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Record W4200131046 · doi:10.1101/2021.12.12.21267685

Statin use in relation to intraocular pressure, glaucoma, and ocular coherence tomography parameters in the UK Biobank

2021· preprint· en· W4200131046 on OpenAlexaff
Jihye Kim, Marianne T. Neary, Hugues Aschard, Mathew M. Palakkamanil, Ron Do, Janey L. Wiggs, Anthony P. Khawaja, Louis R. Pasquale, Jae H. Kang

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsDalhousie University
FundersAlcon Research InstituteNational Institute of General Medical SciencesNational Eye InstituteNational Heart, Lung, and Blood InstituteGlaucoma FoundationAstraZenecaNational Institutes of HealthUK Research and InnovationResearch to Prevent Blindness
KeywordsGlaucomaMedicineOphthalmologyStatinIntraocular pressureLogistic regressionBiobankInternal medicineBioinformatics

Abstract

fetched live from OpenAlex

Abstract Objective To evaluate the relationship between statin use and various glaucoma-related traits. Design Cross-sectional analysis of UK Biobank data. Participants We included 118,153 participants (mean age (SD)=56.8 (8.0) years) with data on statin use (5 statin types – 2006-2010) and corneal-compensated IOP measured in 2009-2013). Also, we included 192,283 participants (with 8,982 self-reported glaucoma cases as of 2006-2010) for the glaucoma analyses. After excluding participants with neurodegenerative diseases, 41,638 participants with global macular retinal nerve fiber layer thickness (mRNFL) and 41,547 participants with ganglion cell inner plexiform layer thickness (mGCIPL) measurements in 2009–2010 were available for analysis. Method We examined associations with statin use utilizing multivariable-adjusted linear regression models for IOP, mRNFL, and mGCIPL and logistic regression models for glaucoma. We assessed whether a 2,673-member polygenic risk score (PRS) identified from a glaucoma multi-trait analysis of genome wide association study (MTAG) modified associations. We performed Mendelian randomization (MR) experiments using 5 gene variants as proxies for the cholesterol-altering effect of statins to investigate associations with various glaucoma-related outcomes. Main Outcome and Measures IOP; glaucoma; mRNFL; mGCIPL. Results Statin users had higher unadjusted mean IOP ± SD (16.3 ± 3.9 mm Hg; n = 20,593 participants) than non-users (15.9 ± 3.8 mm Hg; n = 97,560 participants), but in a multivariable-adjusted model, IOP did not differ by statin use (difference = 0.05 mm Hg; 95% CI: -0.02, 0.13; p = 0.17). Similarly, statin use was not associated with prevalent glaucoma (OR = 1.05; 95% CI: 0.98, 1.13). Statin use was weakly associated with thinner mRNFL (difference = -0.15 microns; 95% CI: -0.28, -0.01; p=0.03) but not with mGCIPL thickness (difference = -0.12 microns; 95% CI: -0.29, 0.05; p=0.17). Among statins, simvastatin and atorvastatin, the two most commonly used statins, were not associated with any glaucoma outcome measures. No association was modified by the glaucoma MTAG PRS ( P interaction ≥ 0.16). MR experiments showed no evidence for a causal association between the cholesterol-altering effect of statins and various glaucoma outcomes (inverse weighted variance p ≥ 0.14). Conclusions Statin use was not associated with lower IOP, lower glaucoma prevalence, thicker mRNFL or thicker mGCIPL in the UK Biobank.

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)0.008
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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.020
GPT teacher head0.259
Teacher spread0.240 · 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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