Brain structure and connectivity mediate the association of lifestyle and cognition: the Maastricht study

N. R. DeJong, J. Jansen, M. P. J. van Boxtel, M. T. Schram, C. Stehouwer, M. V. van Greevenbroek, C. V. D. van der Kallen, A. Koster, S. Eussen, B. D. de Galan, W. Backes, S. Koehler
{"title":"Brain structure and connectivity mediate the association of lifestyle and cognition: the Maastricht study","authors":"N. R. DeJong, J. Jansen, M. P. J. van Boxtel, M. T. Schram, C. Stehouwer, M. V. van Greevenbroek, C. V. D. van der Kallen, A. Koster, S. Eussen, B. D. de Galan, W. Backes, S. Koehler","doi":"10.1093/braincomms/fcae171","DOIUrl":null,"url":null,"abstract":"\n 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 (BAG) assesses brain health by comparing an individual’s neuroimaging-based predicted age with their calendar age. Higher BAG implies accelerated brain aging 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 (GM), cerebrospinal fluid (CSF), white matter hyperintensity (WMH)), cerebral microbleeds (CMB), and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health (LIBRA) 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, executive function and attention). BAG was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIBRA tertiles, BAG and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. Results show that CSF, GM, WMH and CMB best predicted BAG (R2 = 0.455, RMSE = 6.44). In regression analysis, higher LIBRA scores (greater dementia risk) were associated with higher BAG (standardized regression coefficient ß = 0.126, p < 0.001) and worse cognition (ß = -0.046, p = 0.013), while higher BAG was associated with worse cognition (ß=-0.163, p<0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIBRA tertile was mediated by BAG (ßindirect = -0.049, p < 0.001; ßtotal = -0.198, p < 0.001), and an additional 3.8% was mediated via connectivity (ßindirect = -0.006, p < 0.001; ßtotal = -0.150, p < 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIBRA) and cognition can be partially explained by structural brain health markers (BAG) 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.","PeriodicalId":504765,"journal":{"name":"Brain Communications","volume":"16 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/braincomms/fcae171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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 (BAG) assesses brain health by comparing an individual’s neuroimaging-based predicted age with their calendar age. Higher BAG implies accelerated brain aging 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 (GM), cerebrospinal fluid (CSF), white matter hyperintensity (WMH)), cerebral microbleeds (CMB), and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health (LIBRA) 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, executive function and attention). BAG was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIBRA tertiles, BAG and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. Results show that CSF, GM, WMH and CMB best predicted BAG (R2 = 0.455, RMSE = 6.44). In regression analysis, higher LIBRA scores (greater dementia risk) were associated with higher BAG (standardized regression coefficient ß = 0.126, p < 0.001) and worse cognition (ß = -0.046, p = 0.013), while higher BAG was associated with worse cognition (ß=-0.163, p<0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIBRA tertile was mediated by BAG (ßindirect = -0.049, p < 0.001; ßtotal = -0.198, p < 0.001), and an additional 3.8% was mediated via connectivity (ßindirect = -0.006, p < 0.001; ßtotal = -0.150, p < 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIBRA) and cognition can be partially explained by structural brain health markers (BAG) 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.
大脑结构和连通性是生活方式与认知关联的中介:马斯特里赫特研究
一生中接触的风险和保护因素会影响大脑的宏观和微观结构,进而影响认知能力。脑年龄差距(BAG)的概念是通过比较个人基于神经影像学的预测年龄和日历年龄来评估大脑健康状况的。较高的 BAG 意味着大脑加速老化,预计与认知能力下降有关。在这项研究中,我们全面模拟了大量中年人群的脑健康和生活方式因素、脑年龄和认知能力之间的相互关系。在这项研究中,4881 名参与者(平均年龄(±SD)= 59.2 (±8.6),50.1% 为男性)的认知测试得分、生活方式和 3T 磁共振成像数据均来自马斯特里赫特研究(The Maastricht Study)。研究人员计算了全脑体积(灰质(GM)、脑脊液(CSF)、白质高密度(WMH))、脑微出血(CMB)和结构性白质连通性。将生活方式因素合并为改编的 "促进大脑健康的生活方式"(LIBRA)加权总分,得分越高表示痴呆风险越大。认知能力是通过计算三个认知领域(记忆、信息处理速度、执行功能和注意力)的 Z 值平均值得出的。BAG的计算方法是将日历年龄与基于神经影像学的多变量回归模型的预测值进行比较。使用线性回归和结构方程模型对 LIBRA tertiles、BAG 和认知功能之间的路径进行了测试,并对社会人口学和临床混杂因素进行了调整。结果显示,CSF、GM、WMH 和 CMB 对 BAG 的预测效果最好(R2 = 0.455,RMSE = 6.44)。在回归分析中,LIBRA评分越高(痴呆风险越大),BAG越高(标准化回归系数ß=0.126,p<0.001),认知能力越差(ß=-0.046,p=0.013),而BAG越高,认知能力越差(ß=-0.163,p<0.001)。在中介分析中,BAG(ß间接=-0.049,p<0.001;ß总=-0.198,p<0.001)中介了最高和最低LIBRA三分位数之间认知差异的24.7%,另外3.8%通过连接性中介(ß间接=-0.006,p<0.001;ß总=-0.150,p<0.001)。研究结果表明,基于健康和生活方式的风险/保护因素(LIBRA)与认知之间的关联可以部分地通过大脑结构健康标志物(BAG)和白质连接标志物来解释。针对中晚年高危人群的生活方式干预措施可能会有效促进和保护大众的认知功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书