Obesity management for the treatment of type 2 diabetes: emerging evidence and therapeutic approaches
Bibliographic record
Abstract
Excess adiposity can contribute to metabolic complications, such as type 2 diabetes mellitus (T2DM), which poses a significant global health burden. Traditionally viewed as a chronic and irreversible condition, T2DM management has evolved and new approaches emphasizing reversal and remission are emerging. Bariatric surgery demonstrates significant improvements in body weight and glucose homeostasis. However, its complexity limits widespread implementation as a population-wide intervention. The identification of glucagon-like peptide 1 (GLP-1) and the development of GLP-1 receptor agonists (GLP-1RAs) have improved T2DM management and offer promising outcomes in terms of weight loss. Innovative treatment approaches combining GLP-1RA with other gut and pancreatic-derived hormone receptor agonists, such as glucose-dependant insulinotropic peptide (GIP) and glucagon (GCG) receptor agonists, or coadministered with amylin analogues, are demonstrating enhanced efficacy in both weight loss and glycemic control. This review aims to explore the benefits of bariatric surgery and emerging pharmacological therapies such as GLP-1RAs, and dual and triple agonists in managing obesity and T2DM while highlighting the caveats and evolving landscape of treatment options.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".