Familial Hypercholesterolemia Variant and Cardiovascular Risk in Individuals With Elevated Cholesterol

Yiyi Zhang; Jacqueline S Dron; Brandon K Bellows; Amit V Khera; Junxiu Liu; Pallavi P Balte; Elizabeth C Oelsner; Sami Samir Amr; Matthew S Lebo; Anna Nagy; Gina M Peloso; Pradeep Natarajan; Jerome I Rotter; Cristen Willer; Eric Boerwinkle; Christie M Ballantyne; Pamela L Lutsey; Myriam Fornage; Donald M Lloyd-Jones; Lifang Hou; Bruce M Psaty; Joshua C Bis; James S Floyd; Ramachandran S Vasan; Nancy L Heard-Costa; April P Carson; Michael E Hall; Stephen S Rich; Xiuqing Guo; Dhruv S Kazi; Sarah D de Ferranti; Andrew E Moran

doi:10.1001/jamacardio.2023.5366

. 2024 Jan 31;9(3):263–271. doi: 10.1001/jamacardio.2023.5366

Familial Hypercholesterolemia Variant and Cardiovascular Risk in Individuals With Elevated Cholesterol

Yiyi Zhang ^1,^✉, Jacqueline S Dron ^2,³, Brandon K Bellows ¹, Amit V Khera ^2,^4,⁵, Junxiu Liu ⁶, Pallavi P Balte ¹, Elizabeth C Oelsner ¹, Sami Samir Amr ^7,⁸, Matthew S Lebo ^7,⁸, Anna Nagy ⁸, Gina M Peloso ⁹, Pradeep Natarajan ^2,^4,¹⁰, Jerome I Rotter ¹¹, Cristen Willer ^12,^13,¹⁴, Eric Boerwinkle ¹⁵, Christie M Ballantyne ¹⁶, Pamela L Lutsey ¹⁷, Myriam Fornage ¹⁸, Donald M Lloyd-Jones ¹⁹, Lifang Hou ¹⁹, Bruce M Psaty ^20,^21,²², Joshua C Bis ²⁰, James S Floyd ^20,²¹, Ramachandran S Vasan ^23,^24,²⁵, Nancy L Heard-Costa ²⁶, April P Carson ²⁷, Michael E Hall ²⁷, Stephen S Rich ²⁸, Xiuqing Guo ¹¹, Dhruv S Kazi ^4,²⁹, Sarah D de Ferranti ^30,³¹, Andrew E Moran ¹

¹Division of General Medicine, Columbia University, New York, New York

²Cardiovascular Disease Initiative, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge

³Center for Genomic Medicine, Massachusetts General Hospital, Boston

⁴Department of Medicine, Harvard Medical School, Boston, Massachusetts

⁵Division of Cardiology, Brigham and Women’s Hospital, Boston, Massachusetts

⁶Department of Population Health Science and Policy, Icahn School of Medicine, Mount Sinai, New York, New York

⁷Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts

⁸Laboratory for Molecular Medicine, Personalized Medicine, Mass General Brigham, Cambridge, Massachusetts

⁹Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts

¹⁰Department of Medicine, Massachusetts General Hospital, Boston

¹¹The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California

¹²Department of Internal Medicine, University of Michigan, Ann Arbor

¹³Department of Human Genetics, University of Michigan, Ann Arbor

¹⁴Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor

¹⁵Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston

¹⁶Department of Medicine, Baylor College of Medicine, Houston, Texas

¹⁷Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis

¹⁸The Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston

¹⁹Northwestern University, Chicago, Illinois

²⁰Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle

²¹Department of Epidemiology, University of Washington, Seattle

²²Department of Health Systems and Population Health, University of Washington, Seattle

²³The Framingham Heart Study, Framingham, Massachusetts

²⁴Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, Massachusetts

²⁵Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts

²⁶Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts

²⁷Department of Medicine, University of Mississippi Medical Center, Jackson

²⁸Center for Public Health Genomics, University of Virginia, Charlottesville

²⁹Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts

³⁰Department of Cardiology, Boston Children’s Hospital, Boston, Massachusetts

³¹Department of Pediatrics, Harvard Medical School, Boston, Massachusetts

Accepted for Publication: November 22, 2023.

Published Online: January 31, 2024. doi:10.1001/jamacardio.2023.5366

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Corresponding Author: Yiyi Zhang, PhD, Division of General Medicine, Columbia University Medical Center, 622 W. 168th Street, PH9-109A, New York, NY 10032 (yz3160@cumc.columbia.edu).

Author Contributions: Dr. Zhang had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs de Ferranti and Moran contributed equally to this work.

Concept and design: Zhang, Boerwinkle, Hou, Kazi, de Ferranti, Moran.

Acquisition, analysis, or interpretation of data: Zhang, Dron, Bellows, Khera, Liu, Balte, Oelsner, Amr, Lebo, Nagy, Peloso, Natarajan, Rotter, Willer, Boerwinkle, Ballantyne, Lutsey, Fornage, Lloyd-Jones, Psaty, Bis, Floyd, Ramachandran, Heard-Costa, Carson, Hall, Rich, Guo, Kazi, Moran.

Drafting of the manuscript: Zhang.

Critical review of the manuscript for important intellectual content: Dron, Bellows, Khera, Liu, Balte, Oelsner, Amr, Lebo, Nagy, Peloso, Natarajan, Rotter, Willer, Boerwinkle, Ballantyne, Lutsey, Fornage, Lloyd-Jones, Hou, Psaty, Bis, Floyd, Ramachandran, Heard-Costa, Carson, Hall, Rich, Guo, Kazi, de Ferranti, Moran.

Statistical analysis: Zhang, Bellows, Nagy, Guo.

Obtained funding: Khera, Rotter, Willer, Boerwinkle, Lloyd-Jones, Psaty, Ramachandran, de Ferranti, Moran.

Administrative, technical, or material support: Liu, Oelsner, Peloso, Rotter, Willer, Boerwinkle, Bis, Floyd, Ramachandran, Heard-Costa, Hall, Rich.

Supervision: Zhang, Lebo, Natarajan, Hou, Moran.

Conflict of Interest Disclosures: Dr Zhang reported grants from the National Heart, Lung, and Blood Institute during the conduct of the study. Dr Khera reported employment and equity at Verve Therapeutics and personal fees from Novartis, Silence Therapeutics, Amgen, Color Health, and Veritas International during the conduct of the study as well as personal fees from Third Rock Ventures and Foresite Labs outside the submitted work. Dr Balte reported grants from the National Institutes of Health during the conduct of the study. Dr Peloso reported grants from the National Heart, Lung, and Blood Institute during the conduct of the study. Dr Natarajan reported grants from Allelica, Apple, Amgen, Boston Scientific, Genentech/Roche, and Novartis; personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Creative Education Concepts, CRISPR Therapeutics, Eli Lilly, Foresite Labs, Genentech/Roch, GV Pharma, HeartFlow, Magnet Biomedicine, Novartis, Esperion Therapeutics, and TenSixteen Bio; equity from MyOme, Preciseli, and TenSixteen Bio; and other from Vertex Pharmaceuticals (spousal employment) outside the submitted work. Dr Rotter reported grants from the National Institutes of Health during the conduct of the study. Dr Willer reported employment and stock holdings at Regeneron Pharmaceuticals during the conduct of the study. Dr Boerwinkle reported grants from the National Institutes of Health Atherosclerosis Risk in Communities study during the conduct of the study. Dr Ballantyne reported grant support through his institution from Abbott, Akcea, Amgen, Arrowhead, Esperion, Ionis, Merck, New Amsterdam, Novartis, Novo Nordisk, Regeneron, and Roche and consultant fees from Abbott, Alnylam Pharmaceuticals, Althera, Amarin, Amgen, Arrowhead, AstraZeneca, Denka Seiken, Esperion, Genentech, Gilead, Illumina, Ionis, Matinas BioPharma, Merck, New Amsterdam, Novartis, Novo Nordisk, Pfizer, Regeneron, Roche, and TenSixteen Bio. Dr Lutsey reported grants from the National Institutes of Health to her institution during the conduct of the study. Dr Lloyd-Jones reported grants from the National Institutes of Health during the conduct of the study. Dr Psaty reported grants from the National Institutes of Health during the conduct of the study and serving on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. Dr Ramachandran reported grants from the National Institutes of Health during the conduct of the study. Dr Carson reported grants from the National Heart, Lung, and Blood Institute and grants from the National Institute on Minority Health and Health Disparities during the conduct of the study and grants from Amgen outside the submitted work. Dr Kazi reported grants from the National Heart, Lung, and Blood Institute during the conduct of the study. Dr de Ferranti reported grants from the National Institutes of Health during the conduct of the study and personal fees from UpToDate outside the submitted work. Dr Moran reported grants from the National Heart, Lung, and Blood Institute during the conduct of the study. No other disclosures were reported.

Funding/Support: This work was supported primarily by National Institutes of Health grant R01HL141823 (Moran and de Ferranti). Funding support was also provided by grants K08HG010155 and U01HG011719 (Khera) from the National Human Genome Research Institute and K24HL159246 (Lutsey), K23HL130627 and R21HL129924 (Oelsner), and R01HL155081 (Zhang) from the National Heart, Lung, and Blood Institute. The Atherosclerosis Risk in Communities study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute Department of Health and Human Services (contract numbers 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, and 75N92022D00005). The Coronary Artery Risk Development in Young Adults study is conducted and supported by the National Heart, Lung, and Blood Institute in collaboration with the University of Alabama at Birmingham (HHSN268201800005I and HHSN268201800007I), Northwestern University (HHSN268201800003I), University of Minnesota (HHSN268201800006I), and Kaiser Foundation Research Institute (HHSN268201800004I). The Coronary Artery Risk Development in Young Adults study was also partially supported by the Intramural Research Program of the National Institute on Aging and an intra-agency agreement between the National Institute on Aging and the National Heart, Lung, and Blood Institute (AG0005). The Cardiovascular Health Study was supported by contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006, and grants U01HL080295, R01HL105756, and U01HL130114 from the National Heart, Lung, and Blood Institute, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided by R01AG023629 from the National Institute on Aging. The Framingham Heart Study was supported by contracts NO1-HC-25195, HHSN268201500001I, and 75N92019D00031 and grant supplement R01 HL092577-06S1 from the National Heart, Lung, and Blood Institute. The Jackson Heart Study is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. The Multi-Ethnic Study of Atherosclerosis projects are conducted and supported by the National Heart, Lung, and Blood Institute in collaboration with Multi-Ethnic Study of Atherosclerosis investigators (contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1TR001881, DK063491, and R01HL105756). Whole genome sequencing for the TOPMed program was supported by the National Heart, Lung, and Blood Institute. Whole genome sequencing for the National Heart, Lung, and Blood Institute TOPMed: Multi-Ethnic Study of Atherosclerosis (phs001416.v3.p1) was performed at the Broad Institute of MIT and Harvard (3U54HG003067-13S1). Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1). Phenotype harmonization, data management, sample-identity quality control, and general study coordination, were provided by the TOPMed Data Coordinating Center (3R01HL-120393-02S1), and TOPMed MESA Multi-Omics (HHSN2682015000031/HSN26800004). Molecular data for the TOPMed program were supported by the National Heart, Lung, and Blood Institute. See the TOPMed Omics Support Table (eTable 11 in Supplement 1) for study-specific omics support information. Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Data Sharing Statement: See Supplement 2.

Additional Contributions: The authors thank the investigators, staff, and participants of all 6 cohorts and the TOPMed Consortium (Trans-Omics in Precision Medicine; https://topmed.nhlbi.nih.gov/topmed-banner-authorship) for their valuable contributions. They also gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. In addition, the authors thank the Cross-Cohort Collaboration Consortium for their promotion and support of this multicohort effort. A full list of principal Cardiovascular Health Study investigators and institutions can be found at CHS-NHLBI.org. A full list of participating MESA investigators and institutes can be found at http://www.mesa-nhlbi.org.

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Corresponding author.

PMCID: PMC10831623 PMID: 38294787

Key Points

Question

How do familial hypercholesterolemia (FH) genetic variants modify coronary heart disease (CHD) risk among adults with moderate (LDL-C 130-189 mg/dL) and severe (LDL-C≥190 mg/dL) hypercholesterolemia?

Findings

In this pooled cohort study of 21 426 participants followed up with for a median of 18 years, FH variants were associated with a 2-fold higher CHD risk, even among individuals with moderately elevated LDL-C. The increased CHD risk appeared to be largely explained by the substantially higher lifetime cumulative LDL-C exposure in those with an FH variant vs those without.

Meaning

The findings suggest that genetic testing for FH may help refine risk stratification beyond LDL-C alone; clinical research is needed to assess the value of adding genetic testing to traditional phenotypic FH screening.

This pooled cohort study assesses the risk of coronary heart disease associated with familial hypercholesterolemia variants in individuals with moderately to severely elevated low-density lipoprotein cholesterol.

Abstract

Importance

Familial hypercholesterolemia (FH) is a genetic disorder that often results in severely high low-density lipoprotein cholesterol (LDL-C) and high risk of premature coronary heart disease (CHD). However, the impact of FH variants on CHD risk among individuals with moderately elevated LDL-C is not well quantified.

Objective

To assess CHD risk associated with FH variants among individuals with moderately (130-189 mg/dL) and severely (≥190 mg/dL) elevated LDL-C and to quantify excess CHD deaths attributable to FH variants in US adults.

Design, Setting, and Participants

A total of 21 426 individuals without preexisting CHD from 6 US cohort studies (Atherosclerosis Risk in Communities study, Coronary Artery Risk Development in Young Adults study, Cardiovascular Health Study, Framingham Heart Study Offspring cohort, Jackson Heart Study, and Multi-Ethnic Study of Atherosclerosis) were included, 63 of whom had an FH variant. Data were collected from 1971 to 2018, and the median (IQR) follow-up was 18 (13-28) years. Data were analyzed from March to May 2023.

Exposures

LDL-C, cumulative past LDL-C, FH variant status.

Main Outcomes and Measures

Cox proportional hazards models estimated associations between FH variants and incident CHD. The Cardiovascular Disease Policy Model projected excess CHD deaths associated with FH variants in US adults.

Results

Of the 21 426 individuals without preexisting CHD (mean [SD] age 52.1 [15.5] years; 12 041 [56.2%] female), an FH variant was found in 22 individuals with moderately elevated LDL-C (0.3%) and in 33 individuals with severely elevated LDL-C (2.5%). The adjusted hazard ratios for incident CHD comparing those with and without FH variants were 2.9 (95% CI, 1.4-6.0) and 2.6 (95% CI, 1.4-4.9) among individuals with moderately and severely elevated LDL-C, respectively. The association between FH variants and CHD was slightly attenuated when further adjusting for baseline LDL-C level, whereas the association was no longer statistically significant after adjusting for cumulative past LDL-C exposure. Among US adults 20 years and older with no history of CHD and LDL-C 130 mg/dL or higher, more than 417 000 carry an FH variant and were projected to experience more than 12 000 excess CHD deaths in those with moderately elevated LDL-C and 15 000 in those with severely elevated LDL-C compared with individuals without an FH variant.

Conclusions and Relevance

In this pooled cohort study, the presence of FH variants was associated with a 2-fold higher CHD risk, even when LDL-C was only moderately elevated. The increased CHD risk appeared to be largely explained by the higher cumulative LDL-C exposure in individuals with an FH variant compared to those without. Further research is needed to assess the value of adding genetic testing to traditional phenotypic FH screening.

Introduction

Familial hypercholesterolemia (FH) is a genetic disorder that results in premature atherosclerotic cardiovascular disease (CVD) due to lifelong exposure to markedly elevated low-density lipoprotein cholesterol (LDL-C) levels.¹ FH is a tier 1 genetic condition identified by the US Centers for Disease Control and Prevention as having significant potential for public health impact through improved diagnosis and treatment.¹ FH is the most common genetic cause of CVD, affecting 1 in 250 individuals in the US.² Despite national guideline recommendations of universal cholesterol screening beginning in childhood, a 2017 report estimated 90% of FH cases remain undiagnosed in the US.^3,4,5 Further, recent evidence suggests that undiagnosed FH cases may be associated with worse health outcomes, including a greater risk of premature death compared with diagnosed FH cases.⁶

FH can be diagnosed with established clinical criteria, such as the Dutch Lipid Clinic Network, Simon Broome, and American Heart Association criteria, with or without genetic testing.^1,7,8 In individuals with extremely high LDL-C (>250 mg/dL), tendon xanthomas, and a family history of premature coronary heart disease (CHD), a causal FH variant is found in 60% to 80% of cases.^1,9 However, among all individuals with severe hypercholesterolemia (LDL-C ≥190 mg/dL), an FH variant is detected in fewer than 5%.^10,11,12 FH gene variants are associated with greater risks of CHD, even when compared with those without a variant with similar LDL-C levels.¹¹ However, previous studies showed that half of individuals with an FH variant may have LDL-C less than 190 mg/dL, suggesting that FH screening based on severely elevated LDL-C alone may miss a considerable number of individuals with an FH variant.^10,13 Further, CHD risk in individuals with FH variants but LDL-C less than 190 mg/dL has not been well quantified, in part because only those with severe hypercholesterolemia are typically screened for FH gene variants within current clinical practice.⁹

This study sought to quantify the risk for incident CHD associated with FH variants among individuals with moderate hypercholesterolemia (LDL-C 130-189 mg/dL) and severe hypercholesterolemia (LDL-C ≥190 mg/dL), examine if increased CHD risk in individuals with an FH variant may be explained by a greater lifelong cumulative exposure to elevated LDL-C compared with those without a variant, and quantify the population-level effect of FH variants by estimating the excess CHD events and deaths attributable to FH in the US population.

Methods

Study Design and Cohorts

The present analysis was based on data from 6 population-based prospective cohort studies: the Atherosclerosis Risk in Communities (ARIC) study,¹⁴ the Coronary Artery Risk Development in Young Adults (CARDIA) study,¹⁵ the Cardiovascular Health Study (CHS),¹⁶ the Framingham Heart Study Offspring cohort (FHS-O),¹⁷ the Jackson Heart Study (JHS),^18,19 and the Multi-Ethnic Study of Atherosclerosis (MESA).²⁰ Details of the design of each study are reported in the eMethods in Supplement 1. All study protocols were approved by the institutional review boards at participating institutions and all participants provided written informed consent. All phenotypic data were centralized at Columbia University for pooling, harmonization, and analysis, as part of the NHLBI Pooled Cohorts Study.²¹ The current analysis was restricted to 24 528 participants who underwent whole-genome sequencing. We further excluded JHS participants who were simultaneously enrolled in ARIC (n = 1406), participants who had existing CHD at baseline (n = 981), and who were missing key covariates (eg, LDL-C and lipid-lowering medication use; n = 714). The final sample size was 21 426 participants.

Clinical Data Collection

Demographic characteristics, LDL-C, and other cardiovascular risk factors were measured using standardized protocols in each study.^{14,15,16,17,18,19,20} Information on race and ethnicity was included because of reported associations with LDL-C levels and CHD outcomes.²² Information on race and ethnicity was self-reported by the participants using fixed categories. For individuals reporting use of lipid-lowering therapy at the time of LDL-C measurements, we estimated detreated LDL-C values by multiplying the observed LDL-C by 2.00 for the subgroup with LDLR variants, and multiplying the observed LDL-C by 1.43 for everyone else, as implemented previously.^2,11 This was based on prior literature showing that individuals with LDLR variants on average have higher LDL-C compared with those with APOB or PCSK9 variants and are thus more likely to be treated with high-intensity vs moderate-intensity statins.^10,23

Whole-Genome Sequencing and FH Variant Annotation

Details of the sequencing methodology are available in the eMethods in Supplement 1. We analyzed gene sequences of 3 FH genes (encoding LDL receptor [LDLR], apolipoprotein B [APOB], and proprotein convertase subtilisin/kexin type 9 [PCSK9]). Variant annotations based on the American College of Medical Genetics and Genomics criteria and reports from the ClinGen and ClinVar expert panels were used to determine a variant’s pathogenicity. Variants identified as pathogenic or likely pathogenic were considered for analysis in the current study (eTable 1 in Supplement 1).

Outcomes

The primary outcome was time to the first incident CHD event. Events were ascertained and adjudicated using each cohort’s specific protocol and the details are provided in the eMethods and eTable 2 in Supplement 1.

Statistical Analyses

Participant characteristics at the baseline visit were described for the overall pooled cohort, by individual studies, and by FH variant status. We also examined the prevalence of FH variants by levels of detreated LDL-C, sex, race, and ethnicity.

To compare the LDL-C trajectories from young adulthood to middle age (50 to 60 years) according to FH variant status, 46 individuals with an FH variant with at least 1 LDL-C measurement from age 50 to 60 years were matched 1:5 to individuals without a variant on age (within 3 years), sex, race, ethnicity, detreated LDL-C (within 5 mg/dL), and lipid-lowering medication use at the time of their last LDL-C measurement during age 50 to 60 years. These participants had LDL-C assessed a mean of 4.7 times, with the earliest measurement occurring at age 19 years. We fitted a linear mixed-effects model of the repeated detreated LDL-C measures against age and FH variant status, with age modeled as restricted cubic splines with random intercept and slope. Cumulative LDL-C exposure was calculated as the area under the LDL-C vs age trajectory (expressed as mg/dL × years). Time-weighted mean LDL-C was calculated as cumulative LDL-C divided by the total years of exposure.

We used Cox proportional hazards models to quantify the risks for incident CHD associated with FH variants among individuals with moderately elevated LDL-C (130-189 mg/dL) and severely elevated LDL-C (≥190 mg/dL). Age was used as the time scale with left truncation on age at the baseline visit. The base model was adjusted for race, ethnicity, sex, smoking status, body mass index, high-density lipoprotein cholesterol, systolic blood pressure, diabetes, use of lipid-lowering medications, and use of antihypertensive medications at the baseline visit. To assess if the increased CHD risk in individuals with an FH variant might be explained by a higher level of baseline or cumulative LDL-C, we further adjusted for LDL-C levels at study baseline and time-weighted average LDL-C from age 20 years to study baseline in separate models. All models were also stratified by study cohort, allowing the baseline hazard function to vary across different cohorts while maintaining a common set of hazard ratios across cohorts. The proportional hazards assumption was checked by plotting the log (-log[survival]) vs log (survival time) and by using Schoenfeld Residuals, and the proportional hazards assumption was met. Additionally, we performed several sensitivity analyses. Specifically, we excluded individuals who were taking lipid-lowering medication at baseline or during follow-up, we repeated all analyses using the observed LDL-C values instead of detreated LDL-C, and we included individuals with preexisting CHD at baseline in the analysis instead of excluding them.

To assess the population-level effect of FH, we estimated the excess CHD events and deaths attributable to FH in US adults. We used a discrete event simulation version of the well-established CVD Policy Model to project excess CHD events and CHD deaths attributable to an FH variant in US adults 20 years and older with no history of CHD and a detreated LDL-C 130 mg/dL or higher.^24,25 Individuals simulated in the model were identified from the 1999 to 2018 National Health and Nutrition Examination Surveys cycles; details of the model are provided in the eMethods, eTables 3-6, and eFigures 1-2 in Supplement 1. A 2-sided P < .05 determined statistical significance. Analyses were performed using Stata version 16 (StataCorp), TreeAge Pro 2021 (TreeAge Software), and R version 4.0.2 (R Foundation). Data were analyzed from March to May 2023.

Results

The mean (SD) age of study participants at the baseline visit was 52.1 (15.5) years; 12 041 (56.2%) were women; and 1023 (4.8%) self-identified as Hispanic, 6039 (28.2%) as non-Hispanic Black, and 13 752 (64.2%) as non-Hispanic White (Table 1; eTable 7 in Supplement 1). Of the 21 426 individuals included, 63 (0.3%) had an FH variant (all heterozygous FH; 41 in LDLR, 21 in APOB, and 1 in PCSK9). Among individuals with an FH variant, 8 (13%), 22 (35%), and 33 (52%) had LDL-C levels less than 130 mg/dL, 130-189 mg/dL, and 190 mg/dL or greater, respectively (Table 2). FH variant prevalence was similar among men and women, and among non-Hispanic Black and non-Hispanic White individuals.

Table 1. Baseline Characteristics of Study Participants.

Characteristic	No. (%)
Characteristic	Overall (N = 21 426)	ARIC (n = 7646)	CARDIA (n = 2754)	CHS (n = 2532)	FHS-O (n = 2186)	JHS (n = 1699)	MESA (n = 4609)
Year of enrollment		1987-1989	1985-1986	1989-1990, 1992-1993	1971	2000-2004	2000-2002
Study follow-up, median (IQR), y	18 (13-28)	26 (17-28)	30 (29-30)	12 (7-18)	39 (30-41)	13 (13-14)	14 (14-15)
Age, mean (SD), y	52.1 (15.5)	54.3 (5.8)	25.3 (4.2)	72.5 (5.4)	37.3 (9.2)	50.0 (10.9)	61.1 (9.8)
Race and ethnicity^a
Hispanic	1023 (4.8)	0	0	0	0	0	1023 (22.2)
Non-Hispanic Black	6039 (28.2)	1756 (23.0)	1263 (45.9)	203 (8.0)	0	1699 (100.0)	1118 (24.3)
Non-Hispanic White	13 752 (64.2)	5890 (77.0)	1491 (54.1)	2321 (91.7)	2186 (100.0)	0	1864 (40.4)
Other^b	612 (2.9)	0	0	8 (0.3)	0	0	604 (13.1)
Sex
Female	12 041 (56.2)	4359 (57.0)	1535 (55.7)	1502 (59.3)	1157 (52.9)	1114 (65.6)	2374 (51.5)
Male	9385 (43.8)	3287 (43.0)	1219 (44.3)	1030 (40.7)	1029 (47.1)	585 (34.4)	2235 (48.5)
Smoking
Never	10 379 (48.4)	3260 (42.6)	1590 (57.7)	1179 (46.6)	823 (37.6)	1185 (69.7)	2342 (50.8)
Former	6331 (29.5)	2436 (31.9)	365 (13.3)	1061 (41.9)	489 (22.4)	278 (16.4)	1702 (36.9)
Current	4716 (22.0)	1950 (25.5)	799 (29.0)	292 (11.5)	874 (40.0)	236 (13.9)	565 (12.3)
Body mass index, mean (SD)^c	27.2 (5.6)	27.4 (5.3)	24.6 (5.1)	26.2 (4.1)	25.4 (4.2)	32.2 (7.7)	28.2 (5.3)
HDL-C, mean (SD), mg/dL	52.2 (15.5)	52.0 (17.0)	53.0 (13.1)	54.9 (15.7)	51.1 (14.5)	51.9 (14.5)	51.0 (14.7)
LDL-C, mean (SD), mg/dL
Observed	125.8 (36.5)	135.9 (38.8)	109.2 (30.6)	129.8 (35.7)	125.2 (34.1)	125.4 (36.3)	117.5 (31.2)
Detreated^d	128.7 (38.9)	137.6 (41.4)	109.2 (30.6)	132.7 (40.2)	125.6 (35.1)	129.1 (39.2)	124.9 (34.6)
Lipid-lowering medication use	1213 (5.7)	196 (2.6)	0	123 (4.9)	11 (0.5)	125 (7.4)	758 (16.4)
Systolic blood pressure, mean (SD), mm Hg	122.4 (19.5)	120.3 (18.7)	109.8 (11.0)	136.6 (21.4)	120.7 (15.0)	125.7 (16.5)	124.9 (20.6)
Diastolic blood pressure, mean (SD), mm Hg	72.8 (10.8)	73.3 (11.1)	68.3 (9.4)	70.7 (11.3)	78.3 (10.1)	76.7 (8.4)	71.8 (10.2)
Antihypertensive medication use	5532 (25.8)	2076 (27.2)	67 (2.4)	973 (38.4)	35 (1.6)	734 (43.2)	1647 (35.7)
Diabetes	1976 (9.2)	717 (9.4)	16 (0.6)	408 (16.1)	35 (1.6)	230 (13.5)	570 (12.4)

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Abbreviations: ARIC, Atherosclerosis Risk in Communities study; CARDIA, Coronary Artery Risk Development in Young Adults study; CHS, Cardiovascular Health Study; FHS-O, Framingham Heart Study Offspring cohort; HDL-C, high-density lipoprotein cholesterol; JHS, Jackson Heart Study; LDL-C, low-density lipoprotein cholesterol; MESA, Multi-Ethnic Study of Atherosclerosis.

^{^a}

Race and ethnicity data were self-reported by the participants using fixed categories and reported because of reported associations with LDL-C levels and CHD outcomes.

^{^b}

Other category includes Asian or Pacific Islander, Native American, and other/multiple races, consolidated owing to small numbers.

^{^c}

Calculated as weight in kilograms divided by height in meters squared.

^{^d}

For individuals reporting use of lipid-lowering therapy at the time of LDL-C measurements, we estimated detreated LDL-C values by multiplying the observed LDL-C by 2.00 for those with LDLR variants and multiplying the observed LDL-C by 1.43 for everyone else.

Table 2. Prevalence of Familial Hypercholesterolemia (FH) Variants by Levels of Detreated Low-Density Lipoprotein Cholesterol (LDL-C), Sex, Race, and Ethnicity.

FH variant status	FH variant		No FH variant		Difference in detreated LDL-C, mean (SE)
FH variant status	No. (%)	Detreated LDL-C, mean (SD)	No. (%)	Detreated LDL-C, mean (SD)	Difference in detreated LDL-C, mean (SE)
Detreated LDL-C
<130 mg/dL	8 (0.1)	114.5 (13.4)	11 786 (99.9)	101.8 (19.8)	12.7 (4.8)
130-189 mg/dL	22 (0.3)	165.8 (15.7)	8273 (99.7)	152.4 (15.8)	13.4 (3.3)
≥190 mg/dL	33 (2.5)	257.6 (66.7)	1304 (97.5)	218.5 (31.4)	39.1 (11.6)
Sex
Male	26 (0.3)	206.7 (80.3)	9359 (99.7)	128.9 (37.1)	72.3 (12.2)
Female	37 (0.3)	207.8 (70.3)	12 004 (99.7)	128.1 (39.6)	65.9 (9.1)
Race and ethnicity^a
Hispanic	1 (0.1)	252.0	1022 (99.9)	126.9 (36.0)	125.1
Non-Hispanic Black	20 (0.3)	183.4 (58.9)	6019 (99.7)	126.8 (40.4)	56.6 (13.2)
Non-Hispanic White	36 (0.3)	229.1 (79.2)	13 716 (99.7)	129.7 (38.1)	99.4 (13.2)
Other^b	6 (1.0)	149.3 (23.4)	606 (99.0)	120.1 (30.3)	29.2 (9.6)

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^{^a}

Race and ethnicity data were self-reported by the participants using fixed categories and reported because of reported associations with LDL-C levels and CHD outcomes.

^{^b}

Other includes Asian or Pacific Islander, Native American, and other/multiple races, consolidated owing to small numbers.

Individuals with an FH variant had a higher LDL-C level starting from early adulthood compared with matched individuals without a variant with similar LDL-C levels at middle age (Figure). The estimated average cumulative LDL-C exposure during age 20 to 60 years was 8594 mg/dL × years among individuals with a variant vs 6179 mg/dL × years among those without (P < .001). Estimated time-weighted average LDL-C during the same period was 215 mg/dL among those with a variant vs 154 mg/dL among those without (P < .001).

Figure. — Individuals with the familial hypercholesterolemia variant were matched 1:5 to those without on age (within 3 years), sex, race, ethnicity, detreated LDL-C levels (within 5 mg/dL), and lipid-lowering medication use at the time of their last LDL-C measurement during age 50 to 60 years. We fitted a linear mixed-effects model of the repeated detreated LDL-C measures against age and familial hypercholesterolemia variant status, with age modeled as restricted cubic splines with random intercept and slope.

During a median (IQR) follow-up of 18 (13-28) years, 1444 incident CHD events occurred among those with baseline LDL-C 130-189 mg/dL, and 315 events occurred among those with baseline LDL-C 190 mg/dL or greater. Among individuals with baseline LDL-C 130-189 mg/dL, the adjusted hazard ratio for incident CHD was 2.9 (95% CI, 1.4-6.0) comparing those with and without FH variants (Table 3). The hazard ratio associated with FH variant was 2.6 (95% CI, 1.2-5.4) when further adjusting for baseline LDL-C levels, and 1.3 (95% CI, 0.6-2.9) when further adjusting for time-weighted average LDL-C from age 20 years to study baseline, respectively. Among individuals with baseline LDL-C 190 mg/dL and greater, the adjusted hazard ratio for incident CHD was 2.6 (95% CI, 1.4-4.9) comparing those with and without FH variants. The corresponding hazard ratios associated with FH variant were 2.4 (95% CI, 1.3-4.4) and 1.9 (95% CI, 0.9-3.9) when further adjusting for baseline LDL-C and time-weighted average LDL-C from age 20 years to study baseline, respectively. Sensitivity analysis restricted to participants not taking lipid-lowering medication (eTable 8 in Supplement 1), using observed LDL-C instead of detreated LDL-C (eTable 9 in Supplement 1), and including those with preexisting CHD at baseline (eTable 10 in Supplement 1) found similar results.

Table 3. Hazard Ratios for Coronary Heart Disease Associated With Familial Hypercholesterolemia (FH) Variant Status.

FH variant status	Mean (SD)		Incidence rate per 1000 person-years (95% UI)	Hazard ratio (95% CI)
FH variant status	Baseline LDL-C	Time-weighted LDL-C^a	Incidence rate per 1000 person-years (95% UI)	Model 1^b	Model 2^c	Model 3^d
Among participants with baseline detreated LDL-C 130-189 mg/dL (n = 8295)
No FH variant	152.4 (15.8)	133.0 (11.7)	8.7 (8.3-9.2)	1 [Reference]	1 [Reference]	1 [Reference]
FH variant	165.8 (15.7)	181.2 (15.9)	11.5 (4.8-27.7)	2.9 (1.4-6.0)	2.6 (1.2-5.4)	1.3 (0.6-2.9)
Among participants with baseline detreated LDL-C ≥190 mg/dL (n = 1337)
No FH variant	218.5 (31.4)	164.9 (18.2)	12.2 (10.9-13.6)	1 [Reference]	1 [Reference]	1 [Reference]
FH variant	257.6 (66.7)	243.9 (36.7)	14.0 (7.5-26.0)	2.6 (1.4-4.9)	2.4 (1.3-4.4)	1.9 (0.9-3.9)

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Abbreviations: LDL-C, low-density lipoprotein cholesterol; UI, uncertainty interval.

^{^a}

Time-weighted mean (SD) LDL-C from age 20 years to study baseline was estimated using linear mixed-effects model.

^{^b}

Model 1 is adjusted for race, ethnicity, sex, smoking status, body mass index, high-density lipoprotein cholesterol, systolic blood pressure, diabetes, use of lipid-lowering medications, and use of antihypertensive medications and stratified by study cohort.

^{^c}

Model 2: model 1 and LDL-C at study baseline.

^{^d}

Model 3: model 1 and time-weighted average LDL-C from age 20 years to study baseline.

We estimated that among the 73.7 million US adults 20 years and older with no history of CHD and a detreated LDL-C 130 mg/dL and greater, more than 417 000 carry an FH variant (Table 4). In those with a detreated LDL-C 130-189 mg/dL, individuals with an FH variant were projected to experience 28 000 (95% uncertainty interval [UI], 26 000-29 000) excess lifetime CHD events and 12 000 (95% UI, 9000-14 000) excess CHD deaths compared with those without a variant. In those with a detreated LDL-C 190 mg/dL and greater, individuals with an FH variant were projected to experience 54 000 (95% UI, 51 000-56 000) excess lifetime CHD events and 15 000 (95% UI, 11 000-19 000) excess CHD deaths compared with those without a variant. These excess deaths translate to a mean of 1.6 (95% UI, 1.5-1.6) and 1.5 (95% UI, 1.4-1.6) future life years lost in individuals with an FH variant compared with those without with a detreated LDL-C 130-189 mg/dL and 190 mg/dL and greater, respectively.

Table 4. Estimated Incidence Rate of Coronary Heart Disease (CHD) and Life Expectancy by Familial Hypercholesterolemia (FH) Variant Status in US Adults.

Detreated LDL-C, FH variant status	LDL-C 130-189 mg/dL		LDL-C ≥190 mg/dL
Detreated LDL-C, FH variant status	No	Yes	No	Yes
No.	63 457 753	168 750	9 817 684	248 454
CHD event
CHD incidence, rate per 1000 person-years (95% UI)	7.4 (7.3-7.5)	11.4 (11.1-11.6)	11.3 (11.2-11.5)	20.7 (20.2-21.2)
Individuals with incident CHD event, % (95% UI)	38.3 (37.7-38.8)	54.7 (53.5-55.7)	50.1 (49.4-50.7)	71.7 (70.5-72.8)
Incident CHD events, No. (95% UI)	24 306 667 (23 943 689-24 608 980)	92 333 (90 343-93 974)	4 919 351 (4 851 811-4 981 130)	178 103 (175 058-180 829)
Excess incident CHD events (per year), No. (95% UI)	1 [Reference]	668 (619-710)	1 [Reference]	2333 (2197-2458)
Excess incident CHD events (lifetime) (95% UI)	1 [Reference]	27 695 (25 705-29 336)	1 [Reference]	53 609 (50 564-56 335)
CHD death
CHD death, rate per 1000 person-years (95% UI)	4.6 (4.5-4.8)	6.0 (5.8-6.2)	5.1 (4.9-5.3)	6.3 (6.0-6.6)
Individuals with CHD death, % (95% UI)	27.4 (26.2-28.4)	34.3 (33.0-35.5)	29.7 (28.6-30.8)	35.7 (34.0-37.2)
CHD deaths, No. (95% UI)	17 372 296 (16 644 715-18 017 560)	57 841 (55 667-59 962)	2 920 152 (2 805 766-3 023 287)	88 660 (84 531-92 475)
Excess CHD deaths (per y), No. (95% UI)	1 [Reference]	224 (187-261)	1 [Reference]	294 (218-364)
Excess CHD deaths (lifetime), No. (95% UI)	1 [Reference]	11 644 (9471-13 765)	1 [Reference]	14 761 (10 631-18 575)
CHD-free survival from age 20 years, mean (95% UI), y	51.5 (51.4-51.6)	48.0 (47.8-48.3)	44.2 (44.1-44.4)	34.6 (34.3-34.9)
Life expectancy from age 20 years, mean (95% UI), y	58.9 (58.8-59.1)	57.4 (57.2-57.5)	58.1 (57.9-58.2)	56.6 (56.4-56.8)

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Abbreviations: LDL-C, low-density lipoprotein cholesterol; UI, uncertainty interval.

Discussion

In this cohort study including more than 21 000 participants without preexisting CHD from 6 US prospective cohorts who underwent whole-genome sequencing, an FH variant was found in 0.3% of the individuals with moderately elevated LDL-C of 130-189 mg/dL and 2.5% in those with severely elevated LDL-C of 190 mg/dL and greater. Among individuals with either moderately or severely elevated LDL-C, presence of an FH variant was associated with a significantly higher risk of CHD, which appeared to be largely explained by the higher past cumulative exposure to LDL-C among individuals with an FH variant starting from early life. Individuals with an FH variant were projected to experience a substantial number of excess CHD deaths compared with those without a variant.

Despite being a public health priority, the US prevalence of FH defined by both phenotypic and genotypic definitions is not well established.^2,10,26,27 Most previous studies used clinical criteria alone to define FH, with varying estimates depending on the diagnostic definitions.^2,26,27 Studies of National Health and Nutrition Examination Surveys data estimated an FH prevalence ranging from 0.40% to 0.47% using modified Dutch Lipid Clinic Network criteria (LDL-C and personal and family history of premature atherosclerotic CVD).^2,26,28 Among 131 000 individuals from Mayo Clinic, 0.32% met Dutch Lipid Clinic Network criteria for FH using an electronic health records-based phenotyping algorithm.²⁹ The prevalence of genetically confirmed FH in the general US population is not well characterized either, as genetic testing for FH is uncommon.⁹ A sequencing analysis of 20 485 participants from 7 case-control studies and 5 cohort studies found an FH variant in 97 individuals (0.5%).¹¹ In 43 979 unselected individuals in the Geisinger Health System, 172 (0.4%) had an FH variant.¹⁰ Similarly, when applying an algorithm predicting the probability of an FH variant developed from participants in UK Biobank with whole exome sequencing to National Health and Nutrition Examination Surveys participants, an estimated 0.4% of US adults have an FH variant.²⁸ The current analysis extended these previous studies and reported FH variant prevalence by LDL-C levels. We also found similar FH variant prevalence among men and women and among non-Hispanic Black and non-Hispanic White individuals.

Additionally, although prior studies have described moderately elevated LDL-C levels in a large proportion of individuals carrying an FH variant, the clinical implications of FH variant status in this group have not been fully characterized.^10,11 The current study addressed this key knowledge gap by showing that individuals with an FH variant with baseline LDL-C 130-189 mg/dL had roughly a 2-fold higher CHD risk compared with those without a variant with similar baseline LDL-C. Thus, FH variants appear to confer an increased risk even when the LDL-C level is only moderately elevated. Further, the association between FH variants and incident CHD was only slightly attenuated when adjusting for baseline LDL-C level, whereas the association was substantially attenuated and no longer statistically significant when adjusting for cumulative LDL-C exposure from age 20 years to study baseline. These findings suggest that much of the association between FH variants and CHD may be explained by a higher cumulative LDL-C exposure in those with a variant compared to those without, and the presence of FH variants is indicative of substantially higher lifelong cumulative LDL-C levels, an exposure history that cannot be captured by a single measure of LDL-C in middle age. Of note, among individuals with LDL-C 190 mg/dL and greater, the association between FH variants and CHD also attenuated but to a lesser degree when adjusting for cumulative LDL-C exposure. Since we used the mixed-effects model to estimate lifetime LDL-C trajectory and cumulative LDL-C exposure, individuals with more extreme observed LDL-C values are subject to extra shrinkage toward the population mean. Therefore, among participants with LDL-C 190 mg/dL and greater, our cumulative LDL-C estimates are likely conservative and may not fully capture the true cumulative exposure experienced by individuals with an FH variant. Also, the current model did not take into account LDL-C exposures before age 20 years.

Commonly used FH diagnostic criteria, such as the Dutch Lipid Clinic Network criteria, Simon Broome Register Diagnostic Criteria, and American Heart Association criteria, all include genetic testing as a key approach to making a diagnosis of FH.^1,7,8 However, genetic testing for FH is rarely used in the US and is typically reserved for individuals already demonstrating severely elevated LDL-C.^1,9 The Cascade Screening for Awareness and Detection of FH (CASCADE FH) Registry³⁰ showed that genetic testing was reported to be performed in only 3.9% of US individuals with a clinical diagnosis of FH. In contrast, genetic testing for FH has been implemented following positive phenotypic screening and as part of cascade screening in routine primary care in the Netherlands, Norway, the United Kingdom, Australia, and New Zealand.^4,9 Findings from the current analysis suggest that genetic testing for FH has the potential to refine risk stratification beyond LDL-C alone and, with early treatment, could reduce cumulative exposure to high LDL-C and subsequently reduce excess CHD deaths. Genetic testing may also facilitate family-based cascade testing to identify additional previously unscreened relatives with FH who might benefit from diagnosis and treatment.³¹ Of note, although the current analysis estimated that more than 417 000 US adults without a history of CHD and LDL-C 130 mg/dL and greater may carry an FH variant and were projected to experience more than 27 000 excess CHD deaths compared with those without a variant, the incremental prognostic value of FH variants beyond LDL-C (particularly cumulative LDL-C) as well as the cost-effectiveness of genetic testing as a complement to traditional phenotypic FH screening has not been established and needs further study. Further, concerns about more limited understanding of FH variant prevalence in minoritized racial and ethnic groups of non-European ancestry, genetic discrimination, cost of genetic counseling, and need for follow-up of false-positive findings also have to be considered before adopting more widespread FH genetic screening.^9,10

Limitations

This study has several limitations. First, because we studied incident CHD events, participants with CHD at baseline were excluded. As individuals with FH are at a higher risk of premature CHD at baseline, their exclusion may lead to an underestimation of the true population prevalence of genetically confirmed FH and also bias the association between FH genotype and CHD toward the null. However, sensitivity analysis including those individuals with preexisting CHD at baseline found similar results. Second, since detailed lipid-lowering medication types or doses were not available in many of the cohorts, for participants self-reporting lipid-lowering medication use, we estimated detreated LDL-C values by conservatively assuming that a moderate-dose statin would reduce LDL-C levels by 30% and an intensive-dose statin would reduce LDL-C levels by 50%.^2,11 This approach does not account for different medication types or doses, variable adherence, or heterogeneity in drug dose-response.^10,11 However, sensitivity analyses restricting to individuals not taking lipid-lowering medication found consistent results. Third, due to the small number of individuals carrying an FH variant, we were not able to perform Cox analysis stratified by relevant subgroups such as sex and race or examine potential interactions. Fourth, the current variant classification procedure may underestimate the true prevalence of pathogenic FH variants, deeming some variants as of uncertain significance due to currently insufficient evidence for disease association or missing FH variants that have yet to be discovered or validated. This may be especially true in populations with non-European ancestry where the amount of data for pathogenic annotation is more limited.

Conclusions

In this study, the presence of an FH variant was associated with a higher risk of CHD, even when LDL-C levels were only moderately elevated, and was projected to result in a substantial number of excess CHD deaths. The increased CHD risk associated with FH variants appeared to be largely explained by the substantially higher lifetime cumulative LDL-C exposure in individuals with ans FH variant compared with those without, suggesting that FH variants may provide incremental risk insights when only the baseline LDL-C is known, but not if cumulative past LDL-C exposure is known. Further research is needed to assess the incremental value of adding genetic testing to traditional phenotypic FH screening.

Supplement 1.

eMethods

eTable 1. Variant characteristics and evidence in support of pathogenic or likely pathogenic classifications.

eTable 2. Definitions of outcomes by study cohorts.

eTable 3. Incident CVD events and non-CVD death risk equations from the NHLBI Pooled Cohort Study used in the CVD Policy Model.

eTable 4. Baseline characteristics of NHLBI Pooled Cohorts Study and simulated population by FH variant status used in the calibration.

eTable 5. Model calibration performance compared with NHLBI Pooled Cohorts Study population by FH variant status.

eTable 6. Baseline characteristics of US adults by assigned FH variant status at NHANES visit.

eTable 7. Baseline characteristics of study participants by FH variant status

eTable 8. Hazard ratios (95% CI) for coronary heart disease (CHD) associated with FH variant status, among individuals not taking lipid-lowering medication at baseline or during follow-up.

eTable 9. Hazard ratios (95% CI) for coronary heart disease (CHD) associated with FH variant status, based on observed LDL-C instead of detreated LDL-C

eTable 10. Hazard ratios (95% CI) for coronary heart disease (CHD) associated with FH variant status, not excluding individuals with preexisting CHD at baseline.

eTable 11. TOPMed Omics Support Table.

eFigure 1. CVD Policy Model calibration to contemporary US event rates.

eFigure 2. Cumulative incidence of coronary heart disease (CHD) in the CVD Policy Model vs. NHLBI Pooled Cohorts Study by FH variant.

eReferences

jamacardiol-e235366-s001.pdf^{(1.5MB, pdf)}

Supplement 2.

Data sharing statement

jamacardiol-e235366-s002.pdf^{(9.5KB, pdf)}

References

1.Gidding SS, Champagne MA, de Ferranti SD, et al. ; American Heart Association Atherosclerosis, Hypertension, and Obesity in Young Committee of Council on Cardiovascular Disease in Young, Council on Cardiovascular and Stroke Nursing, Council on Functional Genomics and Translational Biology, and Council on Lifestyle and Cardiometabolic Health . The agenda for familial hypercholesterolemia: a scientific statement from the American Heart Association. Circulation. 2015;132(22):2167-2192. doi: 10.1161/CIR.0000000000000297 [DOI] [PubMed] [Google Scholar]
2.de Ferranti SD, Rodday AM, Mendelson MM, Wong JB, Leslie LK, Sheldrick RC. Prevalence of familial hypercholesterolemia in the 1999 to 2012 United States National Health and Nutrition Examination Surveys (NHANES). Circulation. 2016;133(11):1067-1072. doi: 10.1161/CIRCULATIONAHA.115.018791 [DOI] [PubMed] [Google Scholar]
3.Stone NJ, Robinson JG, Lichtenstein AH, et al. ; American College of Cardiology/American Heart Association Task Force on Practice Guidelines . 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25)(suppl 2):S1-S45. doi: 10.1161/01.cir.0000437738.63853.7a [DOI] [PubMed] [Google Scholar]
4.Nordestgaard BG, Benn M. Genetic testing for familial hypercholesterolaemia is essential in individuals with high LDL cholesterol: who does it in the world? Eur Heart J. 2017;38(20):1580-1583. doi: 10.1093/eurheartj/ehx136 [DOI] [PubMed] [Google Scholar]
5.Daniels SR, Gidding SS, de Ferranti SD; National Lipid Association Expert Panel on Familial Hypercholesterolemia . Pediatric aspects of familial hypercholesterolemias: recommendations from the National Lipid Association Expert Panel on Familial Hypercholesterolemia. J Clin Lipidol. 2011;5(3)(suppl):S30-S37. doi: 10.1016/j.jacl.2011.03.453 [DOI] [PubMed] [Google Scholar]
6.Ray KK, Pillas D, Hadjiphilippou S, et al. Premature morbidity and mortality associated with potentially undiagnosed familial hypercholesterolemia in the general population. Am J Prev Cardiol. 2023;15:100580. doi: 10.1016/j.ajpc.2023.100580 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Defesche JC, Lansberg PJ, Umans-Eckenhausen MA, Kastelein JJ. Advanced method for the identification of patients with inherited hypercholesterolemia. Semin Vasc Med. 2004;4(1):59-65. doi: 10.1055/s-2004-822987 [DOI] [PubMed] [Google Scholar]
8.Risk of fatal coronary heart disease in familial hypercholesterolaemia. Scientific Steering Committee on behalf of the Simon Broome Register Group. BMJ. 1991;303(6807):893-896. doi: 10.1136/bmj.303.6807.893 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Sturm AC, Knowles JW, Gidding SS, et al. ; Convened by the Familial Hypercholesterolemia Foundation . Clinical genetic testing for familial hypercholesterolemia: JACC Scientific Expert Panel. J Am Coll Cardiol. 2018;72(6):662-680. doi: 10.1016/j.jacc.2018.05.044 [DOI] [PubMed] [Google Scholar]
10.Abul-Husn NS, Manickam K, Jones LK, et al. Genetic identification of familial hypercholesterolemia within a single US health care system. Science. 2016;354(6319):aaf7000. doi: 10.1126/science.aaf7000 [DOI] [PubMed] [Google Scholar]
11.Khera AV, Won HH, Peloso GM, et al. Diagnostic yield and clinical utility of sequencing familial hypercholesterolemia genes in patients with severe hypercholesterolemia. J Am Coll Cardiol. 2016;67(22):2578-2589. doi: 10.1016/j.jacc.2016.03.520 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Patel AP, Wang M, Fahed AC, et al. Association of rare pathogenic DNA variants for familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and lynch syndrome with disease risk in adults according to family history. JAMA Netw Open. 2020;3(4):e203959. doi: 10.1001/jamanetworkopen.2020.3959 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Zhang Y, Dron JS, Bellows BK, et al. Association of severe hypercholesterolemia and familial hypercholesterolemia genotype with risk of coronary heart disease. Circulation. 2023;147(20):1556-1559. doi: 10.1161/CIRCULATIONAHA.123.064168 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.The Atherosclerosis Risk in Communities (ARIC) study: design and objectives. The ARIC investigators. Am J Epidemiol. 1989;129(4):687-702. doi: 10.1093/oxfordjournals.aje.a115184 [DOI] [PubMed] [Google Scholar]
15.Friedman GD, Cutter GR, Donahue RP, et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988;41(11):1105-1116. doi: 10.1016/0895-4356(88)90080-7 [DOI] [PubMed] [Google Scholar]
16.Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol. 1991;1(3):263-276. doi: 10.1016/1047-2797(91)90005-W [DOI] [PubMed] [Google Scholar]
17.Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. design and preliminary data. Prev Med. 1975;4(4):518-525. doi: 10.1016/0091-7435(75)90037-7 [DOI] [PubMed] [Google Scholar]
18.Sempos CT, Bild DE, Manolio TA. Overview of the Jackson Heart Study: a study of cardiovascular diseases in African American men and women. Am J Med Sci. 1999;317(3):142-146. doi: 10.1016/S0002-9629(15)40495-1 [DOI] [PubMed] [Google Scholar]
19.Taylor HA, Jr., Wilson JG, Jones DW, et al. Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study. Ethn Dis. 2005;15(4 Suppl 6):S6-4-17. [PubMed] [Google Scholar]
20.Bild DE, Bluemke DA, Burke GL, et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol. 2002;156(9):871-881. doi: 10.1093/aje/kwf113 [DOI] [PubMed] [Google Scholar]
21.Oelsner EC, Balte PP, Cassano PA, et al. Harmonization of respiratory data from 9 US population-based cohorts: the NHLBI Pooled Cohorts Study. Am J Epidemiol. 2018;187(11):2265-2278. doi: 10.1093/aje/kwy139 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Frank ATH, Zhao B, Jose PO, Azar KMJ, Fortmann SP, Palaniappan LP. Racial/ethnic differences in dyslipidemia patterns. Circulation. 2014;129(5):570-579. doi: 10.1161/CIRCULATIONAHA.113.005757 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Doi T, Hori M, Harada-Shiba M, et al. Patients with LDLR and PCSK9 gene variants experienced higher incidence of cardiovascular outcomes in heterozygous familial hypercholesterolemia. J Am Heart Assoc. 2021;10(4):e018263. doi: 10.1161/JAHA.120.018263 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Bryant KB, Moran AE, Kazi DS, et al. Cost-effectiveness of hypertension treatment by pharmacists in Black barbershops. Circulation. 2021;143(24):2384-2394. doi: 10.1161/CIRCULATIONAHA.120.051683 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Kohli-Lynch CN, Bellows BK, Zhang Y, et al. Cost-effectiveness of lipid-lowering treatments in young adults. J Am Coll Cardiol. 2021;78(20):1954-1964. doi: 10.1016/j.jacc.2021.08.065 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Bucholz EM, Rodday AM, Kolor K, Khoury MJ, de Ferranti SD. Prevalence and predictors of cholesterol screening, awareness, and statin treatment among US adults with familial hypercholesterolemia or other forms of severe dyslipidemia (1999-2014). Circulation. 2018;137(21):2218-2230. doi: 10.1161/CIRCULATIONAHA.117.032321 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Perak AM, Ning H, de Ferranti SD, Gooding HC, Wilkins JT, Lloyd-Jones DM. Long-term risk of atherosclerotic cardiovascular disease in US adults with the familial hypercholesterolemia phenotype. Circulation. 2016;134(1):9-19. doi: 10.1161/CIRCULATIONAHA.116.022335 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Bellows BK, Khera AV, Zhang Y, et al. Estimated yield of screening for heterozygous familial hypercholesterolemia with and without genetic testing in US adults. J Am Heart Assoc. 2022;11(11):e025192. doi: 10.1161/JAHA.121.025192 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Safarova MS, Liu H, Kullo IJ. Rapid identification of familial hypercholesterolemia from electronic health records: the SEARCH study. J Clin Lipidol. 2016;10(5):1230-1239. doi: 10.1016/j.jacl.2016.08.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Ahmad ZS, Andersen RL, Andersen LH, et al. US physician practices for diagnosing familial hypercholesterolemia: data from the CASCADE-FH registry. J Clin Lipidol. 2016;10(5):1223-1229. doi: 10.1016/j.jacl.2016.07.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Knowles JW, Rader DJ, Khoury MJ. Cascade screening for familial hypercholesterolemia and the use of genetic testing. JAMA. 2017;318(4):381-382. doi: 10.1001/jama.2017.8543 [DOI] [PMC free article] [PubMed] [Google Scholar]

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