BaHLS Cohort: Obesity and Lipid Indices for Type 2 Diabetes Prediction in Elderly Chinese

18,251 participants from the BaHLS cohort in Shenzhen, China, were tracked to evaluate six novel obesity- and lipid-related indices as predictors for Type 2 Diabetes onset. The study, authored by Xiang-yang He and last updated in March 2026, found that indices like TyG and TyHGB showed superior predictive performance (AUC up to 0.737). Results are based on longitudinal analysis using Cox models and ROC curve analysis.

Use Cases

• Compare the predictive power of novel indices (VAI, LAP, TyG, TyHGB) for Type 2 Diabetes based on the study's AUC results.
• Analyze longitudinal associations between obesity/lipid markers and disease onset using the described multivariate Cox proportional hazards model.
• Determine optimal cutoff values for clinical risk stratification based on the ROC curve analysis detailed in the results.
• Investigate nonlinear relationships between metabolic indices and diabetes risk as suggested by the restricted cubic spline (RCS) analysis.

Strengths

• Includes data from a substantial cohort of 18,251 elderly participants.
• Provides hazard ratios and AUC values with confidence intervals for six distinct clinical indices.
• Longitudinal study design tracks Type 2 Diabetes incidence, with 1,350 cases diagnosed by end of 2022.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying data tables is unknown, which may limit suitability assessment.
• Data is specific to a community-dwelling elderly population in Shenzhen, China, which may limit generalizability.

Provenance

Source

BaHLS (BaHLS cohort study), Shenzhen, China.

Collection Method

Prospective cohort study of community-dwelling elderly individuals.

Time Range

Study period up to 2022.

Freshness

Last updated 2026-03-26 05:15:51; freshness should be verified.

Geography

Shenzhen, China.