Sex, age, and ethnic dependency of lipoprotein variants as the risk factors of ischemic heart disease: a detailed study on the different age-classes and genders in Tehran Cardiometabolic Genetic Study (TCGS)

Abstract

Biological processes involving environmental and genetic factors drive the interplay between age- and sex-regulating lipid profile. The relation between variations in the LPA gene with increasing the risk of coronary heart disease is dependent on population differences, sex, and age. The present study tried to do a gene candidate association analysis in people with myocardial infarction (MI) in a 22 year cohort family-based longitudinal cohort study, Tehran Cardiometabolic Genetic Study (TCGS). After adjusting p value by the FDR method, only the association of rs6415084 with the MI probability and the age-of-CHD-onset was significant in males in their middle age (p < 0.005). Surprisingly, a lack of association was observed for the rest of the markers (16 SNPs). These results revealed the moderator effects of age and sex on the association between the genetic variants (SNPs) of LPA and heart disease risk. Our observations may provide new insights into the biology that underlies lipid profile with age or the sexual dimorphism of Lp(a) metabolism. Finally, Lp(a) appears to be an independent risk factor; however, the role of sex and ethnicity is important.

Predictive value of systemic inflammatory response index (SIRI) for complex coronary artery disease occurrence in patients presenting with angina equivalent symptoms

“…The modifiable parameters should be taken into consideration to improve patient outcomes [31]. The findings in the TCGS study, disease onset was related to age and gender differences [32]. The present results indicate age-related risk for CAD.…”

Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study

“…Nonetheless, even when combined with genetic information, these models are insufficient for clinical application [ 59 ]. The first reason is that the sample size of such studies is insufficient to make a conclusive determination; the second reason is that whole-genome information is required in this regard; additionally, the ancestral discrepancy between populations necessarily requires that these models be considered separately for different ethnic groups [ 60 , 61 ]. In this work, we compared predictive models for metabolic syndrome using the information on demographic and clinical and genetic data (functional variants of GCKR gene) on patients from TCGS.…”