ArticleThe journal of prevention of Alzheimer's disease2026
Spatial amyloid-informed multimodal brain age as an early marker of Alzheimer's-related vulnerability and risk stratification.
Article in The journal of prevention of Alzheimer's disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundBrain 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.
objectivesTo develop an amyloid-informed multimodal BAG model and examine its associations with cognition, plasma biomarkers, and functional connectivity across the AD continuum.
designCross-sectional analysis using integrated machine-learning models.
settingChinese Preclinical Alzheimer's Disease Study (CPAS), a cohort recruited from community settings and memory clinics.
participantsNine 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.
resultsHigher 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.
conclusionsAn 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.
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