Development and validation of a nomogram to estimate future risk of metabolic syndrome in middle-aged and elderly people

Abstract

Abstract Aims This study aimed to investigate the prevalence of Metabolic syndrome (MetS) and its influencing factors among middle-aged and elderly Chinese, and to develop a nomogram for predicting MetS. Methods This cross-sectional study were the follow-up visits of the Gannan Medical University cohort study. The participants were permanent residents aged 35 years and above living in Ganzhou, Jiangxi, China. MetS was defined according to the Chinese Diabetes Society (CDS) criteria. Participants’ demographics, history of illness, blood biochemistry data, and anthropometric variables were enrolled into screen significant variables for prediction model of MetS, Subsequently, the data was divided into a training set and a validation set, and nomogram were performed to develop the predictive model of MetS. The training set was used for nomogram model construction and internal verification, and the validation set was used for external verification. Nomogram performance was assessed based on receiver operating characteristic curve (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results A total of 1581 participants were enrolled in the study, and the prevalence of MetS was 27.39% (95%CI:25.19%-29.59%). The age-standardized prevalence was 12.51%. Nine variables (age, residence, occupation, hyperlipidemia, hyperuricemia, family history of hypertension, hip circumference, glycated hemoglobinA1c (HbA1c), BMI, resting heart rate (RHR)) were identified as influencing factors of MetS. The participants were randomly divided into a development cohort (n = 1107,70%) and a validation cohort (n = 474, 30%). The nomogram was verified by internal validation (Area Under Curve (AUC): 0.844) and external validation (AUC: 0.825). The calibration plots showed good agreement in the training sets. Conclusion The prevalence rate of MetS is high in Ganzhou, Jiangxi, China. The nomogram based on nine variables has a good predictive efficacy and can be used to predict the risk of MetS in middle-aged and elderly people.