Abstract

Introduction: Familial Hypercholesterolemia (FH) variants confer risk for coronary artery disease (CAD) even after adjusting for a single baseline LDL measure. Hypothesis: We hypothesized that a model incorporating serial LDL measures over many years may account for the risk associated with FH variants. Methods: We analyzed 418,790 participants in the Million Veteran Program with electronic health records (EHR) spanning up to 15 years prior to and 7 years after enrollment. FH variants were defined using ClinVar and standard bioinformatic approaches. Carriers were identified by custom genotype array. CAD cases were identified by chart codes for acute MI and coronary revascularization. We used a nested case-control design conditional on survival to enrollment. Controls were matched on year of first LDL, time between first LDL and the case index date, age, sex, and ancestry. Logistic regression was used to estimate CAD risk among FH carriers while adjusting for CAD risk factors and the first, maximum (max), or mean LDL prior to the index date. Results: We found 53 LDLR , 2 APOB , and 2 PCSK9 variants, and FH prevalence was 1:303. We identified 23,091 cases with ≥1 LDL measure prior to the diagnosis of CAD and matched 10 controls per case. Cases had a median of 6 LDL measures over a median of 49 months prior to the index date. Carriers had an increased risk for CAD compared to non-carriers. Adjusting for the first, max, and mean LDL progressively attenuated risk of CAD associated with FH, but residual FH risk remained substantial ( Figure ). This pattern did not change in the subset of subjects with the most extensive LDL data nor in the subset of incident cases. We additionally found evidence of modifying effects of sex and ancestry, with higher within-group risk for females and subjects of African ancestry ( Figure ). Conclusion: The risk associated with FH variants cannot be fully captured by the LDL data available in the EHR, even when considering multiple LDL measurements spanning several years.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.