The Global Neurodegeneration Proteomics Consortium: biomarker and drug target discovery for common neurodegenerative diseases and aging
Bibliographic record
Abstract
More than 57 million people globally suffer from neurodegenerative diseases, a figure expected to double every 20 years. Despite this growing burden, there are currently no cures, and treatment options remain limited due to disease heterogeneity, prolonged preclinical and prodromal phases, poor understanding of disease mechanisms, and diagnostic challenges. Identifying novel biomarkers is crucial for improving early detection, prognosis, staging and subtyping of these conditions. High-dimensional molecular studies in biofluids ('omics') offer promise for scalable biomarker discovery, but challenges in assembling large, diverse datasets hinder progress. To address this, the Global Neurodegeneration Proteomics Consortium (GNPC)-a public-private partnership-established one of the world's largest harmonized proteomic datasets. It includes approximately 250 million unique protein measurements from multiple platforms from more than 35,000 biofluid samples (plasma, serum and cerebrospinal fluid) contributed by 23 partners, alongside associated clinical data spanning Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). This dataset is accessible to GNPC members via the Alzheimer's Disease Data Initiative's AD Workbench, a secure cloud-based environment, and will be available to the wider research community on 15 July 2025. Here we present summary analyses of the plasma proteome revealing disease-specific differential protein abundance and transdiagnostic proteomic signatures of clinical severity. Furthermore, we describe a robust plasma proteomic signature of APOE ε4 carriership, reproducible across AD, PD, FTD and ALS, as well as distinct patterns of organ aging across these conditions. This work demonstrates the power of international collaboration, data sharing and open science to accelerate discovery in neurodegeneration research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".