Antidepressants Reduced Risk of Mortality in Patients With Diabetes Mellitus: A Population-Based Cohort Study in Taiwan
Bibliographic record
Abstract
CONTEXT: The effect of antidepressant (ATD) use on mortality in patients with diabetes mellitus (DM) has not yet been sufficiently studied, although comorbid depression is common in this population. OBJECTIVE: To explore the impact of ATDs on mortality among DM patients. DESIGN: A retrospective cohort study in a national database. SETTING: This population-based study used the National Health Insurance Research Database in Taiwan. Since 2000, we identified 53,412 cases of newly diagnosed patients with DM and depression. Patient cases were followed for assessing mortality until 2013. MAIN OUTCOME MEASURE: The association between mortality and ATD use was explored adjusting for cumulative dosing. RESULTS: Using the time-dependent Cox regression model, ATD use was associated with significantly reduced mortality among patients with DM [in the highest dose group: hazard ratio (HR), 0.65; 95% CI, 0.59 to 0.71]. Further analysis showed that differences in mortality existed across ATD categories: selective serotonin reuptake inhibitors (HR, 0.63; 95% CI, 0.56 to 0.71), serotonin-norepinephrine reuptake inhibitors (HR, 0.58; 95% CI, 0.44 to 0.78), norepinephrine-dopamine reuptake inhibitors (HR, 0.20; 95% CI, 0.07 to 0.63), mirtazapine (HR, 0.60; 95% CI, 0.45 to 0.82), tricyclic/tetracyclic antidepressants (HR, 0.73; 95% CI, 0.54 to 0.97), and trazodone (HR, 0.52; 95% CI, 0.29 to 0.91). However, reversible inhibitor of monoamine oxidase A (RIMA) was found to be associated with an increase, rather than a decrease, in total mortality (HR, 1.48; 95% CI, 1.09 to 1.99). CONCLUSION: Most ATDs, but not RIMA, were associated with significantly reduced mortality among a population with comorbid DM and depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".