Predicting atrial fibrillation using a combination of genetic risk score and clinical risk factors.

Atrial Fibrillation / diagnosis Genetic Predisposition to Disease Genome-Wide Association Study Humans Polymorphism, Single Nucleotide Risk Factors
Atrial fibrillation Clinical risk factors Genetic risk score Risk stratification Single nucleotide polymorphisms

Journal

Heart rhythm
ISSN: 1556-3871
Titre abrégé: Heart Rhythm
Pays: United States
ID NLM: 101200317

Informations de publication

Date de publication:
05 2020
Historique:
received: 22 07 2019
accepted: 02 01 2020
pubmed: 14 1 2020
medline: 31 5 2022
entrez: 14 1 2020
Statut: ppublish

Résumé

Atrial fibrillation (AF) has a genetic basis, and environmental factors can modify its actual pathogenesis. The purpose of this study was to construct a combined risk assessment method including both genetic and clinical factors in the Japanese population. We screened a cohort of 540 AF patients and 520 non-AF controls for single nucleotide polymorphisms (SNPs) previously associated with AF by genome-wide association studies. The most strongly associated SNPs after propensity score analysis were then used to calculate a weighted genetic risk score (WGRS). We also enrolled 1018 non-AF Japanese subjects as a validation cohort and monitored AF emergence over several years. Finally, we constructed a logistic model for AF prediction combining WGRS and clinical risk factors. We identified 5 SNPs (in PRRX1, ZFHX3, PITX2, HAND2, and NEURL1) associated with AF after Bonferroni correction. There was a 4.92-fold difference in AF risk between the highest and lowest WGRS calculated using these 5 SNPs (P = 2.32 × 10 This novel predictive model of combined AF-associated SNPs and known clinical risk factors can accurately stratify AF risk in the Japanese population.

Sections du résumé

BACKGROUND
Atrial fibrillation (AF) has a genetic basis, and environmental factors can modify its actual pathogenesis.
OBJECTIVE
The purpose of this study was to construct a combined risk assessment method including both genetic and clinical factors in the Japanese population.
METHODS
We screened a cohort of 540 AF patients and 520 non-AF controls for single nucleotide polymorphisms (SNPs) previously associated with AF by genome-wide association studies. The most strongly associated SNPs after propensity score analysis were then used to calculate a weighted genetic risk score (WGRS). We also enrolled 1018 non-AF Japanese subjects as a validation cohort and monitored AF emergence over several years. Finally, we constructed a logistic model for AF prediction combining WGRS and clinical risk factors.
RESULTS
We identified 5 SNPs (in PRRX1, ZFHX3, PITX2, HAND2, and NEURL1) associated with AF after Bonferroni correction. There was a 4.92-fold difference in AF risk between the highest and lowest WGRS calculated using these 5 SNPs (P = 2.32 × 10
CONCLUSION
This novel predictive model of combined AF-associated SNPs and known clinical risk factors can accurately stratify AF risk in the Japanese population.

Identifiants

DOI: 10.1016/j.hrthm.2020.01.006 PMID: 31931171
pubmed: 31931171
pii: S1547-5271(20)30010-2
doi: 10.1016/j.hrthm.2020.01.006
pii:
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

699-705

Informations de copyright

Copyright © 2020 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Auteurs

Yousaku Okubo (Y)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

Yukiko Nakano (Y)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan. Electronic address: nakanoy@hiroshima-u.ac.jp.

Hidenori Ochi (H)

Research Center for Hepatology and Gastroenterology, Hiroshima University, Hiroshima, Japan; Department of Health Management, Hiroshima Red Cross Hospital & Atomic-bomb Survivors Hospital, Hiroshima, Japan.

Yuko Onohara (Y)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

Takehito Tokuyama (T)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

Chikaaki Motoda (C)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

Michitaka Amioka (M)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

Naoya Hironobe (N)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

Sho Okamura (S)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

Yoshihiro Ikeuchi (Y)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

Syunsuke Miyauchi (S)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

Kazuaki Chayama (K)

Research Center for Hepatology and Gastroenterology, Hiroshima University, Hiroshima, Japan; Department of Gastroenterology and Metabolism, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan.

Yasuki Kihara (Y)

Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan.

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Classifications MeSH

questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Troubles du rythme cardiaque › Fibrillation auriculaire › Predicting atrial fibrillation using a...
questionsmedicales.fr › Phénomènes génétiques › Génotype › Prédisposition génétique à une maladie › Predicting atrial fibrillation using a...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Épidémiologie moléculaire › Étude d'association pangénomique › Predicting atrial fibrillation using a...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Predicting atrial fibrillation using a...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Polymorphisme génétique › Polymorphisme de nucléotide simple › Predicting atrial fibrillation using a...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Risque › Facteurs de risque › Predicting atrial fibrillation using a...