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Cited by 285Genetic Associations and EpidemiologyNutrition, Genetics, and DiseaseGenetics and Physical Performance

The DOI matches its Crossref record; 22 of the 23 references it cites were found in a registry or an index.

Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history

Open access
Tada, Hayato, Melander, Olle, Louie, Judy Z., Catanese, Joseph J., Rowland, Charles M., Devlin, James J., Kathiresan, Sekar, Shiffman, Dov·2016·European Heart Journal
DOI 10.1093/eurheartj/ehv462PMC PMC4744619
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Abstract

AIMS: Genetic risk scores (GRSs) have been associated with coronary heart disease (CHD) in large studies. We asked whether expanding an established 27-variant GRS (GRS27) to a 50-variant GRS (GRS50) improved CHD prediction and whether GRSs are independent of self-reported family history of CHD. METHODS AND RESULTS: The association between GRSs and incident CHD was assessed in Cox models adjusting for established risk factors in 23 595 participants of the Malmö Diet and Cancer study--a prospective, population-based study. During a median follow-up of 14.4 years, 2213 participants experienced a first CHD event. After adjustment for established risk factors, both GRS27 and GRS50 were associated with incident CHD [hazard ratio (HR) = 1.70 for high (top quintile) vs. low (bottom quintile) of GRS27; 95% confidence interval (CI): 1.48-1.94; Ptrend = 1.6 × 10(-15) and HR = 1.92 for GRS50; 95% CI: 1.67-2.20; Ptrend = 6.2 × 10(-22)]. Adding 23 single nucleotide polymorphisms (SNPs) to GRS27 improved risk prediction (P = 3 × 10(-6)). Further adjustment for self-reported family history did not appreciably change the risk estimates of either GRS27 (HR = 1.65; 95% CI: 1.45-1.89) or GRS50 (HR = 1.87; 95% CI: 1.63-2.14). The addition of GRS50 to established risk factors, including self-reported family history, improved discrimination (P < 0.0001) and reclassification (continuous net reclassification improvement index = 0.17, P < 0.0001). In young participants (below median age), those with high GRS50 had 2.4-fold greater risk (95% CI: 1.85-3.12) than those with low GRS50. CONCLUSION: The addition of 23 SNPs to an existing GRS27 improved CHD risk prediction and was independent of self-reported family history. Coronary heart disease risk assessment by GRS could be particularly useful in young individuals.

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Figure 1 Cumulative incidence of coronary heart disease events according to self-reported family history of coronary heart disease and 50-variant genetic risk score. Blue and green: those with high 50-variant genetic risk score with (blue) or without (green) a self-reported family history. Red and black: those with low 50-variant genetic risk score with (red) or without (black) a self-reported family history. Inset: those with intermediate 50-variant genetic risk score with (dashed) or without (dotted) a self-reported family history. FH, self-reported family history. Cumulative incidence was estimated while considering non-coronary heart disease death as competing risk. (A) Participants younger than median age (≤57.6). Median age for this younger group is 51.4 (interquartile range, 48.8–54.2). (B) Participants older than median age (>57.6). Median age for this older group is 64.7 (interquartile range, 61.1–67.7).
Figure 1 Cumulative incidence of coronary heart disease events according to self-reported family history of coronary heart disease and 50-variant genetic risk score. Blue and green: those with high 50-variant genetic risk score with (blue) or without (green) a self-reported family history. Red and black: those with low 50-variant genetic risk score with (red) or without (black) a self-reported family history. Inset: those with intermediate 50-variant genetic risk score with (dashed) or without (dotted) a self-reported family history. FH, self-reported family history. Cumulative incidence was estimated while considering non-coronary heart disease death as competing risk. (A) Participants younger than median age (≤57.6). Median age for this younger group is 51.4 (interquartile range, 48.8–54.2). (B) Participants older than median age (>57.6). Median age for this older group is 64.7 (interquartile range, 61.1–67.7).CC BY-NC · PubMed Central

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Metadata

Authors
Tada, H., Melander, O., Louie, J. Z., Catanese, J. J., Rowland, C. M., Devlin, J. J., Kathiresan, S., & Shiffman, D.
Affiliations
  • Broad Institute (US)
  • Lund University (SE)
  • Quest Diagnostics (United States) (US)
Corresponding author
  • Melander, Olle
  • Kathiresan, Sekar
  • Shiffman, Dov
ORCID
Venue
European Heart Journal
Publisher
Oxford University Press (OUP)
Published
2015-09-20
Volume
37
Issue
6
Pages
561-567
ISSN
  • 0195-668X (ISSN-L)
  • 1522-9645
DOI
10.1093/eurheartj/ehv462
PMC
PMC4744619
OpenAlex
W1874197506
Cited by
285
References
23
Citations by year
2026: 7 · 2025: 20 · 2024: 20 · 2023: 31 · 2022: 34 · 2021: 33 · 2020: 30 · 2019: 32 · 2018: 29 · 2017: 29 · 2016: 19 · 2015: 1
FWCI
17.77
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • European Heart Journal (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • Lund University Publications (Lund University) (submittedVersion)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • Swedish Cancer Society
  • Swedish Medical Research Council
  • Swedish Dairy Association
  • Albert Påhlsson and Gunnar Nilsson Foundations
  • Malmö city council
  • Japanese Circulation Society
  • Massachusetts General Hospital (MGH)
  • MGH
  • Donovan Family Foundation
  • European Research Council (StG-282255)
  • Swedish Heart and Lung Foundation
  • Swedish Research Council
  • Novo Nordisk Foundation
  • Skåne University Hospital
  • Medical Faculty, Lund University;
  • Albert Påhlsson Research Foundation, Region Skåne
  • King Gustav V and Queen Victoria
  • Foundation
  • Marianne and Marcus Wallenberg Foundation
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
Keywords
coronary artery disease, polygenic risk score, single nucleotide polymorphisms, family history (medicine), risk prediction, risk reclassification, net reclassification improvement, incident cardiovascular events, young adults, Malmö Diet and Cancer Study, cardiovascular risk factors, discrimination (statistics)
MeSH terms
Aged, Coronary Artery Disease, Epidemiologic Methods, Female, Humans, Male, Middle Aged, Pedigree, Sweden, Age Distribution, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide

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