Exploring the interaction between klotho and TyG index in cardiovascular risk stratification: A metabolic-inflammatory network analysis with mediation and machine learning insights.

Background: This study explores the relationship of metabolic-inflammatory network and cardiovascular disease (CVD), offering new insights into the roles of Klotho and the Triglyceride-Glucose (TyG) index in CVD pathogenesis. Methods: Data from 5402 adults (mean age: 58.04 ± 10.83 years; 50.96 % female) from the NHANES in 2007-2016 database were analyzed. We proposed a prediction model for CVD risk incorporating Klotho protein, TyG index, and their interaction. The predictive value of these factors was evaluated using machine learning techniques, including random forest analysis and CHAID decision tree modeling. Results: The study found no association between serum alpha-Klotho levels and CVD risk. However, the TyG index was demonstrated to be a significant predictor of CVD risk, particularly when lifestyle and socio-economic factors were not accounted for. TyG values were associated with an increased risk of metabolic syndrome and CVD (Model 1 Conclusions: This study underscores the TyG index as a key biomarker for CVD risk, with the Klotho-TyG interaction improving risk stratification, and supporting early screening, treatment, and personalized interventions for more effective CVD management.