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Epigenetic Age as Both Effect and Modifier of Response to Nasal Corticosteroid Under Diesel Exhaust and Allergen Exposure in Adults With Allergic Rhinitis

2025· article· en· W4410269444 on OpenAlexaff
E. Halbe, Christopher F. Rider, A. Hashimoto, Julia L. MacIsaac, Marcia Smiti Jude, Chaini Konwar, Eun-Soo Lim, P. Johal, A.C.Y. Yuen, Michael S. Kobor, Chris Carlsten

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineCorticosteroidAllergenAsthmaImmunologyAllergyDiesel exhaustDiesel fuelInternal medicine

Abstract

fetched live from OpenAlex

Abstract Rationale: Allergic rhinitis (AR) is a nasal inflammatory disease triggered by exposure to aeroallergens. Exposure to air pollution, including diesel exhaust (DE; a model of traffic-related air pollution) can drive allergic rhinitis (AR) and may alter airway DNA methylation. Nasal corticosteroids (NCS) are anti-inflammatory agents used as first-line treatment for AR, and in vitro studies have also shown budesonide to modulate DNA methylation. The effect of budesonide treatment, in combination with exposures to DE and allergens, on DNA methylation-based biomarkers of age in AR has not been previously studied. Methods: In a double-blinded crossover trial, twenty healthy non-smokers (aged 18-65) with AR used a once-daily budesonide or placebo nasal spray treatment for four weeks each in a randomized order. Allergen and DE exposures were completed twice over two-day periods separated by a ≥2-week washout, starting with allergen two weeks after the start of each treatment arm. A ≥4-week washout was completed between treatment arms. Nasal brushings were collected at the start of each treatment period, as well as before and at 24h and 48h after the initial allergen or DE exposure in each treatment arm. Re-exposures occurred 24h after each initial exposure. Allergen challenges were titrated for each participant, while 2h DE exposures were standardized to 300μg/m3 of PM2.5. DNA methylation was quantified using Illumina EPIC arrays and epigenetic age measurements were computed by the Clock Foundation. Data analysis was completed using linear mixed effects models. Results: The effects of budesonide treatment on various measures of epigenetic age at each timepoint are given in Table 1. Allergen increased Horvath1 age acceleration (AA) following initial (2.58 years [0.29, 4.86], p=0.03) and re-exposure (3.35 years [1.03, 5.56], p=0.006), while decreasing Grim AA (-2.10 years [-3.36, -0.85], p≤0.001) following re-exposure. Re-exposure to DE also decreased Grim AA (-1.33 years [-2.48, -0.19], p=0.02). Grim AA scores moderated budesonide's effect on IL-1β (β=1.20-fold [1.09, 1.32], p=0.02) and VEGF-A (β=1.07-fold [1.03, 1.12], p=0.03) relative to placebo at the DE baseline, as well as an increase in DE's effect upon GM-CSF (β=1.12-fold [1.04, 1.21], p=0.03) and IL-16 (β=1.15-fold [1.06, 1.26], p=0.03) 24h after re-exposure to DE. Conclusions: Budesonide and exposure to allergen and DE significantly affected epigenetic age acceleration. Epigenetic age may moderate cytokine responses to budesonide treatment and common environmental exposures.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.321
Teacher spread0.304 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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