Non-traditional metabolic indices predict incident circadian syndrome in middle-aged and older Chinese adults: a nationwide prospective cohort study and machine learning analysis - PubMed
3 hours ago
- #Metabolic Indices
- #Circadian Syndrome
- #Machine Learning
- Eight non-traditional metabolic indices were analyzed to predict the onset of Circadian Syndrome (CircS) in middle-aged and older Chinese adults.
- CircS adds disturbed sleep and depressive symptoms to traditional metabolic syndrome components.
- The study used data from the China Health and Retirement Longitudinal Study (CHARLS) with 4,325 participants over four years.
- Triglyceride-glucose body mass index (TyG-BMI) showed the strongest positive association with CircS risk.
- Estimated glucose disposal rate (eGDR) demonstrated the most significant inverse relationship with CircS.
- Cholesterol-HDL-C-glucose (CHG) index provided the best discrimination gain for predicting CircS.
- Machine learning analysis identified eGDR as the top predictor, with logistic regression achieving the highest predictive accuracy.
- Incorporating these indices into routine assessments could help identify CircS risk earlier in aging populations.