Glucagon-like peptide-1 receptor agonists to improve cardiorenal outcomes: data from FLOW and beyond
Bibliographic record
Abstract
PURPOSE OF REVIEW: Glucagon-like peptide-1 receptor agonists (GLP1RA), initially approved for glycemic control in type 2 diabetes mellitus (T2DM), have emerged as agents for weight loss, cardiovascular and kidney protection. This review summarizes the evidence supporting the benefits of these therapies on cardiorenal outcomes. RECENT FINDINGS: Clinical trials have consistently demonstrated reductions in major adverse cardiovascular events with GLP1RA treatments. Recently, the FLOW trial revealed that semaglutide reduced the composite outcome of kidney failure, at least 50% decline in estimated glomerular filtration rate, kidney or cardiovascular mortality by 24% in patients with T2DM, thereby establishing GLP1RA as a pillar of therapy in this population. New evidence suggests favorable effects on kidney endpoints in nondiabetic individuals with overweight or obesity. Dedicated trials have also provided evidence for reduction in the risk for heart failure hospitalization and improvement in symptoms in individuals with heart failure with preserved ejection fraction. Subgroup analyses have suggested that GLP1RAs confer additive cardiorenal benefits irrespective of background medication use. SUMMARY: There is increasing evidence that GLP1RA reduces the risk for cardiovascular events, chronic kidney disease progression, and heart failure hospitalizations. Further data on the effect of dual and triple GLP1-based therapies on cardiorenal outcomes is required.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".