Cardiovascular Risk Prediction Modelling with Semaglutide in Patients with Type 1 Diabetes
Bibliographic record
Abstract
Background: Semaglutide, a glucagon-like peptide-1 receptor agonist, improves glycemia, lowers body weight, and reduces cardiovascular (CV) events in people with type 2 diabetes. In type 1 diabetes (T1D), semaglutide also reduces hyperglycemia and body weight, but its impact on CV outcomes remains unknown. Because no CV outcome trials exist in this population, validated risk prediction models can provide insights into potential CV effects. We therefore aimed to evaluate the effect of semaglutide on estimated 10-year CV risk in people with T1D, hypothesizing that semaglutide would lower predicted risk. Methods: This was a secondary analysis of a double-blind, randomized, crossover trial (NCT05205928), evaluating weekly subcutaneous semaglutide (titrated up to 1.0mg weekly for 15 weeks) with automated insulin delivery in adults with T1D. Medical history, demographic, and biochemical data from 22 participants (46±13 yrs, 32.5±5.4 kg/m2, 54% female) were used to estimate the 10-year risk of a first fatal and non-fatal CV event using two validated models: the Steno T1 Risk Engine (ST1RE) and the Scottish Diabetes Research Network (SDRN) risk model. Risk was calculated at baseline and after 15 weeks of treatment with semaglutide or placebo. The percentage change in CV risk from baseline for each intervention was assessed using a paired sample t-test. Results: Median baseline 10-year CV risk was ~12% (SRE: 12.4% [8.5, 20.8], SDRN: 11.5% [4.3, 18.5]). Using the ST1RE model, the median change in CV risk from baseline was -17.0% [-23.2, -6.5] and -5.1% [-20.0, -1.3] for semaglutide and placebo, respectively (placebo-adjusted difference [PAD]: -4.1% [-9.5, -1.4]; P=0.07). Using the SDRN model, the median change in CV risk from baseline was -10.7% [-25.3, -0.8] and -9.5% [-20.5, -2.9], respectively (PAD: 0.4% [-7.6, 6.8]; P=0.78). Conclusion: Semaglutide did not significantly reduce estimated 10-year CV risk in people with T1D and a low-to-moderate baseline risk. However, the reductions observed suggest a trend toward benefit, although the two prediction models yielded differences in effect magnitude. Analyses in larger datasets would help clarify the potential impact of semaglutide on CV risk in T1D.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".