Evidence map›Paper›PMID 42337943›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2026

Dementia etiology classification using NULISA plasma biomarkers and machine learning.

Kelly N DuBois, Subhamoy Pal, Amanda Cook Maher, Judith Heidebrink, Carol Persad, Bruno Giordani, Benjamin M Hampstead, Kelly M Bakulski, David G Morgan, Nicholas M Kanaan

Abstract read
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In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Kelly N DuBoisDepartment of Translational Science and Molecular Medicine, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA.
Subhamoy PalMichigan Alzheimer's Disease Research Center, University of Michigan, Ann Arbor, Michigan, USA.
Amanda Cook MaherMichigan Alzheimer's Disease Research Center, University of Michigan, Ann Arbor, Michigan, USA.
Judith HeidebrinkMichigan Alzheimer's Disease Research Center, University of Michigan, Ann Arbor, Michigan, USA.
Carol PersadDepartment of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.
Bruno GiordaniMichigan Alzheimer's Disease Research Center, University of Michigan, Ann Arbor, Michigan, USA.
Benjamin M HampsteadMichigan Alzheimer's Disease Research Center, University of Michigan, Ann Arbor, Michigan, USA.
Kelly M BakulskiMichigan Alzheimer's Disease Research Center, University of Michigan, Ann Arbor, Michigan, USA.
David G MorganDepartment of Translational Science and Molecular Medicine, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA.
Nicholas M KanaanDepartment of Translational Science and Molecular Medicine, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA.ORCID https://orcid.org/0000-0002-4362-2593

Funding

Treating mild cognitive impairment with transcranial direct current stimulationR01AG058724 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HAMPSTEAD, BENJAMIN MICHAEL · 2018 to 2022
$8.4M
Research Program on Cognition and Neuromodulation Based Interventions (RP-CNBI)R35AG072262 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HAMPSTEAD, BENJAMIN MICHAEL · 2021 to 2025
$4.2M
Maibach-Smiley EndowmentNIA NIH HHS R01AG058724NIA NIH HHS R01AG068388NIA NIH HHS R01AG072262NIA NIH HHS R35 AG072262
6 · The paper itself

Abstract

introductionAccurate 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.

methodsPlasma 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.

resultsNULISA 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.

Indexed as

BiomarkersDementiaMachine LearningAgedAlzheimer DiseaseBoosting Machine Learning AlgorithmsClassification AlgorithmsCognitive DysfunctionDiagnosis, DifferentialFemaleFrontotemporal Lobar DegenerationHumansLewy Body DiseaseMaleProteomicsBiomarkersAlzheimer's diseasedementiafrontotemporal lobar degenerationlewy body diseasemachine learningmultiplex proteomicsplasma biomarkersvascular disease

Identifiers

PMID42337943
PMCPMC13290646

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.