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Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
Published on: April 4, 2012
Causal adiposity and clinical validation of regional fat distribution in PMOS: a multi-modal analysis integrating GBD
Guohong Zhao1,2, Yuxin Jin1, Aili Yang1
1Department of Endocrinology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, PR China.
Background:
Polyendocrine metabolic ovarian syndrome (PMOS), previously named polycystic ovary syndrome (PCOS), is the most common endocrine disorder among women of reproductive age and a leading cause of anovulatory infertility. However, the evolving global burden of PMOS and the role of adiposity, particularly regional fat distribution, remain incompletely understood. We integrated global epidemiological analysis, genetic causal inference, and clinical prediction modeling to investigate the burden and adiposity-related determinants of PMOS.
Methods:
Using the Global Burden of Disease (GBD) 2021 database, we assessed age-specific and socio-demographic index (SDI)-specific patterns of PMOS burden. Two-sample Mendelian randomization (MR) was performed to examine genetic evidence linking overall and regional adiposity traits to PMOS and anovulation-associated infertility. Machine-learning (ML) models were subsequently developed and internally validated in a clinical cohort with detailed body-composition measurements, and SHapley Additive exPlanations (SHAP) were applied to interpret model predictions.
Results:
In 2021, PMOS affected approximately 69.5 million women globally, with an age-standardized prevalence rate of 1,757.8 per 100,000. The highest modeled incidence occurred in the 10-14-year age group, and approximately 12.5 million infertility cases were attributable to PMOS worldwide. MR analyses provided genetic evidence supporting adiposity as a risk factor for PMOS, including body mass index (OR = 2.60, 95% CI 1.84-3.66), body fat percentage (OR = 3.32, 95% CI 1.97-5.58), trunk fat mass (OR = 2.54, 95% CI 1.79-3.60), and left-leg fat mass (OR = 3.44, 95% CI 2.19-5.40), whereas fat-free mass showed no significant association. In ML analyses, XGBoost achieved the best predictive performance in the testing set (AUC = 0.701), and SHAP identified left-leg fat mass, trunk fat mass, percent body fat, and protein mass as the most influential predictors.
Conclusions:
Adiposity, particularly regional fat distribution, appears to be a clinically relevant determinant of PMOS. The convergence of global epidemiological patterns, genetic evidence, and clinical prediction supports the importance of body-composition phenotypes beyond conventional obesity measures in understanding PMOS risk.