Cardiovascular risk reduction with glucagon‐like peptide‐1 receptor agonists is proportional to <scp>HbA1c</scp> lowering in type 2 diabetes: An updated meta‐regression analysis incorporating <scp>FLOW</scp> and <scp>SOUL</scp> trials
Bibliographic record
Abstract
Abstract Aims To evaluate relationships of cardiovascular and kidney outcomes with glycemic or bodyweight reductions in randomised placebo‐controlled trials of glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs), incorporating data from FLOW and SOUL trials. Materials and Methods PubMed and EMBASE were searched up to 22 August 2025 for placebo‐controlled randomized trials of oral or bolus‐type, subcutaneous GLP‐1RAs reporting major adverse cardiovascular events (MACE; a composite of cardiovascular death, myocardial infarction, and stroke) in adults with type 2 diabetes. The primary outcome was MACE; secondary outcomes included heart failure (HF) and kidney outcomes. Random‐effects meta‐analyses were followed by meta‐regression evaluating associations with HbA1c and bodyweight reduction. Results A total of 73 263 individuals were included from 10 trials (ELIXA, LEADER, SUSTAIN‐6, EXSCEL, Harmony Outcomes, PIONEER 6, REWIND, AMPLITUDE‐O, FLOW, and SOUL). GLP‐1RAs reduced MACE by 14% (hazard ratio: 0.86; 95% CI: 0.82 to 0.91; p <0.001), as well as hospitalisation for HF and the composite kidney outcome (both p <0.001). Meta‐regression showed that every 1% extra reduction in HbA1c corresponded to a 27% lower HR for MACE ( p = 0.015; R 2 = 0.61). While HbA1c reduction was not significantly associated with secondary outcomes, the directionality was consistent with MACE. Bodyweight change was not associated with any of the analysed endpoints, including MACE ( p = 0.13; R 2 = 0.21). Conclusions HbA1c reduction, not bodyweight change, was significantly and proportionally associated with MACE risk reduction. HbA1c lowering may serve as a useful surrogate for the cardiovascular improvements associated with GLP‐1RAs in type 2 diabetes.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.047 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".