Efficacy and Safety of SGLT2 Inhibitors in Reducing Glycated Hemoglobin and Weight in Emirati Patients With Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: SGLT2 inhibitors are a new class of drugs that act by inhibiting glucose reabsorption in the proximal renal tubules. Many trials have demonstrated their effectiveness in reducing glycated hemoglobin (HbA1c) and weight, but they have never been examined in Arab or Emirati populations. METHODS: We assessed the efficacy of SGLT2 inhibitors in reducing HbA1c and weight in our population and specifically in an Emirati cohort. We also assessed the effect on fasting blood glucose, blood pressure, lipid profile, serum creatinine, and side effects. RESULTS: The total number of patients was 307. The baseline HbA1c in the Emirati cohort was 8.9±1.7%, which dropped significantly to 8±1.5% at 6 months (P = 0.0001). At 1 year, the mean HbA1c was 8±1.4%, which was significantly different from baseline (P = 0.0001). However, the change in mean HbA1c from 6 months (8±1.5%) to 1 year (8±1.4%) was not statistically significant (P = 0.88). A similar highly significant change was observed when comparing weights at baseline and 6 months in the Emirati population (85.7 ± 17.8 kg vs. 84 ± 17.2 kg, P = 0.0001). Total cholesterol dropped significantly at 6 months (P = 0.008), as did low-density lipoprotein (LDL) (P = 0.005). CONCLUSIONS: The use of SGLT2 inhibitors is associated with significant reductions in HbA1c and weight. Unlike all previous trials, the inhibitors significantly reduced total cholesterol and LDL. Larger trials are needed to reassess their effects on lipid parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".