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已发表论文
非肥胖 2 型糖尿病患者颈动脉粥样硬化的预测列线图的建立与验证
Authors Cui Y, Li L , Li Y, Yu P
Received 10 October 2025
Accepted for publication 28 November 2025
Published 16 December 2025 Volume 2025:18 Pages 4547—4557
DOI https://doi.org/10.2147/DMSO.S572295
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Rebecca Baqiyyah Conway
Yuliang Cui,1,2,* Lingling Li,3,* Ying Li,4 Pei Yu1,5
1NHC Key Laboratory of Hormones and Development and Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, People’s Republic of China; 2Department of Endocrinology, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, 253000, People’s Republic of China; 3Department of Health Management, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, 253000, People’s Republic of China; 4Department of Clinical Laboratory, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, 253000, People’s Republic of China; 5Department of Nephrology & Blood Purification Center, The Second Hospital of Tianjin Medical University, Tianjin, 300211, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Pei Yu, Tianjin Medical University Chu Hsien-I Memorial Hospital, No. 6 Huanrui North Road, Beichen District, Tianjin, People’s Republic of China, Email yupei@tmu.edu.cn
Purpose: To develop and validate an individualized risk prediction model for carotid atherosclerosis (CAS) in non-obese patients with type 2 diabetes mellitus (T2DM), addressing the need for a non-invasive and practical screening tool.
Patients and Methods: This retrospective study analyzed data from a cohort of 1014 non-obese T2DM patients (mean age: 58.65± 11.95 years; 691 males and 323 females) enrolled between 2016 and 2025. We collected a comprehensive set of clinical variables, including demographics, body mass index (BMI), blood pressure, lipid profile, hepatic and renal function, and glycemic indicators. The population was randomly divided into a training set and an internal validation set. Feature selection was performed using univariate and multivariate logistic analysis to identify significant predictors. A nomogram was subsequently constructed based on these independent risk factors. The model’s performance was rigorously evaluated by assessing its discriminative ability with the area under the receiver operating characteristic curve (AUC), its calibration with calibration plots, and its potential clinical net benefit with decision curve analysis (DCA).
Results: The final prediction model incorporated five key clinical variables: age, gender, systolic blood pressure, low-density lipoprotein cholesterol, and fasting blood glucose. The nomogram demonstrated strong and consistent performance, achieving AUC values of 0.828 in the training set and 0.824 in the validation set, indicating high discriminatory power. Calibration curves showed excellent agreement between predicted probabilities and actual observed outcomes. Furthermore, decision curve analysis confirmed the clinical utility of the model for a wide range of risk thresholds.
Conclusion: The validated nomogram provides a reliable and easily applicable tool for the early identification of CAS risk in non-obese individuals with T2DM. This model facilitates personalized risk assessment and supports clinical decision-making for targeted preventive strategies, potentially reducing the incidence of associated cardiovascular and cerebrovascular events.
Keywords: type 2 diabetes mellitus, carotid atherosclerosis, non-obese, nomogram
1NHC Key Laboratory of Hormones and Development and Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, People’s Republic of China; 2Department of Endocrinology, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, 253000, People’s Republic of China; 3Department of Health Management, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, 253000, People’s Republic of China; 4Department of Clinical Laboratory, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, 253000, People’s Republic of China; 5Department of Nephrology & Blood Purification Center, The Second Hospital of Tianjin Medical University, Tianjin, 300211, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Pei Yu, Tianjin Medical University Chu Hsien-I Memorial Hospital, No. 6 Huanrui North Road, Beichen District, Tianjin, People’s Republic of China, Email yupei@tmu.edu.cn
Purpose: To develop and validate an individualized risk prediction model for carotid atherosclerosis (CAS) in non-obese patients with type 2 diabetes mellitus (T2DM), addressing the need for a non-invasive and practical screening tool.
Patients and Methods: This retrospective study analyzed data from a cohort of 1014 non-obese T2DM patients (mean age: 58.65± 11.95 years; 691 males and 323 females) enrolled between 2016 and 2025. We collected a comprehensive set of clinical variables, including demographics, body mass index (BMI), blood pressure, lipid profile, hepatic and renal function, and glycemic indicators. The population was randomly divided into a training set and an internal validation set. Feature selection was performed using univariate and multivariate logistic analysis to identify significant predictors. A nomogram was subsequently constructed based on these independent risk factors. The model’s performance was rigorously evaluated by assessing its discriminative ability with the area under the receiver operating characteristic curve (AUC), its calibration with calibration plots, and its potential clinical net benefit with decision curve analysis (DCA).
Results: The final prediction model incorporated five key clinical variables: age, gender, systolic blood pressure, low-density lipoprotein cholesterol, and fasting blood glucose. The nomogram demonstrated strong and consistent performance, achieving AUC values of 0.828 in the training set and 0.824 in the validation set, indicating high discriminatory power. Calibration curves showed excellent agreement between predicted probabilities and actual observed outcomes. Furthermore, decision curve analysis confirmed the clinical utility of the model for a wide range of risk thresholds.
Conclusion: The validated nomogram provides a reliable and easily applicable tool for the early identification of CAS risk in non-obese individuals with T2DM. This model facilitates personalized risk assessment and supports clinical decision-making for targeted preventive strategies, potentially reducing the incidence of associated cardiovascular and cerebrovascular events.
Keywords: type 2 diabetes mellitus, carotid atherosclerosis, non-obese, nomogram