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Record W4411240630 · doi:10.1002/acn3.70076

Coffee Consumption Is Associated With Later Age‐at‐Onset of Parkinson's Disease

2025· article· en· W4411240630 on OpenAlexafffund
Dariia Kuzovenkova, Lang Liu, Ziv Gan‐Or, Konstantin Senkevich

Bibliographic record

VenueAnnals of Clinical and Translational Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsMcGill University Health CentreMcGill UniversityMontreal Neurological Institute and Hospital
FundersFonds de Recherche du Québec - SantéNational Institute on AgingConsortium canadien en neurodégénérescence associée au vieillissementCelgeneSanofiGlaxoSmithKlineParkinson Study GroupPfizerNational Institute of Neurological Disorders and StrokeVerily Life SciencesBristol-Myers SquibbMichael J. Fox Foundation for Parkinson's ResearchYale UniversityNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsMedicineParkinson's diseaseConsumption (sociology)DiseasePediatricsInternal medicine

Abstract

fetched live from OpenAlex

Observation studies suggest that coffee consumption may lower the risk and delay the age-at-onset (AAO) of Parkinson's disease (PD). The aim of this study was to explore the causal relationship and genetic association between coffee consumption and the AAO, risk, and progression of PD. Using Mendelian randomization, we identified a significant association between coffee consumption and delayed PD AAO (IVW: OR, 1.91; 95% CI 1.53-2.38; p = 8.072e-09), but no causal association or genetic correlation with PD risk or progression. Our findings suggest a potential causal effect of higher coffee consumption on PD AAO, with no evidence of an association with PD risk or progression.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.037
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.162
GPT teacher head0.446
Teacher spread0.284 · 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 teacher head, 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

Citations4
Published2025
Admission routes2
Has abstractyes

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