Intrahepatic Fat Content and Markers of Hepatic Fibrosis in Obese Children

Wu, Wei; Zhang, Hongxi; Xu, Xiaojun; Huang, Ke; Fu, Junfen

doi:10.1155/2016/4890974

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…The 180 cases that received additional hepatic 1 H-MRS were used as the validation set. Hepatic 1 H-MRS using a 1.5-T clinical MR imager while the patient was at rest and supine (Magnetom Avanto, Siemens Healthcare, Erlangen, Germany) ( 31 ). Relative liver lipid content was obtained using the equations verified by Longo et al ( 32 ).…”

Section: Methodsmentioning

confidence: 99%

Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity

et al. 2022

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ObjectiveThis study aims to outline the clinical characteristics of pediatric NAFLD, as well as establish and validate a prediction model for the disease.Materials and methodsThe retrospective study enrolled 3216 children with obesity from January 2003 to May 2021. They were divided into obese without NAFLD, nonalcoholic fatty liver (NAFL), and nonalcoholic steatohepatitis (NASH) groups. Clinical data were retrieved, and gender and chronologic characteristics were compared between groups. Data from the training set (3036) were assessed using univariate analyses and stepwise multivariate logistic regression, by which a nomogram was developed to estimate the probability of NAFLD. Another 180 cases received additional liver hydrogen proton magnetic resonance spectroscopy (1H-MRS) as a validation set.ResultsThe prevalence of NAFLD was higher in males than in females and has increased over the last 19 years. In total, 1915 cases were NAFLD, and the peak onset age was 10-12 years old. Hyperuricemia ranked first in childhood NAFLD comorbidities, followed by dyslipidemia, hypertension, metabolic syndrome (MetS), and dysglycemia. The AUROC of the eight-parameter nomogram, including waist-to-height ratio (WHtR), hip circumference (HC), triglyceride glucose-waist circumference (TyG-WC), alanine aminotransferase (ALT), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1(ApoA1), insulin sensitivity index [ISI (composite)], and gender, for predicting NAFLD was 0.913 (sensitivity 80.70%, specificity 90.10%). Calibration curves demonstrated a great calibration ability of the model.Conclusion and relevanceNAFLD is the most common complication in children with obesity. The nomogram based on anthropometric and laboratory indicators performed well in predicting NAFLD. This can be used as a quick screening tool to assess pediatric NAFLD in children with obesity.

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Section: Methodsmentioning

confidence: 99%

Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity

et al. 2022

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Uric acid and sCD163 as biomarkers for metabolic dysfunction and MAFLD in children and adolescents with overweight and obesity

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Dalstrup Jakobsen,

Kristensen

et al. 2023

Journal of Pediatric Endocrinology and Metabolism

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Objectives The prevalence of childhood obesity increases globally, and noninvasive methods are needed to identify metabolic dysfunction and obesity-related complications such as pediatric metabolic associated fatty liver disease (MAFLD). We investigated whether uric acid (UA) and the macrophage marker soluble form of cysteine scavenger receptor CD163 (sCD163) can be used as biomarkers for deteriorated metabolism or pediatric MAFLD in children with overweight or obesity. Methods Cross-sectional clinical and biochemical data from 94 children with overweight or obesity was included. Surrogate liver markers were calculated, and correlations were investigated using Pearson’s or Spearman’s correlation test. Results UA and sCD163 correlated with BMI standard deviation score (r=0.23, p<0.05; r=0.33, p<0.01) and body fat (r=0.24, p<0.05; r=0.27, p=0.01). UA correlated with triglycerides (ρ=0.21, p<0.05), fat free mass (r=0.33, p<0.01), and gamma-glutamyl transferase (r=0.39, p<0.01). sCD163 correlated with the pediatric NAFLD fibrosis score (r=0.28, p<0.01) and alanine aminotransferase (r=0.28, p<0.01). No correlation was found between UA and pediatric MAFLD. Conclusions UA and sCD163 was identified as markers of a deranged metabolic profile, thus acting as easily accessible biomarkers for obesity and an obesity-related deranged metabolism. Furthermore, increasing levels of sCD163 could be a useful biomarker of pediatric MAFLD. Future prospective studies are warranted.

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Intrahepatic Fat Content and Markers of Hepatic Fibrosis in Obese Children

Cited by 2 publications

References 40 publications

Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity

Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity

Uric acid and sCD163 as biomarkers for metabolic dysfunction and MAFLD in children and adolescents with overweight and obesity

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