Dementia etiology classification using NULISA plasma biomarkers and machine learning

INTRODUCTIONAccurate 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. METHODSPlasma 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. RESULTSNULISA 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. DISCUSSIONThese results support the feasibility of using multiplexed NULISA plasma proteomics, combined with machine learning, for differential diagnosis of complex neurodegenerative dementia etiologies. HighlightsO_LIMultiplex plasma proteomics revealed distinct protein markers of dementia subtypes C_LIO_LISupervised XGBoost classifiers accurately distinguished each dementia etiology C_LIO_LIModel application to unknown etiologies produced interpretable probability profiles C_LIO_LIThe combined NULISA-machine learning framework demonstrates diagnostic feasibility C_LI Research in ContextO_LISystematic review: The authors reviewed the literature using traditional sources (e.g. PubMed), meeting abstracts and presentations. NULISA technology has been utilized to analyze blood biomarkers in people with neurological diseases and differential protein expression based on clinical diagnosis and presumed etiologies has been observed. These citations are appropriately cited. C_LIO_LIInterpretation: Our findings indicate that machine learning can be used in combination with NULISA technology to improve etiology prediction in people with dementia. C_LIO_LIFuture directions: Future studies using larger, more well-balanced participant cohorts will enable better understanding of the plasma biomarkers and demographic factors that best discriminate between dementia etiologies. C_LI