Association between exposure to PM2.5 components and disease aggravation in Parkinson’s disease: an analysis in New York State
Bibliographic record
Abstract
BACKGROUND AND AIM: Studies suggests that long-term fine particle matter (PM2.5) exposure may contribute to aggravation of Parkinson’s disease (PD), but overall results have been inconsistent. Among other factors, the differences may arise from variations in PM2.5 composition. In a previous study in New York State, we found a nonlinear PM2.5–PD association. To further characterize this association, here we evaluated long-term exposure to specific PM2.5 components in the same cohort. METHODS: We used data from the New York Department of Health Statewide Planning and Research Cooperative System (2000–2014) to construct annual county counts of first hospitalizations with a PD diagnosis. We used well-validated prediction models at 1km2 resolution to calculate county-level population-weighted annual concentrations of six PM2.5 components: black carbon, organic matter, nitrate, sulfate, sea salt, and soil. Exposure was assigned based on county of residence. We used mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) and 95% confidence intervals (CI) for a 1-year exposure to each PM2.5 component. We allowed for nonlinear exposure–outcome relationships using penalized splines and accounted for potential geospatial and temporal confounders. RESULTS:We estimated a linear positive association between organic matter and disease aggravation in PD (RR=1.06, 95%CI: 1.04, 1.09 per one standard deviation (SD) increase) and a positive linear association with nitrate (RR=1.06, 95%CI: 1.03, 1.10 per one SD increase). We found no association with sulfate, sea salt, or soil exposure. CONCLUSIONS:Our results support that particle composition of PM2.5 may influence its adverse effects on PD. Specifically, we identified organic matter and nitrate as potentially important components in the PD–PM2.5 association. KEYWORDS: Air pollution, particle composition, Parkinson's disease, long-term exposures, disease aggravation
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".