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Accelerating Biomarker Discovery Through Electronic Health Records, Automated Biobanking, and Proteomics

Wells, Quinn S. (author)
Vanderbilt University
Gupta, Deepak K. (author)
Vanderbilt University
Smith, J. Gustav (author)
Lund University,Lunds universitet,Molecular Epidemiology and Cardiology,Forskargrupper vid Lunds universitet,Lund University Research Groups,Skåne University Hospital
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Collins, Sean P. (author)
Vanderbilt University
Storrow, Alan B. (author)
Vanderbilt University
Ferguson, Jane (author)
Vanderbilt University
Smith, Maya Landenhed (author)
Lund University,Lunds universitet,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Forskargrupper vid Lunds universitet,Artificial Intelligence in CardioThoracic Sciences (AICTS),Lund University Research Groups,Skåne University Hospital
Pulley, Jill M. (author)
Vanderbilt University
Collier, Sarah (author)
Vanderbilt University
Wang, Xiaoming (author)
Vanderbilt University
Roden, Dan M. (author)
Vanderbilt University
Gerszten, Robert E. (author)
Beth Israel Deaconess Medical Center
Wang, Thomas J. (author)
Vanderbilt University
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 (creator_code:org_t)
Elsevier BV, 2019
2019
English 11 s.
In: Journal of the American College of Cardiology. - : Elsevier BV. - 0735-1097. ; 73:17, s. 2195-2205
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinsk bioteknologi -- Medicinsk bioteknologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Medical Biotechnology -- Medical Biotechnology (hsv//eng)

Keyword

biomarkers
electronic health records
heart failure
proteomics

Publication and Content Type

art (subject category)
ref (subject category)

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