Development and Validation of Prediction Model for Risk Reduction of Metabolic Syndrome by Body Weight Control: A Prospective Population-based Study

Abstract

Several studies have reported that weight control is of paramount importance in reducing the risk of metabolic syndrome. Nevertheless, this well-known association does not provide any practical information on how much weight loss in a given period would reduce the risk of metabolic syndrome in individuals in a personalized setting. This study aimed to develop and validate a risk prediction model for metabolic syndrome in 2 years, based on an individual’s baseline health status and body weight after 2 years. We recruited 3,447 and 3,874 participants from the Ansan and Anseong cohorts of the Korean Genome and Epidemiology Study, respectively. Among the former, 8636 longitudinal observations of 2,412 participants (70%) and 3,570 of 1,034 (30%) were used for training and internal validation, respectively. Among the latter, all 15,739 observations of 3,874 participants were used for external validation. Compared to logistic regression, Gaussian Naïve Bayes, random forest, and deep neural network, XGBoost showed the highest performance (area under curve of 0.879) and a significantly enhanced calibration of the predictive score with the prevalence rate. The model was ported onto an application to provide the 2-year probability of developing metabolic syndrome by simulating selected target body weights, based on an individual’s baseline health profiles. Further prospective studies are required to determine whether weight-control programs could lead to favorable health outcomes.