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Plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults
INTRODUCTION: Motoric cognitive risk syndrome (MCR) is characterized by subjective cognitive complaints (SCCs) and slow gait (SG). Metabolomics and lipidomics may potentiate disclosure of the underlying mechanisms of MCR. METHODS: This was a cross-sectional study from the West China Health and Aging...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490321/ https://www.ncbi.nlm.nih.gov/pubmed/36158552 http://dx.doi.org/10.3389/fnagi.2022.977191 |
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author | Li, Wanmeng Sun, Xuelian Liu, Yu Ge, Meiling Lu, Ying Liu, Xiaolei Zhou, Lixing Liu, Xiaohui Dong, Biao Yue, Jirong Xue, Qianli Dai, Lunzhi Dong, Birong |
author_facet | Li, Wanmeng Sun, Xuelian Liu, Yu Ge, Meiling Lu, Ying Liu, Xiaolei Zhou, Lixing Liu, Xiaohui Dong, Biao Yue, Jirong Xue, Qianli Dai, Lunzhi Dong, Birong |
author_sort | Li, Wanmeng |
collection | PubMed |
description | INTRODUCTION: Motoric cognitive risk syndrome (MCR) is characterized by subjective cognitive complaints (SCCs) and slow gait (SG). Metabolomics and lipidomics may potentiate disclosure of the underlying mechanisms of MCR. METHODS: This was a cross-sectional study from the West China Health and Aging Trend cohort study (WCHAT). The operational definition of MCR is the presence of SCCs and SG without dementia or mobility disability. The test and analysis were based on untargeted metabolomics and lipidomics, consensus clustering, lasso regression and 10-fold cross-validation. RESULTS: This study enrolled 6,031 individuals for clinical analysis and 577 plasma samples for omics analysis. The overall prevalence of MCR was 9.7%, and the prevalence of MCR-only, assessed cognitive impairment-only (CI-only) and MCR-CI were 7.5, 13.3, and 2.1%, respectively. By consensus clustering analysis, MCR-only was clustered into three metabolic subtypes, MCR-I, MCR-II and MCR-III. Clinically, body fat mass (OR = 0.89, CI = 0.82–0.96) was negatively correlated with MCR-I, and comorbidity (OR = 2.19, CI = 1.10–4.38) was positively correlated with MCR-III. Diabetes mellitus had the highest ORs above 1 in MCR-II and MCR-III (OR = 3.18, CI = 1.02–9.91; OR = 2.83, CI = 1.33–6.04, respectively). The risk metabolites of MCR-III showed relatively high similarity with those of cognitive impairment. Notably, L-proline, L-cystine, ADMA, and N1-acetylspermidine were significantly changed in MCR-only, and PC(40:3), SM(32:1), TG(51:3), eicosanoic acid(20:1), methyl-D-galactoside and TG(50:3) contributed most to the prediction model for MCR-III. INTERPRETATION: Pre-dementia syndrome of MCR has distinct metabolic subtypes, and SCCs and SG may cause different metabolic changes to develop MCR. |
format | Online Article Text |
id | pubmed-9490321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94903212022-09-22 Plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults Li, Wanmeng Sun, Xuelian Liu, Yu Ge, Meiling Lu, Ying Liu, Xiaolei Zhou, Lixing Liu, Xiaohui Dong, Biao Yue, Jirong Xue, Qianli Dai, Lunzhi Dong, Birong Front Aging Neurosci Neuroscience INTRODUCTION: Motoric cognitive risk syndrome (MCR) is characterized by subjective cognitive complaints (SCCs) and slow gait (SG). Metabolomics and lipidomics may potentiate disclosure of the underlying mechanisms of MCR. METHODS: This was a cross-sectional study from the West China Health and Aging Trend cohort study (WCHAT). The operational definition of MCR is the presence of SCCs and SG without dementia or mobility disability. The test and analysis were based on untargeted metabolomics and lipidomics, consensus clustering, lasso regression and 10-fold cross-validation. RESULTS: This study enrolled 6,031 individuals for clinical analysis and 577 plasma samples for omics analysis. The overall prevalence of MCR was 9.7%, and the prevalence of MCR-only, assessed cognitive impairment-only (CI-only) and MCR-CI were 7.5, 13.3, and 2.1%, respectively. By consensus clustering analysis, MCR-only was clustered into three metabolic subtypes, MCR-I, MCR-II and MCR-III. Clinically, body fat mass (OR = 0.89, CI = 0.82–0.96) was negatively correlated with MCR-I, and comorbidity (OR = 2.19, CI = 1.10–4.38) was positively correlated with MCR-III. Diabetes mellitus had the highest ORs above 1 in MCR-II and MCR-III (OR = 3.18, CI = 1.02–9.91; OR = 2.83, CI = 1.33–6.04, respectively). The risk metabolites of MCR-III showed relatively high similarity with those of cognitive impairment. Notably, L-proline, L-cystine, ADMA, and N1-acetylspermidine were significantly changed in MCR-only, and PC(40:3), SM(32:1), TG(51:3), eicosanoic acid(20:1), methyl-D-galactoside and TG(50:3) contributed most to the prediction model for MCR-III. INTERPRETATION: Pre-dementia syndrome of MCR has distinct metabolic subtypes, and SCCs and SG may cause different metabolic changes to develop MCR. Frontiers Media S.A. 2022-09-07 /pmc/articles/PMC9490321/ /pubmed/36158552 http://dx.doi.org/10.3389/fnagi.2022.977191 Text en Copyright © 2022 Li, Sun, Liu, Ge, Lu, Liu, Zhou, Liu, Dong, Yue, Xue, Dai and Dong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Li, Wanmeng Sun, Xuelian Liu, Yu Ge, Meiling Lu, Ying Liu, Xiaolei Zhou, Lixing Liu, Xiaohui Dong, Biao Yue, Jirong Xue, Qianli Dai, Lunzhi Dong, Birong Plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults |
title | Plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults |
title_full | Plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults |
title_fullStr | Plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults |
title_full_unstemmed | Plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults |
title_short | Plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults |
title_sort | plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490321/ https://www.ncbi.nlm.nih.gov/pubmed/36158552 http://dx.doi.org/10.3389/fnagi.2022.977191 |
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