Timed Bromocriptine-QR Therapy Reduces Progression of Cardiovascular Disease and Dysglycemia in Subjects with Well-Controlled Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes (T2DM) patients, including those in good glycemic control, have an increased risk of cardiovascular disease (CVD). Maintaining good glycemic control may reduce long-term CVD risk. However, other risk factors such as elevated vascular sympathetic tone and/or endothelial dysfunction may be stronger potentiators of CVD. This study evaluated the impact of bromocriptine-QR, a sympatholytic dopamine D2 receptor agonist, on progression of metabolic disease and CVD in T2DM subjects in good glycemic control (HbA1c ≤ 7.0%). METHODS: 1834 subjects (1219 bromocriptine-QR; 615 placebo) with baseline HbA1c ≤ 7.0% derived from the Cycloset Safety Trial (this trial is registered with ClinicalTrials.gov Identifier: NCT00377676), a 12-month, randomized, multicenter, placebo-controlled, double-blind study in T2DM, were evaluated. Treatment impact upon a prespecified composite CVD endpoint (first myocardial infarction, stroke, coronary revascularization, or hospitalization for angina/congestive heart failure) and the odds of losing glycemic control (HbA1c >7.0% after 52 weeks of therapy) were determined. RESULTS: Bromocriptine-QR reduced the CVD endpoint by 48% (intention-to-treat; HR: 0.52 [0.28-0.98]) and 52% (on-treatment analysis; HR: 0.48 [0.24-0.95]). Bromocriptine-QR also reduced the odds of both losing glycemic control (OR: 0.63 (0.47-0.85), p = 0.002) and requiring treatment intensification to maintain HbA1c ≤ 7.0% (OR: 0.46 (0.31-0.69), p = 0.0002). CONCLUSIONS: Bromocriptine-QR therapy slowed the progression of CVD and metabolic disease in T2DM subjects in good glycemic control.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".