Polygenic, metabolic, and clinical risk score utility for cardiometabolic traits in Middle Eastern populations

Cardiometabolic traits such as high- and low-density lipoprotein cholesterol, blood pressure, and insulin resistance are known to increase the risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). The prevalence of T2D in Qatar is 19.8% according to the 2021 statistics of the IDF Diabetes Atlas and it is expected to reach 22.8% by 2045. T2D and CVD are among the top 10 causes of mortality (https://www.who.int/data). Monitoring and managing cardiometabolic traits through lifestyle modifications, medication, and regular health check-ups can help reduce the risk of T2D and CVD. In this proposal, we aim at developing risk scores for cardiometabolic traits, studying their multi-omic basis, and understand their impact on T2D and CVD, using whole genome sequence (WGS) and clinical data (~14000 participants), metabolomics (~3000), and lifestyle data (~14000) in underrepresented Middle Eastern (ME) populations in the Qatar Biobank and Qatar Genome Program. First, we will perform genome-wide association studies (GWAS) using WGS for each cardiometabolic trait, T2D, and CVD for common and rare variants. Novel loci and genes will be investigated for plausible biological mechanisms underlying the studied traits. Second, we will analyze metabolomics data to identify associations between metabolites, cardiometabolic traits, T2D, and CVD. GWAS for all metabolites will be performed and an atlas of metabolite-SNV associations will be developed. Additional metabolomics data will be generated for result replication. Third, by combining WGS and metabolomics data, we will perform mendelian randomization to search for causal relationships between metabolites, diseases (T2D and CVD), and cardiometabolic traits. Fourth, we will develop polygenic risk scores (PRSs) and metabolite risk scores (MRSs) to predict cardiometabolic traits, T2D, and CVD. Existing PRSs, MRSs, and clinical scores (e.g., FINDRISC for T2D and QRISK3 for CVD) will be tested, and combined with the developed scores to create an ensemble score using artificial intelligence (AI) techniques. Fifth, to validate the developed scores and test their clinical utility, we will use the 2-time point QBB longitudinal data (5-year interval) on 2,400 participants. The change of cardiometabolic profiles and incidence of T2D and CVD will be evaluated with respect to the score categories (high, moderate, low). We will set up implementation strategies for validated scores in clinical practice. Our proposal uses existing datasets with a focus on clinical implementation of results. It will directly impact the care of Qataris and ME populations by developing early detection and diagnostic tools for T2D and CVD. Tailored prevention and treatment plans for high-risk individuals may be initiated through lifestyle intervention and early treatment. Research impact will be generated by shedding light on ME populations and studying the multi-omic basis of cardiometabolic traits, T2D, and CVD.

Hamad Bin Khalifa University (HBKU)