Polygenic risk score predicts prevalence of cardiovascular disease in patients with familial hypercholesterolemia Academic Article uri icon

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abstract

  • BACKGROUND: Although familial hypercholesterolemia (FH) is a severe monogenic disease, it has been shown that clinical risk factors and common genetic variants can modify cardiovascular disease (CVD) risk. OBJECTIVE: The aim of the study was to evaluate the polygenic contribution to lipid traits and CVD in FH using genetic risk scores (GRSs). METHODS: Among the 20,434 subjects attending the lipid clinic, we identified and included 725 individuals who carried an FH causing mutation in this retrospective cohort study. We evaluated the association of GRSs for several traits including coronary artery disease (CAD; GRSCAD) as well as plasma concentrations of low-density lipoprotein cholesterol (LDL-C; GRSLDL-C), high-density lipoprotein cholesterol (GRSHDL-C) and triglycerides (GRSTG). RESULTS: A total of 32% (n = 231) of FH subjects presented a CVD event before their first visit. Patients in the highest GRSLDL-C tertile presented an LDL-C 0.4 mmol/L (15.5 mg/dL) higher than the subjects in the lowest tertile (P = .01). The GRSCAD was strongly associated with CVD events (odds ratio 1.80; 95% confidence interval 1.14-2.85; P = .01) even after adjustment for cardiovascular risk factors. Compared with subjects in the first tertile, those in the third GRSCAD tertile had a significantly higher prevalence of events (40.9% vs 24.7%, P < .0001) and a significantly higher number of events (average 0.97 vs 0.57 [P = .0001] events per individual). CONCLUSION: These results indicate that even in the context of a severe monogenic disease such as FH, common genetic variants can significantly modify the disease phenotype. The use of the 192-SNPs GRSCAD may refine CVD risk prediction in FH patients and this could lead to a more personalized approach to therapy.

authors

  • Paquette, Martine
  • Chong, Michael
  • Thériault, Sébastien
  • Dufour, Robert
  • Pare, Guillaume
  • Baass, Alexis

publication date

  • May 2017