Strong Association Between Weight Reduction and Suppression of Cardiovascular Events in Recent Clinical Trials of DPP4 Inhibitors, GLP-1 Receptor Agonists, and SGLT2 Inhibitors
Bibliographic record
Abstract
The 2008 US Food and Drug Administration (FDA) regulation requires to demonstrate in clinical trials of new antidiabetic drugs, that these drugs do not increase the risk of development of cardiovascular events as compared to existing drugs, after adjustment for major risk factors.It has been difficult to show superiority of newer drugs after adjustments for the major risk factors, and in fact, superiority could not be demonstrated in the initial three trials of DPP-4 inhibitors [1][2][3].Therefore, it came as an unexpected surprise that the SGLT2 inhibitor empagliflozin showed superiority in the EMPA-REG OUTCOME study [4].Thereafter, superiority was again demonstrated in one study of SGLT2 inhibitor, CANVAS [5], two studies of GLP-1 receptor agonists, LEADER [6], and SUSTAIN-6 [7], but not in another two studies of GLP-1 receptor agonists, ELIXA [8], and EXCEL [9].Various hypotheses have been proposed in regard to factors influencing the demonstration of superiority, but there is no widely accepted theory.It has been suggested that differences in the patients' background characteristics may be a factor.It is also possible that the incidence of cardiovascular events is not sufficiently high in studies with a short study period, and that there is a bias towards patients with slightly higher risk being in the active drug group.We focused on weight reduction, because weight gain is a risk factor for cardiovascular diseases, independent of disordered glucose metabolism, elevated blood pressure, and abnormal lipid profile [10].We examined the correlations between the body weight changes and hazard ratios before and after treatment in eight studies [1-2, 4-9].In , since the analysis was carried out with semaglutide 0.5 mg and 1 mg treatment respectively, both were adopted.The TECOS study [3] was excluded, as there were no data on the body weight changes in this study.As shown in the Figure 1 [1-9], there was a strong cor-
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".