Shiyu Hu,1,2,* Wenyu Chen,2,* Xiaoli Tan,2,* Ye Zhang,1,2 Jiaye Wang,1,2 Lifang Huang,3 Jianwen Duan4 

1Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China; 2Department of Respiratory Medicine, The Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, People’s Republic of China; 3Health Management Center, The Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, People’s Republic of China; 4Department of Hepatobiliary Surgery, Quzhou People’s Hospital, Quzhou, Zhejiang, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Lifang Huang, Health Management Center, The Affiliated Hospital of Jiaxing University, No. 1882, Zhonghuan South Road, Nanhu District, Jiaxing, Zhejiang, 314001, People’s Republic of China, Email jxhuanglifang@126.com Jianwen Duan, Department of Hepatobiliary Surgery, Quzhou People’s Hospital, No. 2, Zhongloudi, Quzhou, Zhejiang, 324000, People’s Republic of China, Email 695377686@qq.com

Purpose: The purpose of this study is to develop and validate a clinical prediction model for diagnosing Metabolic Syndrome (MetS) based on indicators associated with its occurrence.
Patients and Methods: This study included a total of 26,637 individuals who underwent health examinations at the Jiaxing First Hospital Health Examination Center from January 19, 2022, to December 31, 2022. They were randomly divided into training (n = 18645) and validation (n = 7992) sets in a 7:3 ratio. Firstly, the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm was employed for variable selection. Subsequently, a multifactor Logistic regression analysis was conducted to establish the predictive model, accompanied by nomograms. Thirdly, model validation was performed using Receiver Operating Characteristic (ROC) curves, Harrell’s concordance index (C-index), calibration plots, and Decision Curve Analysis (DCA), followed by internal validation.
Results: In this study, six predictive indicators were selected, including Body Mass Index, Triglycerides, Blood Pressure, High-Density Lipoprotein Cholesterol, Low-Density Lipoprotein Cholesterol, and Fasting Blood Glucose. The model demonstrated excellent predictive performance, with an AUC of 0.978 (0.976– 0.980) for the training set and 0.977 (0.974– 0.980) for the validation set in the nomogram. Calibration curves indicated that the model possessed good calibration ability (Training set: Emax 0.081, Eavg 0.005, P = 0.580; Validation set: Emax 0.062, Eavg 0.007, P = 0.829). Furthermore, decision curve analysis suggested that applying the nomogram for diagnosis is more beneficial when the threshold probability of MetS is less than 89%, compared to either treating-all or treating-none at all.
Conclusion: We developed and validated a nomogram based on MetS risk factors, which can effectively predict the occurrence of MetS. The proposed nomogram demonstrates significant discriminative ability and clinical applicability. It can be utilized to identify variables and risk factors for diagnosing MetS at an early stage.

Keywords: metabolic syndrome, risk factors, nomogram, prediction