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Review

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

1
Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
2
Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
3
Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
4
Information School, University of Washington, Seattle, WA 98195, USA
5
Key Laboratory of Adolescent Cyber Psychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China
6
Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Brain Sci. 2026, 16(1), 33; https://doi.org/10.3390/brainsci16010033
Submission received: 4 December 2025 / Revised: 22 December 2025 / Accepted: 24 December 2025 / Published: 25 December 2025

Abstract

Background/Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.
Keywords: Alzheimer’s disease; brain age; Brain Age Gap; machine learning; amyloid/tau/neurodegeneration framework; longitudinal study; multimodal imaging Alzheimer’s disease; brain age; Brain Age Gap; machine learning; amyloid/tau/neurodegeneration framework; longitudinal study; multimodal imaging

Share and Cite

MDPI and ACS Style

Lin, L.; Li, Y.; Sun, S.; Lin, J.; Wang, Z.; Wu, Y.; Fu, Z.; Gao, H. Brain Age as a Biomarker in Alzheimer’s Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential. Brain Sci. 2026, 16, 33. https://doi.org/10.3390/brainsci16010033

AMA Style

Lin L, Li Y, Sun S, Lin J, Wang Z, Wu Y, Fu Z, Gao H. Brain Age as a Biomarker in Alzheimer’s Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential. Brain Sciences. 2026; 16(1):33. https://doi.org/10.3390/brainsci16010033

Chicago/Turabian Style

Lin, Lan, Yanxue Li, Shen Sun, Jeffery Lin, Ziyi Wang, Yutong Wu, Zhenrong Fu, and Hongjian Gao. 2026. "Brain Age as a Biomarker in Alzheimer’s Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential" Brain Sciences 16, no. 1: 33. https://doi.org/10.3390/brainsci16010033

APA Style

Lin, L., Li, Y., Sun, S., Lin, J., Wang, Z., Wu, Y., Fu, Z., & Gao, H. (2026). Brain Age as a Biomarker in Alzheimer’s Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential. Brain Sciences, 16(1), 33. https://doi.org/10.3390/brainsci16010033

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