One of the major global health and welfare issues is the treatment of obesity and associated metabolic disorders, such as type 2 diabetes mellitus and nonalcoholic fatty liver disease. Obesity, caused by the excessive accumulation of triglycerides in adipose tissues, induces adipocyte dysfunction, followed by inflammation, in adipose tissues and lipotoxicity in nonadipose tissues. Several studies have shown that obesity and glucose homeostasis are influenced by sphingolipid mediators, including ceramide and sphingosine 1-phosphate (S1P). Cellular accumulation of ceramide impairs pancreatic β-cell survival, confers insulin resistance in the liver and the skeletal muscle, and deteriorates adipose tissue inflammation via unknown molecular mechanisms. The roles of S1P are more complicated, because there are five cell-surface S1P receptors (S1PRs: S1P_1–5) which have altered functions, different cellular expression patterns, and inapparent intracellular targets. Recent findings, including those by our group, support the notable concept that the pharmacological activation of S1P₁ or S1P₃ improves obesity and associated metabolic disorders, whereas that of S1P₂ has the opposite effect. In addition, the regulation of S1P production by sphingosine kinase (SphK) is an essential factor affecting glucose homeostasis. This review summarizes the current knowledge on SphK/S1P/S1PR signaling in and against obesity, insulin resistance, and associated disorders.

Background: The use of serum soluble interleukin 2 receptor (sIL-2R) for the diagnosis of febrile illnesses has not been examined. In this study, febrile patients were classified according to etiology and disease, and serum sIL-2R levels were evaluated. We determined whether serum sIL-2R is a useful marker for differentiating between malignant lymphoma (ML) and non-ML patients and between patients with ML and Kikuchi disease, which present similar clinical manifestations. Methods: This study was a cross-sectional study and included 344 patients with uncomplicated hemophagocytic syndrome, who had a fever of 38 °C or higher within 1 week of admission to our institution. Patient serum sIL-2R was measured, and the serum sIL-2R values are shown as median and IQR. Results: Serum sIL-2R increased above the upper reference limit in all disease groups with fever. The serum sIL-2R level in ML patients (n = 13) was 4760 (2120–6730) U/mL and significantly higher (p < 0.001) than the level of 998 (640–1625) U/mL in non-ML patients (n = 331). The serum sIL-2R level in ML patients (n = 13) was also significantly higher (p < 0.001) compared with that in patients with Kikuchi disease (n = 20; 705 (538–1091) U/mL). Conclusions: Serum sIL-2R tends to exceed the upper reference limit in patients with febrile illnesses. We conclude that the measurement of serum sIL-2R is useful for differentiating ML from non-ML and ML from Kikuchi disease.

Background: Whether the prevalence of Helicobacter pylori (H. pylori) infection is lower in hemodialysis patients than that in patients with normal renal function remains controversial. We performed multivariable logistic regression analysis in hemodialysis patients and patients with normal renal function to compare the prevalence of H. pylori infection in these patients. We also investigated the relationship between the prevalence of H. pylori infection and the duration of hemodialysis. Methods: This retrospective study population included hemodialysis patients who received regular hemodialysis in four hospitals and had their anti-H. pylori antibodies measured between January 2014 and February 2021, as well as consenting patients who had their anti-H. pylori antibodies measured between May and June 2021. Subjects with normal renal function who underwent a medical check-up at two hospitals and had their anti-H. pylori antibodies measured between January 2014 and February 2021 were selected to match the hemodialysis patients for age, sex, and time of diagnosis of H. pylori infection. These subjects formed the control group. Results: A total of 218 patients were enrolled in the study, with 109 in the hemodialysis group and 109 in the control group. The prevalence of H. pylori infection in the hemodialysis and control groups was 12.8% (95% confidence interval [CI] 7.2%, 20.6%) and 41.3% (95% CI 31.9%, 51.1%), respectively. To adjust for confounding, the model included body mass index (BMI), smoking status, alcohol consumption, age, sex, and use of gastric medications as covariates. A significant association was observed between the prevalence of H. pylori infection and hemodialysis (odds ratio [OR], 0.32; 95% CI 0.1, 0.97; p = 0.045). In the hemodialysis group, the association between the duration of hemodialysis and the prevalence of H. pylori infection was not clear (OR, 0.98; 95% CI 0.91, 1.06; p = 0.607). Conclusions: The prevalence of H. pylori infection in hemodialysis patients was significantly lower than that observed in patients with normal renal function.

Background: Glucocorticoid (GC) treatments are often used. There is limited information on the prediction of hyperglycaemia after GC administration.Aims: This study aimed to identify the risk factors for hyperglycaemia after glucocorticoid (GC) administration and the need for hypoglycaemic agents to correct it and to develop and validate a novel scoring system for predicting GC‐induced hyperglycaemia.Methods: In a development set, 508 adults receiving prednisolone (PSL) for the first time were divided into two groups based on treatment with or without hypoglycaemic agents. Clinical and laboratory parameters were compared, and risk factors were identified using logistic regression analysis after performing univariate analyses between the two groups. A point‐addition scoring system with several categories and coefficients for each risk factor was constructed to predict the need for hypoglycaemic agents. The scoring system was then applied and validated on two validation sets: A and B.Results: Older age, higher glycated haemoglobin percentage, body mass index and initial PSL dosage were identified as risk factors. The sensitivity, specificity and accuracy of the scoring system were 70.6%, 81.9% and 77.1% in the development set; 75.8%, 78.4% and 77.4% in validation set A; and 79.4%, 73.9% and 75.3% in validation set B respectively. By fitting the total score in the development set and the probability of hyperglycaemia to a logistic curve, a figure was created to show the probability of GC‐induced hyperglycaemia in patients scheduled to receive GC.Conclusion: This scoring system is a novel, valid and reliable tool for predicting GC‐induced hyperglycaemia and the need for hypoglycaemic agents to correct it.