Effect of visceral fat on onset of metabolic syndrome.

Journal: Scientific reports
Published Date:

Abstract

This study analysed the effects of visceral fat on metabolic syndrome (MetS) and developed an algorithm to predict its onset using health examination data from the Iwaki Health Promotion Project in Japan. The dataset included 213 cases of MetS onset within three years and 1320 non-onset cases. The data was split into training and test sets with an 8:2 ratio. In the training set, the MetS onset group had significantly higher visceral fat area than the non-onset group (p < 0.00001). A cut-off value of 82 cm2 for the visceral fat area was determined, with an AUC of 0.86. Additionally, a machine learning algorithm utilizing seven non-invasive factors, including visceral fat, achieved high accuracy with a five-fold cross-validation AUC of 0.90 in the training set and 0.88 in the test set. Visceral fat was identified as the main factor, as supported by the SHAP value. In conclusion, this study found visceral fat to be crucial in predicting the onset of MetS, and a high-accuracy onset prediction algorithm based on non-invasive parameters, including visceral fat, was developed.

Authors

  • Hiroto Bushita
    Health & Wellness Products Research Laboratories, Kao Corp, Tokyo, Japan.
  • Naoki Ozato
    Human Health Care Products Research Laboratories, Kao Corporation, Tokyo, Japan.
  • Kenta Mori
    Human Health Care Products Research Laboratories, Kao Corporation, Tokyo, Japan.
  • Hiromitsu Kawada
    Human Health Care Products Research Laboratories, Kao Corporation, Tokyo, Japan.
  • Yoshihisa Katsuragi
  • Noriko Osaki
  • Tatsuya Mikami
    Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki, 036-8562, Japan.
  • Ken Itoh
    Department of Stress Response Science, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.
  • Koichi Murashita
    Center of Innovation Research Initiatives Organization, Hirosaki University, Hirosaki, Japan.
  • Shigeyuki Nakaji
    Department of Social Health, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.
  • Yoshinori Tamada
    Department of Medical Intelligent Systems, Graduate School of Medicine, Kyoto University, Kyoto, Japan.