Glucagon‐like‐peptide 1 receptor agonism and attempted suicide: A Mendelian randomisation study to assess a potential causal association
Bibliographic record
Abstract
Glucagon-like-peptide 1 receptor agonists (GLP-1RA) have transformed type 2 diabetes (T2D) and obesity management. Multiple regulatory agencies are investigating reported associations between GLP1-RA and increased suicide attempts (SA), but observational data may be prone to confounding. Randomised control trials (RCT) of GLP-1RA were largely undertaken in people at lower risk of SA. Real-world data suggest semaglutide use associates with reduced suicidal ideation and depression but was under-powered to statistically assess risk of SA. Mendelian randomisation (MR) leverages genetic instrument(s) to infer potential causal association between an exposure and an outcome. We undertook MR using missense variants in the gene encoding GLP1R that improve glycemia, lower T2D risk and/or lower BMI, to investigate potential causal association between GLP-1RA and SA. In people of European ancestry, MR did not find evidence genetically proxied GLP1RA increased SA in a general population cohort: (rs10305492, exposure: HbA1c, odds ratio [OR] and 95% confidence interval [CI]: 1.38, 0.41-4.62, p = .60), (rs10305492, exposure: FG, OR 1.27, 0.52-3.13, p = .60) and (rs1042044, exposure BMI, OR 0.30, 0.06-1.48) with concordant results in a multi-ancestry SA case-control cohort. In conclusion, we did not find MR evidence that increased GLP-1RA impacts SA. This awaits confirmation with RCT and real-world data.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".