Brain‐age mediates the association between modifiable risk factors and cognitive decline early in the AD continuum

Background Neuroimaging‐derived brain‐age is a useful biomarker to study the brain’s biological aging process. Brain‐age has shown cross‐sectional associations with cognitive function and modifiable risk factors for dementia. We aimed to study, in cognitively unimpaired (CU) individuals, the mediati...

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Veröffentlicht in:Alzheimer's & dementia 2023-12, Vol.19 (S10), p.n/a
Hauptverfasser: Cumplido‐Mayoral, Irene, Brugulat‐Serrat, Anna, Sánchez‐Benavides, Gonzalo, Escalante, Armand González, Anastasi, Federica, Milà‐Alomà, Marta, Falcon, Carles, Shekari, Mahnaz, Cacciaglia, Raffaele, Minguillon, Carolina, Fauria, Karine, Molinuevo, Jose Luis, Suarez‐Calvet, Marc, Vilaplana, Verónica, Gispert, Juan Domingo
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Sprache:eng
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Zusammenfassung:Background Neuroimaging‐derived brain‐age is a useful biomarker to study the brain’s biological aging process. Brain‐age has shown cross‐sectional associations with cognitive function and modifiable risk factors for dementia. We aimed to study, in cognitively unimpaired (CU) individuals, the mediating role of brain‐age in the association between modifiable risk factors and cognitive changes, and the impact of AD pathology on this role. Method We included 416 CU individuals from the ALFA+ study with available structural MRI, measurements of the global cognitive Preclinical Alzheimer’s Cognitive Composite (PACC) (370 individuals had a follow‐up PACC assessment 3.28±0.27 years later), and lifestyle and cardiovascular risk factors assessments. We computed brain‐age delta as the difference between chronological and predicted brain‐age using a previously pre‐trained machine learning algorithm on structural MRI data. Partial Least Squares Path Modeling (PLS‐PM) was employed to investigate the mediation effect of brain‐age delta between a computed latent variable from modifiable risk factors (cardiovascular, mental health and mood, metabolic/endocrine disease history, and alcohol consumption factors; Table 1) and a latent variable from longitudinal PACC. Statistical bias adjustment was performed to control for the confounding effects of age and sex by using multiple linear regression. The analysis was performed on the whole sample (ALL) and after stratification by amyloid‐ß (Aß) status. Participants were classified as amyloid‐ß positive (Aß+) if CSF Aß42/40
ISSN:1552-5260
1552-5279
DOI:10.1002/alz.081797