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

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.

Keywords: biomarkers; electronic health records; heart failure; proteomics.

Figure 1:. Circulating thrombospondin-2 and angiopoietin-2 levels in patients with and without acute heart failure presenting to the emergency department.

Box plots of circulating thrombospondin-2 (TSP2) and angiopoietin-2 (ANGPT2) levels in the emergency department-based validation cohort (STRATIFY), Wilcoxon rank-sum p < 0.001 for both. AHF = acute heart failure, n = 405. No AHF, n = 447.

Figure 2:. Predicted probability of acute heart failure according to thrombospondin-2 and angiopoietin-2 levels among 619 patients with suspected heart failure and B-type natriuretic peptide > 100 pg/ml in the emergency department.

Among patients presenting to the emergency department in whom the diagnosis of acute heart failure has not been excluded, i.e. B-type natriuretic peptide > 100 pg/ml, additional knowledge of both thrombospondin-2 and angiopoietin-2 levels further stratifies the probability of acute heart failure across a broad range beyond either marker alone. 364/619 (58.8%) patients had adjudicated acute heart failure.

Figure 3:. Cumulative incidence of heart failure according to tertiles of plasma angiopoietin-2 and thrombospondin-2 levels in the Malmö Diet and Cancer Study.

Higher plasma levels of both angiopoietin-2 and thrombospondin-2 are associated with greater risk of incident heart failure, which was defined through validated international classification of diseases codes. The Kaplan-Meier plots are simple curves of the 768 participants followed over time.

Figure 4:. Angiopoietin-2, thrombospondin-2, and NTproBNP levels in advanced heart failure patients before and after heart transplant.

Among advanced heart failure patients undergoing cardiac transplantation (n = 30), levels of angiopoietin-2, thrombospondin-2, and NTproBNP are lower after compared with before transplant, p <0.001 for all. ANGPT2 = angiopoietin-2, TSP2 = thrombospondin-2, NTproBNP = N-terminal pro B-type natriuretic peptide. RFU = relative fluorescence units.

Central Illustration.. Automated Plasma Collection and Proteomic Analysis Procedures.

A) Electronic algorithms are deployed in an electronic health record (EHR) database for real-time identification of subjects with and without the phenotype of interest, e.g. heart failure. B) Discarded clinical blood samples from eligible subjects are flagged for automated collection, plasma extraction, and sample storage. C) Plasma samples are linked with de-identified EHRs and incorporated into a biobank. D) Discovery of novel candidate biomarkers by analysis of plasma from cases and controls using a DNA aptamer-based proteomic platform. E) Candidate biomarkers are validated in conventional cohorts. Numbered solid lines indicated the sequential strategy used in the current study. Other validation strategies (i.e., parallel validation) are also possible, as indicated by dashed lines.