Unmasking covert metabolic syndrome in normal-weight women: regional adiposity and muscle quality outperform BMI in a temporally validated machine learning triage framework.

AIMS: Traditional screening based on body mass index (BMI) fails to identify the metabolically obese normal-weight (MONW) phenotype. We aimed to develop and temporally validate an interpretable machine learning framework utilizing non-invasive surrogate markers of central adiposity (waist-to-height ratio, WHtR) and relative functional muscle capacity (relative handgrip strength, RHGS) for cross-sectional triage of prevalent metabolic syndrome (MetS) in women. METHODS: In 2,418 women (NHANES 2011-2014), we excluded pregnant women and applied independent K-nearest neighbors imputation (k = 5) without data leakage. Using XGBoost, we evaluated baseline M1 (Age + BMI), intermediate M2 (Age + BMI + RHGS), and proposed M3 (Age + WHtR + RHGS). Following TRIPOD Type 3 temporal validation, we trained models on Cycle 2011-2012 (N = 1,167) and tested them on Cycle 2013-2014 (N = 1,251). SHapley Additive exPlanations (SHAP) extracted exploratory, model-dependent decision reference points. RESULTS: In the unstratified cohort, M3 achieved comparable discrimination to M2 (AUC 0.782 vs. 0.769, DeLong p = 0.117). However, in normal-weight women (n = 371), M3 demonstrated marked superiority over M1 (AUC 0.855 [95% CI 0.800-0.910] vs. 0.757 [95% CI 0.678-0.835], p < 0.001) and M2 (AUC 0.801, p = 0.015), with near-perfect calibration (Brier score = 0.068), high rule-out reliability (NPV = 98.9%), and a negative likelihood ratio (LR-) of 0.12 (95% CI 0.06-0.22). SHAP dependence analysis illustrated non-additive muscle-fat interactions, where lower strength (< 0.891 kg/kg) amplified risk primarily when WHtR exceeded 0.529. CONCLUSIONS: Replacing BMI with indices of visceral adiposity and relative strength markedly improves MetS triage in normal-weight women. This temporally validated framework provides a non-invasive pre-laboratory triage tool to prioritize high-risk MONW individuals for comprehensive clinical evaluation.