Dual inhibition of the renin–angiotensin system in high-risk diabetes and risk for stroke and other outcomes
Bibliographic record
Abstract
BACKGROUND: A recent study suggested that addition of a direct renin inhibitor to either an angiotension-converting enzyme (ACE) inhibitor (ACEi) or an angiotensin receptor blocker (ARB) may increase stroke risk in people with diabetes and renal disease. METHODS: We examined the effects of addition of an ACE inhibitor (ramipril) to an ARB (telmisartan) for a mean follow-up of 56 months in people with diabetes [n = 9628, mean age 66 years, baseline blood pressure 144/82 mmHg, BMI 29 kg/m², estimated glomerular filtration rate (eGFR) 73 ml/min, and urine albumin 11 mg/mmol] who participated in the ONTARGET trial, divided by those with (n = 3163) and without (n = 6465) nephropathy. We compared participants on monotherapy with either ramipril or telmisartan with those on dual therapy. RESULTS: SBP decreased more with dual over monotherapy (-7.1 vs. -5.3 mmHg, P < 0.0001) and the same number of strokes occurred (1.19 vs. 1.22 per 100 patient-years; hazard ratio 0.99, 95% confidence interval 0.82-1.20). Stroke rate was higher in participants with than those without diabetic nephropathy (1.5 vs. 1.0 per 100 patient-years), but effects of dual-therapy vs. monotherapy were not different in either subgroup (1.59 vs. 1.55 and 1.01 vs. 1.08 per 100 patient-years; P value for interaction = 0.60). Other cardiovascular and kidney outcomes (dialysis or doubling of serum creatinine) did not differ between dual-therapy and monotherapy in subgroups, but adverse events, namely acute dialysis, hyperkalemia and hypotension, tended to be more frequent with dual therapy, CONCLUSION: A combination of ACEi and ARB does not increase strokes or alter other major cardiovascular or renal events in patients with diabetes, irrespective of the presence of nephropathy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".