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Dan Roden
Faculty Member
Last active: 3/24/2020

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

Wells QS, Gupta DK, Smith JG, Collins SP, Storrow AB, Ferguson J, Smith ML, Pulley JM, Collier S, Wang X, Roden DM, Gerszten RE, Wang TJ
J Am Coll Cardiol. 2019 73 (17): 2195-2205

PMID: 31047008 · PMCID: PMC6501811 · DOI:10.1016/j.jacc.2019.01.074

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). 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.

Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

MeSH Terms (20)

Academic Medical Centers Acceleration Aged Automation Biological Specimen Banks Biomarkers Cohort Studies Electronic Health Records Female Heart Failure Humans Male Middle Aged Proportional Hazards Models Prospective Studies Proteomics Reproducibility of Results Risk Assessment Sensitivity and Specificity Thrombospondins

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