Evidence map›Paper›PMID 41250190›Full record

ArticleMolecular neurodegeneration2025

High-sensitivity plasma proteomics reveals disease-specific signatures and predictive biomarkers of Alzheimer's disease phenotypes in a large mixed-dementia cohort.

Katherine Gong, Jigyasha Timsina, Muhammad Ali, Yike Chen, Menghan Liu, Ciyang Wang, Cyril Pottier, Geoffrey K Feld, Gyujin Heo, Tammie L S Benzinger and 9 more

Abstract read
In one paragraph

Article in Molecular neurodegeneration, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

12 citing papers in PubMed.

  1. Review
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  6. Dementia etiology classification using NULISA plasma biomarkers and machine learning.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Katherine GongDepartment of Psychiatry, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO, 63110, USA.
Jigyasha TimsinaDepartment of Psychiatry, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO, 63110, USA.
Muhammad AliDepartment of Psychiatry, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO, 63110, USA.
Yike ChenDepartment of Psychiatry, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO, 63110, USA.
Menghan LiuDepartment of Psychiatry, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO, 63110, USA.
Ciyang WangDepartment of Psychiatry, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO, 63110, USA.
Cyril PottierNeurogenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA.
Geoffrey K FeldGeocyte LLC, Dublin, OH, USA.
Gyujin HeoDepartment of Psychiatry, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO, 63110, USA.
Tammie L S BenzingerDepartment of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA.
Cyrus A RajiDepartment of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
Beau AncesKnight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, 63110, USA.
Brian A GordonKnight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, 63110, USA.
Julie K WischHope Center for Neurologic Diseases, Washington University St. Louis, St. Louis, MO, 63110, USA.
Suzanne E SchindlerDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
John C MorrisDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
David M HoltzmanDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
Laura Ibanez *Department of Psychiatry, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO, 63110, USA.
Carlos Cruchaga *Department of Psychiatry, Washington University School of Medicine, 4444 Forest Park Ave, St. Louis, MO, 63110, USA. cruchagac@wustl.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Novel plasma assays enabled accurate blood-based biomarkers for neurodegenerative diseases with minimally invasive options for clinical use. Large-scale studies encompassing multiple neurodegenerative diseases using novel multiplex platforms are essential to uncover disease-specific biomarkers and pathways. We generated and analyzed plasma biomarker data using the NULISAseq™ CNS Panel from 3,232 participants with Alzheimer disease (AD), Dementia with Lewy bodies (DLB), Frontotemporal dementia (FTD), Parkinson disease (PD) and cognitively unimpaired individuals, from the Charles F. and Joanne Knight Alzheimer Disease Research Center. We identified proteins associated with disease status and AD-related phenotypes (Clinical Dementia Rating®, CSF Aβ42/Aβ40, Amyloid-PET, and Tau-PET). These proteins were used to identify disease-specific biomarkers and perform pathway analyses. We identified 81 proteins associated with AD, 21 with DLB, four with FTD, and 52 with PD after multiple test correction. Disease comparison showed that PD and DLB had the highest similarity, followed by AD and DLB. Concurrently, each disease also presented disease-specific signatures. Some AD-specific proteins included p-tau217; MSLN and SAA1 were specific to DLB, and FLT1 and PARK7 to PD. We also identified eight proteins associated with Amyloid-PET, eight with Tau-PET, 14 with CSF Aβ42/40 ratio, and 72 with CDR, some of which were specific to each phenotype. We used a data-driven approach to identify the p-tau217 cut-off for biomarker positivity. Plasma p-tau217 achieved an AUC of 0.81 (95% CI: 0.79-0.83) for AD diagnosis and 0.96 (95% CI: 0.94-0.98) for Amyloid positivity. P-tau217 had 93.77% agreement with Amyloid-PET status. Proteins associated with AD were enriched in protein-lipid complex binding pathway, whereas PD associated proteins were enriched in laminin-related pathways. FTD associated proteins were enriched in cytoskeleton proteins. This is the largest plasma NULISA CNS study performed till date and covers the four major neurodegenerative diseases: AD, PD, DLB and FTD. We validated the high classification accuracy of the NULISA plasma p-tau217 and its strong correlation with Amyloid-PET status. We also identified disease-specific proteins that could enhance differential diagnosis. These findings highlight the potential of the NULISA platform as a reliable quantitative tool for research and clinical applications in neurodegenerative diseases.

Indexed as

Alzheimer DiseaseBiomarkersProteomicsAgedAged, 80 and overAmyloid beta-PeptidesCohort StudiesFemaleFrontotemporal DementiaHumansLewy Body DiseaseMaleMiddle AgedParkinson DiseasePhenotypetau ProteinsAmyloid beta-PeptidesBiomarkerstau ProteinsCNS protein profilingNeurodegeneration diagnosticsPlasma assaysPlasma-based diagnostics

Identifiers

PMID41250190
PMCPMC12625197

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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.