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Cited by 831

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

Brain age predicts mortality

Open access
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·2018·Molecular Psychiatry
DOI 10.1038/mp.2017.62
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Abstract

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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Metadata

Authors
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.
Venue
Molecular Psychiatry
Publisher
Springer Science and Business Media LLC
Published
2017-04-25
Volume
23
Issue
5
Pages
1385-1392
ISSN
  • 1359-4184 (ISSN-L)
  • 1476-5578
DOI
10.1038/mp.2017.62
OpenAlex
W2602552939
Cited by
831
References
49
FWCI
34.67
Type
journal article
Language
en
Open access
hybrid

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.