Antihypertensive Therapy and Incidence of Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To systematically review the available evidence examining the effects of the major antihypertensive drug classes on the incidence of type 2 diabetes. RESEARCH DESIGN AND METHODS: The Cochrane Controlled Trials Register, Medline, and Embase were searched for English-language case-control, cohort, and randomized controlled trials involving the major antihypertensive classes and reporting type 2 diabetes as an end point. Reference lists of original studies and narrative reviews were also hand searched. One reviewer (R.P.) performed the electronic searches. Both reviewers independently extracted data and assessed all potentially relevant studies for inclusion and methodological quality. Abstracts were not included, and unpublished studies were not sought. RESULTS: One case-control study, 8 cohort studies, and 14 randomized controlled trials met inclusion criteria. No study examined diabetes incidence as a primary end point. Poor methodological quality limits the conclusions that can be drawn from most nonrandomized trials. Evidence from randomized studies is also potentially limited by several sources of bias, including treatment contamination and bias inherent in post hoc analyses. Data from the highest-quality studies suggest that diabetes incidence is unchanged or increased by thiazide diuretics and beta-blockers and unchanged or decreased by ACE inhibitors, calcium channel blockers, and angiotensin receptor blockers. CONCLUSIONS: The major antihypertensive classes may exert differential effects on diabetes incidence, although current data are far from conclusive. Ongoing placebo-controlled randomized trials involving potentially beneficial drug classes and examining diabetes incidence as a primary end point should provide more definitive evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".