Bushita Hiroto, Ozato Naoki, Mori Kenta, Kawada Hiromitsu, Katsuragi Yoshihisa, Osaki Noriko, Mikami Tatsuya, Itoh Ken, Murashita Koichi, Nakaji Shigeyuki, Tamada Yoshinori
Department of Medical Data Intelligence, Research Center for Health-Medical Data Science, Hirosaki University Graduate School of Medicine, Aomori, Japan.
Human Health Care Products Research Laboratories, Kao Corporation, Tokyo, Japan.
Sci Rep. 2025 May 30;15(1):19012. doi: 10.1038/s41598-025-01389-1.
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.
本研究分析了内脏脂肪对代谢综合征（MetS）的影响，并利用日本磐城健康促进项目的健康检查数据开发了一种预测其发病的算法。该数据集包括三年内发生MetS的213例病例和1320例未发病病例。数据以8:2的比例分为训练集和测试集。在训练集中，MetS发病组的内脏脂肪面积显著高于未发病组（p < 0.00001）。确定内脏脂肪面积的截断值为82 cm²，曲线下面积（AUC）为0.86。此外，一种利用包括内脏脂肪在内的七个非侵入性因素的机器学习算法，在训练集中通过五折交叉验证的AUC为0.90，在测试集中为0.88，实现了高精度。如SHAP值所示，内脏脂肪被确定为主要因素。总之，本研究发现内脏脂肪在预测MetS发病方面至关重要，并开发了一种基于包括内脏脂肪在内的非侵入性参数的高精度发病预测算法。
