Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure
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 combin...
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| Format: | Article |
| Language: | English |
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BMC
2018-11-01
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| Series: | Clinical Proteomics |
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| Online Access: | http://link.springer.com/article/10.1186/s12014-018-9213-1 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Thong Huy Cao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T06:23:19Z |
| format | Article |
| id | doaj.art-7d848e9aa7b4408487993da9ac338a8b |
| institution | Directory Open Access Journal |
| issn | 1542-6416 1559-0275 |
| language | English |
| last_indexed | 2024-12-16T06:23:19Z |
| publishDate | 2018-11-01 |
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| series | Clinical Proteomics |
| spelling | doaj.art-7d848e9aa7b4408487993da9ac338a8b2022-12-21T22:41:04ZengBMCClinical Proteomics1542-64161559-02752018-11-011511910.1186/s12014-018-9213-1Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failureThong Huy Cao0Donald J. L. Jones1Paulene A. Quinn2Daniel Chu Siong Chan3Narayan Hafid4Helen M. Parry5Mohapradeep Mohan6Jatinderpal K. Sandhu7Stefan D. Anker8John G. Cleland9Kenneth Dickstein10Gerasimos Filippatos11Hans L. Hillege12Marco Metra13Piotr Ponikowski14Nilesh J. Samani15Dirk J. Van Veldhuisen16Faiez Zannad17Aeilko H. Zwinderman18Adriaan A. Voors19Chim C. Lang20Leong L. Ng21Department of Cardiovascular Sciences, University of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalDepartment of Cardiovascular Sciences, University of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalDepartment of Cardiovascular Sciences, University of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalDepartment of Cardiovascular Sciences, University of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalDepartment of Cardiovascular Sciences, University of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalDivision of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of DundeeDivision of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of DundeeDepartment of Cardiovascular Sciences, University of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalDivision 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 BerlinRobertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow Royal InfirmaryUniversity of Bergen, Stavanger University HospitalDepartment of Cardiology, Heart Failure Unit, Athens University Hospital Attikon, School of Medicine, National and Kapodistrian University of AthensDepartment of Cardiology, University of GroningenDepartment of Medical and Surgical Specialties, Radiological Sciences and Public Health, Institute of Cardiology, University of BresciaDepartment of Heart Diseases, Wroclaw Medical UniversityDepartment of Cardiovascular Sciences, University of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalDepartment of Cardiology, University of GroningenInserm CIC 1433, Université de Lorrain, CHU de NancyNational Heart Centre SingaporeDepartment of Cardiology, University of GroningenDivision of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of DundeeDepartment of Cardiovascular Sciences, University of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalAbstract 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.http://link.springer.com/article/10.1186/s12014-018-9213-1MALDI-MSHeart failureBiomarkerClinical outcomeProteomics |
| spellingShingle | 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 Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure Clinical Proteomics MALDI-MS Heart failure Biomarker Clinical outcome Proteomics |
| title | Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure |
| title_full | Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure |
| title_fullStr | Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure |
| title_full_unstemmed | Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure |
| title_short | Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure |
| title_sort | using matrix assisted laser desorption ionisation mass spectrometry maldi ms profiling in order to predict clinical outcomes of patients with heart failure |
| topic | MALDI-MS Heart failure Biomarker Clinical outcome Proteomics |
| url | http://link.springer.com/article/10.1186/s12014-018-9213-1 |
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