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Dementia etiology classification using NULISA plasma biomarkers and machine learning.

Dementia etiology classification using NULISA plasma biomarkers and machine learning.

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Grand Rapids, US · Author affiliation

Department of Translational Science and Molecular Medicine, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA.
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Ann Arbor, US · Author affiliation

Michigan Alzheimer's Disease Research Center, University of Michigan, Ann Arbor, Michigan, USA.
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East Lansing, US · Author affiliation

Neuroscience Program, Michigan State University, East Lansing, Michigan, USA.
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Original abstract

INTRODUCTION: Accurate ante mortem 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 Nucleic acid Linked Immuno-Sandwich Assay (NULISA) Central Nervous System 120+ plasma biomarker panel. Differentially abundant protein patterns associated with Alzheimer's disease, 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 (MCI) 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 MCI yielded robust etiologic predictions. DISCUSSION: These results support the feasibility of using multiplexed NULISA plasma proteomics, combined with machine learning, for differential diagnosis of complex neurodegenerative dementia etiologies.

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