The Association of Muscle-Related Factors With Glaucoma and Related Traits in a Large United Kingdom Population
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate the hypothesis that muscle-related factors influence glaucoma risk, we examined the association of grip strength (GS), thigh muscle volume (TMV), and walking pace (WP) with glaucoma and its related traits. Methods: We included UK Biobank participants with data on IOP (N = 114,284), optical coherence tomography (OCT) macular inner retinal layer thickness measures (N = 44,141) and glaucoma status (N = 105,556; 2006-2010). Linear regression was used to evaluate multivariable-adjusted associations of GS, TMV, and WP with IOP and macular inner retinal OCT parameters, and logistic regression was used to evaluate associations with glaucoma status. We additionally examined gene-GS interactions with each outcome using a polygenic risk score (PRS) that combined the effects of 2673 genetic variants associated with glaucoma. Results: After adjustment for key anthropometric, lifestyle, and medical covariables, we found each additional standard deviation (SD) increase in GS (8.6 kg in men and 6.1 kg in women) was associated with thicker macular retinal nerve fiber layer (mRNFL) by 0.08 µm (P = 0.013) and 0.07 µm (P = 0.010) in men and women, respectively; thicker macular ganglion cell-inner plexiform layer (mGCIPL) by 0.12 µm (P = 0.003) and 0.17 µm (P < 0.001); higher IOP by 0.15 millimeters of mercury (mm Hg; P < 0.001) and 0.16 mm Hg (P < 0.001) and lower odds of glaucoma (odds ratio [OR] = 0.83, P < 0.001) in men only. The association with glaucoma was replicated in the independent EPIC-Norfolk cohort. Faster WP and greater TMV were also associated with lower odds of glaucoma in men only (P = 0.004 and P = 0.017, respectively). Stronger GS-IOP associations were observed in participants with a higher level of genetic risk for glaucoma (Pinteraction < 0.001). Conclusions: In this cross-sectional and gene-environment interaction study, factors relating to muscle strength, mass, and function were consistently associated with higher IOP, thicker inner retinal OCT measures in both sexes, and lower odds of glaucoma in men.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".