Effect of Dapagliflozin on Glycemic Control, Weight, and Blood Pressure in Patients with Type 2 Diabetes Attending a Specialist Endocrinology Practice in Canada: A Retrospective Cohort Analysis
Bibliographic record
Abstract
BACKGROUND: In randomized clinical trials, dapagliflozin has been shown to improve glycemic control, weight, and blood pressure. However, there is little real-world evidence of the effectiveness of dapagliflozin. The objective of this study is to investigate the real-world treatment outcomes of patients with type 2 diabetes (T2D) who initiated dapagliflozin in a referral-based endocrinology practice. METHODS: This study was a retrospective cohort analysis of patients with T2D who initiated dapagliflozin in 2015, using data from a large, specialist diabetes registry in Canada. RESULTS: 1520 patients were eligible for analysis. Following 3 to 6 months of treatment, hemoglobin A1c (HbA1c) decreased by a mean of 0.9% ± 1.3% (9.8 ± 14.2 mmol/mol) (P < 0.01), weight decreased 2.2 ± 3.1 kg (P < 0.01), and systolic blood pressure decreased 3.7 ± 14.3 mmHg (P < 0.01). The proportion of patients who achieved glycemic control (HbA1c ≤7.0%) increased from 7.0% at baseline to 27.0% during follow-up. There was also a statistically significant decrease from baseline in body mass index, diastolic blood pressure, fasting glucose, total cholesterol, low-density lipoprotein cholesterol, triglycerides, alanine aminotransferase, and the proportion of patients with microalbuminuria (P < 0.01). A higher baseline HbA1c, shorter duration of diabetes, male gender, and greater weight loss were each independently associated with a greater reduction in HbA1c (P < 0.01). CONCLUSIONS: In a real-world clinical setting in Canada, dapagliflozin produced significant improvements in HbA1c, weight, and blood pressure in patients with T2D, comparable to that seen in randomized clinical trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".