Associations between Ambient Fine Particulate Levels and Disease Activity in Patients with Systemic Lupus Erythematosus (SLE)
Bibliographic record
Abstract
BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic disease of unclear etiology, characterized by an overactive immune system and the production of antibodies that may target normal tissues of many organ systems, including the kidneys. It can arise at any age and occurs mainly in women. OBJECTIVE: Our aim was to evaluate the potential influence of particulate matter (PM) air pollution on clinical aspects of SLE. METHODS: We studied a clinic cohort of SLE patients living on the island of Montreal, followed annually with a structured clinical assessment. We assessed the association between ambient levels of fine PM [median aerodynamic diameter ≤ 2.5 μm (PM2.5)] measured at fixed-site monitoring stations and SLE disease activity measured with the SLE Disease Activity Index, version 2000 (SLEDAI-2K), which includes anti-double-stranded DNA (anti-dsDNA) serum-specific autoantibodies and renal tubule cellular casts in urine, which reflects serious renal inflammation. We used mixed effects regression models that we adjusted for daily ambient temperatures and ozone levels. RESULTS: We assessed 237 patients (223 women) who together had 1,083 clinic visits from 2000 through 2007 (mean age at time of first visit, 41.2 years). PM2.5 levels were associated with anti-dsDNA and cellular casts. The crude and adjusted odds ratios (reflecting a 10-μg/m3 increase in PM2.5 averaged over the 48 hr prior to clinical assessment) were 1.26 [95% confidence interval (CI), 0.96-1.65] and 1.34 (95% CI, 1.02-1.77) for anti-dsDNA antibodies and 1.43 (95% CI, 1.05-1.95) and 1.28 (0.92-1.80) for cellular casts. The total SLEDAI-2K scores were not associated with PM2.5 levels. CONCLUSIONS: We provide novel data that suggest that short-term variations in air pollution may influence disease activity in established autoimmune rheumatic disease in humans. Our results add weight to concerns that pollution may be an important trigger of inflammation and autoimmunity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".