Weight Beneficial Treatments for Type 2 Diabetes
Bibliographic record
Abstract
CONTEXT: The close link between type 2 diabetes and excess body weight highlights the need to consider the weight effects of different treatment regimens. We examine the impact of "weight-friendly" type 2 diabetes pharmacotherapies and suggest treatment strategies that mitigate weight gain. EVIDENCE ACQUISITION: Evidence was identified via PubMed search by class and agent and in bibliographies of review articles, with final articles for inclusion selected by author consensus. EVIDENCE SYNTHESIS: Substantial evidence confirms the weight benefits of metformin and shows that, of the newer available agents, glucagon-like peptide-1 (GLP-1) agonists and amylin analogs promote weight loss. Dipeptidyl peptidase-4 (DPP-4) inhibitors and bile acid sequestrants are weight-neutral. Liraglutide and exenatide appear to have similar effects on weight; however, recent research suggests a potentially greater effect of liraglutide on glycemic control compared to exenatide, when used as a second-line therapy. Mounting evidence suggests that insulin detemir may provide the most favorable weight benefits of available insulins. CONCLUSIONS: Weight-beneficial agents should be considered in patients, particularly obese patients, who fail to reach glycemic targets on metformin therapy. We propose the following treatment choices based on potential weight benefit and blood glucose increment: long-acting GLP-1 agonists (liraglutide), DPP-4 inhibitors, bile acid sequestrants, amylin analogs, and basal insulin for patients with elevated fasting plasma glucose; and short-acting (exenatide) or long-acting GLP-1 agonists, amylin analogs, DPP-4 inhibitors, acarbose, and bile acid sequestrants for patients with elevated postprandial glucose. The weight-sparing effects of insulin detemir, notably in patients with high body mass index, should also be considered when initiating insulin therapy.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".