Objective: This article describes the methodology and summarizes some preliminary results of the GENYAL study aiming to design and validate a predictive model, considering both environmental and genetic factors, that identifies children who would benefit most from actions aimed at reducing the risk of obesity and its complications.
Design: The study is a cluster randomized clinical trial with 5-year follow-up. The initial evaluation was carried out in 2017. The schools were randomly split into intervention (nutritional education) and control schools. Anthropometric measurements, social and health as well as dietary and physical activity data of schoolchildren and their families are annually collected. A total of 26 single nucleotide polymorphisms (SNPs) were assessed. Machine Learning models are being designed to predict obesity phenotypes after the 5-year follow-up.
Settings: Six schools in Madrid.
Participants: A total of 221 schoolchildren (6-8 years old).
Results: Collected results show that the prevalence of excess weight was 19.0, 25.4, and 32.2% (according to World Health Organization, International Obesity Task Force and Orbegozo Foundation criteria, respectively). Associations between the nutritional state of children with mother BMI [β = 0.21 (0.13-0.3), p (adjusted) <0.001], geographical location of the school [OR = 2.74 (1.24-6.22), p (adjusted) = 0.06], dairy servings per day [OR = 0.48 (0.29-0.75), p (adjusted) = 0.05] and 8 SNPs [rs1260326, rs780094, rs10913469, rs328, rs7647305, rs3101336, rs2568958, rs925946; p (not adjusted) <0.05] were found.
Conclusions: These baseline data support the evidence that environmental and genetic factors play a role in the development of childhood obesity. After 5-year follow-up, the GENYAL study pretends to validate the predictive model as a new strategy to fight against obesity.
Clinical trial registration: This study has been registered in ClinicalTrials.gov with the identifier NCT03419520, https://clinicaltrials.gov/ct2/show/NCT03419520.
Keywords: early intervention; machine learning; nutrition; pediatric obesity; single nucleotide polymorphisms.
Copyright © 2022 Marcos-Pasero, Aguilar-Aguilar, de la Iglesia, Espinosa-Salinas, Molina, Colmenarejo, Martínez, Ramírez de Molina, Reglero and Loria-Kohen.