TyG-GLM6: A Composite Index for Predicting Cardiovascular Disease in Chinese Adults

TyG-GLM6, a novel index integrating six metabolic parameters, demonstrated superior predictive value for cardiovascular disease in a nationwide Chinese cohort. The dataset likely contains clinical and biochemical measurements for 3,684 participants aged 45 and older with abnormal glucose metabolism, tracked from 2011 to 2020. Dan-dan Peng authored this research, which was externally validated on 2,105 participants from a tertiary hospital.

Use Cases

• Training machine learning models for CVD risk stratification based on integrated metabolic parameters.
• Benchmarking new predictive indices against nine conventional insulin resistance surrogates.
• Analyzing linear dose-response relationships between composite metabolic scores and CVD incidence.
• Validating clinical prediction tools across sex and age subgroups within a specific demographic.

Strengths

• Includes data from a nationwide prospective cohort study with 3,684 participants.
• Features external validation on an independent cohort of 2,105 participants.
• Compares the novel TyG-GLM6 index against nine conventional surrogate indexes.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small in size (196.1 KB), indicating limited scope.

Provenance

Source

China Health and Retirement Longitudinal Study (CHARLS).

Collection Method

Prospective cohort study analyzing participants with abnormal glucose metabolism.

Time Range

2011–2020

Freshness

Last updated 2026-05-14 04:22:00; freshness should be verified.

Geography

China