Predicting brain health in community-dwelling elderly populations by integrating Gaussian mixture model and plasma biomarkers.

Background: Alzheimer's disease (AD) is the most common type of dementia, and early screening is crucial for intervention.

Objective: Currently, early screening for older adults without dementia primarily rely on cognitive scale. This study aims to explore a more feasible approach.

Methods: Plasma biomarkers (Aβ_42/40, p-tau181 and p-tau217) and Gaussian mixture models (GMM) were utilized for stratifying risk levels in older adults without dementia from the Alzheimer's Disease Neuroimaging Initiative. Linear mixed effects model was employed to compare subsequent pathological and cognitive changes, alongside a comparison with traditional scale-based screening methods. Cox regression model was used to assess the risk of progression to dementia across different biomarker status groups.

Results: Plasma Aβ_42/40 and p-tau217 effectively predicted Aβ PET pathological progression, while p-tau217 also predicted tau PET changes. All three biomarkers could forecast the progression of FDG PET and cognitive function. P-tau217 and p-tau181 significantly modulated pathology-related cognitive impairment. All three biomarkers could predict dementia risk. The screening method combining GMM with plasma biomarkers demonstrates superior predictive ability compared to traditional scale-based approaches.

Conclusions: Our study indicated that the combination of GMM and plasma biomarkers for community screening shows promising potential in monitoring brain health among older adults without dementia. P-tau217 exhibited the best predictive value among the three plasma biomarkers.