Early detection of Alzheimer’s disease pathophysiology using 3D virtual reality navigation: a correlational study with genetic and plasma biomarkers

BACKGROUND We investigated the association between path-integration (PI) errors related to entorhinal cortex function detectable using a 3D virtual reality (VR) navigation system and various biomarkers to explore its potential as an early AD indicator. METHODS The PI capabilities of 111 healthy adults were assessed using a head-mounted 3D VR system. Demographic and cognitive assessments, AD-related plasma biomarkers, and apolipoprotein E genotypes were also evaluated. Predictive factors for PI errors were identified using multivariate linear regression, logistic regression, and random forest. RESULTS PI errors positively correlated with age, plasma levels of glial fibrillary acidic protein [GFAP], neurofilament light, and p-tau181. Multivariate analysis identified plasma GFAP and p-tau181 levels as significant predictors. Random forest analysis and receiver operating characteristic curves underscored plasma p-tau181 levels as the most substantial predictor. DISCUSSION PI errors, particularly in conjunction with plasma p-tau181 levels, could reflect early AD pathophysiology, highlighting their potential as early biomarkers.