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

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: To test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics. Methods: Using the EHR, we 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 three 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 nine candidate biomarkers (p <4.42 × 10−5). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in three 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 NTproBNP (HR per SD increment, 1.35 [95%CI, 1.14 to 1.61, p = 0.0007] for angiopoietin-2, and 1.37 [1.06–1.79, p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80–84%) following cardiac transplant (p <0.001 for both). Conclusions: A novel strategy integrating EHRs, discarded clinical specimens, and proteomics identified two biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.