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Multidimensional Body Composition Modalities and Their Associations with Brain Aging in Chinese Adults

摘要Objective This study examined the associations between multidimensional body composition modalities and brain aging in Chinese adults.Methods Brain age was estimated using ridge regression based on 24 head computed tomography-derived neuroanatomical indicators in a Chinese cohort(n=557).Brain age gap(BAG),the deviation between the predicted brain age and chronological age(CA),was categorized into brain age acceleration(BAG>0)and deceleration(BAG<0)groups.Principal component analysis of 22 correlation-independent body composition indicators identified different body composition modalities.Logistic regression was used to examine the associations between these modalities and the BAG groups.Results The mean absolute error of brain age in predicting CA was 6.41 years.Three body composition modalities were identified:fat mass dominant(characterized by high loading coefficients of body fat mass,fat mass index,visceral fat level,and fat-to-lean mass ratio);fat-free mass dominant;and trunk-leg contrast distribution.The fat mass dominant modality was significantly associated with brain age acceleration(odds ratio[OR]=1.40,95%confidence interval[CI]:1.15-1.71),and the association was robust in sensitivity analyses.Conclusion The fat mass dominant modality was significantly associated with accelerated brain aging.This study suggests integrating deep body composition indicators into clinical and community health screening could aid in targeted prevention of brain aging.

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作者 Qiaoqiao Zhao [1] Yanjie Zhao [2] Jing Ju [3] Liming Zhang [2] Xueqing Jia [2] Duoduo Fu [4] Jiening Yu [2] Kaili Sun [2] Liying Chen [5] Xiaoting Liu [6] Zuyun Liu [2] Yan Zhang [7] Yangzhen Lu [8] Xuan Ge [9] 学术成果认领
作者单位 Department of General Practice,Dongyang People's Hospital,Dongyang 322100,Zhejiang,China [1] Center for Clinical Big Data and Analytics of the Second Affiliated Hospital,Department of Big Data in Health Science School of Public Health,Zhejiang Key Laboratory of Intelligent Preventive Medicine,Zhejiang University School of Medicine,Hangzhou 310058,Zhejiang,China [2] School of Public Health,Zhejiang University School of Medicine,Hangzhou 310058,Zhejiang,China [3] Department of Epidemiology,Harvard T.H.Chan School of Public Health,Boston,MA 02115,USA [4] Department of General Practice,Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310016,Zhejiang,China [5] School of Public Affairs,Zhejiang University,Hangzhou 310058,Zhejiang,China [6] Department of Geriatric Medicine,Huadong Hospital Affiliated to Fudan University,Shanghai 200040,China [7] Department of General Practice,Dongyang Affiliated Hospital of Wenzhou Medical University,Dongyang 322100,Zhejiang,China [8] Health Management Center,Dongyang Affiliated Hospital of Wenzhou Medical University,Dongyang 322100,Zhejiang,China [9]
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DOI 10.3967/bes2025.166
发布时间 2026-04-24(万方平台首次上网日期,不代表论文的发表时间)
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