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Cao TH, Jones DJL, Quinn PA, et al. Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure. Clin Proteomics. 2018;15:35doi: 10.1186/s12014-018-9213-1.
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., & Ng, L. L. (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, 1535. https://doi.org/10.1186/s12014-018-9213-1
Cao, Thong Huy, et al. "Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure." Clinical proteomics vol. 15 (2018): 35. doi: https://doi.org/10.1186/s12014-018-9213-1
Cao TH, Jones DJL, Quinn PA, Chan DCS, Hafid N, Parry HM, Mohan M, Sandhu JK, Anker SD, Cleland JG, Dickstein K, Filippatos G, Hillege HL, Metra M, Ponikowski P, Samani NJ, Van Veldhuisen DJ, Zannad F, Zwinderman AH, Voors AA, Lang CC, Ng LL. Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure. Clin Proteomics. 2018 Nov 02;15:35. doi: 10.1186/s12014-018-9213-1. eCollection 2018. PMID: 30410428; PMCID: PMC6214161.
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Clin Proteomics. 2018 Nov 02;15:35. doi: 10.1186/s12014-018-9213-1. eCollection 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

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

Affiliations

  1. 1Department of Cardiovascular Sciences, University of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QP UK.
  2. 2Leicester Cancer Research Centre, Leicester Royal Infirmary, University of Leicester, Leicester, UK.
  3. 3Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY UK.
  4. 4Division of Cardiology and Metabolism, Department of Cardiology (CVK), and Berlin-Brandenburg Center for Regenerative Therapies (BCRT), German Centre for Cardiovascular Research (DZHK) partner site Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany.
  5. Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow Royal Infirmary, Glasgow, UK.
  6. 6University of Bergen, Stavanger University Hospital, Stavanger, Norway.
  7. 7Department of Cardiology, Heart Failure Unit, Athens University Hospital Attikon, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
  8. 8Department of Cardiology, University of Groningen, Groningen, The Netherlands.
  9. 9Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, Institute of Cardiology, University of Brescia, Brescia, Italy.
  10. 10Department of Heart Diseases, Wroclaw Medical University, Wroclaw, Poland.
  11. 11Cardiology Department, Military Hospital, Wroclaw, Poland.
  12. 12Inserm CIC 1433, Université de Lorrain, CHU de Nancy, Nancy, France.
  13. 13National Heart Centre Singapore, Singapore, Singapore.

PMID: 30410428 PMCID: PMC6214161 DOI: 10.1186/s12014-018-9213-1

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.

Keywords: Biomarker; Clinical outcome; Heart failure; MALDI-MS; Proteomics

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