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Background/Objectives: The pediatric definition of severe obesity (OB) depends on the body mass index (BMI) references. We evaluated different BMI-derived metrics of the World Health Organization (WHO) system to define which cut-off is associated with the highest cardiometabolic risk (CMR); Methods: In this multicentric study, data were retrieved for 3727 youths (1937 boys; 2225 children, 1502 adolescents). OB was defined as BMI > 97th percentile (BMI97th), severe OB was defined as BMI > 99th percentile (BMI99th), BMI ≥ 120% of the 97th percentile (120% BMI97th), or BMI Z-score > 3 (WHO tables), or BMI ≥ the International Obesity Task Force (IOTF) value crossing a BMI of 35 kg/m2 at the age of 18 (IOTF35). The continuous CMR Z-score (sum of residual standardized for age and sex of waist-to-height ratio, systolic and diastolic blood pressure, triglycerides, and HDL-cholesterol x −1) and the cluster of at least two CMR factors (hypertension, high triglycerides, low HDL-cholesterol, and high waist-to-height ratio) were calculated. Results: Continuous CMR Z-score was significantly higher both in children or adolescents with severe OB defined by 120% BMI97th compared to BMI99th (p < 0.0001), while it was lower only in adolescents with severe OB defined by 120% BMI97th compared to BMI Z-score >3 (p < 0.0001). Compared to 120% BMI97th, BMI Z-score > 3 and IOTF35 had higher specificity, but lower sensitivity in identifying children and adolescents with clustered CMR factors. Conclusions: The definition of severe OB based on 120% BMI97th is superior to BMI99th but it is inferior to BMI Z score > 3 as far as the association between severe OB and CMR factors is concerned. Pediatricians should take into consideration the implication of the use of different BMI metrics in those countries that recommend the WHO system. WHO BMI Z-score > 3 and IOTF35 can be used interchangeably to predict cardiometabolic risk. [ABSTRACT FROM AUTHOR]

Background: Pediatric obesity treatment is based on high-intensity lifestyle counseling. However, high dropout rates and low effectiveness have been reported, even in specialized centers. Mobile health technologies have been used to overcome these limits with contrasting results. This study aims at evaluating the effectiveness of a six-month intervention with a mobile app for the treatment of pediatric obesity at 6 and 12 months of follow-up. Methods: Seventy-five patients were randomly assigned to standard care or standard care plus mobile app (2:1) using an online randomizer system. The mobile app delivered high-intensity lifestyle counseling for diet and physical activity. Results: At six months of follow-up, the M-App group showed significantly lower dropout rates compared to standard care (p = 0.01). The risk of dropout was significantly higher in controls compared to the intervention group (OR 3.86, 95% C.I. 1.39–10.42, p = 0.01). After one year, we observed lower albeit non-statistically significant dropout rates in the M-App compared to the standard care group (p = 0.24). No differences were observed in z-score BMI and percentage of BMI reduction between the two groups. Conclusions: Our findings suggest that the mobile app might help in the clinical management of children and adolescents with obesity in terms of dropout reduction. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) presents significant challenges in pediatric care, often remaining underrecognized. This paper provides an overview of pediatric AKI, highlighting its epidemiology, pathophysiology, diagnosis, predisposing conditions, and treatment. AKI in children stems from diverse causes, including renal tubular damage, vasoconstriction, and inflammation. Diagnosis relies on traditional markers such as serum creatinine and urine output, alongside emerging biomarkers such as Cystatin C, NGAL, KIM-1, IL-18, TIMP-2 and IGFBP7, urinary calprotectin, URBP4, L-FABP, and clusterin. Various pediatric conditions predispose to AKI, including type 1 diabetes, pneumonia, bronchiolitis, appendicitis, gastroenteritis, COVID-19, multisystem inflammatory syndrome, sickle cell disease, and malignancies. Treatment entails supportive care with fluid management and, in severe cases, renal replacement therapy. Timely recognition and management are essential to mitigating adverse outcomes. Enhanced awareness and integration of novel biomarkers could improve pediatric AKI care, warranting further research for better diagnosis and management. [ABSTRACT FROM AUTHOR]