Multivariate adaptive regression spline modeling for estimating carotid artery plaque thickness in Chinese men with type 2 diabetes.
CC, Y., CY, H., D, P., TW, C., YJ, L., & JG, L. (2026). Multivariate adaptive regression spline modeling for estimating carotid artery plaque thickness in Chinese men with type 2 diabetes.. The Journal of international medical research. https://doi.org/10.1177/03000605261476583
CC Y, CY H, D P, TW C, YJ L, JG L. Multivariate adaptive regression spline modeling for estimating carotid artery plaque thickness in Chinese men with type 2 diabetes.. The Journal of international medical research. 2026; doi: 10.1177/03000605261476583
CC Y, CY H, D P, et al. Multivariate adaptive regression spline modeling for estimating carotid artery plaque thickness in Chinese men with type 2 diabetes.[J]. The Journal of international medical research. 2026. DOI: 10.1177/03000605261476583.
@article{cc2026,
author = {Yang CC and Hsu CY and Pei D and Chu TW and Liang YJ and Leu JG},
title = {Multivariate adaptive regression spline modeling for estimating carotid artery plaque thickness in Chinese men with type 2 diabetes.},
journal = {The Journal of international medical research},
year = {2026},
doi = {10.1177/03000605261476583},
note = {PMID: 42663572},
}
TY - JOUR AU - Yang CC AU - Hsu CY AU - Pei D AU - Chu TW AU - Liang YJ AU - Leu JG TI - Multivariate adaptive regression spline modeling for estimating carotid artery plaque thickness in Chinese men with type 2 diabetes. T2 - The Journal of international medical research PY - 2026 DO - 10.1177/03000605261476583 AN - PMID:42663572 ER -
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.