4113Glucose lowering drugs or strategies, major adverse cardiovascular events and heart failure outcomes, and association with weight loss - meta-analysis of large cardiovascular outcome trials
Bibliographic record
Abstract
Abstract Background Glucose lowering drugs or strategies (GLDS) have varied effects on major adverse cardiovascular events (MACE) and heart failure (HF) in cardiovascular outcomes trials. Mechanisms driving cardiovascular risk reduction remain elusive. Methods We searched MEDLINE, PubMed, and meeting abstracts up to 11/21/2018 for large GLDS cardiovascular outcome trials (CVOTs) in patients with or at risk for type 2 diabetes. Primary endpoints of MACE and HF were evaluated with random effects risk ratios (RR) and explored by baseline CVD subgroups and meta-regression by weight change across treatment arms. Results In 27 GLDS CVOTs, a total 207,820 patients, median age 63 years, 64% male, 64% CVD and 11% with prior HF were studied over a mean 3.8 years with 20,118 (10%) patients having MACE and 7,212 (4%) a HF event. Compared with standard care, GLDS overall lowered MACE (RR 0.92, P<0.ehz745.01171) but not HF (RR 1.01, P=0.91). Across GLDS, the magnitude and directionality varied modestly for MACE RR (P-int=0.07) but markedly for HF (P-int<0.ehz745.01171). Meta-regression showed a change in HF RR by 6% (95% CI 3%-9%) per 1 kg weight gain/loss between treatment arms (P=0.0006; Figure). In 9 trials of GLDS that achieved marked weight loss (lifestyle, GLP1 agonists, SGLT2 inhibitors), MACE benefit was confined to patients with baseline CVD (RR 0.89 [0.84–0.95] versus without (RR 1.02 [0.91–1.15]; P-int=0.01) with consistent HF effect (RR 0.80 [0.72–0.88] vs RR 0.76 [0.56–1.03]; P-int=0.74). Heart Failure Risk and Changes in Weight Conclusion HF outcomes were improved with GLDS that lower weight. Among diabetes GLDS that lower weight, there was a robust risk reduction in atherothrombotic and heart failure events, with the MACE benefit confined to patients with established CVD. Acknowledgement/Funding Heart and Stroke Foundation
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.042 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".