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Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders

Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict “healthy” brain age. Brain regions most informative for the prediction included...

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Autores principales: Kolbeinsson, Arinbjörn, Filippi, Sarah, Panagakis, Yannis, Matthews, Paul M., Elliott, Paul, Dehghan, Abbas, Tzoulaki, Ioanna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672070/
https://www.ncbi.nlm.nih.gov/pubmed/33203906
http://dx.doi.org/10.1038/s41598-020-76518-z
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author Kolbeinsson, Arinbjörn
Filippi, Sarah
Panagakis, Yannis
Matthews, Paul M.
Elliott, Paul
Dehghan, Abbas
Tzoulaki, Ioanna
author_facet Kolbeinsson, Arinbjörn
Filippi, Sarah
Panagakis, Yannis
Matthews, Paul M.
Elliott, Paul
Dehghan, Abbas
Tzoulaki, Ioanna
author_sort Kolbeinsson, Arinbjörn
collection PubMed
description Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict “healthy” brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk.
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spelling pubmed-76720702020-11-18 Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders Kolbeinsson, Arinbjörn Filippi, Sarah Panagakis, Yannis Matthews, Paul M. Elliott, Paul Dehghan, Abbas Tzoulaki, Ioanna Sci Rep Article Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict “healthy” brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk. Nature Publishing Group UK 2020-11-17 /pmc/articles/PMC7672070/ /pubmed/33203906 http://dx.doi.org/10.1038/s41598-020-76518-z Text en © The Author(s) 2020 Open Access This 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/.
spellingShingle Article
Kolbeinsson, Arinbjörn
Filippi, Sarah
Panagakis, Yannis
Matthews, Paul M.
Elliott, Paul
Dehghan, Abbas
Tzoulaki, Ioanna
Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders
title Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders
title_full Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders
title_fullStr Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders
title_full_unstemmed Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders
title_short Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders
title_sort accelerated mri-predicted brain ageing and its associations with cardiometabolic and brain disorders
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672070/
https://www.ncbi.nlm.nih.gov/pubmed/33203906
http://dx.doi.org/10.1038/s41598-020-76518-z
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