Development of a prediction protocol for the screening of metabolic associated fatty liver disease in children with overweight or obesity. Issue 9 (8th April 2022)

• Record Type: Journal Article
• Authors: Oses, Maddi; Cadenas‐Sanchez, Cristina; Medrano, María; Galbete, Arkaitz; Miranda‐Ferrua, Emiliano; Ruiz, Jonatan R.; Sánchez‐Valverde, Felix; Ortega, Francisco B.; Cabeza, Rafael; Villanueva, Arantxa; Idoate, Fernando; Labayen, Idoia
• Is Part Of: Pediatric obesity. Volume 17:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-08
• Subjects: fatty liver -- metabolic diseases -- Paediatric obesity -- primary health care; Obesity in children -- Periodicals; Obesity in adolescence -- Periodicals; Obesity -- Periodicals; Overweight children -- Periodicals; 618.92398
• DOI: 10.1111/ijpo.12917
• Languages: English
• ISSNs: 1747-7174

Abstract

Summary: Background: The early detection and management of children with metabolic associated fatty liver disease (MAFLD) is challenging. Objective: To develop a non‐invasive and accurate prediction protocol for the identification of MAFLD among children with overweight/obesity candidates to confirmatory diagnosis. Methods: A 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. Results: Children 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%. Conclusions: The 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.