Background: Accurate antemortem differentiation among dementia etiologies remains challenging, particularly for atypical or mixed clinical presentations. Multiplexed plasma proteomics paired with supervised machine learning offers a minimally invasive and accessible approach for differential diagnosis.
Methods: Plasma from 194 participants was analyzed using the NULISA CNS 120+ plasma biomarker panel. Differentially abundant protein patterns associated with AD, frontotemporal lobar degeneration, Lewy body disease, and vascular disease were identified. These features were used to train supervised XGBoost classifier models. Models were then applied to participants with mild cognitive impairment to generate data-driven predictions of etiology.
Results: NULISA plasma biomarkers revealed disease-specific protein patterns. XGBoost classifiers differentiated disease etiologies with high specificity. Application of the models to participants with mild cognitive impairment yielded robust etiologic predictions.
Conclusion: These results support the feasibility of using multiplexed NULISA plasma proteomics, combined with machine learning, for differential diagnosis of complex neurodegenerative dementia etiologies.