Evaluating the use of novel atherogenicity indices and insulin resistance surrogate markers in predicting the risk of coronary artery disease: a case-control investigation with comparison to traditional biomarkers

Background Due to the contribution of coronary artery disease (CAD) to serious cardiovascular events, determining biomarkers that could robustly predict its risk would be of utmost importance. Thus, this research was designed to assess the value of traditional cardio-metabolic indices, and more novel atherogenicity indices and insulin resistance surrogate markers in the identification of individuals at risk of CAD. Methods A case-control survey was conducted, in which 3085 individuals were enrolled. Their clinical and biochemical data were gathered at baseline. The investigated indices included the atherogenic index of plasma (AIP), triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), lipoprotein combine index (LCI), cholesterol index (CHOLINDEX), Castelli's risk indices-I, II (CRI-I, CRI-II), and metabolic score for insulin resistance (METS - IR). To examine the relationship between these variables and CAD risk, multiple regression analyses adjusted for potential confounders were conducted. Results Overall, 774 angiographically confirmed CAD patients (mean age = 54 years) were compared with 3085 controls (mean age = 51 years). Higher triglyceride, total cholesterol and fasting blood sugar levels and lower HDL-C levels were related to an elevated risk of CAD (P-for-trend < 0.001), while the direct association between increased serum LDL-C concentrations and a greater risk of CAD only became apparent when excluding those with diabetes, and statin users. Among novel indices, greater values of the majority of these markers, including AIP, CRI-I, and -II, CHOLINDEX, LCI, and TyG-index, in comparison to the lower values, significantly elevated CAD risk (P-for-trend < 0.001). Conclusion According to the current findings, novel atherogenicity indices and insulin resistance surrogate markers, in particular, AIP, CRI-I and II, CHOLINDEX, LCI, and TyG-index, may be useful in predicting CAD risk.