Table 1_Air pollution, emerging chemical exposures, and systemic lupus erythematosus: a meta-epidemiology study.docx
Bibliographic record
Abstract
Objectives This meta-analysis evaluated the direction and strength of associations between air pollution, emerging chemical pollutants, and systemic lupus erythematosus (SLE) incidence, clarifying distinct relationships by pollutant type. Method By utilizing medical subject headings and keywords from the PubMed and EMBASE databases, a thorough search was conducted for published observational studies linking air pollution and SLE from inception until August 2024. The Newcastle-Ottawa Scale (NOS) was utilized to evaluate the quality of the studies. Statistical analyses were performed using STATA software (version 14.0), with the assessment of publication bias conducted through funnel plots and Egger’s test. Result This meta-analysis encompassed 8 studies published between 2018 and 2024, involving a total of 1,390,348 individuals. We assessed exposure to standard air pollutants and emerging chemical pollutants, specifically including perfluoroalkyl and polyfluoroalkyl substances (PFASs, a type of persistent chemical widely used in nonstick cookware and waterproof products) and bisphenol compounds (BPs, a synthetic chemical primarily used in plastic products and resins). These eight studies identified significant positive associations between SLE incidence and exposure to PM2.5 [OR = 1.16, 95% CI (1.02-1.32), I2 = 62.4%, p=0.031], NO2 [OR = 1.24, 95% CI (1.11-1.38), I² = 0.0%, P = 0.603], and PFASs [OR = 2.47, 95% CI (1.54-2.57)], while O3 exhibited a negative association [OR = 0.83, 95% CI (0.70-0.98), I² = 19.3%, P = 0.290]. No significant links were found for PM10 [OR = 1.11, 95% CI (0.90–1.36), I² = 66.3%, P = 0.031], SO2 [OR = 0.99, 95% CI (0.66-1.48), I² = 79.0%, P = 0.001], and BPs [OR = 1.26, 95% CI (0.80-1.99)]. Sensitivity analyses supported robustness without evidence of publication bias. Conclusion The results of this meta-analysis suggest that air pollutants PM2.5 and NO2 may be potential environmental risk factors for SLE, while the negative correlation with O3 requires further research to validate its potential mechanisms. It is worth noting that although a study on PFASs showed a strong association with SLE, this finding requires further evidence due to the limited number of relevant studies currently available. These findings imply that improving air quality and strengthening regulation of emerging pollutants may reduce the disease burden of SLE. Based on the current strength of evidence, public health policies should prioritize reducing population exposure levels to PM2.5 and NO2, which may help reduce the potential risk of SLE onset. Concurrently, larger-scale studies should be conducted to confirm the association between other environmental pollutants such as PFASs and SLE, providing more comprehensive scientific evidence for the development of targeted environmental health policies. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, PROSPERO (CRD42024581931).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Meta-epidemiology (broad) Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.336 | 0.010 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".