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Validation of neuroimaging-based brain age gap as a mediator between modifiable risk factors and cognition

  • Chang Le Chen
  • , Ming Che Kuo
  • , Pin Yu Chen
  • , Yu Hung Tung
  • , Yung Chin Hsu
  • , Chi Wen Christina Huang
  • , Wing P. Chan
  • , Wen Yih Isaac Tseng

Research output: Contribution to journalArticlepeer-review

Abstract

Neuroimaging-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.

Original languageEnglish
Pages (from-to)61-72
Number of pages12
JournalNeurobiology of Aging
Volume114
DOIs
Publication statusPublished - Jun 2022

Keywords

  • Brain age gap
  • Cognitive aging
  • Machine learning
  • Mediation
  • Modifiable risk factor
  • Neuroimaging

ASJC Scopus subject areas

  • General Neuroscience
  • Ageing
  • Clinical Neurology
  • Developmental Biology
  • Geriatrics and Gerontology

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