Factors Associated With Mild Cognitive Impairment in Patients With Type 2 Diabetes: A Cohort Study.

Background Cognitive dysfunction is increasingly recognized as an important comorbidity of diabetes mellitus (DM). Objective The purpose of this study was to determine the prevalence and predictors of cognitive decline in individuals with type 2 diabetes mellitus (T2DM). Methods This cohort study included patients with type 2 diabetes mellitus aged between 40 and 75 years and with a duration of the evolution of diabetes that is greater than five years admitted in endocrinology consultation of the Sheikh Khalifa ibn Zaid Hospital in Casablanca, Morocco.

Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models.

Aims: This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models.

Methods: First, we conducted three consultations, before, during, and after Ramadan to assess demographics, diabetes history, caloric intake, anthropometric and metabolic parameters. Second, machine learning techniques (Logistic Regression, Support Vector Machine, Naive Bayes, K-nearest neighbor, Decision Tree, Random Forest, Extra Trees Classifier and Catboost) were trained using the data to predict poor glycemic control among patients.

Diabetic Ketoacidosis With Acute Metabolic Pancreatitis: Two Serious Cases.

Hypertriglyceridemia is a rare cause of acute pancreatitis (AP), occupying approximately 7% of cases. The triad of acute pancreatitis, hypertriglyceridemia, and diabetes is a rare event, with a higher death rate. We describe two cases of severe acute metabolic pancreatitis discovered in diabetic ketoacidosis.