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

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...

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Autores principales:	Didac Vidal-Pineiro, Yunpeng Wang, Stine K Krogsrud, Inge K Amlien, William FC 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
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	Aging brain age gap Brain age delta brain decline neuroimaging T1w Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f7bfb3e601d344e5b6d9bf1556cfb70a
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spelling	oai:doaj.org-article:f7bfb3e601d344e5b6d9bf1556cfb70a2021-11-10T14:09:31ZIndividual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change10.7554/eLife.699952050-084Xe69995https://doaj.org/article/f7bfb3e601d344e5b6d9bf1556cfb70a2021-11-01T00:00:00Zhttps://elifesciences.org/articles/69995https://doaj.org/toc/2050-084XBrain 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.Didac Vidal-PineiroYunpeng WangStine K KrogsrudInge K AmlienWilliam FC BaaréDavid Bartres-FazLars BertramAndreas M BrandmaierChristian A DrevonSandra DüzelKlaus EbmeierRichard N HensonCarme JunquéRogier Andrew KievitSimone KühnEsten LeonardsenUlman LindenbergerKathrine S MadsenFredrik MagnussenAthanasia Monika MowinckelLars NybergJames M RoeBarbara SeguraStephen M SmithØystein SørensenSana SuriRene WesterhausenAndrew ZaleskyEnikő ZsoldosKristine Beate WalhovdAnders FjelleLife Sciences Publications LtdarticleAgingbrain age gapBrain age deltabrain declineneuroimagingT1wMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Aging brain age gap Brain age delta brain decline neuroimaging T1w Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	Aging brain age gap Brain age delta brain decline neuroimaging T1w Medicine R Science Q Biology (General) QH301-705.5 Didac Vidal-Pineiro Yunpeng Wang Stine K Krogsrud Inge K Amlien William FC 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 Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change
description	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.
format	article
author	Didac Vidal-Pineiro Yunpeng Wang Stine K Krogsrud Inge K Amlien William FC 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
author_facet	Didac Vidal-Pineiro Yunpeng Wang Stine K Krogsrud Inge K Amlien William FC 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
author_sort	Didac Vidal-Pineiro
title	Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change
title_short	Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change
title_full	Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change
title_fullStr	Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change
title_full_unstemmed	Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change
title_sort	individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/f7bfb3e601d344e5b6d9bf1556cfb70a
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Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change

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