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- Aragam, Krishna G., et al.
(author)
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Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery
- 2019
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In: Circulation. - 0009-7322. ; 139:4, s. 489-501
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Journal article (peer-reviewed)abstract
- Heart failure (HF) is a morbid and heritable disorder for which the biological mechanisms are incompletely understood. We therefore examined genetic associations with HF in a large national biobank, and assessed whether refined phenotypic classification would facilitate genetic discovery. Methods: We defined all-cause HF among 488 010 participants from the UK Biobank and performed a genome-wide association analysis. We refined the HF phenotype by classifying individuals with left ventricular dysfunction and without coronary artery disease as having nonischemic cardiomyopathy (NICM), and repeated a genetic association analysis. We then pursued replication of lead HF and NICM variants in independent cohorts, and performed adjusted association analyses to assess whether identified genetic associations were mediated through clinical HF risk factors. In addition, we tested rare, loss-of-function mutations in 24 known dilated cardiomyopathy genes for association with HF and NICM. Finally, we examined associations between lead variants and left ventricular structure and function among individuals without HF using cardiac magnetic resonance imaging (n=4158) and echocardiographic data (n=30 201). Results: We identified 7382 participants with all-cause HF in the UK Biobank. Genome-wide association analysis of all-cause HF identified several suggestive loci (P<1×10 -6 ), the majority linked to upstream HF risk factors, ie, coronary artery disease (CDKN2B-AS1 and MAP3K7CL) and atrial fibrillation (PITX2). Refining the HF phenotype yielded a subset of 2038 NICM cases. In contrast to all-cause HF, genetic analysis of NICM revealed suggestive loci that have been implicated in dilated cardiomyopathy (BAG3, CLCNKA-ZBTB17). Dilated cardiomyopathy signals arising from our NICM analysis replicated in independent cohorts, persisted after HF risk factor adjustment, and were associated with indices of left ventricular dysfunction in individuals without clinical HF. In addition, analyses of loss-of-function variants implicated BAG3 as a disease susceptibility gene for NICM (loss-of-function variant carrier frequency=0.01%; odds ratio,12.03; P=3.62×10 -5 ). Conclusions: We found several distinct genetic mechanisms of all-cause HF in a national biobank that reflect well-known HF risk factors. Phenotypic refinement to a NICM subtype appeared to facilitate the discovery of genetic signals that act independently of clinical HF risk facto rs and that are associated with subclinical left ventricular dysfunction.
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| 2. |
- Wells, Quinn S., et al.
(author)
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Accelerating Biomarker Discovery Through Electronic Health Records, Automated Biobanking, and Proteomics
- 2019
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In: Journal of the American College of Cardiology. - : Elsevier BV. - 0735-1097. ; 73:17, s. 2195-2205
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Journal article (peer-reviewed)abstract
- Background: Circulating biomarkers can facilitate diagnosis and risk stratification for complex conditions such as heart failure (HF). Newer molecular platforms can accelerate biomarker discovery, but they require significant resources for data and sample acquisition. Objectives: The purpose of this study was to test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics. Methods: Using the electronic health record, the authors identified patients with and without HF, retrieved their discarded plasma samples, and screened these specimens using a DNA aptamer-based proteomic platform (1,129 proteins). Candidate biomarkers were validated in 3 different prospective cohorts. Results: In an automated manner, plasma samples from 1,315 patients (31% with HF) were collected. Proteomic analysis of a 96-patient subset identified 9 candidate biomarkers (p < 4.42 × 10 −5 ). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in 3 separate validation cohorts. In an emergency department–based registry of 852 dyspneic patients, the 2 biomarkers improved discrimination of acute HF compared with a clinical score (p < 0.0001) or clinical score plus B-type natriuretic peptide (p = 0.02). In a community-based cohort (n = 768), both biomarkers predicted incident HF independent of traditional risk factors and N-terminal pro–B-type natriuretic peptide (hazard ratio per SD increment: 1.35 [95% confidence interval: 1.14 to 1.61; p = 0.0007] for angiopoietin-2, and 1.37 [95% confidence interval: 1.06 to 1.79; p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80% to 84%) following cardiac transplant (p < 0.001 for both). Conclusions: A novel strategy integrating electronic health records, discarded clinical specimens, and proteomics identified 2 biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.
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