TY - JOUR
T1 - Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change
AU - Vidal-Pineiro, Didac
AU - Wang, Yunpeng
AU - Krogsrud, Stine K.
AU - Amlien, Inge K.
AU - Baaré, William F.C.
AU - Bartres-Faz, David
AU - Bertram, Lars
AU - Brandmaier, Andreas M.
AU - Drevon, Christian A.
AU - Düzel, Sandra
AU - Ebmeier, Klaus
AU - Henson, Richard N.
AU - Junqué, Carme
AU - Kievit, Rogier Andrew
AU - Kühn, Simone
AU - Leonardsen, Esten
AU - Lindenberger, Ulman
AU - Madsen, Kathrine S.
AU - Magnussen, Fredrik
AU - Mowinckel, Athanasia Monika
AU - Nyberg, Lars
AU - Roe, James M.
AU - Segura, Barbara
AU - Smith, Stephen M.
AU - Sørensen, Øystein
AU - Suri, Sana
AU - Westerhausen, Rene
AU - Zalesky, Andrew
AU - Zsoldos, Enikő
AU - Walhovd, Kristine Beate
AU - Fjell, Anders
N1 - Funding Information:
BASE-II has been supported by the German Federal Ministry of Education and Research under grant numbers 16SV5537/16SV5837/16SV5538/16SV5536K/01UW0808/01U-W0706/01GL1716A/01GL1716B. Part of the computation was performed on the Norwegian high-performance computation resources, sigma2, through the project no. NN9767K. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from award 203139/Z/16/Z from the Wellcome Trust. Data used in the preparation of this article were partially obtained from the AIBL funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), which was made available at the ADNI database (http://www.loni.usc.edu/ADNI). UK Biobank is generously supported by its founding funders the Wellcome Trust and UK Medical Research Council, as well as the Department of Health, Scottish Government, the Northwest Regional Development Agency, British Heart Foundation and Cancer Research UK. The organization has over 150 dedicated members of staff, based in multiple locations across the UK.
Publisher Copyright:
© Vidal-Pineiro et al. This article is distributed under the terms of the Creative Commons Attribution License,.
PY - 2021/11
Y1 - 2021/11
N2 - 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 indepen-dent 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.
AB - 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 indepen-dent 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.
UR - http://www.scopus.com/inward/record.url?scp=85120157840&partnerID=8YFLogxK
U2 - 10.7554/eLife.69995
DO - 10.7554/eLife.69995
M3 - Journal articles
C2 - 34756163
AN - SCOPUS:85120157840
SN - 2050-084X
VL - 10
JO - eLife
JF - eLife
M1 - e69995
ER -