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Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease
BACKGROUND: Although lipid metabolite dysfunction contributes substantially to clinical signs and pathophysiology of Alzheimer’s disease (AD), how dyslipidemia promoting neuropathological processes and brain functional impairment subsequently facilitates the progression of AD remains unclear. METHOD...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724298/ https://www.ncbi.nlm.nih.gov/pubmed/36474263 http://dx.doi.org/10.1186/s12967-022-03786-w |
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author | Wang, Qing Zang, Feifei He, Cancan Zhang, Zhijun Xie, Chunming |
author_facet | Wang, Qing Zang, Feifei He, Cancan Zhang, Zhijun Xie, Chunming |
author_sort | Wang, Qing |
collection | PubMed |
description | BACKGROUND: Although lipid metabolite dysfunction contributes substantially to clinical signs and pathophysiology of Alzheimer’s disease (AD), how dyslipidemia promoting neuropathological processes and brain functional impairment subsequently facilitates the progression of AD remains unclear. METHODS: We combined large-scale brain resting-state networks (RSNs) approaches with canonical correlation analysis to explore the accumulating effects of lipid gene- and protein-centric levels on cerebrospinal fluid (CSF) biomarkers, dynamic trajectory of large-scale RSNs, and cognitive performance across entire AD spectrum. Support vector machine model was used to distinguish AD spectrum and pathway analysis was used to test the influences among these variables. RESULTS: We found that the effects of accumulation of lipid-pathway genetic variants and lipoproteins were significantly correlated with CSF biomarkers levels and cognitive performance across the AD spectrum. Dynamic trajectory of large-scale RSNs represented a rebounding mode, which is characterized by a weakened network cohesive connector role and enhanced network incohesive provincial role following disease progression. Importantly, the fluctuating large-scale RSNs connectivity was significantly correlated with the summative effects of lipid-pathway genetic variants and lipoproteins, CSF biomarkers, and cognitive performance. Moreover, SVM model revealed that the lipid-associated twenty-two brain network connections represented higher capacity to classify AD spectrum. Pathway analysis further identified dyslipidemia directly influenced brain network reorganization or indirectly affected the CSF biomarkers and subsequently caused cognitive decline. CONCLUSIONS: Dyslipidemia exacerbated cognitive decline and increased the risk of AD via mediating large-scale brain networks integrity and promoting neuropathological processes. These findings reveal a role for lipid metabolism in AD pathogenesis and suggest lipid management as a potential therapeutic target for AD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03786-w. |
format | Online Article Text |
id | pubmed-9724298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97242982022-12-07 Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease Wang, Qing Zang, Feifei He, Cancan Zhang, Zhijun Xie, Chunming J Transl Med Research BACKGROUND: Although lipid metabolite dysfunction contributes substantially to clinical signs and pathophysiology of Alzheimer’s disease (AD), how dyslipidemia promoting neuropathological processes and brain functional impairment subsequently facilitates the progression of AD remains unclear. METHODS: We combined large-scale brain resting-state networks (RSNs) approaches with canonical correlation analysis to explore the accumulating effects of lipid gene- and protein-centric levels on cerebrospinal fluid (CSF) biomarkers, dynamic trajectory of large-scale RSNs, and cognitive performance across entire AD spectrum. Support vector machine model was used to distinguish AD spectrum and pathway analysis was used to test the influences among these variables. RESULTS: We found that the effects of accumulation of lipid-pathway genetic variants and lipoproteins were significantly correlated with CSF biomarkers levels and cognitive performance across the AD spectrum. Dynamic trajectory of large-scale RSNs represented a rebounding mode, which is characterized by a weakened network cohesive connector role and enhanced network incohesive provincial role following disease progression. Importantly, the fluctuating large-scale RSNs connectivity was significantly correlated with the summative effects of lipid-pathway genetic variants and lipoproteins, CSF biomarkers, and cognitive performance. Moreover, SVM model revealed that the lipid-associated twenty-two brain network connections represented higher capacity to classify AD spectrum. Pathway analysis further identified dyslipidemia directly influenced brain network reorganization or indirectly affected the CSF biomarkers and subsequently caused cognitive decline. CONCLUSIONS: Dyslipidemia exacerbated cognitive decline and increased the risk of AD via mediating large-scale brain networks integrity and promoting neuropathological processes. These findings reveal a role for lipid metabolism in AD pathogenesis and suggest lipid management as a potential therapeutic target for AD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03786-w. BioMed Central 2022-12-06 /pmc/articles/PMC9724298/ /pubmed/36474263 http://dx.doi.org/10.1186/s12967-022-03786-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Wang, Qing Zang, Feifei He, Cancan Zhang, Zhijun Xie, Chunming Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease |
title | Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease |
title_full | Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease |
title_fullStr | Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease |
title_full_unstemmed | Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease |
title_short | Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease |
title_sort | dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the alzheimer’s disease |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724298/ https://www.ncbi.nlm.nih.gov/pubmed/36474263 http://dx.doi.org/10.1186/s12967-022-03786-w |
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