SUMMARY Euglycemic diabetic ketoacidosis (DKA) is a rare complication of diabetes mellitus (DM) characterized by metabolic acidosis, ketosis, and blood glucose levels < 250 mg/dL. The prevalence of euglycemic DKA is increasing with the popularity of ketogenic (low-carbohydrate) diets. We present herein the case of a patient with newly diagnosed type 1 DM who developed euglycemic DKA following a ketogenic diet. A 22-year-old woman presented to the emergency department with malaise, fatigue, nausea, and vomiting. She had no family history

Introduction: Non-alcoholic fatty liver disease, now termed metabolic dysfunction-associated steatotic liver disease (MASLD), represents a significant health burden worldwide. Patients with Cushing's syndrome (CS), a condition characterized by excessive cortisol production, may be at an elevated risk for MASLD due to associated metabolic disturbances.Material and methods: This study aimed to evaluate the predictive value of the hepatic steatosis index (HSI) in diagnosing MASLD in patients with CS. 101 endogenous CS patients were included, and hepatosteatosis was assessed using ultrasonography. HSI scores were calculated, and associations with clinical and biochemical parameters were analyzed.Results: Hepatosteatosis was observed in 62.4% of CS patients, with all these individuals meeting the criteria for MASLD. The HSI demonstrated hepatosteatosis with high sensitivity (92.1%) and specificity (78.9%) compared to ultrasonography. Factors significantly correlated with hepatosteatosis included higher BMI, diabetes, hypertension, and hyperlipidemia. There was no correlation with basal morning cortisol, 24-hour urinary free cortisol, low-dose dexamethasone suppression tests, or the etiology of CS.Conclusions: Our study represents a pioneering effort to explore MASLD in patients with endogenous CS by evaluating the HSI as a diagnostic tool. Utilizing the HSI, we demonstrated high sensitivity and specificity in diagnosing hepatosteatosis, emphasizing its potential as a valuable non-invasive tool in this population. Furthermore, our study fills a significant gap in the literature by being the first to investigate the predictive power of HSI for MASLD diagnosis, specifically in CS patients. Integrating these findings into clinical practice could enhance the early detection and management of MASLD in endogenous CS, ultimately improving patient outcomes.