A Systematic Review and Meta-analysis of Systemic Antihypertensive Medications With Intraocular Pressure and Glaucoma
Bibliographic record
Abstract
PURPOSE: We synthesized the literature on the association between systemic antihypertensive medications with intraocular pressure (IOP) and glaucoma. Antihypertensive medications included β-blockers, calcium channel blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and diuretics. DESIGN: Systematic review and meta-analysis. METHODS: Databases were searched for relevant articles until December 5, 2022. Studies were eligible if they examined (1) the association between systemic antihypertensive medications with glaucoma or (2) the association between systemic antihypertensive medications with IOP in those without glaucoma or ocular hypertension. The protocol was registered at PROSPERO (International Prospective Register of Systematic Reviews; registration ID: CRD42022352028). RESULTS: A total of 11 studies were included in the review and 10 studies in the meta-analysis. The 3 studies on IOP were cross-sectional, whereas the 8 studies on glaucoma were primarily longitudinal. In the meta-analysis, β-blockers were associated with a lower odds of glaucoma (odds ratio: 0.83, 95% CI: 0.75-0.92, 7 studies, n = 219,535) and lower IOP (β: -0.53, 95% CI: -1.05 to -0.02, 3 studies, n = 28,683). Calcium channel blockers were associated with a higher odds of glaucoma (odds ratio: 1.13, 95% CI: 1.03-1.24, 7 studies, n = 219,535) but not with IOP (β: -0.11, 95% CI: -0.25 to 0.03, 2 studies, n = 20,620). There were no consistent associations between angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, or diuretics with glaucoma or IOP. CONCLUSIONS: Systemic antihypertensive medications have heterogeneous effects on glaucoma and IOP. Clinicians should be aware that systemic antihypertensive medications may mask elevated IOP or positively or negatively affect the risk of glaucoma.
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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".