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Dementia etiology classification using NULISA plasma biomarkers and machine learning
Kelly N. Dubois, Subhamoy Pal, Amanda Cook Maher, Judith L. Heidebrink, Carol C. Persad, Bruno Giordani, Benjamin M. Hampstead, Kelly M. Bakulski et al.. Alzheimer s & Dementia, 2026-06-01
Topic: Amyloid, tau and blood-test diagnosis
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. These results support the feasibility of using multiplexed NULISA plasma proteomics, combined with machine learning, for differential diagnosis of complex neurodegenerative dementia etiologies. Highlights: Multiplex plasma proteomics revealed distinct protein markers of dementia subtypesSupervised XGBoost classifiers accurately distinguished each dementia etiologyModel application to unknown etiologies produced interpretable probability profilesThe combined NULISA-machine learning framework demonstrates diagnostic feasibility. Research in Context: Systematic 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. Our findings indicate that machine learning can be used in combination with NULISA technology to improve etiology prediction in people with dementia.Future 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.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Dubois, K. N., Pal, S., Maher, A. C., Heidebrink, J. L., Persad, C. C., Giordani, B., et al. (2026). Dementia etiology classification using NULISA plasma biomarkers and machine learning. Alzheimer s & Dementia. https://doi.org/10.1002/alz.71603
Vancouver
Dubois KN, Pal S, Maher AC, Heidebrink JL, Persad CC, Giordani B, et al. Dementia etiology classification using NULISA plasma biomarkers and machine learning. Alzheimer s & Dementia. 2026. doi:10.1002/alz.71603. PMID: 41394895.
BibTeX
@article{dubois2026-41394895,
author = {Dubois, Kelly N. and Pal, Subhamoy and Maher, Amanda Cook and Heidebrink, Judith L. and Persad, Carol C. and Giordani, Bruno and Hampstead, Benjamin M. and Bakulski, Kelly M. and others},
title = {{Dementia etiology classification using NULISA plasma biomarkers and machine learning}},
journal = {Alzheimer s \& Dementia},
year = {2026},
doi = {10.1002/alz.71603},
pmid = {41394895},
url = {https://doi.org/10.1002/alz.71603}
}
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Key findings
- 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.
- These results support the feasibility of using multiplexed NULISA plasma proteomics, combined with machine learning, for differential diagnosis of complex neurodegenerative dementia etiologies.
- Highlights: Multiplex plasma proteomics revealed distinct protein markers of dementia subtypesSupervised XGBoost classifiers accurately distinguished each dementia etiologyModel application to unknown etiologies produced interpretable probability profilesThe combined NULISA-machine learning framework demonstrates diagnostic feasibility.
Why it matters
Using NULISA plasma protein measurements with machine learning, this study differentiated dementia etiologies with high specificity and also made predictions for people with mild cognitive impairment. It supports a diagnostic approach and does not test any treatment, cure, or prevention.
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Study details
- Study designsystematic review
- Participants194
- Evidence strengthHigh
How this study scored 62/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for effect reported because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s & Dementia) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A systematic review of all the studies on the question |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 2 / 4 | 194 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | A review of other studies — randomisation is not credited here |
| Times cited | 1 / 7 | Cited 2 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 4 / 6 | Cited 9.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Nicholas M. Kanaan: h-index 46 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Michigan: institution h-index 2,240 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41394895) |
| Free to read | 2 / 2 | Free to read |
| Total | 62 / 100 | — |
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