A Systematic Review of Drug Therapy to Delay or Prevent Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To systematically review the evidence for the prevention of type 2 diabetes by pharmacological therapies. RESEARCH DESIGN AND METHODS: Randomized controlled trials and cohort studies examining the effect of oral hypoglycemic agents, antiobesity agents, antihypertensive agents, statins, fibrates, and estrogen on the incidence of type 2 diabetes were identified from MEDLINE, EMBASE, the Cochrane Controlled Trials Registry, and searches of reference lists. Two reviewers independently assessed studies for inclusion and performed data extraction. RESULTS: Ten studies of oral hypoglycemic agents and 15 studies of nonoral hypoglycemic agents were found. Oral hypoglycemic agents and orlistat are the only drugs that have been studied in randomized controlled trials with diabetes incidence as the primary end point. In the largest studies of 2.5-4.0 years' duration, metformin (relative risk [RR] 0.69, 95% CI 0.57-0.83), acarbose (0.75, 0.63-0.90), troglitazone (0.45, 0.25-0.83), and orlistat (hazard ratio [HR] 0.63, 95% CI 0.46-0.86) have all been shown to decrease diabetes incidence compared with placebo; however, follow-up rates varied from 43 to 96%. Current evidence for statins, fibrates, antihypertensive agents, and estrogen is inconclusive. In addition, the critical question of whether drugs are preventing, or simply delaying, onset of diabetes remains unresolved. CONCLUSIONS: Currently, no single agent can be definitively recommended for diabetes prevention. Future studies should be designed with diabetes incidence as the primary outcome and should be of sufficient duration to differentiate between genuine diabetes prevention as opposed to simple delay or masking of this condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".