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

Dídac Vidal-Piñeiro ,Simone Kühn,Andrew Zalesky,Esten Leonardsen,Inge K Amlien,James M Roe,Øystein Sørensen,Carme Junqué,Ulman Lindenberger,Sandra Düzel,Lars Bertram,Yunpeng Wang,Sana Suri,Lars Nyberg,Fredrik Magnussen,Enikő Zsoldos ,Kathrine Skak Madsen ,René Westerhausen ,Rogier A Kievit ,Stephen M Smith ,William F.c Baaré ,Athanasia M Mowinckel ,Richard Henson ,Andreas M Brandmaier ,David Bartrés‐Faz ,Bàrbara Segura ,Stine Kleppe Krogsrud ,Klaus P Ebmeier ,Drevon Ca ,Kristine B Walhovd ,Anders M Fjell

doi:10.7554/elife.69995

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.

Highlights

The concept of brain age is increasingly used to capture inter-individual differences in the integrity of the aging brain[1]
The biological age of the brain is estimated typically by applying machine learning to magnetic resonance imaging (MRI) data to predict chronological age
These findings do not support the claim that the individual variations in the cross-sectional brain age metric captures across-subject differences in the ongoing rate of brain aging

Summary

Introduction

The concept of brain age is increasingly used to capture inter-individual differences in the integrity of the aging brain[1]. The difference between predicted brain age and actual chronological age (brain age delta) reflects the deviation from the expected norm and is often used to index brain health. Brain age delta has been related to brain, mental, and cognitive health and proved valuable in predicting outcomes such as mortality[1,2,3]. It is assumed that brain age delta reflects past and ongoing neurobiological aging processes[1,3,4,5,6]. It is common to interpret positive brain age deltas as reflecting a steeper rate of brain aging; often dubbed as accelerated aging[1,4,6]

Methods

Results

Conclusion