Association of antidepressants with cataracts and glaucoma: a disproportionality analysis using the reports to the United States Food and Drug Administration Adverse Event Reporting System (FAERS) pharmacovigilance database
Bibliographic record
Abstract
BACKGROUND: Antidepressants are commonly prescribed for mood disorders. Epidemiological studies suggest antidepressant use may be associated with cataracts and glaucoma. We aim to investigate the association between antidepressants and cataracts and glaucoma. METHODS: ) were calculated for antidepressants (ie, selective serotonin reuptake inhibitors [SSRIs], selective norepinephrine reuptake inhibitors [SNRIs], serotonin-norepinephrine-dopamine reuptake inhibitors, serotonin modulators and stimulators, serotonin antagonists and reuptake inhibitors [SARIs], norepinephrine reuptake inhibitors, norepinephrine-dopamine reuptake inhibitors, tricyclic antidepressants [TCAs], tetracyclic antidepressants [TeCAs], and monoamine oxidase inhibitors [MAOIs]). The reference agent was acetaminophen. RESULTS: TeCAs and MAOIs were significantly associated with a decreased risk of cataracts (ROR = 0.11-0.65 and 0.16-0.69, respectively). TCAs, brexanolone, esketamine, and opipramol reported an increased cataract risk (ROR = 1.31-12.81). For glaucoma, SSRIs, SNRIs, SARIs, TCAs, MAOIs, and other investigated antidepressants reported significant RORs ranging from 1.034 to 21.17. There was a nonsignificant association of angle closure glaucoma (ACG) and open angle glaucoma (OAG) with the investigated antidepressants. LIMITATIONS: For adverse event cases, multiple suspected product names are listed, and as cases are not routinely verified, there may be a possibility of duplicate reports and causality cannot be established. CONCLUSION: Most of the investigated antidepressants were associated with a lower risk of cataract reporting. TCAs, brexanolone, esketamine, and opipramol were associated with greater odds of cataract. For most antidepressants, there was an insignificant increase in reports of ACG and OAG.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".