The journal of prevention of Alzheimer's disease · 2026
Spatial amyloid-informed multimodal brain age as an early marker of Alzheimer's-related vulnerability and risk stratification.
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Cui Liang, Wang Qing-Min, Zhang Zhen, Wang Min, Tu You-Yi, Jiang Jie-Hui, Guan Yi-Hui, Li Yue-Hua, Xie Fang, Guo Qi-Hao
Аннотация
BACKGROUND: Brain age gap (BAG)-the difference between predicted and chronological age-captures neurobiological aging, but MRI-only models insufficiently reflect Alzheimer's disease (AD) pathology. Whether incorporating regional amyloid-β (Aβ) positron emission tomography (PET) improves sensitivity to early AD processes remains unknown.
OBJECTIVES: To develop an amyloid-informed multimodal BAG model and examine its associations with cognition, plasma biomarkers, and functional connectivity across the AD continuum.
DESIGN: Cross-sectional analysis using integrated machine-learning models.
SETTING: Chinese Preclinical Alzheimer's Disease Study (CPAS), a cohort recruited from community settings and memory clinics.
PARTICIPANTS: Nine hundred ninety community-dwelling adults spanning normal cognition, subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia.
MEASUREMENTS: Regional Aβ-PET and structural MRI informed BAG estimation. Cognitive tests, plasma biomarkers (p-tau217, p-tau181, neurofilament light [NfL], glial fibrillary acidic protein [GFAP], Aβ42/40), and hippocampus-default mode network (DMN) connectivity from resting-state fMRI were assessed.
RESULTS: Higher BAG was associated with greater odds of SCD, MCI, or dementia across the cohort, with stronger effects in Aβ-positive individuals. BAG explained more cognitive variance than global Aβ burden and was linked to multidomain cognitive deficits. Elevated BAG corresponded to higher p-tau217, p-tau181, NfL, and GFAP and lower Aβ42/40, indicating early biomarker alterations. BAG was also associated with reduced hippocampus-DMN connectivity.
CONCLUSIONS: An amyloid-informed multimodal BAG model captures convergent AD-related pathology, biomarker alterations, and cognitive vulnerability beyond amyloid burden alone, supporting its value for individualized risk s2tratification and prevention-focused assessment.
OBJECTIVES: To develop an amyloid-informed multimodal BAG model and examine its associations with cognition, plasma biomarkers, and functional connectivity across the AD continuum.
DESIGN: Cross-sectional analysis using integrated machine-learning models.
SETTING: Chinese Preclinical Alzheimer's Disease Study (CPAS), a cohort recruited from community settings and memory clinics.
PARTICIPANTS: Nine hundred ninety community-dwelling adults spanning normal cognition, subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia.
MEASUREMENTS: Regional Aβ-PET and structural MRI informed BAG estimation. Cognitive tests, plasma biomarkers (p-tau217, p-tau181, neurofilament light [NfL], glial fibrillary acidic protein [GFAP], Aβ42/40), and hippocampus-default mode network (DMN) connectivity from resting-state fMRI were assessed.
RESULTS: Higher BAG was associated with greater odds of SCD, MCI, or dementia across the cohort, with stronger effects in Aβ-positive individuals. BAG explained more cognitive variance than global Aβ burden and was linked to multidomain cognitive deficits. Elevated BAG corresponded to higher p-tau217, p-tau181, NfL, and GFAP and lower Aβ42/40, indicating early biomarker alterations. BAG was also associated with reduced hippocampus-DMN connectivity.
CONCLUSIONS: An amyloid-informed multimodal BAG model captures convergent AD-related pathology, biomarker alterations, and cognitive vulnerability beyond amyloid burden alone, supporting its value for individualized risk s2tratification and prevention-focused assessment.