Multimodal Integration of Gait Dysfunction, Amyloid PET, and Plasma Biomarkers for Differentiating Etiological...

To investigate gait characteristics and plasma biomarkers in individuals with mild cognitive impairment (MCI) stratified by amyloid-β (Aβ) positivity on positron emission tomography (PET), and to evaluate the predictive value for Alzheimer's disease (AD)-related MCI.

A total of 168 participants were enrolled, including 50 amyloid PET-negative MCI (MCI-), 51 amyloid PET-positive MCI (MCI+), and 61 cognitively normal (CN) individuals. Gait assessments were conducted using a multi-sensor motion analysis system during dual-task paradigms (cognitive load superimposed on locomotion). Plasma samples were analyzed for neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and phosphorylated tau at threonine 217 (p-tau217) via ultra-sensitive immunoassays.

Gait analyses identified 125 features, with 69 distinguishing MCI+ from CN and 36 differentiating MCI+ from MCI-. Receiver operating characteristic (ROC) analyses showed that dual-task gait under the countdown and animal-naming conditions (gait-countdown [GCD] and gait-animal naming [GAN]) discriminated MCI+ from CN with an AUC of 0.850. The combined models integrating GCD and GAN with plasma GFAP or p-tau217 yielded AUCs of 0.919 and 0.951, respectively. Similarly, GCD&GAN features demonstrated an AUC of 0.862 for distinguishing MCI+ from MCI-, with GFAP (AUC = 0.933) and p-tau217 (AUC = 0.986) enhancing predictive performance.

This study provides evidence that dual-task gait assessments, when combined with plasma biomarkers such as GFAP and p-tau217, may improve the discriminatory power for identifying AD-related MCI. The integration of biomechanical and molecular markers holds promise for advancing early detection strategies and therapeutic monitoring in AD.