The Impact of Antidepressant Therapy on Glycemic Control in Canadian Primary Care Patients With Diabetes Mellitus
Bibliographic record
Abstract
Context: Depression is common in people with diabetes and is associated with poor glycemic control. Evidence suggests that certain antidepressants (AD) increase the risk of poor control. Few population-based studies have examined the impact of individual ADs on glycemic control. This study’s objective is to measure the impact of Citalopram, Amitriptyline, Venlafaxine, Trazodone and Escitalopram on glycated hemoglobin (HbA1c) in Canadian primary care patients with diabetes. Methods: A retrospective study of electronic medical records (EMR) from 115 primary care practices across Canada was undertaken. Data were obtained from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). The sample population comprised 1084 diabetic patients with 1127 prescriptions of one of the five selected ADs and with baseline and post-exposure HbA1c measurements. Generalized linear mixed models were computed to estimate the effect of the ADs on HbA1c. Results: Mean HbA1c ratios for Amitriptyline, Venlafaxine, Trazodone and Escitalopram were all numerically lower than Citalopram. The confidence intervals included the minimum detectable effect, however the differences were not statistically significant. The lowest clinically relevant HbA1c ratios, relative to Citalopram, were found in patients prescribed Trazodone and Escitalopram. Accounting for the prescription of Trazodone for indications other than depression, this research suggests that Escitalopram may be safer than Citalopram for people with diabetes and depression, in terms of its effect on blood glucose. Conclusion: This study can inform future research examining the relationship between ADs and blood glucose and provides insight into the limitations pertaining to the use of health data in health research. Future research should seek to control for, across multiple time points: depression symptoms, depression severity, depression duration, weight, diabetes medication, tobacco and alcohol consumption and other medications with a known impact on blood glucose.
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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) | 0.000 | 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".