Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change.
Authors
Wang, Yunpeng
Krogsrud, Stine K
Amlien, Inge K
Baaré, William Fc
Bertram, Lars
Düzel, Sandra
Junqué, Carme
Leonardsen, Esten
Madsen, Kathrine S
Roe, James M
Segura, Barbara
Sørensen, Øystein
Suri, Sana
Westerhausen, Rene
Zalesky, Andrew
Walhovd, Kristine Beate
Fjell, Anders
Publication Date
2021-11-10Journal Title
Elife
ISSN
2050-084X
Publisher
eLife Sciences Publications, Ltd
Volume
10
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Vidal-Pineiro, D., Wang, Y., Krogsrud, S. K., Amlien, I. K., Baaré, W. F., Bartres-Faz, D., Bertram, L., et al. (2021). Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change.. Elife, 10 https://doi.org/10.7554/eLife.69995
Description
Funder: Norges Forskningsråd; FundRef: http://dx.doi.org/10.13039/501100005416
Funder: Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg; FundRef: http://dx.doi.org/10.13039/501100012319
Funder: NIHR Biomedical Research Centre, Oxford; FundRef: http://dx.doi.org/10.13039/501100013373
Funder: Knut and Alice Wallenberg Foundation; FundRef: http://dx.doi.org/10.13039/501100004063
Funder: ICREA Academia Award
Abstract
Brain age is a widely used index for quantifying individuals' brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.
Keywords
Research Article, Neuroscience, Aging, brain age gap, Brain age delta, brain decline, neuroimaging, T1w, Human
Sponsorship
H2020 European Research Council (732592)
H2020 European Research Council (283634 725025)
H2020 European Research Council (313440)
María de Maeztu Unit of Excellence (Institute of Neurosciences,University of Barcelona) (MDM-2017-0729)
European Research Council (677804)
UK Medical Research Council (G1001354)
Charitable Trust (1117747)
Alzheimer’s Research UK (441)
Norges Forskningsråd (324882)
Medical Research Council (SUAG/046 G101400)
Identifiers
69995
External DOI: https://doi.org/10.7554/eLife.69995
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330593
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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