Establishment of a Risk Prediction Model for Metabolic Syndrome in High Altitude Areas in Qinghai Province, China: A Cross-Sectional Study

Abstract

Yanting Ma,1 Yongyuan Li,2 Zhanfeng Zhang,3 Guomei Du,4 Ting Huang,1 Zhi Zhong Zhao,5 Shou Liu,1 Zhancui Dang1 1Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, People’s Republic of China; 2Disease Control department, Huangzhong District health Bureau, Xining, Qinghai, People’s Republic of China; 3Huangzhong District, Duoba County Health Services Center, Xining, Qinghai, People’s Republic of China; 4Clinical Laboratory, Qinghai Red Cross Hospital, Xining, Qinghai, People’s Republic of China; 5Disease Control Department, Qinghai Provincial Center for Endemic Disease Control and Prevention, Xining, Qinghai, People’s Republic of ChinaCorrespondence: Shou Liu; Zhancui Dang, Email [email protected]; [email protected]: The prevalence of metabolic syndrome (MetS) is increasing worldwide, and early prediction of MetS risk is highly beneficial for health outcomes. This study aimed to develop and validate a nomogram to predict MetS risk in Qinghai Province, China, and it provides a methodological reference for MetS prevention and control in Qinghai Province, China.Patients and Methods: A total of 3073 participants living between 1900 and 3710 meters above sea level in Qinghai Province participated in this study between March 2014 and March 2016. We omitted 12 subjects who were missing diagnostic component data for MetS, ultimately resulting in 3061 research subjects, 70% of the subjects were assigned randomly to the training set, and the remaining subjects were assigned to the validation set. The least absolute shrinkage and selection operator (LASSO) regression analysis method was used for variable selection via running cyclic coordinate descent with 10-fold cross-validation. Multivariable logistic regression was then performed to develop a predictive model and nomogram. The receiver operating characteristic (ROC) curves was used for model evaluation, and calibration plot and decision curve analysis (DCA) were used for model validation.Results: Of 24 variables studied, 6 risk predictors were identified by LASSO regression analysis: hyperlipidaemia, hyperglycemia, abdominal obesity, systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI). A prediction model including these 6 risk factors was constructed and displayed good predictability with an area under the ROC curve of 0.914 for the training set and 0.930 for the validation set. DCA revealed that if the threshold probability of MetS is less than 82%, the application of this nomogram is more beneficial than both the treat-all or treat-none strategies.Conclusion: The nomogram developed in our study demonstrated strong discriminative power and clinical applicability, making it a valuable reference for meets prevention and control in the plateau areas of Qinghai Province.Keywords: metabolic syndrome, nomogram, prediction model, plateau section

Keywords

• metabolic syndrome
• nomogram
• prediction model
• plateau section