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: 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

A genetic risk score for hypertension is associated with risk of thoracic aortic aneurysm

A genetic risk score (GRS) based on 29 single nucleotide polymorpysms (SNPs) associated with high blood pressure (BP) was prospectively associated with development of hypertension, stroke and cardiovascular events. The aim of the present study was to evaluate the impact of this GRS on the incidence of aortic disease, including aortic dissection (AD), rupture or surgery of a thoracic (TAA) or abdominal (AAA) aortic aneurysm. More than 25,000 people from the Swedish Malmo Diet and Cancer Study had information on at least 24 SNPs and were followed up for a median\u2009 65\u200918 years. The number of BP elevating alleles of each SNPs, weighted by their effect size in the discovery studies, was summed into a BP-GRS. In Cox regression models, adjusted for traditional cardiovascular risk factors including hypertension, we found significant associations of the BP-GRS, prospectively, with incident TAA (hazard ratio (HR) 1.64 (95% confidence interval (CI) 1.081-2.475 comparing the third vs. the first tertile; p\u2009=\u20090.020) but not with either AAA or aortic dissection. Calibration, discrimination and reclassification analyses show modest improvement in prediction using the BP-GRS in addition to the model which used only traditional risk factors. A GRS for hypertension associates with TAA suggesting a link between genetic determinants of BP and aortic disease. The effect size is small but the addition of more SNPs to the GRS might improve its discriminatory capability