Metabolic Syndrome (MS) is prevalant in China, especially according to the pediatric obesity group. Based on the MS-CHN2012 definition for Chinese children and adolescents the need to explore and establish a convienent MS screening become imminent. This study aims to investigate the optimal cut-off values, compare the accuracy for the (TriGlycerides (TG) to High-Density Lipoprotein Cholesterol (HDL-C)) (TG/HDL-C) ratio and Homeostasis Model Assessment Insulin Resistance (HOMA-IR) indexs to identify Metabolic Syndrome in obese pediatric population in China. A total sample of 976 children (female286 male690, BMI > =95percentile) aged from 6–16 years underwent a medical assessment including a physical examination and investigations of total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, insulin, glucose, and oral glucose tolerance test to identify the components of Metabolic Syndrome. The validity and accuracy between TG/HDL-C ratio and HOMA-IR were compared by Receiver Operating Characteristics analysis (ROC). TG/HDL-C ratio achieved a larger ROC Area under Curve (AUC = 0.843) than HOMA-IR indexes (0.640, 0.625 for HOMA1-IR, HOMA2-IR respectively) to screen for Metabolic Syndrome. The cut-off values for MS were: TG/HDL-C ratio > 1.25 (sensitivity: 80 %; specificity: 75 %), HOMA1-IR > 4.59 (sensitivity: 58.7 %; specificity: 65.5 %) and HOMA2-IR > 2.76 (sensitivity: 53.2 %; specificity: 69.5 %). The results kept robust after stratified by gender, age group and pubertal stage. TG/HDL-C ratio was a better indicator than the HOMA-IR to screen for a positive diagnosis for MS. Furthermore, the TG/HDL-C ratio was superior to the HOMA-IR indexes even after the control of possible confusions from the gender, age group and puberty stage. TG/HDL-C ratio proved a better index than HOMA-IR in screening for MS in obese children and adolescents. TG/HDL-C ratio has a discriminatory power in detecting potential MS in the Chinese obese pediatric population.

Wiedemann–Steiner syndrome (WDSTS) is a rare genetic disorder characterized by facial gestalt, neurodevelopmental delay, skeletal anomalies and growth retardation, which is caused by variation of KMT2A gene. To date, only 2 Chinese WDSTS patients have been reported. Here, we report the phenotypes and KMT2A gene variations in 14 unrelated Chinese WDSTS patients and investigate the phenotypic differences between the Chinese and French cohorts. Next generation sequencing was performed for each patient, and the variants in the KMT2A gene were validated by Sanger sequencing. The phenotypes of 16 Chinese WDSTS patients were summarized and compared to 33 French patients. Genetic sequencing identified 13 deleterious de novo KMT2A variants in 14 patients, including 10 truncating, 2 missenses and 1 splicing variants. Of the 13 variants, 11 are novel and two have been reported previously. One of the patients is mosaic in the KMT2A gene. The variation spectra and phenotypic profiles of the Chinese WDSTS patients showed no difference with patients of other ethnicities; however, differ in the frequencies of several clinical features. We demonstrated that variations in the KMT2A gene can lead to both advanced and delayed bone age. We identified 6 novel phenotypes, which include microcephaly, deep palmar crease, external ear deformity, carpal epiphyseal growth retardation, dyslipidemia, and glossoptosis. In addition, patients harbored missense variants in the CXXC zinc finger domain of KMT2A showed more severe neurophenotypes. Our study consists of the largest cohort of Chinese WDSTS patients that continues to expand the WDSTS phenotypic and variation spectrum. Our results support the notion that the CXXC zinc finger domain of KMT2A gene is a hotspot for missense variants associated with more severe neurophenotypes.

Context: Diagnosis of non-chromosomal type disorders of sex development (DSD) has long been challenging. There is still no research on overview of a large Chinese DSD cohort. Objective: To determine the etiologic diagnosis through unbiased large-scale panel sequencing and whole-exome sequencing (WES) within a large Chinese DSD cohort. Design: Patients were recruited according to the inclusion criteria of DSD. The applied panel contains 2742 known disease-causing genes, including all known diagnostic genes for DSD. Methods: Targeted panel sequencing (TPS) was performed, and identified candidate variants were verified. Variant pathogenicities were evaluated according to established guidelines. WES was performed for randomly selected negative samples. Results: This study included 125 patients. Seventy-five variants were identified by TPS and 31 variants were reported for the first time. Pathogenic and likely pathogenic variants accounted for 38.7 and 30.7%, respectively. On the basis of clinical certainty, etiologic diagnostic rates of 46.9 and 10.3% were obtained for 46,XY and 46,XX DSD patients, respectively. We reported novel candidate genes (BMPR1B, GNAS, GHR) and regions of copy number variants outside the expected DSD genotype–phenotype correlation and determined a founder mutation (SRD5A2 p.R227Q) in patients with 5α-reductase deficiency. Further WES in randomly selected negative samples identified only one among 14 negative samples as a variant of uncertain significance, indicating that WES did not improve the diagnostic rate. Conclusions: This is the first report of the applying unbiased TPS in a large Chinese cohort of patients with 46,XY and 46,XX DSD. Our findings expand the gene, mutation and phenotype spectra of the rare types of DSD in the Chinese population and provide new insight into the current understanding of the etiologies of DSD.

Short stature may be caused by a multitude of conditions, including genetic and non-genetic causes. Over the last decade, advances in genetic sequencing technologies have revolutionized our understanding of the underlying physiology of growth and greatly increased our ability to identify genetic etiologies of short stature. The current guideline provides a general overview of the approach to the evaluation of a child with short stature, followed by recommendations identifying factors in the medical and family history, physical examination, radiographic, and laboratory work up which increase the likelihood of identifying a genetic etiology. An algorithm is proposed for the genetic workup of individuals with short stature based on their clinical presentation. The benefits and risks of genetic testing are discussed as well.

This study is aimed at examining the impact of ChatGPT on pediatric endocrine and metabolic conditions, particularly in the areas of screening and diagnosis, in both Chinese and English modes. A 40-question questionnaire covering the four most common pediatric endocrine and metabolic conditions was posed to ChatGPT in both Chinese and English three times each. Six pediatric endocrinologists evaluated the responses. ChatGPT performed better when responding to questions in English, with an unreliable rate of 7.5% compared to 27.5% for Chinese questions, indicating a more consistent response pattern in English. Among the reliable questions, the answers were more comprehensive and satisfactory in the English mode. We also found disparities in ChatGPT’s performance when interacting with different target groups and diseases, with improved performance for questions posed by clinicians in English and better performance for questions related to diabetes and overweight/obesity in Chinese for both clinicians and patients. Language comprehension, providing incomprehensive answers, and errors in key data were the main contributors to the low scores, according to reviewer feedback.Conclusion: Despite these limitations, as ChatGPT continues to evolve and expand its network, it has significant potential as a practical and effective tool for clinical diagnosis and treatment. What is Known:• The deep learning-based large-language model ChatGPT holds great promise for improving clinical practice for both physicians and patients and has the potential to increase the speed and accuracy of disease screening and diagnosis, as well as enhance the overall efficiency of the medical process. However, the reliability and appropriateness of AI model responses in specific field remains unclear.• This study focused on the reliability and appropriateness of AI model responses to straightforward and fundamental questions related to the four most prevalent pediatric endocrine and metabolic disorders, for both healthcare providers and patients, in different language scenarios. What is New:• The AI model performed better when responding to questions in English, with more consistent, as well as more comprehensive and satisfactory responses. In addition, we also found disparities in ChatGPT’s performance when interacting with different target groups and different diseases.• Despite these limitations, as ChatGPT continues to evolve and expand its network, it has significant potential as a practical and effective tool for clinical diagnosis and treatment.