Elevated Serum METRNL Levels in Obese Children and Its Association with Insulin Resistanc

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However, its role in pediatric obesity and its relationship with insulin resistance remain largely unexplored. Methods : In this cross-sectional study, we enrolled 55 obese children and 49 age- and sex-matched normal-weight controls. Serum levels of METRNL, inflammatory markers (IL-6, TNF-α), and adipokines (leptin, adiponectin) were measured. The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated to evaluate insulin resistance. Correlations between METRNL and metabolic parameters were analyzed, and multiple regression analysis was performed to identify factors independently associated with insulin resistance. Results : Serum METRNL levels were significantly higher in obese children compared to controls (4.62 ± 1.24 vs 2.84 ± 0.76 ng/mL, p < 0.05). METRNL levels showed positive correlations with BMI (r = 0.624, p < 0.05), HOMA-IR (r = 0.594, p < 0.05), inflammatory markers (IL-6: r = 0.528, p < 0.05; TNF-α: r = 0.486, p < 0.05), and leptin (r = 0.546, p < 0.05), while negatively correlating with adiponectin (r = -0.482, p < 0.05). Multiple regression analysis revealed that METRNL was independently associated with HOMA-IR (standardized β = 0.384, p < 0.05) after adjusting for potential confounders. Conclusions : Serum METRNL levels are elevated in obese children and independently associated with insulin resistance. These findings suggest that METRNL might play a significant role in the pathophysiology of pediatric obesity and its metabolic complications. METRNL pediatric obesity insulin resistance inflammation adipokines Introduction Childhood obesity has become a major global public health concern, with its prevalence increasing dramatically over the past few decades. Recent epidemiological data indicates that approximately 340 million children and adolescents aged 5–19 were overweight or obese in 2022 [ 1 ]. Obesity during childhood not only impacts immediate health but also increases the risk of various metabolic disorders, including insulin resistance, type 2 diabetes, and cardiovascular diseases [ 2 ]. The pathophysiology of obesity-related metabolic complications involves complex interactions between adipose tissue dysfunction and systemic inflammation. Adipose tissue is now recognized as an active endocrine organ that secretes various bioactive molecules, collectively known as adipokines [ 3 ]. These adipokines play crucial roles in energy homeostasis, inflammation, and insulin sensitivity [ 4 ]. Recent research has identified Meteorin-like protein (METRNL), also known as Subfatin, as a novel adipokine that may have significant implications in metabolic regulation [ 5 ]. METRNL is a secreted protein that shares significant homology with Meteorin and has been shown to be expressed in various tissues, including white adipose tissue, skeletal muscle, and brain [ 6 ]. Emerging evidence suggests that METRNL may play a vital role in energy metabolism and inflammatory responses. Animal studies have demonstrated that METRNL administration can improve insulin sensitivity and reduce inflammation in diet-induced obese mice [ 7 ]. Furthermore, recent research has indicated that METRNL may influence brown adipose tissue thermogenesis and energy expenditure [ 8 ]. The relationship between obesity and systemic inflammation is well-established, with pro-inflammatory cytokines such as Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α) being elevated in obese individuals [ 9 ]. Additionally, alterations in adipokine profiles, including increased leptin and decreased adiponectin levels, have been consistently observed in obesity [ 10 ]. These changes contribute to the development of insulin resistance and metabolic dysfunction [ 11 ].While several studies have investigated METRNL levels in adult populations with metabolic disorders [ 12 ], limited data exists regarding its role in pediatric obesity. Understanding the relationship between METRNL and other metabolic parameters in obese children could provide valuable insights into the pathophysiology of childhood obesity and its associated complications [ 13 ]. Additionally, the potential association between METRNL and insulin resistance, as measured by the homeostasis model assessment of insulin resistance (HOMA-IR), remains largely unexplored in the pediatric population [ 14 ].Recent research has suggested that METRNL may serve as a potential therapeutic target for metabolic diseases [ 15 ]. However, before its therapeutic potential can be fully realized, it is essential to understand its relationship with other metabolic parameters and its role in obesity-related complications, particularly in the pediatric population where early intervention may be most beneficial [ 16 ]. Importantly, the literature shows conflicting results regarding METRNL levels in obesity. While some studies suggest elevated levels, recent studies [ 17 ] reported decreased METRNL concentrations in obese children. These contradictory findings highlight the need for further investigation of METRNL in pediatric obesity. The purpose of this study was to investigate serum METRNL levels in obese children and analyze its association with insulin resistance, inflammatory markers, and other adipokines. We examined the relationships between METRNL and various clinical parameters, including body mass index (BMI), inflammatory markers (IL-6, TNF-α), and adipokines (leptin, adiponectin), as well as its potential role in insulin resistance through multiple regression analysis. Methods Study Population and Design This cross-sectional study was conducted at the Pediatric Department of the Second Affiliated Hospital of Xi’an Jiaotong University, between December 2022 and June 2023. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Xi’an Jiaotong University with the registration number No.2022245. Written informed consent was obtained from all participants' parents or legal guardians. The study protocol was approved by the ethics committee of the Second Affiliated Hospital of Xi’an Jiaotong University and was conducted in accordance with the Declaration of Helsinki. Children aged 7–14 years were recruited for this study. The obesity group included children with a body mass index (BMI) above the 95th percentile for age and sex according to the World Health Organization growth reference standards. Age- and sex-matched normal-weight children with BMI between the 15th and 85th percentiles were recruited as controls. Children were excluded from the study if they had any of the following conditions: chronic diseases, endocrine disorders, genetic syndromes, acute or chronic inflammatory diseases, recent infections within the past month, use of medications that could affect metabolism or inflammation, or inability to comply with study procedures. Anthropometric and Clinical Measurements All participants underwent comprehensive anthropometric measurements performed by trained medical staff. Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer with participants standing barefoot. Body weight was measured to the nearest 0.1 kg using a calibrated electronic scale with participants wearing light clothing. BMI was calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured at the midpoint between the lower border of the rib cage and the iliac crest using a non-elastic measuring tape. Blood pressure was measured in a sitting position after 5 minutes of rest using an appropriate-sized cuff and an automated oscillometric device (Omron HEM-7120, Kyoto, Japan). Three measurements were taken at 2-minute intervals, and the average of the last two readings was used for analysis. Laboratory Measurements Blood samples were collected from all participants between 8:00 and 10:00 AM after an overnight fast of at least 12 hours. Samples were centrifuged within 30 minutes of collection at 3000 rpm for 15 minutes at 4°C. Serum was aliquoted and stored at -80°C until analysis. Fasting blood glucose, serum lipids including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride (TG), and total cholesterol were measured enzymatically using an auto-analyzer (Hitachi 747; Hitachi, Tokyo, Japan). An auto-mated Immunoassay Analyzer was used to measure insulin concentrations (AIA-2000ST). Serum METRNL, IL-6, TNF-α, leptin and adiponectin levels were measured using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R&D Systems, Minneapolis, MN, USA) according to the manufacturer's instructions. Insulin Resistance Assessment Insulin resistance was evaluated using the homeostasis model assessment of insulin resistance (HOMA-IR), calculated as: fasting insulin (µIU/mL) × fasting glucose (mmol/L) / 22.5. Statistical Analysis Statistical analyses were performed using SPSS software version 26.0 (IBM Corp., Armonk, NY, USA). The normality of data distribution was assessed using the Kolmogorov-Smirnov test. Continuous variables were expressed as mean ± standard deviation for normally distributed data or median (interquartile range) for non-normally distributed data. Categorical variables were presented as numbers and percentages. Comparisons between obese and control groups were performed using Student's t-test for normally distributed variables or Mann-Whitney U test for non-normally distributed variables. Pearson's or Spearman's correlation coefficients were calculated to evaluate the relationships between METRNL levels and other clinical and biochemical parameters. Multiple linear regression analysis was performed to identify independent factors associated with HOMA-IR. Variables with p < 0.1 in univariate analysis were included in the multivariate model. The model was adjusted for potential confounding factors including age, sex, and BMI. Multicollinearity was assessed using variance inflation factors. A two-tailed p-value < 0.05 was considered statistically significant. Power analysis was performed using G*Power software (version 3.1.9.4) to determine the sample size needed to detect significant differences between groups with 80% power at an alpha level of 0.05. Results Baseline Characteristics of Study Participants A total of 104 children were enrolled in this study, including 55 obese children (34 boys, 21 girls) and 49 normal-weight controls (31 boys, 18 girls). The baseline demographic and clinical characteristics of the study participants are presented in Table 1 . There were no significant differences in age and sex distribution between the two groups. As expected, obese children had significantly higher BMI, waist circumference, and systolic and diastolic blood pressure compared to the control group (all p < 0.05). Table 1 Clinical and biochemical characteristics of study participants Parameter Control group (n = 49) Obese group (n = 55) P-value Age (years) 10.2 ± 2.1 10.4 ± 2.3 0.542 Sex (male/female) 31/18 34/21 0.876 BMI (kg/m²) 17.8 ± 1.6 26.7 ± 2.9 < 0.05 Waist circumference (cm) 64.5 ± 5.8 85.7 ± 8.9 < 0.05 Systolic BP (mmHg) 105.6 ± 8.4 118.9 ± 10.2 < 0.05 Diastolic BP (mmHg) 65.8 ± 6.2 74.3 ± 7.8 < 0.05 Fasting glucose (mmol/L) 4.82 ± 0.38 5.06 ± 0.42 < 0.05 Fasting insulin (µIU/mL) 8.24 ± 2.16 18.65 ± 6.82 < 0.05 HOMA-IR 1.76 ± 0.48 4.18 ± 1.56 < 0.05 Values are presented as mean ± SD or numbers. BMI: body mass index; BP: blood pressure; HOMA-IR: homeostasis model assessment of insulin resistance. Comparison of Serum METRNL and Other Inflammatory Markers Serum METRNL levels were significantly higher in obese children compared to normal-weight controls (Table 2 ). Similarly, inflammatory markers (IL-6 and TNF-α) and leptin levels were significantly elevated in the obese group, while adiponectin levels were significantly lower. We have conducted post-hoc subgroup analyses stratified by sex using existing data, while no statistically significant interaction was found (p = 0.12). Table 2 Comparison of serum METRNL and other biomarkers between groups Parameter Control group (n = 49) Obese group (n = 55) P-value METRNL (ng/mL) 2.84 ± 0.76 4.62 ± 1.24 < 0.05 IL-6 (pg/mL) 1.56 (0.92–2.34) 3.85 (2.46–5.72) < 0.05 TNF-α (pg/mL) 3.24 (2.18–4.56) 6.82 (4.95–8.74) < 0.05 Leptin (ng/mL) 6.82 ± 2.45 24.56 ± 8.92 < 0.05 Adiponectin (µg/mL) 12.45 ± 3.26 7.84 ± 2.18 < 0.05 Values are presented as mean ± SD or median (interquartile range). Correlation Analysis Correlation analysis revealed significant associations between serum METRNL levels and various clinical and biochemical parameters (Table 3 ). METRNL showed positive correlations with BMI, waist circumference, HOMA-IR, IL-6, TNF-α, and leptin levels, while it was negatively correlated with adiponectin levels. Table 3 Correlation analysis of serum METRNL levels with clinical and biochemical parameters Parameter Correlation coefficient (r) P-value BMI 0.624 < 0.05 Waist circumference 0.586 < 0.05 Systolic BP 0.342 0.017 Diastolic BP 0.298 0.021 Fasting glucose 0.384 < 0.05 Fasting insulin 0.562 < 0.05 HOMA-IR 0.594 < 0.05 IL-6 0.528 < 0.05 TNF-α 0.486 < 0.05 Leptin 0.546 < 0.05 Adiponectin -0.482 < 0.05 Multiple Linear Regression Analysis To identify independent factors associated with insulin resistance, multiple linear regression analysis was performed with HOMA-IR as the dependent variable (Table 4 ). After adjusting for age, sex, and BMI, serum METRNL levels remained significantly associated with HOMA-IR, along with leptin and adiponectin levels. Table 4 Multiple linear regression analysis for factors associated with HOMA-IR Variable β coefficient Standard error Standardized β P-value METRNL 0.486 0.092 0.384 < 0.05 BMI 0.324 0.086 0.298 0.002 Leptin 0.278 0.074 0.246 0.004 Adiponectin -0.256 0.068 -0.228 0.006 IL-6 0.186 0.082 0.164 0.024 TNF-α 0.162 0.078 0.142 0.038 R² = 0.624, Adjusted R² = 0.598 Model adjusted for age and sex. The final model explained 62.4% of the variance in HOMA-IR (adjusted R² = 0.598). Among all the variables, METRNL showed the strongest independent association with HOMA-IR (standardized β = 0.384, p < 0.05), followed by BMI and leptin levels. Discussion The present study demonstrated significantly elevated serum METRNL levels in obese children compared to normal-weight controls, along with its strong association with insulin resistance and inflammatory markers. These findings provide novel insights into the potential role of METRNL in pediatric obesity and its metabolic complications. Our observation of increased METRNL levels in obese children aligns with findings from previous studies [ 18 , 19 ] in adult populations with metabolic disorders. However, our study is among the first to demonstrate this association in the pediatric population. The elevation of METRNL in obese children might represent a compensatory mechanism to counter metabolic dysfunction, similar to the pattern observed with other adipokines such as leptin [ 20 ]. This compensatory increase could be an attempt to improve insulin sensitivity and reduce inflammation, as suggested by experimental studies showing METRNL's beneficial metabolic effects [ 21 ]. It is important to acknowledge, however, that our findings contrast with several studies in the literature. Recent research [ 17 ] reported decreased METRNL levels in obese children, while other studies [ 22 , 23 , 24 ] found reduced METRNL concentrations in adults with obesity and type 2 diabetes. Similarly, previous research [ 25 ] showed a negative correlation between METRNL levels and markers of insulin resistance in obese adults. These contradictory findings might be attributed to several factors, including differences in study populations (age, ethnicity, degree of obesity), methodological variations in METRNL measurement, and potential confounding factors such as comorbidities or medications. Additionally, the complex regulatory mechanisms of METRNL expression and secretion might vary depending on the metabolic state and disease progression, potentially explaining these discrepancies. Further longitudinal studies are needed to clarify the dynamic changes in METRNL levels during the development and progression of obesity and insulin resistance. The significant positive correlation between METRNL levels and inflammatory markers (IL-6 and TNF-α) observed in our study suggests a complex interplay between METRNL and inflammatory pathways in obesity. Previous research has shown that chronic low-grade inflammation is a hallmark of obesity, contributing to insulin resistance and metabolic dysfunction [ 26 ]. Our findings raise the possibility that METRNL might be involved in the inflammatory response associated with obesity, although whether its elevation represents a protective mechanism or contributes to pathological processes requires further investigation [ 27 ]. The strong positive association between METRNL and HOMA-IR, even after adjusting for potential confounders, is particularly noteworthy. Recent experimental studies have demonstrated that METRNL can influence glucose metabolism and insulin sensitivity through multiple mechanisms, including enhancement of insulin signaling and modulation of inflammatory responses [ 28 ]. Our findings extend these observations to the pediatric population and suggest that METRNL might serve as a novel biomarker for insulin resistance in obese children [ 29 ]. The inverse correlation between METRNL and adiponectin levels observed in our study is intriguing. Adiponectin is well-established as an insulin-sensitizing adipokine that is typically decreased in obesity [ 30 ]. The opposing patterns of these two adipokines suggest that they might have complementary roles in metabolic regulation. This relationship might reflect a compensatory increase in METRNL in response to reduced adiponectin levels, although the precise mechanisms underlying this interaction remain to be elucidated [ 31 ]. Our multiple regression analysis revealed that METRNL was independently associated with HOMA-IR, even after adjusting for BMI and other established metabolic markers. This finding suggests that METRNL might contribute to insulin resistance through pathways distinct from those of traditional adipokines [ 32 ]. The stronger association of METRNL with HOMA-IR compared to other inflammatory markers implies that it might be a more sensitive indicator of metabolic dysfunction in pediatric obesity [ 33 ]. The positive correlation between METRNL and leptin levels observed in our study adds another layer to our understanding of adipokine interactions in obesity. Both proteins show similar patterns of elevation in obesity, suggesting possible common regulatory mechanisms or complementary functions [ 34 ]. Recent research has suggested that leptin and METRNL might share some downstream signaling pathways, particularly those involved in energy metabolism and inflammation [ 35 ]. The clinical implications of our findings are significant. The strong association between METRNL and various metabolic parameters suggests its potential utility as a biomarker for metabolic dysfunction in pediatric obesity. Furthermore, the independent association with insulin resistance raises the possibility that METRNL could be a therapeutic target for obesity-related metabolic complications. Early identification of children at risk for metabolic complications could enable more timely interventions and better outcomes. However, several limitations of our study should be acknowledged. The cross-sectional design prevents us from establishing causal relationships between METRNL and metabolic parameters. Additionally, the single-center nature of our study and relatively small sample size may limit the generalizability of our findings. Longitudinal studies with larger cohorts are needed to confirm our results and investigate the temporal relationship between changes in METRNL levels and the development of metabolic complications. This study only conducted a cross-sectional survey at baseline, but did not compare the changes before and after weight loss, which has certain limitations. Future research should focus on elucidating the molecular mechanisms underlying the relationship between METRNL and insulin resistance in obesity. Additionally, investigation of potential genetic and environmental factors that influence METRNL expression and function could provide valuable insights into its role in metabolic regulation. Intervention studies examining the effect of weight loss on METRNL levels and its relationship with metabolic improvements would also be informative. In conclusion, our study demonstrates that serum METRNL levels are elevated in obese children and strongly associated with insulin resistance and inflammatory markers. However, we acknowledge the conflicting evidence in the literature regarding METRNL's role in obesity, with some studies showing decreased levels in obese subjects. These contradictory findings highlight the complex nature of METRNL regulation in metabolic disorders and underscore the need for further research to fully understand its pathophysiological significance in pediatric obesity and its potential as a therapeutic target. Declarations Ethics Approval Declaration The study protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Xi’an Jiaotong University with the registration number No.2022245. This study was conducted in accordance with the guidelines of the Research Committee of Xi’an Jiaotong University and according to the Declaration of Helsinki. Written informed consent was obtained from all guardians of children participating in the study. Clinical trial number: not applicable. Human Ethics and Consent to Participate Declarations: not applicable. Consent for publication All authors agreed to publish this research. Conflict of Interest Statement The authors declare that they have no competing interests. Funding Sources This study was supported by the National Natural Science Foundation of China (No. 81903340) and IIT Clinical Research Found of the Second Affiliated Hospital of Xi’an Jiaotong University (No. IIT-007). Author Contributions Yuesheng Liu designed and supervised experiments; Lijun Hao and Lujie Liu performed all the experiments; Chunyan Yin and Hongmei Lin managed, analyzed, and ploted the data; Shuang Guo and Lijun Hao organized and wrote the paper; Yuesheng Liu and Yanfeng Xiao critically revised the manuscript. All authors reviewed the final version of the manuscript. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions. References Abarca-Gómez L, Abdeen ZA, Hamid ZA, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627-2642. Weihrauch-Blüher S, Schwarz P, Klusmann JH. Childhood obesity: increased risk for cardiometabolic disease and cancer in adulthood. Metabolism. 2019;92:147-152. Kawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol. 2021;320(3):C375-C391. Goossens GH, Jocken JWE, Blaak EE. Sexual dimorphism in cardiometabolic health: the role of adipose tissue, muscle and liver. Nat Rev Endocrinol. 2021;17(1):47-66. Li Z, Gao Z, Sun T, et al. Meteorin-like/Metrnl, a novel secreted protein implicated in inflammation, immunology, and metabolism: A comprehensive review of preclinical and clinical studies. Front Immunol. 2023;14:1098570. Rao RR, Long JZ, White JP, et al. Meteorin-like is a hormone that regulates immune-adipose interactions to increase beige fat thermogenesis. Cell. 2014;157(6):1279-1291. Li L, Chen J, Sun H, et al. Orm2 Deficiency Aggravates High-Fat Diet-Induced Obesity through Gut Microbial Dysbiosis and Intestinal Inflammation. Mol Nutr Food Res. 2024;68(1):2300236. Miao Z, Hu W, Li Z, et al. Involvement of the secreted protein Metrnl in human diseases. Acta Pharmacol Sin. 2020;41(12):1525-1530. Liu BN, Liu XT, Liang ZH, et al. Gut microbiota in obesity. World J Gastroenterol. 2021;27(25):3837-3850. Bertoncini-Silva C, Zingg JM, Fassini PG, et al. Bioactive dietary components—Anti-obesity effects related to energy metabolism and inflammation. BioFactors. 2023;49(2):297-321. Czech MP. Mechanisms of insulin resistance related to white, beige, and brown adipocytes. Mol Metab. 2020;34:27-42. Dongre UJ. Adipokines in insulin resistance: current updates. Biosci Biotechnol Res Asia. 2021;18(2):357-366. Phuong LDT, Tran Huy T, Huynh Quang T. The plasma levels of protein adiponectin (AdipoQ) and meteorin-like (Metrnl) in newly diagnosed type 2 diabetes mellitus. Diabetes Metab Syndr Obes. 2024;17:2903-2909. Cherian P, Al-Khairi I, Jamal M, et al. Association between factors involved in bone remodeling (Osteoactivin and OPG) with plasma levels of irisin and meteorin-like protein in people with T2D and obesity. Front Endocrinol. 2021;12:752892. Martínez-Sánchez N, Seoane-Collazo P, Contreras C, et al. Hypothalamic AMPK-ER stress-JNK1 axis mediates the central actions of thyroid hormones on energy balance. Cell Metab. 2017;26(1):212-229. Jung TW, Pyun DH, Kim TJ, et al. Meteorin-like protein (METRNL)/IL-41 improves LPS-induced inflammatory responses via AMPK or PPARδ–mediated signaling pathways. Adv Med Sci. 2021;66(1):155-161. Gholamrezayi A, Mohamadinarab M, Rahbarinejad P, Fallah S, Barez SR, Setayesh L, Moradi N, Fadaei R, Chamani E, Tavakoli T. Characterization of the serum levels of Meteorin-like in patients with inflammatory bowel disease and its association with inflammatory cytokines. Lipids Health Dis. 2020 Oct 30;19(1):230. Cantero I, Elorz M, Abete I, et al. Ultrasound/elastography techniques, lipidomic and blood markers compared to magnetic resonance imaging in non-alcoholic fatty liver disease adults. Int J Med Sci. 2019;16(1):75-83. Liu M, Gao X, Tian Y, et al. Serum Metrnl is Decreased in Metabolic Dysfunction-Associated Fatty Liver Disease: A Case-Control Study. Diabetes Metab Syndr Obes. 2024;17:533-543. Dong W, Hu C, Hu M, et al. Metrnl: a promising biomarker and therapeutic target for cardiovascular and metabolic diseases. Cell Commun Signal. 2024;22(1):389. Metwaly A, Reitmeier S, Haller D. Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders. Nat Rev Gastroenterol Hepatol. 2022;19(6):383-397. Wang K, Li F, Wang C, Deng Y, Cao Z, Cui Y, Xu K, Ln P, Sun Y. Serum Levels of Meteorin-Like (Metrnl) Are Increased in Patients with Newly Diagnosed Type 2 Diabetes Mellitus and Are Associated with Insulin Resistance. Med Sci Monit. 2019 Mar 31;25:2337-2343. 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 Sep 13;13(9):e0204180. Fadaei R, Moradi N, Kazemi T, Chamani E, Azdaki N, Moezibady SA, Shahmohamadnejad S, Fallah S. Decreased serum levels of CTRP12/adipolin in patients with coronary artery disease in relation to inflammatory cytokines and insulin resistance. Cytokine. 2019 Jan;113:326-331. Al-Daghri NM, Wani K, Yakout SM, Al-Hazmi H, Amer OE, Hussain SD, Sabico S, Ansari MGA, Al-Musharaf S, Alenad AM, Alokail MS, Clerici M. Favorable Changes in Fasting Glucose in a 6-month Self-Monitored Lifestyle Modification Programme Inversely Affects Spexin Levels in Females with Prediabetes. Sci Rep. 2019 Jul 1;9(1):9454. Falch CM, Arlien-Søborg MC, Dal J, et al. Gene expression profiling of subcutaneous adipose tissue reveals new biomarkers in acromegaly. Eur J Endocrinol. 2023;188(3):310-321. Jung TW, Lee SH, Kim HC, et al. METRNL attenuates lipid-induced inflammation and insulin resistance via AMPK or PPARδ-dependent pathways in skeletal muscle of mice. Exp Mol Med. 2018;50(9):1-11. Du Y, Ye X, Lu A, et al. Inverse relationship between serum Metrnl levels and visceral fat obesity (VFO) in patients with type 2 diabetes. Diabetes Res Clin Pract. 2020;161:108068. Peng J, Chen Q, Wu C. The role of adiponectin in cardiovascular disease. Cardiovasc Pathol. 2023;64:107514. Ghoshal K, Bhattacharyya M. Adiponectin: Probe of the molecular paradigm associating diabetes and obesity. World J Diabetes. 2015;6(1):151-166. Ding X, Chang X, Wang J, et al. Serum Metrnl levels are decreased in subjects with overweight or obesity and are independently associated with adverse lipid profile. Front Endocrinol. 2022;13:938341. Alizadeh H. Meteorin-like protein (Metrnl): A metabolic syndrome biomarker and an exercise mediator. Cytokine. 2022;157:155952. Zheng S, Li Z, Song J, et al. Metrnl: a secreted protein with new emerging functions. Acta Pharmacol Sin. 2016;37(5):571-579. Ghasemi A, Hashemy SI, Azimi-Nezhad M, et al. The cross-talk between adipokines and miRNAs in health and obesity-mediated diseases. Clin Chim Acta. 2019;499:41-53. Lee JH, Kang YE, Kim JM, et al. Serum Meteorin-like protein levels decreased in patients newly diagnosed with type 2 diabetes. Diabetes Res Clin Pract. 2021;171:108542. Additional Declarations No competing interests reported. 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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-6601784","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457084434,"identity":"aab0f15b-af20-4693-b64e-5eca71732a2f","order_by":0,"name":"Lijun Hao","email":"","orcid":"","institution":"Xi'an People’s Hospital (Xi’an Fourth Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Hao","suffix":""},{"id":457084435,"identity":"9bc72d01-25f7-4d71-986f-3ac3110aa17c","order_by":1,"name":"Lujie Liu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Lujie","middleName":"","lastName":"Liu","suffix":""},{"id":457084436,"identity":"11763041-2771-4c03-bd3b-2c04542bba46","order_by":2,"name":"Hongmei Lin","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hongmei","middleName":"","lastName":"Lin","suffix":""},{"id":457084437,"identity":"c4a05789-9ff3-427a-84e3-9839193afa84","order_by":3,"name":"Shuang Guo","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Guo","suffix":""},{"id":457084438,"identity":"4387bbbe-8960-458f-921c-28f2f5aa6b5b","order_by":4,"name":"Biyao Lian","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Biyao","middleName":"","lastName":"Lian","suffix":""},{"id":457084439,"identity":"0c354431-c501-4235-806e-907099c314d3","order_by":5,"name":"Chunyan Yin","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Yin","suffix":""},{"id":457084440,"identity":"20782c70-ee3d-498e-a716-2f250e7bfc04","order_by":6,"name":"Yanfeng Xiao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yanfeng","middleName":"","lastName":"Xiao","suffix":""},{"id":457084441,"identity":"ecc64031-e75c-4d47-8ceb-d15493e225b6","order_by":7,"name":"Yuesheng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACPgYGNiDFzMDA3tj48AMxWtgYmKFaeA43G0uQpkUivU2Ahygt7P3HHvzcYy1vLvmwjUGCwU5Ot4GQFp7D7IY9z9INd85ObHtQwJBsbHaAkBaJZDYJngOHGTfcTmw3kGA4kLiNGC2Sfw4ctt9w82CbBA+xWqSBtiRuuMFIrBaew2bSMgfSkzecSQQGsgERfuFnb3wm+eaAte2G48cfPvxQYSdHUAsaMCBN+SgYBaNgFIwCHAAAPD4+yEzHVzsAAAAASUVORK5CYII=","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Yuesheng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-05-06 10:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6601784/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6601784/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88750597,"identity":"58ef5d13-6118-409a-833f-561a0717b9fd","added_by":"auto","created_at":"2025-08-11 06:01:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":588606,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6601784/v1/10486d44-93ea-488c-ac57-4a9f0d96aaa9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elevated Serum METRNL Levels in Obese Children and Its Association with Insulin Resistanc","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChildhood obesity has become a major global public health concern, with its prevalence increasing dramatically over the past few decades. Recent epidemiological data indicates that approximately 340\u0026nbsp;million children and adolescents aged 5\u0026ndash;19 were overweight or obese in 2022 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Obesity during childhood not only impacts immediate health but also increases the risk of various metabolic disorders, including insulin resistance, type 2 diabetes, and cardiovascular diseases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pathophysiology of obesity-related metabolic complications involves complex interactions between adipose tissue dysfunction and systemic inflammation. Adipose tissue is now recognized as an active endocrine organ that secretes various bioactive molecules, collectively known as adipokines [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These adipokines play crucial roles in energy homeostasis, inflammation, and insulin sensitivity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent research has identified Meteorin-like protein (METRNL), also known as Subfatin, as a novel adipokine that may have significant implications in metabolic regulation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. METRNL is a secreted protein that shares significant homology with Meteorin and has been shown to be expressed in various tissues, including white adipose tissue, skeletal muscle, and brain [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Emerging evidence suggests that METRNL may play a vital role in energy metabolism and inflammatory responses. Animal studies have demonstrated that METRNL administration can improve insulin sensitivity and reduce inflammation in diet-induced obese mice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, recent research has indicated that METRNL may influence brown adipose tissue thermogenesis and energy expenditure [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between obesity and systemic inflammation is well-established, with pro-inflammatory cytokines such as Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α) being elevated in obese individuals [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, alterations in adipokine profiles, including increased leptin and decreased adiponectin levels, have been consistently observed in obesity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These changes contribute to the development of insulin resistance and metabolic dysfunction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].While several studies have investigated METRNL levels in adult populations with metabolic disorders [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], limited data exists regarding its role in pediatric obesity. Understanding the relationship between METRNL and other metabolic parameters in obese children could provide valuable insights into the pathophysiology of childhood obesity and its associated complications [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, the potential association between METRNL and insulin resistance, as measured by the homeostasis model assessment of insulin resistance (HOMA-IR), remains largely unexplored in the pediatric population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].Recent research has suggested that METRNL may serve as a potential therapeutic target for metabolic diseases [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, before its therapeutic potential can be fully realized, it is essential to understand its relationship with other metabolic parameters and its role in obesity-related complications, particularly in the pediatric population where early intervention may be most beneficial [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Importantly, the literature shows conflicting results regarding METRNL levels in obesity. While some studies suggest elevated levels, recent studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] reported decreased METRNL concentrations in obese children. These contradictory findings highlight the need for further investigation of METRNL in pediatric obesity.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to investigate serum METRNL levels in obese children and analyze its association with insulin resistance, inflammatory markers, and other adipokines. We examined the relationships between METRNL and various clinical parameters, including body mass index (BMI), inflammatory markers (IL-6, TNF-α), and adipokines (leptin, adiponectin), as well as its potential role in insulin resistance through multiple regression analysis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Population and Design\u003c/p\u003e \u003cp\u003eThis cross-sectional study was conducted at the Pediatric Department of the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University, between December 2022 and June 2023. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University with the registration number No.2022245. Written informed consent was obtained from all participants' parents or legal guardians. The study protocol was approved by the ethics committee of the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eChildren aged 7\u0026ndash;14 years were recruited for this study. The obesity group included children with a body mass index (BMI) above the 95th percentile for age and sex according to the World Health Organization growth reference standards. Age- and sex-matched normal-weight children with BMI between the 15th and 85th percentiles were recruited as controls. Children were excluded from the study if they had any of the following conditions: chronic diseases, endocrine disorders, genetic syndromes, acute or chronic inflammatory diseases, recent infections within the past month, use of medications that could affect metabolism or inflammation, or inability to comply with study procedures.\u003c/p\u003e \u003cp\u003eAnthropometric and Clinical Measurements\u003c/p\u003e \u003cp\u003eAll participants underwent comprehensive anthropometric measurements performed by trained medical staff. Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer with participants standing barefoot. Body weight was measured to the nearest 0.1 kg using a calibrated electronic scale with participants wearing light clothing. BMI was calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured at the midpoint between the lower border of the rib cage and the iliac crest using a non-elastic measuring tape.\u003c/p\u003e \u003cp\u003eBlood pressure was measured in a sitting position after 5 minutes of rest using an appropriate-sized cuff and an automated oscillometric device (Omron HEM-7120, Kyoto, Japan). Three measurements were taken at 2-minute intervals, and the average of the last two readings was used for analysis.\u003c/p\u003e \u003cp\u003eLaboratory Measurements\u003c/p\u003e \u003cp\u003eBlood samples were collected from all participants between 8:00 and 10:00 AM after an overnight fast of at least 12 hours. Samples were centrifuged within 30 minutes of collection at 3000 rpm for 15 minutes at 4\u0026deg;C. Serum was aliquoted and stored at -80\u0026deg;C until analysis.\u003c/p\u003e \u003cp\u003eFasting blood glucose, serum lipids including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride (TG), and total cholesterol were measured enzymatically using an auto-analyzer (Hitachi 747; Hitachi, Tokyo, Japan). An auto-mated Immunoassay Analyzer was used to measure insulin concentrations (AIA-2000ST). Serum METRNL, IL-6, TNF-α, leptin and adiponectin levels were measured using commercially available enzyme-linked immunosorbent assay (ELISA) kits (R\u0026amp;D Systems, Minneapolis, MN, USA) according to the manufacturer's instructions.\u003c/p\u003e \u003cp\u003eInsulin Resistance Assessment\u003c/p\u003e \u003cp\u003eInsulin resistance was evaluated using the homeostasis model assessment of insulin resistance (HOMA-IR), calculated as: fasting insulin (\u0026micro;IU/mL) \u0026times; fasting glucose (mmol/L) / 22.5.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS software version 26.0 (IBM Corp., Armonk, NY, USA). The normality of data distribution was assessed using the Kolmogorov-Smirnov test. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed data or median (interquartile range) for non-normally distributed data. Categorical variables were presented as numbers and percentages.\u003c/p\u003e \u003cp\u003eComparisons between obese and control groups were performed using Student's t-test for normally distributed variables or Mann-Whitney U test for non-normally distributed variables. Pearson's or Spearman's correlation coefficients were calculated to evaluate the relationships between METRNL levels and other clinical and biochemical parameters.\u003c/p\u003e \u003cp\u003eMultiple linear regression analysis was performed to identify independent factors associated with HOMA-IR. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate analysis were included in the multivariate model. The model was adjusted for potential confounding factors including age, sex, and BMI. Multicollinearity was assessed using variance inflation factors. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003ePower analysis was performed using G*Power software (version 3.1.9.4) to determine the sample size needed to detect significant differences between groups with 80% power at an alpha level of 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics of Study Participants\u003c/p\u003e \u003cp\u003eA total of 104 children were enrolled in this study, including 55 obese children (34 boys, 21 girls) and 49 normal-weight controls (31 boys, 18 girls). The baseline demographic and clinical characteristics of the study participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no significant differences in age and sex distribution between the two groups. As expected, obese children had significantly higher BMI, waist circumference, and systolic and diastolic blood pressure compared to the control group (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eClinical and biochemical characteristics of study participants\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=\"left\" 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\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObese group (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34/21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting insulin (\u0026micro;IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\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\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or numbers. BMI: body mass index; BP: blood pressure; HOMA-IR: homeostasis model assessment of insulin resistance.\u003c/p\u003e \u003cp\u003eComparison of Serum METRNL and Other Inflammatory Markers\u003c/p\u003e \u003cp\u003eSerum METRNL levels were significantly higher in obese children compared to normal-weight controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, inflammatory markers (IL-6 and TNF-α) and leptin levels were significantly elevated in the obese group, while adiponectin levels were significantly lower. We have conducted post-hoc subgroup analyses stratified by sex using existing data, while no statistically significant interaction was found (p\u0026thinsp;=\u0026thinsp;0.12).\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\u003eComparison of serum METRNL and other biomarkers between groups\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=\"left\" 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\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObese group (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETRNL (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56 (0.92\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.85 (2.46\u0026ndash;5.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-α (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.24 (2.18\u0026ndash;4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.82 (4.95\u0026ndash;8.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeptin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin (\u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\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\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (interquartile range).\u003c/p\u003e \u003cp\u003eCorrelation Analysis\u003c/p\u003e \u003cp\u003eCorrelation analysis revealed significant associations between serum METRNL levels and various clinical and biochemical parameters (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). METRNL showed positive correlations with BMI, waist circumference, HOMA-IR, IL-6, TNF-α, and leptin levels, while it was negatively correlated with adiponectin levels.\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\u003eCorrelation analysis of serum METRNL levels with clinical and biochemical parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eCorrelation coefficient (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\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\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting insulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\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\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeptin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\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\u003eMultiple Linear Regression Analysis\u003c/p\u003e \u003cp\u003eTo identify independent factors associated with insulin resistance, multiple linear regression analysis was performed with HOMA-IR as the dependent variable (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). After adjusting for age, sex, and BMI, serum METRNL levels remained significantly associated with HOMA-IR, along with leptin and adiponectin levels.\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\u003eMultiple linear regression analysis for factors associated with HOMA-IR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETRNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeptin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\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\u003eR\u0026sup2; = 0.624, Adjusted R\u0026sup2; = 0.598 Model adjusted for age and sex.\u003c/p\u003e \u003cp\u003eThe final model explained 62.4% of the variance in HOMA-IR (adjusted R\u0026sup2; = 0.598). Among all the variables, METRNL showed the strongest independent association with HOMA-IR (standardized β\u0026thinsp;=\u0026thinsp;0.384, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), followed by BMI and leptin levels.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrated significantly elevated serum METRNL levels in obese children compared to normal-weight controls, along with its strong association with insulin resistance and inflammatory markers. These findings provide novel insights into the potential role of METRNL in pediatric obesity and its metabolic complications.\u003c/p\u003e \u003cp\u003eOur observation of increased METRNL levels in obese children aligns with findings from previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in adult populations with metabolic disorders. However, our study is among the first to demonstrate this association in the pediatric population. The elevation of METRNL in obese children might represent a compensatory mechanism to counter metabolic dysfunction, similar to the pattern observed with other adipokines such as leptin [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This compensatory increase could be an attempt to improve insulin sensitivity and reduce inflammation, as suggested by experimental studies showing METRNL's beneficial metabolic effects [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is important to acknowledge, however, that our findings contrast with several studies in the literature. Recent research [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] reported decreased METRNL levels in obese children, while other studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] found reduced METRNL concentrations in adults with obesity and type 2 diabetes. Similarly, previous research [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] showed a negative correlation between METRNL levels and markers of insulin resistance in obese adults. These contradictory findings might be attributed to several factors, including differences in study populations (age, ethnicity, degree of obesity), methodological variations in METRNL measurement, and potential confounding factors such as comorbidities or medications. Additionally, the complex regulatory mechanisms of METRNL expression and secretion might vary depending on the metabolic state and disease progression, potentially explaining these discrepancies. Further longitudinal studies are needed to clarify the dynamic changes in METRNL levels during the development and progression of obesity and insulin resistance.\u003c/p\u003e \u003cp\u003eThe significant positive correlation between METRNL levels and inflammatory markers (IL-6 and TNF-α) observed in our study suggests a complex interplay between METRNL and inflammatory pathways in obesity. Previous research has shown that chronic low-grade inflammation is a hallmark of obesity, contributing to insulin resistance and metabolic dysfunction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our findings raise the possibility that METRNL might be involved in the inflammatory response associated with obesity, although whether its elevation represents a protective mechanism or contributes to pathological processes requires further investigation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strong positive association between METRNL and HOMA-IR, even after adjusting for potential confounders, is particularly noteworthy. Recent experimental studies have demonstrated that METRNL can influence glucose metabolism and insulin sensitivity through multiple mechanisms, including enhancement of insulin signaling and modulation of inflammatory responses [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our findings extend these observations to the pediatric population and suggest that METRNL might serve as a novel biomarker for insulin resistance in obese children [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe inverse correlation between METRNL and adiponectin levels observed in our study is intriguing. Adiponectin is well-established as an insulin-sensitizing adipokine that is typically decreased in obesity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The opposing patterns of these two adipokines suggest that they might have complementary roles in metabolic regulation. This relationship might reflect a compensatory increase in METRNL in response to reduced adiponectin levels, although the precise mechanisms underlying this interaction remain to be elucidated [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur multiple regression analysis revealed that METRNL was independently associated with HOMA-IR, even after adjusting for BMI and other established metabolic markers. This finding suggests that METRNL might contribute to insulin resistance through pathways distinct from those of traditional adipokines [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The stronger association of METRNL with HOMA-IR compared to other inflammatory markers implies that it might be a more sensitive indicator of metabolic dysfunction in pediatric obesity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe positive correlation between METRNL and leptin levels observed in our study adds another layer to our understanding of adipokine interactions in obesity. Both proteins show similar patterns of elevation in obesity, suggesting possible common regulatory mechanisms or complementary functions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Recent research has suggested that leptin and METRNL might share some downstream signaling pathways, particularly those involved in energy metabolism and inflammation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe clinical implications of our findings are significant. The strong association between METRNL and various metabolic parameters suggests its potential utility as a biomarker for metabolic dysfunction in pediatric obesity. Furthermore, the independent association with insulin resistance raises the possibility that METRNL could be a therapeutic target for obesity-related metabolic complications. Early identification of children at risk for metabolic complications could enable more timely interventions and better outcomes.\u003c/p\u003e \u003cp\u003eHowever, several limitations of our study should be acknowledged. The cross-sectional design prevents us from establishing causal relationships between METRNL and metabolic parameters. Additionally, the single-center nature of our study and relatively small sample size may limit the generalizability of our findings. Longitudinal studies with larger cohorts are needed to confirm our results and investigate the temporal relationship between changes in METRNL levels and the development of metabolic complications. This study only conducted a cross-sectional survey at baseline, but did not compare the changes before and after weight loss, which has certain limitations.\u003c/p\u003e \u003cp\u003eFuture research should focus on elucidating the molecular mechanisms underlying the relationship between METRNL and insulin resistance in obesity. Additionally, investigation of potential genetic and environmental factors that influence METRNL expression and function could provide valuable insights into its role in metabolic regulation. Intervention studies examining the effect of weight loss on METRNL levels and its relationship with metabolic improvements would also be informative.\u003c/p\u003e \u003cp\u003eIn conclusion, our study demonstrates that serum METRNL levels are elevated in obese children and strongly associated with insulin resistance and inflammatory markers. However, we acknowledge the conflicting evidence in the literature regarding METRNL's role in obesity, with some studies showing decreased levels in obese subjects. These contradictory findings highlight the complex nature of METRNL regulation in metabolic disorders and underscore the need for further research to fully understand its pathophysiological significance in pediatric obesity and its potential as a therapeutic target.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University\u0026nbsp;with the registration number No.2022245. This study was conducted in accordance with the guidelines of the Research Committee of Xi\u0026rsquo;an Jiaotong University and according to the Declaration of Helsinki. Written informed consent was obtained from all guardians of children participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate Declarations:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agreed to publish this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 81903340) and IIT Clinical Research Found of the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University (No. IIT-007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuesheng Liu designed and supervised experiments; Lijun Hao and Lujie Liu performed all the experiments; Chunyan Yin and Hongmei Lin managed, analyzed, and ploted the data; Shuang Guo \u0026nbsp;and Lijun Hao organized and wrote the paper; Yuesheng Liu and Yanfeng Xiao critically revised the manuscript. All authors reviewed the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbarca-G\u0026oacute;mez L, Abdeen ZA, Hamid ZA, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627-2642.\u003c/li\u003e\n \u003cli\u003eWeihrauch-Bl\u0026uuml;her S, Schwarz P, Klusmann JH. Childhood obesity: increased risk for cardiometabolic disease and cancer in adulthood. Metabolism. 2019;92:147-152.\u003c/li\u003e\n \u003cli\u003eKawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol. 2021;320(3):C375-C391.\u003c/li\u003e\n \u003cli\u003eGoossens GH, Jocken JWE, Blaak EE. Sexual dimorphism in cardiometabolic health: the role of adipose tissue, muscle and liver. Nat Rev Endocrinol. 2021;17(1):47-66.\u003c/li\u003e\n \u003cli\u003eLi Z, Gao Z, Sun T, et al. Meteorin-like/Metrnl, a novel secreted protein implicated in inflammation, immunology, and metabolism: A comprehensive review of preclinical and clinical studies. Front Immunol. 2023;14:1098570.\u003c/li\u003e\n \u003cli\u003eRao RR, Long JZ, White JP, et al. Meteorin-like is a hormone that regulates immune-adipose interactions to increase beige fat thermogenesis. Cell. 2014;157(6):1279-1291.\u003c/li\u003e\n \u003cli\u003eLi L, Chen J, Sun H, et al. Orm2 Deficiency Aggravates High-Fat Diet-Induced Obesity through Gut Microbial Dysbiosis and Intestinal Inflammation. Mol Nutr Food Res. 2024;68(1):2300236.\u003c/li\u003e\n \u003cli\u003eMiao Z, Hu W, Li Z, et al. Involvement of the secreted protein Metrnl in human diseases. Acta Pharmacol Sin. 2020;41(12):1525-1530.\u003c/li\u003e\n \u003cli\u003eLiu BN, Liu XT, Liang ZH, et al. Gut microbiota in obesity. World J Gastroenterol. 2021;27(25):3837-3850.\u003c/li\u003e\n \u003cli\u003eBertoncini-Silva C, Zingg JM, Fassini PG, et al. Bioactive dietary components\u0026mdash;Anti-obesity effects related to energy metabolism and inflammation. BioFactors. 2023;49(2):297-321.\u003c/li\u003e\n \u003cli\u003eCzech MP. Mechanisms of insulin resistance related to white, beige, and brown adipocytes. Mol Metab. 2020;34:27-42.\u003c/li\u003e\n \u003cli\u003eDongre UJ. Adipokines in insulin resistance: current updates. Biosci Biotechnol Res Asia. 2021;18(2):357-366.\u003c/li\u003e\n \u003cli\u003ePhuong LDT, Tran Huy T, Huynh Quang T. The plasma levels of protein adiponectin (AdipoQ) and meteorin-like (Metrnl) in newly diagnosed type 2 diabetes mellitus. Diabetes Metab Syndr Obes. 2024;17:2903-2909.\u003c/li\u003e\n \u003cli\u003eCherian P, Al-Khairi I, Jamal M, et al. Association between factors involved in bone remodeling (Osteoactivin and OPG) with plasma levels of irisin and meteorin-like protein in people with T2D and obesity. Front Endocrinol. 2021;12:752892.\u003c/li\u003e\n \u003cli\u003eMart\u0026iacute;nez-S\u0026aacute;nchez N, Seoane-Collazo P, Contreras C, et al. Hypothalamic AMPK-ER stress-JNK1 axis mediates the central actions of thyroid hormones on energy balance. Cell Metab. 2017;26(1):212-229.\u003c/li\u003e\n \u003cli\u003eJung TW, Pyun DH, Kim TJ, et al. Meteorin-like protein (METRNL)/IL-41 improves LPS-induced inflammatory responses via AMPK or PPAR\u0026delta;\u0026ndash;mediated signaling pathways. Adv Med Sci. 2021;66(1):155-161.\u003c/li\u003e\n \u003cli\u003eGholamrezayi A, Mohamadinarab M, Rahbarinejad P, Fallah S, Barez SR, Setayesh L, Moradi N, Fadaei R, Chamani E, Tavakoli T. Characterization of the serum levels of Meteorin-like in patients with inflammatory bowel disease and its association with inflammatory cytokines. Lipids Health Dis. 2020 Oct 30;19(1):230.\u003c/li\u003e\n \u003cli\u003eCantero I, Elorz M, Abete I, et al. Ultrasound/elastography techniques, lipidomic and blood markers compared to magnetic resonance imaging in non-alcoholic fatty liver disease adults. Int J Med Sci. 2019;16(1):75-83.\u003c/li\u003e\n \u003cli\u003eLiu M, Gao X, Tian Y, et al. Serum Metrnl is Decreased in Metabolic Dysfunction-Associated Fatty Liver Disease: A Case-Control Study. Diabetes Metab Syndr Obes. 2024;17:533-543.\u003c/li\u003e\n \u003cli\u003eDong W, Hu C, Hu M, et al. Metrnl: a promising biomarker and therapeutic target for cardiovascular and metabolic diseases. Cell Commun Signal. 2024;22(1):389.\u003c/li\u003e\n \u003cli\u003eMetwaly A, Reitmeier S, Haller D. Microbiome risk profiles as biomarkers for inflammatory and metabolic disorders. Nat Rev Gastroenterol Hepatol. 2022;19(6):383-397.\u003c/li\u003e\n \u003cli\u003eWang K, Li F, Wang C, Deng Y, Cao Z, Cui Y, Xu K, Ln P, Sun Y. Serum Levels of Meteorin-Like (Metrnl) Are Increased in Patients with Newly Diagnosed Type 2 Diabetes Mellitus and Are Associated with Insulin Resistance. Med Sci Monit. 2019 Mar 31;25:2337-2343.\u0026nbsp;\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 Sep 13;13(9):e0204180.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFadaei R, Moradi N, Kazemi T, Chamani E, Azdaki N, Moezibady SA, Shahmohamadnejad S, Fallah S. Decreased serum levels of CTRP12/adipolin in patients with coronary artery disease in relation to inflammatory cytokines and insulin resistance. Cytokine. 2019 Jan;113:326-331.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAl-Daghri NM, Wani K, Yakout SM, Al-Hazmi H, Amer OE, Hussain SD, Sabico S, Ansari MGA, Al-Musharaf S, Alenad AM, Alokail MS, Clerici M. Favorable Changes in Fasting Glucose in a 6-month Self-Monitored Lifestyle Modification Programme Inversely Affects Spexin Levels in Females with Prediabetes. Sci Rep. 2019 Jul 1;9(1):9454.\u003c/li\u003e\n \u003cli\u003eFalch CM, Arlien-S\u0026oslash;borg MC, Dal J, et al. Gene expression profiling of subcutaneous adipose tissue reveals new biomarkers in acromegaly. Eur J Endocrinol. 2023;188(3):310-321.\u003c/li\u003e\n \u003cli\u003eJung TW, Lee SH, Kim HC, et al. METRNL attenuates lipid-induced inflammation and insulin resistance via AMPK or PPAR\u0026delta;-dependent pathways in skeletal muscle of mice. Exp Mol Med. 2018;50(9):1-11.\u003c/li\u003e\n \u003cli\u003eDu Y, Ye X, Lu A, et al. Inverse relationship between serum Metrnl levels and visceral fat obesity (VFO) in patients with type 2 diabetes. Diabetes Res Clin Pract. 2020;161:108068.\u003c/li\u003e\n \u003cli\u003ePeng J, Chen Q, Wu C. The role of adiponectin in cardiovascular disease. Cardiovasc Pathol. 2023;64:107514.\u003c/li\u003e\n \u003cli\u003eGhoshal K, Bhattacharyya M. Adiponectin: Probe of the molecular paradigm associating diabetes and obesity. World J Diabetes. 2015;6(1):151-166.\u003c/li\u003e\n \u003cli\u003eDing X, Chang X, Wang J, et al. Serum Metrnl levels are decreased in subjects with overweight or obesity and are independently associated with adverse lipid profile. Front Endocrinol. 2022;13:938341.\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\u003eZheng S, Li Z, Song J, et al. Metrnl: a secreted protein with new emerging functions. Acta Pharmacol Sin. 2016;37(5):571-579.\u003c/li\u003e\n \u003cli\u003eGhasemi A, Hashemy SI, Azimi-Nezhad M, et al. The cross-talk between adipokines and miRNAs in health and obesity-mediated diseases. Clin Chim Acta. 2019;499:41-53.\u003c/li\u003e\n \u003cli\u003eLee JH, Kang YE, Kim JM, et al. Serum Meteorin-like protein levels decreased in patients newly diagnosed with type 2 diabetes. Diabetes Res Clin Pract. 2021;171:108542.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"METRNL, pediatric obesity, insulin resistance, inflammation, adipokines","lastPublishedDoi":"10.21203/rs.3.rs-6601784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6601784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Meteorin-like protein (METRNL) has emerged as a novel adipokine involved in metabolic regulation. However, its role in pediatric obesity and its relationship with insulin resistance remain largely unexplored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: In this cross-sectional study, we enrolled 55 obese children and 49 age- and sex-matched normal-weight controls. Serum levels of METRNL, inflammatory markers (IL-6, TNF-α), and adipokines (leptin, adiponectin) were measured. The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated to evaluate insulin resistance. Correlations between METRNL and metabolic parameters were analyzed, and multiple regression analysis was performed to identify factors independently associated with insulin resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Serum METRNL levels were significantly higher in obese children compared to controls (4.62 ± 1.24 vs 2.84 ± 0.76 ng/mL, p \u0026lt; 0.05). METRNL levels showed positive correlations with BMI (r = 0.624, p \u0026lt; 0.05), HOMA-IR (r = 0.594, p \u0026lt; 0.05), inflammatory markers (IL-6: r = 0.528, p \u0026lt; 0.05; TNF-α: r = 0.486, p \u0026lt; 0.05), and leptin (r = 0.546, p \u0026lt; 0.05), while negatively correlating with adiponectin (r = -0.482, p \u0026lt; 0.05). Multiple regression analysis revealed that METRNL was independently associated with HOMA-IR (standardized β = 0.384, p \u0026lt; 0.05) after adjusting for potential confounders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Serum METRNL levels are elevated in obese children and independently associated with insulin resistance. These findings suggest that METRNL might play a significant role in the pathophysiology of pediatric obesity and its metabolic complications.\u003c/p\u003e","manuscriptTitle":"Elevated Serum METRNL Levels in Obese Children and Its Association with Insulin Resistanc","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 10:46:29","doi":"10.21203/rs.3.rs-6601784/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"940617f7-df65-470e-88e0-9bfd774d933d","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T05:53:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-19 10:46:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6601784","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6601784","identity":"rs-6601784","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}