Exposure to fine particulate matter in adults is associated with immune cell gene expression related to inflammation, the electron transport chain, and cell cycle regulation
Bibliographic record
Abstract
Abstract Exposure to air pollution and an unhealthy built environment increase disease risk by impacting metabolic risk factors and inflammation, potentially via epigenetic modifications and effects on gene expression. We aimed to explore associations between fine particulate matter (PM2.5), black carbon, ozone, nitrogen dioxide, distance to nearest water body, normalized difference vegetation index, and impervious surface and gene expression profiles in adults. This study is a part of the LongITools project and includes cross-sectional data from the Rotterdam Study, a population-based cohort study, and NoMa, a randomized controlled trial. Environmental exposures were assigned using land-use regression (LUR) models and satellite data. Gene expression was assessed with whole blood RNA sequencing (Rotterdam Study, n = 758) and microarray analyses in peripheral blood mononuclear cells (NoMa, n = 100). We analysed transcriptomic profiles and enriched pathways associated with each of the environmental exposures. PM2.5 had the strongest gene expression associations, while only a few significant associations were observed for the other environmental exposures. In both populations, exposure to PM2.5 was associated with genes and pathways related to inflammation, oxidative stress, DNA metabolism, cell cycle regulation, histones, electron transport chain, oxidative phosphorylation, and neural signalling. This study is limited by different methods for RNA quantification, a cross-sectional design, and a small sample size. However, in both populations, exposure to PM2.5 resulted in the maximum number of associations with gene expression. In conclusion, PM2.5 is strongly associated with various gene expression profiles, which provide information about the underlying mechanisms of the detrimental health effects of exposure to PM2.5.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".