Clinical characteristics affecting weight loss in an East Asian population receiving semaglutide: A STEP 6 subgroup analysis.

Objective: To explore the effects of semaglutide versus placebo on body weight (BW) by subgroups of baseline characteristics.

Methods: In STEP 6, Japanese and Korean adults with overweight or obesity were randomized to subcutaneous semaglutide 2.4 mg, semaglutide 1.

Immune checkpoint inhibitor-related diabetes mellitus associated with high signal intensity in diffusion-weighted magnetic resonance imaging of the pancreas at an early clinical stage.

Introduction: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment but can give rise to immune-related adverse events such as ICI-related diabetes mellitus (DM).

Case Presentation: We herein present the case of a 59-year-old Japanese man with malignant melanoma who developed ICI-related DM after 18 months of nivolumab treatment. He experienced marked hyperglycemia and diabetic ketoacidosis without a personal or family history of diabetes.

Metyrapone single administration, as a possible predictive tool of its dosage and timing in Cushing's syndrome.

Metyrapone is commonly used in the initial management of Cushing's syndrome to reduce hypercortisolemia, but its optimal dosage and timing can vary significantly between patients. Currently, there are limited guidelines on adjustment methods for its administration to individual needs. This study aimed to evaluate responsiveness of each patient to metyrapone and identify the patient characteristics associated with the indices of cortisol responsiveness following a low-dose metyrapone.

Prediction of insulin requirements by explainable machine learning for individuals with type 1 diabetes.

Objective: Adverse events related to insulin therapy remain common in individuals with type 1 diabetes. Optimization of insulin dose can reduce the frequency of these events and help to prevent macrovascular and microvascular complications. The aim of the present study was to develop machine learning models to predict the total daily dose (TDD) of insulin on the basis of data available in routine clinical practice, to evaluate the performance of the models, and to interpret the relation between its predictions and features.