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

Insulin resistance coexist with many metabolic disorders, whether these disorders were promotors or pathway-factors for the association of insulin resistance and cardiovascular disease (CVD) remained unclear. We aimed to investigate the pathways related to elevated the triglyceride-glucose (TyG) index and pathways through elevated TyG index to the occurrence of CVD in Chinese adults. A total of 96,506 participants were enrolled from the Kailuan study. Bayesian network model with the max-min hill climbing algorithm and maximum likelihood estimation was applied to identify factors and pathways related to the TyG index, and quantitatively infer the impact of associated factors on elevated TyG index and the occurrence of CVD by computing conditional probabilities. A final Bayesian network was constructed with 14 nodes and 25 arcs, creating 28 pathways related to elevated TyG index and 8 pathways from elevated TyG index to CVD. Elevated TyG index was causally associated with CVD, the condition probability was 11.9%. Pathways to elevated TyG index were mainly through unhealthy lifestyles and the subsequent increase in lipid profiles, especially smoking and low-density lipoprotein cholesterol. The most important pathway from elevated TyG index to CVD was through overweight/obesity, hypertension, and chronic kidney disease, with a condition probability of 18.5%. The maximum relative change rate related to elevated TyG index was observed for overweight/obesity (64.3%). Elevated TyG index was causally associated with the risk of CVD, a combined control of lifestyles and metabolic factors may contribute to the reduction of TyG index and the prevention of CVD.