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Background <p>Individuals with prediabetes face an increased risk of cardiovascular (CV) complications, which can ultimately lead to premature mortality. However, existing risk stratification tools are not targeted for people with prediabetes. We aimed to develop a simple explainable model to predict if a person will develop a fatal CV outcome or not among people with prediabetes.</p> Methods <p>Participants ≥ 45&#xa0;years with prediabetes (HbA1c 39–47&#xa0;mmol/mol (5.7–6.4%)) and established CV disease and overweight/obesity were included. A binary logistic regression model was trained to predict CV death using stratified threefold cross-validation. The model’s risk estimates were calibrated, and the predictive capability was evaluated using receiver operating characteristic (ROC) area under the curve (AUC), precision and recall.</p> Results <p>In total 5636 participants with 182 (3.2%) CV deaths (mean time-to-event of 2.0&#xa0;years) and a mean trial duration of 3.3&#xa0;years were included. Seven easily collected demographic and clinical variables were selected for the model. Discrimination (ROC AUC) was acceptable at 0.730 (95% CI 0.659–0.801). Applying our prediabetes cohort on existing benchmark models developed for major adverse cardiovascular events (MACE) in a general and type 2 diabetes population without prior CVD, demonstrated lower performance for CV death (ROC AUC: 0.630 (SCORE2) and 0.643 (SCORE2-Diabetes)) compared to our model. Lower performance was also observed for predicting MACE in our cohort (ROC AUC: 0.596 and 0.603) using the established models compared to the original populations (ROC AUC: 0.739 and 0.66–0.73). No comparative models for people with prediabetes and prior CVD exists. Thus, even with the limitations in different populations and outcome targets, this indicates that prediabetes-specific prediction models could potentially improve early prevention in this high-risk population.</p> Conclusion <p>We have developed a prediabetes-specific proof-of-concept model that predicts whether a person is at high risk of cardiovascular death. External validation of the model is crucial before adoption to a real-world setting to clarify whether the model generalizes beyond the studied population.</p> Graphical abstract <p></p>

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Predicting cardiovascular death in overweight/obese people with prediabetes using machine learning—a proof-of-concept study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月17日

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  • Amalie K. Andersen,
  • Kristine Færch,
  • Dorte Vistisen,
  • Bernt J. von Scholten,
  • Nils B. Jørgensen,
  • Simon L. Cichosz,
  • Morten H. Jensen

摘要

Background

Individuals with prediabetes face an increased risk of cardiovascular (CV) complications, which can ultimately lead to premature mortality. However, existing risk stratification tools are not targeted for people with prediabetes. We aimed to develop a simple explainable model to predict if a person will develop a fatal CV outcome or not among people with prediabetes.

Methods

Participants ≥ 45 years with prediabetes (HbA1c 39–47 mmol/mol (5.7–6.4%)) and established CV disease and overweight/obesity were included. A binary logistic regression model was trained to predict CV death using stratified threefold cross-validation. The model’s risk estimates were calibrated, and the predictive capability was evaluated using receiver operating characteristic (ROC) area under the curve (AUC), precision and recall.

Results

In total 5636 participants with 182 (3.2%) CV deaths (mean time-to-event of 2.0 years) and a mean trial duration of 3.3 years were included. Seven easily collected demographic and clinical variables were selected for the model. Discrimination (ROC AUC) was acceptable at 0.730 (95% CI 0.659–0.801). Applying our prediabetes cohort on existing benchmark models developed for major adverse cardiovascular events (MACE) in a general and type 2 diabetes population without prior CVD, demonstrated lower performance for CV death (ROC AUC: 0.630 (SCORE2) and 0.643 (SCORE2-Diabetes)) compared to our model. Lower performance was also observed for predicting MACE in our cohort (ROC AUC: 0.596 and 0.603) using the established models compared to the original populations (ROC AUC: 0.739 and 0.66–0.73). No comparative models for people with prediabetes and prior CVD exists. Thus, even with the limitations in different populations and outcome targets, this indicates that prediabetes-specific prediction models could potentially improve early prevention in this high-risk population.

Conclusion

We have developed a prediabetes-specific proof-of-concept model that predicts whether a person is at high risk of cardiovascular death. External validation of the model is crucial before adoption to a real-world setting to clarify whether the model generalizes beyond the studied population.

Graphical abstract