Chen, Chang-LeChang-LeChenMING-CHE KUOChen, Pin-YuPin-YuChenTung, Yu-HungYu-HungTungHsu, Yung-ChinYung-ChinHsuHuang, Chi-Wen ChristinaChi-Wen ChristinaHuangChan, Wing PWing PChanTseng, Wen-Yih IsaacWen-Yih IsaacTseng2023-06-132023-06-132022-060197-4580https://scholars.lib.ntu.edu.tw/handle/123456789/632653Neuroimaging-based brain age gap (BAG) is presumably a mediator linking modifiable risk factors to cognitive changes, but this has not been verified yet. To address this hypothesis, modality-specific brain age models were constructed and applied to a population-based cohort (N = 326) to estimate their BAG. Structural equation modeling was employed to investigate the mediation effect of BAG between modifiable risk factors (assessed by 2 cardiovascular risk scores) and cognitive functioning (examined by 4 cognitive assessments). The association between higher burden of modifiable risk factors and poorer cognitive functioning can be significantly mediated by a larger BAG (multimodal: p = 0.014, 40.8% mediation proportion; white matter-based: p = 0.023, 15.7% mediation proportion), which indicated an older brain. Subgroup analysis further revealed a steeper slope (p = 0.019) of association between cognitive functioning and multimodal BAG in the group of higher modifiable risks. The results confirm that BAG can serve as a mediating indicator linking risk loadings to cognitive functioning, implicating its potential in the management of cognitive aging and dementia.enBrain age gap; Cognitive aging; Machine learning; Mediation; Modifiable risk factor; Neuroimaging[SDGs]SDG3journal article10.1016/j.neurobiolaging.2022.03.006354134842-s2.0-85127749299WOS:000794105800005https://api.elsevier.com/content/abstract/scopus_id/85127749299