Brain Age as a Biomarker in Alzheimer’s Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential

<b>Background/Objectives</b>: Alzheimer's disease (AD) has a prolonged preclinical phase and marked heterogeneity. Brain age and the Brain Age Gap (BAG), derived from neuroimaging and machine learning (ML), offer a non-invasive, system-level indicator of brain integrity, with potential relevance for early detection, risk stratification, and intervention monitoring. This review summarizes the conceptual basis, imaging characteristics, biological relevance, and explores its potential clinical utility of BAG across the AD continuum. <b>Methods</b>: We conducted a narrative synthesis of evidence from morphometric structural magnetic resonance imaging (sMRI), connectivity-based functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI), alongside recent advances in deep learning architectures and multimodal fusion techniques. We further examined associations between BAG and the Amyloid/Tau/Neurodegeneration (A/T/N) framework, neuroinflammation, cognitive reserve, and lifestyle interventions. <b>Results</b>: BAG may reflect neurodegeneration associated with AD, showing greater deviations in individuals with mild cognitive impairment (MCI) and early AD, and is correlated with tau pathology, neuroinflammation, and metabolic or functional network dysregulation. Multimodal and deep learning approaches enhance the sensitivity of BAG to disease-related deviations. Longitudinal BAG changes outperform static BAG in forecasting cognitive decline, and lifestyle or exercise interventions can attenuate BAG acceleration. <b>Conclusions</b>: BAG emerges as a promising, dynamic, integrative, and modifiable complementary biomarker with the potential for assessing neurobiological resilience, disease staging, and personalized intervention monitoring in AD. While further standardization and large-scale validation are essential to support clinical translation, BAG provides a novel systems-level perspective on brain health across the AD continuum.

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Publication Details

Journal
Brain Sciences
Published
2025-12-25
DOI
https://doi.org/10.3390/brainsci16010033
Citations
1
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
1.47

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article

Brain Age as a Biomarker in Alzheimer’s Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential

Jeffery Lin, Yutong Wu, Shen Sun, Hongjian Gao et al.
1 citations
Brain Sciences
Dementia and Cognitive Impairment Research
1.47
article

Brain Age as a Biomarker in Alzheimer’s Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential

Jeffery Lin, Yutong Wu, Shen Sun, Hongjian Gao, Lan Lin, Ziyi Wang, Zhenrong Fu, Yanxue Li
article en
1 citations

Abstract

<b>Background/Objectives</b>: Alzheimer's disease (AD) has a prolonged preclinical phase and marked heterogeneity. Brain age and the Brain Age Gap (BAG), derived from neuroimaging and machine learning (ML), offer a non-invasive, system-level indicator of brain integrity, with potential relevance for early detection, risk stratification, and intervention monitoring. This review summarizes the conceptual basis, imaging characteristics, biological relevance, and explores its potential clinical utility of BAG across the AD continuum. <b>Methods</b>: We conducted a narrative synthesis of evidence from morphometric structural magnetic resonance imaging (sMRI), connectivity-based functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI), alongside recent advances in deep learning architectures and multimodal fusion techniques. We further examined associations between BAG and the Amyloid/Tau/Neurodegeneration (A/T/N) framework, neuroinflammation, cognitive reserve, and lifestyle interventions. <b>Results</b>: BAG may reflect neurodegeneration associated with AD, showing greater deviations in individuals with mild cognitive impairment (MCI) and early AD, and is correlated with tau pathology, neuroinflammation, and metabolic or functional network dysregulation. Multimodal and deep learning approaches enhance the sensitivity of BAG to disease-related deviations. Longitudinal BAG changes outperform static BAG in forecasting cognitive decline, and lifestyle or exercise interventions can attenuate BAG acceleration. <b>Conclusions</b>: BAG emerges as a promising, dynamic, integrative, and modifiable complementary biomarker with the potential for assessing neurobiological resilience, disease staging, and personalized intervention monitoring in AD. While further standardization and large-scale validation are essential to support clinical translation, BAG provides a novel systems-level perspective on brain health across the AD continuum.

Brain SciencesVol. 16(1)
University of Washington (US), Beijing University of Technology (CN), Central China Normal University (CN), Ministry of Education (SA)
National Natural Science Foundation of China
Openalex Percentile: Top 13%
Dementia and Cognitive Impairment Research
1.47
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