Increased risk of type 2 diabetes in antidepressant users: evidence from a 6‐year longitudinal study in the E3N cohort
Bibliographic record
Abstract
AIM: To examine the association between antidepressant medication use and the risk of type 2 diabetes. METHODS: Data were obtained from the E3N study (Étude Épidémiologique de Femmes de la Mutuelle Générale de l'Éducation Nationale), a French cohort study initiated in 1990, with questionnaire-based follow-up every 2 or 3 years. Exposure to antidepressants was obtained from drug reimbursement files available from 2004 onwards, and individually matched with questionnaire data. Cases of type 2 diabetes were identified from drug reimbursements. Cox proportional-hazard regression models were used, with drug exposure considered as a time-varying parameter. RESULTS: Of the 63 999 women who were free of drug-treated type 2 diabetes at baseline in 2005, 1124 developed type 2 diabetes over the 6-year follow-up. Current use of antidepressants was associated with an increased risk of type 2 diabetes [hazard ratio 1.34 (95% CI 1.12, 1.61)] compared to non-users. When the different types of antidepressants were considered, women who currently used selective serotonin reuptake inhibitors, imipramine-type, 'other' or 'mixed' antidepressants had a 1.25-fold (95% CI 0.99, 1.57), 1.66-fold (95% CI 1.12, 2.46), 1.35-fold (95% CI 1.00, 1.84) and 1.82-fold (95% CI 0.85, 3.86) increase in risk of type 2 diabetes compared to non-users, respectively. CONCLUSION: Our study suggests a positive association between antidepressant use and the risk of type 2 diabetes among women. If this association is confirmed, screening and surveillance of glucose levels should be considered in the context of antidepressant therapy. Further studies assessing the underlying mechanisms of this association are needed. (ClinicalTrials.gov identifier: NCT03285230).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".