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Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer’s Disease

Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer’s disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma...

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Autores principales: Huang, Yi-Long, Lin, Chao-Hsiung, Tsai, Tsung-Hsien, Huang, Chen-Hua, Li, Jie-Ling, Chen, Liang-Kung, Li, Chun-Hsien, Tsai, Ting-Fen, Wang, Pei-Ning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535253/
https://www.ncbi.nlm.nih.gov/pubmed/34681563
http://dx.doi.org/10.3390/ijms222010903
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author Huang, Yi-Long
Lin, Chao-Hsiung
Tsai, Tsung-Hsien
Huang, Chen-Hua
Li, Jie-Ling
Chen, Liang-Kung
Li, Chun-Hsien
Tsai, Ting-Fen
Wang, Pei-Ning
author_facet Huang, Yi-Long
Lin, Chao-Hsiung
Tsai, Tsung-Hsien
Huang, Chen-Hua
Li, Jie-Ling
Chen, Liang-Kung
Li, Chun-Hsien
Tsai, Ting-Fen
Wang, Pei-Ning
author_sort Huang, Yi-Long
collection PubMed
description Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer’s disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD.
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spelling pubmed-85352532021-10-23 Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer’s Disease Huang, Yi-Long Lin, Chao-Hsiung Tsai, Tsung-Hsien Huang, Chen-Hua Li, Jie-Ling Chen, Liang-Kung Li, Chun-Hsien Tsai, Ting-Fen Wang, Pei-Ning Int J Mol Sci Article Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer’s disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD. MDPI 2021-10-09 /pmc/articles/PMC8535253/ /pubmed/34681563 http://dx.doi.org/10.3390/ijms222010903 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
Huang, Yi-Long
Lin, Chao-Hsiung
Tsai, Tsung-Hsien
Huang, Chen-Hua
Li, Jie-Ling
Chen, Liang-Kung
Li, Chun-Hsien
Tsai, Ting-Fen
Wang, Pei-Ning
Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer’s Disease
title Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer’s Disease
title_full Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer’s Disease
title_fullStr Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer’s Disease
title_full_unstemmed Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer’s Disease
title_short Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer’s Disease
title_sort discovery of a metabolic signature predisposing high risk patients with mild cognitive impairment to converting to alzheimer’s disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535253/
https://www.ncbi.nlm.nih.gov/pubmed/34681563
http://dx.doi.org/10.3390/ijms222010903
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