Non-traditional metabolic indices predict incident circadian syndrome in middle-aged and older Chinese adults: a nationwide prospective cohort study and machine learning analysis

Background Circadian syndrome (CircS) augments the conventional metabolic syndrome construct by adding disturbed sleep and depressive features. Whether composite metabolic indices that combine insulin-resistance, atherogenic-lipid, and inflammatory signals can forecast CircS prior to its onset has not been systematically investigated. The present work evaluated eight such composite markers in a population-based sample of Chinese adults aged 45 years or older. Methods Drawing on the China Health and Retirement Longitudinal Study (CHARLS), we followed 4,325 CircS-free adults from 2011 through 2015. Eight baseline metabolic composites were analysed through robust-variance modified Poisson regression, four-knot restricted cubic splines, incremental receiver operating characteristic (ROC) metrics, bidirectional mediation under a quasi-Bayesian framework, multiple sensitivity checks, and a head-to-head benchmarking of 10 machine-learning algorithms complemented by SHapley Additive exPlanations (SHAP) interpretation. Results Over 4 years, 1,025 incident CircS cases (23.7%) accrued. Every index remained independently linked to CircS once multivariable adjustment was applied. The steepest positive gradient belonged to the triglyceride-glucose body mass index (TyG-BMI; extreme-quartile risk ratio [RR] 4.56, 95% CI 3.35—6.21; per-standard-deviation RR 1.87, 95% CI 1.66—2.10), whilst the estimated glucose disposal rate (eGDR) demonstrated the most pronounced inverse gradient (RR 0.28, 95% CI 0.20—0.38). The largest discrimination gain belonged to the cholesterol–HDL-C–glucose (CHG) index (area under the curve [AUC] 0.737; continuous net reclassification improvement 0.379; DeLong P < 0.001). Reverse-path mediation indicated that the CHG index and the metabolic score for insulin resistance (METS-IR) jointly carried part of the high-sensitivity C-reactive protein (hs-CRP)–CircS signal. On the held-out test set, logistic regression reached the top area under the curve (0.746), and the XGBoost SHAP ranking placed eGDR first among predictors. Conclusions Eight non-traditional metabolic composites anticipated incident CircS, and within this panel eGDR, TyG-BMI, and the CHG index carried the most consistent predictive information. Incorporating such readily obtainable indices into routine assessment could facilitate earlier CircS risk identification in ageing populations.