<b><i>Background:</i></b> Nonalcoholic fatty liver disease (NAFLD) is the leading hepatic disease in children, ranging from steatosis to steatohepatitis and fibrosis. Age, sex, hormonal levels, pubertal stages, genetic risk- and epigenetic factors are among the many influencing factors. Appearing predominantly in children with obesity, but not exclusively, it is the liver’s manifestation of the metabolic syndrome but can also exist as an isolated entity. <b><i>Summary:</i></b> Pediatric NAFLD differs from the adult phenotype. This narrative review on NAFLD in children with obesity provides an overview of the current knowledge on risk factors, screening, and diagnostic methods, as well state-of-the-art treatment. The recent discussion on the proposition of a new nomenclature – Metabolic [Dysfunction-] Associated Liver Disease – is featured, and current gaps of knowledge are discussed. <b><i>Key Messages:</i></b> Currently, there is no international consensus on screening and monitoring of pediatric NAFLD. With lifestyle interventions being the cornerstone of treatment, no registered pharmacological treatment for pediatric NAFLD is available. Development and validation of additional noninvasive biomarkers, scores and imaging tools suitable to subcategorize, screen and monitor pediatric patients are necessary. With a variety of upcoming and promising agents, clear recommendations for pediatric nonalcoholic steatohepatitis trials are urgently needed.

Summary Background Relationships between movement‐related behaviours and metabolic health remain underexplored in adolescents with obesity. Objectives To compare profiles of sedentary time (more sedentary, SED+ vs. less sedentary, SED−), moderate to vigorous physical activity (MVPA) time (more active, MVPA+ vs. less active, MVPA−) and combinations of behaviours (SED−/MVPA+, SED−/MVPA−, SED+/MVPA+, SED+/MVPA−) in regard to metabolic health. Methods One hundred and thirty‐four subjects (mean age 13.4 ± 2.2 yrs, mean body mass index [BMI] 98.9 ± 0.7 percentile, 48.5% females) underwent 24 h/7 day accelerometry, anthropometric, body composition, blood pressure (BP), lipid profile and insulin resistance (IR) assessments. Results Metabolic health was better in SED− [lower fat mass (FM) percentage (p < 0.05), blood pressure (BP) (p < 0.05), homeostasis model assessment of insulin resistance (HOMA‐IR) (p < 0.001) and metabolic syndrome risk score (MetScore) (p < 0.001), higher high‐density lipoprotein‐cholesterol (HDL‐c) (p = 0.001)] vs. SED+ group and in MVPA+ [lower triglyceridemia (TG), (p < 0.05), HOMA‐IR (p < 0.01) and MetScore (p < 0.001), higher HDL‐c (p < 0.01)] vs. MVPA− group after adjustment with age, gender, maturation and BMI. SED−/MVPA+ group had the best metabolic health. While sedentary (p < 0.001) but also MVPA times (p < 0.001) were lower in SED−/MVPA− vs. SED+/MVPA+, SED−/MVPA− had lower FM percentage (p < 0.05), HOMA‐IR (p < 0.01) and MetScore (p < 0.05) and higher HDL‐c (p < 0.05), independently of BMI. Sedentary time was positively correlated with HOMA‐IR and Metscore and negatively correlated with HDL‐c after adjustment with MVPA (p < 0.05). MVPA was negatively correlated with HOMA‐IR, BP and MetScore and positively correlated with HDL‐c after adjustment with sedentary time (p < 0.05). Conclusion Lower sedentary time is associated with a better metabolic health independently of MVPA and might be a first step in the management of pediatric obesity when increasing MVPA is not possible.

Carbohydrate counting (CHC) is the established form of calculating bolus insulin for meals in children with type 1 diabetes (T1DM). With the widespread use of continuous glucose monitoring (CGM) observation time has become gapless. Recently, the impact of fat, protein and not only carbohydrates on prolonged postprandial hyperglycaemia have become more evident to patients and health-care professionals alike. However, there is no unified recommendation on how to calculate and best administer additional bolus insulin for these two macronutrients. The aim of this review is to investigate: the scientific evidence of how dietary fat and protein influence postprandial glucose levels; current recommendations on the adjustment of bolus insulin; and algorithms for insulin application in children with T1DM. A PubMed search for all articles addressing the role of fat and protein in paediatric (sub-)populations (<18 years old) and a mixed age population (paediatric and adult) with T1DM published in the last 10 years was performed. Conclusion: Only a small number of studies with a very low number of participants and high degree of heterogeneity was identified. While all studies concluded that additional bolus insulin for (high) fat and (high) protein is necessary, no consensus on when dietary fat and/or protein should be taken into calculation and no unified algorithm for insulin therapy in this context exists. A prolonged postprandial observation time is necessary to improve individual metabolic control. Further studies focusing on a stratified paediatric population to create a safe and effective algorithm, taking fat and protein into account, are necessary.

Metabolic syndrome (MetS) is highly prevalent in children and adolescents with obesity and places them at an increased risk of cardiovascular-related diseases. However, the associations between objectively measured movement-related behaviors and MetS diagnosis remain unexplored in youths with obesity. The aim was to compare profiles of sedentary (SED) time (more sedentary, SED+ vs. less sedentary, SED−), moderate to vigorous physical activity (MVPA) time (more active, MVPA+ vs. less active, MVPA−) and combinations of behaviors (SED−/MVPA+, SED−/MVPA−, SED+/MVPA+, SED+/MVPA−) regarding the MetS diagnosis. One hundred and thirty-four adolescents with obesity (13.4 ± 2.2 years) underwent 24 h/7 day accelerometry, waist circumference (WC), blood pressure (BP), high-density lipoprotein-cholesterol (HDL-c), triglycerides (TG) and insulin-resistance (IR) assessments. Cumulative cardiometabolic risk was assessed by using (i) MetS status (usual dichotomic definition) and (ii) cardiometabolic risk z-score (MetScore, mean of standardized WC, BP, IR, TG and inverted HDL-c). SED− vs. SED+ and MVPA+ vs. MVPA− had lower MetS (p < 0.01 and p < 0.001) and MetScore (p < 0.001). SED−/MVPA+ had the lowest risk. While SED and MVPA times were lower in SED−/MVPA− vs. SED+/MVPA+ (p < 0.001), MetScore was lower in SED−/MVPA− independently of body mass index (BMI) (p < 0.05). MVPA, but not SED, time was independently associated with MetS diagnosis (p < 0.05). Both MVPA (p < 0.01) and SED times (p < 0.05) were associated with MetScore independently of each other. A higher MVPA and lower SED time are associated with lower cumulative cardiometabolic risk.