@article{QI2026, 
author = {Yanmeng QI and Xueyan HAN and Yecheng LIU},
title = {Association of Fat-to-Muscle Ratio with New-Onset Diabetes Mellitus: A Cross-Sectional Study},
year = {2026},
journal = {Medical Journal of Peking Union Medical College Hospital},
volume = {17},
number = {4},
pages = {1073-1080},
keywords = {fat to muscle ratio, new-onset diabetes, disease prediction, body mass index},
url = {https://www.sciopen.com/article/10.12290/xhyxzz.2026-0024},
doi = {10.12290/xhyxzz.2026-0024},
abstract = {ObjectiveTo analyze the association between fat-to-muscle ratio (FMR) and new-onset diabetes, and to compare its predictive value with that of traditional anthropometric indicators.MethodsThis cross-sectional study enrolled healthy adults who underwent health checkups at the Health Management Center of the International Medical Department (Xidan Campus), Peking Union Medical College Hospital, between January 2020 and June 2024. Clinical data were collected, including general information[body weight, body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), etc.], laboratory tests[fasting blood glucose (FBG), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), etc.], and body composition parameters[percent body fat (PBF), body fat mass (BFM), visceral fat area (VFA), fat-free mass (FFM), skeletal muscle mass (SMM), skeletal muscle index (SMI), basal metabolic rate (BMR), FMR, etc.]. Participants were divided into a new-onset diabetes group and a non-new-onset diabetes group (including individuals with normoglycemia and those with prediabetes). Multivariate Logistic regression was used to analyze the association between FMR and new-onset diabetes. FMR was categorized into quartiles (Q1-Q4, with Q1 as reference), and odds ratios (OR) with 95% confidence intervals (CI) were calculated for each group. The Cochran-Armitage trend test was used to assess linear trends. Generalized additive models (GAM) were applied for curve fitting to further explore potential nonlinear relationships. The predictive value of FMR for new-onset diabetes was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsA total of 2877 participants were enrolled, including 1601 males and 1276 females, with a mean age of (52.0±11.1) years. There were 160 cases in the new-onset diabetes group and 2717 in the non-new-onset diabetes group. Compared with the non-new-onset diabetes group, the new-onset diabetes group had a higher proportion of males and older age (both P < 0.05); significantly higher body weight, BMI, WHR, WHtR, and body composition parameters (BFM, SMM, FFM, PBF, SMI, VFA, BMR, FMR) (all P < 0.05); and significantly elevated FBG, HbA1c, TG, LDL-C, and lower HDL-C (all P < 0.05). Multivariate Logistic regression showed that after adjusting for age, sex, smoking and drinking history, history of hypertension, BMI, and blood lipids, FMR remained significantly and positively associated with new-onset diabetes (OR=3.98, 95% CI: 1.69-9.34, P < 0.05). After quartile categorization, a non-linear positive association was observed between FMR and new-onset diabetes (P for trend=0.019). ROC curve analysis showed that the AUCs of BMI, WHtR, WHR, and FMR for predicting new-onset diabetes were 0.769(95% CI: 0.737-0.802), 0.760(95% CI: 0.726-0.794), 0.726(95% CI: 0.689-0.762), and 0.610(95% CI: 0.570-0.651), respectively. DeLong test with Bonferroni correction revealed that the AUC of FMR was significantly lower than those of BMI, WHtR, and WHR (all corrected P < 0.001).ConclusionsFMR is significantly associated with new-onset diabetes, but its predictive performance is inferior to that of BMI, WHtR, and WHR, suggesting limited value as an independent screening tool. FMR may serve as an adjunctive indicator for body composition assessment.}
}