1862-LB: Glucagon-Like Peptide 1 Receptor Agonists and the Risk of Suicide and Self-Harm among Patients with Type 2 Diabetes
Bibliographic record
Abstract
Introduction & Objective: Increased reports of thoughts of suicide and self-harm among users of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have prompted multiple regulatory agencies to conduct reviews on the drug class. There is an urgent need to assess the safety of these drugs in a real-world setting. We sought to determine whether GLP-1 RA use is associated with an increased risk of suicide and self-harm when compared with dipeptidyl peptidase-4 (DPP-4) inhibitors among patients with type 2 diabetes. Methods: Using primary care, hospitalization and mortality data from the United Kingdom, we assembled a cohort of patients with type 2 diabetes newly prescribed GLP-1 RAs or DPP-4 inhibitors between January 2007 and December 2020. We used propensity score fine stratification weighting to balance the exposure groups on over 40 potential confounders, including age, sex, smoking, BMI, history of mental health disorders and behaviours associated with self-harm and suicide attempt, socioeconomic status, proxies for diabetes severity, common comorbidities, other medication use, and markers of health-seeking behaviour. Patients were followed using an on-treatment exposure definition. We fit weighted Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident suicide or self-harm. Results: The cohort included 36,083 new users of GLP-1 RAs and 234,186 new users of DPP-4 inhibitors. Crude analyses indicated a twofold increase in the risk of suicide and self-harm associated with GLP-1 RA use (HR 1.99, 95% CI 1.65-2.41); however, the weighted model showed no increased risk (HR 0.99, 95% CI 0.77-1.29). Conclusions: The use of GLP-1 RAs was not associated with an increased risk of suicide or self-harm in this large, population-based study from the United Kingdom. Increased reporting of thoughts of suicide and self-harm are likely due to confounding factors rather than a causal relationship. Disclosure S. Shapiro: None. L. Azoulay: Advisory Panel; Pfizer Inc. Consultant; Roche Diagnostics. H. Yin: None. O. Yu: None. S. Rej: Other Relationship; AbbVie Inc. Stock/Shareholder; Aifred Health. S. Suissa: Consultant; Boehringer-Ingelheim, Novartis Canada. Speaker's Bureau; Covispharma. Consultant; AtaraBio. Speaker's Bureau; Merck & Co., Inc. Consultant; Panalgo. Funding Canadian Institutes of Health Research (FDN-143328)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".