Effects of Telmisartan on Glucose Levels in People at High Risk for Cardiovascular Disease but Free From Diabetes
Bibliographic record
Abstract
OBJECTIVE: Several large clinical trials suggest that ACE inhibitors may reduce the incidence of diabetes. Less is known about the effects of angiotensin receptor blockers (ARBs) on reducing incident diabetes or leading to regression of impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) to normoglycemia. RESEARCH DESIGN AND METHODS: Participants were 3,488 adults at high risk for cardiovascular disease but free from diabetes (mean age 67 years; 61% male) in the Telmisartan Randomized Assessment Study in ACE Intolerant Subjects With Cardiovascular Disease (TRANSCEND) study. The participants were randomized to the ARB telmisartan 80 mg (n = 1,726) or placebo (n = 1,762) in addition to usual care. RESULTS: During a median 56 months, 21.8% of participants treated with telmisartan and 22.4% of those on placebo developed diabetes (relative ratio 0.95 [95% CI 0.83-1.10]; P = 0.51). Participants originally diagnosed with IFG and/or IGT were equally likely to regress to normoglycemia (26.9 vs. 24.5%) or to progress to incident diabetes (20.1 vs. 21.1%; P = 0.59) on telmisartan or placebo. CONCLUSIONS: There was no evidence that addition of the ARB telmisartan to usual care prevents incident diabetes or leads to regression of IFG or IGT in people at high risk for cardiovascular disease but free from diabetes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".