LDLR Variant Classification for Improved Cardiovascular Risk Prediction in Familial Hypercholesterolemia

Author:

Ibrahim Shirin,Hartgers Merel L.,Reeskamp Laurens F.ORCID,Zuurbier Linda,Defesche Joep,Kastelein John J.P.ORCID,Stroes Erik S.G.ORCID,Hovingh G. KeesORCID,Huijgen Roeland

Abstract

AbstractBackgroundFamilial Hypercholesterolemia (FH) is a genetic disorder marked by high LDL cholesterol and an increased premature coronary artery disease (CAD) risk. Current dichotomous classification of LDL receptor gene (LDLR) variants may inadequately capture patient variability in LDL cholesterol levels and CAD risk. This study assessed a novel approach for assessingLDLRvariant severity using variant-specific LDL cholesterol percentiles.MethodsParticipants of the Dutch FH cascade screening program were screened for 456LDLRvariants. Sex- and age-specific LDL cholesterol percentiles were computed for eachLDLRvariant carrier. These percentiles were used to calculate the mean LDL cholesterol percentile for each variant. Based on the variant-specific LDL cholesterol percentiles, carriers were grouped into the following strata: <75th, 75th-88th, 88th-92nd, 92nd-96.5th, 96.5th-98th, and ≥98thpercentile. Additionally, variants were categorized into class 1 (LDLR deficient) and non-class 1 (often LDLR defective) variants. CAD risk between carriers in the different strata and non-carriers was compared using a Cox proportional hazard model.ResultsOut of 35,257 participants, 12,485 (36%)LDLRvariant carriers were identified. Carriers had a 5-fold higher CAD risk compared with non-carriers. Hazard ratios for CAD increased gradually from 2.2 (95%CI 0.97-5.0) to 12.0 (95%CI 5.5-24.8) across the strata. A 7.3-fold and 3.9-fold increased CAD risk was observed in carriers of class 1 and non-class 1LDLRvariants, respectively.ConclusionsThis study presents a refined approach for classifyingLDLRvariants based on their impact on LDL cholesterol levels, allowing for more precise, genotype-specific CAD risk estimation in FH patients compared with traditional methods.

Publisher

Cold Spring Harbor Laboratory

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