BLOOD PRESSURE-LOWERING, ANTIHYPERTENSIVE TREATMENT AND INCIDENT DIABETES
Bibliographic record
Abstract
Objective: To investigate the effect of blood pressure lowering and specific antihypertensives on the risk of new-onset type 2 diabetes, leveraging the strengths of both genetic data and individual-level data from randomised trials of blood pressure-lowering treatments. Design and method: We pooled individual-level data of 145,939 participants from nineteen randomised clinical trials. We used stratified Cox proportional hazard models, with fixed treatment effects, and participants as the unit of analysis. Analyses were complemented with Mendelian randomisation studies using naturally randomised genetic variants associated with systolic blood pressure and genetic variants encoding the therapeutic targets of each drug class. Results: After a median of 4.4 years follow-up, 9,883 participants were diagnosed with new-onset diabetes in clinical trials. Blood pressure-lowering treatment was found to reduce the risk of diabetes by 11% (hazard ratio [HR] per 5-mm Hg reduction in systolic blood pressure 0.89 [95% confidence interval [CI] 0.84 to 0.95]). Similarly, in the Mendelian randomisation study, each 5-mm Hg genetically-influenced lower systolic blood pressure was associated with an 11% lower risk of diabetes (odds ratio [OR] 0.88 [95% CI 0.84 to 0.92]). Evidence from genetic data and trials were also consistent in that angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) reduced the risk of diabetes, and beta-blockers increased this risk. There was no evidence of an effect for calcium channel blockers and findings for thiazide diuretics were inconsistent; while genetic analysis showed a reduction in risk, the mostly indirect estimation from network analysis of trials suggested an increased risk compared with placebo. Conclusions: We found consistent evidence supporting that lowering blood pressure reduces the risk of new-onset diabetes. However, this protective effect was not uniform among different antihypertensive classes. Whereas ACEIs and ARBs reduced the risk of new-onset diabetes, the converse was observed with beta-blockers and possibly with thiazide diuretics. In determining which class of drug to use to manage elevated blood pressure, consideration of diabetes risk may help inform clinical decision-making.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".