Multivariate adaptive regression spline modeling for estimating carotid artery plaque thickness in Chinese men with...

ObjectiveThe incidence of type 2 diabetes continues to increase worldwide, with cardiovascular disease as the leading cause of mortality in this population. Carotid artery plaque thickness serves as a non-invasive marker of atherosclerosis. This study applied multivariate adaptive regression spline analysis to estimate artery plaque thickness using routinely available clinical data in Chinese men with type 2 diabetes.MethodsIn this retrospective cross-sectional study, 648 male patients with type 2 diabetes were analyzed using demographic and biochemical data. Multiple linear regression analysis served as a benchmark comparison. Model performance was evaluated using repeated train-test splits, with hyperparameter tuning conducted exclusively on training data, followed by bootstrap-corrected internal validation. An estimation equation was constructed based on basis functions derived from multivariate adaptive regression spline analysis.ResultsMean participant age was 63.16 ± 11.78 years, mean diabetes duration was 12.26 ± 7.15 years, mean glycosylated hemoglobin level was 7.76 ± 1.66%, and mean artery plaque thickness was 0.943 ± 0.38 mm. Multivariate adaptive regression spline demonstrated lower prediction errors than multiple linear regression across multiple metrics. Five key predictors were identified, including age, diabetes duration, low-density lipoprotein cholesterol level, microalbumin-to-creatinine ratio, and diastolic blood pressure, each with clinically meaningful threshold effects.ConclusionsA multivariate adaptive regression spline-based equation incorporating five routinely available clinical variables was developed for estimating arterial plaque thickness. Although multivariate adaptive regression spline outperformed multiple linear regression, the model's modest explanatory power (R² ≈ 0.21) indicates that it should be viewed as a potential screening adjunct rather than a definitive diagnostic tool. External validation in independent and further validation on diverse populations is warranted.