Genetic predisposition of metabolic syndrome independent of weight in premature atherosclerosis

Abstract Background Family history is a significant predictor of premature atherosclerotic cardiovascular events, which suggest that a genetic susceptibility underlies the development of premature atherosclerosis. Rather than the influence of single classic risk factors, such as hypertension, diabetes and dyslipidemia, these risk factors could be part of an underlying genetic insulin resistance or metabolic syndrome Methods Data on patients and their first degree family members visiting premature atherosclerosis clinic in a university medical centre, from 2007 until 2023 was analyzed. We divided them in controls (CACs=0 and Age >40 years), subclinical ASCVD (sASCVD) (CAC score ≥80th percentile) and premature ASCVD (PAS). We used principal component analysis (PCA) for clustering the predictor variables and generalized linear mixed model (GLMM) to analyze the mutual relationship between different clusters. The model performance was further validated in predicting the premature ASCVD. Result There were 1551 individuals divided across control (n=438), sASCVD (n=309) and PAS (804). We found 12 variables univariate significantly related to sASCVD. Using a principal component analysis (PCA) approach, these variables were loaded across 4 clusters which were defined as 1. Metabolic (elevated glucose or diabetes, elevated blood pressure or Hypertension, elevated Triglycerides, elevated waist circumference and elevated LDL-C); 2. Liver (elevated ALAT, elevated GGT, elevated triglycerides, decreased thrombocytes) 3. Inflammatory (decreased HDL-C, elevated Triglycerides and elevated leucocyte count) and 4. Lipid (elevated Lp(a) and elevated LDL-C). After mutual correction and model building, the metabolic (OR; 95% CI) (1.82; 1.48-2.27) and Liver clusters (1.20; 1.00-1.45) were only found to be significantly associated with risk of sASCVD. However, we did not find any association of inflammatory (1.19; 0.98-1.44) and lipid cluster (1.19; 0.99-1.41) with risk of sASCVD. Besides, association of these clusters with risk of sASCVD significantly varies among families suggested from variance of intercept when family was kept as a random effect in GLMM model and no interaction was noticed with BMI. We checked the performance of this model on premature atherosclerosis cohort and found model had a good discriminatory (AUC, 0.762; 0.735-0.790; P=0.001) ability for predicting PAS [Figure 1]. Conclusion In conclusion, our findings are suggestive of a significant association between metabolic syndrome factors and an increased risk of sASCVD in individuals with family history of premature atherosclerosis, regardless of weight. This association suggests the potential genetic predisposition towards premature atherosclerosis driving the observed metabolic parameters clustering in these individuals. Besides, the model demonstrated robust discriminatory power to predict premature atherosclerosis.ROC curve for premature atherosclerosis