Effects of circuit resistance training on serum myokine METRNL, cytokines, insulin resistance, body composition, and lipid profile in overweight participants: A 6-week intervention study

Effects of circuit resistance training on serum myokine METRNL, cytokines, insulin resistance, body composition, and lipid profile in overweight participants: A 6-week intervention study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of circuit resistance training on serum myokine METRNL, cytokines, insulin resistance, body composition, and lipid profile in overweight participants: A 6-week intervention study Hamid Alizadeh, Alireza Safarzade This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4945904/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2025 Read the published version in Sport Sciences for Health → Version 1 posted 13 You are reading this latest preprint version Abstract Objectives : This study investigated the effects of circuit resistance training (CRT) on Meteorin-like protein (METRNL), interleukin-4 (IL-4), interleukin-13 (IL-13), and metabolic health markers in overweight individuals. Methods : Thirty overweight male university students (BMI 25-30 kg/m²) were randomly assigned to a 6-week CRT intervention group (n=15) and a control group (n=15). The CRT program comprised three weekly 45-minute sessions at 60-70% of one-repetition maximum. Serum METRNL, IL-4, IL-13, insulin resistance index, body composition, and lipid profile were measured pre-and post-intervention. Results : The CRT group showed significant improvements compared to controls. Body mass index and body fat percentage decreased, while serum METRNL, IL-4, and IL-13 levels increased significantly (p<0.05). Metabolic health markers improved, with reductions in fasting blood glucose, fasting insulin, HOMA-IR, total cholesterol, triglycerides, and LDL-C, and increased HDL-C (p<0.05). Lean body mass remained unchanged between groups. Conclusions : CRT effectively enhances METRNL secretion, potentially contributing to improved immune and metabolic functions in overweight individuals. This suggests its potential as a therapeutic strategy for managing obesity-related immunometabolic disorders, warranting further investigation. Obesity Exercise Immunometabolism Myokines Anti-Inflammatory Response Metabolic Health Introduction The global rise in obesity has intensified research into the relationship between physical activity and metabolic health [ 1 ]. Exercise immunometabolism, an emerging field, explores the complex interactions between exercise, immune function, and metabolism [ 2 ]. Central to this field are myokines, muscle-derived cytokines that mediate many of exercise's beneficial effects [ 2 ]. METRNL (Meteorin-like) is a recently characterized adipomyokine that exemplifies the intricate interplay between immune function and metabolism [ 3 ]. This pleiotropic protein exerts significant immunomodulatory and metabolic effects. Animal studies have shown that METRNL promotes macrophage polarization towards an anti-inflammatory M2 phenotype, attenuating chronic low-grade inflammation associated with metabolic disorders [ 3 ]. Concurrently, it enhances insulin sensitivity [ 4 – 6 ], and fat oxidation [ 7 ] in peripheral tissues, while stimulating adipose tissue browning and adaptive thermogenesis [ 3 ]. The interplay between METRNL and metabolic markers highlights its potential as both a biomarker for metabolic health and a therapeutic target [ 8 ]. Clinical studies have linked serum METRNL levels with various metabolic parameters, including body mass index (BMI), insulin sensitivity, glucose tolerance, and lipid profiles [ 8 ]. These associations underscore METRNL's role not only as an indicator of metabolic health but also as a mediator of the positive effects of exercise on systemic inflammation and metabolic regulation. Notably, METRNL's expression and secretion are upregulated by exercise, contributing to the beneficial immunometabolic effects of physical activity [ 9 ]. A key mechanism underlying these effects, as demonstrated in animal models, involves METRNL's ability to stimulate the production of interleukin-4 (IL-4) and interleukin-13 (IL-13) [ 3 ]. These anti-inflammatory cytokines are crucial in maintaining adipose tissue homeostasis and regulating immunometabolism, particularly in the context of obesity [ 10 ]. This preclinical evidence has established that exercise-induced METRNL secretion initiates a cascade of immunomodulatory events that helps mitigate obesity-associated inflammation and metabolic dysfunction [ 3 ]. However, it is important to note that while the immunogenic effects of METRNL have been well-established in animal models, their translation to human physiology remains largely unexplored. This gap in our understanding presents a critical area for further investigation, particularly in the context of obesity and exercise interventions in humans. While much research has focused on traditional exercise forms, circuit resistance training (CRT) has emerged as a promising modality for improving both metabolic health and muscular strength [ 11 ]. CRT combines resistance exercises with minimal rest periods, offering cardiovascular benefits alongside strength improvements [ 11 ]. However, the effects of CRT on myokine production, especially METRNL, and its impact on metabolic and immune function remain largely unexplored in the context of obesity. To this end, this study aimed to investigate the chronic effects of CRT on METRNL, IL-4, and IL-13 levels as potential mechanisms underlying exercise-induced improvements in obesity markers. Hence, the primary objective of this study was to investigate the impact of a 6-week circuit resistance training program on serum concentrations of Meteorin-like protein (METRNL), interleukin-4 (IL-4), and interleukin-13 (IL-13) in overweight individuals. Additionally, we aimed to examine the associations between changes in serum METRNL levels and alterations in both cytokine profiles and key metabolic health indicators. Methods Study Design This study was a randomized controlled trial (RCT) designed to evaluate the effects of circuit resistance training on various health outcomes. The trial featured two parallel groups: an intervention group that participated in a structured circuit resistance training program and a control group that maintained their usual physical activity levels. The intervention period lasted for six weeks, with assessments conducted at baseline and 48 hours’ post-intervention to measure the effects of the training. Participants Overweight, untrained male students (N = 50) who responded to flyers posted on campus notice boards and in dormitories at our institution were considered for this study. Following an initial phone interview, 45 candidates were invited for a comprehensive health evaluation conducted by a physician. This evaluation encompassed a medical history review, overall health assessment, body composition analysis, and metabolic risk factor evaluation. Out of the 45 initially screened individuals, 30 met the inclusion criteria. These criteria required participants to be between 20 and 30 years old, non-smokers, and have a Body Mass Index (BMI) ranging from 25 to 30 kg/m 2 . Participants were also required to be free from any conditions that would preclude participation, such as cardiovascular, metabolic, or musculoskeletal disorders. Exclusion criteria included cardiometabolic diseases, acute illness or infection, and the use of medications or supplements that could affect the study outcomes. Participants consuming any medication or supplements were excluded from the study. As all participants dined at the university cafeteria, they adhered to an identical diet. Detailed information about the study was provided to all potential participants. Among those who met the inclusion criteria (body weight: 80 ± 3.5kg, height: 170 ± 3cm, BMI: 27.7 ± 1.55 Kg.m 2 , age: 25 ± 4 years) and did not meet any exclusion criteria (N = 30), all agreed to participate and provided written informed consent. Participants were then randomly assigned to either the exercise training group (n = 15) or the control group (n = 15). The sample size for each group was determined to ensure the study was adequately powered to detect differences in the primary outcome, the change in total serum METRNL levels. We based our analysis on variability from a previous study [ 12 ] that utilized a similar study design (Pre-post values). Using Gpower software, we concluded that a minimum of 15 participants per group was necessary to achieve the power of 0.80. Therefore, the total required sample size was 30 participants, with 15 in the CRT group and 15 in the control group. This sample size is sufficient to detect significant differences in METRNL levels between the groups, effectively meeting our research objectives. The Research Ethics Review Committee of the University of Mazandaran reviewed and approved the study protocol (IR.UMZ. REC. 2269045). After obtaining written informed consent from each participant, the clinical evaluations were conducted. Exercise Intervention Participants assigned to the exercise group underwent a 6-week supervised circuit resistance training (CRT) program, with sessions scheduled three times per week on non-consecutive days to ensure adequate recovery. Each session lasted approximately 45 minutes, beginning with a 5-minute warm-up, followed by 35 minutes of circuit training, and concluding with a 5-minute cool-down period. Prior to the commencement of the training program, each participant’s one-repetition maximum (1RM) was determined for each exercise. This process involved a familiarization session where participants performed each exercise with progressively heavier weights until they could complete only one repetition with proper form. This assessment was supervised by experienced trainers to ensure accuracy and safety. Participants performed exercises at 60–70% of their 1RM, a range considered moderate to somewhat hard, ensuring effective muscle engagement and adaptation without causing excessive fatigue or risk of injury. Each exercise was performed for 8–12 repetitions per set, with participants completing three sets of each exercise. A 1-2-minute rest period was allowed between sets to ensure adequate recovery, monitored using a stopwatch to maintain consistency. The circuit included a variety of resistance exercises targeting major muscle groups, such as squats, lunges, and leg presses for the lower body; bench presses, rows, and shoulder presses for the upper body; and planks, Russian twists, and leg raises for the core. Intensity was monitored using the Rated Perceived Exertion (RPE) scale, aiming to maintain a score of 13–15 (moderate to somewhat hard) throughout the training session. Trainers provided real-time feedback and adjustments based on participant performance and feedback. For instance, if a participant reported that the exercise felt too easy, the weight was increased slightly for the next set. The program was progressively adjusted every two weeks. This involved reassessing the 1RM for each exercise and increasing the training load by 5–10% if participants could comfortably complete more than 12 repetitions per set. This progressive overload ensured continuous improvement in strength and endurance. Additionally, trainers introduced variations of exercises to keep the program engaging and to target muscles from different angles. Participants assigned to the control group were asked to maintain their current lifestyle and dietary intakes during the study period. Table 1 summarizes exercise intervention program details. Table 1 Exercise Intervention Program Details Aspect Description Duration 6 weeks Frequency 3 sessions per week (non-consecutive days) Session Length 45 minutes Warm-Up 5 minutes Circuit Training 35 minutes Cool-Down 5 minutes Initial Assessment One-repetition maximum (1RM) for each exercise Intensity 60–70% of 1RM Repetitions per Set 8–12 repetitions Sets per Exercise 3 sets Rest Between Sets 1–2 minutes Exercises Included Squats-Lunges-Leg presses-Bench presses-Rows-Shoulder presses-Planks-Russian twists-Leg raises Intensity Monitoring Rated Perceived Exertion (RPE) scale (target: 13–15) Progressive Overload Reassessment of 1RM every 2 weeks; Increase training load by 5–10% Data Collection All outcome measures were collected at baseline (pre-intervention) and following the 6-week exercise intervention period (circuit resistance training). Blood samples were collected after a 10-12-hour overnight fast. Body composition was assessed using a body analyzer composition apparatus, and participants completed questionnaires related to their physical activity levels and dietary intake. Blood Sample Collection All blood samples were collected in the morning (between 7:00 AM and 9:00 AM) after an overnight fast of 10–12 hours. Participants were instructed to refrain from eating or drinking anything except water during the fasting period. They were also asked to avoid strenuous physical activity and alcohol consumption for 72 hours prior to blood collection. A total of 10 mL of venous blood was drawn from each participant by a certified phlebotomist using standard venipuncture techniques. Blood was collected from the antecubital vein with participants in a seated position. Immediately after collection, all tubes were gently inverted 8–10 times to ensure proper mixing. Serum separator tubes (SST) were allowed to clot at room temperature for 30 minutes before centrifugation. All samples were centrifuged within 1 hour of collection at 1500 x g for 15 minutes at 4°C. After centrifugation, serum and plasma were aliquoted into labeled micro-centrifuge tubes. For each participant, multiple aliquots were prepared to avoid repeated freeze-thaw cycles. All aliquots were immediately stored at -80°C until analysis. Serum METRNL Levels Fasting blood samples were collected and serum was separated by centrifugation. Serum METRNL concentrations were determined using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (Kit: ZellBio, Germany, Sensitivity: 0.06 ng/ml) according to the manufacturer's instructions. The assay sensitivity was 5 ng/mL, with intra- and inter-assay coefficients of variation of 4.2% and 6.8%, respectively. Serum Cytokine Levels Serum levels of IL-4 and IL-13 were measured using a multiplex bead-based immunoassay (Bio-Plex Pro™ Human Cytokine Assay, Bio-Rad Laboratories, USA) following the manufacturer's protocol. The assay sensitivities for IL-4 and IL-13 were 0.3 pg/mL and 0.7 pg/mL, respectively. Intra- and inter-assay coefficients of variation were < 5% for both cytokines. Serum insulin, glucose, and Insulin Resistance index Insulin resistance was assessed using the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Fasting glucose was measured using the hexokinase method on an automated analyzer (GlucoStar 3000, MediTech Systems, Germany). Fasting insulin was quantified using a chemiluminescent immunoassay (InsuliCheck™, Nordic Diagnostics, Sweden). HOMA-IR was calculated using the formula: HOMA-IR = (fasting glucose [mmol. L − 1 ] × fasting insulin [µU. mL − 1 ]) / 22.5. Serum Lipid Profile Fasting blood samples were analyzed for lipid profiles using an automated clinical chemistry analyzer (LipidPro 5000, ChemMed Corp., Japan). Total cholesterol and triglycerides were measured using enzymatic colorimetric methods. HDL cholesterol was determined using a direct method with polyethylene glycol-modified enzymes. LDL cholesterol was calculated using the Friedewald formula. Body Composition parameters Body composition parameters including body weight, lean mass, and body fat percentage were assessed using a body composition analyzer (Medigate Company Inc., Dan-dong Gunpo, Korea). Height was also measured in a barefoot standing position with a wall-mounted stadiometer (SECA, Germany). Body mass index (BMI, kg.m − 2 ) was calculated using weight (kg) and height (m) values. Statistical Analysis Data are expressed as mean ± SD. The normality of distribution for dependent variables was assessed using the Shapiro-Wilk test. A two-factor (time: pre and post; group: control and exercise) with mixed-design ANOVA followed by an LSD post-hoc test was performed for all the continuous variables to evaluate the effect of exercise in different groups. In addition, effect sizes (i.e., Cohen’s d) were calculated by dividing the pre-post change throughout exercise training by the pooled standard deviation of the pre-test scores for the specific group (i.e., control vs. exercise training group). Thus, effect sizes for the exercise or control group were calculated by the pre-post change for that specific group by the pooled standard deviation of the pre-test scores for the entire group. All statistical analyses were performed using GraphPad Prism Version 10, with a type I error rate of α = 0.05. Results Body Composition Parameters Prior to conducting the main analyses, the normality of data distribution was assessed for all body composition parameters using the Shapiro-Wilk test. For normally distributed data, independent t-tests were used to assess baseline differences between groups, and a two-factor (time: pre and post; group: control and exercise) mixed-design ANOVA followed by an LSD post-hoc test was employed to analyze the effects of the intervention over time. No statistically significant differences were observed between the Control and CRT groups at baseline for any of the body composition parameters (p > 0.05). Table 2 summarizes the changes observed in response to the 6-week circuit resistance training (CRT) intervention. Body Weight (BW) Analysis of body weight data revealed a significant interaction effect between the groups over time [F (1, 56) = 33.7, P < 0.0001]. The Control group exhibited a slight increase in body weight (0.9 ± 2.97 kg, Cohen's d = 0.30), whereas the CRT group demonstrated a significant decrease (-2.5 ± 3.75 kg, Cohen's d = -0.67). The between-group difference was statistically significant (P = 0.001), suggesting that the CRT intervention effectively reduced body weight compared to the control condition. Body Mass Index (BMI) A significant interaction effect was observed for BMI [F (1, 56) = 23.9, P < 0.0001]. The Control group showed a minor increase in BMI (0.4 ± 1.85 kg.m-2, Cohen's d = 0.22), while the CRT group exhibited a substantial decrease (-2.0 ± 1.75 kg.m-2, Cohen's d = -1.14). The between-group difference was statistically significant (P = 0.001), indicating that the CRT intervention effectively reduced BMI, with a large effect size. Body Fat Percentage (BF%) Body fat percentage analysis revealed a significant interaction effect [F (1, 56) = 74.32, P < 0.0001]. The Control group experienced a slight increase in BF% (0.7 ± 3.70%, Cohen's d = 0.19), whereas the CRT group demonstrated a substantial decrease (-3.5 ± 3.64%, Cohen's d = -0.96). The between-group difference was statistically significant (P = 0.025), suggesting that the CRT intervention effectively reduced body fat percentage compared to the control condition, with a large effect size. Lean Body Mass (LBM) Analysis of LBM data showed no significant interaction effect [F (1, 56) = 0.35, P = 0.56], and the between-group difference was not statistically significant (P = 0.56). The Control group exhibited a minimal increase in LBM (0.065 ± 3.54 kg, Cohen's d = 0.02), while the CRT group demonstrated a slightly larger increase (1.065 ± 3.89 kg, Cohen's d = 0.27). However, these changes were not statistically significant between groups. Table 2 Changes in body composition parameters in both groups after 6 weeks (mean ± SDs). Parameter Group Pre-test Post-test Change ES Interaction (Group×time) Between-Group BW [kg] Control 78.5 ± 2.1 79.4 ± 2.1 0.9 ± 2.97 0.30 F (1, 56) = 33.7, P < 0.0001 P = 0.001 CRT 81.5 ± 2.5 79 ± 2.8 − 2.5 ± 3.75 † − 0.67 BMI [kg.m − 2 ] Control 26.5 ± 1.20 26.9 ± 1.40 + 0.4 ± 1.85 0.22 [F (1, 56) = 23.9, P < 0.0001] P = 0.001 CRT 27 ± 0.70 25 ± 1.60 -2.0 ± 1.75 † -1.14 BF% Control 31 ± 2.6 31.7 ± 2.6 + 0.7 ± 3.70 0.19 [F (1, 56) = 74.32, P < 0.0001] P = 0.025 CRT 32 ± 2.2 28.5 ± 2.9 −3.5 ± 3.64 † − 0.96 LBM [kg] Control 54.16 ± 2.50 54.23 ± 2.51 + 0.065 ± 3.54 0.02 [F (1, 56) = 0.35, P = 0.56] P = 0.56 CRT 55.42 ± 2.47 56.48 ± 3.04 + 1.065 ± 3.89 0.27 Cohen's d (ES) is based on the change in each parameter (post-test minus pre-test) for each group. BW: Body Weight; BMI: body mass index; BF%: body fat percent; LBM: Lean Body Mass. Interaction Effects Indicates the statistical significance of the interaction between the group and time for each parameter. Between-Group Comparison (Post-hoc comparison) Highlights significant differences between the exercise and control groups at the post-training phase ( † ). Glucose and Lipid Homeostasis Parameters Prior to conducting the main analyses, the normality of data distribution was assessed for all glucose and lipid homeostasis parameters using the Shapiro-Wilk test. For normally distributed data, independent t-tests were used to assess baseline differences between groups, and a two-factor (time: pre and post; group: control and exercise) mixed-design ANOVA followed by an LSD post-hoc test was employed to analyze the effects of the intervention over time. No statistically significant differences were observed between the Control and CRT groups at baseline for any of the parameters (p > 0.05). Table 3 summarizes the changes observed in response to the 6-week circuit resistance training (CRT) intervention. Fasting Blood Glucose (FBG) A significant interaction effect was observed between group and time [F (1, 8) = 7.08, P = 0.025]. The Control group showed a minimal increase in FBG (5.4 ± 0.10 to 5.45 ± 0.11 mmol/L, change: 0.05 ± 0.15 mmol/L, ES: 0.48), while the CRT group demonstrated a substantial decrease (5.6 ± 0.19 to 4.7 ± 0.50 mmol/L, change: -0.9 ± 0.53 mmol/L, ES: -2.38). The between-group difference was statistically significant (p = 0.008), indicating that the CRT intervention led to a significant reduction in FBG compared to the control group. Insulin A significant interaction effect was found for insulin levels [F (1, 8) = 9.88, P = 0.014]. The Control group exhibited a slight increase (11.5 ± 1.50 to 11.9 ± 1.70 µU/mL, change: 0.4 ± 2.27 µU/mL, ES: 0.25), whereas the CRT group showed a notable decrease (11.4 ± 1.40 to 9.3 ± 1.10 µU/mL, change: -2.1 ± 1.78 µU/mL, ES: -1.67). The between-group difference was statistically significant (p = 0.048). Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) A highly significant interaction effect was observed for HOMA-IR [F (1, 16) = 20.21, P = 0.0004]. The Control group showed a slight increase (2.76 ± 0.23 to 2.88 ± 0.23, change: 0.12 ± 0.33, ES: 0.52), while the CRT group demonstrated a substantial decrease (2.85 ± 0.44 to 1.94 ± 0.31, change: -0.91 ± 0.54, ES: -2.39). The between-group difference was statistically significant (p = 0.04). Total Cholesterol (TC) A highly significant interaction effect was found for total cholesterol levels [F (1, 28) = 17.50, P = 0.0003]. The Control group showed a slight increase (201 ± 3.03 to 203 ± 3.03 mg/dl, change: 2 ± 4.29 mg/dl, ES: 0.66), while the CRT group exhibited a marked decrease (204.1 ± 9.4 to 190 ± 5.40 mg/dl, change: -14.1 ± 10.84 mg/dl, ES: -1.84). The between-group difference was statistically significant (p = 0.010). Triglycerides (TG) A significant interaction effect was observed for triglyceride levels [F (1, 16) = 9.608, P = 0.007]. The Control group showed a minimal increase (121.6 ± 3.07 to 122.6 ± 3.07 mg/dl, change: 1 ± 4.34 mg/dl, ES: 0.33), whereas the CRT group demonstrated a considerable decrease (122.2 ± 8.7 to 109.4 ± 8.1 mg/dl, change: -12.8 ± 11.89 mg/dl, ES: -1.52). The between-group difference was statistically significant (p = 0.012). Low-Density Lipoprotein Cholesterol (LDL-C) A highly significant interaction effect was found for LDL-C levels [F (1, 24) = 16.52, P = 0.0004]. The Control group showed a slight increase (134.6 ± 2.67 to 137 ± 2.67 mg/dl, change: 2.4 ± 3.78 mg/dl, ES: 0.90), while the CRT group exhibited a decrease (136.3 ± 6.8 to 126.7 ± 67 mg/dl, change: -9.6 ± 67.33 mg/dl, ES: -0.20). The between-group difference was statistically significant (p = 0.008). High-Density Lipoprotein Cholesterol (HDL-C) A highly significant interaction effect was observed for HDL-C levels [F (1, 16) = 20.57, P = 0.0003]. The Control group showed a slight decrease (40.2 ± 0.95 to 39.2 ± 0.95 mg/dl, change: -1 ± 1.34 mg/dl, ES: -1.05), while the CRT group demonstrated an increase (39.2 ± 2.42 to 43 ± 2.42 mg/dl, change: 3.8 ± 3.42 mg/dl, ES: 1.57). The between-group difference was statistically significant (p = 0.017). Table 3 Changes in glucose and lipid metabolism parameters in both groups after 6 weeks (mean ± SDs). Parameter Group Pre-test Post-test Change ES Interaction (Group×time) Between-Group FBG [mmol. L − 1 ] Control 5.4 ± 0.10 5.45 ± 0.11 0.05 ± 0.15 0.48 [F (1, 8) = 7.08, P = 0.025] p = 0.008 CRT 5.6 ± 0.19 4.7 ± 0.50 −0.9 ± 0.53 † -2.38 Insulin [µU. mL − 1 ] Control 11.5 ± 1.50 11.9 ± 1.70 0.4 ± 2.27 0.25 [F (1, 8) = 9.88, P = 0.014] p = 0.048 CRT 11.4 ± 1.40 9.3 ± 1.10 −2.1 ± 1.78 † -1.67 HOMA-IR Control 2.76 ± 0.23 2.88 ± 0.23 0.12 ± 0.33 0.52 [F (1, 16) = 20.21, P = 0.0004] p = 0.04 CRT 2.85 ± 0.44 1.94 ± 0.31 −0.91 ± 0.54 † -2.39 TC [mg/dl] Control 201 ± 3.03 203 ± 3.03 2 ± 4.29 0.66 [F (1, 28) = 17.50, P = 0.0003] p = 0.010 CRT 204.1 ± 9.4 190 ± 5.40 −14.1 ± 10.84 † -1.84 TG [mg/dl] Control 121.6 ± 3.07 122.6 ± 3.07 1 ± 4.34 0.33 [F (1, 16) = 9.608, P = 0.007] p = 0.012 CRT 122.2 ± 8.7 109.4 ± 8.1 −12.8 ± 11.89 † -1.52 LDL-C [mg/dl] Control 134.6 ± 2.67 137 ± 2.67 2.4 ± 3.78 0.90 [F (1, 24) = 16.52, P = 0.0004] p = 0.008 CRT 136.3 ± 6.8 126.7 ± 67 −9.6 ± 67.33 † -0.20 HDL-C [mg/dl] Control 40.2 ± 0.95 39.2 ± 0.95 −1 ± 1.34 -1.05 [F (1, 16) = 20.57, P = 0.0003] p = 0.017 CRT 39.2 ± 2.42 43 ± 2.42 3.8 ± 3.42 † 1.57 Cohen's d (ES) is based on the change in each parameter (post-test minus pre-test) for each group. Interaction Effects Indicates the statistical significance of the interaction between the group and time for each parameter. Between-Group Comparison (Post-hoc comparison) Highlights significant differences between the exercise and control groups at the post-training phase (†). FBG: Fasting Blood Glucose; TC: total cholesterol; HDLC: high-density lipoprotein cholesterol; LDLC: low-density lipoprotein cholesterol TG: triglycerides; HOMA-IR: homeostasis model assessment for insulin resistance. METRNL and cytokines levels Prior to conducting the main analyses, the normality of data distribution was assessed for METRNL and interleukins using the Shapiro-Wilk test. For normally distributed data, independent t-tests were used to assess baseline differences between groups, and a two-factor (time: pre and post; group: control and exercise) mixed-design ANOVA followed by an LSD post-hoc test was employed to analyze the effects of the intervention over time. No statistically significant differences were observed between the Control and CRT groups at baseline for any of the parameters (p > 0.05). Table 4 summarizes the changes observed in response to the 6-week circuit resistance training (CRT) intervention. METRNL A 2x2 mixed ANOVA revealed a significant interaction effect between group and time (F(1, 20) = 216.2, P < 0.0001). Post-hoc analysis, likely using independent t-tests, indicated a significant between-group difference (P < 0.0001). The CRT group demonstrated a substantially larger increase in METRNL levels (Δ = 0.60 ± 0.18 ng/ml, ES = 2.6) compared to the control group (Δ = 0.04 ± 0.16 ng/ml, ES = 0.4). IL-4 The 2x2 mixed ANOVA yielded a significant interaction effect between group and time (F(1, 56) = 35.39, P < 0.0001). Subsequent between-group comparisons, presumably using independent t-tests, showed a significant difference (P = 0.008). The CRT group exhibited a more pronounced increase in IL-4 levels (Δ = 1.15 ± 1.06 pg/mL, ES = 1.64) relative to the control group (Δ = 0.25 ± 0.71 pg/mL, ES = 0.5). IL-13 Similarly, a 2x2 mixed ANOVA indicated a significant interaction effect between group and time (F(1, 56) = 60.20, P < 0.0001). Post-hoc analysis, likely employing independent t-tests, revealed a significant between-group difference (P = 0.002). The CRT group demonstrated a markedly larger increase in IL-13 levels (Δ = 1.2 ± 1.53 pg/mL, ES = 1.5) compared to the control group (Δ = 0.1 ± 1.20 pg/mL, ES = 0.12). Table 4 Changes in METRNL and interleukins in both groups after 6 weeks (mean ± SDs). Parameter Group Pre-test Post-test Change ES Interaction (Group×time) Between-Group METRNL [ng. ml − 1 ] Control 1.36 ± 0.1 1.40 ± 0.12 0.04 ± 0.16 0.4 [F (1, 20) = 216.2, P < 0.0001] (P < 0.0001) CRT 1.30 ± 0.1 1.90 ± 0.15 0.60 ± 0.18 † 2.6 IL-4 [pg. mL − 1 ] Control 1.8 ± 0.5 2.05 ± 0.5 0.25 ± 0.71 0.5 [F (1, 56) = 35.39, P < 0.0001] P = 0.008 CRT 1.9 ± 0.7 3.05 ± 0.8 1.15 ± 1.06 † 1.64 IL-13 [pg. mL − 1 ] Control 2.3 ± 0.8 2.4 ± 0.9 0.1 ± 1.20 0.12 [F (1, 56) = 60.20, P < 0.0001] P = 0.002 CRT 2.5 ± 0.8 3.7 ± 1.3 1.2 ± 1.53 † 1.5 Cohen's d (ES) is based on the change in each parameter (post-test minus pre-test) for each group. Interaction Effects Indicates the statistical significance of the interaction between the group and time for each parameter. Between-Group Comparison (Post-hoc comparison) Highlights significant differences between the exercise and control groups at the post-training phase ( † ). Correlation results Table 5 summarizes correlations between changes in serum METRNL levels with cytokines and metabolic parameters. Pearson correlation analyses revealed significant and positive correlations between changes in serum METRNL levels and changes in serum levels of IL-4 (r = 0.912, p < 0.0001) and IL-13 (r = 0.913, p < 0.0001) and HDL (r = 0.872, p < 0.0001). Moreover, serum METRNL level changes showed significant and negative correlations with glucose homeostasis markers, glucose (r = 0.948, p < 0.0001), insulin (r = 0.928, p < 0.0001) and HOMA-IR (r = 0.904, p < 0.0001), lipid metabolism markers, TC (r = 0.785, p < 0.0001), TG (r = 0.943, p < 0.0001), LDL (r = 0.689, p < 0.0001), and body composition markers, BMI (r = 0.736, p < 0.0001) and BF% (r = 0.964, p < 0.0001). Table 5 Correlations between changes in serum METRNL levels with cytokines and metabolic parameters Parameters Pearson correlation Sig. (2-tailed) Number BMI -0.736 ** P < 0.0001 60 BF% -0.964 ** P < 0.0001 60 Glucose -0.948 ** P < 0.0001 60 Insulin -0.928 ** P < 0.0001 60 HOMA-IR -0.904 ** P < 0.0001 60 TG -0.943 ** P < 0.0001 60 TC -0.785 ** P < 0.0001 60 LDL-C -0.689 ** P < 0.0001 60 HDL-C 0.872 ** P < 0.0001 60 IL-4 0.912 ** P < 0.0001 60 IL-3 0.913 ** P < 0.0001 60 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Discussion This study investigated the effects of a 6-week circuit resistance training (CRT) program on serum levels of Meteorin-like protein (METRNL), interleukin-4 (IL-4), and interleukin-13 (IL-13), along with associated changes in metabolic health markers in overweight individuals. Our findings revealed that CRT led to significant increases in serum levels of METRNL, IL-4, and IL-13, coupled with improvements in body composition, insulin sensitivity, and lipid profiles. These results underscore the potential of CRT as an effective intervention for enhancing metabolic health through the modulation of exercise-induced myokines. METRNL has emerged as a novel myokine with significant implications for energy metabolism and immune regulation [ 9 ]. Our study's findings add to the growing body of evidence highlighting its multifaceted role in metabolic regulation. Consistent with previous reports [ 12 ], we demonstrated that 6 weeks of CRT led to a significant increase in serum METRNL levels in our participants. Our study strongly supports METRNL's role in exercise-induced anti-inflammatory responses in the context of obesity [ 13 ]. The overweight, untrained males who completed the 6-week CRT program exhibited a significant increase in serum METRNL levels, which positively correlated with elevated IL-4 and IL-13 levels. These results suggest that resistance training can effectively upregulate METRNL and its associated anti-inflammatory cytokines, broadening our understanding of how resistance training can enhance metabolic health by promoting an anti-inflammatory environment through myokine modulation. Importantly, the elevated METRNL levels were positively correlated with improvements in insulin resistance, as measured by indices such as fasting blood glucose and HOMA-IR. The beneficial effects of increased METRNL on insulin sensitivity observed in our study align with existing literature. METRNL has been shown to promote white adipocyte differentiation and lipid metabolism, while also inhibiting adipose inflammation, all of which contribute to enhanced insulin action [ 4 , 5 ]. Additionally, METRNL has been reported to increase glucose uptake in skeletal muscle cells and improve glucose tolerance in animal models of obesity and type 2 diabetes [ 6 ]. Furthermore, CRT has been shown to increase resting levels of METRNL in individuals with T2D, which is significantly associated with improved fasting blood glucose levels and insulin resistance [ 12 ] which is in agreement with our findings suggesting that the exercise-induced increase in METRNL may be a key mediator in ameliorating insulin resistance in our participants. On the other hand, there are significantly inverse correlations between METRNL levels and insulin resistance in clinical studies [ 14 – 17 ], further support the notion that modulating METRNL may be a promising therapeutic approach for managing insulin resistance and T2D. Furthermore, our study observed that the increase in serum METRNL levels was accompanied by improvements in the participants' lipid profiles. Specifically, we found that the elevated METRNL levels were associated with decreases in total cholesterol, triglycerides, and LDL-cholesterol, as well as an increase in HDL-cholesterol. These results are consistent with previous studies demonstrating the role of METRNL in regulating various components of the blood lipid panel [ 18 – 20 ]. These clinical negative correlations between METRNL levels and adverse lipid parameters, even after adjusting for potential confounders, support the notion that modulating METRNL could be a viable therapeutic approach for managing dyslipidemia. Furthermore, our study's findings reveal a significant relationship between circuit resistance training, serum METRNL levels, and changes in body composition. The observed inverse association between increased serum METRNL levels and reductions in BMI and body fat percentage following the training intervention adds to the growing body of evidence supporting METRNL's role in metabolic regulation and adiposity. These results are consistent with several previous studies exploring the relationship between METRNL and obesity. For instance, Li et al. [ 4 ] reported increased adipocyte Metrnl expression in high-fat diet-induced obese mice, suggesting a potential compensatory mechanism in response to metabolic stress. Similarly, Loffler et al. [ 21 ] observed increased adipocyte Metrnl expression in obese children, further supporting the link between METRNL and adiposity in humans. However, the literature presents some conflicting findings regarding circulating METRNL levels in obesity. While our results align with studies by Dadmanesh et al. [ 14 ], who found an inverse correlation between BMI and blood METRNL, and Pellitero et al. [ 22 ], who reported lower blood METRNL levels in obese humans, other studies have reported contrasting results. For example, Alkhairi et al. [ 23 ] and Wang et al. [ 24 ] observed higher blood METRNL levels in obese individuals. These discrepancies highlight the complex nature of METRNL regulation and suggest that factors beyond BMI alone, such as physical activity levels, dietary patterns, or metabolic health status, may influence circulating METRNL levels. The inverse relationship we observed between changes in METRNL levels and body composition parameters following exercise intervention is particularly intriguing. This finding supports the hypothesis that METRNL may play a crucial role in exercise-induced improvements in body composition, possibly through its effects on both white and brown adipose tissue functions. This aligns with the proposed function of METRNL as an exercise-induced myokine that promotes the browning of white adipose tissue and increases energy expenditure, as suggested by Rao, et al. [ 3 ], Bae [ 7 ], Javaid, et al. [ 13 ]. Furthermore, our results are consistent with studies investigating the effects of weight loss interventions on METRNL levels. Jamal et al. [ 25 ] reported increased Metrnl protein levels in adipose tissue and skeletal muscle following sleeve gastrectomy in diet-induced obese rats, although they observed decreased circulating Metrnl levels. Similarly, Schmid et al. [ 26 ] found increased serum METRNL levels after sleeve gastrectomy in severely obese patients. These findings, along with our results, suggest that interventions leading to improvements in body composition, whether through exercise or bariatric surgery, may influence METRNL expression and circulation. In summary, our findings further support the multifaceted role of METRNL in metabolic homeostasis. The exercise-induced increase in serum METRNL levels was associated with improvements in anti-inflammatory responses, insulin resistance, lipid profile, and body composition parameters in our study participants. These results add to the growing evidence suggesting that modulating METRNL may be a promising therapeutic target for the management of metabolic disorders, such as insulin resistance, dyslipidemia, and obesity. Furthermore, this study highlights the potential of METRNL as a biomarker for exercise-induced anti-inflammatory responses in overweight individuals. Future studies should continue to explore the underlying mechanisms and the clinical implications of METRNL in the context of metabolic health and exercise, including long-term interventions, comparisons with other exercise modalities, and investigations into potential combination therapies targeting METRNL pathways. Study Limitations While this study demonstrates significant correlations between increased METRNL levels and improvements in metabolic health markers, it is crucial to acknowledge that correlation does not imply causation. This limitation necessitates a cautious interpretation of the findings and underscores the need for further research to validate the observed relationships. The associations between elevated METRNL levels and metabolic improvements, such as enhanced insulin sensitivity and better lipid profiles, suggest a potential role for METRNL in mediating these effects. However, these correlations alone do not establish a direct causal relationship. To determine whether METRNL directly influences these metabolic changes, more rigorous experimental approaches, including controlled interventions and mechanistic studies, are required. A significant limitation of the study is the lack of tissue-specific measurements. While METRNL and cytokine levels were assessed in serum, their levels in key metabolic tissues, such as skeletal muscle, adipose tissue, and the liver, were not measured. Tissue-specific measurements would offer deeper insights into the roles of METRNL and cytokines in metabolic regulation and help clarify whether serum levels accurately reflect their activity within these tissues. Moreover, the study does not provide mechanistic data to explain how METRNL might influence anti-inflammatory cytokines like IL-4 and IL-13 or contribute to metabolic health. Understanding the underlying mechanisms requires detailed studies, possibly involving tissue biopsies, molecular analyses, or animal models, to explore how METRNL exerts its effects at the cellular and tissue levels. To establish a causal link, functional studies that manipulate METRNL levels are necessary. Approaches such as overexpression or knockdown models in animal studies or in vitro experiments would help determine whether changes in METRNL are directly responsible for the observed metabolic improvements or merely associated with these changes as a result of exercise. Finally, the study did not investigate the temporal dynamics of METRNL and cytokine responses during the CRT program. Exploring the timing of these changes relative to metabolic improvements could clarify the sequence of events and strengthen the case for a causal relationship between METRNL and the observed metabolic benefits. Conclusions In conclusion, the results of this study reinforce the importance of incorporating resistance training into exercise programs aimed at improving metabolic health. The ability of CRT to elevate METRNL levels and stimulate anti-inflammatory cytokines suggests that this type of training is not only effective for weight management and metabolic improvement but also for modulating inflammation, which is a critical factor in the pathogenesis of obesity-related diseases. While this study provides valuable correlations between METRNL and metabolic health markers, establishing a causal link requires further research. Future studies should focus on tissue-specific measurements, mechanistic analyses, and functional experiments to unravel the precise role of METRNL in exercise-induced metabolic adaptations. Declarations Acknowledgments The authors wish to express their appreciation to the participants and hospital staff for their sincere cooperation throughout the study. Funding : This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declarations of interest : none References Türkel İ, Özerkliğ B, Atakan MM, Aktitiz S, Koşar ŞN, Yazgan B. Exercise and metabolic health: the emerging roles of novel exerkines. Current Protein and Peptide Science. 2022;23:437-55. Bay ML, Pedersen BK. Muscle-organ crosstalk: focus on immunometabolism. Frontiers in physiology. 2020;11:567881. Rao RR, Long JZ, White JP, Svensson KJ, Lou J, Lokurkar I, et al. Meteorin-like is a hormone that regulates immune-adipose interactions to increase beige fat thermogenesis. Cell. 2014;157:1279-91. Li Z-Y, Song J, Zheng S-L, Fan M-B, Guan Y-F, Qu Y, et al. Adipocyte Metrnl antagonizes insulin resistance through PPARγ signaling. Diabetes. 2015;64:4011-22. Jung TW, Lee SH, Kim H-C, Bang JS, Abd El-Aty A, Hacımüftüoğlu A, et al. METRNL attenuates lipid-induced inflammation and insulin resistance via AMPK or PPARδ-dependent pathways in skeletal muscle of mice. Experimental & molecular medicine. 2018;50:1-11. Lee JO, Byun WS, Kang MJ, Han JA, Moon J, Shin MJ, et al. The myokine meteorin‐like (metrnl) improves glucose tolerance in both skeletal muscle cells and mice by targeting AMPKα2. The FEBS Journal. 2020;287:2087-104. Bae JY. Aerobic exercise increases meteorin‐like protein in muscle and adipose tissue of chronic high‐fat diet‐induced obese mice. BioMed research international. 2018;2018:6283932. Alizadeh H. Meteorin-like protein (Metrnl): A metabolic syndrome biomarker and an exercise mediator. Cytokine. 2022;157:155952. Li Z, Gao Z, Sun T, Zhang S, Yang S, Zheng M, et al. Meteorin-like/Metrnl, a novel secreted protein implicated in inflammation, immunology, and metabolism: A comprehensive review of preclinical and clinical studies. Frontiers in immunology. 2023;14:1098570. Man K, Kallies A, Vasanthakumar A. Resident and migratory adipose immune cells control systemic metabolism and thermogenesis. Cellular & molecular immunology. 2022;19:421-31. Ramos-Campo DJ, Andreu Caravaca L, Martinez-Rodriguez A, Rubio-Arias JÁ. Effects of resistance circuit-based training on body composition, strength and cardiorespiratory fitness: a systematic review and meta-analysis. Biology. 2021;10:377. Tayebi SM, Golmohammadi M, Eslami R, Shakiba N, Costa PB. The effects of eight weeks of circuit resistance training on serum METRNL levels and insulin resistance in individuals with type 2 diabetes. Journal of Diabetes & Metabolic Disorders. 2023;22:1151-8. Javaid HMA, Sahar NE, ZhuGe D-L, Huh JY. Exercise inhibits NLRP3 inflammasome activation in obese mice via the anti-inflammatory effect of meteorin-like. Cells. 2021;10:3480. Dadmanesh M, Aghajani H, Fadaei R, Ghorban K. Lower serum levels of Meteorin-like/Subfatin in patients with coronary artery disease and type 2 diabetes mellitus are negatively associated with insulin resistance and inflammatory cytokines. PloS one. 2018;13:e0204180. El-Ashmawy HM, Selim FO, Hosny TA, Almassry HN. Association of low serum Meteorin like (Metrnl) concentrations with worsening of glucose tolerance, impaired endothelial function and atherosclerosis. Diabetes research and clinical practice. 2019;150:57-63. Chung HS, Hwang SY, Choi JH, Lee HJ, Kim NH, Yoo HJ, et al. Implications of circulating Meteorin-like (Metrnl) level in human subjects with type 2 diabetes. Diabetes research and clinical practice. 2018;136:100-7. Zheng S-L, Li Z-Y, Zhang Z, Wang D-S, Xu J, Miao C-Y. Evaluation of two commercial enzyme-linked immunosorbent assay kits for the detection of human circulating Metrnl. Chemical and Pharmaceutical Bulletin. 2018;66:391-8. Ding X, Chang X, Wang J, Bian N, An Y, Wang G, et al. Serum Metrnl levels are decreased in subjects with overweight or obesity and are independently associated with adverse lipid profile. Frontiers in Endocrinology. 2022;13:938341. Liu ZX, Ji HH, Yao MP, Wang L, Wang Y, Zhou P, et al. Serum Metrnl is associated with the presence and severity of coronary artery disease. Journal of cellular and molecular medicine. 2019;23:271-80. Qi Q, Hu W-j, Zheng S-l, Zhang S-l, Le Y-y, Li Z-y, et al. Metrnl deficiency decreases blood HDL cholesterol and increases blood triglyceride. Acta Pharmacologica Sinica. 2020;41:1568-75. Löffler D, Landgraf K, Rockstroh D, Schwartze J, Dunzendorfer H, Kiess W, et al. METRNL decreases during adipogenesis and inhibits adipocyte differentiation leading to adipocyte hypertrophy in humans. International journal of obesity. 2017;41:112-9. Pellitero S, Piquer-Garcia I, Ferrer-Curriu G, Puig R, Martínez E, Moreno P, et al. Opposite changes in meteorin-like and oncostatin m levels are associated with metabolic improvements after bariatric surgery. International journal of obesity. 2018;42:919-22. AlKhairi I, Cherian P, Abu-Farha M, Madhoun AA, Nizam R, Melhem M, et al. Increased expression of meteorin-like hormone in type 2 diabetes and obesity and its association with irisin. Cells. 2019;8:1283. Wang K, Li F, Wang C, Deng Y, Cao Z, Cui Y, et al. Serum levels of meteorin-like (Metrnl) are increased in patients with newly diagnosed type 2 diabetes mellitus and are associated with insulin resistance. Medical science monitor: international medical journal of experimental and clinical research. 2019;25:2337. Jamal MH, Abu-Farha M, Al-Khaledi G, Al-Sabah S, Ali H, Cherian P, et al. Effect of sleeve gastrectomy on the expression of meteorin-like (METRNL) and Irisin (FNDC5) in muscle and brown adipose tissue and its impact on uncoupling proteins in diet-induced obesity rats. Surgery for Obesity and Related Diseases. 2020;16:1910-8. Schmid A, Karrasch T, Schäffler A. Meteorin-like protein (Metrnl) in obesity, during weight loss and in adipocyte differentiation. Journal of Clinical Medicine. 2021;10:4338. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Feb, 2025 Read the published version in Sport Sciences for Health → Version 1 posted Editorial decision: Revision requested 12 Oct, 2024 Reviews received at journal 24 Sep, 2024 Reviews received at journal 19 Sep, 2024 Reviews received at journal 18 Sep, 2024 Reviews received at journal 17 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviewers invited by journal 24 Aug, 2024 Editor assigned by journal 21 Aug, 2024 Submission checks completed at journal 21 Aug, 2024 First submitted to journal 20 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4945904","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":356340908,"identity":"a0f8b297-483d-4ba3-900a-951f12fb0b2b","order_by":0,"name":"Hamid Alizadeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACAwYGxgMJQKJBgvkAsoQEPi0MUC1sCQxImghoYQBr4TFA1oIbmDMwPzjw4I+dbL90z+fPH9sYEtf2H2D88IPBIh+XFssGNoMDCTzJxjPnnN0mcRCoZduBA8ySPQwSlg24HHYAiBIkmBM33MjdxgDWcrCBQRroFwOcfjnA/uFAgkF94v4bOY8/gLUcZmD+jV8LD9CWhMOJGyRyGCAOO8bAht+WwzwFBxIOHDeecSPNTOLMOQnjbWcY2yx7DPBoOd6+8eGPP9Wy/TOSH3+oKLOR3Xb+8OEbPyrqcGphYEblgmKQsQESX6NgFIyCUTAKyAYAwANb5nS/Z+AAAAAASUVORK5CYII=","orcid":"","institution":"University of Mazandaran","correspondingAuthor":true,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Alizadeh","suffix":""},{"id":356340909,"identity":"4aa55356-1879-40dc-80c3-214c84fabce0","order_by":1,"name":"Alireza Safarzade","email":"","orcid":"","institution":"University of Mazandaran","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Safarzade","suffix":""}],"badges":[],"createdAt":"2024-08-20 14:51:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4945904/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4945904/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11332-025-01350-9","type":"published","date":"2025-02-26T15:57:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77623093,"identity":"3e2ffff2-ae70-4d6b-9795-ace246b2814c","added_by":"auto","created_at":"2025-03-03 16:11:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1165793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4945904/v1/277c42d7-6553-449d-9707-ab1589562088.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of circuit resistance training on serum myokine METRNL, cytokines, insulin resistance, body composition, and lipid profile in overweight participants: A 6-week intervention study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global rise in obesity has intensified research into the relationship between physical activity and metabolic health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Exercise immunometabolism, an emerging field, explores the complex interactions between exercise, immune function, and metabolism [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Central to this field are myokines, muscle-derived cytokines that mediate many of exercise's beneficial effects [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. METRNL (Meteorin-like) is a recently characterized adipomyokine that exemplifies the intricate interplay between immune function and metabolism [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This pleiotropic protein exerts significant immunomodulatory and metabolic effects. Animal studies have shown that METRNL promotes macrophage polarization towards an anti-inflammatory M2 phenotype, attenuating chronic low-grade inflammation associated with metabolic disorders [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Concurrently, it enhances insulin sensitivity [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and fat oxidation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] in peripheral tissues, while stimulating adipose tissue browning and adaptive thermogenesis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe interplay between METRNL and metabolic markers highlights its potential as both a biomarker for metabolic health and a therapeutic target [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Clinical studies have linked serum METRNL levels with various metabolic parameters, including body mass index (BMI), insulin sensitivity, glucose tolerance, and lipid profiles [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These associations underscore METRNL's role not only as an indicator of metabolic health but also as a mediator of the positive effects of exercise on systemic inflammation and metabolic regulation.\u003c/p\u003e \u003cp\u003eNotably, METRNL's expression and secretion are upregulated by exercise, contributing to the beneficial immunometabolic effects of physical activity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A key mechanism underlying these effects, as demonstrated in animal models, involves METRNL's ability to stimulate the production of interleukin-4 (IL-4) and interleukin-13 (IL-13) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These anti-inflammatory cytokines are crucial in maintaining adipose tissue homeostasis and regulating immunometabolism, particularly in the context of obesity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This preclinical evidence has established that exercise-induced METRNL secretion initiates a cascade of immunomodulatory events that helps mitigate obesity-associated inflammation and metabolic dysfunction [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, it is important to note that while the immunogenic effects of METRNL have been well-established in animal models, their translation to human physiology remains largely unexplored. This gap in our understanding presents a critical area for further investigation, particularly in the context of obesity and exercise interventions in humans.\u003c/p\u003e \u003cp\u003eWhile much research has focused on traditional exercise forms, circuit resistance training (CRT) has emerged as a promising modality for improving both metabolic health and muscular strength [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. CRT combines resistance exercises with minimal rest periods, offering cardiovascular benefits alongside strength improvements [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the effects of CRT on myokine production, especially METRNL, and its impact on metabolic and immune function remain largely unexplored in the context of obesity.\u003c/p\u003e \u003cp\u003eTo this end, this study aimed to investigate the chronic effects of CRT on METRNL, IL-4, and IL-13 levels as potential mechanisms underlying exercise-induced improvements in obesity markers. Hence, the primary objective of this study was to investigate the impact of a 6-week circuit resistance training program on serum concentrations of Meteorin-like protein (METRNL), interleukin-4 (IL-4), and interleukin-13 (IL-13) in overweight individuals. Additionally, we aimed to examine the associations between changes in serum METRNL levels and alterations in both cytokine profiles and key metabolic health indicators.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study was a randomized controlled trial (RCT) designed to evaluate the effects of circuit resistance training on various health outcomes. The trial featured two parallel groups: an intervention group that participated in a structured circuit resistance training program and a control group that maintained their usual physical activity levels. The intervention period lasted for six weeks, with assessments conducted at baseline and 48 hours\u0026rsquo; post-intervention to measure the effects of the training.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eOverweight, untrained male students (N\u0026thinsp;=\u0026thinsp;50) who responded to flyers posted on campus notice boards and in dormitories at our institution were considered for this study. Following an initial phone interview, 45 candidates were invited for a comprehensive health evaluation conducted by a physician. This evaluation encompassed a medical history review, overall health assessment, body composition analysis, and metabolic risk factor evaluation. Out of the 45 initially screened individuals, 30 met the inclusion criteria. These criteria required participants to be between 20 and 30 years old, non-smokers, and have a Body Mass Index (BMI) ranging from 25 to 30 kg/m\u003csup\u003e2\u003c/sup\u003e. Participants were also required to be free from any conditions that would preclude participation, such as cardiovascular, metabolic, or musculoskeletal disorders. Exclusion criteria included cardiometabolic diseases, acute illness or infection, and the use of medications or supplements that could affect the study outcomes. Participants consuming any medication or supplements were excluded from the study. As all participants dined at the university cafeteria, they adhered to an identical diet. Detailed information about the study was provided to all potential participants. Among those who met the inclusion criteria (body weight: 80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5kg, height: 170\u0026thinsp;\u0026plusmn;\u0026thinsp;3cm, BMI: 27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55 Kg.m\u003csup\u003e2\u003c/sup\u003e, age: 25\u0026thinsp;\u0026plusmn;\u0026thinsp;4 years) and did not meet any exclusion criteria (N\u0026thinsp;=\u0026thinsp;30), all agreed to participate and provided written informed consent. Participants were then randomly assigned to either the exercise training group (n\u0026thinsp;=\u0026thinsp;15) or the control group (n\u0026thinsp;=\u0026thinsp;15).\u003c/p\u003e \u003cp\u003eThe sample size for each group was determined to ensure the study was adequately powered to detect differences in the primary outcome, the change in total serum METRNL levels. We based our analysis on variability from a previous study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] that utilized a similar study design (Pre-post values). Using Gpower software, we concluded that a minimum of 15 participants per group was necessary to achieve the power of 0.80. Therefore, the total required sample size was 30 participants, with 15 in the CRT group and 15 in the control group. This sample size is sufficient to detect significant differences in METRNL levels between the groups, effectively meeting our research objectives. The Research Ethics Review Committee of the University of Mazandaran reviewed and approved the study protocol (IR.UMZ. REC. 2269045). After obtaining written informed consent from each participant, the clinical evaluations were conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExercise Intervention\u003c/h2\u003e \u003cp\u003eParticipants assigned to the exercise group underwent a 6-week supervised circuit resistance training (CRT) program, with sessions scheduled three times per week on non-consecutive days to ensure adequate recovery. Each session lasted approximately 45 minutes, beginning with a 5-minute warm-up, followed by 35 minutes of circuit training, and concluding with a 5-minute cool-down period. Prior to the commencement of the training program, each participant\u0026rsquo;s one-repetition maximum (1RM) was determined for each exercise. This process involved a familiarization session where participants performed each exercise with progressively heavier weights until they could complete only one repetition with proper form. This assessment was supervised by experienced trainers to ensure accuracy and safety. Participants performed exercises at 60\u0026ndash;70% of their 1RM, a range considered moderate to somewhat hard, ensuring effective muscle engagement and adaptation without causing excessive fatigue or risk of injury. Each exercise was performed for 8\u0026ndash;12 repetitions per set, with participants completing three sets of each exercise. A 1-2-minute rest period was allowed between sets to ensure adequate recovery, monitored using a stopwatch to maintain consistency. The circuit included a variety of resistance exercises targeting major muscle groups, such as squats, lunges, and leg presses for the lower body; bench presses, rows, and shoulder presses for the upper body; and planks, Russian twists, and leg raises for the core. Intensity was monitored using the Rated Perceived Exertion (RPE) scale, aiming to maintain a score of 13\u0026ndash;15 (moderate to somewhat hard) throughout the training session. Trainers provided real-time feedback and adjustments based on participant performance and feedback. For instance, if a participant reported that the exercise felt too easy, the weight was increased slightly for the next set. The program was progressively adjusted every two weeks. This involved reassessing the 1RM for each exercise and increasing the training load by 5\u0026ndash;10% if participants could comfortably complete more than 12 repetitions per set. This progressive overload ensured continuous improvement in strength and endurance. Additionally, trainers introduced variations of exercises to keep the program engaging and to target muscles from different angles. Participants assigned to the control group were asked to maintain their current lifestyle and dietary intakes during the study period. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes exercise intervention program details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExercise Intervention Program Details\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 sessions per week (non-consecutive days)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSession Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 minutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarm-Up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 minutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircuit Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 minutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCool-Down\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 minutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne-repetition maximum (1RM) for each exercise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026ndash;70% of 1RM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepetitions per Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026ndash;12 repetitions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSets per Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 sets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRest Between Sets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 minutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercises Included\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquats-Lunges-Leg presses-Bench presses-Rows-Shoulder presses-Planks-Russian twists-Leg raises\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity Monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRated Perceived Exertion (RPE) scale (target: 13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgressive Overload\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReassessment of 1RM every 2 weeks; Increase training load by 5\u0026ndash;10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eAll outcome measures were collected at baseline (pre-intervention) and following the 6-week exercise intervention period (circuit resistance training). Blood samples were collected after a 10-12-hour overnight fast. Body composition was assessed using a body analyzer composition apparatus, and participants completed questionnaires related to their physical activity levels and dietary intake.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBlood Sample Collection\u003c/h2\u003e \u003cp\u003eAll blood samples were collected in the morning (between 7:00 AM and 9:00 AM) after an overnight fast of 10\u0026ndash;12 hours. Participants were instructed to refrain from eating or drinking anything except water during the fasting period. They were also asked to avoid strenuous physical activity and alcohol consumption for 72 hours prior to blood collection. A total of 10 mL of venous blood was drawn from each participant by a certified phlebotomist using standard venipuncture techniques. Blood was collected from the antecubital vein with participants in a seated position. Immediately after collection, all tubes were gently inverted 8\u0026ndash;10 times to ensure proper mixing. Serum separator tubes (SST) were allowed to clot at room temperature for 30 minutes before centrifugation. All samples were centrifuged within 1 hour of collection at 1500 x g for 15 minutes at 4\u0026deg;C. After centrifugation, serum and plasma were aliquoted into labeled micro-centrifuge tubes. For each participant, multiple aliquots were prepared to avoid repeated freeze-thaw cycles. All aliquots were immediately stored at -80\u0026deg;C until analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSerum METRNL Levels\u003c/h2\u003e \u003cp\u003eFasting blood samples were collected and serum was separated by centrifugation. Serum METRNL concentrations were determined using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (Kit: ZellBio, Germany, Sensitivity: 0.06 ng/ml) according to the manufacturer's instructions. The assay sensitivity was 5 ng/mL, with intra- and inter-assay coefficients of variation of 4.2% and 6.8%, respectively.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSerum Cytokine Levels\u003c/h2\u003e \u003cp\u003eSerum levels of IL-4 and IL-13 were measured using a multiplex bead-based immunoassay (Bio-Plex Pro\u0026trade; Human Cytokine Assay, Bio-Rad Laboratories, USA) following the manufacturer's protocol. The assay sensitivities for IL-4 and IL-13 were 0.3 pg/mL and 0.7 pg/mL, respectively. Intra- and inter-assay coefficients of variation were \u0026lt;\u0026thinsp;5% for both cytokines.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSerum insulin, glucose, and Insulin Resistance index\u003c/h2\u003e \u003cp\u003eInsulin resistance was assessed using the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Fasting glucose was measured using the hexokinase method on an automated analyzer (GlucoStar 3000, MediTech Systems, Germany). Fasting insulin was quantified using a chemiluminescent immunoassay (InsuliCheck\u0026trade;, Nordic Diagnostics, Sweden). HOMA-IR was calculated using the formula: HOMA-IR = (fasting glucose [mmol. L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e] \u0026times; fasting insulin [\u0026micro;U. mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]) / 22.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSerum Lipid Profile\u003c/h2\u003e \u003cp\u003eFasting blood samples were analyzed for lipid profiles using an automated clinical chemistry analyzer (LipidPro 5000, ChemMed Corp., Japan). Total cholesterol and triglycerides were measured using enzymatic colorimetric methods. HDL cholesterol was determined using a direct method with polyethylene glycol-modified enzymes. LDL cholesterol was calculated using the Friedewald formula.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBody Composition parameters\u003c/h2\u003e \u003cp\u003eBody composition parameters including body weight, lean mass, and body fat percentage were assessed using a body composition analyzer (Medigate Company Inc., Dan-dong Gunpo, Korea). Height was also measured in a barefoot standing position with a wall-mounted stadiometer (SECA, Germany). Body mass index (BMI, kg.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) was calculated using weight (kg) and height (m) values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. The normality of distribution for dependent variables was assessed using the Shapiro-Wilk test. A two-factor (time: pre and post; group: control and exercise) with mixed-design ANOVA followed by an LSD post-hoc test was performed for all the continuous variables to evaluate the effect of exercise in different groups. In addition, effect sizes (i.e., Cohen\u0026rsquo;s d) were calculated by dividing the pre-post change throughout exercise training by the pooled standard deviation of the pre-test scores for the specific group (i.e., control vs. exercise training group). Thus, effect sizes for the exercise or control group were calculated by the pre-post change for that specific group by the pooled standard deviation of the pre-test scores for the entire group. All statistical analyses were performed using GraphPad Prism Version 10, with a type I error rate of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBody Composition Parameters\u003c/h2\u003e \u003cp\u003ePrior to conducting the main analyses, the normality of data distribution was assessed for all body composition parameters using the Shapiro-Wilk test. For normally distributed data, independent t-tests were used to assess baseline differences between groups, and a two-factor (time: pre and post; group: control and exercise) mixed-design ANOVA followed by an LSD post-hoc test was employed to analyze the effects of the intervention over time. No statistically significant differences were observed between the Control and CRT groups at baseline for any of the body composition parameters (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the changes observed in response to the 6-week circuit resistance training (CRT) intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBody Weight (BW)\u003c/h2\u003e \u003cp\u003eAnalysis of body weight data revealed a significant interaction effect between the groups over time [F (1, 56)\u0026thinsp;=\u0026thinsp;33.7, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]. The Control group exhibited a slight increase in body weight (0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97 kg, Cohen's d\u0026thinsp;=\u0026thinsp;0.30), whereas the CRT group demonstrated a significant decrease (-2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75 kg, Cohen's d = -0.67). The between-group difference was statistically significant (P\u0026thinsp;=\u0026thinsp;0.001), suggesting that the CRT intervention effectively reduced body weight compared to the control condition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBody Mass Index (BMI)\u003c/h2\u003e \u003cp\u003eA significant interaction effect was observed for BMI [F (1, 56)\u0026thinsp;=\u0026thinsp;23.9, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]. The Control group showed a minor increase in BMI (0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85 kg.m-2, Cohen's d\u0026thinsp;=\u0026thinsp;0.22), while the CRT group exhibited a substantial decrease (-2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75 kg.m-2, Cohen's d = -1.14). The between-group difference was statistically significant (P\u0026thinsp;=\u0026thinsp;0.001), indicating that the CRT intervention effectively reduced BMI, with a large effect size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBody Fat Percentage (BF%)\u003c/h2\u003e \u003cp\u003eBody fat percentage analysis revealed a significant interaction effect [F (1, 56)\u0026thinsp;=\u0026thinsp;74.32, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]. The Control group experienced a slight increase in BF% (0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70%, Cohen's d\u0026thinsp;=\u0026thinsp;0.19), whereas the CRT group demonstrated a substantial decrease (-3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64%, Cohen's d = -0.96). The between-group difference was statistically significant (P\u0026thinsp;=\u0026thinsp;0.025), suggesting that the CRT intervention effectively reduced body fat percentage compared to the control condition, with a large effect size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLean Body Mass (LBM)\u003c/h2\u003e \u003cp\u003eAnalysis of LBM data showed no significant interaction effect [F (1, 56)\u0026thinsp;=\u0026thinsp;0.35, P\u0026thinsp;=\u0026thinsp;0.56], and the between-group difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.56). The Control group exhibited a minimal increase in LBM (0.065\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54 kg, Cohen's d\u0026thinsp;=\u0026thinsp;0.02), while the CRT group demonstrated a slightly larger increase (1.065\u0026thinsp;\u0026plusmn;\u0026thinsp;3.89 kg, Cohen's d\u0026thinsp;=\u0026thinsp;0.27). However, these changes were not statistically significant between groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in body composition parameters in both groups after 6 weeks (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInteraction (Group\u0026times;time)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBetween-Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBW [kg]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e78.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e79.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF \u003csub\u003e(1, 56)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;33.7, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e81.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI [kg.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 56)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;23.9, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e-2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 56)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;74.32, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLBM [kg]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e54.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e54.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.065\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 56)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.35, P\u0026thinsp;=\u0026thinsp;0.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e55.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e56.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;1.065\u0026thinsp;\u0026plusmn;\u0026thinsp;3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCohen's d (ES)\u003c/b\u003e is based on the change in each parameter (post-test minus pre-test) for each group.\u003c/p\u003e \u003cp\u003eBW: Body Weight; BMI: body mass index; BF%: body fat percent; LBM: Lean Body Mass.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInteraction Effects\u003c/strong\u003e \u003cp\u003eIndicates the statistical significance of the interaction between the group and time for each parameter.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBetween-Group Comparison (Post-hoc comparison)\u003c/strong\u003e \u003cp\u003eHighlights significant differences between the exercise and control groups at the post-training phase (\u003cb\u003e\u0026dagger;\u003c/b\u003e).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eGlucose and Lipid Homeostasis Parameters\u003c/h2\u003e \u003cp\u003ePrior to conducting the main analyses, the normality of data distribution was assessed for all glucose and lipid homeostasis parameters using the Shapiro-Wilk test. For normally distributed data, independent t-tests were used to assess baseline differences between groups, and a two-factor (time: pre and post; group: control and exercise) mixed-design ANOVA followed by an LSD post-hoc test was employed to analyze the effects of the intervention over time. No statistically significant differences were observed between the Control and CRT groups at baseline for any of the parameters (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the changes observed in response to the 6-week circuit resistance training (CRT) intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFasting Blood Glucose (FBG)\u003c/h2\u003e \u003cp\u003eA significant interaction effect was observed between group and time [F (1, 8)\u0026thinsp;=\u0026thinsp;7.08, P\u0026thinsp;=\u0026thinsp;0.025]. The Control group showed a minimal increase in FBG (5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 to 5.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 mmol/L, change: 0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 mmol/L, ES: 0.48), while the CRT group demonstrated a substantial decrease (5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 to 4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 mmol/L, change: -0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 mmol/L, ES: -2.38). The between-group difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.008), indicating that the CRT intervention led to a significant reduction in FBG compared to the control group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eInsulin\u003c/h2\u003e \u003cp\u003eA significant interaction effect was found for insulin levels [F (1, 8)\u0026thinsp;=\u0026thinsp;9.88, P\u0026thinsp;=\u0026thinsp;0.014]. The Control group exhibited a slight increase (11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50 to 11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70 \u0026micro;U/mL, change: 0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27 \u0026micro;U/mL, ES: 0.25), whereas the CRT group showed a notable decrease (11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40 to 9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10 \u0026micro;U/mL, change: -2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78 \u0026micro;U/mL, ES: -1.67). The between-group difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.048).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eHomeostatic Model Assessment of Insulin Resistance (HOMA-IR)\u003c/h2\u003e \u003cp\u003eA highly significant interaction effect was observed for HOMA-IR [F (1, 16)\u0026thinsp;=\u0026thinsp;20.21, P\u0026thinsp;=\u0026thinsp;0.0004]. The Control group showed a slight increase (2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 to 2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23, change: 0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33, ES: 0.52), while the CRT group demonstrated a substantial decrease (2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44 to 1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31, change: -0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54, ES: -2.39). The between-group difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eTotal Cholesterol (TC)\u003c/h2\u003e \u003cp\u003eA highly significant interaction effect was found for total cholesterol levels [F (1, 28)\u0026thinsp;=\u0026thinsp;17.50, P\u0026thinsp;=\u0026thinsp;0.0003]. The Control group showed a slight increase (201\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03 to 203\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03 mg/dl, change: 2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29 mg/dl, ES: 0.66), while the CRT group exhibited a marked decrease (204.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4 to 190\u0026thinsp;\u0026plusmn;\u0026thinsp;5.40 mg/dl, change: -14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84 mg/dl, ES: -1.84). The between-group difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eTriglycerides (TG)\u003c/h2\u003e \u003cp\u003eA significant interaction effect was observed for triglyceride levels [F (1, 16)\u0026thinsp;=\u0026thinsp;9.608, P\u0026thinsp;=\u0026thinsp;0.007]. The Control group showed a minimal increase (121.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07 to 122.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07 mg/dl, change: 1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34 mg/dl, ES: 0.33), whereas the CRT group demonstrated a considerable decrease (122.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 to 109.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 mg/dl, change: -12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.89 mg/dl, ES: -1.52). The between-group difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.012).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLow-Density Lipoprotein Cholesterol (LDL-C)\u003c/h2\u003e \u003cp\u003eA highly significant interaction effect was found for LDL-C levels [F (1, 24)\u0026thinsp;=\u0026thinsp;16.52, P\u0026thinsp;=\u0026thinsp;0.0004]. The Control group showed a slight increase (134.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67 to 137\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67 mg/dl, change: 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78 mg/dl, ES: 0.90), while the CRT group exhibited a decrease (136.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 to 126.7\u0026thinsp;\u0026plusmn;\u0026thinsp;67 mg/dl, change: -9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;67.33 mg/dl, ES: -0.20). The between-group difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eHigh-Density Lipoprotein Cholesterol (HDL-C)\u003c/h2\u003e \u003cp\u003eA highly significant interaction effect was observed for HDL-C levels [F (1, 16)\u0026thinsp;=\u0026thinsp;20.57, P\u0026thinsp;=\u0026thinsp;0.0003]. The Control group showed a slight decrease (40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95 to 39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95 mg/dl, change: -1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34 mg/dl, ES: -1.05), while the CRT group demonstrated an increase (39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42 to 43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42 mg/dl, change: 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42 mg/dl, ES: 1.57). The between-group difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in glucose and lipid metabolism parameters in both groups after 6 weeks (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInteraction (Group\u0026times;time)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBetween-Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG [mmol. L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 8)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;7.08, P\u0026thinsp;=\u0026thinsp;0.025]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInsulin [\u0026micro;U. mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 8)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9.88, P\u0026thinsp;=\u0026thinsp;0.014]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 16)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;20.21, P\u0026thinsp;=\u0026thinsp;0.0004]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTC [mg/dl]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e201\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e203\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 28)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;17.50, P\u0026thinsp;=\u0026thinsp;0.0003]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e204.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e190\u0026thinsp;\u0026plusmn;\u0026thinsp;5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTG [mg/dl]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e121.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e122.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 16)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9.608, P\u0026thinsp;=\u0026thinsp;0.007]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e122.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e109.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.89\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLDL-C [mg/dl]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e134.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e137\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 24)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;16.52, P\u0026thinsp;=\u0026thinsp;0.0004]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e136.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e126.7\u0026thinsp;\u0026plusmn;\u0026thinsp;67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;67.33\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHDL-C [mg/dl]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 16)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;20.57, P\u0026thinsp;=\u0026thinsp;0.0003]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCohen's d (ES)\u003c/b\u003e is based on the change in each parameter (post-test minus pre-test) for each group.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInteraction Effects\u003c/strong\u003e \u003cp\u003eIndicates the statistical significance of the interaction between the group and time for each parameter.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBetween-Group Comparison (Post-hoc comparison)\u003c/strong\u003e \u003cp\u003eHighlights significant differences between the exercise and control groups at the post-training phase (\u0026dagger;).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFBG: Fasting Blood Glucose; TC: total cholesterol; HDLC: high-density lipoprotein cholesterol; LDLC: low-density lipoprotein cholesterol TG: triglycerides; HOMA-IR: homeostasis model assessment for insulin resistance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eMETRNL and cytokines levels\u003c/h2\u003e \u003cp\u003ePrior to conducting the main analyses, the normality of data distribution was assessed for METRNL and interleukins using the Shapiro-Wilk test. For normally distributed data, independent t-tests were used to assess baseline differences between groups, and a two-factor (time: pre and post; group: control and exercise) mixed-design ANOVA followed by an LSD post-hoc test was employed to analyze the effects of the intervention over time. No statistically significant differences were observed between the Control and CRT groups at baseline for any of the parameters (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the changes observed in response to the 6-week circuit resistance training (CRT) intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eMETRNL\u003c/h2\u003e \u003cp\u003eA 2x2 mixed ANOVA revealed a significant interaction effect between group and time (F(1, 20)\u0026thinsp;=\u0026thinsp;216.2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Post-hoc analysis, likely using independent t-tests, indicated a significant between-group difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The CRT group demonstrated a substantially larger increase in METRNL levels (Δ\u0026thinsp;=\u0026thinsp;0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 ng/ml, ES\u0026thinsp;=\u0026thinsp;2.6) compared to the control group (Δ\u0026thinsp;=\u0026thinsp;0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 ng/ml, ES\u0026thinsp;=\u0026thinsp;0.4).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIL-4\u003c/h3\u003e\n\u003cp\u003eThe 2x2 mixed ANOVA yielded a significant interaction effect between group and time (F(1, 56)\u0026thinsp;=\u0026thinsp;35.39, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Subsequent between-group comparisons, presumably using independent t-tests, showed a significant difference (P\u0026thinsp;=\u0026thinsp;0.008). The CRT group exhibited a more pronounced increase in IL-4 levels (Δ\u0026thinsp;=\u0026thinsp;1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06 pg/mL, ES\u0026thinsp;=\u0026thinsp;1.64) relative to the control group (Δ\u0026thinsp;=\u0026thinsp;0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71 pg/mL, ES\u0026thinsp;=\u0026thinsp;0.5).\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eIL-13\u003c/h2\u003e \u003cp\u003eSimilarly, a 2x2 mixed ANOVA indicated a significant interaction effect between group and time (F(1, 56)\u0026thinsp;=\u0026thinsp;60.20, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Post-hoc analysis, likely employing independent t-tests, revealed a significant between-group difference (P\u0026thinsp;=\u0026thinsp;0.002). The CRT group demonstrated a markedly larger increase in IL-13 levels (Δ\u0026thinsp;=\u0026thinsp;1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53 pg/mL, ES\u0026thinsp;=\u0026thinsp;1.5) compared to the control group (Δ\u0026thinsp;=\u0026thinsp;0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20 pg/mL, ES\u0026thinsp;=\u0026thinsp;0.12).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in METRNL and interleukins in both groups after 6 weeks (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInteraction (Group\u0026times;time)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBetween-Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMETRNL [ng. ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 20)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;216.2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIL-4 [pg. mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 56)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;35.39, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIL-13 [pg. mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e[F \u003csub\u003e(1, 56)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;60.20, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCohen's d (ES)\u003c/b\u003e is based on the change in each parameter (post-test minus pre-test) for each group.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInteraction Effects\u003c/strong\u003e \u003cp\u003eIndicates the statistical significance of the interaction between the group and time for each parameter.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBetween-Group Comparison (Post-hoc comparison)\u003c/strong\u003e \u003cp\u003eHighlights significant differences between the exercise and control groups at the post-training phase (\u003cb\u003e\u0026dagger;\u003c/b\u003e).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes correlations between changes in serum METRNL levels with cytokines and metabolic parameters. Pearson correlation analyses revealed significant and positive correlations between changes in serum METRNL levels and changes in serum levels of IL-4 (r\u0026thinsp;=\u0026thinsp;0.912, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and IL-13 (r\u0026thinsp;=\u0026thinsp;0.913, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and HDL (r\u0026thinsp;=\u0026thinsp;0.872, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Moreover, serum METRNL level changes showed significant and negative correlations with glucose homeostasis markers, glucose (r\u0026thinsp;=\u0026thinsp;0.948, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), insulin (r\u0026thinsp;=\u0026thinsp;0.928, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.904, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), lipid metabolism markers, TC (r\u0026thinsp;=\u0026thinsp;0.785, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), TG (r\u0026thinsp;=\u0026thinsp;0.943, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), LDL (r\u0026thinsp;=\u0026thinsp;0.689, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and body composition markers, BMI (r\u0026thinsp;=\u0026thinsp;0.736, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and BF% (r\u0026thinsp;=\u0026thinsp;0.964, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between changes in serum METRNL levels with cytokines and metabolic parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.736\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.964\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.948\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.928\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.904\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.943\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.785\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.689\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.912\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e** Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e \u003cp\u003e* Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the effects of a 6-week circuit resistance training (CRT) program on serum levels of Meteorin-like protein (METRNL), interleukin-4 (IL-4), and interleukin-13 (IL-13), along with associated changes in metabolic health markers in overweight individuals. Our findings revealed that CRT led to significant increases in serum levels of METRNL, IL-4, and IL-13, coupled with improvements in body composition, insulin sensitivity, and lipid profiles. These results underscore the potential of CRT as an effective intervention for enhancing metabolic health through the modulation of exercise-induced myokines.\u003c/p\u003e \u003cp\u003eMETRNL has emerged as a novel myokine with significant implications for energy metabolism and immune regulation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Our study's findings add to the growing body of evidence highlighting its multifaceted role in metabolic regulation. Consistent with previous reports [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], we demonstrated that 6 weeks of CRT led to a significant increase in serum METRNL levels in our participants.\u003c/p\u003e \u003cp\u003eOur study strongly supports METRNL's role in exercise-induced anti-inflammatory responses in the context of obesity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The overweight, untrained males who completed the 6-week CRT program exhibited a significant increase in serum METRNL levels, which positively correlated with elevated IL-4 and IL-13 levels. These results suggest that resistance training can effectively upregulate METRNL and its associated anti-inflammatory cytokines, broadening our understanding of how resistance training can enhance metabolic health by promoting an anti-inflammatory environment through myokine modulation.\u003c/p\u003e \u003cp\u003eImportantly, the elevated METRNL levels were positively correlated with improvements in insulin resistance, as measured by indices such as fasting blood glucose and HOMA-IR. The beneficial effects of increased METRNL on insulin sensitivity observed in our study align with existing literature. METRNL has been shown to promote white adipocyte differentiation and lipid metabolism, while also inhibiting adipose inflammation, all of which contribute to enhanced insulin action [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, METRNL has been reported to increase glucose uptake in skeletal muscle cells and improve glucose tolerance in animal models of obesity and type 2 diabetes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, CRT has been shown to increase resting levels of METRNL in individuals with T2D, which is significantly associated with improved fasting blood glucose levels and insulin resistance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] which is in agreement with our findings suggesting that the exercise-induced increase in METRNL may be a key mediator in ameliorating insulin resistance in our participants. On the other hand, there are significantly inverse correlations between METRNL levels and insulin resistance in clinical studies [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], further support the notion that modulating METRNL may be a promising therapeutic approach for managing insulin resistance and T2D.\u003c/p\u003e \u003cp\u003eFurthermore, our study observed that the increase in serum METRNL levels was accompanied by improvements in the participants' lipid profiles. Specifically, we found that the elevated METRNL levels were associated with decreases in total cholesterol, triglycerides, and LDL-cholesterol, as well as an increase in HDL-cholesterol. These results are consistent with previous studies demonstrating the role of METRNL in regulating various components of the blood lipid panel [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These clinical negative correlations between METRNL levels and adverse lipid parameters, even after adjusting for potential confounders, support the notion that modulating METRNL could be a viable therapeutic approach for managing dyslipidemia.\u003c/p\u003e \u003cp\u003eFurthermore, our study's findings reveal a significant relationship between circuit resistance training, serum METRNL levels, and changes in body composition. The observed inverse association between increased serum METRNL levels and reductions in BMI and body fat percentage following the training intervention adds to the growing body of evidence supporting METRNL's role in metabolic regulation and adiposity. These results are consistent with several previous studies exploring the relationship between METRNL and obesity. For instance, Li et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] reported increased adipocyte Metrnl expression in high-fat diet-induced obese mice, suggesting a potential compensatory mechanism in response to metabolic stress. Similarly, Loffler et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] observed increased adipocyte Metrnl expression in obese children, further supporting the link between METRNL and adiposity in humans.\u003c/p\u003e \u003cp\u003eHowever, the literature presents some conflicting findings regarding circulating METRNL levels in obesity. While our results align with studies by Dadmanesh et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], who found an inverse correlation between BMI and blood METRNL, and Pellitero et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], who reported lower blood METRNL levels in obese humans, other studies have reported contrasting results. For example, Alkhairi et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and Wang et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] observed higher blood METRNL levels in obese individuals. These discrepancies highlight the complex nature of METRNL regulation and suggest that factors beyond BMI alone, such as physical activity levels, dietary patterns, or metabolic health status, may influence circulating METRNL levels.\u003c/p\u003e \u003cp\u003eThe inverse relationship we observed between changes in METRNL levels and body composition parameters following exercise intervention is particularly intriguing. This finding supports the hypothesis that METRNL may play a crucial role in exercise-induced improvements in body composition, possibly through its effects on both white and brown adipose tissue functions. This aligns with the proposed function of METRNL as an exercise-induced myokine that promotes the browning of white adipose tissue and increases energy expenditure, as suggested by Rao, et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], Bae [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], Javaid, et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, our results are consistent with studies investigating the effects of weight loss interventions on METRNL levels. Jamal et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reported increased Metrnl protein levels in adipose tissue and skeletal muscle following sleeve gastrectomy in diet-induced obese rats, although they observed decreased circulating Metrnl levels. Similarly, Schmid et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] found increased serum METRNL levels after sleeve gastrectomy in severely obese patients. These findings, along with our results, suggest that interventions leading to improvements in body composition, whether through exercise or bariatric surgery, may influence METRNL expression and circulation.\u003c/p\u003e \u003cp\u003eIn summary, our findings further support the multifaceted role of METRNL in metabolic homeostasis. The exercise-induced increase in serum METRNL levels was associated with improvements in anti-inflammatory responses, insulin resistance, lipid profile, and body composition parameters in our study participants. These results add to the growing evidence suggesting that modulating METRNL may be a promising therapeutic target for the management of metabolic disorders, such as insulin resistance, dyslipidemia, and obesity. Furthermore, this study highlights the potential of METRNL as a biomarker for exercise-induced anti-inflammatory responses in overweight individuals. Future studies should continue to explore the underlying mechanisms and the clinical implications of METRNL in the context of metabolic health and exercise, including long-term interventions, comparisons with other exercise modalities, and investigations into potential combination therapies targeting METRNL pathways.\u003c/p\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eWhile this study demonstrates significant correlations between increased METRNL levels and improvements in metabolic health markers, it is crucial to acknowledge that correlation does not imply causation. This limitation necessitates a cautious interpretation of the findings and underscores the need for further research to validate the observed relationships. The associations between elevated METRNL levels and metabolic improvements, such as enhanced insulin sensitivity and better lipid profiles, suggest a potential role for METRNL in mediating these effects. However, these correlations alone do not establish a direct causal relationship. To determine whether METRNL directly influences these metabolic changes, more rigorous experimental approaches, including controlled interventions and mechanistic studies, are required.\u003c/p\u003e \u003cp\u003eA significant limitation of the study is the lack of tissue-specific measurements. While METRNL and cytokine levels were assessed in serum, their levels in key metabolic tissues, such as skeletal muscle, adipose tissue, and the liver, were not measured. Tissue-specific measurements would offer deeper insights into the roles of METRNL and cytokines in metabolic regulation and help clarify whether serum levels accurately reflect their activity within these tissues. Moreover, the study does not provide mechanistic data to explain how METRNL might influence anti-inflammatory cytokines like IL-4 and IL-13 or contribute to metabolic health. Understanding the underlying mechanisms requires detailed studies, possibly involving tissue biopsies, molecular analyses, or animal models, to explore how METRNL exerts its effects at the cellular and tissue levels.\u003c/p\u003e \u003cp\u003eTo establish a causal link, functional studies that manipulate METRNL levels are necessary. Approaches such as overexpression or knockdown models in animal studies or in vitro experiments would help determine whether changes in METRNL are directly responsible for the observed metabolic improvements or merely associated with these changes as a result of exercise. Finally, the study did not investigate the temporal dynamics of METRNL and cytokine responses during the CRT program. Exploring the timing of these changes relative to metabolic improvements could clarify the sequence of events and strengthen the case for a causal relationship between METRNL and the observed metabolic benefits.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the results of this study reinforce the importance of incorporating resistance training into exercise programs aimed at improving metabolic health. The ability of CRT to elevate METRNL levels and stimulate anti-inflammatory cytokines suggests that this type of training is not only effective for weight management and metabolic improvement but also for modulating inflammation, which is a critical factor in the pathogenesis of obesity-related diseases. While this study provides valuable correlations between METRNL and metabolic health markers, establishing a causal link requires further research. Future studies should focus on tissue-specific measurements, mechanistic analyses, and functional experiments to unravel the precise role of METRNL in exercise-induced metabolic adaptations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to express their appreciation to the participants and hospital staff for their sincere cooperation throughout the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest\u003c/strong\u003e: none\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eT\u0026uuml;rkel İ, \u0026Ouml;zerkliğ B, Atakan MM, Aktitiz S, Koşar ŞN, Yazgan B. Exercise and metabolic health: the emerging roles of novel exerkines. Current Protein and Peptide Science. 2022;23:437-55.\u003c/li\u003e\n\u003cli\u003eBay ML, Pedersen BK. Muscle-organ crosstalk: focus on immunometabolism. Frontiers in physiology. 2020;11:567881.\u003c/li\u003e\n\u003cli\u003eRao RR, Long JZ, White JP, Svensson KJ, Lou J, Lokurkar I, et al. Meteorin-like is a hormone that regulates immune-adipose interactions to increase beige fat thermogenesis. Cell. 2014;157:1279-91.\u003c/li\u003e\n\u003cli\u003eLi Z-Y, Song J, Zheng S-L, Fan M-B, Guan Y-F, Qu Y, et al. Adipocyte Metrnl antagonizes insulin resistance through PPAR\u0026gamma; signaling. Diabetes. 2015;64:4011-22.\u003c/li\u003e\n\u003cli\u003eJung TW, Lee SH, Kim H-C, Bang JS, Abd El-Aty A, Hacım\u0026uuml;ft\u0026uuml;oğlu A, et al. METRNL attenuates lipid-induced inflammation and insulin resistance via AMPK or PPAR\u0026delta;-dependent pathways in skeletal muscle of mice. Experimental \u0026amp; molecular medicine. 2018;50:1-11.\u003c/li\u003e\n\u003cli\u003eLee JO, Byun WS, Kang MJ, Han JA, Moon J, Shin MJ, et al. The myokine meteorin‐like (metrnl) improves glucose tolerance in both skeletal muscle cells and mice by targeting AMPK\u0026alpha;2. The FEBS Journal. 2020;287:2087-104.\u003c/li\u003e\n\u003cli\u003eBae JY. Aerobic exercise increases meteorin‐like protein in muscle and adipose tissue of chronic high‐fat diet‐induced obese mice. BioMed research international. 2018;2018:6283932.\u003c/li\u003e\n\u003cli\u003eAlizadeh H. Meteorin-like protein (Metrnl): A metabolic syndrome biomarker and an exercise mediator. Cytokine. 2022;157:155952.\u003c/li\u003e\n\u003cli\u003eLi Z, Gao Z, Sun T, Zhang S, Yang S, Zheng M, et al. Meteorin-like/Metrnl, a novel secreted protein implicated in inflammation, immunology, and metabolism: A comprehensive review of preclinical and clinical studies. Frontiers in immunology. 2023;14:1098570.\u003c/li\u003e\n\u003cli\u003eMan K, Kallies A, Vasanthakumar A. Resident and migratory adipose immune cells control systemic metabolism and thermogenesis. Cellular \u0026amp; molecular immunology. 2022;19:421-31.\u003c/li\u003e\n\u003cli\u003eRamos-Campo DJ, Andreu Caravaca L, Martinez-Rodriguez A, Rubio-Arias J\u0026Aacute;. Effects of resistance circuit-based training on body composition, strength and cardiorespiratory fitness: a systematic review and meta-analysis. Biology. 2021;10:377.\u003c/li\u003e\n\u003cli\u003eTayebi SM, Golmohammadi M, Eslami R, Shakiba N, Costa PB. The effects of eight weeks of circuit resistance training on serum METRNL levels and insulin resistance in individuals with type 2 diabetes. Journal of Diabetes \u0026amp; Metabolic Disorders. 2023;22:1151-8.\u003c/li\u003e\n\u003cli\u003eJavaid HMA, Sahar NE, ZhuGe D-L, Huh JY. Exercise inhibits NLRP3 inflammasome activation in obese mice via the anti-inflammatory effect of meteorin-like. Cells. 2021;10:3480.\u003c/li\u003e\n\u003cli\u003eDadmanesh M, Aghajani H, Fadaei R, Ghorban K. Lower serum levels of Meteorin-like/Subfatin in patients with coronary artery disease and type 2 diabetes mellitus are negatively associated with insulin resistance and inflammatory cytokines. PloS one. 2018;13:e0204180.\u003c/li\u003e\n\u003cli\u003eEl-Ashmawy HM, Selim FO, Hosny TA, Almassry HN. Association of low serum Meteorin like (Metrnl) concentrations with worsening of glucose tolerance, impaired endothelial function and atherosclerosis. Diabetes research and clinical practice. 2019;150:57-63.\u003c/li\u003e\n\u003cli\u003eChung HS, Hwang SY, Choi JH, Lee HJ, Kim NH, Yoo HJ, et al. Implications of circulating Meteorin-like (Metrnl) level in human subjects with type 2 diabetes. Diabetes research and clinical practice. 2018;136:100-7.\u003c/li\u003e\n\u003cli\u003eZheng S-L, Li Z-Y, Zhang Z, Wang D-S, Xu J, Miao C-Y. Evaluation of two commercial enzyme-linked immunosorbent assay kits for the detection of human circulating Metrnl. Chemical and Pharmaceutical Bulletin. 2018;66:391-8.\u003c/li\u003e\n\u003cli\u003eDing X, Chang X, Wang J, Bian N, An Y, Wang G, et al. Serum Metrnl levels are decreased in subjects with overweight or obesity and are independently associated with adverse lipid profile. Frontiers in Endocrinology. 2022;13:938341.\u003c/li\u003e\n\u003cli\u003eLiu ZX, Ji HH, Yao MP, Wang L, Wang Y, Zhou P, et al. Serum Metrnl is associated with the presence and severity of coronary artery disease. Journal of cellular and molecular medicine. 2019;23:271-80.\u003c/li\u003e\n\u003cli\u003eQi Q, Hu W-j, Zheng S-l, Zhang S-l, Le Y-y, Li Z-y, et al. Metrnl deficiency decreases blood HDL cholesterol and increases blood triglyceride. Acta Pharmacologica Sinica. 2020;41:1568-75.\u003c/li\u003e\n\u003cli\u003eL\u0026ouml;ffler D, Landgraf K, Rockstroh D, Schwartze J, Dunzendorfer H, Kiess W, et al. METRNL decreases during adipogenesis and inhibits adipocyte differentiation leading to adipocyte hypertrophy in humans. International journal of obesity. 2017;41:112-9.\u003c/li\u003e\n\u003cli\u003ePellitero S, Piquer-Garcia I, Ferrer-Curriu G, Puig R, Mart\u0026iacute;nez E, Moreno P, et al. Opposite changes in meteorin-like and oncostatin m levels are associated with metabolic improvements after bariatric surgery. International journal of obesity. 2018;42:919-22.\u003c/li\u003e\n\u003cli\u003eAlKhairi I, Cherian P, Abu-Farha M, Madhoun AA, Nizam R, Melhem M, et al. Increased expression of meteorin-like hormone in type 2 diabetes and obesity and its association with irisin. Cells. 2019;8:1283.\u003c/li\u003e\n\u003cli\u003eWang K, Li F, Wang C, Deng Y, Cao Z, Cui Y, et al. Serum levels of meteorin-like (Metrnl) are increased in patients with newly diagnosed type 2 diabetes mellitus and are associated with insulin resistance. Medical science monitor: international medical journal of experimental and clinical research. 2019;25:2337.\u003c/li\u003e\n\u003cli\u003eJamal MH, Abu-Farha M, Al-Khaledi G, Al-Sabah S, Ali H, Cherian P, et al. Effect of sleeve gastrectomy on the expression of meteorin-like (METRNL) and Irisin (FNDC5) in muscle and brown adipose tissue and its impact on uncoupling proteins in diet-induced obesity rats. Surgery for Obesity and Related Diseases. 2020;16:1910-8.\u003c/li\u003e\n\u003cli\u003eSchmid A, Karrasch T, Sch\u0026auml;ffler A. Meteorin-like protein (Metrnl) in obesity, during weight loss and in adipocyte differentiation. Journal of Clinical Medicine. 2021;10:4338.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sport-sciences-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssfh","sideBox":"Learn more about [Sport Sciences for Health](http://link.springer.com/journal/11332)","snPcode":"11332","submissionUrl":"https://submission.nature.com/new-submission/11332/3","title":"Sport Sciences for Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Obesity, Exercise Immunometabolism, Myokines, Anti-Inflammatory Response, Metabolic Health","lastPublishedDoi":"10.21203/rs.3.rs-4945904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4945904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: This study investigated the effects of circuit resistance training (CRT) on Meteorin-like protein (METRNL), interleukin-4 (IL-4), interleukin-13 (IL-13), and metabolic health markers in overweight individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Thirty overweight male university students (BMI 25-30 kg/m²) were randomly assigned to a 6-week CRT intervention group (n=15) and a control group (n=15). The CRT program comprised three weekly 45-minute sessions at 60-70% of one-repetition maximum. Serum METRNL, IL-4, IL-13, insulin resistance index, body composition, and lipid profile were measured pre-and post-intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The CRT group showed significant improvements compared to controls. Body mass index and body fat percentage decreased, while serum METRNL, IL-4, and IL-13 levels increased significantly (p\u0026lt;0.05). Metabolic health markers improved, with reductions in fasting blood glucose, fasting insulin, HOMA-IR, total cholesterol, triglycerides, and LDL-C, and increased HDL-C (p\u0026lt;0.05). Lean body mass remained unchanged between groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: CRT effectively enhances METRNL secretion, potentially contributing to improved immune and metabolic functions in overweight individuals. This suggests its potential as a therapeutic strategy for managing obesity-related immunometabolic disorders, warranting further investigation.\u003c/p\u003e","manuscriptTitle":"Effects of circuit resistance training on serum myokine METRNL, cytokines, insulin resistance, body composition, and lipid profile in overweight participants: A 6-week intervention study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-20 11:03:09","doi":"10.21203/rs.3.rs-4945904/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-12T18:27:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-24T11:22:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-19T13:43:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-18T16:50:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T19:54:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46436687651031402247635920430894338865","date":"2024-09-03T05:46:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94589920062859998780310794021998355157","date":"2024-08-30T11:03:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34149657538243781448294893424687298513","date":"2024-08-30T10:19:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198054115994191445699813422428359344098","date":"2024-08-26T19:15:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-25T01:34:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-21T11:29:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-21T11:28:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sport Sciences for Health","date":"2024-08-20T14:50:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sport-sciences-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssfh","sideBox":"Learn more about [Sport Sciences for Health](http://link.springer.com/journal/11332)","snPcode":"11332","submissionUrl":"https://submission.nature.com/new-submission/11332/3","title":"Sport Sciences for Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f2ea89d2-383b-4ce9-a907-acd943e435ab","owner":[],"postedDate":"September 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T16:07:55+00:00","versionOfRecord":{"articleIdentity":"rs-4945904","link":"https://doi.org/10.1007/s11332-025-01350-9","journal":{"identity":"sport-sciences-for-health","isVorOnly":false,"title":"Sport Sciences for Health"},"publishedOn":"2025-02-26 15:57:52","publishedOnDateReadable":"February 26th, 2025"},"versionCreatedAt":"2024-09-20 11:03:09","video":"","vorDoi":"10.1007/s11332-025-01350-9","vorDoiUrl":"https://doi.org/10.1007/s11332-025-01350-9","workflowStages":[]},"version":"v1","identity":"rs-4945904","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4945904","identity":"rs-4945904","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}