LDLR variant classification for improved cardiovascular risk prediction in familial hypercholesterolemia.

ABSTRACT

BACKGROUND AND AIMS: Familial 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 determining LDLR variant severity using variant-specific LDL cholesterol percentiles. METHODS: Participants of the Dutch FH cascade screening program were screened for 456 LDLR variants. For each LDLR variant carrier, a sex- and age-specific LDL cholesterol percentile was derived from the LDL cholesterol level measured at study entry, i.e. generally from the blood drawn for DNA analysis. 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 LDL cholesterol strata <75th, 75th-88th, 88th-92nd, 92nd-96.5th, 96.5th-98th, and ≥98th percentile. Additionally, variants were categorized into class 1 (LDLR deficient) and non-class 1 (often LDLR defective) variants. CAD risk between carriers in the different LDL cholesterol strata and non-carriers was compared using a Cox proportional hazard model. RESULTS: Out of 35,067 participants, 12,485 (36 %) LDLR variant carriers (mean age 38.0 ± 20.0 years, 47.7 % male) 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 LDL cholesterol strata. A 7.3-fold and 3.9-fold increased CAD risk was observed in carriers of class 1 and non-class 1 LDLR variants, respectively. CONCLUSIONS: This study presents a refined approach for classifying LDLR variants based on their impact on LDL cholesterol levels, allowing for more precise, genotype-specific CAD risk estimation in FH patients compared with traditional methods.