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Fahed AC, Wang M, Patel AP, Ajufo E, Maamari DJ, Aragam KG, Brockman DG, Vosburg T, Ellinor PT, Ng K, Khera AV. Association of the Interaction Between Familial Hypercholesterolemia Variants and Adherence to a Healthy Lifestyle With Risk of Coronary Artery Disease. JAMA Netw Open 2022; 5:e222687. [PMID: 35294538 PMCID: PMC8928007 DOI: 10.1001/jamanetworkopen.2022.2687] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
IMPORTANCE Familial hypercholesterolemia variants impair clearance of cholesterol from the circulation and increase risk of coronary artery disease (CAD). The extent to which adherence to a healthy lifestyle is associated with a lower risk of CAD in carriers and noncarriers of variants warrants further study. OBJECTIVE To assess the association of the interaction between familial hypercholesterolemia variants and adherence to a healthy lifestyle with risk of CAD. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used 2 independent data sets with gene sequencing and lifestyle data from the UK Biobank: a case-control study of 4896 cases and 5279 controls and a cohort study of 39 920 participants. Participants were recruited from 22 sites across the UK between March 21, 2006, and October 1, 2010. The case-control study included participants with CAD and controls at enrollment. The cohort study used a convenience sample of individuals with available gene sequencing data. Statistical analysis was performed from April 2, 2019, to January 20, 2022. EXPOSURES Pathogenic or likely pathogenic DNA variants classified by a clinical laboratory geneticist and adherence to a healthy lifestyle based on a 4-point scoring system (1 point for each of the following: healthy diet, regular exercise, not smoking, and absence of obesity). MAIN OUTCOMES AND MEASURES Coronary artery disease, defined as myocardial infarction in the case-control study, and myocardial infarction, ischemic heart disease, or coronary revascularization procedure in the cohort study. RESULTS The case-control study included 10 175 participants (6828 men [67.1%]; mean [SD] age, 58.6 [7.2] years), and the cohort study included 39 920 participants (18 802 men [47.1%]; mean [SD] age at the end of follow-up, 66.4 [8.0] years). A variant was identified in 35 of 4896 cases (0.7%) and 12 of 5279 controls (0.2%), corresponding to an odds ratio of 3.0 (95% CI, 1.6-5.9), and a variant was identified in 108 individuals (0.3%) in the cohort study, in which the hazard ratio for CAD was 3.8 (95% CI, 2.5-5.8). However, this risk appeared to vary according to lifestyle categories in both carriers and noncarriers of familial hypercholesterolemia variants, without a significant interaction between carrier status and lifestyle (odds ratio, 1.2 [95% CI, 0.6-2.5]; P = .62). Among carriers, a favorable lifestyle conferred 86% lower risk of CAD compared with an unfavorable lifestyle (hazard ratio, 0.14 [95% CI, 0.04-0.41]). The estimated risk of CAD by the age of 75 years varied according to lifestyle, ranging from 10.2% among noncarriers with a favorable lifestyle to 24.0% among noncarriers with an unfavorable lifestyle and ranging from 34.5% among carriers with a favorable lifestyle to 66.2% among carriers with an unfavorable lifestyle. CONCLUSIONS AND RELEVANCE This study suggests that, among carriers and noncarriers of a familial hypercholesterolemia variant, significant gradients in risk of CAD are noted according to adherence to a healthy lifestyle pattern. Similar to the general population, individuals who carry familial hypercholesterolemia variants are likely to benefit from lifestyle interventions to reduce their risk of CAD.
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Affiliation(s)
- Akl C. Fahed
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Minxian Wang
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Aniruddh P. Patel
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Ezimamaka Ajufo
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, UT Southwestern Medical Center, Dallas, Houston, Texas
| | - Dimitri J. Maamari
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Krishna G. Aragam
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Deanna G. Brockman
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Trish Vosburg
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Patrick T. Ellinor
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, Massachusetts
| | - Amit V. Khera
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Medicine, UT Southwestern Medical Center, Dallas, Houston, Texas
- Verve Therapeutics, Cambridge, Massachusetts
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Brockman DG, Petronio L, Dron JS, Kwon BC, Vosburg T, Nip L, Tang A, O'Reilly M, Lennon N, Wong B, Ng K, Huang KH, Fahed AC, Khera AV. Design and user experience testing of a polygenic score report: a qualitative study of prospective users. BMC Med Genomics 2021; 14:238. [PMID: 34598685 PMCID: PMC8485114 DOI: 10.1186/s12920-021-01056-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 08/12/2021] [Indexed: 12/28/2022] Open
Abstract
Background Polygenic scores—which quantify inherited risk by integrating information from many common sites of DNA variation—may enable a tailored approach to clinical medicine. However, alongside considerable enthusiasm, we and others have highlighted a lack of standardized approaches for score disclosure. Here, we review the landscape of polygenic score reporting and describe a generalizable approach for development of a polygenic score disclosure tool for coronary artery disease. Methods We assembled a working group of clinicians, geneticists, data visualization specialists, and software developers. The group reviewed existing polygenic score reports and then designed a two-page mock report for coronary artery disease. We then conducted a qualitative user-experience study with this report using an interview guide focused on comprehension, experience, and attitudes. Interviews were transcribed and analyzed for themes identification to inform report revision. Results Review of nine existing polygenic score reports from commercial and academic groups demonstrated significant heterogeneity, reinforcing the need for additional efforts to study and standardize score disclosure. Using a newly developed mock score report, we conducted interviews with ten adult individuals (50% females, 70% without prior genetic testing experience, age range 20–70 years) recruited via an online platform. We identified three themes from interviews: (1) visual elements, such as color and simple graphics, enable participants to interpret, relate to, and contextualize their polygenic score, (2) word-based descriptions of risk and polygenic scores presented as percentiles were the best recognized and understood, (3) participants had varying levels of interest in understanding complex genomic information and therefore would benefit from additional resources that can adapt to their individual needs in real time. In response to user feedback, colors used for communicating risk were modified to minimize unintended color associations and odds ratios were removed. All 10 participants expressed interest in receiving a polygenic score report based on their personal genomic information. Conclusions Our findings describe a generalizable approach to develop a polygenic score report understandable by potential patients. Although additional studies are needed across a wider spectrum of patient populations, these results are likely to inform ongoing efforts related to polygenic score disclosure within clinical practice. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-021-01056-0.
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Affiliation(s)
- Deanna G Brockman
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building
- CPZN 6.256, Boston, MA, 02114, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lia Petronio
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jacqueline S Dron
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bum Chul Kwon
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Trish Vosburg
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Genomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lisa Nip
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew Tang
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mary O'Reilly
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Niall Lennon
- Genomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bang Wong
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Katherine H Huang
- Pattern Visualization Team, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akl C Fahed
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building
- CPZN 6.256, Boston, MA, 02114, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Amit V Khera
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building
- CPZN 6.256, Boston, MA, 02114, USA. .,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Medicine, Harvard Medical School, Boston, MA, USA.
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