Effect of visceral fat on onset of metabolic syndrome

Abstract

Abstract Background Metabolic syndrome (MetS) increases the risk of cardiovascular and lifestyle-related diseases. Therefore, early detection is important to prevent MetS. This study analysed the effects of visceral fat on MetS using health examination. A MetS onset prediction algorithm was developed. Methods Health examination data were obtained from the Iwaki Health Promotion Project conducted in Aomori Prefecture in Japan, wherein labels indicated the development of MetS within the three years (213 onset and 1320 non-onset cases). The data were divided into training and test data (8:2 ratio), and 18 onset prediction models were developed to support the SHapley Additive exPlanations (SHAP) value. The onset labels and non-invasive input data were used as the output and input variables, respectively. We selected the model with the highest area under the curve (AUC) score when conducting five-fold cross validation, and the AUC of the test data was calculated. Feature impact was calculated based on SHAP. Results There were 169 and 1058 people in the metabolic and non-metabolic syndrome groups, respectively. The visceral fat area was significantly higher in the onset group than in the non-onset group (p < 0.00001). The cut-off value based on the receiver operating characteristic curve was 82 cm2, and the AUC was 0.86. Machine learning was employed on six items reported to contribute to the onset of MetS in addition to visceral fat to build an onset prediction algorithm. The cross-validation AUC = 0.90 and test AUC = 0.88 indicated a high-accuracy algorithm. The visceral fat was found to be the main factor, as confirmed by conventional feature importance in machine learning. Conclusions Visceral fat is crucial to determining the onset of MetS in the future. A high-accuracy onset prediction algorithm was developed based on non-invasive parameters, including visceral fat.