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

doi:10.21203/rs.3.rs-4945904/v1

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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

https://doi.org/10.21203/rs.3.rs-4945904/v1

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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

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.

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/m². 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.m², 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.

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
BW [kg]	CRT	81.5 ± 2.5	79 ± 2.8	− 2.5 ± 3.75†	− 0.67	F _{(1, 56)} = 33.7, P < 0.0001	P = 0.001
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
BMI [kg.m^− 2]	CRT	27 ± 0.70	25 ± 1.60	-2.0 ± 1.75†	-1.14	[F _{(1, 56)} = 23.9, P < 0.0001]	P = 0.001
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
BF%	CRT	32 ± 2.2	28.5 ± 2.9	−3.5 ± 3.64†	− 0.96	[F _{(1, 56)} = 74.32, P < 0.0001]	P = 0.025
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
LBM [kg]	CRT	55.42 ± 2.47	56.48 ± 3.04	+ 1.065 ± 3.89	0.27	[F _{(1, 56)} = 0.35, P = 0.56]	P = 0.56

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
FBG [mmol. L^− 1]	CRT	5.6 ± 0.19	4.7 ± 0.50	−0.9 ± 0.53†	-2.38	[F _{(1, 8)} = 7.08, P = 0.025]	p = 0.008
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
Insulin [µU. mL^− 1]	CRT	11.4 ± 1.40	9.3 ± 1.10	−2.1 ± 1.78†	-1.67	[F _{(1, 8)} = 9.88, P = 0.014]	p = 0.048
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
HOMA-IR	CRT	2.85 ± 0.44	1.94 ± 0.31	−0.91 ± 0.54†	-2.39	[F _{(1, 16)} = 20.21, P = 0.0004]	p = 0.04
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
TC [mg/dl]	CRT	204.1 ± 9.4	190 ± 5.40	−14.1 ± 10.84†	-1.84	[F _{(1, 28)} = 17.50, P = 0.0003]	p = 0.010
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
TG [mg/dl]	CRT	122.2 ± 8.7	109.4 ± 8.1	−12.8 ± 11.89†	-1.52	[F _{(1, 16)} = 9.608, P = 0.007]	p = 0.012
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
LDL-C [mg/dl]	CRT	136.3 ± 6.8	126.7 ± 67	−9.6 ± 67.33†	-0.20	[F _{(1, 24)} = 16.52, P = 0.0004]	p = 0.008
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
HDL-C [mg/dl]	CRT	39.2 ± 2.42	43 ± 2.42	3.8 ± 3.42†	1.57	[F _{(1, 16)} = 20.57, P = 0.0003]	p = 0.017

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)
METRNL [ng. ml^− 1]	CRT	1.30 ± 0.1	1.90 ± 0.15	0.60 ± 0.18†	2.6	[F _{(1, 20)} = 216.2, P < 0.0001]	(P < 0.0001)
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
IL-4 [pg. mL^− 1]	CRT	1.9 ± 0.7	3.05 ± 0.8	1.15 ± 1.06†	1.64	[F _{(1, 56)} = 35.39, P < 0.0001]	P = 0.008
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
IL-13 [pg. mL^− 1]	CRT	2.5 ± 0.8	3.7 ± 1.3	1.2 ± 1.53†	1.5	[F _{(1, 56)} = 60.20, P < 0.0001]	P = 0.002

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).

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.

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.

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

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No competing interests reported.

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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

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Abstract

Introduction

Methods

Study Design

Participants

Exercise Intervention

Data Collection

Blood Sample Collection

Serum METRNL Levels

Serum Cytokine Levels

Serum insulin, glucose, and Insulin Resistance index

Serum Lipid Profile

Body Composition parameters

Statistical Analysis

Results

Body Composition Parameters

Body Weight (BW)

Body Mass Index (BMI)

Body Fat Percentage (BF%)

Lean Body Mass (LBM)

Glucose and Lipid Homeostasis Parameters

Fasting Blood Glucose (FBG)

Insulin

Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)

Total Cholesterol (TC)

Triglycerides (TG)

Low-Density Lipoprotein Cholesterol (LDL-C)

High-Density Lipoprotein Cholesterol (HDL-C)

METRNL and cytokines levels

METRNL

IL-4

IL-13

Correlation results

Discussion

Study Limitations

Conclusions

Declarations

References

Additional Declarations

Status:

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