Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study
Authors: DeJong Nathan R , Jansen Jacobus F A , van Boxtel Martin P J , Schram Miranda T , Stehouwer Coen D A , van Greevenbroek Marleen M J , van der Kallen Carla J H , Koster Annemarie , Eussen Simone J P M , de Galan Bastiaan E , Backes Walter H , Köhler Sebastian 
Published: May 2024

Life-course exposure to risk and protective factors impacts brain macro- and micro-structure, which in turn affects cognition. The concept of brain-age gap assesses brain health by comparing an individual’s neuroimaging-based predicted age with their calendar age. A higher BAG implies accelerated brain ageing and is expected to be associated with worse cognition. In this study, we comprehensively modelled mutual associations between brain health and lifestyle factors, brain age and cognition in a large, middle-aged population. For this study, cognitive test scores, lifestyle and 3T MRI data for n = 4881 participants [mean age (± SD) = 59.2 (±8.6), 50.1% male] were available from The Maastricht Study, a population-based cohort study with extensive phenotyping. Whole-brain volumes (grey matter, cerebrospinal fluid and white matter hyperintensity), cerebral microbleeds and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health weighted sum score, with higher score indicating greater dementia risk. Cognition was calculated by averaging z-scores across three cognitive domains (memory, information processing speed and executive function and attention). Brain-age gap was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIfestyle for BRAin health tertiles, brain-age gap and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. The results show that cerebrospinal fluid, grey matter, white matter hyperintensity and cerebral microbleeds best predicted brain-age gap (R2 = 0.455, root mean squared error = 6.44). In regression analysis, higher LIfestyle for BRAin health scores (greater dementia risk) were associated with higher brain-age gap (standardized regression coefficient β = 0.126, P &lt; 0.001) and worse cognition (β = −0.046, P = 0.013), while higher brain-age gap was associated with worse cognition (β=−0.163, P &lt; 0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIfestyle for BRAin health tertile was mediated by brain-age gap (βindirect = −0.049, P &lt; 0.001; βtotal = −0.198, P &lt; 0.001) and an additional 3.8% was mediated via connectivity (βindirect = −0.006, P &lt; 0.001; βtotal = −0.150, P &lt; 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIfestyle for BRAin health) and cognition can be partially explained by structural brain health markers (brain-age gap) and white matter connectivity markers. Lifestyle interventions targeted at high-risk individuals in mid-to-late life may be effective in promoting and preserving cognitive function in the general public.