Multivariate adaptive regression spline modeling for estimating carotid artery plaque thickness in Chinese men with type 2 diabetes

Objective The 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. Methods In 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. Results Mean 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. Conclusions A 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 (R 2 ≈ 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.