Exposure to Pesticides and Welding Hastens the Age-at-Onset of Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: The age-at-onset (AAO) of Parkinson's disease (PD) is thought to be influenced by environmental factors and polygenic predispositions. Professional exposures to pesticides and toxic metals were shown to be associated with an earlier onset in small sample studies. AIM OF STUDY: The aim of this study was to confirm the association between professional exposures to pesticides and toxic metals and the AAO of PD, on a larger cohort of patients, defined with a clinic-based ascertainment scheme. METHODS: We used an incident cohort of 290 patients recruited through three designated movement disorder clinics in the province of Quebec, Canada. Patients completed a detailed questionnaire regarding professional exposures to pesticides and toxic metals. We compared the AAO in patients without prior professional exposure (N = 170) and those with exposure to pesticides (N = 53) or toxic metals through welding (N = 30). We further subdivided patients exposed to pesticides according to the frequency and proximity of their contacts. RESULTS: Patients with prior exposure to pesticides (AAO = 54.74 years) or toxic metals (54.27 years) had a significantly earlier AAO compared to the control group (59.26 years) (p = 0.003). In those exposed to pesticides, closer (p = 0.03) and more frequent (p = 0.02) contacts were negatively correlated with AAO. CONCLUSION: Exposure to pesticides and toxic metals were both associated with an earlier onset of PD, an effect that was greater with higher levels of exposure, both in terms of frequency and proximity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".