Comparison of single-point insulin sensitivity estimator and other markers to predict metabolic syndrome in children and adolescents.

Background: Reliable markers are crucial for the early detection and management of pediatric metabolic syndrome (MS).

Methods: 1593 children and adolescents were included from Korea National Health and Nutrition Examination Survey 2019-2021. We assessed homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides-glucose index (TyG), triglycerides/high-density lipoprotein cholesterol (TG/HDL), the Metabolic Score for IR (METS-IR), and single-point insulin sensitivity estimator (SPISE) as predictors of MS.

Development and Validation of a Machine Learning-Based Model for Methimazole Dosage Adjustment in Children and Adolescents with Hyperthyroidism.

This study developed a machine learning model using data from 142 children and adolescents with hyperthyroidism, with external validation conducted on 63 patients from another institution. Input variables included age, sex, height SDS, weight SDS, BMI SDS, T3, free T4, TSH, follow-up interval, and previous methimazole dose. SHAP analysis identified T3, TSH, and free T4 as the most influential factors.

Dual-energy X-ray absorptiometry and bioelectrical impedance analysis are useful for predicting metabolic syndrome and nonalcoholic fatty liver disease in children and adolescents.

Purpose: Assessing body composition is essential for evaluating metabolic syndrome (MS) and nonalcoholic fatty liver disease (NAFLD). However, studies on the relationship between these conditions and dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) in children and adolescents remain limited. This study aimed to investigate the associations between DXA and BIA parameters, and their utility in predicting MS and NAFLD in the youth.

Prediction Model for Insulin Resistance and Implications for MASLD in Youth: A Novel Marker, the Pediatric Insulin Resistance Assessment Score.

Purpose: Insulin resistance (IR) is a condition closely associated with cardiovascular risk factors and metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a significant IR-related complication. We aimed to develop a predictive model for IR in youths and implicate this model for MASLD.

Materials And Methods: A total of 1588 youths from the population-based data were included in the training set.

Optimal Cutoffs of Fatty Liver Index and Hepatic Steatosis Index in Diagnosing Pediatric Metabolic Dysfunction-associated Steatotic Liver Disease.

Background & Aims: Despite the high global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD), effective noninvasive diagnostic tools in pediatric populations remain limited. We aimed to validate the predictive performance of the fatty liver index (FLI) and hepatic steatosis index (HSI) in diagnosing MASLD in the young population and propose optimal cutoff points for the diagnosis of MASLD and severe MASLD.

Methods: This cross-sectional study included data from 1158 and 203 youths from the United States National Health and Nutrition Examination Survey (NHANES) 2017 to 2020 and real-world clinical environments, respectively.

Identification of pediatric MASLD using insulin resistance indices.

Background & Aims: We investigated the triglyceride-to-high density lipoprotein (HDL) ratio (TG/HDL), triglyceride-glucose index (TyG), single-point insulin sensitivity estimator (SPISE), and metabolic score for insulin resistance (METS-IR) as potential predictors of pediatric metabolic dysfunction-associated steatotic liver disease (MASLD) by addressing the limited research on insulin-resistance markers in this population.

Methods: This cross-sectional study included data from 1,150 and 260 youths from the National Health and Nutrition Examination Survey (NHANES) and a real-world clinic, respectively. Hepatic steatosis was assessed using transient elastography and abdominal sonography.

Development of a simplified prediction model for diagnosing progressive central precocious puberty using clinical and pelvic ultrasound parameters.

This study aimed to explore the predictive value of clinical and pelvic ultrasound parameters for diagnosing central precocious puberty (CPP) and to establish a clinically useful simplified prediction model to differentiate progressive CPP (P-CP) from nonprogressive precocious puberty (N-PP). Girls aged <9 years with secondary sexual development who underwent a gonadotropin-releasing hormone stimulation test and pelvic ultrasound between September 2020 and November 2023 were retrospectively included and divided into the P-CP and N-PP groups. Logistic regression analysis was used to determine the significant parameters and develop prediction models.

Analysis of risk factors affecting obesity in Korean adolescents: based on the 2017-2020 Korea national health and nutrition examination survey.

Background: Obesity, which is caused by various congenital factors, lifestyle changes, and modernized eating habits, has recently emerged as a serious health concern in children and adolescents. According to the Korea National Health and Nutrition Examination Survey (KNHANES) conducted from 2007 to 2020, the prevalence of obesity and overweight in youth has shown an increasing trend over time. Notably, obesity has been studied in several studies.

Bioelectrical impedance analysis parameters are superior to liver enzymes in predicting metabolic dysfunction-associated steatotic liver disease in young adults.

Background/aims: The predictive value of bioelectrical impedance analysis (BIA) parameters and liver enzymes for metabolic dysfunction-associated steatotic liver disease (MASLD) in young adults is unclear. We aimed to investigate the usefulness of BIA parameters and liver enzymes in MASLD screening in young adults.

Methods: Overall, 2,647 participants aged 19-30 years were evaluated by sex.

Development and validation of a machine learning model for predicting pediatric metabolic syndrome using anthropometric and bioelectrical impedance parameters.

Objective: Metabolic syndrome (MS) is a risk factor for cardiovascular diseases, and its prevalence is increasing among children and adolescents. This study developed a machine learning model to predict MS using anthropometric and bioelectrical impedance analysis (BIA) parameters, highlighting its ability to handle complex, nonlinear variable relationships more effectively than traditional methods such as logistic regression.

Methods: The study included 359 youths from the Korea National Health and Nutrition Examination Survey (KNHANES; 16 MS, 343 normal) and 174 youths from real-world clinical data (66 MS, 108 normal).

Association between Bioelectrical Impedance Parameters, Magnetic Resonance Imaging Muscle Parameters, and Fatty Liver Severity in Children and Adolescents.

Background/aims: To evaluate the associations between pediatric fatty liver severity, bioelectrical impedance analysis (BIA), and magnetic resonance imaging parameters, including total psoas muscle surface area (tPMSA) and paraspinal muscle fat (PMF).

Methods: Children and adolescents who underwent BIA and liver magnetic resonance imaging between September 2022 and November 2023 were included. Linear regression analyses identified predictors of liver proton density fat fraction (PDFF) including BIA parameters, tPMSA, and PMF.

Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging-Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature: Cross-Sectional, Multicenter Study.

Background: Growth hormone deficiency (GHD) and idiopathic short stature (ISS) are the major etiologies of short stature in children. For the diagnosis of GHD and ISS, meticulous evaluations are required, including growth hormone provocation tests, which are invasive and burdensome for children. Additionally, sella magnetic resonance imaging (MRI) is necessary for assessing etiologies of GHD, which cannot evaluate hormonal secretion.

Comparison of SPISE and METS-IR and Other Markers to Predict Insulin Resistance and Elevated Liver Transaminases in Children and Adolescents.

Backgruound: Studies on predictive markers of insulin resistance (IR) and elevated liver transaminases in children and adolescents are limited. We evaluated the predictive capabilities of the single-point insulin sensitivity estimator (SPISE) index, metabolic score for insulin resistance (METS-IR), homeostasis model assessment of insulin resistance (HOMA-IR), the triglyceride (TG)/ high-density lipoprotein cholesterol (HDL-C) ratio, and the triglyceride-glucose index (TyG) for IR and alanine aminotransferase (ALT) elevation in this population.

Methods: Data from 1,593 participants aged 10 to 18 years were analyzed using a nationwide survey.

The Impact of Artificial Intelligence on Radiologists' Reading Time in Bone Age Radiograph Assessment: A Preliminary Retrospective Observational Study.

To evaluate the real-world impact of artificial intelligence (AI) on radiologists' reading time during bone age (BA) radiograph assessments. Patients (<19 year-old) who underwent left-hand BA radiographs between December 2021 and October 2023 were retrospectively included. A commercial AI software was installed from October 2022.

Impact of Respiratory Motion on the Quantification of Pediatric Hepatic Steatosis Using Two Different Ultrasonography Machines.

Purpose: This study aimed to investigate the effect of respiratory motion on hepatic steatosis quantification using ultrasound attenuation imaging (ATI) or ultrasound-guided attenuation parameter (UGAP) in pediatric patients.

Materials And Methods: Pediatric patients (aged ≤18 years) who underwent liver ultrasonography (US) with ATI or UGAP between May 2022 and February 2023 were included retrospectively. Median, interquartile range (IQR), and IQR/median values were calculated in both free-breathing (FB) and breath-holding (BH) states.

Long-term tracking of glycosylated hemoglobin levels across the lifespan in type 1 diabetes: from infants to young adults.

Purpose: Glycosylated hemoglobin (HbA1c) is commonly used as a monitoring tool in diabetes. Due to the potential influence of insulin resistance (IR), HbA1c level may fluctuate over a person's lifetime. This study explores the long-term tracking of HbA1c level in individuals diagnosed with type 1 diabetes mellitus (T1DM) from infancy to early adulthood.

Prediction of insulin resistance and elevated liver transaminases using serum uric acid and derived markers in children and adolescents.

Objective: To investigate the relationship of serum uric acid (Uacid) and derived parameters as predictors of insulin resistance (IR) and elevated liver transaminases in children and adolescents METHODS: Data of 1648 participants aged 10-18 years was analyzed using nationwide survey. Logistic regression analysis was performed with IR and elevated liver transaminases as dependent variables, and odds ratios (ORs) and 95% confidence intervals (CIs) for tertiles 2 and 3 of each parameter in comparison to tertile 1, which served as the reference. Receiver operating characteristic (ROC) curves were generated to assess predictability of the parameters for IR and elevated liver transaminases.

Bioelectrical impedance parameters add incremental value to waist-to-hip ratio for prediction of metabolic dysfunction associated steatotic liver disease in youth with overweight and obesity.

Introduction: Metabolic dysfunction-associated steatotic liver disease (MASLD) presents a growing health concern in pediatric populations due to its association with obesity and metabolic syndrome. Bioelectrical impedance analysis (BIA) offers a non-invasive and potentially effective alternative for identifying MASLD risk in youth with overweight or obesity. Therefore, this study aimed to assess the utility of BIA for screening for MASLD in the youth.

Effectiveness and safety of pamidronate treatment in nonambulatory children with low bone mineral density.

Purpose: Nonambulatory pediatric patients may have low bone mineral density (BMD) and increased risk of pathologic fractures. Though bisphosphonate therapy is the mainstream medical intervention in these children, clinical data regarding this treatment are limited. Therefore, this study aimed to evaluate the effectiveness and safety of bisphosphonate therapy in such children.

Comparison of agranulocytosis and anti-neutrophil cytoplasmic antibody-associated vasculitis caused by two antithyroid drugs: A pharmacovigilance study using the WHO international database.

Background: Methimazole (MMI) and propylthiouracil (PTU) are commonly used for patients with thyrotoxicosis. Agranulocytosis and antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is associated with high morbidity and mortality, requiring appropriate interventions. In this study, we compared adverse drug effects associated with MMI and PTU using a real-world large pharmacovigilance database.

The influence of pituitary volume on the growth response in growth hormone-treated children with growth hormone deficiency or idiopathic short stature.

Purpose: Magnetic resonance imaging (MRI) can be used for assessing the morphology of the pituitary gland in children with short stature. The purposes of this study were: (1) to determine if pituitary volume (PV) can distinguish patients with growth hormone (GH) deficiency from those with idiopathic short stature (ISS), (2) to validate an association between PV and severity of GH deficiency, and (3) to compare PV between good and poor response groups in children with GH deficiency or ISS after 1 year of treatment.

Methods: Data were collected from the medical records of 152 children with GH deficiency or ISS who underwent GH stimulation test, sella MRI, and GH treatment for at least 1 year.

Parental metabolic syndrome and elevated liver transaminases are risk factors for offspring, even in children and adolescents with a normal body mass index.

Introduction: The parent-child correlation in metabolic syndrome (MetS) and elevated transaminases is sparsely researched. We assessed the correlation of parental MetS and elevated transaminase status with these conditions in their children.

Methods: Data of 4,167 youths aged 10-18 years were analyzed in a population-based survey, and the parental characteristics were stratified by the presence or absence of MetS or alanine aminotransferase (ALT) elevation in their children.

Bone Age Estimation and Prediction of Final Adult Height Using Deep Learning.

Purpose: The appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to predict the final adult height of Korean children.

Materials And Methods: A total of 1678 radiographs from 866 children, for which the interpretation results were consistent between two pediatric endocrinologists, were used to train and validate the deep learning model.