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Updated: Jun 25, 2026
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Dementia etiology classification using NULISA plasma biomarkers and machine learning
Kelly N DuBois1,2, Subhamoy Pal2, Amanda Cook Maher2,3
1Department of Translational Science and Molecular Medicine, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA.
Summary
Multiplexed plasma proteomics using NULISA and machine learning can accurately differentiate dementia causes, even in complex cases. This minimally invasive approach aids in diagnosing neurodegenerative diseases like Alzheimer's.
Area of Science:
- Neuroscience
- Biomarker Discovery
- Machine Learning in Medicine
Background:
- Accurate ante mortem differentiation of dementia etiologies is challenging, especially for atypical or mixed presentations.
- Multiplexed plasma proteomics offers a minimally invasive diagnostic approach.
- Supervised machine learning can enhance differential diagnosis capabilities.
Purpose of the Study:
- To evaluate the feasibility of using multiplexed plasma proteomics and machine learning for differential diagnosis of dementia.
- To identify plasma protein patterns associated with specific neurodegenerative diseases.
- To predict dementia etiology in individuals with mild cognitive impairment (MCI).
Main Methods:
- Plasma samples from 194 participants were analyzed using the Nucleic acid Linked Immuno-Sandwich Assay (NULISA) CNS 120+ biomarker panel.
- Differentially abundant proteins linked to Alzheimer's disease, frontotemporal lobar degeneration, Lewy body disease, and vascular disease were identified.
- XGBoost classifier models were trained on protein patterns and applied to MCI participants for etiologic prediction.
Main Results:
- NULISA plasma biomarkers revealed distinct protein patterns for different dementia etiologies.
- XGBoost classifiers demonstrated high specificity in differentiating disease causes.
- The models successfully generated robust etiologic predictions for individuals with MCI.
Conclusions:
- Multiplexed NULISA plasma proteomics combined with machine learning is feasible for diagnosing complex dementia.
- This approach provides a data-driven method for predicting neurodegenerative disease etiology.
- The findings support the clinical utility of plasma proteomics in dementia diagnostics.
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