Objective The aim of this study was to examine the associations between the visceral fat area (VFA) and the subcutaneous fat area (SFA) as estimated by the dual impedance method with a body composition monitor (BCM) and the diagnostic components of metabolic syndrome in a middle-aged Japanese population. Methods The subjects included 303 men (average age 51.3±9.0 years old) and 345 women (average age 40.0±9.4 years old). The VFA and SFA were estimated by BCM, and the associations among the components of metabolic syndrome (waist circumference, blood pressure and related blood sample tests) were evaluated. Results VFA showed positive correlations with waist circumference, HbA1c, high-density lipoprotein (HDL)/low-density lipoprotein (LDL) cholesterol, triglyceride and uric acid level in men, while showing positive correlations with waist circumference, HDL cholesterol, triglyceride and HbA1c in women. The estimated SFA showed positive correlations with systolic blood pressure, HDL/LDL cholesterol and triglyceride in men, and HDL cholesterol and triglyceride in women. A receiver operating characteristic (ROC) analysis showed the estimated VFA to be as effective as WC to identify subject with metabolic syndrome. Conclusion By estimating the VFA using BCM, it may be possible to identify patients at risk of developing metabolic syndrome and hyperuricemia.

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Concentrations of RLP-TG found in the TG along with its particle size are significantly increased in postprandial plasma compared with fasting plasma. Therefore, non-fasting TG determination better reflects the presence of higher RLP concentrations in plasma.

Background Approximately 2.4 million patients in Japan would benefit from treatment for thyroid disease, including Graves’ disease and Hashimoto’s disease. However, only 450,000 of them are receiving treatment, and many patients with thyroid dysfunction remain largely overlooked. In this retrospective study, we aimed to develop and conduct preliminary testing on a machine learning method for screening patients with hyperthyroidism and hypothyroidism who would benefit from prompt medical treatment. Methods We collected electronic medical records and medical checkup data from four hospitals in Japan. We applied four machine learning algorithms to construct classification models to distinguish patients with hyperthyroidism and hypothyroidism from control subjects using routine laboratory tests. Performance evaluation metrics such as sensitivity, specificity, and the area under receiver operating characteristic (AUROC) were obtained. Techniques such as feature importance were further applied to understand the contribution of each feature to the machine learning output. Results The results of cross-validation and external evaluation indicated that we achieved high classification accuracies (AUROC = 93.8% for hyperthyroidism model and AUROC = 90.9% for hypothyroidism model). Serum creatinine (S-Cr), mean corpuscular volume (MCV), and total cholesterol were the three features that were most strongly correlated with the hyperthyroidism model, and S-Cr, lactic acid dehydrogenase (LDH), and total cholesterol were correlated with the hypothyroidism model. Conclusions We demonstrated the potential of machine learning approaches for diagnosing the presence of thyroid dysfunction from routine laboratory tests. Further validation, including prospective clinical studies, is necessary prior to application of our method in the clinic.