Ann Med. 2026 Dec;58(1):2682583. doi: 10.1080/07853890.2026.2682583. Epub 2026 Jun 6.
ABSTRACT
BACKGROUND AND AIMS: Metabolic syndrome (MetS) is highly prevalent among perimenopausal and postmenopausal women and poses a major public health challenge because of its association with cardiovascular disease, type 2 diabetes, and premature mortality. However, prediction tools for this population remain limited. Therefore, this study aimed to develop a Body Roundness Index (BRI)-based prediction model for MetS by integrating cross-sectional machine learning and longitudinal assessment.
METHODS AND RESULTS: Cross-sectional models were trained using NHANES 2007-2020 and validated in the Affiliated Hospital of Dalian University (2023-2024). Sixteen predictors were selected via LASSO and Boruta, and eight models were evaluated using AUC, calibration, and decision curve analysis. SHAP ranked high-contribution factors. Longitudinal analysis used a 10-year cohort. Annualized change rates and cumulative exposure metrics of five key predictors were combined with baseline values to build Cox models, compared by C-index and time-dependent ROC.The artificial neural network (ANN) demonstrated optimal cross-sectional performance (internal AUC: 0.854; external AUC: 0.878) with good calibration and clinical benefit. SHAP identified BRI, WBC, ALT, MCV, and AST as top contributors, with BRI showing the strongest impact. Longitudinal analysis revealed that integrating annual change rates and annual cumulative exposure of these five predictors achieved optimal discriminative ability (C-index: 0.847), with time-dependent AUCs of 0.853, 0.859, and 0.847 at 1, 3, and 5 years, respectively.
CONCLUSION: BRI is significantly associated with MetS in perimenopausal and postmenopausal women. The ANN model provides an efficient cross-sectional screening tool, while incorporating longitudinal trajectories of BRI and key laboratory indicators enhances long-term MetS risk prediction.
PMID:42250232 | DOI:10.1080/07853890.2026.2682583