Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Cao, T. H. et al. (2018) Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure. Clinical Proteomics, 15, 35. (doi: 10.1186/s12014-018-9213-1) (PMID:30410428) (PMCID:PMC6214161)

[img]
Preview
Text
174934.pdf - Published Version
Available under License Creative Commons Attribution.

1MB

Abstract

Background: Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF. Methods: A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF. Results: After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005). Conclusions: The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF.

Item Type:Articles
Additional Information:This work was funded by the European Union FP7 Project [FP7-242209-BIOSTAT-CHF; EudraCT 2010-020808-29].
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Cleland, Professor John
Authors: Cao, T. H., Jones, D. J. L., Quinn, P. A., Chan, D. C. S., Hafid, N., Parry, H. M., Mohan, M., Sandhu, J. K., Anker, S. D., Cleland, J. G., Dickstein, K., Filippatos, G., Hillege, H. L., Metra, M., Ponikowski, P., Samani, N. J., Van Veldhuisen, D. J., Zannad, F., Zwinderman, A. H., Voors, A. A., Lang, C. C., and Ng, L. L.
Subjects:R Medicine > R Medicine (General)
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Robertson Centre
Journal Name:Clinical Proteomics
Publisher:BioMed Central
ISSN:1559-0275
ISSN (Online):1559-0275
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Clinical Proteomics 15: 35
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record