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Record W4414146997 · doi:10.1101/2025.09.06.25335210

No Evidence for Genetic Role of the Tumor Necrosis Factor Pathway in Parkinson’s Disease

2025· preprint· en· W4414146997 on OpenAlexafffund
Morvarid Ghamgosar Shahkhali, Lang Liu, Emma N. Somerville, Alastair J. Noyce, Ziv Gan-Or, Konstantin Senkevich

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicStudies on Chitinases and Chitosanases
Canadian institutionsMcGill University Health CentreMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Neurological Disorders and StrokeFonds de Recherche du Québec - SantéNational Institutes of HealthConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsMendelian randomizationDiseaseTumor necrosis factor alphaGeneTumor necrosis factor αSignal transductionRisk factorMendelian inheritance

Abstract

fetched live from OpenAlex

Tumor necrosis factor (TNF) inhibition is under investigation as a therapeutic strategy for Parkinson's disease (PD) and REM sleep behavior disorder (RBD), yet supporting genetic evidence is limited. We used Summary-data-based Mendelian Randomization (SMR) to test whether expression level of ten TNF-related genes were causally linked to PD risk, PD progression, or RBD risk. We also analyzed associations between common and rare variants in these genes, and performed pathway specific polygenic risk score analysis, with PD. Overall, our findings do not support a genetic link between the TNF signaling and PD or RBD, arguing against this pathway as a genetically validated therapeutic target.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.023
GPT teacher head0.277
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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