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

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posted on 2018-11-26, 16:59 authored by Thong Huy Cao, Donald J. L. Jones, Paulene A. Quinn, Daniel Chu Siong Chan, Narayan Hafid, Helen M. Parry, Mohapradeep Mohan, Jatinderpal K. Sandhu, Stefan D. Anker, John G. Cleland, Kenneth Dickstein, Gerasimos Filippatos, Hans L. Hillege, Marco Metra, Piotr Ponikowski, Nilesh J. Samani, Dirk J. Van Veldhuisen, Faiez Zannad, Aeilko H. Zwinderman, Adriaan A. Voors, Chim C. Lang, Leong L. Ng
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.

Funding

This work was supported by the John and Lucille van Geest Foundation and the National Institute for Health Research Leicester Biomedical Research Centre. This work was funded by the European Union FP7 Project [FP7-242209-BIOSTAT-CHF; EudraCT 2010-020808-29].

History

Citation

Clinical Proteomics, 2018, 15:35

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Cancer Research Centre

Version

  • VoR (Version of Record)

Published in

Clinical Proteomics

Publisher

BMC (part of Springer Nature)

issn

1559-0275

eissn

1559-0275

Acceptance date

2018-10-26

Copyright date

2018

Available date

2018-11-26

Publisher version

https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-018-9213-1

Notes

The datasets generated during the current study are available from the corresponding author on reasonable request. Data analysed during this study are included in this published article (and its Additional files).

Language

en

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