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Brain age predicts mortality

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ul...

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Autores principales: Cole, J H, Ritchie, S J, Bastin, M E, Valdés Hernández, M C, Muñoz Maniega, S, Royle, N, Corley, J, Pattie, A, Harris, S E, Zhang, Q, Wray, N R, Redmond, P, Marioni, R E, Starr, J M, Cox, S R, Wardlaw, J M, Sharp, D J, Deary, I J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984097/
https://www.ncbi.nlm.nih.gov/pubmed/28439103
http://dx.doi.org/10.1038/mp.2017.62
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author Cole, J H
Ritchie, S J
Bastin, M E
Valdés Hernández, M C
Muñoz Maniega, S
Royle, N
Corley, J
Pattie, A
Harris, S E
Zhang, Q
Wray, N R
Redmond, P
Marioni, R E
Starr, J M
Cox, S R
Wardlaw, J M
Sharp, D J
Deary, I J
author_facet Cole, J H
Ritchie, S J
Bastin, M E
Valdés Hernández, M C
Muñoz Maniega, S
Royle, N
Corley, J
Pattie, A
Harris, S E
Zhang, Q
Wray, N R
Redmond, P
Marioni, R E
Starr, J M
Cox, S R
Wardlaw, J M
Sharp, D J
Deary, I J
author_sort Cole, J H
collection PubMed
description Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, ‘brain-predicted age’, derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.
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spelling pubmed-59840972018-06-04 Brain age predicts mortality Cole, J H Ritchie, S J Bastin, M E Valdés Hernández, M C Muñoz Maniega, S Royle, N Corley, J Pattie, A Harris, S E Zhang, Q Wray, N R Redmond, P Marioni, R E Starr, J M Cox, S R Wardlaw, J M Sharp, D J Deary, I J Mol Psychiatry Original Article Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, ‘brain-predicted age’, derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death. Nature Publishing Group 2018-05 2017-04-25 /pmc/articles/PMC5984097/ /pubmed/28439103 http://dx.doi.org/10.1038/mp.2017.62 Text en Copyright © 2018 The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Original Article
Cole, J H
Ritchie, S J
Bastin, M E
Valdés Hernández, M C
Muñoz Maniega, S
Royle, N
Corley, J
Pattie, A
Harris, S E
Zhang, Q
Wray, N R
Redmond, P
Marioni, R E
Starr, J M
Cox, S R
Wardlaw, J M
Sharp, D J
Deary, I J
Brain age predicts mortality
title Brain age predicts mortality
title_full Brain age predicts mortality
title_fullStr Brain age predicts mortality
title_full_unstemmed Brain age predicts mortality
title_short Brain age predicts mortality
title_sort brain age predicts mortality
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984097/
https://www.ncbi.nlm.nih.gov/pubmed/28439103
http://dx.doi.org/10.1038/mp.2017.62
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