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The value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy
OBJECTIVE: To construct a prediction model based on metabolic profiling for predicting the response to cardiac resynchronization therapy (CRT). METHODS: Peripheral venous (PV) and coronary sinus (CS) blood samples were collected from 25 patients with heart failure (HF) at the time of CRT implantatio...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Science Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6689524/ https://www.ncbi.nlm.nih.gov/pubmed/31447892 http://dx.doi.org/10.11909/j.issn.1671-5411.2019.07.002 |
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author | Zhu, Meng-Ruo Fulati, Zibire Liu, Yang Wang, Wen-Shuo Wu, Qian Su, Yan-Gang Chen, Hai-Yan Shu, Xian-Hong |
author_facet | Zhu, Meng-Ruo Fulati, Zibire Liu, Yang Wang, Wen-Shuo Wu, Qian Su, Yan-Gang Chen, Hai-Yan Shu, Xian-Hong |
author_sort | Zhu, Meng-Ruo |
collection | PubMed |
description | OBJECTIVE: To construct a prediction model based on metabolic profiling for predicting the response to cardiac resynchronization therapy (CRT). METHODS: Peripheral venous (PV) and coronary sinus (CS) blood samples were collected from 25 patients with heart failure (HF) at the time of CRT implantation, and PV blood samples were obtained from ten healthy controls. The serum samples were analyzed by liquid chromatography-mass spectrometry (LC-MS). As per the clinical and echocardiographic assessment at the 6-month follow-up, the HF patients were categorized as CRT responders and non-responders. RESULTS: HF patients had altered serum metabolomic profiles that were significantly different from those of the healthy controls. Differential metabolites were also observed between CRT responders and non-responders. A prediction model for CRT response (CRT-Re) was constructed using the concentration levels of the differential metabolites, L-arginine and taurine. The optimal cutoff value of the CRT-Re model was found to be 0.343 by ROC analysis (sensitivity, 88.2%; specificity, 87.5%; Area under curve (AUC) = 0.897, P = 0.002). The concentration levels of the differential metabolites, L-arginine and lysyl-gamma-glutamate, in PV serum were significantly correlated with that in CS serum (r = 0.945 and 0.680, respectively, all P < 0.001). CONCLUSIONS: Our results suggest that serum-based metabolic profiling may be a potential complementary screening tool for predicting the outcome of CRT. |
format | Online Article Text |
id | pubmed-6689524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Science Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-66895242019-08-23 The value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy Zhu, Meng-Ruo Fulati, Zibire Liu, Yang Wang, Wen-Shuo Wu, Qian Su, Yan-Gang Chen, Hai-Yan Shu, Xian-Hong J Geriatr Cardiol Research Article OBJECTIVE: To construct a prediction model based on metabolic profiling for predicting the response to cardiac resynchronization therapy (CRT). METHODS: Peripheral venous (PV) and coronary sinus (CS) blood samples were collected from 25 patients with heart failure (HF) at the time of CRT implantation, and PV blood samples were obtained from ten healthy controls. The serum samples were analyzed by liquid chromatography-mass spectrometry (LC-MS). As per the clinical and echocardiographic assessment at the 6-month follow-up, the HF patients were categorized as CRT responders and non-responders. RESULTS: HF patients had altered serum metabolomic profiles that were significantly different from those of the healthy controls. Differential metabolites were also observed between CRT responders and non-responders. A prediction model for CRT response (CRT-Re) was constructed using the concentration levels of the differential metabolites, L-arginine and taurine. The optimal cutoff value of the CRT-Re model was found to be 0.343 by ROC analysis (sensitivity, 88.2%; specificity, 87.5%; Area under curve (AUC) = 0.897, P = 0.002). The concentration levels of the differential metabolites, L-arginine and lysyl-gamma-glutamate, in PV serum were significantly correlated with that in CS serum (r = 0.945 and 0.680, respectively, all P < 0.001). CONCLUSIONS: Our results suggest that serum-based metabolic profiling may be a potential complementary screening tool for predicting the outcome of CRT. Science Press 2019-07 /pmc/articles/PMC6689524/ /pubmed/31447892 http://dx.doi.org/10.11909/j.issn.1671-5411.2019.07.002 Text en Institute of Geriatric Cardiology http://creativecommons.org/licenses/by-nc-sa/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License, which allows readers to alter, transform, or build upon the article and then distribute the resulting work under the same or similar license to this one. The work must be attributed back to the original author and commercial use is not permitted without specific permission. |
spellingShingle | Research Article Zhu, Meng-Ruo Fulati, Zibire Liu, Yang Wang, Wen-Shuo Wu, Qian Su, Yan-Gang Chen, Hai-Yan Shu, Xian-Hong The value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy |
title | The value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy |
title_full | The value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy |
title_fullStr | The value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy |
title_full_unstemmed | The value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy |
title_short | The value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy |
title_sort | value of serum metabolomics analysis in predicting the response to cardiac resynchronization therapy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6689524/ https://www.ncbi.nlm.nih.gov/pubmed/31447892 http://dx.doi.org/10.11909/j.issn.1671-5411.2019.07.002 |
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