The association between triglyceride glucose-frailty index and cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national prospective cohort study
Combining blood sugar and muscle-weakness measures predicts heart and metabolic disease risk better than either alone, helping identify vulnerable aging adults.
In 2961 CHARLS participants followed for 9 years, the composite TyG-FI index showed non-linear, threshold-defined associations with cardiometabolic multimorbidity and improved risk stratification over TyG or frailty index alone. Machine learning models incorporating TyG-FI reached AUC ≈0.81, supporting cost-effective CMM prediction in aging populations.
What the study was
- Study design
- Prospective national cohort study with machine learning models (CHARLS, 2011–2020)
- Population
- Chinese adults aged ≥45 years, CHARLS longitudinal study
- Sample size
- 2961
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- Cardiovascular Diabetology
Why it surfaced
Prospective CHARLS cohort with ML models demonstrating TyG-FI superior CMM prediction; directly relevant to cardiometabolic risk stratification in aging populations; limited by single-ethnicity (Chinese) design.
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