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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".