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

Didac Vidal-Pineiro; Yunpeng Wang; Stine K. Krogsrud; Inge K. Amlien; William F.C. Baaré; David Bartres-Faz; Lars Bertram; Andreas M. Brandmaier; Christian A. Drevon; Sandra Düzel; Klaus Ebmeier; Richard N. Henson; Carme Junqué; Rogier Andrew Kievit; Simone Kühn; Esten Leonardsen; Ulman Lindenberger; Kathrine S. Madsen; Fredrik Magnussen; Athanasia Monika Mowinckel; Lars Nyberg; James M. Roe; Barbara Segura; Stephen M. Smith; Øystein Sørensen; Sana Suri; Rene Westerhausen; Andrew Zalesky; Enikő Zsoldos; Kristine Beate Walhovd; Anders Fjell

doi:10.7554/eLife.69995

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

Didac Vidal-Pineiro^*, Yunpeng Wang, Stine K. Krogsrud, Inge K. Amlien, William F.C. Baaré, David Bartres-Faz, Lars Bertram, Andreas M. Brandmaier, Christian A. Drevon, Sandra Düzel, Klaus Ebmeier, Richard N. Henson, Carme Junqué, Rogier Andrew Kievit, Simone Kühn, Esten Leonardsen, Ulman Lindenberger, Kathrine S. Madsen, Fredrik Magnussen, Athanasia Monika MowinckelLars Nyberg, James M. Roe, Barbara Segura, Stephen M. Smith, Øystein Sørensen, Sana Suri, Rene Westerhausen, Andrew Zalesky, Enikő Zsoldos, Kristine Beate Walhovd, Anders Fjell

^*Korrespondierende/r Autor/-in für diese Arbeit

119 Zitate (Scopus)

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 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.

Originalsprache	Englisch
Aufsatznummer	e69995
Zeitschrift	eLife
Jahrgang	10
DOIs	https://doi.org/10.7554/eLife.69995
Publikationsstatus	Veröffentlicht - 11.2021

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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.

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10.7554/eLife.69995

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Vidal-Pineiro, D., Wang, Y., Krogsrud, S. K., Amlien, I. K., Baaré, W. F. C., Bartres-Faz, D., Bertram, L., Brandmaier, A. M., Drevon, C. A., Düzel, S., Ebmeier, K., Henson, R. N., Junqué, C., Kievit, R. A., Kühn, S., Leonardsen, E., Lindenberger, U., Madsen, K. S., Magnussen, F., ... Fjell, A. (2021). Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change. eLife, 10, Artikel e69995. https://doi.org/10.7554/eLife.69995

@article{ca8fccbb9fe041d3b998469dddf24e59,

title = "Individual variations in {\textquoteleft}brain age{\textquoteright} relate to early-life factors more than to longitudinal brain change",

abstract = "Brain age is a widely used index for quantifying individuals{\textquoteright} 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.",

author = "Didac Vidal-Pineiro and Yunpeng Wang and Krogsrud, \{Stine K.\} and Amlien, \{Inge K.\} and Baar{\'e}, \{William F.C.\} and David Bartres-Faz and Lars Bertram and Brandmaier, \{Andreas M.\} and Drevon, \{Christian A.\} and Sandra D{\"u}zel and Klaus Ebmeier and Henson, \{Richard N.\} and Carme Junqu{\'e} and Kievit, \{Rogier Andrew\} and Simone K{\"u}hn and Esten Leonardsen and Ulman Lindenberger and Madsen, \{Kathrine S.\} and Fredrik Magnussen and Mowinckel, \{Athanasia Monika\} and Lars Nyberg and Roe, \{James M.\} and Barbara Segura and Smith, \{Stephen M.\} and {\O}ystein S{\o}rensen and Sana Suri and Rene Westerhausen and Andrew Zalesky and Enik{\H o} Zsoldos and Walhovd, \{Kristine Beate\} and Anders Fjell",

note = "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: {\textcopyright} Vidal-Pineiro et al. This article is distributed under the terms of the Creative Commons Attribution License,.",

year = "2021",

month = nov,

doi = "10.7554/eLife.69995",

language = "English",

volume = "10",

journal = "eLife",

issn = "2050-084X",

publisher = "eLife Sciences Publications Ltd",

}

Vidal-Pineiro, D, Wang, Y, Krogsrud, SK, Amlien, IK, Baaré, WFC, Bartres-Faz, D, Bertram, L, Brandmaier, AM, Drevon, CA, Düzel, S, Ebmeier, K, Henson, RN, Junqué, C, Kievit, RA, Kühn, S, Leonardsen, E, Lindenberger, U, Madsen, KS, Magnussen, F, Mowinckel, AM, Nyberg, L, Roe, JM, Segura, B, Smith, SM, Sørensen, Ø, Suri, S, Westerhausen, R, Zalesky, A, Zsoldos, E, Walhovd, KB & Fjell, A 2021, 'Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change', eLife, Jg. 10, e69995. https://doi.org/10.7554/eLife.69995

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 - https://www.scopus.com/pages/publications/85120157840

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 -

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

Abstract

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