ArticlePediatric obesity2022
Development of a prediction protocol for the screening of metabolic associated fatty liver disease in children with overweight or obesity.
Article in Pediatric obesity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Differences in Fat Depots, Cardiometabolic Risk Factors, and Insulin Resistance Between Active and Passive School Commuters.Pediatric obesity · 2026Article
- Evaluation for Fatty Liver Infiltration Should Become Standard in Pediatric Cholecystectomy Patients.Cureus · 2025Article
- Abdominal skeletal muscle quality and quantity are independently and jointly associated with cardiometabolic health in children.International journal of obesity (2005) · 2025Article
- Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk.BMC public health · 2024Article
- Metabolic dysfunction associated fatty liver disease: The new nomenclature and its impact.World journal of gastroenterology · 2023Article
- Development of a prediction protocol for the screening of metabolic associated fatty liver disease in children with overweight or obesity.Pediatric obesity · 2022Article
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe early detection and management of children with metabolic associated fatty liver disease (MAFLD) is challenging.
objectiveTo develop a non-invasive and accurate prediction protocol for the identification of MAFLD among children with overweight/obesity candidates to confirmatory diagnosis.
methodsA total of 115 children aged 8-12 years with overweight/obesity, recruited at a primary care, were enrolled in this cross-sectional study. The external validation was performed using a cohort of children with overweight/obesity (N = 46) aged 8.5-14.0 years. MAFLD (≥5.5% hepatic fat) was diagnosed by magnetic resonance imaging (MRI). Fasting blood biochemical parameters were measured, and 25 candidates' single nucleotide polymorphisms (SNPs) were determined. Variables potentially associated with the presence of MAFLD were included in a multivariate logistic regression.
resultsChildren with MAFLD (36%) showed higher plasma triglycerides (TG), insulin, homeostasis model assessment of insulin resistance (HOMA-IR), alanine aminotransferase (ALT), aspartate transaminase (AST), glutamyl-transferase (GGT) and ferritin (p < 0.05). The distribution of the risk-alleles of PPARGrs13081389, PPARGrs1801282, HFErs1800562 and PNLPLA3rs4823173 was significantly different between children with and without MAFLD (p < 0.05). Three biochemical- and/or SNPs-based predictive models were developed, showing strong discriminatory capacity (AUC-ROC: 0.708-0.888) but limited diagnostic performance (sensitivity 67%-82% and specificity 63%-69%). A prediction protocol with elevated sensitivity (72%) and specificity (84%) based on two consecutive steps was developed. The external validation showed similar results: sensitivity of 70% and specificity of 85%.
conclusionsThe HEPAKID prediction protocol is an accurate, easy to implant, minimally invasive and low economic cost tool useful for the early identification and management of paediatric MAFLD in primary care.
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