Pathways from insulin resistance to incident cardiovascular disease: a Bayesian network analysis

Area of Science:

• Metabolic Health
• Cardiovascular Disease Epidemiology
• Biostatistics

Background:

• Insulin resistance is linked to metabolic disorders and cardiovascular disease (CVD), but the exact pathways remain unclear.
• The triglyceride-glucose (TyG) index is a marker of insulin resistance.
• Investigating the TyG index's role in CVD pathways is crucial for understanding metabolic health.

Purpose of the Study:

• To investigate pathways associated with an elevated TyG index.
• To examine pathways through which an elevated TyG index contributes to CVD occurrence.
• Focus on Chinese adult population.

Main Methods:

• Utilized data from 96,506 participants in the Kailuan study.
• Applied Bayesian network models with max-min hill climbing and maximum likelihood estimation.
• Quantitatively inferred causal impacts using conditional probabilities.

Main Results:

• A Bayesian network identified 28 pathways related to elevated TyG index and 8 pathways from TyG index to CVD.
• Elevated TyG index showed a causal association with CVD (11.9% probability).
• Key pathways to elevated TyG index involved unhealthy lifestyles (smoking) and lipid profiles (LDL-C); key pathways to CVD involved overweight/obesity, hypertension, and CKD (18.5% probability).

Conclusions:

• Elevated TyG index is causally associated with increased CVD risk.
• Combined control of lifestyle and metabolic factors can reduce TyG index and prevent CVD.
• Findings highlight the importance of managing metabolic health for cardiovascular protection.