Effects of Sodium-Glucose Cotransporter 2 Inhibitors on Body Weight, BMI, and Body Composition in Adults With Type 2 Diabetes Mellitus: A Systematic Review
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) is a major global health issue, affecting millions and leading to significant healthcare costs. Sodium-glucose cotransporter 2 (SGLT2) inhibitors have emerged as a potential treatment option. Still, their effects on body weight, body mass index (BMI), and body composition compared to other diabetes medications or placebos remain unclear. This systematic review investigates these effects in adults with T2DM. A comprehensive literature search was conducted from June 20, 2024, to July 5, 2024, across six databases and one register: PubMed, MEDLINE, Cochrane Library (CENTRAL), Europe PMC, ScienceDirect, ClinicalTrials.gov, and EBSCO Open Dissertation, yielding 2,425 records. Following the application of inclusion and exclusion criteria, 13 studies were selected for final analysis, encompassing a sample size of 37,619 participants, adhering to Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines. The quality of the included studies was assessed using the Cochrane Risk-of-Bias Tool for randomized controlled trials and the Newcastle-Ottawa Scale for observational studies. Results indicate that SGLT2 inhibitors are significantly associated with reductions in body weight and BMI compared to other diabetes medications and placebo. These findings suggest that SGLT2 inhibitors improve glycemic control and facilitate effective weight management, underscoring their potential role in comprehensive diabetes care. Future research should focus on long-term outcomes and the integration of SGLT2 inhibitors into individualized treatment plans for patients with T2DM.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".