Aim: To describe the Turkish generalized lipodystrophy (GL) cohort with the frequency of each complication and the death rate during the period of the follow‐up. Methods: This study reports on 72 patients with GL (47 families) registered at different centres in Turkey that cover all regions of the country. The mean ± SD follow‐up was 86 ± 78 months. Results: The Kaplan–Meier estimate of the median time to diagnosis of diabetes and/or prediabetes was 16 years. Hyperglycaemia was not controlled in 37 of 45 patients (82.2%) with diabetes. Hypertriglyceridaemia developed in 65 patients (90.3%). The Kaplan–Meier estimate of the median time to diagnosis of hypertriglyceridaemia was 14 years. Hypertriglyceridaemia was severe (≥ 500 mg/dl) in 38 patients (52.8%). Seven (9.7%) patients suffered from pancreatitis. The Kaplan–Meier estimate of the median time to diagnosis of hepatic steatosis was 15 years. Liver disease progressed to cirrhosis in nine patients (12.5%). Liver disease was more severe in congenital lipodystrophy type 2 (CGL2). Proteinuric chronic kidney disease (CKD) developed in 32 patients (44.4%) and cardiac disease in 23 patients (31.9%). Kaplan–Meier estimates of the median time to diagnosis of CKD and cardiac disease were 25 and 45 years, respectively. Females appeared to have a more severe metabolic disease, with an earlier onset of metabolic abnormalities. Ten patients died during the follow‐up period. Causes of death were end‐stage renal disease, sepsis (because of recurrent intestinal perforations, coronavirus disease, diabetic foot infection and following coronary artery bypass graft surgery), myocardial infarction, heart failure because of dilated cardiomyopathy, stroke, liver complications and angiosarcoma. Conclusions: Standard treatment approaches have only a limited impact and do not prevent the development of severe metabolic abnormalities and early onset of organ complications in GL.

Neonatal diabetes mellitus (NDM) may be transient or permanent, and the majority is caused by genetic mutations. Early diagnosis is essential to select the patients who will respond to oral treatment. In this investigation, we aimed to present the phenotype and genotype of our patients with NDM and share our experience in a single tertiary center. A total of 16 NDM patients from 12 unrelated families are included in the study. The clinical presentation, age at diagnosis, perinatal and family history, consanguinity, gender, hemoglobin A1c, C-peptide, insulin, insulin autoantibodies, genetic mutations, and response to treatment are retrospectively evaluated. The median age at diagnosis of diabetes was five months (4 days-18 months) although six patients with a confirmed genetic diagnosis were diagnosed >6 months. Three patients had KCNJ11 mutations, six had ABCC8 mutations, three had EIF2AK3 mutations, and one had a de novo INS mutation. All the permanent NDM patients with KCNJ11 and ABCC8 mutations were started on sulfonylurea treatment resulting in a significant increase in C-peptide level, better glycemic control, and discontinuation of insulin. Although NDM is defined as diabetes diagnosed during the first six months of life, and a diagnosis of type 1 diabetes is more common between the ages of 6 and 24 months, in rare cases NDM may present as late as 12 or even 24 months of age. Molecular diagnosis in NDM is important for planning treatment and predicting prognosis. Therefore, genetic testing is essential in these patients.

Objective: 22q11.2 deletion syndrome (22q11.2 DS) is the most common chromosomal microdeletion disorder. Associated problems in 22q11.2 DS may include cardiac abnormalities, immune dysfunction, facial dysmorphism, with endocrine, genitourinary and gastrointestinal problems, and developmental delay. The aim of this study was to evaluate and present all endocrinological findings of patients with 22q11.2 DS from a single center.Methods: All participants had confirmed 22q11.2 DS by fluorescence in situ hybridization with hypoparathyroidism. Data were retrieved by retrospective review of patient records.Results: A total of 17 patients were reviewed. On physical examination, all patients had similar dysmorphic features. The median age at diagnosis was 45 days (1 day-13 years). Most cases (64.7%, 11/17) were diagnosed with hypoparathyroidism incidentally after routine tests. At the time of diagnosis, mean calcium was 7.04 & PLUSMN;0.80 mg/dL, phosphorus was 6.2 & PLUSMN;1.1 mg/dL, and median parathyroid hormone (PTH) was 11.5 (3.7-47.6) ng/L. Transient hypoparathyroidism was detected in five cases (29.4%). There was no significant difference between patients with permanent or transient hypoparathyroidism regarding gender, age at diagnosis, calcium, phosphorus, and PTH levels. However, vitamin D levels were significantly lower in the transient group (p=0.036). During follow-up, short stature, obesity, and type 2 diabetes mellitus were absent. Thyroid autoantibodies were detected in two patients with normal thyroid function tests. Despite there being no pathological short stature, final stature was shorter than the general population (mean height standard deviation score:-0.94 & PLUSMN;0.83).Conclusion: Hypocalcemia may be detected during acute illness in some cases where hypocalcemia appears at later ages. There was no significant difference between permanent and transient hypoparathyroidism cases in terms of PTH level. Recognition of the more specific facial findings is important to trigger investigation of genetic variants, additional anomalies, and for follow-up.

The use of large language models (LLMs) in healthcare has expanded significantly with advances in natural language processing. Models, such as ChatGPT and Google Gemini, are increasingly used to generate human-like responses to questions, including those posed by patients and their families. With the rise in the incidence of type 1 diabetes (T1D) among children, families frequently seek reliable answers regarding the disease. Previous research has focused on type 2 diabetes, but studies on T1D in a pediatric population remain limited. This study aimed to evaluate and compare the performance and effectiveness of different LLMs when answering common questions about T1D. This cross-sectional, comparative study used questions frequently asked by children with T1D and their parents. Twenty questions were selected from inquiries made to pediatric endocrinologists via social media. The performance of ChatGPT-3.5 ChatGPT-4 ChatGPT-4o was assessed using a standard prompt for each model. The responses were evaluated by five pediatric endocrinologists interested in diabetes using the General Quality Scale (GQS), a 5-point Likert scale, assessing factors such as accuracy, language simplicity, and empathy. All five LLMs responded to the 20 selected questions, with their performance evaluated by GQS scores. ChatGPT-4o had the highest mean score (3.78 ± 1.09), while Gemini had the lowest (3.40 ± 1.24). Despite these differences, no significant variation was observed between the models (p = 0.103). However, ChatGPT-4o, ChatGPT-4, and Gemini Advanced produced the highest-quality answers compared to ChatGPT-3.5 and Gemini, scoring consistently between 3 and 4 points. ChatGPT-3.5 had the smallest variation in response quality, indicating consistency but not reaching the higher performance levels of other models. This study demonstrated that all evaluated LLMs performed similarly in answering common questions about T1D. LLMs such as ChatGPT-4o and Gemini Advanced can provide above-average, accurate, and patient-friendly answers to common questions about T1D. Although no significant differences were observed, the latest versions of LLMs show promise for integration into healthcare, provided they continue to be evaluated and improved. Further research should focus on developing specialized LLMs tailored for pediatric diabetes care.

Objectives: To determine the importance of serum prolactin (PRL) in the detection of pituitary stalk interruption syndrome (PSIS) in children with multiple pituitary hormone deficiency (MPHD). We hypothesized that PRL elevation might be a diagnostic indicator of pituitary stalk pathologies. Methods: Clinical, radiological, and laboratory features of the 50 cases of MPHD were studied. Results: The median age at presentation of the 50 cases (52%, n=26 were female) was 6.61 (0.02–18.9) years. PSIS was detected in 60% (n=30), pituitary hypoplasia in 32% (n=16), partial empty sella in 6% (n=3), and only 2% (n=1) was reported as normal. Out of 50 patients, 21.3% (n=10) were hypoprolactinemic, 44.7% (n=19) were normoprolactinemic, and 34% (n=16) were hyperprolactinemic. The median PRL value was 27.85 (4.21–130) ng/mL in patients with PSIS and 5.57 (0–41.8) ng/mL in patients without PSIS. Additional hormone deficiencies, especially ACTH and LH were detected in follow-up. Conclusions: Patients with normal or high prolactin levels deserve special attention regarding the possibility of PSIS. Furthermore, we emphasize the importance of regular follow-up and monitoring for multiple pituitary hormone deficiencies in all patients with a single pituitary hormone deficiency.