Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study (2024)

DeJong, N. R., Jansen, J. F. A., van Boxtel, M. P. J., Schram, M. T., Stehouwer, C. D. A., van Greevenbroek, M. M. J., van der Kallen, C. J. H., Koster, A., Eussen, S. J. P. M., de Galan, B. E., Backes, W. H. (2024). Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study. Brain Communications, 6(3), Article 171. https://doi.org/10.1093/braincomms/fcae171

DeJong, Nathan R ; Jansen, Jacobus F A ; van Boxtel, Martin P J et al. / Brain structure and connectivity mediate the association between lifestyle and cognition : The Maastricht Study. In: Brain Communications. 2024 ; Vol. 6, No. 3.

@article{157ad69ad6fb4b3da5428dcb423c8bda,

title = "Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study",

abstract = "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 = 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 -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 ( = 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 = 0.126, < 0.001) and worse cognition ( = -0.046, = 0.013), while higher brain-age gap was associated with worse cognition ( =-0.163, < 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 ( = -0.049, < 0.001; = -0.198, < 0.001) and an additional 3.8% was mediated via connectivity ( = -0.006, < 0.001; = -0.150, < 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.",

keywords = "brain reserve, cognition, connectivity, lifestyle, resilience",

author = "DeJong, {Nathan R} and Jansen, {Jacobus F A} and {van Boxtel}, {Martin P J} and Schram, {Miranda T} and Stehouwer, {Coen D A} and {van Greevenbroek}, {Marleen M J} and {van der Kallen}, {Carla J H} and Annemarie Koster and Eussen, {Simone J P M} and {de Galan}, {Bastiaan E} and Backes, {Walter H} and Sebastian K{\"o}hler",

year = "2024",

month = may,

day = "16",

doi = "10.1093/braincomms/fcae171",

language = "English",

volume = "6",

journal = "Brain Communications",

issn = "2632-1297",

publisher = "Oxford University Press",

number = "3",

}

DeJong, NR, Jansen, JFA, van Boxtel, MPJ, Schram, MT, Stehouwer, CDA, van Greevenbroek, MMJ, van der Kallen, CJH, Koster, A, Eussen, SJPM, de Galan, BE, Backes, WH 2024, 'Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study', Brain Communications, vol. 6, no. 3, 171. https://doi.org/10.1093/braincomms/fcae171

Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study. / DeJong, Nathan R; Jansen, Jacobus F A; van Boxtel, Martin P J et al.
In: Brain Communications, Vol. 6, No. 3, 171, 16.05.2024.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Brain structure and connectivity mediate the association between lifestyle and cognition

T2 - The Maastricht Study

AU - DeJong, Nathan R

AU - Jansen, Jacobus F A

AU - van Boxtel, Martin P J

AU - Schram, Miranda T

AU - Stehouwer, Coen D A

AU - van Greevenbroek, Marleen M J

AU - van der Kallen, Carla J H

AU - Koster, Annemarie

AU - Eussen, Simone J P M

AU - de Galan, Bastiaan E

AU - Backes, Walter H

AU - Köhler, Sebastian

PY - 2024/5/16

Y1 - 2024/5/16

N2 - 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 = 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 -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 ( = 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 = 0.126, < 0.001) and worse cognition ( = -0.046, = 0.013), while higher brain-age gap was associated with worse cognition ( =-0.163, < 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 ( = -0.049, < 0.001; = -0.198, < 0.001) and an additional 3.8% was mediated via connectivity ( = -0.006, < 0.001; = -0.150, < 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.

AB - 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 = 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 -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 ( = 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 = 0.126, < 0.001) and worse cognition ( = -0.046, = 0.013), while higher brain-age gap was associated with worse cognition ( =-0.163, < 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 ( = -0.049, < 0.001; = -0.198, < 0.001) and an additional 3.8% was mediated via connectivity ( = -0.006, < 0.001; = -0.150, < 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.

KW - brain reserve

KW - cognition

KW - connectivity

KW - lifestyle

KW - resilience

U2 - 10.1093/braincomms/fcae171

DO - 10.1093/braincomms/fcae171

M3 - Article

SN - 2632-1297

VL - 6

JO - Brain Communications

JF - Brain Communications

IS - 3

M1 - 171

ER -

DeJong NR, Jansen JFA, van Boxtel MPJ, Schram MT, Stehouwer CDA, van Greevenbroek MMJ et al. Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study. Brain Communications. 2024 May 16;6(3):171. doi: 10.1093/braincomms/fcae171