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MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention

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Irene Brusini, Eilidh MacNicol, Eugene Kim, Örjan Smedby, Chunliang Wang, Eric Westman, Mattia Veronese, Federico Turkheimer, Diana Cash

Original languageEnglish
Pages (from-to)204-215
Number of pages12
JournalNeurobiology of Aging
Volume109
Early online date14 Oct 2021
DOIs
E-pub ahead of print14 Oct 2021
PublishedJan 2022

Bibliographical note

Funding Information: The study was funded by the UK Biotechnology and Biological Sciences Research Council (BB/N009088/1). IB was supported by the joint research funds of KTH Royal Institute of Technology and Stockholm County Council (HMT). EM was supported by the UK Medical Research Council (MR/N013700/1) and Kings College London as a member of the MRC Doctoral Training Partnership in Biomedical Sciences. MV was supported by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and Kings College London. The views expressed are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Publisher Copyright: © 2021 The Authors

King's Authors

Abstract

The difference between brain age predicted from MRI and chronological age (the so-called BrainAGE) has been proposed as an ageing biomarker. We analyse its cross-species potential by testing it on rats undergoing an ageing modulation intervention. Our rat brain age prediction model combined Gaussian process regression with a classifier and achieved a mean absolute error (MAE) of 4.87 weeks using cross-validation on a longitudinal dataset of 31 normal ageing rats. It was then tested on two groups of 24 rats (MAE = 9.89 weeks, correlation coefficient = 0.86): controls vs. a group under long-term environmental enrichment and dietary restriction (EEDR). Using a linear mixed-effects model, BrainAGE was found to increase more slowly with chronological age in EEDR rats (p=0.015 for the interaction term). Cox regression showed that older BrainAGE at 5 months was associated with higher mortality risk (p=0.03). Our findings suggest that lifestyle-related prevention approaches may help to slow down brain ageing in rodents and the potential of BrainAGE as a predictor of age-related health outcomes.

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