Effect of Semaglutide on Regression and Progression of Glycemia in People With Overweight or Obesity but Without Diabetes in the SELECT Trial
Bibliographic record
Abstract
OBJECTIVE: To determine whether semaglutide slows progression of glycemia in people with cardiovascular disease and overweight or obesity but without diabetes. RESEARCH DESIGN AND METHODS: In a multicenter, double-blind trial, participants aged ≥45 years, with BMI ≥27 kg/m2, and with preexisting cardiovascular disease but without diabetes (HbA1c <6.5%) were randomized to receive subcutaneous semaglutide (2.4 mg weekly) or placebo. Major glycemic outcomes were HbA1c and proportions achieving biochemical normoglycemia (HbA1c <5.7%) and progressing to biochemical diabetes (HbA1c ≥6.5%). RESULTS: Of 17,604 participants, 8,803 were assigned to semaglutide and 8,801 to placebo. Mean ± SD intervention exposure was 152 ± 56 weeks and follow-up 176 ± 40 weeks. In both treatment arms mean nadir HbA1c for participants was at 20 weeks. Thereafter, HbA1c increased similarly in both arms, with a mean difference of -0.32 percentage points (95% CI -0.33 to -0.30; -3.49 mmol/mol [-3.66 to -3.32]) and with the difference favoring semaglutide throughout the study (P < 0.0001). Body weight plateaued at 65 weeks and was 8.9% lower with semaglutide. At week 156, a greater proportion treated with semaglutide were normoglycemic (69.5% vs. 35.8%; P < 0.0001) and a smaller proportion had biochemical diabetes by week 156 (1.5% vs. 6.9%; P < 0.0001). The number needed to treat was 18.5 to prevent a case of diabetes. Both regression and progression were dependent on glycemia at baseline, with the magnitude of weight reduction important in mediating 24.5% of progression and 27.1% of regression. CONCLUSIONS: In people with preexisting cardiovascular disease and overweight or obesity but without diabetes, long-term semaglutide increases regression to biochemical normoglycemia and reduces progression to biochemical diabetes but does not slow glycemic progression over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".