Association between a polygenic and family risk score on the prevalence and incidence of myocardial infarction in the KORA-F3 study.


Journal

Atherosclerosis
ISSN: 1879-1484
Titre abrégé: Atherosclerosis
Pays: Ireland
ID NLM: 0242543

Informations de publication

Date de publication:
07 2022
Historique:
received: 03 02 2022
revised: 03 05 2022
accepted: 18 05 2022
pubmed: 1 6 2022
medline: 29 6 2022
entrez: 31 5 2022
Statut: ppublish

Résumé

Genetic risk scores for common diseases as myocardial infarction (MI) gain increasing attention for individual's risk prediction. One might wonder if assessing family history becomes redundant. It was the aim of this study to evaluate the amount of shared information between family history and genetic risk scores and to assess their independent and combined effects on prevalent and incident MI risk. A genome wide polygenic risk score (PGS) and one family risk score (FamRS) were calculated in a population-based study from Southern Germany (n = 3071) with up to 11 years of follow-up. Logistic and Cox Regression models were used adjusting for lifestyle and classical risk factors. A right shift in MI risk for increasing values of PGS was found, with. considerably increasing ORs along the top quantiles of PGS (OR = 3.03 for top 10%; OR = 5.55 for top 2.5%). The PGS was not associated with incident MI cases, though. The FamRS was significantly associated with both prevalent and incident MI cases with an OR of 2.9 for participants with a strong positive family history compared to average. ORs and HRs did hardly change in a combined model including both measures, indicating independent contribution to MI risk. The simultaneous addition of PGS and FamRS to a model including classical risk factors significantly enhanced prediction for prevalent cases and non-cases (p = 3.28 × 10 These findings emphasize that both genetic information and family history are relevant for the determination of MI risk and that neither of them can replace the other.

Sections du résumé

BACKGROUND AND AIMS
Genetic risk scores for common diseases as myocardial infarction (MI) gain increasing attention for individual's risk prediction. One might wonder if assessing family history becomes redundant. It was the aim of this study to evaluate the amount of shared information between family history and genetic risk scores and to assess their independent and combined effects on prevalent and incident MI risk.
METHODS
A genome wide polygenic risk score (PGS) and one family risk score (FamRS) were calculated in a population-based study from Southern Germany (n = 3071) with up to 11 years of follow-up. Logistic and Cox Regression models were used adjusting for lifestyle and classical risk factors.
RESULTS
A right shift in MI risk for increasing values of PGS was found, with. considerably increasing ORs along the top quantiles of PGS (OR = 3.03 for top 10%; OR = 5.55 for top 2.5%). The PGS was not associated with incident MI cases, though. The FamRS was significantly associated with both prevalent and incident MI cases with an OR of 2.9 for participants with a strong positive family history compared to average. ORs and HRs did hardly change in a combined model including both measures, indicating independent contribution to MI risk. The simultaneous addition of PGS and FamRS to a model including classical risk factors significantly enhanced prediction for prevalent cases and non-cases (p = 3.28 × 10
CONCLUSIONS
These findings emphasize that both genetic information and family history are relevant for the determination of MI risk and that neither of them can replace the other.

Identifiants

pubmed: 35636145
pii: S0021-9150(22)00250-7
doi: 10.1016/j.atherosclerosis.2022.05.014
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

10-17

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Florian Schnitzer (F)

Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria.

Lukas Forer (L)

Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria.

Sebastian Schönherr (S)

Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria.

Christian Gieger (C)

Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

Harald Grallert (H)

Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

Florian Kronenberg (F)

Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria.

Annette Peters (A)

Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Research Center for Cardiovascular Research (DZHK e.V.), Partner Site Munich Heart Alliance, Munich, Germany; Chair of Epidemiology, Ludwig-Maximilians Universität München, Munich, Germany.

Claudia Lamina (C)

Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria. Electronic address: Claudia.Lamina@i-med.ac.at.

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Classifications MeSH