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Record W2894716597 · doi:10.1136/lupus-2018-lsm.86

EF-06 Anti-nuclear antibodies (ANA) and air pollution: ultrafine particles and ozone

2018· article· en· W2894716597 on OpenAlexafffundabout
Sasha Bernatsky, Audrey Smargiassi, Shouao Wang, May Y. Choi, Scott Weichenthal, Marianne Hatzopoulou, Marvin J. Fritzler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of TorontoUniversity of CalgaryUniversité de MontréalMcGill University
FundersCanadian Institutes of Health ResearchNational Institutes of HealthLupus Research AllianceLupus Research Institute
KeywordsMedicineUltrafine particlePopulationLogistic regressionAir pollutionOzoneInternal medicineEnvironmental healthMeteorology

Abstract

fetched live from OpenAlex

Background Previous studies suggest links between air pollution (i.e. fine particulate matter, PM2.5) and serum antibodies related to rheumatic diseases. No one has yet examined associations between anti-nuclear antibodies (ANA) and ultrafine particles (UFP) or ozone (O3), both of which can enter through the lungs and may have the potential to trigger systemic effects. Methods Our analyses were based within the CARTaGENE general population cohort (n=20,000) based in the province of Quebec, Canada. We determined baseline ANA (HEp-2000, Immuno Concepts) on a random sample. Air pollutant exposures were assigned by linking subjects’ residential postal codes with estimated levels (determined by hybrid approaches including satellite imagery and modelling). We performed multivariable logistic regression models for the outcome of positive ANA, assessing for independent effects of UFP (available for Montreal only) and O3 in separate models, adjusting for age, sex, smoking, and self-reported ancestry. Results ANA positivity of at least 1:160 occurred in 713 (20%) of 3578 randomly selected patients tested. The ANA positive subjects were more likely than ANA negative subjects to be female (63% vs 49%) while the average age (55.4 vs 54.0) and percent never-smokers (37% vs 40%) were similar. There was missing information on covariates for 232 subjects, which therefore were not included in the model estimates. There was a trend for higher average UFP: exposure in ANA positive subjects (24 606 particles/cm3, standard deviation, SD 4979) versus ANA negative (24328, SD 5078), while average ozone levels were very similar (22.5 vs 22.6 µg/m). The multivariable model (table 1) for UFP showed a trend to higher levels in the ANA positive group (1.008, 95% CI 0.982 to 1.034) while in the multivariable model for O3 the OR was very close to the null value (0.996, 95% CI 0.965 to 1.029). In all models, risk factors for ANA positivity included older age and female sex, with trends for lower ANA positivity in French Canadians. Conclusions We saw a non-significant trend towards higher UFP levels in ANA positive versus negative subjects, while O3 levels seemed very similar in the two groups. Expected trends for more ANA positivity with older age and female sex was seen. Further study of UFP levels with a larger sample size is in progress. If confirmed, these results may strengthen the hypothesis that air pollution is an environmental trigger of immune system activation. Acknowledgements This research was supported by the CIHR. CARTaGENE is a member of the Canadian Project for Tomorrow cohort.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.685

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.029
GPT teacher head0.285
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes3
Has abstractyes

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