GLP-1 medicine and non-arteritic anterior ischemic optic neuropathy: Literature review and perspectives
Bibliographic record
Abstract
GLP-1 therapeutics (defined as GLP-1 medicine hereafter), including GLP-1 receptor (GLP-1R) agonists (i.e. semaglutide) and glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 receptor dual agonist (i.e. tirzepatide), were initially developed for diabetes treatment. Semaglutide and tirzepatide were then approved for treating obesity and body weight management. Beyond their capability in improving glucose disposal and lowering body weight, clinical trials are undertaking to assess their beneficial effect in other metabolic disorders. However, several recent retrospective studies indicated that semaglutide treatment increased the risk of non-arteritic anterior ischemic optic neuropathy (NAION). A few other studies, however, reported a lack of such association, or that the increased risk was very moderate. As an untreatable disorder, NAION mainly affects middle-aged and elderly people, associated with hyperglycemia and hyperlipidemia. Here, we highlighted additional beneficial effects of GLP-1 medicine, reviewed literature on the above association, and presented our view on future investigations. Based on literature controversy, the intrinsic limitations of retrospective studies, and the existence of neuroprotective effect of GLP-1 in neural systems, future meta-analyses are essential to quantitatively determine the potential association. Animal models may be utilized to explore the underlying mechanisms, if the association indeed exists.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".