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Amino Acid Biosignature in Plasma among Ischemic Stroke Subtypes
Ischemic stroke is a neurovascular disorder caused by reduced or blockage of blood flow to the brain, which may permanently affect motor and cognitive abilities. The diagnostic of stroke is performed using imaging technologies, clinical evaluation, and neuropsychological protocols, but no blood test...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360633/ https://www.ncbi.nlm.nih.gov/pubmed/30800679 http://dx.doi.org/10.1155/2019/8480468 |
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author | Goulart, Vânia A. M. Sena, Marcelo M. Mendes, Thiago O. Menezes, Helvécio C. Cardeal, Zenilda L. Paiva, Maria J. N. Sandrim, Valéria C. Pinto, Mauro C. X. Resende, Rodrigo R. |
author_facet | Goulart, Vânia A. M. Sena, Marcelo M. Mendes, Thiago O. Menezes, Helvécio C. Cardeal, Zenilda L. Paiva, Maria J. N. Sandrim, Valéria C. Pinto, Mauro C. X. Resende, Rodrigo R. |
author_sort | Goulart, Vânia A. M. |
collection | PubMed |
description | Ischemic stroke is a neurovascular disorder caused by reduced or blockage of blood flow to the brain, which may permanently affect motor and cognitive abilities. The diagnostic of stroke is performed using imaging technologies, clinical evaluation, and neuropsychological protocols, but no blood test is available yet. In this work, we analyzed amino acid concentrations in blood plasma from poststroke patients in order to identify differences that could characterize the stroke etiology. Plasma concentrations of sixteen amino acids from patients with chronic ischemic stroke (n = 73) and the control group (n = 16) were determined using gas chromatography coupled to mass spectrometry (GC-MS). The concentration data was processed by Partial Least Squares-Discriminant Analysis (PLS-DA) to classify patients with stroke and control. The amino acid analysis generated a first model able to discriminate ischemic stroke patients from control group. Proline was the most important amino acid for classification of the stroke samples in PLS-DA, followed by lysine, phenylalanine, leucine, and glycine, and while higher levels of methionine and alanine were mostly related to the control samples. The second model was able to discriminate the stroke subtypes like atherothrombotic etiology from cardioembolic and lacunar etiologies, with lysine, leucine, and cysteine plasmatic concentrations being the most important metabolites. Our results suggest an amino acid biosignature for patients with chronic stroke in plasma samples, which can be helpful in diagnosis, prognosis, and therapeutics of these patients. |
format | Online Article Text |
id | pubmed-6360633 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-63606332019-02-24 Amino Acid Biosignature in Plasma among Ischemic Stroke Subtypes Goulart, Vânia A. M. Sena, Marcelo M. Mendes, Thiago O. Menezes, Helvécio C. Cardeal, Zenilda L. Paiva, Maria J. N. Sandrim, Valéria C. Pinto, Mauro C. X. Resende, Rodrigo R. Biomed Res Int Research Article Ischemic stroke is a neurovascular disorder caused by reduced or blockage of blood flow to the brain, which may permanently affect motor and cognitive abilities. The diagnostic of stroke is performed using imaging technologies, clinical evaluation, and neuropsychological protocols, but no blood test is available yet. In this work, we analyzed amino acid concentrations in blood plasma from poststroke patients in order to identify differences that could characterize the stroke etiology. Plasma concentrations of sixteen amino acids from patients with chronic ischemic stroke (n = 73) and the control group (n = 16) were determined using gas chromatography coupled to mass spectrometry (GC-MS). The concentration data was processed by Partial Least Squares-Discriminant Analysis (PLS-DA) to classify patients with stroke and control. The amino acid analysis generated a first model able to discriminate ischemic stroke patients from control group. Proline was the most important amino acid for classification of the stroke samples in PLS-DA, followed by lysine, phenylalanine, leucine, and glycine, and while higher levels of methionine and alanine were mostly related to the control samples. The second model was able to discriminate the stroke subtypes like atherothrombotic etiology from cardioembolic and lacunar etiologies, with lysine, leucine, and cysteine plasmatic concentrations being the most important metabolites. Our results suggest an amino acid biosignature for patients with chronic stroke in plasma samples, which can be helpful in diagnosis, prognosis, and therapeutics of these patients. Hindawi 2019-01-20 /pmc/articles/PMC6360633/ /pubmed/30800679 http://dx.doi.org/10.1155/2019/8480468 Text en Copyright © 2019 Vânia A. M. Goulart et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Goulart, Vânia A. M. Sena, Marcelo M. Mendes, Thiago O. Menezes, Helvécio C. Cardeal, Zenilda L. Paiva, Maria J. N. Sandrim, Valéria C. Pinto, Mauro C. X. Resende, Rodrigo R. Amino Acid Biosignature in Plasma among Ischemic Stroke Subtypes |
title | Amino Acid Biosignature in Plasma among Ischemic Stroke Subtypes |
title_full | Amino Acid Biosignature in Plasma among Ischemic Stroke Subtypes |
title_fullStr | Amino Acid Biosignature in Plasma among Ischemic Stroke Subtypes |
title_full_unstemmed | Amino Acid Biosignature in Plasma among Ischemic Stroke Subtypes |
title_short | Amino Acid Biosignature in Plasma among Ischemic Stroke Subtypes |
title_sort | amino acid biosignature in plasma among ischemic stroke subtypes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360633/ https://www.ncbi.nlm.nih.gov/pubmed/30800679 http://dx.doi.org/10.1155/2019/8480468 |
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