Ambient Air Pollution Associations with Retinal Morphology in the UK Biobank
Bibliographic record
Abstract
Purpose: Because air pollution has been linked to glaucoma and AMD, we characterized the relationship between pollution and retinal structure. Methods: We examined data from 51,710 UK Biobank participants aged 40 to 69 years old. Ambient air pollution measures included particulates and nitrogen oxides. SD-OCT imaging measured seven retinal layers: retinal nerve fiber layer, ganglion cell-inner plexiform layer, inner nuclear layer, outer plexiform layer + outer nuclear layer, photoreceptor inner segments, photoreceptor outer segments, and RPE. Multivariable regression was used to evaluate associations between pollutants (per interquartile range increase) and retinal thickness, adjusting for age, sex, race, Townsend deprivation index, body mass index, smoking status, and refractive error. Results: Participants exposed to greater particulate matter with an aerodynamic diameter of <2.5 µm (PM2.5) and higher nitrogen oxides were more likely to have thicker retinal nerve fiber layer (β = 0.28 µm; 95% CI, 0.22-0.34; P = 3.3 × 10-20 and β = 0.09 µm; 95% CI, 0.04-0.14; P = 2.4 × 10-4, respectively), and thinner ganglion cell-inner plexiform layer, inner nuclear layer, and outer plexiform layer + outer nuclear layer thicknesses (P < 0.001). Participants resident in areas of higher levels of PM2.5 absorbance were more likely to have thinner retinal nerve fiber layer, inner nuclear layer, and outer plexiform layer + outer nuclear layers (β = -0.16 [95% CI, -0.22 to -0.10; P = 5.7 × 10-8]; β = -0.09 [95% CI, -0.12 to -0.06; P = 2.2 × 10-12]; and β = -0.12 [95% CI, -0.19 to -0.05; P = 8.3 × 10-4], respectively). Conclusions: Greater exposure to PM2.5, PM2.5 absorbance, and nitrogen oxides were all associated with apparently adverse retinal structural features.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".