Cognitive components derived from traditional neuropsychological tests and their associations with plasma p-tau217 and p-tau181 in mild cognitive impairment: a multisite analysis.
TL;DR
The findings indicate that a robust four-factor cognitive structure can be identified in clinically defined MCI cohorts without prior biological selection, and the association between latent memory factors and plasma p-tau217, observed primarily in cohorts with higher biomarker burden or clearer amnestic profiles, highlights the potential for blood-based biomarkers to refine risk assessment in routine clinical practice.
Abstract
Background
Currently, no single biomarker can reliably identify preclinical Alzheimer's disease (AD), particularly at or before the mild cognitive impairment (MCI) stage. Given the heterogeneity of MCI, integrative approaches are needed to improve early risk stratification.
Objectives
(i) To derive robust latent cognitive components from a multicenter, clinically defined MCI cohort using principal component analysis (PCA); (ii) to investigate the associations between these components and plasma p-tau217 and p-tau181 levels.
Methods
Data from 742 MCI participants in the AI-Mind cohort were analyzed. Cognitive domains were derived using PCA with varimax rotation and tested for associations with plasma p-tau biomarkers using site-specific linear regressions, adjusted for age, sex, and education.
Results
A reproducible four-component cognitive structure emerged (memory, executive/processing speed, verbal fluency, visuospatial ability), with memory as the most p-tau-sensitive domain. The p-tau217 measure showed stronger associations with memory than p-tau181, though effects varied by site.
Conclusion
The findings indicate that a robust four-factor cognitive structure can be identified in clinically defined MCI cohorts without prior biological selection. The association between latent memory factors and plasma p-tau217, observed primarily in cohorts with higher biomarker burden or clearer amnestic profiles, highlights the potential for blood-based biomarkers to refine risk assessment in routine clinical practice.