Shaping the future of metabolic syndrome: genetics, prognosis and individual tailoring. (25th November 2020)

• Record Type: Journal Article
• Authors: Sousa, J.A; Mendonca, M.I; Pereira, A; Monteiro, J.P; Temtem, A.M; Santos, M; Mendonca, F.V; Sousa, A.C; Rodrigues, M; Henriques, E; Ornelas, I; Freitas, A.I; Freitas, A.D; Reis, P
• Is Part Of: European heart journal. Volume 41:(2020)Supplement 2
• Publication Date: 2020-11-25
• Subjects: Lipid Metabolism, Metabolic Syndrome, Diabetes; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/ehjci/ehaa946.3826
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: Metabolic syndrome (MetS), characterized by a cluster of cardiovascular risk factors, is considered to be the major health hazard of modern world and a 21st century epidemic. Recent GWAS have identified several susceptibility regions involved in lipid metabolism and oxidation, also associated with MetS. Genetic risk score (GRS) is an emerging method that attempts to establish correlation between SNPs and clinical phenotypes. Aim: Evaluate the value of a GRS encompassing SNPs involved in lipidic metabolism and oxidation pathways, in predicting CAD outcome (MACEs and long-term cardiovascular Mortality) in a coronary population with MetS. Methods: 1101 coronary patients and MetS, were selected from the GENEMACOR study. Genotyping was performed by TaqMan allelic discrimination assay. A Multiplicative score (mGRS) was constructed according to the multiplicative model with variants belonging to the lipid and oxidative axes (PSRC1, PCSK9, KIF6, ZNF259, LPA, APO E, PON192, PON108, PON55, MTHFR677, MTHFR1298, MTHFD1L). This GRS was categorized using the mean (higher vs lower than mean). Cumulate Mortality Hazards Model (Cox regression) adjusted for age, gender, smoking, hypertension, dyslipidaemia, diabetes, hsCRP, eGFR, Ejection fraction (EF), and multivessel disease) was used to find independent predictors of cardiovascular outcome. We performed Kaplan-Meier Survival curves for both groups (higher vs lower than mean GRS) and log-rank test to compare survival distributions in both groups. Results: The following variables have emerged independently associated with time to MACE occurrence: mGRS (HR=1.31 95% CI (1.07; 1.59); p=0.008), male gender, EF and multivessel disease. Concerning cardiovascular mortality, mGRS also remained an independent predictor (HR=1.44 (1.04–1.99); p=0.028) alongside age, smoking, diabetes and EF. The Log-Rank test showed significant differences between the two curves related to MACE occurrence and cardiovascular mortality (p=0.001 and 0.002, respectively). The Kaplan-Meier survival showed that as mGRS increases, patient survival decreases. Conclusion: In patients with MetS, a GRS comprising variants in lipidic and oxidative pathways, proved to be a useful stratification tool, identifying patients likely to have a worst prognosis over time. Our data further underlines the additive potential and clinical utility of genetic information in shaping secondary prevention. Funding Acknowledgement: Type of funding source: None