Environmental impacts on clinical outcomes and the epigenome in patients with fibrotic interstitial lung disease
Bibliographic record
Abstract
Fibrotic interstitial lung diseases (fILDs) are a group of conditions characterized by lung scarring, functional limitation, and a high morbidity and mortality. Air pollution and socioeconomic disadvantage have been linked with adverse outcomes in idiopathic pulmonary fibrosis (IPF), the most common form of fILD, but these impacts have not been evaluated in large, geographically-diverse cohorts of patients with fILD. This dissertation sought to evaluate the impact of particulate matter with a diameter of <=2.5um (PM2.5) and neighborhood-level disadvantage on clinical outcomes, genomic, and epigenomic mechanisms of disease in a diverse cohort of patients with fILD. Aim 1 demonstrated that patients with fILD who live in neighborhoods with greater disadvantage in the U.S., but not Canadian cohort, experience increased mortality and lower odds of receiving lung transplant. This work highlights how health disparities may be exacerbated by healthcare system structure, and raises questions of how environmental factors in disadvantaged neighborhoods contribute to these adverse outcomes. Aim 2 demonstrated that increased exposures to PM2.5 and its anthropogenic constituents (particularly sulfate, nitrate, and ammonium) are associated with increased mortality and worse lung function in fILDs. Aim 3 demonstrated that PM2.5 and its constituents can influence DNA methylation (DNAm) patterns and telomere length in patients with fILD, and that these molecular changes may mediate PM2.5-mortality associations. We found that increased PM2.5, sulfate, and ammonium exposures were associated with higher global DNAm using an ELISA-based assay. Then, epigenome-wide association studies identified multiple CpGs associated with high exposures to PM2.5. Most significant CpGs were found in analyses of sulfate, ammonium, and sea salt PM2.5 constituents, highlighting the potentially greater mechanistic relevance for these components of the PM2.5 mixture. Lastly, we found that higher exposure to PM2.5 and anthropogenic constituents was associated with shorter telomere length, which mediates a portion of the PM2.5-mortality association. By highlighting environmental impacts on clinical outcomes and molecular mechanisms of disease, we have unveiled potential causal pathways for how such exposures contribute to the development and progression of fILDs. This research has direct impacts on public health by providing critical data to inform environmental health policies that protect vulnerable populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.006 | 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 teacher head, 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".