Characterising the covariance pattern between lifestyle factors and structural brain measures: a multivariable replication study of two independent ageing cohorts

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Modifiable lifestyle factors have been shown to promote healthy brain ageing. However, studies have typically focused on a single factor at a time. Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle-brain relationship. Here, canonical correlation analyses (CCA) examined the relationship between nine lifestyle factors and seven MRI-derived indices of brain structure. The resulting covariance pattern was further explored with Bayesian regressions. CCA analyses were first conducted on a Danish cohort of older adults (n = 251) and then replicated in a British cohort (n = 668). In both cohorts, the latent factors of lifestyle and brain structure were positively correlated (UK: r = .37, p < 0.001; Denmark: r = .27, p < 0.001). In the cross-validation study, the correlation between lifestyle-brain latent factors was r = .10, p = 0.008. However, the pattern of associations differed between datasets. These findings suggest that baseline characterisation and tailoring towards the study sample may be beneficial for achieving targeted lifestyle interventions.
OriginalsprogEngelsk
TidsskriftNeurobiology of Aging
Vol/bind131
Sider (fra-til)115-123
ISSN0197-4580
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This work was supported by funding from the Global Brain Health Institute (GBHI), Alzheimer’s Association and Alzheimer’s Society (grant GBHI ALZ UK-21–723783 ). The LISA study was supported by the Nordea Foundation (grant from Center for Healthy Aging, University of Copenhagen , Denmark) and the Whitehall MRI Sub-study was funded by the UK Medical Research Council ( G1001354 ). HRS holds a 5-year professorship in precision medicine at the Faculty of Health Sciences and Medicine, University of Copenhagen which is sponsored by the Lundbeck Foundation (grant R186–2015-2138 ). ND is supported by funding from the Lundbeck Foundation (grant R380–2021-1269 ). OJH was funded by a Novo Nordisk Foundation Exploratory Interdisciplinary Synergy grant (ref. NNF20OC0064869 ). KPE was supported by EU Horizon 2020 grant agreement number 732592 (Lifebrain). We are grateful to Dr. Athanasia Mowinckel for help with the processing of MR images from the Whitehall cohort.

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© 2023 The Author(s)

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