Does Lithium Lower the Risk of Glaucoma—A Danish Nationwide Study
Bibliographic record
Abstract
BACKGROUND: Glaucoma is an optic neuropathy caused by neurodegenerative loss of retinal ganglion cells (RGC). Mechanisms that contribute to RGC death include some of the same mechanisms involved in bipolar disorders (BD) and are the same mechanisms which are targeted by lithium (Li). We conducted a pharmacoepidemiological, population study in Denmark testing the links between Li prescriptions and risk of glaucoma. STUDY DESIGN: A nationwide, register-based historical, prospective cohort study of participants who were alive and over the age of 18 between January 1, 1996 and December 31, 2019. Participants were followed from the start of study until censoring or glaucoma. STUDY RESULTS: A total of 7,683,398 individuals (51.3% females) contributing 121,366,461 person-years were included in the study. In the general population, Li exposure was associated with developing glaucoma (HR = 1.10, 95% CI = 1.02-1.19, p = 0.01), but this association was not present in the population with BD (HR = 1.07, 95% CI = 0.93-1.22, p = 0.34). In the cumulative dosage analyses of the entire population, people with no Li prescription (HR = 0.78 95% CI = 0.66-0.93, p = 0.01) and between 365 defined daily doses (DDDs) and 5 × 365 DDDs of Li showed significantly reduced risk of glaucoma, relative to at least one prescription (HR = 0.79, 95% CI = 0.64-0.99, p < 0.05). CONCLUSION: BDs as indexed by Li prescription are associated with a greater risk of glaucoma. This is in keeping with generally increased rates of medical comorbidities in BD. While there was no clear dose-response relationship, some of the higher cumulative exposures to Li might be protective relative to a single prescription.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".