The pesticide chlorpyrifos increases the risk of Parkinson’s disease
Bibliographic record
Abstract
BACKGROUND: Pesticides as a class have been associated with an increased risk of Parkinson’s disease (PD), but it is unclear which specific pesticides contribute to this association and whether it is causal. Since chlorpyrifos (CPF) exposure has been implicated as a risk factor for PD, we investigated its association to incident PD and if this association is biologically plausible using human, rodent, and zebrafish (ZF) studies. METHODS: The association of CPF with PD was performed using the UCLA PEG cohort (829 PD and 824 control subjects), the pesticide use report and geocoding the residence and work locations to estimate exposures. For the mammalian studies, 6 months old male mice were exposed to CPF by inhalation (consistent with human exposures) for 11 weeks and behavioral and stereological pathological analyses were performed. Transgenic ZF were utilized to determine the mechanism of CPF neurotoxicity. RESULTS: Long-term residential exposure to CPF was associated with more than a 2.5-fold increased risk of developing PD. Mice exposed to aerosolized CPF developed motor impairment, dopaminergic neuron loss, microglial activation, and an increase in pathological α-synuclein (α-syn). Using ZF, we found that CPF-induced dopaminergic neuron loss was at least partially due to autophagy dysfunction and synuclein accumulation, as knocking down LC3 recapitulated the dopaminergic neuron loss and restoring autophagic flux or eliminating synuclein reduced neuronal vulnerability. CONCLUSIONS: CPF exposure is associated with an increased risk of developing PD and relevant exposures in animal models establish biological plausibility. In addition to establishing a new risk factor for PD, we identified new therapeutic targets for disease modification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".