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Altered Metabolic Profiles of the Plasma of Patients with Amyotrophic Lateral Sclerosis
Currently, there is no objective biomarker to indicate disease progression and monitor therapeutic effects for amyotrophic lateral sclerosis (ALS). This study aimed to identify plasma biomarkers for ALS using a targeted metabolomics approach. Plasma levels of 185 metabolites in 36 ALS patients and 3...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699018/ https://www.ncbi.nlm.nih.gov/pubmed/34944760 http://dx.doi.org/10.3390/biomedicines9121944 |
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author | Chang, Kuo-Hsuan Lin, Chia-Ni Chen, Chiung-Mei Lyu, Rong-Kuo Chu, Chun-Che Liao, Ming-Feng Huang, Chin-Chang Chang, Hong-Shiu Ro, Long-Sun Kuo, Hung-Chou |
author_facet | Chang, Kuo-Hsuan Lin, Chia-Ni Chen, Chiung-Mei Lyu, Rong-Kuo Chu, Chun-Che Liao, Ming-Feng Huang, Chin-Chang Chang, Hong-Shiu Ro, Long-Sun Kuo, Hung-Chou |
author_sort | Chang, Kuo-Hsuan |
collection | PubMed |
description | Currently, there is no objective biomarker to indicate disease progression and monitor therapeutic effects for amyotrophic lateral sclerosis (ALS). This study aimed to identify plasma biomarkers for ALS using a targeted metabolomics approach. Plasma levels of 185 metabolites in 36 ALS patients and 36 age- and sex-matched normal controls (NCs) were quantified using an assay combining liquid chromatography with tandem mass spectrometry and direct flow injection. Identified candidates were correlated with the scores of the revised ALS Functional Rating Scale (ALSFRS-r). Support vector machine (SVM) learning applied to selected metabolites was used to differentiate ALS and NC subjects. Forty-four metabolites differed significantly between ALS and NC subjects. Significant correlations with ALSFRS-r score were seen in 23 metabolites. Six of them showing potential to distinguish ALS from NC—asymmetric dimethylarginine (area under the curve (AUC): 0.829), creatinine (AUC: 0.803), methionine (AUC: 0.767), PC-acyl-alkyl C34:2 (AUC: 0.808), C34:2 (AUC: 0.763), and PC-acyl-acyl C42:2 (AUC: 0.751)—were selected for machine learning. The SVM algorithm using selected metabolites achieved good performance, with an AUC of 0.945. In conclusion, our findings indicate that a panel of metabolites were correlated with disease severity of ALS, which could be potential biomarkers for monitoring ALS progression and therapeutic effects. |
format | Online Article Text |
id | pubmed-8699018 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86990182021-12-24 Altered Metabolic Profiles of the Plasma of Patients with Amyotrophic Lateral Sclerosis Chang, Kuo-Hsuan Lin, Chia-Ni Chen, Chiung-Mei Lyu, Rong-Kuo Chu, Chun-Che Liao, Ming-Feng Huang, Chin-Chang Chang, Hong-Shiu Ro, Long-Sun Kuo, Hung-Chou Biomedicines Article Currently, there is no objective biomarker to indicate disease progression and monitor therapeutic effects for amyotrophic lateral sclerosis (ALS). This study aimed to identify plasma biomarkers for ALS using a targeted metabolomics approach. Plasma levels of 185 metabolites in 36 ALS patients and 36 age- and sex-matched normal controls (NCs) were quantified using an assay combining liquid chromatography with tandem mass spectrometry and direct flow injection. Identified candidates were correlated with the scores of the revised ALS Functional Rating Scale (ALSFRS-r). Support vector machine (SVM) learning applied to selected metabolites was used to differentiate ALS and NC subjects. Forty-four metabolites differed significantly between ALS and NC subjects. Significant correlations with ALSFRS-r score were seen in 23 metabolites. Six of them showing potential to distinguish ALS from NC—asymmetric dimethylarginine (area under the curve (AUC): 0.829), creatinine (AUC: 0.803), methionine (AUC: 0.767), PC-acyl-alkyl C34:2 (AUC: 0.808), C34:2 (AUC: 0.763), and PC-acyl-acyl C42:2 (AUC: 0.751)—were selected for machine learning. The SVM algorithm using selected metabolites achieved good performance, with an AUC of 0.945. In conclusion, our findings indicate that a panel of metabolites were correlated with disease severity of ALS, which could be potential biomarkers for monitoring ALS progression and therapeutic effects. MDPI 2021-12-18 /pmc/articles/PMC8699018/ /pubmed/34944760 http://dx.doi.org/10.3390/biomedicines9121944 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chang, Kuo-Hsuan Lin, Chia-Ni Chen, Chiung-Mei Lyu, Rong-Kuo Chu, Chun-Che Liao, Ming-Feng Huang, Chin-Chang Chang, Hong-Shiu Ro, Long-Sun Kuo, Hung-Chou Altered Metabolic Profiles of the Plasma of Patients with Amyotrophic Lateral Sclerosis |
title | Altered Metabolic Profiles of the Plasma of Patients with Amyotrophic Lateral Sclerosis |
title_full | Altered Metabolic Profiles of the Plasma of Patients with Amyotrophic Lateral Sclerosis |
title_fullStr | Altered Metabolic Profiles of the Plasma of Patients with Amyotrophic Lateral Sclerosis |
title_full_unstemmed | Altered Metabolic Profiles of the Plasma of Patients with Amyotrophic Lateral Sclerosis |
title_short | Altered Metabolic Profiles of the Plasma of Patients with Amyotrophic Lateral Sclerosis |
title_sort | altered metabolic profiles of the plasma of patients with amyotrophic lateral sclerosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699018/ https://www.ncbi.nlm.nih.gov/pubmed/34944760 http://dx.doi.org/10.3390/biomedicines9121944 |
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