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

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Abstract 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.

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