Introduction
Type 2 diabetes mellitus (T2DM) has emerged as a critical global public health challenge, with its high prevalence and associated mortality imposing substantial burdens on healthcare systems and economies.1 T2DM represents a significant global public health crisis. Current data indicate that approximately 589 million adults aged 20 to 79 years are living with diabetes worldwide, corresponding to a prevalence rate of 11.1%, with over 90% of these cases attributed to T2DM. Notably, 252 million individuals remain undiagnosed. In 2024, diabetes directly caused 3.4 million deaths, accounting for 9.3% of global mortality. Projections suggest that by 2050, the number of cases will increase to 853 million, affecting 13% of the adult population.2 Abdominal obesity, characterized by visceral fat accumulation, is a well-established risk factor for T2DM, exacerbating disease progression through multiple mechanisms, including disruption of glucose and lipid metabolism, chronic low-grade inflammation, and aggravated insulin resistance.3
In China, over 60% of adult diabetic patients are classified as overweight or obese. Data from a national cross-sectional survey conducted between 2015 and 2017, using Chinese BMI classification standards,4,5 revealed that 42.4% of adults with type 2 diabetes mellitus (T2DM) were overweight (BMI 24.0–27.9 kg/m²), while 5.3% were classified as obese (BMI ≥ 28.0 kg/m²). Notably, although the overall obesity rates in the Chinese population are lower than those in Caucasian populations, there is a significant predisposition toward abdominal fat deposition, resulting in a higher prevalence of visceral adiposity. Among Chinese adults with T2DM, 39.7% meet the criteria for abdominal obesity, defined as a visceral fat area (VFA) of ≥ 100 cm².6,7 Strikingly, abdominal obesity affects 49.8% of overweight and 83.1% of obese T2DM patients, whereas it affects 11.5% of those with normal weight.8 Growing evidence suggests that fat distribution, rather than total fat mass, plays a more critical role in metabolic health. Individuals with similar BMI values may present divergent cardiometabolic profiles,9 as excessive visceral fat accumulation significantly increases the risk of cardiovascular disease (CVD) and T2DM.10,11
Meteorin Like Protein (Metrnl), a recently identified adipokine, is secreted by skeletal muscle and adipose tissue in response to exercise and cold exposure.12 It regulates glucose metabolism by enhancing glucose uptake and ameliorating insulin resistance.13 Spiegelman et al first demonstrated Metrnl’s ability to improve glucose tolerance and increase energy expenditure.14 Additionally, Metrnl induces the expression of anti-inflammatory genes, suggesting a protective metabolic role. Li et al further revealed that adipose-specific Metrnl knockout exacerbates insulin resistance in mice, whereas its overexpression confers protection.15 Clinical studies support these findings: Lee et al and Zheng et al observed significantly reduced serum Metrnl levels in newly diagnosed T2DM patients, which inversely correlated with hyperglycemia and insulin resistance. Similarly, Pellitero et al reported lower Metrnl levels in obese individuals compared to normal-weight controls,16 while Chung et al identified negative correlations between serum Metrnl and waist circumference (WC), body weight, and BMI.17,18 Notably, Du et al found that T2DM patients with visceral fat obesity (VFO) exhibit diminished serum Metrnl levels, further implicating its role in abdominal fat metabolism.20–22 Despite these advances, the relationship between serum Metrnl and diabetic abdominal obesity remains underexplored. Therefore, this study aims to investigate whether circulating Metrnl concentrations are associated with abdominal obesity in T2DM patients.23
Recent studies have highlighted the potential of novel therapeutic strategies, such as stimuli-responsive systems, biomimetic nanoparticles, and multifunctional co-delivery platforms, to overcome the challenges of treating metabolic diseases such as type 2 diabetes. These innovations, especially in nanoparticle-mediated immunomodulation and combination immunotherapy, have driven advances in the treatment of metabolic diseases and may provide new avenues for targeting adipokines like Metrnl.24 These methods are expected to further elucidate the role of Metrnl in metabolic health and its potential as a therapeutic target.25
Materials and Methods
Study Subjects
A total of 281 participants (195 men and 86 postmenopausal women) aged 45 to 78 years (mean age 61.4 ± 6.5 years) were evaluated. A cross-sectional study was conducted involving patients with type 2 diabetes who were admitted to The Second People’s Hospital of Changzhou, the Third Affiliated Hospital of Nanjing Medical University between January 2023 and May 2024. Diabetes was defined according to the criteria established by the American Diabetes Association, Diabetes mellitus cases were confirmed by oral glucose tolerance testing. The study focused exclusively on men and postmenopausal women. Patients with type 1 diabetes, active hepatitis or liver cirrhosis, chronic renal failure, congestive heart failure, anemia, malignant tumors, parathyroid dysfunction, abnormal thyroid function, current infectious conditions, severe disabilities, severe malnutrition, or mental disorders were excluded from the study.
Ethical Consideration
This study was conducted in accordance with the Declaration of Helsinki. The protocol for this research was reviewed and approved by the ethics committee of The Second People’s Hospital of Changzhou, the Third Affiliated Hospital of Nanjing Medical University, Approval No. [2023]KY013-01, and informed consent was obtained from all participants prior to the examinations.
Methodology
All subjects completed a questionnaire regarding their present and past illnesses, medical therapies, and other health-related behaviors. All participants underwent physical measurements using standardized methods. Height was measured to the nearest 0.1 cm using a height measuring instrument, while weight was recorded to the nearest 0.1 kg on a calibrated digital platform scale, with participants wearing no outer clothing, hats, shoes, or carrying any items in their pockets. Body Mass Index (BMI) was calculated by dividing weight in kilograms by the square of height in meters (kg/m²). WHtR was determined by dividing Waist Circumference by Height.
Laboratory Measurements
Fasting blood samples for Metrnl, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and creatinine (Cr) were collected in the morning after a 10-hour overnight fast. All blood samples were immediately separated by centrifugation at 1000g, aliquoted, and stored at −80°C. Serum Metrnl concentrations were measured using enzyme-linked immunosorbent assay (ELISA) commercial kits (R&D Systems, Minneapolis, MN, USA). Serum levels of TC, TG, HDL-C, LDL-C, and Cr were measured enzymatically using a Cobas 8000 modular analyzer series (Roche Diagnostics GmbH, Mannheim, Germany).
Body composition was assessed using Dual-energy X-ray Absorptiometry (DXA) with a whole-body scanner (Hologic Discovery Wi, Bedford, MA, USA). This device was equipped with Hologic APEX software (version 4.5.3) for precise analysis of various body composition metrics. The specific measurement parameters included Waist Circumference (WC), Visceral Fat Area (VFA), Android Fat Percentage (the percentage of fat in the abdominal region), Gynoid Fat Percentage (the percentage of fat in the hip region), and the Android-to-Gynoid Fat Ratio (A/G, which compares abdominal fat percentage to hip fat percentage). During the measurement process, participants lay flat on the scanning bed and maintained a standardized posture. The whole-body scanner conducted a low-dose X-ray scan of the entire body. DXA technology employs two different energies of X-rays to penetrate body tissues. By detecting the degree of X-ray attenuation, it can accurately differentiate between adipose tissue, lean body mass (including muscle and bone), and bone mineral content. The Hologic APEX software further analyzes the scanned data to generate specific parameters regarding fat distribution across various body regions.
Statistical Analysis
Data were analyzed using Free Statistics software (version 1.7; developed by Pavel Beránek, Czech Republic). Descriptive statistics were performed on all patient data. Normality of continuous variables was assessed using the Shapiro–Wilk test (or Kolmogorov–Smirnov test). Normally distributed data are presented as mean ± standard deviation (SD), and differences between two groups were compared using the independent t-test. Non-normally distributed data are presented as median (interquartile range), and differences between two groups were compared using non-parametric tests (Mann–Whitney U-test) Categorical data are presented as frequencies (percentages). To explore potential associations between various variables and body fat distribution parameters, univariate linear regression analyses were first performed. Subsequently, to control for potential confounding factors, multivariate linear regression analyses were employed to examine the independent association between serum Metrnl levels and body fat-related indicators. Additionally, a restricted cubic spline (RCS) method was utilized to visually assess potential linear or non-linear relationships between serum Metrnl levels and body fat-related indicators. A p-value < 0.05 was considered statistically significant.
Results
Clinical Characteristics of the Study Subjects
The general characteristics of the study groups are presented in Table 1. Gender-stratified analysis revealed distinct metabolic profiles between the sexes. Male patients exhibited a larger waist circumference (WC) and higher levels of serum Metrnl and creatinine. In contrast, female patients demonstrated significantly higher levels of serum TC, HDL-C, WHtR, A/G, android percent fat, and gynoid percent fat. There were no significant differences in age,BMI, duration of T2DM, TG, and LDL-C.
|
Table 1 Comparison of General Clinical Characteristics in Patients with T2DM
|
Relationships Between Clinical Parameters and Different Adiposity Indices
To further investigate the relationships between clinical parameters and various adiposity indices, we conducted univariate linear regression analyses (Table 2). The results indicated that sex, BMI, TG, HDL-C, LDL-C, CR, WC, WHtR, and Metrnl were significantly associated with body fat distribution. Notably, Metrnl exhibited negative correlations with the adiposity index A/G (β = −0.01; 95% CI: −0.02~0.00), android percent fat (β = −0.77; 95% CI: −1.11 to −0.42), and gynoid percent fat (β = −0.39; 95% CI: −0.66 to −0.12).
 |
Table 2 Relationships Between Clinical Parameters and Different Adiposity Indices
|
Relationship Between Different Adiposity Indices and the Serum Metrnl Levels
Multivariate linear regression analysis (Table 3) showed that serum Metrnl levels remained inversely associated with both A/G ratio (β = −0.01; 95% CI: −0.02 ~ 0.00) and android percent fat (β = −0.30; 95% CI: −0.56 ~ −0.05) even after adjusting for potential confounders related to Metrnl levels.
 |
Table 3 Relationship Between Different Adiposity Indices and the Serum Metrnl Levels
|
Subgroup Analysis by Sex on the Association Between Serum Metrnl Levels and A/G Linear Relationship Between Serum Metrnl Levels and A/G
To further clarify the influence of sex on the Metrnl-body fat distribution relationship (Table 4), we conducted subgroup analyses and found no significant interaction effects between groups.
 |
Table 4 Subgroup Analysis by Sex
|
We further conducted a multivariate linear regression analysis in male patients (Table 5), which showed that serum Metrnl levels remained inversely associated with both the A/G ratio (β = −0.01; 95% CI: −0.03 ~ 0.00) and android percent fat (β = −0.46; 95% CI: −0.81 ~ −0.10) even after adjusting for potential confounders related to Metrnl levels.
 |
Table 5 Serum Metrnl and Adiposity Indices in Male Patients
|
To explore the potential impact of gender-specific factors on the relationship between serum Metrnl levels and body fat distribution, we conducted a subgroup analysis focused on patients with polycystic ovary syndrome (PCOS). However, no significant differences in Metrnl levels were observed between overweight and obese individuals with PCOS. This finding aligns with previous studies which have suggested that the influence of Metrnl on fat distribution may be moderated by other metabolic or hormonal factors specific to female patients, such as the altered hormonal profile in PCOS. These results emphasize the complexity of the relationship between Metrnl and abdominal adiposity and indicate that gender-specific conditions, such as PCOS, may alter the associations observed in a general T2DM population.
Linear Relationship Between Serum Metrnl Levels and A/G
To further investigate the linear correlation between serum Metrnl levels and the A/G ratio, we conducted a restricted cubic spline (RCS) analysis. As illustrated in Figure 1, after adjusting for covariates such as age, sex, BMI, TG, HDL-C, LDL-C and CR, serum Metrnl levels exhibited a significant linear inverse relationship with adiposity indices. The restricted cubic spline analysis uses the cubic spline function to fit the nonlinear relationship between variables. The linear test result (p value) only represents the statistical significance of the linear component in the overall trend, rather than negating the potential nonlinear correlation. When p > 0.05, the linear relationship may be a reasonable simplification of the true correlation; however, even if p < 0.05, it is still recommended to combine the curve shape to explain.
 |
Figure 1 Linear associations between serum Metrnl and A/G (A) Overall population, the p-value for non-linearity was 0.647; (B) Male subgroup. Adjusted for: age, BMI, TG, LDL-C, HDL-C, and CR. The p-value for non-linearity was 0.221.
|
Discussion
This cross-sectional study, which included 281 participants, examined the correlation between serum Metrnl levels and body fat distribution, highlighting, for the first time, the potential influence of gender differences. The findings indicated that elevated serum Metrnl levels were significantly associated with a reduced A/G ratio (the ratio of android to gynoid fat) and a lower percentage of android fat. Conversely, lower serum Metrnl levels may suggest an increased risk of abdominal fat accumulation. Furthermore, restricted cubic spline (RCS) analysis revealed a linear relationship between serum Metrnl levels and the A/G ratio. In male T2DM patients, for every 1 unit decrease in serum Metrnl, the percentage of Android fat may increase by 0.85% (95% CI: 0.44% to 1.27%). It has been established that the A/G ratio can effectively predict insulin resistance and cardiovascular risk factors.26 The A/G ratio emphasizes the relative distribution of fat rather than its absolute quantity; a higher A/G ratio indicates a greater concentration of fat in the abdominal region, which is strongly correlated with increased health risks. Previous studies have demonstrated a negative correlation between Metrnl and insulin resistance.27,28 However, current literature regarding the relationship between obesity and serum Metrnl levels remains contentious. Specifically, a subgroup analysis of patients with polycystic ovary syndrome did not reveal significant differences in Metrnl levels among overweight and obese individuals.29 Conversely, Wang et al observed significantly elevated serum Metrnl concentrations in obese populations, a finding supported by studies conducted on Arab populations.30 These discrepancies may be attributed to ethnic differences, variations in age, gender, sample size, and the duration of diabetes. In the present study, we found that low serum Metrnl levels were closely associated with increased abdominal fat in T2DM patients, particularly among males, where the A/G ratio was independently correlated with serum Metrnl levels. To our knowledge, this is the first report demonstrating a direct link between serum Metrnl concentration and the A/G ratio. Another significant finding of this study is the independent negative correlation between serum Metrnl levels and the A/G ratio. Du et al confirmed that elevated serum Metrnl levels significantly decreased with visceral fat area (VFA). While our previous research established a significant association between visceral fat obesity (VFO) and Metrnl, the current study enhances this understanding by investigating the Android-to-Gynoid Fat Ratio (a/g ratio), a metric that reflects the relative predominance of metabolically active visceral fat. This suggests that the imbalance between visceral and subcutaneous fat depots, as indicated by the a/g ratio, may serve as a more critical and independent determinant of Metrnl dysregulation than the quantity of visceral fat alone. Additionally, the hormonal fluctuations characteristic of women, especially those related to the menstrual cycle and postmenopausal status, could modulate fat distribution and Metrnl levels. Estrogen, for example, is known to influence fat storage patterns, favoring the accumulation of subcutaneous fat in the lower body (gynoid fat). After menopause, however, the decrease in estrogen levels leads to a shift toward more visceral fat deposition, which could potentially alter the impact of Metrnl on fat distribution. Previous studies have shown that hormonal factors in women may mask or alter the associations between adipokines like Metrnl and body fat distribution.19 Therefore, this study goes beyond merely characterizing visceral fat accumulation, offering new insights into the significance of abdominal fat compartmental dynamics in the pathophysiology of Metrnl and proposing the a/g ratio as a more effective tool for risk stratification. Research has indicated a statistically significant association between higher body mass index (BMI) and serum Metrnl levels (SMD = −0.688, 95% CI: −1.348 to −0.028, P = 0.041). Pellitero et al found that patients with obesity exhibited lower serum Metrnl levels compared to normal-weight controls. Insulin resistance is a primary contributor to T2DM. In this study involving T2DM patients, we also found no correlation between serum Metrnl levels and body weight, BMI, or waist circumference (WC). Preclinical studies have shown that adipose tissue-specific knockout or transgenic overexpression of Metrnl in mice did not result in changes in body weight, fat mass, or adipose tissue distribution. The results of Chung et al are consistent with ours. However, some studies have reported a significant positive correlation between Metrnl levels and TC and BMI. Compared to normal-weight subjects, overweight and obese individuals exhibited increased serum Metrnl levels, which were significantly positively correlated with BMI and WC. Löffler et al also reported increased Metrnl expression in the adipose tissue of obese children compared to their lean counterparts. These inconsistencies may arise from the inherent limitations of BMI, which only reflects overall obesity and fails to accurately assess the distribution characteristics and metabolic state of adipose tissue. The predicted relationship exists independently of BMI and waist circumference, suggesting that Metrnl may reflect fat distribution characteristics that traditional indicators cannot adequately capture.
This study performed a stratified analysis by gender, revealing stronger correlations in male patients for the A/G ratio (β = −0.01, 95% CI: −0.03 to 0.00) and Android Percent Fat (β = −0.85, 95% CI: −1.27 to −0.44), while no significant correlation was observed in Gynoid Percent Fat. In contrast, female patients exhibited no statistically significant correlations for these variables. This finding diverges from previous research, which has not extensively explored gender-specific analyses. We posit that gender differences in fat distribution may significantly contribute to this phenomenon. Typically, female patients present with a higher Gynoid Percent Fat, whereas male patients tend to exhibit a greater Android Percent Fat. The distribution pattern (eg, higher hip fat percentage) is generally associated with lower health risks. Conversely, the accumulation of abdominal fat in male patients is closely linked to metabolic risks, including insulin resistance and cardiovascular disease. However, in female patients, the correlation between Metrnl and the A/G ratio may be obscured or diminished, potentially due to variations in fat distribution patterns and hormonal levels.
This study presents several limitations. Firstly, the cross-sectional design, while elucidating correlations between serum Metrnl levels and parameters of body fat distribution, does not allow for the establishment of causality. Secondly, the study exclusively involved patients with T2DM and did not include a healthy control group, thereby precluding comparisons between diabetic and non-diabetic populations. Furthermore, the relatively small sample size may have masked potential gender differences in the effects of Metrnl. The limitations associated with the sample size also restricted the capacity to perform comprehensive subgroup analyses, such as stratified evaluations based on age, duration of diabetes, or treatment regimen. Notably, the limited number of female participants may further constrain the generalizability of the findings. Future research should consider employing a longitudinal design, increasing both the sample size and age range, and exploring the effects of variations in Metrnl levels on body fat distribution and metabolic health.
Conclusion
This observational study investigated the association between circulating Metrnl levels and body fat distribution in individuals with T2DM. Our findings indicate that lower serum Metrnl levels are significantly correlated with increased abdominal adiposity, particularly visceral fat, and a higher android-to-gynoid fat ratio (A/G ratio). Notably, these associations were more pronounced in male patients, highlighting potential sex differences in fat distribution and metabolic risk. The inverse relationship between serum Metrnl and abdominal fat accumulation, especially the A/G ratio, suggests that Metrnl may play a role in regulating abdominal obesity and related metabolic dysfunction in T2DM patients.
However, it is important to acknowledge the observational nature of this study, which limits our ability to infer causality. While these results provide compelling evidence of an association between Metrnl levels and fat distribution, further mechanistic studies are necessary to elucidate the underlying biological pathways through which Metrnl influences adiposity and metabolic health. Longitudinal studies involving larger, more diverse cohorts are warranted to explore the potential clinical applications of Metrnl as a biomarker for metabolic risk stratification and to better understand its role in the pathophysiology of T2DM and its complications.
Acknowledgments
We would like to express our sincere gratitude to Dr. Du for their invaluable guidance in the study design and execution. We also thank Dr. Zhao and Dr. Liu for their contributions to data collection and analysis. Special thanks are extended to the participants of this study for their cooperation. This research was supported by The Second People’s Hospital of Changzhou, the Third Affiliated Hospital of Nanjing Medical University.
Disclosure
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References
1. Bhupathiraju SN. Hu FB.Epidemiology of obesity and diabetes and their cardiovascular complications. Circ Res. 2016;118(11):1723–1735.
2. Ogbonna SU, Ezeani IU. Risk factors of thyroid dysfunction in patients with type 2 diabetes mellitus. Front Endocrinol. 2019;10:440. doi:10.3389/fendo.2019.00440
3. Thong EP, Lim SS. Obesity, diabetes and reproductive health. Semin Reprod Med. 2020;38(6):331–332. doi:10.1055/s-0041-1727221
4. Piché ME, Poirier P, Lemieux I, Després JP. Overview of epidemiology and contribution of obesity and body fat distribution to cardiovascular disease: an update. Prog Cardiovasc Dis. 2018;61(2):103–113. doi:10.1016/j.pcad.2018.06.004
5. Zhang C, Rexrode KM, van Dam RM, Li TY, Hu FB. Hu FB.Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women. Circulation. 2008;117(13):1658–1667. doi:10.1161/CIRCULATIONAHA.107.739714
6. Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444(7121):881–887. doi:10.1038/nature05488
7. Mayes JS, Watson GH. Direct effects of sex steroid hormones on adipose tissues and obesity. Obes Rev. 2004;5(4):197–216. doi:10.1111/j.1467-789X.2004.00152.x
8. Vasan SK, Osmond C, Canoy D, et al. Comparison of regional fat measurements by dual-energy X-ray absorptiometry and conventional anthropometry and their association with markers of diabetes and cardiovascular disease risk. Int J Obes. 2018;42(4):850–857. doi:10.1038/ijo.2017.289
9. Kuk JL, Katzmarzyk PT, Nichaman MZ, Church TS, Blair SN, Ross R. Visceral fat is an independent predictor of all-cause mortality in men. Obesity. 2006;14(2):336–341. doi:10.1038/oby.2006.43
10. Okosun IS, Seale JP, Lyn R. Commingling effect of gynoid and android fat patterns on cardiometabolic dysregulation in normal weight American adults. Nutr Diabetes. 2015;5(5):e155. doi:10.1038/nutd.2015.5
11. Neeland IJ, Poirier P, Després JP. Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management. Circulation. 2018;137(13):1391–1406. doi:10.1161/CIRCULATIONAHA.117.029617
12. Ayebi SM, Motaghinasab S, Eslami R, et al. Impact of 8-week cold- and warm water swimming training combined with cinnamon consumption on serum METRNL, HDAC5, and insulin resistance levels in diabetic male rats. Heliyon. 2024;10(8):e29742. doi:10.1016/j.heliyon.2024.e29742
13. Cheng JX, Yu K. New discovered adipokines associated with the pathogenesis of obesity and type 2 diabetes. Diabetes Metab Syndr Obes. 2022;15:2381–2389. doi:10.2147/DMSO.S376163
14. 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. doi:10.1016/j.cell.2014.03.065
15. Lu QB, Ding Y, Liu Y, et al. Metrnl ameliorates diabetic cardiomyopathy via inactivation of cGAS/STING signaling dependent on LKB1/AMPK/ULK1-mediated autophagy. J Adv Res. 2023;51:161–179. doi:10.1016/j.jare.2022.10.014
16. Li ZY, Song J, Zheng SL, et al. Adipocyte metrnl antagonizes insulin resistance through PPARγ signaling. Diabetes. 2015;64(12):4011–4022. doi:10.2337/db15-0274
17. Lee JH, Kang YE, Kim JM, et al. Serum meteorin-like protein levels decreased in patients newly diagnosed with type 2 diabetes. Diabet Res Clin Pract. 2018;135:7–10. doi:10.1016/j.diabres.2017.10.005
18. Zheng SL, Li ZY, Zhang Z, Wang DS, Xu J, Miao CY. Evaluation of two commercial enzyme-linked immunosorbent assay kits for the detection of human circulating metrnl. Chem Pharm Bull. 2018;66(4):391–398. doi:10.1248/cpb.c17-00846
19. Sites CK, Toth MJ, Cushman M, et al. Menopause-related differences in inflammation markers and their relationship to body fat distribution and insulin-stimulated glucose disposal. Fertil Steril. 2002;77(1):128–135. doi:10.1016/S0015-0282(01)02934-X
20. Pellitero S, Piquer-Garcia I, Ferrer-Curriu G, et al. Opposite changes in meteorin-like and oncostatin m levels are associated with metabolic improvements after bariatric surgery. Int J Obes. 2018;42(4):919–922. doi:10.1038/ijo.2017.268
21. Huang D, Liu X, Gao X, et al. Meteorin-like protein/METRNL/interleukin-41 ameliorates atopic dermatitis-like inflammation. Allergy. 2025;80(2):474–488. doi:10.1111/all.16150
22. Du Y, Ye X, Lu A. Lu A, et al.Inverse relationship between serum Metrnl levels and visceral fat obesity (VFO) in patients with type 2 diabetes. Diabet Res Clin Pract. 2020;161:108068. doi:10.1016/j.diabres.2020.108068
23. Alizadeh H. Dual role of metrnl: exercise-induced benefits and potential cancer implications. J Immunother Cancer. 2025;13(5):e011666. doi:10.1136/jitc-2025-011666
24. Dogiparthi LK, Bukke SPN, Thalluri C, et al. The role of genomics and proteomics in drug discovery and its application in pharmacy. Discov Appl Sci. 2025;7(6):552. doi:10.1007/s42452-025-07155-2
25. Chung HS, Hwang SY, Choi JH, et al. Implications of circulating meteorin-like (metrnl) level in human subjects with type 2 diabetes. Diabet Res Clin Pract. 2018;136:100–107. doi:10.1016/j.diabres.2017.11.031
26. Zhao D, Bai H, Bo Y. Association between composite indices of femoral neck strength and odds of hip fracture. Arch Osteoporos. 2024;19(1):76. doi:10.1007/s11657-024-01436-w
27. Samsell L, Regier M, Walton C, Cottrell L. Importance of android/gynoid fat ratio in predicting metabolic and cardiovascular disease risk in normal weight as well as overweight and obese children. J Obes. 2014;2014:846578. doi:10.1155/2014/846578
28. Fouani FZ, Fadaei R, Moradi N, et al. Circulating levels of meteorin-like protein in polycystic ovary syndrome: a case-control study. PLoS One. 2020;15(4):e0231943. doi:10.1371/journal.pone.0231943
29. Wang K, Li F, Wang C, et al. Serum levels of meteorin-like (metrnl) are increased in patients with newly diagnosed type 2 diabetes mellitus and are associated with insulin resistance. Med Sci Monit. 2019;25:2337–2343. doi:10.12659/MSM.915331
30. AlKhairi I, Cherian P, Abu-Farha M, et al. Increased expression of meteorin-like hormone in type 2 diabetes and obesity and its association with Irisin. Cells. 2019;8(10):1283. doi:10.3390/cells8101283