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
eng
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: British Heart Foundation
Funder: Cancer Research UK
Funder: Knut and Alice Wallenberg Foundation
Funder: NIHR Biomedical Research Centre, Oxford
Funder: Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg
Funder: ICREA Academia Award
Funder: National Institute for Health Research (NIHR)
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
Human, Aging, Neuroscience, Neuroimaging, T1w, Brain Age Gap, Brain Decline, Brain Age Delta
Sponsorship
Medical Research Council (MC_UU_00005/8)
Biotechnology and Biological Sciences Research Council (BB/H008217/1)
European Commission (732592)
Identifiers
PMC8580481, 34756163
External DOI: https://doi.org/10.7554/eLife.69995
This record's URL: https://www.repository.cam.ac.uk/handle/1810/332306
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