Fine particulate matter air pollution and anti-nuclear antibodies
Bibliographic record
Abstract
OBJECTIVES: Air pollution has been increasingly linked to systemic autoimmune rheumatic diseases (SARDs), but studies regarding fine particulate matter (PM2.5) and SARD-related serological biomarkers are limited. We aimed to assess the association between exposure to ambient PM2.5 and ANA in the general population. METHODS: Serum samples of 3548 subjects (collected between 2010 and 2013) were randomly selected from the Ontario Health Study general population cohort. We examined ANA titres using indirect immunofluorescence assay on HEp-2 cells. Annual average ambient PM2.5 levels (estimated by satellite images and a chemical transport model) for the 5-year period before sera collection were assigned based on subjects' six-character residential postal codes. We used multivariable logistic regression models to compute odds ratios (ORs) for ANA positivity by separately setting ambient PM2.5 exposure as a continuous and a categorical variable, adjusting for basic socio-demographic factors, smoking status and urban-rural status. RESULTS: Comparing the highest vs lowest quartile of PM2.5 exposure, the ORs for ANA titres ≥1:640 and ≥1:1280 were 1.46 (95% CI: 1.02, 2.10) and 1.54 (95% CI: 1.06, 2.60) respectively. Although ORs were also positive for lower titres (i.e. ≥1:160 and ≥1:320), their CIs included unity. When PM2.5 was set as a continuous variable, the OR (always >1.00) was increased as the ANA titre increased but CIs always contained unity at any titre thresholds. CONCLUSIONS: PM2.5 exposure was associated with ANA positivity at high titres. This strengthens the argument for systemic immune system effects of air pollution, which could in turn lead to autoimmune diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".