min each time. Table 2 lists the antibody information and dilution methods. ## 4.3.5. Determination of mRNA Expression Levels of Clock-Related Genes in Mouse Liver Tissue Total RNA was extracted from liver tissue samples using a total tissue RNA extraction kit, and cDNA was extracted and reverse transcribed using a reverse transcription kit. PCR technology was used to detect the expression of related genes, and GAPDH was taken as the target gene, and the relative mRNA expression of target genes was calculated by the 2-△△Ct method [35]. The procedure was as follows: total RNA extraction from mouse liver → reverse transcription and cDNA synthesis and detection → fluorescence quantitative PCR detection. The primer sequences of each gene are shown in Table 3. ## 4.4. Data Analysis and Processing SPSS Statistics 25 software was used to perform one-way ANOVA on the experimental data, and Duncan’s test was used for multiple comparison analysis. 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--- title: 'The ENDORSE Feasibility Study: Exploring the Use of M-Health, Artificial Intelligence and Serious Games for the Management of Childhood Obesity' authors: - Konstantia Zarkogianni - Evi Chatzidaki - Nektaria Polychronaki - Eleftherios Kalafatis - Nicolas C. Nicolaides - Antonis Voutetakis - Vassiliki Chioti - Rosa-Anna Kitani - Kostas Mitsis - Κonstantinos Perakis - Maria Athanasiou - Danae Antonopoulou - Panagiota Pervanidou - Christina Kanaka-Gantenbein - Konstantina Nikita journal: Nutrients year: 2023 pmcid: PMC10057317 doi: 10.3390/nu15061451 license: CC BY 4.0 --- # The ENDORSE Feasibility Study: Exploring the Use of M-Health, Artificial Intelligence and Serious Games for the Management of Childhood Obesity ## Abstract Childhood obesity constitutes a major risk factor for future adverse health conditions. Multicomponent parent–child interventions are considered effective in controlling weight. Τhe ENDORSE platform utilizes m-health technologies, Artificial Intelligence (AI), and serious games (SG) toward the creation of an innovative software ecosystem connecting healthcare professionals, children, and their parents in order to deliver coordinated services to combat childhood obesity. It consists of activity trackers, a mobile SG for children, and mobile apps for parents and healthcare professionals. The heterogeneous dataset gathered through the interaction of the end-users with the platform composes the unique user profile. Part of it feeds an AI-based model that enables personalized messages. A feasibility pilot trial was conducted involving 50 overweight and obese children (mean age 10.5 years, $52\%$ girls, $58\%$ pubertal, median baseline BMI z-score 2.85) in a 3-month intervention. Adherence was measured by means of frequency of usage based on the data records. Overall, a clinically and statistically significant BMI z-score reduction was achieved (mean BMI z-score reduction −0.21 ± 0.26, p-value < 0.001). A statistically significant correlation was revealed between the level of activity tracker usage and the improvement of BMI z-score (−0.355, $$p \leq 0.017$$), highlighting the potential of the ENDORSE platform. ## 1. Introduction Childhood obesity is a major public health challenge worldwide. If left untreated, it is linked to numerous health complications, both physical (insulin resistance, dyslipidemia, non-alcoholic fatty liver disease, and arterial hypertension) and psychological (low self-esteem and depression) [1,2]. According to the World Health Organization (WHO), 340 million children and adolescents aged 5–19 were overweight or obese in 2016, thus highlighting the need to develop efficient interventions for the management of childhood obesity [3]. However, conventional interventions are not always accessible due to high costs, commute inconveniences, and lack of facilities. Additionally, $80\%$ of overweight and obese children hesitate to engage in physical activities, therefore requiring the implementation of creative interventions [4]. Multicomponent interventions (dietary, physical activity, educational, and behavioral) constitute first-line treatments for childhood overweight and obesity [1,5,6]. According to the US Preventive Services Task Force, clinically significant weight reduction is considered a drop in BMI z-score by 0.2 units. To achieve this reduction, intensive, multicomponent interventions are required with a minimum of 26 contact hours and a duration of 3–6 months [5]. Smart digital health interventions are considered an effective means for accommodating certain issues raised by the child’s socioeconomic, environmental, and health status through tracking and monitoring physiological, psychological, behavioral, and lifestyle parameters that help healthcare professionals in adapting the treatment plan. For this reason, many studies have explored the use of technological components, such as mobile applications, text messages, websites, and wearables, in weight management programs for adults and children and have produced promising results [7,8,9,10,11]. Especially during the COVID-19 era, e-health programs have become more important in the fight against childhood obesity [12]. Ambulatory care via mobile devices can also contribute to optimizing healthcare processes by saving considerable staff and patient time while endorsing individuals in self-health management [13]. Parental involvement is considered important for the successful weight management of children with obesity, not only in traditional interventions [5,6] but also in digital health interventions [14]. Despite the recognized benefits and advantages of Computerized Decision Support Systems (CDSS) incorporating Machine Learning (ML) techniques [15,16,17], there is a limited number of interventions that have been based on these technologies [18]. The type of interventions have been varying across (i) utilizing Electronic Health Records (EHRs) to produce alerts based on the BMI, (ii) using a table app for educational purposes, goal setting, and videoconferences between family and health coaches, (iii) delivering to the families and health providers a web site for comparing measurements and lifestyle information with the established clinical guidelines, (iv) applying exergames and video-chat in a gaming console with the aim to support self-health management. Some of these interventions have also incorporated wearable activity trackers. ML has been applied for exploring the classification of physical activity, investigating correlations between proximity to a supermarket and weight control and predicting weight. Most of these ML approaches have been based on decision trees, random forests, artificial neural networks, and linear and logistic regressions. The development of a smart digital multicomponent intervention engaging all the primary agents (e.g., parents and healthcare professionals with different expertise) in childhood obesity management remains challenging. The ENDORSE project faces this challenge through the use of advanced Information and Computing Technologies (ICT), including mobile health (m-health), sensing, gamification, and ML technologies capable of delivering tools and services facilitating weight management while engaging the active involvement of children, parents, pediatricians, nutritionists, psychologists, and endocrinologists. Self-monitoring, goal setting, and positive feedback constitute the core components of the ENDORSE behavioral obesity intervention that are based on the self-determination theory (SDT) [19]. This feasibility study has been conducted to elucidate the potential of technically implementing the ENDORSE innovative software ecosystem into clinical practice while investigating its usability and acceptability. Secondary objectives have been to examine whether participating in this e-health weight management program had an impact on children’s BMI z-score, diet quality, physical activity, screen time, and sleep duration. ## 2.1. The ENDORSE Platform as a Means for the Management of Childhood Obesity The ENDORSE platform consisted of several modules [20]: (i) a mobile serious game (SG) to promote effective behavioral lifestyle changes in overweight/obese children; (ii) a physical activity tracker facilitating physical activity monitoring and sleep tracking; and (iii) mobile applications for parents and healthcare professionals, enabling remote health monitoring. Apart from the end-user modules, the platform also featured the ENDORSE recommendation system that was responsible for producing personalized content such as reports, messages, and in-game missions. To guarantee data privacy and data security, sensitive information stored on the ENDORSE platform was encrypted, while access to the platform was mediated through the deployment of a dedicated identity and access controller that was also responsible for the registration of the participants. The ENDORSE SG included a variety of mini-games in the form of educational and action missions. Through the action missions, the user collected in-game currency and food ingredients. The collected food ingredients were used as primary materials in an educational mini-game focusing on preparing a lunch meal. The users were rewarded with regard to their ability to prepare a balanced meal. The in-game currency was spent on avatar customization. The ENDORSE SG also featured space for presenting daily educational personalized messages generated by the ENDORSE recommendation system. The ENDORSE parental mobile apps provided various functionalities to support self-monitoring, such as logging goal-related achievements and body weight, while exchanging messages with healthcare professionals. The parental mobile app also hosted specific sections for displaying messages generated by the ENDORSE Recommendation System on a daily basis, the dietary plan as drafted by healthcare professionals, and educational material. At their initial log-in to the mobile app, the parents were required to fill in several forms for medical, nutritional, behavioral, and psychological assessment. The ENDORSE mobile app that was available to healthcare professionals had the same template as the parental mobile app that featured further functionalities, such as: (i) editing the medical assessment form; (ii) editing, formatting, sending, and changing the dietary plan; (iii) entering and changing weekly goals; and (iv) sending feedback messages to the parents. To guarantee data security in the frame of the ENDORSE mobile apps, several security measures were employed, including [1] the establishment of a secure connection between the ENDORSE mobile application, the ENDORSE server, and the ENDORSE Firebase platform instance; [2] the local encryption of the stored data; and [3] enabling the appearance of the name of the participant in the healthcare professionals’ screen only after the healthcare professional proceeded with scanning the unique QR code produced from the participant’s ENDORSE parental mobile app, otherwise an arbitrary alphanumeric code was visible on the screen after having registered the participant in the ENDORSE platform and having associated the participant with specific healthcare professionals. Physical activity trackers: The Fitbit ACE 2 (for kids) was utilized, which had the ability to track steps, calories, and sleep characteristics (duration and quality of sleep). Fitbit Ace 2 featured Bluetooth connectivity and incorporated memory for up to 7 days of movement data per minute and up to 30 days of total daily activity data. It was waterproof and had a five-day battery autonomy. Physical activity tracking (steps per day) and sleep duration were available, through the cloud, to the ENDORSE platform for further processing from the ENDORSE Recommendation System. Children were advised to wear the activity tracker daily. For younger children (<8 years) and those who did not feel comfortable wearing the activity tracker on a daily basis, a recommendation for a minimum of 2 weekdays and 1 rest day was given. The ENDORSE Recommendation System leveraged data collected from various sources, including the physical activity tracker, the ENDORSE mobile apps, and the ENDORSE SG. It incorporated an engine able to produce personalized weekly reports providing information regarding the child’s achievements in the ENDORSE SG, physical activity as recorded by means of the physical activity tracker, and weight logs. Personalization was further supported through the integration of an AI-based engine able to select at a weekly basis suitable game missions and messages to be presented to the end users [20]. To this end, a pool of messages was created, including tips and advice on a healthy lifestyle. ## 2.2. Study Design This ENDORSE study was a 12-week pilot study with a pre- and post-intervention design since data collection occurred at baseline and immediately post-intervention. The main objective was to investigate the potential of utilizing the ENDORSE platform as a supportive means to standardize weight management care of overweight and obese children. This study was driven by Self Determination Theory with an emphasis on providing appropriate plans, meaningful rational, and positive/informational feedback to all participants in a respectful manner [19,21]. The clinical protocol was approved by the bioethics committee of the “Aghia Sophia” Children’s Hospital (protocol number: 4760, date of approval 10 March 2021), and all mothers provided written informed parental consent to participate in this study. This study included two phases (pre-pilot and pilot) and was executed from March 2021 to May 2022. The pre-pilot phase aimed at the clinical implementation of the ENDORSE platform in order to recognize and resolve technical issues while receiving valuable feedback from the participants that led to improvements (Figure 1) toward releasing the platform’s pilot version. The recruiting and monitoring procedure was conducted at the Obesity outpatient Clinic of the First Department of Pediatrics of the National and Kapodistrian University of Athens at the “Aghia Sophia” Children’s Hospital in Athens, Greece. All participants were recruited from the Obesity outpatient Clinic (treatment-seeking population). The majority of the participants resided in urban settings, i.e., most were residents of Athens ($90\%$, $$n = 40$$). The USSF sample size calculator for pre- and post-intervention (paired t-test) was used [22] in order to estimate the sample size that should be recruited for producing statistically significant evidence on BMI z-score changes. The threshold probability for rejecting the null hypothesis was $5\%$, while the probability of failing to reject the null hypothesis under the alternative hypothesis was equal to $20\%$. A medium value (e.g., 0.5) of effect size was applied, and the SD of the BMI z-score reduction was set to 1. The estimated sample size was equal to 34, yet the total recruited sample consisted of more participants (e.g., 50 chidren and their mothers) in order to account for potential dropouts. Nonrandomized recruitment was used to enroll participants into 3 consecutive groups that applied different versions of the ENDORSE platform for a time period of 12 weeks (Figure 1). In this pilot study, the differences between the active control group and the intervention group were the absence of the ENDORSE SG and the de-activation of the ENDORSE Recommendation System. In the active control group, personalized messages were sent at the beginning of each week to the participating mothers by a member of the clinical team (pediatrician or nutritionist). In the intervention group, the weekly messages were designed by the clinical team (with relevant content to the pre-specified weekly behavioral goal) and were sent automatically to all participants by the ENDORSE Recommendation System at the beginning of each week. In both groups, mothers were able to exchange messages with the clinical team via the application. Children aged 6–14 years with a BMI > 85th centile were eligible to participate in this study. Overweight and obese were classified according to the age- and sex-specific definition of the International Obesity Task Force (IOTF) [23]. The exclusion criteria included secondary causes of obesity such as endocrine causes (hypothyroidism, Cushing syndrome, growth hormone deficiency), known genetic syndromes linked to obesity (Down syndrome, Prader Willi syndrome), and serious developmental disorders (severe Autism Spectrum Disorders (ASD) or severe Attention Deficit Hyperactivity Disorder (ADHD)). ## 2.3.1. Medical Assessment At baseline and at the end of this study, a full clinical assessment was performed by a pediatric endocrinologist, including the assessment of the pubertal stage. Children were grouped as prepubertal (Tanner stage 1) and as pubertal (including Tanner stages 2, 3, 4, and 5). Girls were staged according to breast development and pubic hair growth, and boys were staged according to pubic hair growth and male genital stages [24]. Anthropometrics: Height was measured to the nearest 1 mm by means of a stadiometer (Holtain Limited) while children were standing without shoes, with their backs to the wall, and with their bodies up straight. Body weight was measured to the nearest 0.1 kg in light clothing using a mobile digital scale (Tefal Bodysignal). Each BMI was standardized by conversion to a z-score (BMI z-score) in groups defined by age and sex according to the Centers for Disease Control and Prevention (CDC) growth charts 2000 [25]. Because BMI z-scores are known to be inaccurate at values greater than the 97th centile, adjusted z-scores were used for these children ($$n = 46$$, $92\%$ of the whole sample) [26]. Waist circumference was measured with an elastic tape at the point halfway between the last palpable rib and the tip of the iliac crest with an accuracy of 0.1 cm. The waist-to-height ratio (WHR) above 0.5 was considered a measure of central adiposity [27]. Blood pressure (mmHg) was measured with an electronic blood pressure meter (Microlife Gentle). Two measurements were taken at each visit, and the mean value was documented. ## 2.3.2. Nutritional Assessment and Dietary Intervention At baseline, a thorough nutritional assessment was made by a nutritionist. A diet history interview was used to collect information on dietary habits, eating behavior, and parental feeding practices in order to design a nutritional intervention and an individualized meal plan. The Mediterranean dietary pattern, as described in the Greek National Dietary Guidelines for Infants, Children, and Adolescents, formed the basis of the individualized meal plan with macronutrient balance (15–$20\%$ protein, 30–$35\%$ fat, 50–$55\%$ carbohydrates) [28]. Energy requirements were calculated using the equations of the Institute of Medicine for use in the pediatric population with excess weight [29] and adjusted according to weight maintenance or weight loss goals [30]. The dietary plan included a wide variety of foods relevant to each child’s preferences to encourage dietary adhesion. The nutritional intervention was designed according to the wishes, abilities, and goals set for each child/adolescent. In addition, it aimed to reinforce feeding practices that promote parental involvement, self-regulation, and autonomy of children and adolescents against the implementation of restrictive feeding practices [31,32]. ## 2.3.3. Children’s Health Behaviors Questionnaire In order to evaluate food habits and other health behaviors of the children (exercise, sedentary life, sleep), a clinical questionnaire was used, specifically designed for the needs of this study, which was completed by a member of the clinical team together with the mothers at the beginning and the end of the intervention. Food habits were assessed by questions based on the National Dietary Guidelines of Greece for Children and Adolescents and indicative serving suggestions by age group as reported in the Guidelines [28,33]. It included 22 questions regarding food groups, food items of interest, and frequency of consumption with the aim to picture the National Dietary Recommendations described briefly in the “Ten steps to healthy eating for children and adolescents” [28]. Weekly consumption of fruits, vegetables, whole grains, fast food, sugar-sweetened beverages (SSB), and energy-dense but low-nutrient packaged products (highly processed snacks) were used as indicators of the children’s diet quality [1]. The same questionnaire included questions about the children’s weekly frequency of organized physical activity, as well as the daily hours that children spent in front of screens for entertainment purposes, the children’s daily sleep duration, and the quality of their sleep (see Supplementary File S1, Health behaviors questionnaire). ## 2.3.4. Psychological Assessment At baseline, a thorough psychological assessment was performed by a health psychologist, who assessed psychologically the eligibility of mothers and their children for their participation in this study based on a clinical interview with structured open-ended questions. By the time the mothers were interviewed by the psychologist, they had completed several psychometric tests via the application for themselves: PHQ-9: Greek version of the Patient Health Questionnaire −9 by Kroenke et al., which is a screening tool for detecting depressive symptoms in adults [34,35]; EAT-26: Eating Attitudes Test-26 by Garner et al. [ 36], a Greek version by Simos [37], which is a screening tool for detecting eating disorders in adults; the Comprehensive Feeding Practices Questionnaire by Musher-Eizenman et Holub [38], a Greek version by Michou et al. [ 39], that is a questionnaire evaluating 6 feeding practices (monitoring, control of feeding by the child, pressure to eat, restriction in food intake, use of food as a reward and/or for emotional regulation and guidance for healthy eating); and one questionnaire for their child—SDQ: Parent’s version of the Strengths and Difficulties Questionnaire by Goodman [40], a Greek version by Bibou-Nakou et al. [ 41], which is a screening tool for detecting behavioral and emotional problems in children and adolescents. Mothers also completed a questionnaire via the application specially designed for the needs of the research, which included questions about sex, age, weight, height, nationality, marital status, level of education, and type of work. The body mass index of the parents was calculated based on self-reported weight and height: BMI = weight (kg)/height2 (m2). Their categorization into normal weight (BMI: 18.5 kg/m2 to 24.9 kg/m2), overweight (BMI: 25 kg/m2 to 29.9 kg/m2), and obese (BMI ≥ 30 kg/m2) was done according to the definition of overweight and obesity of the World Health Organization [3]. The results were discussed with the psychologist in person or over the telephone. At the end of the intervention, a second psychological assessment was performed by the same psychologist, and mothers were asked again to complete these psychometric questionnaires via the application. During the interview, emphasis was given to known psychosocial factors associated with childhood obesity (bullying, weight stigma, and depressive symptoms) [42,43,44]. Although the risk of developing an eating disorder following family-based obesity interventions is extremely low [45,46], there are no data from digital interventions addressing this issue [7,9]. During the initial psychological and medical/nutritional assessment, emphasis was given to parents that weight reduction should be gradual even for children with severe obesity (no more than 1 kg/week). Parents were advised to weigh their children once weekly. If weight loss was more than 1 kg/week, they were advised to contact a member of the clinical team for a thorough evaluation for excessive energy restrictions (meal skipping, purging, fasting, excessive exercise, etc.), as recommended by the American Academy of Pediatrics [47]. ## 2.4. Study Implementation At the time this ENDORSE study was conducted, “Aghia Sophia” Children’s Hospital was a reference center for children with COVID-19 infection. The applied health protection measures to minimize contamination with the SARS-CoV2 virus hampered the recruitment and monitoring procedures making it difficult to enroll the intervention and control groups simultaneously. For this reason, the participants of the two pilot groups were recruited and monitored consecutively. At the beginning of this study, the mother participants had one 90-min face-to-face session with the clinical team (pediatrician and nutritionist) in order to be trained in utilizing the ENDORSE platform (activity tracker, serious game, app). Moreover, during these sessions, the weight goals were set together with the mothers and in accordance with the American Academy of Pediatrics guidelines [30]. Health behavior goals (Table 1) were also explicitly discussed, and the importance of monitoring the goals and weight was thoroughly explained to participants. ## 2.4.1. Self-Monitoring of Behavior and Outcome The mother participants were responsible for entering the behavioral goals in the ENDORSE parental mobile application on a daily basis and for monitoring their child’s weight on a weekly basis. Children were asked to monitor their physical activity (steps/day) via the physical activity tracker. In the pre-pilot phase, the weekly goals were jointly defined by the mothers and a member of the clinical team. Mothers were encouraged to choose no more than 2 goals each week for monitoring. The clinical team had the opportunity to change weekly goals via the app at any given time during the 12 weeks of intervention at the mother’s request. In the pilot phase, all weekly goals for monitoring were set by the clinical team at the beginning of the intervention in a prespecified order (each goal was monitored by the mothers for 2 consecutive weeks): physical activity plus screen time goal; breakfast goal; mid-morning snack goal; lunch goal; afternoon snack goal; and dinner goal. Mothers were still given the opportunity to change weekly goals via the application at any given time during this study. Positive and informational feedback constituted an important part of the self-monitoring procedure. The weekly reports that were sent to the mothers via the endorsed recommendation system contained informational feedback, while the messages sent by the clinical team were both positive and informational. ## 2.4.2. Educational Material At the time of the face-to-face sessions, an educational booklet was given to mothers as a guide for optimal weight management (also available in pdf version via the mobile application). The educational material was developed by the clinical team based on National Dietary Guidelines [28] and aimed to educate mothers and children/adolescents to improve family dietary habits. Emphasis was given on diet quality, food group education (e.g., servings of fruit, vegetables, whole grains, nuts, etc.), portion size education, energy-dense nutrient-poor snacks and sugar-sweetened beverage reduction, and label reading. Recipes with easy-to-prepare healthy choices for main meals and snacks, together with a shopping list, were also provided to all participants. The educational material included a brief description of parental feeding practices according to the recent classification by Di Pasquale et Rivolta following SDT principles [31]: Relatedness—enhancing food parenting practices (family meals, child’s involvement in preparing meals); Competence—enhancing food parenting practices (clear and consistent rules related to food, availability and accessibility of healthy food, nutrition education, parental modeling); Autonomy—enhancing food parenting practices (guided choices, discussing and negotiating with the child food choices). The educational material also included advice to parents about eating behaviors that have a genetic basis and affect appetite in different ways (low satiety responsiveness, food responsiveness, and food fussiness) [49] (see Supplementary File S1, Educational material). ## 2.5.1. Descriptive Statistics and Pre-Post Intervention Descriptive statistics were used to assess baseline participant characteristics (demographical, clinical, and behavioral), separated according to group (means ± SD, median with 25th and 75th centile or as absolute values with percentages). For this pilot study, the Chi-square test was used for categorical variables, while the Independent sample t-test was used for normally distributed variables and the Mann–Whitney U test for non-normally distributed variables. The Shapiro–Wilk test was applied to check for normality. Paired sample t-tests were deployed to assess pre- and post-intervention changes within each of the three groups for normally distributed variables and the Wilcoxon test for not normally distributed variables. All p-values were two-sided, and the level of significance was set at 0.05. Statistically significant correlations between the degree of adherence of all participants ($$n = 45$$) and pre- vs. post-intervention changes in the BMI z-score and health behaviors (intake of fruits, vegetables, fast food, highly processed snacks, and SSBs, changes in physical activity, screen time, sleep duration) were explored via the Spearman’s Rank Correlation test. For the statistical analysis of health behaviors (diet, physical activity, screen use, and sleep), categorical variables were transformed into ordinal variables according to the following assumptions: 1–2 times/day: 1.5; 3–4 times/day: 3.5; 5–6 times/day: 5.5; 7–8 times/day: 7.5; >9 times/day: 9.5; 1–2 times/week: 1.5; 3–4 times/week: 3.5; 5–7 times/week: 6; never/rarely: 0; 60–120 min/day: 90; 30–60 min/day: 45; <30 min/day: 15; >4 h/day: 4.5; 3–4 h/day: 3.5; 2–3 h/day: 2.5; 1–2 h/day: 1.5; <1 h/day: 0.5; <7 h/day: 6.5, 7–8 h/day: 7.5; 9–11 h/day: 10. ## 2.5.2. Feasibility and Acceptability The feasibility of the ENDORSE platform was measured by means of adherence to this study’s protocol, attrition rate, and perceived helpfulness. The criteria for the acceptability were based on Davis’ theory of the Technology Acceptance Model focused on the ease of use and perceived usefulness as rated by the participants [50]. The adherence to the ENDORSE intervention was measured by means of frequency of usage. To this end, objective usage metrics were identified and estimated based on the data records for each module. Specific usage metrics of the parental mobile app included the number of days of usage, the number of days containing self-monitoring data (weight, goals), and the number of messages that were exchanged between the clinical team and the participants. Regarding the physical activity tracker, the number of nights with sleep recordings, the average time of sleep per day (min), the average time of usage per day (hours), the number of days with step recordings, and the average steps per day were calculated. The usage metrics for the ENDORSE SG were identified as the days of usage and the number of mini-games (action, educational) that were completed by the children. Based on the above usage metrics, an adherence score was estimated that reflected the number of days of usage for each module. Specifically, the days of usage of the ENDORSE parental mobile app [1] were calculated as the maximum between the total days containing self-monitoring goal-related records (goal monitoring) and the weekly weight records (weight monitoring):score_mobile_app = max (7 × weight_monitoring, goal_monitoring)[1] Regarding the adherence score to the physical activity tracker [3], the product between the average daily usage (h) and the total days of usage was divided by a predefined minimum daily hour of active usage (10 h):score_activity_tracker = average_daily_usage × total_days_of_usage/10[2] The adherence levels were estimated by applying specific thresholds on the obtained individual adherence scores: low (<25 days); medium (between 25 and 50 days); high (>50 days). These thresholds were common across all modules except for the case of the physical activity tracker usage in the pre-pilot phase, as it was applied by the participants for a longer period of up to 120 days since the duration of the pre-pilot phase was extended due to technical issues that hampered the functionality of the ENDORSE platform. Therefore, the corresponding thresholds were set as 40 and 80 days. The individual adherence levels were encoded in 1, 2, and 3 for low, medium, and high, respectively, in order to estimate the overall adherence score that was calculated by adding the corresponding encoded levels. Low, medium, or high adherence levels regarding the combined use of the physical activity tracker and the parental ENDORSE mobile apps were obtained by applying thresholds 2 and 4. Taking into consideration the low adherence to the ENDORSE SG that was observed over almost all children, one child had more than 25 days of usage and was considered to be an outlier, and it was excluded from the estimation of the overall adherence score and level that were used to explore statistically significant correlations. Perceived usefulness, ease of use, and perceived helpfulness were obtained using a customized self-report 5-point Likert scale questionnaire post-intervention (see Supplementary File S1, Postintervention feasibility questionnaire). The statistical analysis was performed in Python and Excel. ## 3.1. Baseline Characteristics The baseline characteristics of the recruited participants are depicted in Table 2 for both pilot phases. The pre-pilot phase included 20 children ($60\%$ boys) with a mean age of 11 years. The mean BMI z-score was 2.85, while all children had a waist-to-height ratio greater than 0.5. According to the IOTF criteria [23], $55\%$ of the children were obese and $45\%$ severely obese (>$120\%$ of the 95th centile for height and age). Children’s mothers had a mean age of 44 years and BMI of 30.7, while $95\%$ of them were Greek and $65\%$ were married. Most mothers ($60\%$) were secondary school graduates, and $70\%$ of them were employed. A total of 30 children ($40\%$ boys) with a mean age of 10 years participated in the pilot phase. The mean BMI z-score in the control group was 2.71, while in the intervention group was 2.89. All children had a waist-to-height ratio greater than 0.5. According to the IOTF criteria [23], $33.3\%$ and $46.7\%$ of children were severely obese in the control and intervention groups, respectively. There were statistically significant differences between the groups in terms of age and height ($p \leq 0.05$). The participating mothers of this pilot study ($$n = 30$$) had a mean age of 43.6 years and a mean BMI of 29.6, while $96.7\%$ were Greek, $86.7\%$ married, and $46.7\%$ were university education graduates. Figure 2 illustrates the baseline dietary habits of the recruited participants. Prior to the pre-pilot phase of this study, $55\%$ and $60\%$ of the children consumed one–two times/day a serving of fruit and vegetables, respectively, which was lower than the target intake of two–three servings/day for their age according to the National Dietary Guidelines [28]. In addition, $90\%$ of children reported an intake of more than seven servings of grains/day, while only one–two servings/day were reported as whole grains for $70\%$ of children. For $90\%$ of the children, the intake of fast-food products was more than once a week, while the intake of a serving of processed snacks for $40\%$ of the children was more than three times a week. Additionally, $50\%$ of the children reported consuming a portion of SSB more than once a week. Prior to the pilot phase of this study, $80\%$ of the children in the control group and $73.4\%$ of the children in the intervention group consumed a portion of fruit at a frequency equal to or less than one–two times a day without a statistically significant difference between the groups. Moreover, the largest percentage of children for both pilot groups reported consuming vegetables at a frequency equal to or less than one–two times a day. All the children for both pilot groups reported an intake of more than seven servings of grains per day, while only $80\%$ of children in the control group and $46.7\%$ in the intervention group reported one–two servings/day of whole grains. Regarding the intake of fast food and highly processed snacks, the majority of children consumed a portion at a frequency equal to or greater than one–two times a week, while the intake of a portion of SSB was of similar frequency for the majority of children. There was no statistically significant difference between the control and intervention groups for all food groups. Other health behaviors in terms of physical activity, screen time, and sleep duration (Figure 3) were also assessed at baseline. Focusing on the pre-pilot group, $60\%$ of children exercised less than 1 h per day, and $75\%$ of them did not perform systematic physical activity. In addition, $90\%$ of children spent more than 2 h a day in front of screens, both on weekdays and on the weekends. A total of $65\%$ of children slept less than recommended for their age; however, most children ($75\%$) had no sleep difficulties. Regarding the pilot groups, $93.3\%$ of children in the control group and $46.7\%$ of children in the intervention group exercised less than 1 h a day. The number of participants performing physical activity less than 30 min per day was statistically significantly higher in the control group than in the intervention group. Additionally, the majority of children reported more than 2 h of screen time per day on both weekdays and weekends ($66.7\%$ and $86.7\%$ for the control group versus $40\%$ and $93.3\%$ for the intervention group, respectively). Regarding sleep time, $46.7\%$ of children for both groups slept less than recommended for their age; however, the majority of children ($93.3\%$ vs. $86.7\%$) had no sleep difficulties, with no statistically significant differences between groups. ## 3.2. Adherence Results This ENDORSE pilot study had a low attrition rate corresponding to $10\%$, $13.33\%$, and $6.66\%$ in the pre-pilot, control, and intervention group, respectively. The degree of adherence was estimated by means of calculating particular metrics indicative of the frequency of usage of each ENDORSE module (e.g., parental mobile app, activity tracker, game). ## 3.2.1. Usage Metrics Table 3 presents the usage metrics relevant to the parental mobile app. Within the frame of this pre-pilot study, a small percentage of participants had zero usage, contrary to this pilot study, where all participants interacted with the mobile app. An increased variability was observed among participants in terms of frequency of use since a high standard deviation was estimated regarding days of usage, weight registration, and monitoring of weekly goals. A comparison between the pre-pilot and pilot groups was conducted in order to investigate whether the pilot version of the ENDORSE parental mobile app promoted higher adherence than the corresponding pre-pilot version. Although no statistically significant differences were revealed, all the usage metrics were improved, demonstrating that substantial enhancements were applied toward releasing the pilot version of the ENDORSE platform. Several usage metrics were extracted based on the sleep minutes and the number of steps as recorded by means of the physical activity tracker (Table 4). A percentage of the participants ($20\%$) did not apply the physical activity tracker during the day, while $24.44\%$ of them didn’t have sleep records. The number of days with step records varied among the participants, yet the average time of usage per day was adequately high for most of them. The average time of sleep per day (7 to 8 h) was marginally insufficient, and the average steps per day were mainly lower than the target value of 10,000 steps. Figure 4 depicts the number of participants applying the physical activity tracker per intervention week. An overall reduction in the users was observed as the intervention weeks advanced, reflecting the users’ initial excitement and its progressive decrease. The control group demonstrated the highest, yet not statistically important, usage metrics against those achieved by the pre-pilot and intervention groups. This was justified through the participants’ intensive communication with the medical team (number of communication messages: 4.23 ± 5.64) that promoted their adherence. The usage metrics relevant to the ENDORSE game were extracted based on the days of interaction with this module and the number of completing the action and educational mini-games (Table 5). Low adherence was observed in the pre-pilot group due to the limited content of the game. A statistically significant difference was observed between the pre-pilot and the intervention group in terms of days of usage (3.86 ± 3.96 vs. 14.57 ± 8.93), highlighting that the enriched game content improved adherence. However, sustainability in participants’ engagement was not achieved, and for this reason, the usage metrics were low. ## 3.2.2. Level and Score of Adherence The level and score of adherence for each module (ENDORSE mobile app, physical activity tracker) and group are presented in Figure 5. The overall adherence level across both modules is also depicted. A high percentage of participants ($75\%$) belonged to medium to high overall adherence levels. Most of the participants ($62\%$) in the control group presented a high adherence in terms of utilizing the physical activity tracker and the ENDORSE parental mobile app. Improvement in adherence levels was observed between the pre-pilot and pilot groups since the percentage of high adherence increased from $22\%$ to $41\%$, and the percentage of low adherence decreased from $33\%$ to $19\%$. Statistically significant correlations between the score of adherence and pre- vs. post-intervention changes in the BMI z-score and health behaviors (intake of fruits, vegetables, fast food, highly processed snacks, and SSBs, changes in physical activity, screen time, and sleep duration) were explored. In this direction, Spearman’s Rank Correlation test was applied to the obtained outcomes over all participants. A statistically significant correlation between the score of adherence and the BMI z-score change was revealed (−0.299, $$p \leq 0.046$$), yet there were no statistically significant correlations in terms of changes in health behaviors. The changes in BMI z-score were not statistically correlated with changes in health behaviors. A statistically significant correlation was revealed between the physical activity tracker’s average usage and changes in BMI z-score (−0.355, $$p \leq 0.017$$). ## 3.2.3. Acceptability Table 6 presents a summary of the participant responses to the postintervention questionnaire for the pre-pilot and intervention groups, respectively. Although this summary provides a subjective estimation of the level of acceptance, it could be inferred that the participants found the ENDORSE modules sufficiently useful, helpful, and easy to use. In addition, statistically significant differences were revealed between the pre-pilot and pilot versions of the ENDORSE game and parental mobile app, demonstrating that substantial improvements were applied. ## 3.3.1. Pre-Pilot Study Following the intervention, the mean BMI z-score of children decreased significantly (−0.24, $$p \leq 0.001$$), while no other significant changes were observed in the anthropometric variables. With regard to dietary habits, fruits increased by 0.6 portions/day ($p \leq 0.05$) and vegetables by 0.62 portions/day ($p \leq 0.05$), while intake of fast-food products decreased by 0.5 portions per week ($p \leq 0.05$). In addition, children in this pre-pilot study increased their minutes of physical activity (mean 26.67 min/day) and sleep time (mean 0.81 h/day) while decreasing the time spent in front of screens ($p \leq 0.05$), both on the weekdays (mean decrease 0.61 h/day) and on the weekends (mean decrease 0.78 h/day). ## 3.3.2. Pilot Study The change in BMI z-score of children in the intervention group was −0.16 (p-value = 0.002), while in the control group, BMI z-score decreased by −0.21 (borderline significant, p-value = 0.068), but between groups, there was no statistically significant difference. No statistically significant differences were observed regarding the other anthropometric characteristics. Regarding the dietary intakes, mean fruit and vegetable intake increased (mean increase of 0.61 and 0.79 servings/day for the control group vs. 0.64 and 1.03 servings/day for the intervention group, respectively) with a statistically significant increase ($p \leq 0.05$) within both groups. However, there was no statistically significant difference in fruit and vegetable intake between the control and intervention groups. A borderline significant difference between the groups was found in highly processed snacks, where a greater mean reduction was noted in the intervention group (−0.79 vs. −0.5 servings/week, $$p \leq 0.068$$) and in SSB, where a greater reduction was noted in the control group (−2.81 vs. −0.32 servings/week, $$p \leq 0.068$$). Physical activity increased for both groups (mean increase in physical activity 34.62 vs. 11.79 min/day for the control and intervention groups, respectively) with a statistically significant increase within groups ($p \leq 0.05$). Additionally, screen time decreased over the week with a mean decrease of 0.69 versus 0.07 h per day for the control and intervention groups, with a statistically significant decrease within groups ($p \leq 0.05$). Regarding sleep time, there was a mean increase of 0.38 h/day for the control group and 0.36 h/day for the intervention group ($p \leq 0.05$). A borderline significant difference existed between the groups in terms of minutes of physical activity ($$p \leq 0.061$$) and screen time during the week ($$p \leq 0.085$$); however, the largest difference was noted in the control group. ## 3.3.3. Overall Changes Considering the whole population of participants ($$n = 45$$) in both phases, there was a clinically significant reduction in BMI z-score (mean BMI z-score reduction: −0.21 ± 0.26, p-value < 0.001), an increase in fruit intake (mean increase in daily servings 0.62, $p \leq 0.001$), in vegetable intake (mean increase in daily servings 0.80, $p \leq 0.001$), and a decrease in fast food intake (mean decrease in weekly consumption −0.22, $$p \leq 0.042$$). Additionally, there was an increase in physical activity (mean increase in minutes of physical activity 24.33, $p \leq 0.001$), a reduction in hours of television viewing on weekdays (mean reduction in daily hours of television viewing −0.47, $$p \leq 0.005$$), and an increase in sleep hours (mean increase in sleep hours 0.54, $$p \leq 0.005$$). Furthermore, at the time of the second psychological assessment (in-person or over the telephone) of the mothers who completed the program ($$n = 45$$), no adverse psychological events were mentioned to the teams’ psychologists, and all mothers were satisfied with their participation in the program. ## 4.1. Main Findings The outcomes of this ENDORSE feasibility study revealed important findings that can help elucidate the advantages and challenges regarding the use of technological solutions in the management of childhood obesity. Engagement and adherence were demonstrated to play crucial roles in BMI reduction, a finding that is in accordance with the reported results obtained by metadata analysis [7]. However, the authors in this meta-analysis highlight the fact that most studies do not consistently measure and report adherence data, and when reported, adherence rates are often suboptimal and decline over time [7]. In order to provide reliable measures of adherence, several objective indicators were obtained reflecting the frequency of usage for each module and for the overall ENDORSE platform. The level of adherence varied among the participants, while in some modules (e.g., physical activity tracker), a reduction over time was observed. Comparing the adherence to the ENDORSE protocol with the protocol that applied other CDSS-based interventions for the management of childhood obesity, it can be implied that a low attrition rate ($10\%$) was achieved while most of the studies ($62.5\%$) were labeled as weak to moderate in terms of dropouts [18]. Additionally, the ENDORSE intervention plan was characterized as helpful, useful, and easy to use by the participants. This is particularly important, taking into consideration the high prevalence of severe obesity in the recruited samples. The benefit of applying wearables for physical activity monitoring toward weight controlling in childhood obesity was also demonstrated within the ENDORSE feasibility trial since a statistically significant correlation was revealed between the activity tracker average usage and the reduction of the BMI z-score (−0.355, $$p \leq 0.017$$). This finding is in accordance with the outcomes of a recent systematic review and meta-analysis investigating the effectiveness of interventions supported by activity trackers in preventing and treating childhood obesity [51]. Wearable device interventions had statistically significant beneficial effects on BMI, BMI z-score, body weight, and body fat. Another important finding concerns the high adherence observed in most participants in the control group ($62\%$). Τhe active control group included personalized messages sent by the clinical team on a weekly basis to all participating mothers via the application. This has probably generated a more frequent exchange of messages between the mothers and the clinical team compared to the intervention group, where the weekly messages were fully automated. Taking into consideration that our sample consisted mainly of treatment-seeking obese ($50\%$) and severely obese ($42\%$) participants with special needs, having a personal coach can contribute significantly to increased adherence. Similar findings were observed in a CDSS-based intervention clinical trial incorporating a health coach who performed four telephone calls with the participants’ parents within one year of intervention, and this led to better weight outcomes [52]. The low engagement with the ENDORSE’s SG highlights the great challenge of designing an SG featuring a balance between user attractiveness and scientific background in accordance with the existing literature [53,54,55]. However, the significant enhancement that was observed in terms of engagement in the pilot version compared to the pre-pilot version supports the effectiveness of the SG’s gameplay flow and conceptual framework. Potential improvements could refer to the implementation of more mini-games (missions) and the improvement of the artwork. Regarding the secondary objectives of this ENDORSE feasibility study, the e-health intervention managed to achieve clinically and statistically significant BMI z-score reduction (mean BMI z-score reduction: −0.21, p-value < 0.001) and significant change in health behaviors in all participants. This is in accordance with results from meta-analytic studies, showing that digital health interventions can be effective in treating childhood obesity [7,11,14]. A recent meta-analysis of 32 randomized controlled trials of technology-based interventions for childhood obesity treatment found a small but significant effect on weight outcomes (d = −0.13, $$p \leq 0.001$$), although 27 of 33 treatment studies ($79\%$) did not find significant differences between treatment and comparators [7]. Another meta-analysis of nine clinical studies using self-monitoring via mobile health technologies in pediatric weight management also found a small but significant overall effect size ($d = 0.42$) of the interventions on weight status [56]. Regarding health behaviors, overall ($$n = 45$$), there was an increase in fruit intake (mean increase in daily servings 0.62, $p \leq 0.001$), an increase in vegetable intake (mean increase in daily servings 0.80, $p \leq 0.001$), a decrease in fast food intake (mean decrease in weekly consumption −0.22, $$p \leq 0.042$$), an increase in physical activity (24.33 min/day, $p \leq 0.001$), a reduction of screen exposure on weekdays (−0.47 h/day, $$p \leq 0.005$$) and an increase in sleep time of 0.54 h/day ($$p \leq 0.005$$). A recent review of the impact of family-based digital interventions for obesity prevention and treatment on obesity-related outcomes in primary school-aged children [11] reported significant improvements in physical activity in two intervention studies [57,58] and in diet quality in terms of increasing energy from healthy core foods and decreasing energy from energy-dense nutrient-poor foods in one intervention study [59], while no significant effects were reported for screen time in one intervention study [60]. Specifically regarding fruits and vegetable consumption, a recent systematic review with meta-analysis of in-site and digital nutrition interventions in children and adolescents with overweight or obesity, including the intervention groups of 34 randomized control trials (RCT), reported an increased fruit and vegetable intakes from 0.6 to 1.5 servings/day at time periods up to 12 months from baseline [61]. ## 4.2. Limitations This ENDORSE feasibility study was conducted under highly challenging and difficult conditions imposed by the application of public measures to control the COVID-19 pandemic making it thus impossible to consider a control group following the standard of care due to the restricted clinic visitation policies. The size of the recruited sample was also affected and limited to a feasible number of participants. Furthermore, the participant’s lifestyle (e.g., diet, physical activity) was strongly influenced by the containment measures causing major changes in physical activity and diet with respect to those under normal conditions. The categorization based on the BMI could also be considered as a limitation since it was not indicative of body fat levels and fat-free mass. Children with normal weight might have low muscle levels, while active overweight children might have lower body fat and higher fat-free mass levels. Positive changes in fruit and vegetable intake and reduction in energy-dense nutrient-poor foods are of high importance as they have the potential to positively influence child weight status [1]. However, the food intakes of the children were assessed by an interview-delivered semi-quantitative questionnaire formed specifically for the needs of our study. The dietary outcomes were derived from the frequencies of consumption of food items or food group sections of the questionnaire converted to continuous variables for the need of the analysis, as mentioned in the methodology. This conversion must be kept in consideration with the interpretation of the results. Despite the limits of this dietary assessment method, the dietary intervention successfully focused on food-based guidance rather than nutrient-focused advice, as dietary interventions targeting intakes of specific food groups are more likely to result in the intended change in the targeted food(s) [61]. ## 5. 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--- title: Anticancer and Targeting Activity of Phytopharmaceutical Structural Analogs of a Natural Peptide from Trichoderma longibrachiatum and Related Peptide-Decorated Gold Nanoparticles authors: - Francesca Moret - Luca Menilli - Celeste Milani - Giorgia Di Cintio - Chiara Dalla Torre - Vincenzo Amendola - Marta De Zotti journal: International Journal of Molecular Sciences year: 2023 pmcid: PMC10057332 doi: 10.3390/ijms24065537 license: CC BY 4.0 --- # Anticancer and Targeting Activity of Phytopharmaceutical Structural Analogs of a Natural Peptide from Trichoderma longibrachiatum and Related Peptide-Decorated Gold Nanoparticles ## Abstract In the large field of bioactive peptides, peptaibols represent a unique class of compounds. They are membrane-active peptides, produced by fungi of the genus Trichoderma and known to elicit plant defenses. Among the short-length peptaibols, trichogin GA IV is nonhemolytic, proteolysis-resistant, antibacterial, and cytotoxic. Several trichogin analogs are endowed with potent activity against phytopathogens, thus representing a sustainable alternative to copper for plant protection. In this work, we tested the activity of trichogin analogs against a breast cancer cell line and a normal cell line of the same derivation. Lys-containing trichogins showed an IC50 below 12 µM, a peptide concentration not significantly affecting the viability of normal cells. Two analogs were found to be membrane-active but noncytotoxic. They were anchored to gold nanoparticles (GNPs) and further investigated for their ability to act as targeting agents. GNP uptake by cancer cells increased with peptide decoration, while it decreased in the corresponding normal epithelial cells. This work highlights the promising biological properties of peptaibol analogs in the field of cancer therapy either as cytotoxic molecules or as active targeting agents in drug delivery. ## 1. Introduction Peptide-based therapy is considered a promising alternative to small-molecule and monoclonal antibody therapies, being characterized by fewer off-target effects than the former and economically advantageous over the latter [1,2,3]. Several peptide-based drugs currently on the market are used for the treatment of metabolic diseases [4,5], but they are actively studied also for their anticancer and antimicrobial potential [6,7]. Unfortunately, most of the therapeutic peptides cannot be administered orally since they are easily degraded by proteolytic enzymes [8]. Among natural, bioactive peptides there are peptaibols [9,10], an important class of antimicrobial peptides that show resistance to enzymatic degradation, the ability to interact with phospholipid membranes and a stable helical structure [11,12,13]. Lipopeptaibols are a family of short-length peptaibols featuring an N-terminal long-chain acyl group [14,15]. The progenitor of lipopeptaibols is trichogin GA IV, a weakly amphiphilic peptide of ten amino acid residues, featuring a 1,2-aminoalcohol at its C-terminus and a 1-octanoyl group at its N-terminus [16,17]. Trichogin GA IV is produced by the fungus Tricoderma longibrachiatum [18] as a defensive weapon against other microorganisms [19,20]. Over the years, several analogs of trichogin GA IV have been produced and evaluated with the aim of applying them as plant-protection products against fungal phytopathogens [21,22,23,24,25] (Table S1) and also as antimicrobial agents against Gram+ and Gram− bacteria (S. aureus, S. epidermis, P. aeruginosa, E. coli) [26,27]. Recently, specific amino acid substitutions on the peptide backbone allowed us to identify trichogin analogs (e.g., K6-Lol and K6-NH2, Table 1) endowed with potent activity against ovarian cancer and Hodgkin lymphoma [28]. Indeed, it was demonstrated in vitro that both peptides exerted cytotoxicity due to membrane permeabilization in different cancer cell lines, including some tumor cells resistant to doxorubicin or cisplatin treatments. Importantly, notwithstanding a quite comparable cytotoxic effect, K6-Lol and K6-NH2 demonstrated higher and more rapid uptake in cancer cells with respect to normal ones, thus suggesting a potential to selectively interact with malignant tissues in vivo. In the frame of the same systematic substitution of trichogin GA IV amino acids, in the present work, a dozen water-soluble analogs (Table 1), either known [21,29,30] or newly designed, were efficiently produced by ecofriendly solid-phase peptide synthesis and tested in vitro for anticancer activity and selective interaction against breast cancer cells (MDA-MB-231) with respect to normal cells of the same derivation (MCF-10A). We selected a triple negative breast cancer (TNBC) as in vitro model over other BC subtypes since the frequency of its diagnosis has dramatically increased over the recent years, especially among young women [31,32]. Indeed, the TNBC survival rate remains the lowest with respect to those of other BC subtypes: targeted hormonal therapies are almost ineffective, since TNBC cells lack estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2) [33,34]. While the majority of trichogin GA IV analogs herein described displayed potent anticancer activity in the low micromolar range, four of the screened peptides were able to interact with cancer cells very efficiently but without exerting any cytotoxicity. Those peptide sequences derive from trichogin analogs that have already showed potent membrane interaction and poor cytotoxicity [29]. Thus, in the present study we also report on the possibility to exploit them as targeting agent for the decoration of gold nanoparticles (GNPs) to be used for drug delivery or diagnostic purposes in the TNBC therapy context. Great efforts are currently directed toward the development of targeted therapies which often combine chemotherapeutics/immunotherapeutics with unprecedented nanomaterials [35,36]. Notwithstanding the great impact of the nanotechnology revolution in the field of drug delivery, with the commercialization and clinical application of some traditional anticancer drugs in the form of NPs [37,38], an increase in the selectivity of nanosystem uptake by the target organs is still necessary. To this end, we coupled the peptides with GNPs obtained by laser ablation in liquid (LAL) [39]. These GNPs are free of capping agents or ligands, and hence their surface is readily available for conjugation with thiolated compounds without the need for place-exchange reactions [40,41,42]. In addition, the GNPs are ultrapure because they are produced in aqueous solutions containing only 0.2 mM NaCl, without the addition of other chemical compounds which may interfere with the peptides structure and integrity [43]. For these reasons, and for the other advantages such as the fast, easy, reproducible, robust, clean, green and cost-effective synthesis, GNPs obtained by LAL have been frequently exploited as building blocks in bioconjugates for biotechnological applications [40,41,42,43,44]. ## 2.1. Peptide Synthesis Trichogin analogs were produced by manual solid phase peptide synthesis (SPPS) following the procedure described in [21]. The peptide sequences are reported in Table 1, together with the plant-protection properties of the known peptides. The protocol foresees the use of ecofriendly reagents, such as Oxyma pure [45] and diisopropylcarbodiimide (DIC) as active agents and the use of ecofriendly ethyl acetate/dimethylsulfoxide mixtures instead of dimethylformamide (DMF) as solvent [46,47], thus limiting the impact of the peptide synthesis on the environment. The good purity of the crude peptides allowed us to purify them to >$95\%$ by medium-pressure liquid chromatography using an Isolera Prime instrument (Biotage, Uppsala, Sweden), further reducing the waste of organic solvents compared to preparative HPLC purification. Phytosanitary trichogin analogs endowed with positive charge(s) were included in the present work based on promising literature data on the cytotoxicity of some of them (Table 2). Cationic [48], membrane-active peptides should indeed have an advantage in interacting with tumor cells since the latter have a higher concentration of negatively charged glycoproteins and phosphatidylserine in their membrane than healthy cells [49,50]. The newly designed and produced sequences Leu4-NH2 and Api8-NH2 (Table 1) are cheaper versions of the related Leu4-Lol and Api8-Lol peptaibols (endowed with a C-terminal Leucinol instead of the -Leu-NH2) in that the SPPS 2-chlorotrityl resin preloaded with the 1,2-aminoalcohol Lol [51,52] is very expensive compared to the rink amide resin herein used to produce Leu4-NH2 and Api8-NH2. Leu4-NH2 was selected because of the peculiar biological activity of the trichogin analog Leu4-Lol [27], which was proven to be able to interact with phospholipid membranes while being inactive both towards bacteria and eukaryotic cells [27,29]. Such behavior makes it a promising candidate as a drug carrier, for example, bound to nanoparticles. To this aim, the related Leu4-SH (Table 1) bearing a lipoyl moiety to be anchored to nanoparticles [53] was produced. Api8-NH2 was chosen because its Lol-bearing counterpart Api8-Lol was found to be selective against cancer cell lines, leaving healthy eukaryotic cells unaffected (Table 2) [29]. Similarly, Api8-SH (Table 1) was produced to decorate gold nanoparticles and to study its potential as a drug carrier. To study cell internalization by flow cytometry of the two sequences Leu4 and Api8, the related fluorescein 5[6]-isothiocyanate (FITC)-labeled analogs (Leu4-FITC and Api8-FITC) were also synthesized on a 2-chlorotrityl resin preloaded with the ethylendiamine linker. The fluorescent dye was coupled at the C-terminus in solution, using Merck-supplied FITC. The new sequences designed and synthesized in the present work (Table 1) were all obtained in high yield (>$60\%$) and purity (≥$95\%$). The chemical characterization of the new sequences by means of HPLC and high-resolution electrospray ionization mass analysis (HR-ESIMS) is reported in the Supporting Materials (Figures S1–S12). ## 2.2. Synthesis and Characterization of Peptide-Decorated Gold Nanoparticles GNPs were synthesized by LAL, according to a previously described procedure [39,42,43] in distilled water with 2 × 10−4 M NaCl. The final GNP concentration was 22.8 × 10−9 M (value obtained by UV absorption analysis using the protocol described in [54]), and the Feret average size was 13 nm (evaluated by dynamic light scattering, DLS). The concentration of thiol-bearing peptide analogs to be used to saturate the GNP surface was estimated from the nanoparticle concentration and size and assuming a packing density of two peptide molecules per square nanometer of nanoparticles. The final concentration of Api8-SH in the GNP-containing solution was 24.2 µM. A lower concentration of 18.2 µM was used for the hydrophobic peptide Leu4-SH to avoid GNP aggregation and precipitation. Indeed, the DLS spectra of peptide-decorated GNPs (Figure S13, Supporting Materials) showed that their mean radius is increased compared to naked GNPs, indicating the onset of GNP aggregation, probably due to peptide–peptide interactions. Api8-S-GNPs form aggregates with an average diameter of 44.13 nm, while Leu4-S-GNPs have a larger mean diameter (over 1000 nm). The absorption spectra of the GNPs before and after 5-hour incubation with the peptides (Figure S14, Supporting Materials) indicate the red shift (from 520 nm to 535 nm for Api8-SH and 550 nm for Leu4-SH) and the broadening of the plasmon absorption band, which are indicative of particle aggregation [55]. Transmission electron microscopy (TEM) images (Figure S15, Supporting Materials) further confirmed the different aggregation state of the GNP constructs, in agreement with DLS-derived mean diameters, since the free GNPs are homogeneously dispersed as isolated particles on the carbon film of the TEM grid, while peptide-decorated GNPs show different aggregates of particles. ## 2.3. Conformational Study by Circular Dichroism (CD) A conformational study on the new peptides Api8-NH2 and Leu4-NH2 and related analogs was performed by CD [56]. The presence of a C-terminal amide allows the onset of an additional hydrogen bond compared with the corresponding 1,2-aminoalcohol, thus increasing the strength of the helical conformation, which is known to be essential for the biological activity of peptaibols [57]. Api is a Cα-tetrasubstituted residue that shares the same helix-inducing ability of Aib [58,59]; therefore, Api8-NH2 is expected to maintain the helical structure of the parent peptide trichogin GA IV [60]. Leu4-NH2 should retain the peculiar helix–loop–helix conformation of the related Leu4-Lol (with a C-terminal Leucinol instead of the -Leu-NH2) [27]. Such a 3D-structure seems mainly responsible for its peculiar bioactivity [27]. Initially, the 3D-structure adopted by Api8-NH2, Leu4-NH2, Api8-FITC, Leu4-FITC, Api8-SH, and Leu4-SH have been characterized in solution by CD under a variety of experimental conditions (see Supporting Materials, Figures S16–S20). The results highlighted the onset of a right-handed, mixed α-/310-helical conformation for all the analogs [61,62], as expected, with a switch towards a full α-helix in the presence of micelles of sodium dodecyl sulfate in water, namely the membrane-mimicking environment tested. The CD profile of Api8-NH2 and Leu4-NH2 was also acquired in the presence of cells (cell line MCF-10A) (Figure 1). This study also provided information on the stability of the peptides in the biological environment [63]. Trichogin analogs are usually resistant to the action of proteolytic enzymes thanks to the presence of the noncoded α-amino acid Aib in their sequences [27,64]. Indeed, the CD analysis confirmed both the stability of the peptides in the presence of cells and the conservation of their helical structure, with the positions of the two negative maxima falling at the canonical wavelengths for a α-helix, namely about 208 and 220 nm [65]. Comparing the CD profiles of the peptides in water (Supporting Materials) and in the presence of cells (Figure 1) it is clear that the negative maximum centered at about 222 nm is more pronounced in the latter conditions for both peptides. This modification on CD spectra is usually associated with a switch towards a more α-helical conformation [61,62]. The profile in the presence of cells resembles that recorded in the membrane-mimicking environment (micelles of sodium dodecyl sulfate, SDS 100 mM in H2O; Supporting Materials, Figures S16 and S19) for both peptides. This observation seems to point out the presence of peptide–membrane interactions for both trichogin analogs also when in contact with cells. The CD analysis was also performed on the peptides Api8-SH and Leu4-SH once linked to GNPs (Api8-GNPs and Leu4-GNPs) to confirm the persistence of the helical conformation (Figure 2). The analysis revealed a right-handed, mixed α-/310-helical conformation for both peptides also when linked to the GNPs, in line with the results obtained for the corresponding free peptides. ## 2.4. Membrane Leakage Study We verified the ability of Api8-NH2 and Leu4-NH2 to cause leakage of the entrapped 5 [6]-carboxyfluorescein (CF) dye from small unilamellar vesicles (SUVs) [66] made of two lipid mixtures: phosphatidylethanolamine (PE)/phosphatidylglycerol (PG) 7:3, namely a phospholipid composition with a net negative charge, thus mimicking the tumor cell membrane [50]; and phosphatidylcholine (PC) and cholesterol (Ch) $\frac{7}{3}$, a zwitterionic model membrane mimicking the erythrocytic one [67]. The assay follows fluorescence increase in response to CF release from SUVs in terms of percentage with respect to the full membrane disruption caused by Triton addition. The results are reported in Figure 3. As expected, the peptides are able to cause membrane leakage with subsequent release of the fluorescent dye almost as effectively as the parent peptide trichogin GA IV. The results are in line with the literature on Leu4-Lol [27]. This result allowed us to conclude that both peptides retain the ability to interact with model membranes of the parent peptide. Leakage experiments cannot be performed on FITC-containing peptides since fluorescence from FITC interferes with that from the CF release. ## 2.5. In Vitro Cytotoxicity of Trichogin GA IV Analogs in Breast Cells The in vitro anticancer activity of trichogin analogs with plant-protection properties (Table 1) was assessed by comparing the exerted cytotoxicity in MDA-MB-231 TNBC cells and in normal epithelial breast MCF-10A cells. Table 2 summarizes the cytotoxic activity against other cancer cell lines previously reported in the literature for some of them. Table 2 \| Acronym \| IC50 a (µM) and Target Cancer Cell Lines b \| IC50 a (µM) and Target Cancer Cell Lines b.1 \| IC50 a (µM) and Target Cancer Cell Lines b.2 \| IC50 a (µM) and Target Cancer Cell Lines b.3 \| IC50 a (µM) and Target Cancer Cell Lines b.4 \| Hemolysis/Healthy Cells \| References \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Acronym \| HeLa \| A549 \| A431 \| T67 \| Ov-HL \| Hemolysis/Healthy Cells \| References \| \| TRIC \| 4–8 \| 4–6 \| 4–6 \| 2 \| n.d. c \| nonhemolytic \| [29,30] \| \| Api8-NH2 \| - d \| - d \| - d \| - d \| - d \| - d \| this work \| \| Api8-Lol e \| >20 f \| n.d. \| n.d. \| 13 \| n.d. \| nonhemolytic f/nontoxic g \| [29] \| \| Leu4-NH2 \| - \| - \| - \| - \| - \| - \| this work \| \| Leu4-Lol e \| >20 \| nt \| nt \| >20 \| n.d. \| nonhemolytic/nontoxic \| [29,68] \| \| K2569-Lol \| - \| - \| - \| - \| - \| - \| this work \| \| K259-NH2 \| - \| - \| - \| - \| - \| - \| this work \| \| K259-Lol \| 6 \| 8 \| 5 \| n.d. \| n.d. \| nonhemolytic \| [68] \| \| K25-Lol \| 7 \| 10 \| 5 \| n.d. \| 7–13 \| nonhemolytic <16 µM \| [68] \| \| K56-Lol \| 10 \| 16 \| 8 \| 7 \| n.d. \| nonhemolytic/nontoxic \| [29,68] \| \| K6-NH2 \| n.d. \| n.d. \| n.d. \| n.d. \| 7–13 \| n.d. \| [28] \| \| K6-Lol e \| 2–8 \| 8 \| 5 \| 4 \| n.d. \| nonhemolytic<16 µM \| [28,29,30] \| \| K2-NH2 \| - \| - \| - \| - \| - \| - \| this work \| \| K2-Lol e \| 7 \| 8 \| 6 \| n.d. \| n.d. \| nonhemolytic \| [30,68] \| \| Api8-FITC \| - \| - \| - \| - \| - \| - \| this work \| \| Leu4-FITC \| - \| - \| - \| - \| - \| - \| this work \| \| Api8-SH \| - \| - \| - \| - \| - \| - \| this work \| \| Leu4-SH \| - \| - \| - \| - \| - \| - \| this work \| Cells were incubated with increasing concentrations of peptides for 24 h before assessing cell viability with MTS assay in order to create dose/response curves (Figure 4) and calculate the half maximal inhibitory concentration (IC50) values for each peptaibol (Table 3). For convenience, peptides have been subdivided based on their C-terminal substituents, i.e., leucinol (lol) or amide (-NH2). Leu4-NH2, Api8-NH2 and Leu4-Lol did not significantly affect the viability of both cell lines in the peptide concentration range tested (Figure 4). For all the other peptaibols, a concentration-dependent cell viability reduction was observed. The cytotoxicity is higher against the cancer than the normal cell line, as reflected by the calculated IC50 values (Table 3). Indeed, while the IC50 values measured in MDA-MB-231 cells are in the concentration range 8–12 µM (with the exception of K259-NH2, with an IC50 of about 13 µM), viability in MCF-10A cells did not drop below $50\%$ for any of the peptides tested in the aforementioned concentration range. Of note, the IC50 values on normal cells are 5 µM higher than those measured in cancer cells. We note that none of the peptaibols were found cytotoxic against epithelial breast cells up to 10 µM, while in cancer cells most of them exerted significant viability reduction (killing activity of about $90\%$ for K6-NH2 and K2-NH2, $60\%$ for K259-Lol and K25-Lol, $30\%$ for K2569-Lol, $20\%$ for K56-Lol). This result is in line with the observation that K6-NH2 is taken up faster by ovarian cancer (OvCa) and Hodgkin lymphoma (HL) cell lines than normal cells [28]. Generally, the C-terminal moiety does not seem to significantly impact on peptide cytotoxicity, the effect of -NH2 or -Lol analogs being quite comparable. Again, this observation confirms and broadens the significance of the data reported for K6-Lol and K6-NH2 on OvCa and HL cell lines [28]. The cost-effective amino alcohol-to-amide substitution at the peptide C-terminus can therefore be considered conservative in terms of cytotoxic activity. ## 2.6. In Vitro Uptake of Selected Peptaibols For this study, we decided to focus our attention on the unique, noncytotoxic, yet membrane-active peptides Leu4-NH2 and Api8-NH2 to study them as potential cancer-targeting agents. To assess if they displayed any selectivity for breast cancer vs. breast normal cells, we synthesized their FITC-conjugated version, Api8-FITC and Leu4-FITC, and studied their internalization by measuring the cellular uptake by flow cytometry (Figure 5). As reference, K6-NH2 internalization was measured as well, since for this specific peptide, previous studies already reported an increased uptake in tumor cells (HDLM-2 and A2780 tumor cell lines) with respect to normal healthy cells, such as peripheral blood mononuclear cells (PBMCs) or fibroblasts [28]. As clearly visible in Figure 5, the internalization rate of all the three peptide was significantly higher in MDA-MB-231 cells with respect to MCF-10A cells, indicating some cancer cell selectivity. Moreover, while the increase in concentration did not result in an increased uptake in normal cells, a concentration-dependent uptake was measured for all three peptides in cancer cells. At the highest concentration tested, Leu4-FITC, Api8-FITC and K6-FITC were internalized about 5, 3, and 4-folds more, respectively, in MDA-MB-231 with respect to MCF-10A cells. Of note, none of the FITC-conjugated peptaibols were cytotoxic in both cell lines up to 10 µM (Figure S23, Supporting Materials). ## 2.7. Uptake of GNPs Decorated with Peptaibols in Breast Cancer Cells and Macrophages Cell uptake of the peptide-decorated GNPs was assessed after 24 h incubation by transmission electron microscopy (TEM) in samples counterstained with uranyl acetate. The results were further confirmed by atomic absorption spectrometry (AAS). First, we verified the compatibility of the peptide-decorated GNPs toward our in vitro cell models, in order to exclude any increased uptake due to their possible intrinsic cytotoxicity. As confirmed by the graphs of Figure S24 in the Supporting Materials, peptide-decorated GNP suspensions were almost noncytotoxic against both cell lines after 24 h cell incubation up to a concentration corresponding to 10 µM of peptide (with the exception of a few cases in which we measured a $15\%$ viability reduction; see statistic for significance). A concentration of peptide-GNPs corresponding to 5 µM peptide, devoid of any possible cytotoxic effect, was thus used for TEM analysis (Figure 6). In Figure 6a,b are reported a few representative images, showing that all GNP samples are internalized in both cell lines but to a significantly different extent. Indeed, completely different trends of GNP uptake were observed both among cell lines and among the samples. Unexpectedly, by observing tens of TEM-acquired images, it appeared that all types of GNPs were taken up to a greater extent in normal MCF-10A cells than in the tumor cells, in the increasing order: Leu4-GNPs < Api8-GNPs < GNPs. The opposite trend was observed in tumor MDA-MB-231 cells, with Leu4-GNPs showing the highest intracellular uptake, followed by Api8-GNPs and nude GNPs. Therefore, it appears clear that: (i) GNP functionalization impacts the internalization rate; (ii) GNP uptake is cell-line dependent; (iii) GNPs are internalized by endocytosis (Figure 6). Indeed, MDA-MB-231 invariably contained groups of GNPs entrapped in vesicular components in the cytoplasm (e.g., endosome/lysosomes); magnified images 5, 8, and 11 (Figure 6a) clearly show that those organelles contain a higher number of NPs in the case of Leu4-GNPs, followed by Api8-GNPs, and nude NPs. In MCF-10A cells, the elevated number of GNPs internalized, especially in the case of nude GNPs and Api8-GNPs, entailed the formation of larger vesicles, with hundreds of NPs inside (Figure 6(b6,b9)). To verify TEM internalization results with a more quantitative analysis, we performed AAS under the same experimental conditions. AAS measurements (Figure 6c) indeed confirmed the internalization trend previously observed: the uptake of all GNP formulations was significantly higher in MCF-10A than MDA-MB-231 cells, with a ratio of about 5.5: 3.5: 1.2 fold for nude GNPs, Api8-GNPs, Leu4NPs, respectively. Importantly, AAS data revealed that the presence of peptaibols on the surface of GNPs increased GNP accumulation in TNBC cells (with respect to the nude counterpart) while decreasing it in normal epithelial cells of the same derivatization. Of note, using Leu4 as targeting agent resulted in a quite similar internalization rate among our two model cell lines. DLS measurements on Leu4-GNPs indicated an average hydrodynamic diameter > 1000 nm, very likely due to the formation of aggregates between particles in those experimental conditions. On the other hand, as shown in Figure S15 (Supporting Materials), the diameter of single Leu4-S-GNPs does not exceed 30–40 nm, i.e., the same dimension range as that of Api8-S-GNPs. Image 12 of Figure 6 (MDA-MB-231 cells) shows aggregated Leu4-GNPs interacting with plasma membrane as well as NPs within endocytic vesicles, confirming that, at least in the cell lines considered in this study, aggregates of gold NPs are promptly internalized. In any case, further experiments using other normal and cancer cell lines are needed to confirm the observations reported herein, which indicated Leu4 as the most appropriated peptaibol for targeted drug delivery or therapeutic applications. In the latter context, another fundamental characteristic that a nanosystem must possess is the capacity to avoid recognition/clearance by the reticuloendothelial system (RES) components, mainly macrophages (e.g., Kupffer cells) resident in liver and spleen [69]. NP recognition is mainly mediated by serum proteins absorbed on the surface of the nanosystem, the so called “protein corona”. Since the conjugation of GNPs with peptaibols could influence macrophage phagocytosis, we analyzed the interactions of the different GNP formulations with human macrophages, derived from monocytes isolated from buffy coats, by means of TEM analysis. As visible in the representative images of Figure S25 in the Supporting Materials, a very similar extent of GNP capture by macrophages was observed, independently of the presence of peptaibol decoration on NP surfaces. ## 3. Discussion Trichogin is a naturally-occurring, cytotoxic peptaibol produced by a fungus of the genus Trichoderma, which is used in organic farming. Antitumor activity of several trichogin analogs endowed with plant protection properties have been assessed against breast cancer cells (cell line MDA-MB-231) as well as new analogs, obtained in high yield and purity via a protocol with limited environmental impact. The cytotoxicity to normal cells of the same derivation (cell line MCF-10A) was also tested. All Lys-containing trichogin analogs showed antitumor activity with an IC50 between 8 and 13 µM, a concentration range where healthy cells are still about $90\%$ viable. Such Aib-containing, membrane-active cationic peptides represent a promising class of potential antitumor compounds since they combine affinity to tumor cell membranes—rich in phosphoserine [70] with a limited chance to elicit resistance since they do not have a specific target [71]. In addition, Aib is known to increase peptide proteolytic resistance [72,73]. The assays allowed us to identify two analogs (Leu4-NH2 and Api8-NH2) as potential targeting agents/drug carriers [74] since they displayed membrane activity devoid of cytotoxicity. Circular dichroism analysis carried out in the presence of breast cells showed the presence of the helical structure, thus proving the stability of the two analogs in the biological environment. GNPs decorated with the thiol-containing versions of those analogs were produced and characterized. Circular dichroism (CD) confirmed the presence of the helical conformation for the peptides even when anchored to the nanoparticles. Intracellular uptake was evaluated both in cancer and in the corresponding normal cell line. Peptide decoration increased GNP-uptake by cancer cells with Leu4-S-GNPs being internalized best. In the meantime, peptide-decoration also limited GNP-intake by normal cells, again with Leu4-S-GNPs showing the highest uptake reduction. NPs are known to be recognized by macrophages, thus limiting their possible side effects on off-target cells [75]. Peptide-decoration might perturb this feature [76]. Nonetheless, we found that GNP decoration with the two peptides did not alter GNP capture by macrophages. ## 4.1. Peptide Synthesis Api8-NH2 and Leu4-NH2 peptides were obtained following the solid-phase peptide synthesis (SPPS) procedures described in [21,27] on a Rink Amide resin (Novabiochem, Merck Biosciences, La Jolla, CA, USA). The C-terminal FITC analogs Api-FITC and Leu-FITC were synthesized by manual SPPS on a 1,2-diaminoethane-trityl resin (Iris Biotech, Marktredwitz, Germany). Fmoc-deprotection was achieved by treatment with $20\%$ piperidine solution in N,N-dimethylformamide (DMF). The deprotection step was repeated twice (5 and 10 min, respectively). The coupling steps were generally carried out exploiting Oxyme pure and diisopropylcarbodiimide (DIC) as activating agents. The coupling reactions involving Aib residues were doubled. All 1 h coupling steps were carried out with three equivalent excess of the activated residue. Capping of the N-terminal α-NH2 with 1-octanoyc acid was achieve by reaction with four equivalents of 1-Octanoic acid, Oxyma pure, and DIC. The 1 h coupling was repeated twice. Peptide cleavage from the Rink amide resin was achieved by 2.5 h acid treatment with the standard mixture: trifluoroacetic acid, TFA, $95\%$; water, $2.5\%$; triisopropylsilane, TIS, $2.5\%$. C-terminal amino alcohol-containing peptides were cleaved by several treatments with $30\%$ hexafluoroisopropanol (HFIP) in dichloromethane, as described in [77]. The filtrates, after precipitation in diethyl ether, were collected and concentrated under a flow of N2. The crude peptides were used without purification to obtain the respective, FITC-bearing, compounds, by reaction with 2 equivalents of FITC in DMF in the presence of diisopropylethylamine. The workup of the reaction mixtures, dissolved in dichloromethane, involved washings with H2O. Each organic phase was washed with H2O three times, dried over Na2SO4, filtered, and evaporated to dryness. Boc removal from the precursor Api (Boc)-FITC was achieved by dissolving the peptide in HCl 3M in methanol. The reaction was left stirring until quantitative Boc-removal (2 h, followed by HPLC). Crude peptides were purified by medium-pressure liquid chromatography on a Biotage Isolera Prime Instrument. Yield: Api8-NH2, $60.28\%$; Leu4-NH2 $72.5\%$. The purified fractions were characterized by analytical RP-HPLC on a Jupiter Phenomenex (Torrance, CA, USA) C18 column (4.6 × 250 mm, 5μm, 300 Å) using an Agilent (Santa Clara, CA, USA) 1200 HPLC pump. The binary elution system used was A, H2O/CH3CN (9:1 v/v) + $0.05\%$ trifluoroacetic acid (TFA); B, CH3CN/H2O (9:1 v/v) + $0.05\%$TFA; flow rate 1 ml/min; spectrophotometric detection at λ = 216 nm. Electrospray ionization, high-resolution mass spectrometry (ESI-HRMS) was performed by using a Waters Micromass instrument (Milford, MA, USA). HPLC and ESI-MS spectra, and characterization of the synthetic segments obtained during solution phase synthesis are reported in the Supporting Materials. ## 4.2. Circular Dichroism The CD spectra were obtained on a Jasco (Tokyo, Japan) J-1500 spectropolarimeter. Fused quartz cells (Hellma, Mühlheim, Germany) of 1 mm path length were used. The values are expressed in terms of [θ]T, total molar ellipticity (deg∙cm2∙dmol−1). Spectrograde MeOH and TFE (Acros, Geel, Belgium) were used as solvents. The CD measurements in cells were performed as follows: 1 × 106 cells were harvested, washed twice in PBS and incubated in the same buffer with peptides (concentrations: Api8, 5 × 10−5 M; Leu4, 1 × 10−5 M) for 1 h at 37°C in a rotating mixer before recording the CD spectra. ## 4.3. Gold Nanoparticles Synthesis and Characterization GNPs LAL synthesis was performed with the laser pulses at 1064 nm (6 ns, 50 Hz) focused to 8 J/cm2 with an f 100 mm lens on a $99.99\%$ pure Au plate dipped in distilled water with 2 × 10−4 M NaCl [39,42,43]. The laser beam ablated a circular area of 5 mm in diameter and the ablation cell was mounted on a motorized XY scanning stage (Standa, Vilnius, Lithuania) managed with a 2-axis stepper and a DC motor controller. UV-vis spectroscopy was performed in 0.2 cm optical path quartz cuvettes using a JASCO (Tokyo, Japan) V770 spectrophotometer. DLS analysis was performed using a Malvern (Malvern, UK) Zetasizer Nano ZS in ZEN0040 cells. TEM was performed on a FEI (Hillsboro, OR, USA) Tecnai G2 12 instrument operating at 100 kV and equipped with a Veleta (Olympus Soft Imaging System, Denver, CO, USA) digital camera. Peptide decoration was performed by mixing a concentrated solution of peptide with the solution of GNPs as obtained from LAL, obtaining a final concentration of 24.2 µM for Api8-SH and 18.2 µM for Leu4-SH, respectively. The solution was incubated overnight, then washed to remove excess peptide before use. ## 4.4. Leakage Phospholipids (phosphatidylethanolamine (PE) and phosphatidylglycerol (PG)) were supplied by Avanti Polar Lipids (Alabaster, AL, USA). The two lipids were combined in a 7:3 w/w ratio in a test tube, dissolved in chloroform, and then dried over a nitrogen flux to obtain a lipid film. The lipid film was then hydrated with a solution of 5[6]-carboxyfluorescein (CF) in 30 mM HEPES buffer (pH 7.4) at room temperature overnight. The resulting vesicle suspension was sonicated twice for 30 min over an ice bath to break the multilamellar structure, to obtain small unilamellar vesicles (SUVs). The suspension was loaded in a gel filtration column Sephadex G-75 (Merck) to remove the excess of CF. The obtained SUVs concentrated solution was diluted with buffer (100 mM NaCl, 5 mM HEPES, pH 7.4) to a working concentration of 0.06 mM. The working solution of SUVs was stored at 4 °C and used within 24 h. Fluorescence (measured on a MPF-66 spectrofluorimeter, Perkin Elmer, Waltham, MA, USA) was used to evaluate peptide-induced dye leakage from the liposomes. Onto each cuvette containing a fixed volume of the SUVs working solution (2.5 mL), increasing amounts of peptide solution (s) in water (or methanol for the non-water-soluble analogs) were added to achieve increasing [peptide]/[lipid] molar ratios (R−1). The fluorescence was recorded for each cuvette, at 520 nm with λexc = 488 nm, before peptide addition and after 20 min incubation with the peptide. The released CF at time t (%CF) was determined as: %CF = (Ft − F0)/(FT − F0) × 100, where F0 is the fluorescence intensity recorded for SUVs before peptide addition; Ft is the fluorescence intensity of vesicles at the time t: 20 min after peptide addition; and FT is the total fluorescence intensity determined by disrupting the SUVs by adding $10\%$ v/v Triton X-100 in water (50 μL). The results are reported on a logarithmic scale. ## 4.5. Cell Lines MDA-MB-231 (human triple-negative breast cancer) and MCF10A (human nonmalignant breast epithelial) cell lines were purchased from American Type Culture Collection (ATCC, Rockville, MD, USA). MDA-MB-231 were grown in Dulbecco’s Modified Eagle Medium (DMEM) with GlutamaxTM supplemented with $10\%$ heat inactivated fetal bovine serum (FBS) and antibiotics (100 U/mL streptomycin, 100 μg/mL penicillin G). MCF10A cells were cultured in DMEM/F12 medium supplemented with $5\%$ horse serum, 20 ng/mL EGF, 0.5 mg/mL hydrocortisone, 100 ng/mL cholera toxin, 10 μg/mL insulin, and antibiotics. Human macrophages were derived from monocytes extracted from fresh buffy coats of healthy donors (obtained from Azienda Ospedaliera Padova, Padova, Italy) by centrifugation over a Ficoll–Hypaque step gradient and subsequent Percoll gradient (Sigma-Aldrich, Munich, Germany). All cell culture medium and supplements were purchased from Life Technologies or Sigma-Aldrich (Munich, Germany), while sterile plasticware was purchased from Falcon® (Corning, New York, NY, USA). ## 4.6. In Vitro Cytotoxicity of Peptaibols and Peptaibol-Decorated GNPs The cytotoxicity of peptides and free Api8- and Leu4-decorated GNPs was assessed with the MTS assay (CellTiter 96® AQueous One Solution Cell Proliferation Assay, Promega, Milan, Italy) in cancer MDA-MB-231 and normal MCF-10A cells exposed to increasing concentrations of peptaibols/GNPs for 24 h. Cells (8 × 103 cells/well for MDA-MB-231, 6 × 103 cells/well for MCF-10A) were seeded in 96-well plates, and after 24 h the medium was replaced with a fresh one containing peptaibols/GNPs. For MTS assay, the medium was replaced with 100 μL of serum-free medium and 20 μL of the CellTiter 96® reagent. After 60–90 min, the absorbance at 492 nm was measured with a Multiskan Go (Thermo Fischer Scientific, Carlsbad, CA, USA) plate reader and cell viability was expressed as a function of absorbance relative to that of control cells (considered as $100\%$ viability). IC50 values for each peptaibol treatment were extrapolated from the relative dose–response curves obtained with the software GraphPad Prism 9.5. ## 4.7. Cellular Uptake of FITC-Conjugated Peptides Flow cytometry and peptides conjugated with FITC, namely Api8-FITC and Leu4-FITC, were used to assess the capacity of peptaibols to be internalized by breast cancer cells, Briefly, 8 × 104 MDA-MB-231 and MCF-10A cells/well were grown in 24-well plates for 24 h and then incubated for 24 h with 5 μM peptaibols. At the end of the incubation time, cells were washed twice with Versene solution and detached from the plates with trypsin that was neutralized by the addition of FBS. Those washings both before cell detachment from plates and after cell recovery and centrifugation removed any peptides not strongly associated with cell plasma membranes, assuring FITC signals measured by flow cytometry were referred exclusively to internalized peptides. Cells were centrifuged and resuspended in Versene before measuring FITC fluorescence using a BD FortessaTM X-20 flow cytometer (Becton Dickinson, San Jose, CA, USA). For each sample, 104 events were acquired and analyzed using the FACSDiva 9.0 software. ## 4.8. Cellular Uptake of GNPs Measured by TEM The uptake of GNPs and peptide-decorated GNPs by breast cells was assessed by TEM. Briefly, 8 × 104 MDA-MB-231 and MCF-10A cells/well were grown in 24-well plates for 24 h and then incubated for 24 h with a GNPs concentration corresponding to a final 5 μM of peptide. Cells were then fixed in $2.5\%$ glutaraldehyde in 0.1 M phosphate buffer at pH 7.4 for 1 h at room temperature and then washed three times with phosphate buffer (10 min each wash). The samples were post-fixed in $1\%$ osmium tetroxide in 0.1 M phosphate buffer pH 7.4 for 1 h at room temperature and dehydrated in ethanol from 10 to $100\%$ (three times) for 10 min each step and then included in epoxy resin. The samples were sectioned with an Ultrotome V ultramicrotome (LKB instruments, Victoria, TX, USA). Thin sections (80–100 nm) were counterstained with uranyl acetate and lead citrate and then observed with a Tecnai G2 transmission electron microscope (FEI Company, Hillsboro, OR, USA) operating at 100 kV. TEM analysis was performed also to assess the extent of recognition and capture by human macrophages. Briefly, 3 × 105 monocytes isolated from buffy coats were seeded per well in 24 wells/plate and cultured for 7 days in RPMI-1640 medium (Life Technologies, Delhi, India) supplemented with $20\%$ FBS and 100 ng/mL of human macrophage colony-stimulating factor (Peprotech, Neuilly-sur-Seine, France) to promote macrophage differentiation. On day 4 from the seed, the macrophage colony-stimulating factor was added again. For AuNP uptake experiments, 7-day-old macrophages were incubated for 24 h with GNPs, Leu4-GNPs, or Api8-GNPs with a final peptide concentration of 5 μM. The samples were then processed as mentioned above for mammary cell lines. ## 4.9. Cellular Uptake of GNPs Measured by Atomic Absorption Spectroscopy Briefly, 4 × 105 MDA-MB-231 and MCF-10A cells/well were seeded in 12-well plates and allowed to growth for 24 h before incubating them with GNPs or peptide-decorated GNPs (at a 5 μM peptide concentration). At the end of the 24 h GNP incubation, cells were washed twice with PBS and detached from the plates with trypsin that was neutralized by the addition of FBS. Cells were centrifuged and washed twice with Versene solution. A fraction of cell suspension was taken for subsequent quantification of total protein content using Pierce BCA Protein Assay kit (Thermo Fisher, Waltham, MA, USA), while the remaining suspension was digested using a mixture consisting of 1 part of $33\%$ hydrogen peroxide and 2 parts of $27\%$ nitric acid. The obtained solutions were then diluted 1:100 using purified water and analyzed by atomic absorption spectroscopy (AAS) to assess gold concentration on a Varian AA240 Zeeman instrument equipped with a GTA120 graphite furnace, a Zeeman background corrector, and an autosampler (Varian Inc., Palo Alto, CA, USA). The total gold content was then expressed as ng Auμg protein. ## 5. Conclusions and Future Perspectives The present article described the promising antitumor properties of structural analogs of the natural peptide trichogin GA IV and highlighted the chance to exploit them also as targeting agents by exploiting the unique combination of effective membrane selectivity and lack of cytotoxicity. We demonstrated Leu4-NH2 and Api8-NH2 selectivity towards breast tumor cell, both alone and when conjugated to GNPs, thus opening the way to their functionalization with anticancer drugs. 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--- title: Development of pH-Responsive N-benzyl-N-O-succinyl Chitosan Micelles Loaded with a Curcumin Analog (Cyqualone) for Treatment of Colon Cancer authors: - Sasikarn Sripetthong - Fredrick Nwude Eze - Warayuth Sajomsang - Chitchamai Ovatlarnporn journal: Molecules year: 2023 pmcid: PMC10057334 doi: 10.3390/molecules28062693 license: CC BY 4.0 --- # Development of pH-Responsive N-benzyl-N-O-succinyl Chitosan Micelles Loaded with a Curcumin Analog (Cyqualone) for Treatment of Colon Cancer ## Abstract This work aimed at preparing nanomicelles from N-benzyl-N,O-succinyl chitosan (NBSCh) loaded with a curcumin analog, 2,6-bis((3-methoxy-4-hydroxyphenyl) methylene) cyclohexanone, a.k.a. cyqualone (CL), for antineoplastic colon cancer chemotherapy. The CL-loaded NBSCh micelles were spherical and less than 100 nm in size. The entrapment efficiency of CL in the micelles ranged from 13 to $39\%$. Drug release from pristine CL was less than $20\%$ in PBS at pH 7.4, whereas the release from CL-NBSCh micelles was significantly higher. The release study of CL-NBSCh revealed that around $40\%$ of CL content was released in simulated gastric fluid at pH 1.2; 79 and $85\%$ in simulated intestinal fluids at pH 5.5 and 6.8, respectively; and $75\%$ in simulated colonic fluid at pH 7.4. CL-NBSCh showed considerably high selective cytotoxicity towards mucosal epithelial human colon cancer (HT-29) cells and lower levels of toxicity towards mouse connective tissue fibroblasts (L929). CL-NBSCh was also more cytotoxic than the free CL. Furthermore, compared to free CL, CL-NBSCh micelles were found to be more efficient at arresting cell growth at the G2/M phase, and induced apoptosis earlier in HT-29 cells. Collectively, these results indicate the high prospective potential of CL-loaded NBSCh micelles as an oral therapeutic intervention for colon cancer. ## 1. Introduction Cancer is a leading cause of morbidity worldwide, resulting in the death of an estimated 9.6 million persons in 2018 [1]. Colorectal cancers are one of the most prevalent types of cancer and second most common cause of cancer-related death in both males and females [2]. Cancer is conventionally treated by a number of approaches, including surgery, radiation and chemotherapy [3,4]. Intravenously injected chemotherapeutics are still the mainstay of cancer treatment [5]. However, systemic drug delivery following intravenous injection for treatment of colon cancer raises major problems of drug toxicity and side effects, including nausea and vomiting, diarrhea, decreased white blood cell count, anemia, fatigue, nerve damage, pain and skin reactions [6,7]. These complications severely diminish patients’ quality of life and present major obstacles to successful chemotherapy. Local delivery of cancer chemotherapeutics to tumor sites in the lung and breast following intravenous administration has the potential to reduce the administered dose and minimize adverse side effects by concentrating the drug at the site of action. Various intravenous drug delivery strategies have evolved to realize this potential, including antibody-directed enzyme prodrug therapy (ADEPT) [8,9] and targeting of drug-loaded nanoparticles to specific receptors on cancer cells [10,11] by incorporating ligands in the nanoparticle surface. However, phagocytic uptake of circulating nanomedicines, resulting in short blood residence times, has limited their application in clinically available therapeutics. The potential advantages of orally administered nanoparticles for delivery of drugs to the colon for treatment of inflammatory bowel disease and colorectal cancer are receiving increasing attention [12]. Nanoparticles less than 500 nm in size have been reported to traverse the mucosal layer and be taken up by solid tumors [13,14]. As a result, research on nanomedicines for applications in colon cancer therapy has increased significantly to include a variety of nanocarriers and nanoparticle in microcapsule systems. Nanomicelles are self-assembled nano-sized structures, typically 10–200 nm in dimension, which may be produced from biodegradable and biocompatible amphiphilic block polymers and are therefore attractive as drug delivery systems. Micelles have previously been developed as intravenous nanocarriers for anticancer drugs to enhance ‘passive’ targeted delivery to tumor sites [15]. The micelles have been shown to easily pass through the ‘leaky’ vasculature associated with tumor tissues (extravasation) and elevate the drug concentration in the tumor. Biodegradation of the micelles avoids their accumulation in the cell, impairment of cellular processes and toxic side effects [7,16]. Chitosan has been widely investigated for the production of drug delivery systems in the form of carriers [17], due to favorable biocompatibility, biodegradability and low toxicity [18]. Curcumin-loaded N-benzyl-N,O-succinyl chitosan (NBSCh) micelles have been reported to exhibit intense cytotoxicity towards cervical cancer cells in vitro [19]. Curcumin is a hydrophobic polyphenol derived from the rhizome of the herb Curcuma longa. The compound has demonstrated a wide spectrum of biological and pharmacological activities, including antioxidant, anti-inflammatory, antimicrobial and anticancer [20,21]. However, curcumin’s therapeutic potential has been limited by poor solubility and instability under physiological conditions. Recently, new curcumin analogs were synthesized via structural modifications of the curcumin scaffold with enhanced physicochemical parameters. Some of the analogs were observed with enhanced bioavailability, due to enhanced transmembrane passage properties and delayed metabolism [22]. The curcumin analogue, 2,6-bis((3-methoxy-4-hydroxyphenyl) methylene) cyclohexanone (cyqualone (CL); Figure 1), was found to significantly decrease tumor cell proliferation and angiogenesis in adenocarcinoma of the rat prostate (PLS10)-bearing mice compared with curcumin [23,24]. CL has been reported to have cytotoxic activities against brain, breast, leukemia, pancreatic and prostate cancer cells, both in drug-sensitive and drug resistance cell lines, while exhibiting low toxicity in normal cells [23,24,25,26,27,28]. Due to limited bio-accessibility, bioavailability and stability of curcumin, a number of previous studies had focused on improving these crucial parameters using nanoparticle strategies [29,30,31,32,33,34,35]. Among these, nanomicelle preparation using chitosan derivatives is one of the most interesting approaches. For example, N-naphthyl-N,O-succinyl chitosan (NSCS) and N-octyl-N,O-succinyl chitosan (OSCS) nanomicelles containing curcumin were prepared [36,37] and found to be pH-sensitive and appropriate for colon-targeted drug delivery. The curcumin-loaded nanomicelles showed higher anti-colon cancer activity against HT-29 cells (IC50 = 6.18 ± 0.18 μg/mL) than plain curcumin (IC50 = 11.38 ± 3.07 μg/mL) [37]. In this study, we describe the development of a novel oral antineoplastic formulation based on N-benzyl-N,O-succinyl chitosan (NBSCh) micelle-encapsulated CL for efficacious colon cancer chemotherapy. The CL-loaded micelles were prepared by dialysis method and characterized in terms of size and morphology (TEM), chemical composition (NMR, FT-IR) and drug release behavior in simulated gastric and intestinal fluids. Furthermore, the anticancer activity of the micelles was thoroughly investigated using a human mucosal epithelial colon cancer cell line (HT-29). The findings from this work are expected to have positive implications in the effective treatment of colon cancer. ## 2.1. Synthesis and Characterization of N-benzyl-N,O-succinyl Chitosan (NBSCh) NBSCh was successfully synthesized by reductive amination and succinoylation [19], resulting in a yellow powder with $63\%$ product yield. The obtained N-benzyl chitosan (NBCh) and NBSCh were properly characterized using 1H-NMR (Figure S2) and FT-IR analysis (Figure S1), and the results are presented in Supporting Information. Elemental analysis was used to calculate the degree of substitution of N-benzyl group and N,O-succinyl groups of NBCh. Data from the elemental analysis (Table 1) revealed that the structure of the chitosan starting material changed to NBCh according to substitution of benzyl groups for the N–atom of the amino group of chitosan. Analysis showed increasing %C and %H, but reducing %N. Moreover, the change in elemental compositions of NBSCh compared to that of NBCh confirmed the structural change after the reaction. NBSCh contained succinyl moiety on both N- and O atoms of the chitosan backbone, resulting in lower %C and %N compared to that of NBCh, whereas the higher %O of NBSCh relative to NBCh was due to the oxygen content of the substituting succinyl group in NBSCh. The elemental analysis data were used for calculation of the degree of substitution with succinyl radical (DSS) of NBSCh and resulting in 1.02 DSS. ## 2.2. Preparation and Characterization of Micelles The NBSCh is endowed with hydrophobic and hydrophilic segments on the chitosan backbone and therefore had the potential to form micelles through self-assembly. The dialysis method employed in this study yielded yellow-colored NBSCh micellar solutions (Figure 2). The mean size of the micelles in these solutions was 60.1 ± 1.20 nm and displayed a narrow size distribution (PDI value 0.096). The CMC of NBSCh was 0.014 mg/mL (Figure S3). Loading of some synthetic hydrophobic compounds with polymeric micelles have been observed, with a predominant increase in their pharmacological profile [38]. In the current study, the curcumin analog was successfully loaded into NBSCh micelles via hydrophobic–hydrophobic interactions between the cyqualone and hydrophobic core of the NBSCh micelles. TEM micrographs of CL-loaded NBSCh micelles revealed spherically shaped structures, with a consistent diameter of around 50 nm for both the blank and CL-loaded NBSCh micelles (Figure 3). Moreover, the size of CL-NBSCh was not markedly different from the blank samples. Size of the CL-micelles observed was 62.4 ± 0.9, zeta potential was −31.0 ± 0.6, and PDI was 0.236 ± 0.022. Nanoparticles with sizes less than 200 nm have been reported to be potentially useful carriers for anticancer drug delivery [39,40]. The CL-loaded NBSCh micelles were characterized by a negative surface charge, with zeta potential values around −30 mV. The negative charges of the succinyl moieties could prevent aggregation and improve stability of the drug-loaded micelles in aqueous media. Moreover, NBSCh possessed high numbers of carboxyl groups on its surface, such that NBSCh micelles could be mucoadhesive through interactions with mucus via hydrogen bonding, van der Waals interactions, polymer chain interpenetration and hydrophobic forces [19]. The effect of CL concentration on CL entrapment efficiency (EE) and loading capacity (LC) of NBSCh micelles were calculated according to Equations [1] and [2], respectively and the results are summarized in Table 2. The NBSCh concentration of the starting DMSO solution was kept constant at 1 mg/mL. The %EE remained constant (44.4–$48.7\%$) with increasing CL concentration, indicating saturation of the available micelles. The loading capacity increased significantly from $4.25\%$ to $32.75\%$ with increasing CL concentration. Several factors in the drug were encapsulated in micelles: the hydrophobic interactions between CL and hydrophobic moieties of NBSCh, and the miscibility between NBSCh and CL. If CL interacted with the hydrophobic polymer chain more than with solvent, high-incorporation efficiency would be observed. However, if CL interacted with the hydrophobic polymer chain less than the solvent, CL would be precipitated [37,41]. [ 1]%EE=wt of CL encapsulated in micelleswt of CL used for micelles preparation×100 [2]%LC=wt of CL encapsulated in micellesTotal wt of the obtained dried micelle×100 CL-loaded NBSCh micelles were prepared by mixing solutions of CL stock solution (100, 300, 600 and 1000 µL of 10 mg/mL in DMSO) with NBSCh in DMSO (1 mg/mL) according to the information in Table 2, followed by dialysis against distilled water. The NBSCh concentration of the starting DMSO solution remained constant at 1 mg/mL. ## 2.3. In Vitro Release of CL from NBSCh Micelles The release profiles of CL in gastric fluid (SGF), simulated extracellular tumor cells fluid (ETC, pH 5.5), simulated intestinal fluid (SIF, pH 6.8) and simulated colon fluid (SCF, pH 7.4) are shown in Figure 4 and Figure 5. Free CL dissolved slowly in each medium, reaching a maximum of $28\%$ (pH = 1.2), $20\%$ (pH = 5.5), $26\%$ (pH = 6.8) and $16\%$ (pH = 7.4) after 18 h. The release profile of CL from CL-loaded NBSCh micelles in SGF (pH = 1.2) showed a similar pattern to that obtained for the dissolution of CL in SGF. However, the amount of CL in ETC (pH 5.5), SIF (pH 6.8), and SCF (pH 7.4) releasing from CL-loaded micelles was significantly higher than that from free CL. The maximum release of CL from CL-NBSCh micelles in pH 5.5, pH 6.8 and pH 7.4 media was found to be 79, 85 and $75\%$, respectively (Figure 4 and Figure 5). This behavior may be influenced by the pKa1 of 4.21 [42] of succinic moieties in the micelles. At pH 5.5, 6.8 and 7.4, the succinic groups would be ionized, possibly resulting in a loose micelle structure, which facilitated the release of the entrapped CL [19,37]. The release behavior of CL was found to be dependent on the pH of the release medium. TEM morphologies, particle sizes and zeta potentials findings confirmed that morphologies, sizes, and zeta potentials change of the micelles varied with pH. In acidic medium, the succinic acid were in unionized form, whereas, at pH lower than 4.1, the pKa1 of succinic acid, the micelle aggregated like a cluster, resulting in larger-sized nanomicelles, leading to larger hydrodynamic diameter with a positive charge (Figure 6b,c). The TEM image of the micelles in a low pH medium revealed a spherical shape, suggesting that the micelles retained their integrity and modulated the release of the drug under strong acidic condition at a slow pace (Figure 6a). As such, the prepared NBSCh micellar system could be useful for improving the bioavailability of the drug. Unlike in the stomach, where the micelles could retain their spherical morphology, on reaching the simulated small intestine and colon with pH values in the range of 6.8–7.4, the micelles are dissociated, facilitating the release of their drug load. The pH values of the intracellular compartments of tumors are within 5.0–6.5, and the tumor extracellular environment are in the range of pH 6.5–7.2 [43]. Therefore, the NBSCh micelles can be adapted towards pH-triggered accelerated drug release at intracellular sites, such as the acidic tumor tissues or tumor cells. Interestingly, the solubility improvement of the drug could enhance its bioavailability. Xu et al. [ 2015] reported that quercetin loaded MPEG–PCL micelles could improve drug release, and an in vivo study showed that quercetin loaded MPEG–PCL micelles could enhance the T$\frac{1}{2}$ and Cmax of quercetin and could better improve the colon cancer cytotoxicity than pure quercetin [44]. ## 2.4. Effect of Temperature on the Stability of CL-Loaded NBSCh Micelle Powders The storage stability of CL-loaded NBSCh micelle powders was evaluated by determining the sizes, zeta potentials of the micelle, and the content (%) of CL remaining at the end of the storage period (Figure 7). Redispersion of dried powders in distilled water resulted in an increase in micelle size to around 130 nm after 15 days’ storage, compared to 100 nm of non-freeze-dried preparations. Storage for 120 days at 30 °C resulted in a slight increase in size (135 nm) compared with storage at 4 °C (120 nm), indicating a fairly good storage stability. All samples showed only a slight increase in zeta potential values during storage, providing a further indication of their stability (Figure 7a). The storage stability of the CL-loaded micelle powder samples at 4 and 30 °C was further investigated by measuring the amount of CL that remained at various storage times (Figure 7b). More than $90\%$ of the initial content of CL was found remaining after 120 days’ storage, demonstrating superb stability of the freeze-dried micelle powders containing glycine as a cryoprotectant. ## 2.5. CL-Loaded NBSCh Micelles Were Selective and Cytotoxic to HT-29 Colon Cancer Cells Cyqualone has been previously shown to reverse P-gp-mediated multidrug resistance (MDR) at a low dose (2.5 µM), which is potentially advantageous for avoiding side effects in chemotherapy [21]. The compound was also reported to exhibit higher anti-invasion properties against castration-resistant prostate cancer cells than curcumin [45] and inhibited both MMP-2- and MMP-9-defined MMP activities [46]. In the present study, blank micelles showed minimal toxicity towards both normal (L929) mouse fibroblasts and human colon cancer (HT-29) cells. Increasing micelle concentrations from 1–200 µg/mL resulted in a gradual reduction in viability of both cell types but remained over $80\%$. Figure 8 demonstrates HT-29 (a) and L929 (b) cells’ viability (%) of the CL, CL micelle and blank micelle by MTT assay. The cytotoxic effect of CL and CL-loaded NBSCh micelles against human colon mucosal epithelial cancer cells (HT-29) and mouse connective tissue fibroblasts (L929) in terms of IC50 values is summarized in Table 3. Free CL demonstrated similar toxicity towards both human colon cancer cells and normal mouse fibroblasts (IC50 approximately 10 µg/mL). CL-loaded micelles, on the other hand, exhibited significantly higher toxicity against the cancer cell line (IC50 = 3.4 µg/mL) compared to free CL by a factor of three. This behavior suggests that the micelle carrier is taken up efficiently by colon cancer cells, thus enhancing the transport of CL. Kansom et al. [ 2018] prepared andrographolide (or 3A.1)-loaded naphthyl-grafted succinyl chitosan (NSC), octyl-grafted succinyl chitosan (OSC) and benzyl-grafted succinyl chitosan (BSC) nanopolymeric micelles. Similarly, to our results, they found that 3A.1-loaded nanopolymeric micelles showed significantly lower IC50 against HT-29 colon cancer than the pure drug [47]. Importantly, in the present study, CL-loaded micelles were found to demonstrate substantially high levels of selectivity, as indicated by the IC50 value of 24.3 µg/mL obtained when tested against mouse fibroblasts compared to 3.4 µg/mL against colon cancer cells. In addition, the toxicity of CL-loaded micelles to normal cells was almost four times lower than that of CL, suggesting a shielding effect by the micelle core. This could be due to a nanomicelle with a size about 60 nm being potentially better at entering cancer cells via the pore of the epithelial cell of the cancer cell and better preventing the EPR effect than a normal cell; nanomicelles could also prevent the elimination of pure compounds via the EPR effect. ## 2.6. Cellular Uptake of CL-Loaded NBSCh Micelles The cellular uptake of NBSCh micelles by human colon mucosal epithelial cancer cells (HT-29) was monitored following exposure for 6 and 24 h using CLSM (Figure 9). Blue fluorescence was used to identify the cell nucleus stained by Hoechst 33,342 dye, while green fluorescence was used to identify the micelles labelled by FITC. Greater cellular uptake was apparent after 24 h compared with 6 h, and NBSCh micelles had moved very close to the nucleus at 24 h. The CLSM study indicated that NBSCh micelles were able to penetrate the cell membrane, possibly by caveolae-mediated endocytosis, and enter the cytoplasm [15,16]. Micelles’ translocation may be facilitated by their anionic surface charge, which favors interaction with cationic lipid domains in the cell membrane. ## 2.7. CL-Loaded NBSCh Micelles Promoted Early Apoptosis in HT-29 Cancer Cells During apoptosis, phagocytes receive signals via phosphatidylserine (PS) in the extracellular membrane—for example, to initiate cell clearance. Annexin V binds explicitly to the PS of apoptotic cells in early apoptosis, whereas propidium iodide (PI) can stain the nucleus of late apoptosis (necrotic) cells due to loss of membrane integrity. HT-29 cells were exposed to free CL and CL-loaded NBSCh micelles for 24 h at the CL IC50 value of 10.6 µg/mL for free CL and 3.4 µg/mL for CL in micelles. Double staining using Annexin V-FITC and PI, followed by flow cytometry, was performed to distinguish between early apoptosis and necrosis. Viable cells do not bind to AnnexinV-FITC or PI. Flow cytometry analysis demonstrated that HT-29 colon cancer cells retained more than $85\%$ cell viability following exposure to culture media (control) or blank micelles. Early apoptotic cells (AnnexinV- FITC bound and negative for PI (lower right quadrant)) were found to represent 14.0 ± $1.7\%$ of the population following exposure to CL, while CL-loaded NBSCh micelles induced a significant 3-fold increase in early apoptosis (39.0 ± $3.0\%$ of the total population) compared to free CL. This behavior is consistent with earlier observations pointing towards the efficient uptake of NBSCh micelles by HT-29 cancer cells (Section 2.6), and indicated the potential of CL-loaded micelles for application in cancer chemotherapy. Necrotic or late apoptotic cells are expected to be positive for both AnnexinV-FITC and PI (Figure 10, upper right quadrant). This accounted for approximately $12.6\%$ of the cell population (Figure 10, upper right quadrant). These observations indicated that, after 24 h exposure to CL or CL-loaded micelles, most of the HT-29 cells had not entered the late apoptosis phase. ## 2.8. Cell Cycle for Antiproliferative Effect of CL and CL-Loaded Micelles on HT-29 Human Colon Cancer Cells The cell cycle is a process where a cell grows and divides to create a copy of itself. The cell cycle involves four main phases: G1, S, G2 and M phases. G1 is the starting phase, when the cell is ready to get into DNA replication before entry into the S phase. The S phase is where DNA is replicated. Once DNA has been replicated, the cell enters G2 phase, which prepares the cell for division. Finally, the cell is divided during M phase. In this study, flow cytometry was employed to understand the antiproliferative effects of free CL, blank NBSCh micelles and CL-loaded NBSCh micelles on the cell cycle of HT-29 human colon cancer cells. Formulations containing CL were added to the cell cultures to provide an IC50 value of CL 10.6 µg/mL (pure CL) and 3.4 µg/mL (CL in micelles). The DNA content of the cells following 24 h exposure was analyzed by flow cytometry after labelling with propidium iodide (PI) and converted to % cell number in each cell cycle (Figure 11). The blank NBSCh micelles exhibited no significant effect on the cell cycle compared with untreated controls. CL-loaded micelles caused a significant reduction in cell number in the G1 phase (approximately $60\%$ of the population, compared with $70\%$ for control samples), but no effect was observed in the S phase. Approximately $15\%$ of the total cell population existed in the S phase of growth. The percentage of cells in the G2/M phase was significantly increased from around 17.32 ± $2.64\%$ to 26.42 ± $2.76\%$ following exposure to CL-loaded NBSCh micelles for 24 h, compared with free CL, indicating an improvement in the drug-loaded micelle ability to induce cell cycle arrest and exert a 1.53-fold increase in the inhibition of cancer cell division (Figure 11) [40,48]. This observation correlated well with the efficient uptake of CL-loaded NBSCh micelles (Section 2.6) and the significant 3-fold increase in early apoptosis (39.0 ± 3.0 % of the total population) compared with free CL (Section 2.7). Overall, these findings demonstrated that CL-loaded micelles hold high promise for applications in colon cancer chemotherapy. ## 3.1. Chemicals The curcumin analogue, 2,6-bis((3-methoxy-4-hydroxyphenyl) methylene) cyclohexanone (cyqualone, CL, >$99\%$ purity) was previously synthesized, purified and well characterized in our lab. Chitosan with molecular weight of 30,000 g/mol and ≥$80\%$ degree of deacetylation was obtained from Bannawach Bio-Line Co. Ltd., Bangkok, Thailand. Benzaladehyde, analytical grade, was purchased from Panreac., Barcelona, Spain. Glacial acetic acid, methanol, dimethylformamide and sodium hydroxide were purchased from Labscan Asia, Bangkok Thailand. 4-Nitrobenzoyl chloride was obtained from Fluka, Munich, Germany. Succinic anhydride and sodium cyanoborohydride were obtained from Sigma-Aldrich, Munich, Germany. ## 3.2. Reagents and Cell Lines for Cytotoxicity Studies Mucosal epithelial human colon cancer cell line (HT-29) and mouse connective tissue fibroblasts (L929) were purchased from ATCC®, Manassas, VA, USA. Dulbecco’s Modified Eagle’s Medium (DMEM), fetal bovine serum (FBS) and $0.25\%$ trypsin–EDTA (1X) were purchased from Gibco (Invitrogen, Carlsbad, CA, USA). Phosphate buffered saline pH 7.4 (PBS) was purchased from Sigma–Aldrich, Munich, Germany. 3-[4,5-Dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (for MTT assay of cell viability) and Hoechst 33,342 were purchased from InvitrogenTM Molecular Probes®, Eugene, OR, USA. Dimethyl sulfoxide (DMSO) was of analytical grade and purchased from Labscan, Bangkok, Thailand. Guava Cell Cycle Reagents for Flow Cytometry was purchased from Merck, Darmstadt, Germany and FITC Annexin V Apoptosis Detection Kit I was obtained from BD PharmingenTM, Franklin Lakes, NJ, USA. ## 3.3. Synthesis of N-benzyl-N,O-succinyl Chitosan (NBSCh) N-benzyl-N,O-succinyl chitosan (NBSCh) was synthesized by reductive amination and succinoylation in two stages, according to a previous report [19] (Figure 12). Chitosan 10 g (equivalent to 0.35 mol of glucosamine) was dissolved in 500 mL $1\%$ acetic acid at room temperature. A solution of benzaldehyde in ethanol (0.06 g/mL, 300 mL) was added and the mixture was stirred continuously at room temperature for 24 h. NaOH ($15\%$, 1 mL) was then added to adjust the pH of the mixture to 5. Subsequently, sodium cyanoborohydride (1.89 g, 0.03 mol) was added to the mixture with further stirring at room temperature for 24 h. The resulting solution was subjected to dialysis (cellulose membrane MW cut off 12,000–14,000, Membrane Filtration Products, Seguin, TX, USA) against distilled water for 3 days. The dialyzed solution was lyophilized (freeze dryer, DELTA 2-24 LSC, Darmstadt, Germany), resulting in a white powder of N-benzyl chitosan (NBCh). NBCh was characterized using FT-IR on Perkin-Elmer FT-IR model 1600 spectrometer (Llantrisant, UK) and NMR on FT-NMR spectrometer (500 MHz, Unity Inova, Varian, Darmstadt, Germany). N-benzyl-N,O-succinyl chitosan (NBSCh) was then synthesized by succinoylation as described. Briefly, N-benzyl chitosan NBCh (5 g) was dispersed in 450 mL dimethylformamide (DMF) 450 mL, and succinic anhydride (15 g, 0.30 mol) was added. The reaction mixture was stirred under nitrogen purge for 24 h, resulting in a clear yellow solution. Excess of succinic anhydride and DMF were eliminated by dialysis against distilled water for 3 days. The final solution in the dialysis bag was freeze-dried to give a yellow powder prior to characterization by FT-IR and 1H-NMR. ## 3.4. Preparation of Blank NBSCh Micelles and CL-Loaded NBSCh Micelles Blank NBSCh micelles were prepared using a dialysis method. A solution of 10 mg of NBSCh in 10 mL DMSO was prepared at room temperature and subjected to dialysis against distilled water (cellulose membrane, MW. cut off = 3500) for 18 h. The solution in the dialysis tubing was filtered using a nylon-membrane syringe filter (pore size = 0.22 µm, Vertical Chromatography, Bangkok, Thailand). The CL-loaded NBSCh micelles were prepared by using the same procedure as the blank as follows: NBSCh powder 10 mg was dissolved in in DMSO (10 mL) at room temperature. Different concentrations of CL solutions in DMSO (100, 300, 600 and 1000 μL of 10 mg/mL prepared as solutions, having final concentrations of 0.1, 0.3 0.6 and 1 mg/mL, were added to each tube of the NBSCh solution while stirring at room temperature for 1 h to provide clear yellow solutions. The resulting solutions were then dialyzed against distilled water for 24 h (cellulose membrane, MW cut off = 3500), and the final micellar solution was filtered through 0.22 µm PVDF filter (Vertical, Bangkok, Thailand). Finally, glycine was added to the filtrates to obtain a final concentration of $1\%$ w/v before freeze-drying (freeze drier, DELTA 2-24 LSC, Darmstadt, Germany) to produce light yellow powder. ## 3.5. Determination of Critical Micelle Concentration (CMC) The Critical Micelle Concentration (CMC) of NBSCh micelles was determined by means of a fluorescence spectroscopic technique. NBSCh solutions in distilled water (4 mL) at various concentrations (0.002–1 mg/mL) were placed in separate tubes, and 0.1 mM pyrene in acetone (10 µL) was added to each sample. The mixtures were sonicated at room temperature for 15 min, followed by heating at 50 °C for 2 h, before being kept overnight in the dark at room temperature to reach equilibrium. The fluorescence intensity of the samples was measured using a fluorescence spectrophotometer (Cary Eclipse, Perkin Elmer Ltd., Wokingham, UK) at an excitation wavelength of 335 nm. The emission intensities were recorded at 373 and 382 nm to investigate the shift in NBSCh hydrophobic microdomains, by monitoring the change in intensity ratio (I1/I3) at 373 nm (I1) and 382 nm (I3). The CMC (mg/mL) was calculated after fitting the semi-log plot of the intensity ratio I1/I3 vs. concentrations of NBSCh. ## 3.6.1. Size and Zeta Potential Measurements The micelle size, size distribution, and zeta potential of CL-loaded NBSCh micelles were measured by dynamic light scattering using a Zetasizer Nano ZS (Zetasizer Nano ZS, Malvern, UK). Samples were freshly prepared in distilled water and filtered through a 0.22 µm filter prior to analysis. ## 3.6.2. Micellar Morphology Determination The shape and surface appearance of the CL-loaded NBSCh micelles were observed by transmission electron microscopy (TEM, JEM-2010, JEOL, Tokyo, Japan). The micellar solutions in distilled water were dropped onto copper grids (200 mesh) and stained with uranyl acetate solution ($1\%$, w/v) for subsequent TEM examination. ## 3.7. The Entrapment Efficiency (EE) and Loading Capacity (LC) of CL in NBSCh Micelles The entrapment efficiency and loading capacity of CL-loaded NBSCh micelles was determined by using HPLC. The test sample was prepared by mixing CL-loaded NBSCh micellar solution with DMSO to give a clear yellow solution. The CL content of the test samples was estimated by a HPLC system consisting of a Chromaster 5110 pump and a Chromaster 5430 Diode Array Detector. The output signal was monitored and processed using a Chromaster 60 MPa System. A Phenomenex® C18 250 mm × 4.6 mm, 5 µm column was used with a mobile phase containing acetonitrile and 0.01 mM trifluoroacetic acid at the ratio of 30:70 (v/v). The flow rate of the mobile phase was 1.5 mL/min. The detection wavelength was set at 385 nm. The entrapment efficiency (%) and loading capacity (%) of CL in the micelles were quantified by comparison of elution peak areas obtained for CL-micellar samples with the chromatograms of standard solutions of CL in acetonitrile. ## 3.8. In Vitro Release of CL from NBSCh Micelles In vitro release of CL from NBSCh micelles was evaluated using a dialysis method in simulated gastric fluid [SGF, (HCl, pH 1.2)], simulated intestinal fluid [SIF, (PBS, pH 6.8)], extracellular tumor cells [ETC (PBS, pH 5.5)] and simulated colonic fluid [SCF, (PBS, pH 7.4)]. Solutions of CL-loaded NBSCh micelles were prepared in each release medium to obtain a CL concentration of about 50 µg/mL, and 5 mL of each solution was transferred to a dialysis tube (MW cut off 3.5 kDa) and immersed into 50 mL of each respective release medium. The release study was performed at 37 °C in an incubator (ES80 shaker, Grant Instruments, Cambridgeshire, UK) shaking at 120 rpm. At intervals of 0.5, 1, 2, 3, 5, 7, 9, 12, 15, 18 and 24 h, 1 mL of the released medium outside the dialysis tube was collected and replaced with 1 mL of a fresh medium at the same temperature. The amount of CL released from the NBSCh micelles was quantified by HPLC, and the release behavior was presented as a cumulative release (%) versus time. All drug release tests were performed in triplicate. High-performance liquid chromatography (HPLC) was carried out using a Hitachi system based on a Chromaster 5110 pump and a Chromaster 5430 Diode Array Detector. The output signal was monitored and processed using a Chromaster 60 MPa. ## 3.9. Determination of Stability of Freeze-Dried CL-Loaded NBSCh Micelle The storage stability of CL-loaded NBSCh micelle powders was investigated by storing solid samples in well-closed vials at 4 °C and 30 °C for 16 weeks. After the indicated storage time, the samples were taken and evaluated for their sizes and zeta potentials. The CL content of each sample was determined by mean of HPLC. Samples were prepared in the same manner as %EE and %DL investigation. ## 3.10.1. Determination of In Vitro Cytotoxicity of CL-Loaded NBSCh Micelles The mucosal epithelial human colon cancer cell line (HT-29) and mouse connective tissue fibroblasts (L929) were maintained in Dulbecco’s Modified Eagle Medium (DMEM) containing $10\%$ (v/v) fetal bovine serum [FBS]. Sub-confluent monolayers were trypsinized and seeded at a density of 2 × 104 cells/well in 96-well plates and grown in $10\%$ FBS DMEM at 37 °C for 24 h in a $5\%$ CO2 atmosphere. The cytotoxicity activity of the test samples was determined using the 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide (MTT)-based colorimetric assay, which quantifies the number of viable cells after 24 h. The exponentially growing cells were washed twice with PBS (pH = 7.4) and incubated for 24 h with 100 µL of fresh medium (control) or medium containing CL or CL-loaded NBSCh micelles. Cell survival was assessed by adding MTT solution (50 µL of 5 mg/mL MTT in PBS) and incubated for 4 h. DMSO (100 µL) was then added to dissolve the formazan crystals formed. Absorbance (Ab) of the samples in the 96-well plate was measured by a microplate reader (SPECTRO star Nano, Beckman Coulter, Raleigh, NC, USA) at 570 nm. Percentage cell viability was calculated using the following equation, Equation [3]:[3]Cytotoxicity %=Ab570control−Ab570sample ×100Ab570control ## 3.10.2. Determination of Cellular Uptake of NBSCh Micelles Cellular uptake of NBSCh micelles was performed following conjugation with fluorescein isothiocyanate (FITC). Briefly, NBSCh (3 mmol) was reacted with 0.06 mmol N-hydroxysuccinimide (NHS) and 0.036 mmol N,N′-dicyclohexylcarbodiimide (DCC) in 20 mL of DMSO at room temperature for 12 h. Ethylenediamine (0.03 mmol) was added to the mixture and underwent continuous stirring for 2 h, after which FITC solution in DMSO (12 mg/5 mL) was added, with continuous stirring for 24 h to complete the reaction. The resulting mixture was dialyzed against distilled water to remove free FITC to obtain FITC-labelled NBSCh micelles. Cellular uptake of FITC-labelled NBSCh micelles was investigated using a confocal laser scanning microscopy (Zeiss LSM 800, Zeiss, Germany). HT-29 cells at a concentration of 3 × 105 cells/well (1 mL) were seeded onto sterilized glass coverslips and incubated overnight in a 6-well plate for 24 h. Thereafter, the medium was removed and washed twice with PBS (pH 7.4). FITC-labelled micelles in $10\%$ FBS DMEM medium (250 µg/mL, IC50 value of blank NBSCh micelles) was added and incubated at 37 °C under $5\%$ CO2 for 6 and 24 h. The cells were then washed twice with PBS (pH 7.4), fixed with cold $70\%$ ethanol for 10 min and washed twice with PBS (pH 7.4). Hoechst 33,342 dye (2’-[4-ethoxyphenyl]-5-[4-methyl-1-piperazinyl]-2,5’-bi-1H-benzimidazole trihydrochloride trihydrate) was used to stain the cellular nuclei. The Hoechst 33,342 solution (10 mg/mL) was diluted in sterile water at a ratio of 1:1000 v/v and 2 mL of this solution was added to the fixed cells in each well. After 30 min of incubation at 37 °C under $5\%$ CO2, the solution above the cells was aspirated and the cells were washed twice with PBS (pH 7.4). The fixed cells attached to the coverslips were examined using a confocal laser scanning microscopy (Zeiss LSM 800, Zeiss, Jena, Germany). ## 3.10.3. Cell Apoptosis Study Cell apoptosis was investigated by using annexin V-FITC/propidium iodide (PI) double staining to detect both apoptotic and necrotic cells. HT-29 cells were maintained in $10\%$ FBS DMEM medium to attain a density of $80\%$ confluence. Then, sub-cultured monolayers were trypsinized and cells were seeded into a 6-well culture plate (1 × 106 cells/well). After 24 h, cells were washed twice with PBS (pH = 7.4) and incubated with 1 mL of blank micelles or CL-loaded NBSCh micelles containing CL at the IC50 value against HT29 cells. The cells were incubated at 37 °C for 24 h in a $5\%$ CO2 atmosphere. Following incubation, the cells were dispersed using trypsin–EDTA solution, centrifuged at 500× g for 5 min and the cell pellets were re-suspended in PBS (pH 7.4). The cells were then stained with annexin V–FITC (5 µL) and PI (3 µL) by incubating for 15 min in the dark at room temperature. Finally, 100 µL of annexin V binding buffer was added. Cells were analyzed using a flow cytometer (Amis Image X-MKII system, Merck, Germany), with green fluorescence (FITC) detected at an excitation wavelength of 535 nm and red (PI) fluorescence detected at 550 nm wavelength. ## 3.10.4. Cell Cycle Analysis for Antiproliferative Effect of CL and CL-Loaded Micelles Flow cytometry was carried out to investigate the antiproliferative effect of CL and CL-loaded NBSCh micelles on the cell cycle. HT-29 cells (1 × 106 cells/well) were seeded in 6-well culture plates and incubated for 24 h in the presence or absence of CL or CL-loaded NBSCh micelles at the IC50 value of CL 10.6 µg/mL (pure CL) and 3.4 µg/mL(CL in micelles). Following incubation, the cells were trypsinized, washed twice with PBS and centrifuged at 500× g for 5 min. The collected cells were fixed using $70\%$ ethanol at 4 °C overnight and washed twice with PBS. The cell pellets were re-suspended with 200 µL of PI and incubated at 30 °C in the dark for 30 min. Finally, the cells were analyzed using Flow cytometer (Amis Image X-MKII system, Merck, Germany). ## 3.11. Statistical Analysis Statistical analysis was performed using one-way ANOVA, and differences were considered to be significant at $p \leq 0.05.$ Analysis was carried out using the statistical software package SPSS, version 17.0 (SPSS 17.0 for Windows, SPSS Inc., Chicago, IL, USA). All studies were performed at least in triplicate and the data are presented as mean ± standard deviation. ## 4. Conclusions In this study, the improved water solubility and anti-colon cancers activities of a curcumin analog, cyqualone (CL) was achieved by N-benzyl-N,O-succinyl chitosan (NBSCh) nanomicelles construction. The CL-loaded NBSCh nanomicelles were prepared by the dialysis method provided high loading capacity. The obtained nanomicelles were spherical. The release of CL from the obtained nanomicelles could be controlled by the pH environments. 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--- title: Effect of Frozen-Thawed Embryo Transfer on the Metabolism of Children in Early Childhood authors: - Ze-Han Dong - Ting Wu - Chen Zhang - Kai-Zhen Su - Yan-Ting Wu - He-Feng Huang journal: Journal of Clinical Medicine year: 2023 pmcid: PMC10057347 doi: 10.3390/jcm12062322 license: CC BY 4.0 --- # Effect of Frozen-Thawed Embryo Transfer on the Metabolism of Children in Early Childhood ## Abstract Background: As a routine procedure in assisted reproductive technology (ART), it is crucial to assess the safety of frozen and thawed embryo transfer (FET). We aimed to investigate the metabolic profile of children conceived through FET in their early childhood. Method: A total of 147 children between the age of 1.5 and 4 years old, conceived through FET or naturally conceived (NC), were recruited. A total of 89 children, 65 in the FET group and 24 in the NC group (matched with the FET group based on children’s BMI) were included in the final statistical analysis of biochemical markers and metabolomics. Results: Children conceived through FET had a lower level of fasting insulin level and HORM-IR and a higher level of fasting glucose and APOE as compared to children naturally conceived. Metabolomics showed that there were 16 small differential metabolites, mainly including amino acids, carnitines, organic acids, butyric, and secondary bile acid between two groups, which enriched in Nitrogen metabolism, Butanoate metabolism, Phenylalanine metabolism, and D-Arginine and D-ornithine metabolism pathways. Conclusion: Although the FET group had a significantly higher level of APOE and fasting glucose, it cannot yet be considered that children in the FET group had an obvious disorder of glucose and lipid metabolism. However, the potentially more active intestinal flora and lower carnitine levels of children in the FET group suggested by metabolomics are worth further exploration. ## 1. Introduction As the most widely used treatment for infertility, more than 8 million children worldwide have been born through assisted reproductive technology (ART) up to 2018 [1]. Since the first successful frozen-thawed embryo transfer (FET) was reported in 1983, it has become a very important part of assisted reproductive technology (ART) [2]. Even though FET can significantly increase the cumulative pregnancy rate and reduce the risk of multiple gestations and ovarian hyperstimulation syndrome (OHSS) [3], there have been concerns regarding its potential adverse effects. FET has been identified as a potential risk factor for pregnancy-induced hypertension, large for gestational age, and macrosomia [4,5,6,7], all of which might raise the risk of metabolic dysfunction and cardiovascular problems [8,9]. In addition, the exposure of gametes and embryos to the non-physiological environment during the critical preimplantation period may lead to epigenetic disorder of the growth and metabolic systems of the offspring and result in potential long-term health effects. Animal studies have shown that IVF-ET (in vitro fertilization and embryo transfer) offspring have impaired glucose metabolism, including altered fasting glucose levels and impaired glucose tolerance (IGT) [10,11,12,13]. Recently, we discovered that FET-conceived male mouse offspring had glucose metabolic abnormalities, mainly manifesting insulin resistance [14]. Observations from human studies indicate that body fat composition in IVF children is disturbed, and children conceived by IVF/ICSI (intracytoplasmic sperm injection) have less favorable glucose and cardiovascular metabolic profiles in childhood when compared with naturally conceived children [15,16]. However, human research on the metabolic profile of children conceived through FET is limited. One follow-up study found that children conceived through FET frequently have abnormal lipid metabolism. [ 17]. Metabolomics, also known as the comprehensive profiling of small molecule metabolites in cells, is the study of the types, quantities, and changes of endogenous metabolites in biological systems, which has undergone a rapid evolution in the past two decades [18]. In the present study, we aimed to investigate the metabolic profile of FET offspring in early childhood at the level of macromolecular metabolites and small molecule changes and explore its potential impact on the metabolism in early childhood, with the hope to conduct early interventions to possible metabolic abnormalities of FET offspring. ## 2.1. Study Design and Population This study was a prospective study. From September 2018 to November 2019, 182 children born from FET and 66 naturally conceived children were recruited based on the electronic Case Report Forms (e-CRF) data in the International Peace Maternity and Child Health Hospital (IPMCH). Inclusion criteria were singleton birth children born after 28 gestational weeks and aged 1.5–4 years on the follow-up day. The exclusion criteria were as follows: [1] The mother had a history of severe liver and kidney dysfunction, diabetes, cancer, or autoimmune system disease; [2] One or both of their parents’ BMI was greater than 28 before pregnancy; [3] Children with severe congenital malformations, chromosomal abnormalities, or congenital metabolic diseases (as described in the previous study [19,20]); [4] Children developed from embryo preserved by the conventional slow freezing method. In total, 118 children from the FET group and 29 from the NC group who consented to provide venous blood were included in biochemical and metabolomic analyses. For a better comparison between the two groups, we matched NC cases with FET cases based on children’s BMI at the ratio of 1:3; in all, 24 children naturally conceived and 65 children born from FET were finally included for statistical analysis. This study was approved by the hospital’s scientific research ethics committee ((GKLW) 2016-21), and each participant signed an informed consent form for sample and data collection. ## 2.2. Medical History Sociodemographic characteristics, birth characteristics, and other potential confounding factors were collected by a questionnaire that was administered by specially trained investigators. Specifically, month age = (date of examination—date of birth)/30, gestational week (GW) refers to the exact days of the gestational week (e.g., 39 weeks + 5 days = 39.7 weeks). The diagnosis of GDM (gestational diabetes mellitus) was confirmed if fasting plasma glucose was ≥5.1 mmol/L (≥92 mg/dl), 1-h plasma glucose was ≥10.0 mmol/L (≥180 mg/dl), or 2-h plasma was ≥8.5 mmol/L (≥153 mg/dL). Preterm birth (PTB): gestational week <37 weeks; low birthweight (LB): birthweight < 2500 g; macrosomia: birth weight ≥ 4000 g; birth weight ≤10th percentile was defined as small for gestational age (SGA), and ≥90th percentile was defined as large for gestational age (LGA). As for alcohol consumption, a positive result was defined as having consumed alcohol at least once one year before childbirth. A positive result for smoking was defined as a history of smoking, and a positive result for passive smoking was defined as exposure to second-hand smoking during pregnancy. ## 2.3. IVF Procedures The ovarian stimulation protocol includes conventional protocols (GnRH-agonist long protocol, short protocol, and GnRH antagonist protocol), modified mild protocol, and individualized combined protocol [21,22,23,24]. The choice of different protocols is based on patients’ age, infertility diagnosis, and ovarian reserve test results. Oocytes were collected 34–36 h after ovulation induction [25] and inseminated using traditional IVF or ICSI. Fertilized oocytes were cultured in a cleavage medium until day 2 or day 3 before being cryopreserved via vitrification [26]. Natural cycles, hormone replacement cycles, and human menopausal gonadotropin (HMG)-stimulated cycles were used to administer endometrial preparation [27]. The embryos were thawed and transferred on a day when the maternal estradiol levels and endometrial thickness were well-prepared. ## 2.4. Biochemical Analysis The children’s peripheral venous blood was collected after they were fasting (>8 h). Plasma was collected in EDTA tubes after centrifugation at 3000 rpm for 10 min and stored in a −80 °C refrigerator. Fasting blood glucose (FBG) was measured using a glucometer ((ACCU-CHEK (Roche) glucose meter). Plasma concentrations of total cholesterol (CHOD-POD method), triglyceride (GPO-PAP method), HDL-cholesterol (direct method), and LDL-cholesterol (direct method) were measured by TBA 120 FR chemistry analyzer (Toshiba Co., Tokyo, Japan) [28]. The original formula was used to calculate the homeostasis model assessment (HOMA) as a marker of insulin resistance [29]. ## 2.5.1. Sample Preparation All targeted metabolite standards were obtained from Sigma-Aldrich (St. Louis, MO, USA), Steraloids Inc. (Newport, RI, USA), and TRC Chemicals (Toronto, ON, Canada). All standards were precisely weighed and prepared in water, methanol, sodium hydroxide solution, or hydrochloric acid solution to yield individual stock solutions containing 5.0 mg/mL. An appropriate amount of each stock solution was mixed to create stock calibration solutions. To reduce sample degradation, samples were thawed on ice, 25 μL of plasma was added to a 96-well plate, and the plate was transferred to the workstation (Biomek 4000, Beckman Coulter, Inc., Brea, CA, USA). Each sample received 100 μL of ice methanol with partial internal standards and was vortexed vigorously for 5 min. The plate was returned to the workstation after centrifugation at 4000 g for 30 min (Allegra X-15R, Beckman Coulter, Inc., Indianapolis, IN, USA). Then, 30 μL of supernatant was transferred to a clean 96-well plate, and each well-received 20 μL of freshly prepared derivative reagents. 350 μL of ice-cold $50\%$ methanol solution was added to dilute the sample after it had been sealed and derivatized at 30 °C for 60 min. The plate was then stored at −20 °C for 20 min before being centrifuged at 4 °C for 30 min. 135 μL of supernatant was transferred to a new 96-well plate, each with 15μL internal standards. The left wells were treated with serial dilutions of derivatized stock standards. ## 2.5.2. Data Analysis and Processing Metabolomic analysis was performed against 210 standard metabolites using an ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system (ACQUITY UPLC-XEVO TQ-S, Waters Corp., Milford, MA, USA); a similar method was prescribed in previous studies [30,31]. The following are brief descriptions of the optimized instrument settings: Temperatures for the sample manager and column were set to 10 °C and 40 °C, respectively. The injection volume was set at 5 μL, with a flow rate of 0.4 mL/min. The mobile phases were $0.1\%$ formic acid (A) and acetonitrile (B). The gradient conditions were set as follows: 0–1 min, $5\%$ B; 1–12 min, $5\%$ → $80\%$ B; 12–15 min, 80–$95\%$ B; 15–16 min, $100\%$ B; 18–18.1 min, $100\%$ → $5\%$ B, 18.1–20 min, $5\%$ B. The following instrument parameters were chosen for the mass spectrometer: voltage,1.5 Kv (ESI+)2.0(ESI−), source temperature, 150 °C; desolvation temperature, 550 °C; desolvation gas flow, 1000 L/h. The raw data files generated by UPLC-MS/MS were processed using Quan MET software (v2.0, Metabo Profile, Shanghai, China) to perform peak integration, calibration, and quantification of each metabolite. ## 2.6. Statistical Analysis The propensity score matching method (PSM) was adopted to match the NC cases for the FET cases. Statistical data analysis was conducted using SPSS (IBM, Armonk, NY, USA). Continuous variables with normal distribution were presented as mean ± standard deviation (SD), while continuous variables with nonnormal distribution were presented as median (first quartile, third quartile); the differences in the continuous variables between the two groups were tested using the t-test or Mann–Whitney U test. Noncontinuous data were presented as a percentage, and differences were detected using the Pearson χ2 test or Fisher’s exact test. A p-value less than 0.05 was deemed significant. Based on Xplore MET’s one-stop analysis software platform (including embedded statistical R software (3.2.1) code and a link to the KEGG database) for metabolomic data analysis, interpretation, and visual mapping. Orthogonal projection to latent structure discriminant analysis (OPLS-DA) was used in multivariate statistical analysis. A permutation test (1000 times) was performed for the statistical validation of the OPLS-DA model. Univariate analysis was also performed (t-test or Mann–Whitney U Test) to determine the differential metabolites between the two groups. The involved pathways based on the differential metabolites were identified using the KEGG-has library (http://www.genome.jp/kegg-bin/showpathway, accessed on 6 February 2023). ## 3.1. Baseline Characteristics The comparison of baseline characteristics between the FET group and the NC group was summarized in Table 1. The median age of children in the NC group on the follow-up day was higher than children in the FET group by about four months. Other characteristics, including maternal and paternal factors, were comparable between the two groups. ## 3.2. Biochemical Profile The biochemical profile of the FET group compared with the NC group is presented in Table 2. Children in the FET group have a significantly higher concentration of APOE than in the NC group. As for glucose metabolism, the fasting insulin level and HOMA-IR index were significantly decreased, while the fasting glucose level significantly increased in the FET group. No significant difference was observed in other biochemicals between the two groups. ## 3.3. Metabolomic Profile In total, 210 small molecule metabolites (µmol/L) from three metabolic pathways, including glucose, amino acid, and lipid metabolism, were detected in plasma; details of category and KEGG number are presented in Table S1. The stacked bars of the relative abundance of various metabolite types in each sample and the Z-score heatmap are shown in Figures S1 and S2, respectively. The results of OPLS-DA (orthogonal partial least-squares discrimination analysis) score plots and the permutation test are presented in Figure 1. However, OPLS-DA failed to completely separate the two groups of small molecule metabolite. Based on univariate analysis, 16 differential metabolites were screened out (Figure 2), with Maleic acid, TLCA (Taurolithocholic acid), and two fatty acids named Butyric acid and Isocaproic acid increased in the FET group. The higher metabolites in the NC group as compared to the FET group were three kinds of amino acid (Histidine, Tyrosine, Ornithine) and organic acid (Azelaic acid, Isocitric acid, 2-Hydroxy-3-methyl-butyric acid, Methylmalonic acid), three kinds of Carnitines (Palmitoyl carnitine, Stearyl carnitine, Linoleyl carnitine), Fructose, and p−Hydroxy phenylacetic acid (Benzenoids). See the box plot (Figure S3) for a more intuitive comparison. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, enriched metabolic pathways based on differential metabolites from two groups were displayed in the bubble plot (Figure 3). The affected pathways were Nitrogen metabolism, Butanoate metabolism, Phenylalanine metabolism, and D-Arginine and D-ornithine metabolism pathways. ## 4.1. Glycolipid Metabolism Glucose homeostasis plays a critical role in sustaining stable growth and metabolic status for an individual. A meta-analysis found that ART offspring had higher fasting insulin levels but no significant difference in fasting glucose or HOMA-IR when compared to non-ART offspring [32]. A human study demonstrated that children conceived by ART have significantly higher fasting blood glucose and serum insulin levels than children conceived naturally [16]. Our previous animal study showed the decreased insulin tolerance of FET-conceived male offspring, with higher HOMA-IR index and higher serum insulin level post glucose injected than the mouse conceived of natural conception. In addition, mice offspring in both the IVF-chow and FET-chow groups had higher serum TG, LDL, and lower HDL levels than those in the NC-chow group, indicating dyslipidemia [14]. In the present study, the FET group has a decreased level of fasting insulin and HOMA-IR but a higher level of fasting glucose and APOE. A large number of previous adult-based studies show that the acceleration of lipid decomposition, the entry of total free fatty acid (FFA) into the blood, and the promotion of inflammation are potential intermediate mechanisms of the interaction between obesity and insulin resistance. Adults with obesity and insulin resistance have an accumulation of total FFA, particularly saturated fatty acids (high-risk factor for type 2 diabetes). It is also accompanied by extensive fatty acid oxidation defects, with the products of incomplete fatty acid oxidation accumulating in the circulation. However, there were no significant differences in total FFA (free fatty acids) or SFA (saturated fatty acids) concentrations between the two groups, according to our findings (Table S2). However, the FET group has a significantly higher UFA and PUFA. Therefore, in the present study, we did not observe significant metabolic characteristics related to glucose and lipid metabolism disorder in the FET group. At the same time, metabolomics showed a decrease in three kinds of carnitines in the FET group. Carnitine is a low-molecular-weight compound that plays a specific role in the mitochondrial oxidation of long-chain fatty acids. Low carnitine availability was suggested to contribute to metabolic inflexibility and impaired glucose tolerance, and carnitine supplementation improves the formation of acetyl-carnitine and rescues metabolic flexibility in IGT subjects [33]. At the same time, a study investigated the plasma profile of subjects with nonalcoholic fatty liver disease (NAFLD), steatosis, and steatohepatitis (NASH) showed higher concentrations of free carnitine, butyryl carnitine, and methyl butyryl carnitine in NASH. Evidence obtained from animal studies revealed that embryonic exposure to culture components affected the body mass and adiposity of adult offspring in mice [34]. A human study demonstrated that children born after IVF have exaggerated weight gain in late infancy and that such catch-up growth appeared to correlate with the fetal growth pattern itself, regardless of birth weight [35]. Therefore, it cannot be ruled out that children conceived through FET have decreased insulin secretion from beta cells in the pancreatic islet, which results in increased fasting glucose levels, and the changes in lipid metabolism may appear in later childhood period. What is more, it should pay attention to the change in carnitine level in FET offspring and its potential relationship with glycolipid metabolism. ## 4.2. Amino Acid Metabolism The association between BCAAs, aromatic amino acids, with insulin resistance and type 2 diabetes has been well-established during the past decades [36]. A systematic review evaluating potential metabolite markers of prediabetes and type 2 diabetes suggested that several blood amino acids were associated with the risk of developing type 2 diabetes, including isoleucine, leucine, valine, tyrosine, and phenylalanine [37]. In the children’s study, amino acids linked to insulin resistance, obesity, and impaired glucose tolerance included branched-chain amino acids (BCCAs), phenylalanine (aromatic amino acid), aspartic acid, arginine, histidine, sarcosine, with abnormally high levels of BCCAs being potentially important risk biomarkers [38,39,40,41]. In the present study, no differences were observed in BCAAs between the FET group and the NC group. However, histidine, tyrosine, and ornithine were significantly decreased in the FET group, besides, Phenylalanine metabolism and D-Arginine and D-ornithine metabolism were affected in the FET group as compared to the NC group. Phenylalanine and tyrosine belong to aromatic amino acids, which are essential amino acids for the human body and are mainly produced by intestinal bacteria, especially Escherichia coli. Most phenylalanine is oxidized to tyrosine by phenylalanine hydroxylase in the body. Both are involved in synthesizing essential neurotransmitters and hormones and in the body’s metabolism of glucose and fat body. Histidine is a non-essential amino acid for adults but an essential amino acid for children. Ornithine is mainly involved in the urea cycle and plays an important role in the excretion of nitrogen in the body. Therefore, the tyrosine production and urea cycle may decrease in children of the FET group. Still, there were no significant changes in amino acid metabolism related to insulin resistance or type 2 diabetes. ## 4.3. Bile Acids (BAs) and Short-Chain Fatty Acids (SFACs) Metabolism Hepatocytes take cholesterol as raw material and synthesize primary BAs through multiple steps. The primary BAs can combine with glycine, taurine, and other substances to form conjugated Bas, including glycol cholic acid, taurocholic acid, glycol chenodeoxycholic acid, and taurochenodeoxycholic acid. After the BAs are discharged into the intestinal cavity, the conjugated primary BAs are hydrolyzed by bacteria in the ileum and upper colon to free primary BAs, which then undergoes 7-position-dehydroxylation to form secondary BAs- bile acid is converted into deoxycholic acid, and chenodeoxycholic acid is converted into lithocholic acid. More than $95\%$ of various BAs discharged into the intestine will be reabsorbed. The reabsorbed BAs enter the liver through the portal vein, and the liver cells re-uptake it and convert it into conjugated BAs, which are discharged into the intestine again, thus forming the enterohepatic circulation, which reuses the limited BAs and promotes digestion and absorption of lipids. SCFAs are primarily produced by the gut microbiota through the fermentation of dietary fiber or carbohydrates [42]; amino acid fermentation also contributes to SCFAs [43]. Unlike the acetate production pathway, which is widely distributed among bacterial groups [43], butyrate production depends on a surprisingly small number of organisms, dominated by Faecalibacterium prausnitzii, Eubacterium rectale, Eubacterium hallii, and R. bromii [44]. It is known that SCFAs, particularly butyrate, play a significant role in maintaining the colonic epithelium and act as a preferred fuel of colonocytes. The increased conjugated secondary BAs (Taurolithocholic acid), butyrate, and the affected butanoate metabolism pathway in the FET group may implicate the more active gut microbiota of children born from FET. In addition, the change in the nitrogen metabolism pathway may be related to the degradation of amino acids by intestinal flora, which can produce SFCA and NH4+. The above-mentioned decrease in tyrosine of the FET group may be the result of imbalanced degradation and synthesis of amino acids by intestinal flora. Considering the potential regulatory role of SCFAs in glucose homeostasis, we may speculate that the more active enterohepatic circulation and increased butyrate can promote lipid and glucose metabolism, but several studies indicate the opposite. A study involving 40 individuals with self-reported diabetes and 60 controls indicates that patients with diabetes exhibit a higher rate of conversion of primary and secondary bile acids by the gut microflora [45]. The study mentioned above showed markedly higher levels of glycocholate, taurocholate, and glycochenodeoxycholate in subjects with NAFLD [46]. Some other studies have reported that intestinal SCFAs concentrations were significantly increased in obese individuals [47,48,49], and high levels of SCFAs may be caused by an imbalance of phyla Firmicutes and Bacteroidetes. A study based on adolescents suggested that plasma SCFA concentrations were positively related to phyla Firmicutes/Bacteroidetes, body mass index, and visceral fat [48]. A high-quality study also revealed that individuals with dysbiosis associated with high fecal SCFAs are prone to increase intestinal permeability, obesity, and cardiovascular disease [50]. Thus, it is interesting to further explore the intestinal flora and BA pool composition of children born from FET. To summarize, our research found that children conceived through FET have a different metabolite profile as compared to children conceived naturally. Although the FET group has a significantly higher level of APOE and fasting glucose, it cannot yet be considered that children in the FET group have an obvious disorder of glucose and lipid metabolism. However, the potentially more active intestinal flora and lower carnitine levels of children in the FET group suggested by metabolomics are worth further exploration. To our knowledge, we demonstrated the metabolic profile of children born after FET in early childhood for the first time. However, some limitations should be considered when interpreting our study. First, the sample size of the present study, particularly the NC group, is small. A further study with a larger sample size and a longer follow-up period will be required to observe the long-term metabolic profile of FET offspring. Second, the present statistical method of metabolomics cannot exclude other confounding factors which could influence children’s metabolism, such as dietary patterns and nutrition, to which the parents of the FET group may pay more attention. However, we balanced the children’s BMI between the two groups, which may reduce this impact to some extent. Third, blood tested for metabolomics comes from only one vein of the children, which may not fully reflect their metabolism profile. Further research is needed to explore the intestinal flora and fecal metabolomics of FET offspring. ## 5. Conclusions Children conceived through FET have a lower level of fasting insulin level, HORM-IR, and a higher level of fasting glucose and APOE. There are 16 small differential metabolites, mainly including amino acids, carnitines, organic acids, butyric, and secondary bile acid, between two groups, which are enriched in four metabolism pathways. 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--- title: MLN4924 Promotes Self-Renewal of Limbal Stem Cells and Ocular Surface Restoration authors: - Qingjian Li - Yankun Shen - Shinan Wu - Hong Wei - Jie Zou - Sanhua Xu - Qian Ling - Min Kang - Hui Huang - Xu Chen - Yi Shao journal: Journal of Personalized Medicine year: 2023 pmcid: PMC10057348 doi: 10.3390/jpm13030379 license: CC BY 4.0 --- # MLN4924 Promotes Self-Renewal of Limbal Stem Cells and Ocular Surface Restoration ## Abstract Objective: To study the role of MLN4924 in corneal stem cell maintenance and corneal injury repair. Methods: In cell experiments, the Sprague–Dawley (SD) rat corneal epithelial cells were co-cultured with mitomycin C-inactivated mouse feeder cells in a supplemental hormonal epithelial medium (SHEM) with or without MLN4924. Cells were photographed using an optical microscope. Furthermore, we performed crystal violet, polymerase chain reaction (PCR), and immunofluorescence staining on limbal stem cells (LSCs). In animal experiments, we scraped the corneal epithelium with a central corneal diameter of 4 mm in SD rats. The area of the corneal epithelial defect was calculated by fluorescein sodium staining. Results: LSCs in the MLN4924 group had significantly proliferated. The MLN4924 treatment evidently enhanced the clone formation rate and clone area of LSCs. The expression levels of Ki67, p63, ABCG2, Bmi1, and C/EBPδ increased in LSCs after MLN4924 treatment, whereas the expression of K12 decreased. At 12 and 24 h after scraping, the corneal epithelium recovery rate in the eyes of the MLN4924-treated rats was accelerated. Conclusions: MLN4924 can maintain stemness, reduce differentiation, promote the proliferative capacity of rat LSCs, and accelerate corneal epithelial wound healing in SD rats. ## 1. Introduction The cornea is the transparent portion of the eye, which focuses light on the retina for optical conduction [1]. The corneal epithelium is the outermost layer of the cornea and undergoes regular regeneration by limbal stem cells (LSCs). The epithelium maintains corneal transparency, protects the eyes from damage and infection, and helps the immune response by producing cytokines [2]. Thoft et al. proposed the X, Y, Z hypothesis of corneal epithelial reproduction. They assumed that during the corneal stable period, LSCs produced transient amplifying cells, which migrated inward and forward and became differential corneal epithelial cells [3]. Loss or dysfunction of LSCs leads to ingrowth of the conjunctival epithelium, corneal stromal neovascularization, and corneal opacity [4]. Ocular surface diseases that cause corneal stem cell defects, such as Stevens–Johnson syndrome, chemical burns, emission damage, widespread microbic infections, and hereditary conditions, may threaten vision and lead to blindness [5]. Data showed that corneal blindness ranked as the second leading inducement of global blindness, affecting 23 million people and thus increasing a substantial burden on medical resources [6]. The usual treatment is surgical transplantation of the donor cornea, which has been practiced for more than a century [7]. Moreover, LSC transplantation can also be used as an alternative therapy [8]. However, comprehensive studies have shown that LSCs have the crucial features of epithelial stem cells, which are infrequent, undifferentiated static cells with self-updating capacity, high proliferation potentiality, and tissue reproduction capacity [9]. LSCs respond to the conversion of corneal epithelial cells by differentiating into progenitor and proliferating cells, which split and relocate to the centric corneal basement layer to supplement the corneal epithelium [10]. This process promotes corneal wound healing by stimulating the proliferation of LSCs internally and externally and directing differentiation into corneal epithelial cells. Ubiquitination adjusts steady-state protein stages mainly via the ubiquitin-proteasome targeted degradation system. Unlike ubiquitination, neddylation can regulate the functionality, structure, and orientation of its substrates [11]. Neddylation is the reversible covalent conjugation of the ubiquitin-like molecule NEDD8 (a developmental down-regulated protein 8 expressed by neuronal precursor cells) with the lysine residues of its substrates [12]. Analogous to ubiquitination, neddylation is initiated by a continuous cascade of NEDD8 activating enzyme E1, NEDD8 binding enzyme E2, and substrate-specific NEDD8-E3 ligase [13]. Neddylation is a highly conserved post-translational modification that allows cells to respond quickly and effectively to various stimuli [14]. Under physiological conditions, the level of neddylation is low. However, as a response to cellular stress or DNA damage, such as viral infection, inflammation, and cancer [15], activation of neddylation alters proteome homeostasis and affects stem cell proliferation [16]. MLN4924, also named Pevonedistat, is an especially tiny molecular suppressant of activating enzymes (NAE). It binds to the NAE active loci and forms a steady MLN4924–NEDD8 adduct, similar to adenylate–NEDD4, but suppresses further enzymatic processes, thereby obstructing the whole neddylation modification [17]. MLN4924 blocks the entire neddylation modification and inactivates all members of the Cullin-RING Ligase family by inhibiting E1. Cullin-RING ligase is often overexpressed in many types of human cancers [18]. MLN4924 exhibits impressive anti-cancer activity against a variety of human cancer cells in a wide range of preclinical environments by inducing growth arrest, apoptosis, autophagy, and senescence [19]. In addition, MLN4924 can promote wound healing induced by epidermal growth factor (EGF) in mouse skin. Low doses of MLN4924 can regulate the proliferation and differentiation of stem cells and have new applications in stem cell remedies [20]. This study investigated the effect of MLN4924 on the proliferation of LSCs and corneal wound healing. Our findings may provide a further understanding of the effects of MLN4924 on corneal repair management, therefore offering novel therapeutic approaches for treating corneal injuries. ## 2.1. Preparation of Feeder Cells NIH 3T3 cells were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with $10\%$ fetal bovine serum (FBS) and $1\%$ Penicillin-Streptomycin (PS). When NIH 3T3 cells reached 80–$90\%$ confluence, the culture medium was discarded, and the pre-prepared NIH 3T3 cell culture medium containing 10 μg/mL mitomycin was added. After 2 h of incubation at 37 °C, the medium was moved, the cells were washed with 1 × Hank’s Buffered Saline Solution (HBSS), replaced with a new NIH 3T3 cell culture medium, and placed back in the cell incubator with a CO2 concentration of $5\%$ and a temperature of 37 °C. ## 2.2. Isolation and Culture of LSCs The limbus sample (~1 mm width) was obtained from the eyeballs of freshly sacrificed adult female Sprague–Dawley (SD) rats. Five rats were used for limbal tissue collection, and the cells from five rats were pooled together. The limbal tissue was washed 3 times with 1 × HBSS containing $10\%$ FBS and $10\%$ PS, 10 min each time. The clean limbal tissue was transferred to supplemental hormonal epithelial medium (SHEM) containing 2 U/mL Dispase II and then digested at 4 °C for 8 h. SHEM medium was made of DMEM/F12, $0.5\%$ dimethyl sulfoxide (DMSO), 0.5 μg/mL hydrocortisone, 10 ng/mL mouse epidermal growth factor, 10 μg/mL insulin-transferrin-selenium-x, $5\%$ FBS, and $1\%$ PS. Subsequently, the isolated epithelial tissue was dissociated into single cells with $0.25\%$ Trypsin-EDTA. The NIH 3T3 cells were also digested into single cells. Corneal epithelial cells were mixed with NIH 3T3 cells according to a density of 1000 cells/cm2 of limbal epithelial cells and 4.5 × 104 cells/cm2 of NIH 3T3 cells and cultured with SHEM medium. To verify the effects of MLN4924 on LSCs, MLN4924 was added to the SHEM medium. Cells were photographed using an optical microscope (Leica, Wetzlar, Germany). ## 2.3. Crystal Violet Staining After the LSCs had been cultured for 12 days, they were fixed with $4\%$ paraformaldehyde solution at room temperature for 20 min and then washed 3 times with 1 × phosphate-buffered saline (PBS), 5 min each time. We added crystal violet staining solution at a concentration of $0.4\%$, stained at 25 °C for 30 min, and flushed 3 times with 1 × PBS buffer, 10 min each time. Finally, the sample was thoroughly dried in the air at room temperature and photographed with a camera (Canon, Tokyo, Japan). ## 2.4. Immunofluorescence Staining After the LSCs had been cultured for 9 days, they were fixed with $4\%$ paraformaldehyde solution at room temperature for 20 min and then washed 3 times with 1 × PBS, 5 min each time. The cells were incubated with cell permeabilization solution ($0.2\%$ TritonX-100) for 20 min at room temperature. After 1 h of blocking with $2\%$ bovine serum albumin (BSA), the samples were incubated with the primary antibody (1:200 dilution) diluted in $1\%$ BSA/PBS at 4 °C overnight. Rabbit anti-Ki67 (ab16667) and anti-K12 (ab185627) were purchased from Abcam (Cambridge, UK). After washing the primary antibody off, the cells were incubated with the secondary antibody (1:300 dilution) at room temperature for 1 h. Alexa Fluor 594-conjugated IgG (A21207) and Alexa Fluor 488-conjugated IgG (A21206) were purchased from Invitrogen (Eugene, OR, USA). The samples were finally mounted with H-1200 (containing DAPI) and photographed with a fluorescence microscope camera system (Leica, Wetzlar, Germany). ## 2.5. RNA Extraction and Polymerase Chain Reaction (PCR) Total RNA was extracted from LSCs using Trizol reagent (ThermoFisher Scientific, Waltham, MA, USA). The RNA concentration was detected by a nucleic acid/protein analyzer (Beckman Coulter, Bria, CA, USA). Equal amounts of RNA were reverse-transcribed to cDNA by PrimeScript™ RT Master Mix (TaKaRa, Shiga, Japan) following the manufacturer’s protocol. The PCR was performed using Taq Pro Universal SYBR qPCR Master Mix (Vazyme, Nanjing, Jiangsu, Chnia) on a LightCycler® 96 instrument (Roche, Basel, Switzerland). The PCR program used was as follows: preincubation at 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 10 s, annealing at 60 °C for 20 s, extension at 72 °C for 20 s, and final melting at 95 °C for 10 s, 65 °C for 60 s, and 97 °C for 1 s. Results were calculated using the 2-△△CT method to evaluate relative gene expression levels. The primers were: β-actin, 5’-CACCCGCGAGTACAACCTTC-3’ (forward) and 5’-CCCATACCCACCATCACACC-3’ (reverse). Ki67, 5’-CTCAGCTCCTGCCTGTTTGG-3’ (forward) and 5’-ACTTGAGTTGGATTGGCGGA-3’ (reverse); p63, 5’-CAGCACACGATCGAGACGTA-3’ (forward), and 5’-CGGGACTCCACAAGCTCATT-3’ (reverse); ABCG2, 5’-GTAGGTCGGTGTGCGAGTCAG-3’ (forward), and 5’-AAGGCCGTTCTTGTTTCTCTGT-3’ (reverse); Bmi1, 5’-AGCGGTGGCTGGATGC-3’ (forward), and 5’-GGTCTCCAAGTAACGCACAA-3’ (reverse); K12, 5’-AGCTAACGCGGAACTGGAAA-3’ (forward), and 5’-CTCTCCGCTCTTGGTGAGGT-3’ (reverse). ## 2.6. Western Blotting Proteins from LSCs were extracted with a cold lysis buffer composed of protease and phosphatase inhibitors (ThermoFisher Scientific, Waltham, MA, USA). Protein concentration was measured by a BAC protein assay kit (ThermoFisher Scientific, Waltham, MA, USA). Equal amounts of protein extracts (15 μg) were subjected to electrophoresis on $10\%$ acrylamide gels and then transferred to polyvinylidene difluoride membranes (Roche, Basel, Switzerland). After being blocked in $5\%$ BSA for 1 h at 25 °C, the membranes were incubated overnight at 4 °C with primary antibodies for anti-p63 (1:1000 dilution, ab124762, Abcam, Cambridge, UK), C/EBPδ (1:1000 dilution, ab65081, Abcam, Cambridge, UK), and K12 (ab185627, Abcam, Cambridge, UK). After 3 washes with Tris-buffered saline containing $0.05\%$ Tween-20 for 10 min each, the membranes were incubated with HRP-conjugated goat anti-rabbit IgG secondary antibody (1: 5000 dilution, 31460, ThermoFisher Scientific, Waltham, MA, USA) for 1 h at room temperature. An HRP-conjugated mouse anti-β-actin antibody (1:1000 dilution, A3854, Sigma-Aldrich, Saint Louis, MO, USA) was used for protein quantification. The results were detected by an enhanced chemiluminescence reagent kit (NCM Biotech, Suzhou, China) and recorded by the transilluminator (Bio-Rad, Hercules, CA, USA). ## 2.7. Corneal Epithelial Scratch Model Animal experiments were authorized by the Institutional Animal Care and Use Committee of Xiamen University School of Medicine. All animals were treated in accordance with national and international animal welfare rules. Under the surgical microscope, a corneal trephine with a diameter of 4 mm was used to align the corneas from 180 to 200 g female SD rats. The corneal trephine was rotated to make a circular incision in the corneal epithelium. A corneal scraper was used to remove the epithelium in the corneal ring incision. To verify the effect of MLN4924 on the healing rate of the corneal epithelium, eye drops (10 μL) containing 1 μM MLN4924 or $0.1\%$ DMSO were topically administered 4 times daily 24 h before the wound healing assay for a consecutive 2-day period. Corneal fluorescein sodium staining and a slit lamp microscope (66 Vision-Tech, Suzhou, Jiangsu, China) were used to calculate the area of the corneal epithelial defect. ## 2.8. Statistical Analysis All statistical analyses were performed with the GraphPad Prism software (San Diego, CA, USA). Data are presented as mean ± standard deviation (SD). A one-way analysis of variance, or t-test, was used to assess the distinctions between the groups. $p \leq 0.05$ indicated statistically significant differences. ## 3.1. MLN4924 Promotes Clone Formation Rate and Clone Area of LSCs The clones were visualized by crystal violet staining. The clone formation rate was calculated as follows: clone formation rate (%) = number of clones/number of seeded cells × $100\%$. The results showed that the clone formation rate and clone area of LSCs increased after MLN4924 treatment, as shown in Figure 1. Out of the different concentrations of MLN4924, 100 nM showed the best results. ## 3.2. MLN4924 Improves the Proliferative Capacity of LSCs In Vitro To examine the effect of MLN4924 on LSCs, 100 nM MLN4924 was used for the in vitro experiments. The growth rate of LSCs in a clone was calculated according to the following formulas: growth rate (%) = (number of LSCs 2 days after treatment − number of LSCs before treatment)/number of LSCs before treatment × $100\%$. It could be observed under an optical microscope that after 2 days of treatment, the LSCs of the MLN4924 group significantly proliferated when compared with the control group (Figure 2). ## 3.3. MLN4924 Increases the Expression Levels of Ki67 and p63 in LSCs The Ki67 index was calculated as the percentage of Ki67-positive cells relative to the total number of LSCs within the same clone. Immunofluorescence staining showed that Ki67 increased in LSCs after MLN4924 treatment. PCR analysis showed significant increases in Ki67 and p63 expression in the MLN4924 group. Western blotting showed that the p63 protein level in the MLN4924 group was higher than that in the control group, as shown in Figure 3. ## 3.4. MLN4924 Enhances the Preservation of Stemness of LSCs PCR analysis revealed a higher expression level of C/EBPδ mRNA and a lower level of K12 mRNA in the MLN4924 group. Furthermore, immunofluorescence staining showed that K12 decreased in the MLN4924 group. Western blotting showed that the C/EBPδ protein expression level increased in LSCs after MLN4924 treatment, whereas the protein level of K12 decreased. The results reflect that MLN4924 can maintain stemness and reduce the differentiation of LSCs, as shown in Figure 4. ## 3.5. MLN4924 Promotes Corneal Wound Healing The corneal epithelium is constantly renewed by LSCs that exclusively reside in the corneoscleral junction. At 12 and 24 h after scraping the epithelium with a diameter of 4 mm in the central cornea of SD rats, the recovery rate of corneal epithelium in the MLN4924 eye was accelerated, as shown in Figure 5. ## 4. Discussion Limbus is the interim zone between the pellucid cornea and opaque sclera [21]. LSCs are mature stem cells that further differentiate into the corneal epithelium. Functional LSCs are crucial for maintaining the entire corneal surface and corneal transparency. The normal limbus and LSCs act as a barrier to prevent conjunctival epithelial cells from invading the cornea [22]. LSCs are quiescent stem cells that can be activated to divide symmetrically and asymmetrically, producing proliferative progenitor cells that produce mature corneal epithelial cells [23]. Our results demonstrated that the addition of exogenous MLN4924 in vivo and in vitro could effectively maintain the stemness of LSCs and promote LSC proliferation. This finding provides a new idea for constructing a limbal equivalent for ocular surface reconstruction. The limbus acts as a barrier between the cornea and the conjunctiva, blocking conjunctivalization and neovascularization of the cornea [24]. The asymmetric division of an LSC can produce a limbal stem daughter cell and a transient amplifying cell. During migration, LSCs differentiate until they become squamous cells and detach from the corneal surface [25]. Many studies have proven the barrier function of the corneal limbus. Chung et al. researched the orientation of corneal epithelial stem cells in rats during their growth and proposed that stem cells were distributed in the basal layer of the cornea and limbal epithelium. When the cornea matures, the epithelial stem cells become concentrated in the limbus [26]. By continuously culturing cells from the ocular surface, Pellegrini et al. discovered that cells from the limbal biopsy experienced 85 doublings, while cells from the central cornea failed to survive in continuous culture, the result of which proved that cells in the marginal zone of the corneal epithelium possessed strong proliferative capability [27]. Stem cells at the edge of the Vogt fence are particularly abundant. These fences are radial fibrovascular ridges, primarily centered on the upper and lower edges [28]. LSCs and the limbal microenvironment play an important role in maintaining corneal stability and corneal immunity. The depletion of LSCs and the devastation of stem cell conditions may alter corneal stability [24]. Under pathological circumstances, the conjunctival epithelium can shift at the edge of the limbus; in the worst case, it can lead to inflammatory fibrovascular conjunctival centripetal pacing [29]. In recent decades, scientists have promoted more efficient methods for corneal injury, including continuous treatment, surgical technology, and advanced transplantation methods [30]. Our study proposes that specific ubiquitin-activating enzyme inhibitors could promote the proliferation of LSCs, which provides a new way of treating corneal blindness. Recent studies have demonstrated that neddylation is associated with many pathophysiological states, such as adipogenesis, cardiac homeostasis, synapse formation, and tumor development [11,31,32,33]. MLN4924 is a specific inhibitor of NAE1. NAE1 can lead to an increase in the autophagy flux of vascular smooth muscle cells [34,35]. MLN4924 has shown good prospects for the treatment of a variety of malignant tumors [36]. It has also exhibited anti-tumor effects in preclinical xenograft models of solid tumors and blood cancers, including acute myeloid leukemia and lymphoma models [37]. Neddylation regulates the tumor microenvironment composed of tumor cells, immune cells, etc. and plays a role in tumor progression [38]. NEDD acylation can inhibit the anti-tumor activity of tumor-associated macrophages, T cells, and dendritic cells [39]. MLN4924 is undergoing several clinical studies to evaluate its anticancer effects on solid tumors and leukemia. Five phase I clinical trials have been completed, suggesting that MLN4924 alone or in combination with chemotherapy is safe and shows effective therapeutic effects [40]. Moreover, the potential anticancer mechanism of MLN4924 is its inhibitory effect on NAE activity by binding to NAE to produce a covalent NEDD8-MLN4924-adduct [41]. Therefore, MLN4924 can effectively prevent the neddylation of all Cullins, leading to the accumulation of its substrates, which in turn triggers DNA replication stress, DNA damage response, cell cycle arrest, apoptosis, autophagy, and senescence [42,43]. Neddylation pathway components and CRL1/SCF E3 ligase are potential anticancer biomarkers, and MLN4924 can be a potential drug for cancer treatment [44]. The data showed that MLN4924 significantly inhibited the growth of renal cancer cells by blocking Cullin1 neddylation and subsequent substrate accumulation. In addition, MLN4924 prevented the migration of renal cancer cells by up-regulating E-cadherin and inhibiting Vimentin [45]. Recent studies have also used MLN4924 to block NAE1 and inhibit neddylation, which can eventually block terminal myoblast differentiation [46]. Studies have shown that sRANKL-activated TRAF6 leads to the automatic amplification of NFAT-c1 and promotes osteoclast differentiation [47]. MLN4924 can inhibit the levels of TRAF6, phosphorylated ERK, phosphorylated p38MAPK, and phosphorylated JNK. This means that in the early stages, neddylation inhibition can reduce the downstream signal of the sRANKL pathway. In addition, μCT analysis of trabecular bone microstructure showed deterioration of bone microstructure in the OVX/sRANKL group, which was reversed by MLN4924, indicating that it can inhibit sRANKL-induced bone loss and may be a new target for the treatment of osteoporosis [48]. For self-renewing hematopoietic stem cells, MLN4924 regulates ubiquitination and neddylation and promotes hematopoietic stem cell proliferation through cyclin-dependent kinase inhibitors [49]. Therefore, we believe that MLN4924 can promote corneal healing by increasing the proliferation of LSC while reducing their differentiation. Previous studies have shown that the mitotic marker Ki67 of LSCs determined by immunohistochemical staining is an important proliferation marker, and K12 is an important differentiation marker [50,51,52]. C-MYC is one of four Yamanaka factors that reprogram fibroblasts into induced pluripotent stem cells [53]. It portrays an essential character in the self-renewal of stem cells [54]. The researchers observed a dose-dependent increase in c-MYC after MLN4924 treatment in multiple cell lines and determined that there was a clear causal relationship between c-MYC accumulation and the stimulating effect of MLN4924. They found that MLN4924 treatment caused c-MYC accumulation in wild-type cells, tested the response of isogenic lines to MLN4924 and found that MLN49244 could still stimulate tumor sphere formation in FBXW7 empty cells [20]. Therefore, the stimulation effect of MLN4924 may be mediated by the c-MYC/FBXW7 axis. Ki67 is usually used as a prognostic marker in clinical practice due to its specificity to proliferating cells and its ability to detect cell cycles. The positive staining of Ki67 protein and other markers in the tumor samples of patients can be used to grade the primary tumor and metastasis [55,56]. Moreover, Ki67 staining also has prognostic value in predicting cancer survival and recurrence [57]. The p63 gene is a member of the p53 family and is essential for the development of epithelial cells and the regulation of epithelial cell proliferation and differentiation. The p63 gene exists in two different subtypes, TAp63 and ΔNp63 [58]. Among them, ΔNp63 portrays an essential character in HNSCC cell survival and inhibits the p73-dependent pro-apoptotic transcription program [59]. The ΔNp63α/HDAC$\frac{1}{2}$ complex is also considered to be an important tumor maintenance factor [60]. ABCG2 is a well-characterized ABC transporter. Some cell experiments have confirmed that the ABCG2 level is a key determinant of cell sensitivity to MLN4924 [61]. It has been reported that MLN4924 may be a potential substrate of ABCG2, which can stimulate the ATPase activity of ABCG2 in a concentration-dependent manner [62]. The data from cell experiments showed that the sensitivity of ABCG2-overexpressing cells to MLN4924 was significantly reduced. Knockdown of ABCG2 could partially restore the sensitivity of MLN4924, indicating that it played an important role in controlling the sensitivity of MLN4924 [63]. Our experiments showed that the ABCG2 of LSCs increased after MLN4924 treatment, which may subsequently cause stem cell resistance to MLN4924, which is also a situation that needs our attention in clinical application. Bmi1 is the core component of the polycomb repressor complex, which mediates gene silencing through the monoubiquitination of histone H2A [64]. In addition, Bmi1 is an important stem cell self-renewal factor [65]. More importantly, researchers found that circ_001680 could promote the number of cancer stem cells in colorectal cancer by regulating Bmi1 through miR-340 [66]. Our cytological experiments showed that Ki67, p63, ABCG2, and Bmi1 of SD rat LSCs increased and K12 decreased after MLN4924 treatment, indicating that MLN4924 could maintain the stemness of LSCs and reduce differentiation. Based on the results from animal experiments, we observed that MLN4924 could accelerate corneal epithelial recovery. This finding indicated that MLN4924 could promote the migration of limbal epithelial cells and wound healing. This not only provides a broader clinical prospect for MLN4924 but also offers a more feasible treatment for corneal limbus injury. ## 5. 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