Title

Screening and advanced lipid phenotyping in familial hypercholesterolemia: The very large database of lipids study-17 (VLDL-17)

Document Type

Article

Department

Cardiology

Abstract

Background: Familial hypercholesterolemia (FH) is an autosomal dominant dyslipidemia characterized by defective low-density lipoprotein (LDL) clearance. The aim of this study was to compare Friedewald-estimated LDL cholesterol (LDL-C) to biologic LDL-C in individuals screening positive for FH and then further characterize FH phenotypes.
Methods: We studied 1,320,581 individuals from the Very Large Database of Lipids, referred from 2009 to 2011 for Vertical Auto Profile ultracentrifugation testing. Friedewald LDL-C was defined as the cholesterol content of LDL-C, intermediate-density lipoprotein cholesterol, and lipoprotein(a) cholesterol (Lp(a)-C), with LDL-C representing biologic LDL-C. Using Friedewald LDL-C, we phenotypically categorized patients by the National Lipid Association guideline age-based screening thresholds for FH. In those meeting criteria, we categorized patients using population percentile-equivalent biologic LDL-C cutpoints and explored Lp(a)-C and remnant lipoprotein cholesterol (RLP-C) levels.
Results: Overall, 3829 patients met phenotypic criteria for FH by Friedewald LDL-C screening (FH+). Of those screening FH+, 78.8% were above and 21.2% were below the population percentile-equivalent biologic LDL-C. The mean difference in Friedewald biologic LDL-C percentiles was -0.01 (standard deviation, 0.17) for those above, and 1.92 (standard deviation, 9.16) for those below, respectively. Over 1 of 3 were found to have an elevated Lp(a)-C and over 50% had RLP-C greater than 95th percentile of the entire VLDL population.
Conclusions: Of those who screened FH+, Friedewald and biologic LDL-C levels were closely correlated. Large proportions of the FH+ group had excess levels of Lp(a)-C and RLP-C. Future studies are warranted to study these mixed phenotypic groups and determine the role for further risk stratification and treatment algorithms.

Comments

This work was published before the author joined Aga Khan University.

Publication

Journal of Clinical Lipidology

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