Advanced

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

Wells, Quinn S.; Gupta, Deepak K.; Smith, J. Gustav LU ; Collins, Sean P.; Storrow, Alan B.; Ferguson, Jane; Smith, Maya Landenhed LU ; Pulley, Jill M.; Collier, Sarah and Wang, Xiaoming, et al. (2019) In Journal of the American College of Cardiology 73(17). p.2195-2205
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... (More)


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.

(Less)
Please use this url to cite or link to this publication:
author
, et al. (More)
(Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
biomarkers, electronic health records, heart failure, proteomics
in
Journal of the American College of Cardiology
volume
73
issue
17
pages
11 pages
publisher
Elsevier USA
external identifiers
  • scopus:85064450747
ISSN
0735-1097
DOI
10.1016/j.jacc.2019.01.074
language
English
LU publication?
yes
id
5baa4c6e-773d-4ed9-b3c8-c7a8888a6c41
date added to LUP
2019-05-03 10:09:08
date last changed
2019-08-14 04:35:48
@article{5baa4c6e-773d-4ed9-b3c8-c7a8888a6c41,
  abstract     = {<p><br>
                                                         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 &lt; 4.42 × 10                             <br>
                            <sup>−5</sup><br>
                                                         ). 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 &lt; 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 &lt; 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.                         <br>
                        </p>},
  author       = {Wells, Quinn S. and Gupta, Deepak K. and Smith, J. Gustav and Collins, Sean P. and Storrow, Alan B. and Ferguson, Jane and Smith, Maya Landenhed and Pulley, Jill M. and Collier, Sarah and Wang, Xiaoming and Roden, Dan M. and Gerszten, Robert E. and Wang, Thomas J.},
  issn         = {0735-1097},
  keyword      = {biomarkers,electronic health records,heart failure,proteomics},
  language     = {eng},
  number       = {17},
  pages        = {2195--2205},
  publisher    = {Elsevier USA},
  series       = {Journal of the American College of Cardiology},
  title        = {Accelerating Biomarker Discovery Through Electronic Health Records, Automated Biobanking, and Proteomics},
  url          = {http://dx.doi.org/10.1016/j.jacc.2019.01.074},
  volume       = {73},
  year         = {2019},
}