Statin Use in Relation to Intraocular Pressure, Glaucoma, and Ocular Coherence Tomography Parameters in the UK Biobank
Bibliographic record
Abstract
Purpose: The purpose of this study was to evaluate the relationship between statin use and glaucoma-related traits. Methods: In a cross-sectional study, we included 118,153 UK Biobank participants with data on statin use and corneal-compensated IOP. In addition, we included 192,283 participants (8982 cases) with data on glaucoma status. After excluding participants with neurodegenerative diseases, 41,638 participants with macular retinal nerve fiber layer thickness (mRNFL) and 41,547 participants with macular ganglion cell inner plexiform layer thickness (mGCIPL) were available for analysis. We examined associations of statin use with IOP, mRNFL, mGCIPL, and glaucoma status utilizing multivariable-adjusted regression models. We assessed whether a glaucoma polygenic risk score (PRS) modified associations. We performed Mendelian randomization (MR) experiments to investigate associations with various glaucoma-related outcomes. Results: Statin users had higher unadjusted mean IOP ± SD than nonusers, but in a multivariable-adjusted model, IOP did not differ by statin use (difference = 0.05 mm Hg, 95% confidence interval [CI] = -0.02 to 0.13, P = 0.17). Similarly, statin use was not associated with prevalent glaucoma (odds ratio [OR] = 1.05, 95% CI = 0.98 to 1.13). Statin use was weakly associated with thinner mRNFL (difference = -0.15 microns, 95% CI = -0.28 to -0.01, P = 0.03) but not with mGCIPL thickness (difference = -0.12 microns, 95% CI = -0.29 to 0.05, P = 0.17). No association was modified by the glaucoma PRS (Pinteraction≥ 0.16). MR experiments showed no evidence for a causal association between the cholesterol-altering effect of statins and several glaucoma traits (inverse weighted variance P ≥ 0.14). Conclusions: We found no evidence of a protective association between statin use and glaucoma or related traits after adjusting for key confounders.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".