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 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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".