Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change

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 Fc

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 - © 2021, Vidal-Pineiro et al.

PY - 2021/11/10

Y1 - 2021/11/10

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 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.

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 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.

U2 - 10.7554/eLife.69995

DO - 10.7554/eLife.69995

M3 - SCORING: Journal article

C2 - 34756163

VL - 10

JO - ELIFE

JF - ELIFE

SN - 2050-084X

M1 - e69995

ER -