Assessment of practical applicability and clinical relevance of a commonly used LDL-C polygenic score in patients with severe hypercholesterolemia

Background and aims: Low-density lipoprotein cholesterol (LDL-C) levels vary in patients with familial hypercholesterolemia (FH) and can be explained by a single deleterious genetic variant or by the aggregate effect of multiple, common small-effect variants that can be captured in a polygenic score (PS). We set out to investigate the contribution of a previously published PS to the inter-individual LDL-C variation and coronary artery disease (CAD) risk in patients with a clinical FH phenotype.Methods: First, in a cohort of 628 patients referred for genetic FH testing, we evaluated the distribution of a PS for LDL-C comprising 12 genetic variants. Next, we determined its association with coronary artery disease (CAD) risk using UK Biobank data.Results: The mean PS was higher in 533 FH-variant-negative patients (FH/M-) compared with 95 FH-variant carriers (1.02 vs 0.94, p < 0.001). 39% of all patients had a PS equal to the top 20% from a population-based reference cohort and these patients were less likely to carry an FH variant (OR 0.22, 95% CI 0.10-0.48) compared with patients in the lowest 20%. In UK Biobank data, the PS explained 7.4% of variance in LDL-C levels and was associated with incident CAD. Addition of PS to a prediction model using age and sex and LDL-C did not increase the c-statistic for predicting CAD risk.Conclusions: This 12-variant PS was higher in FH/M-patients and associated with incident CAD in UK Biobank data. However, the PS did not improve predictive accuracy when added to the readily available characteristics age, sex and LDL-C, suggesting limited discriminative value for CAD.

Automated summary

LDL-C levels vary in patients with familial hypercholesterolemia (FH). This study found that a polygenic score (PS) comprising 12 genetic variants was higher in FH patients and associated with incident coronary artery disease (CAD). However, adding the PS to readily available characteristics did not improve the accuracy of CAD risk prediction, suggesting limited discriminative value for CAD.