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Monogenic diabetes mellitus, which is diabetes caused by a defect in a single gene that is associated with β cell function or insulin action, accounts for 1% to 6% of all pediatric diabetes cases. Accurate diagnosis is important, as the effective treatment differs according to genetic etiology in some types of monogenic diabetes: high-dose sulfonylurea treatment in neonatal diabetes caused by activating mutations in KCNJ11 or ABCC8; low-dose sulfonylurea treatment in HNF1A/HNF4A-diabetes; and no treatment in GCK diabetes. Monogenic diabetes should be suspected by clinicians for certain combinations of clinical features and laboratory results, and approximately 80% of monogenic diabetes cases are misdiagnosed as type 1 diabetes or type 2 diabetes. Here, we outline the types of monogenic diabetes and the clinical implications of genetic diagnosis.

Background: Pediatric obesity has increased over the decades, and in particular, severe pediatric obesity has become a serious public health problem. A concern has arisen that the COVID-19 pandemic may exacerbate the incidence of childhood obesity.Current Concepts: The consequences of severe pediatric obesity are more devastating than those of moderate obesity. Children with severe obesity are at a greater risk for hypertension, type 2 diabetes, metabolic syndrome, non-alcoholic fatty liver disease, atherosclerosis, and adult obesity. Correct assessment and diagnosis of a child with severe obesity is key to successful therapy. A thorough history and physical examination are important in identifying monogenic obesity or metabolic syndrome. Eating behaviors and psychosocial factors should be assessed to improve weight management outcomes. Treatment options for severe pediatric obesity include lifestyle modification, pharmacotherapy, and metabolic and bariatric surgery. Even though progress has been made with regard to the treatment of obesity, safe and effective treatment of severe pediatric obesity is challenging.Discussion and Conclusion: More efforts and innovations are needed to find a solution for the huge medical and emotional burden the children with severe obesity and their families are enduring.

Purpose Adolescent idiopathic scoliosis (AIS) is the most common form of scoliosis and occurs in children between 10 to 18 years old, during periods of growth spurts and puberty changes. In patients with central precocious puberty (CPP), due to early growth spurt, AIS is expected to develop before 10 years of age. Both AIS and CPP are more common in girls than in boys. The aim of this study was to determine the prevalence of AIS in girls with CPP and to evaluate the effect of treatment with gonadotropin-releasing hormone (GnRH) agonists on progression of scoliosis in these patients. Methods We retrospectively reviewed medical records of 553 girls, 338 with CPP and 215 without CPP. Scoliosis angle was measured on the standing frontal radiograph of each patient according to the Cobb method. Patients with a Cobb angle of 10° or more were diagnosed with scoliosis. For girls with CPP, followup spine radiographs were collected 1 year after treatment with GnRH agonists. Progression of scoliosis before and after treatment was compared in terms of Cobb angle changes. Results AIS was more prevalent in girls that were affected by CPP compared to controls without CPP (11.5% vs. 6.0%, CPP girls vs. non-CPP girls, respectively, P=0.031). The peak serum luteinizing hormone level positively correlated with Cobb angle (R2=0.015, P=0.023) in the CPP group. No progression of scoliosis was observed in CPP girls after one year of GnRH agonist treatment. Additionally, the prevalence of scoliosis decreased in CPP girls after 1 year of the treatment. Conclusion We report that the prevalence of AIS is higher in girls with CPP than in non-CPP patients. A regular follow-up schedule for spine radiographs should be considered to reduce the risk of progression. Furthermore, GnRH agonist treatment for CPP may have a suppressive effect on progression of AIS.

Background Triptorelin depot is largely used to treat central precocious puberty (CPP) in children, and a 3-month depot has been introduced. However, data about the 3-month gonadotropin-releasing hormone use for treatment of CPP in Korean girls are not available. This study was conducted to compare the efficacy of a triptorelin 11.25 mg 3-month depot with that of a 3.75 mg 1-month depot in suppressing pubertal development for the treatment of CPP. Methods A retrospective study, including 106 girls with CPP treated with triptorelin, was conducted. Fifty patients were treated with a triptorelin 3-month depot, and 56 were treated with a triptorelin 1-month depot. Serum luteinizing hormone (LH), follicle-stimulating hormone, and estradiol levels were analysed every 6 months after the visit. The height and bone age of each patient was evaluated at the beginning of treatment, after 6 months, and one year after therapy. Results The baseline characteristics of the girls treated with a 3-month depot were similar to those of the girls treated with a 1-month depot. A suppressed levels of LH to the triptorelin injection (serum LH < 2.5 IU/L) at 6 months was seen in 90.0% and 98.2% of the girls treated with the 3-month and 1-month depots, respectively (P = 0.160). After 1 year of treatment, a suppressed levels of LH was seen in 93.5% and 100% of the girls treated with the 3-month and 1-month depots, respectively (P = 0.226). Height velocity showed no significant difference between the two groups. Degree of bone age advancement decreased from 1.22 ± 0.07 and 1.22 ± 0.08 years at baseline (P = 0.914) to 1.16 ± 0.07 and 1.17 ± 0.08 in the girls treated with the 3-month and 1-month depots after 1 year, respectively (P = 0.481). Conclusion This study showed that the efficacy of long-acting triptorelin 3-month was comparable to 1-month depot regarding hormonal suppression and inhibition of bone maturation. The triptorelin 11.25 mg 3-month depot is an effective treatment for girls with CPP., Graphical Abstract

Puberty is the transitional period from childhood to adult that leads to growth spurt, sexual maturation and attainment of reproductive capacity. Precocious puberty is defined when secondary sexual characteristics develop before the age of eight for girls and nine for boys. Central precocious puberty (CPP) is diagnosed when the process is driven by premature activation of hypothalamic gonadotropin-releasing hormone (GnRH) secretion. Many factors promote CPP, and the thyroid function is thought to be one of them. In our previous study, thyroid stimulating hormone (TSH) was higher in the CPP group than that of the participants without CPP. This elevation of TSH in CPP is said to be associated with pubertal luteinizing hormone (LH) elevation. The aim of this study was to evaluate the causal relationship between TSH and LH in CPP patients. A total of 221 girls diagnosed with CPP and treated with GnRH agonists were included. All participants except one showed LH suppression (peak LH < 3 IU/L), and serum levels of follicle stimulating hormone (FSH) were also lower after the treatment. These results indicate that puberty has slowed down and that the patients were successfully treated for CPP. As for thyroid hormones, TSH was significantly lower and free thyroxine (fT4) levels were higher after 12 months of GnRH agonist treatment compared with baseline. With GnRH agonist treatment, the serum levels of LH and TSH were decreased, suggesting that the increase in serum TSH levels is associated with premature LH elevation in girls with CPP.

Background: Bone age assessments (BAAs) is an important clinical modality to investigate endocrine, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich-Pyle (GP) or the Tanner-Whitehouse (TW) method. However, both clinical procedures show several limitations, from significant intra- and inter-operator variability to examination effort of clinicians. To address these problems, several automated approaches have been proposed; nevertheless, some disparity still exists between automated BAAs and manual BAAs to be employed in clinical practice. To overcome this disparity, deep learning-based bone age assess software using GP and TW3 hybrid method has been developed. In this study, we evaluate the accuracy and efficiency of the new automated hybrid software system for bone age assessment and validate its feasibility in clinical practice. Materials and Methods: Greulich-Pyle (GP) and Tanner-Whitehouse (TW3) hybrid method-based deep-learning technique was used to develop the automated software system for bone age assessment. Total 102 radiographs from children with the chronological age of 4.9-17.0 years (mean age 10.9±2.3, 51 cases for females and 51 cases for males) were selected and bone age was estimated with this software. For validation of the automated software system, three human experts have manually performed BAAs at expert’s discretion based on GP method for accuracy estimation and one naïve radiologist performed BAAs with automated software system assist and BAAs reading time was recorded in each session for efficiency evaluation. The performance of automated software system was assessed by comparing mean absolute difference (MAD) between the system estimates and the experts manual BAAs.Results: The results of bone age assessment by human experts and automated software system showed no significant difference between the two groups. Each assessed average of bone age were 11.39 ± 2.74 and 11.35 ± 2.76, respectively. MAD was 0.39 years between automated software system BAAs and experts manual BAAs. The 95% confidence interval of the MAD was 0.33 years and 0.45 years. BAAs reading time was reduced from 56.81 sec (95% confidence interval 52.81 - 60.81 sec) in naïve manual BAAs to 31.72 sec (95% confidence interval 29.74 - 33.69 sec) in automated software system assisted BAAs and statistically significant (p < 0.001). MAD showed 0.42 years between naïve manual BAAs and the software-assisted BAAs (95% confidence interval 0.31-0.47 years).Conclusion: The newly developed GP and TW3 hybrid automated software system were reliable for bone age assessments with equivalent accuracy to human experts. Also, the automated system appeared to enhance efficiency by reducing reading times without compromising diagnostic accuracy.

Objective To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33–0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.