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"GENYAL" Study to Childhood Obesity Prevention: Methodology and Preliminary Results

Helena Marcos-Pasero1,2, Elena Aguilar-Aguilar1, Rocío de la Iglesia3

  • 1Nutrition and Clinical Trials Unit, GENYAL Platform, IMDEA-Food Institute, CEI UAM + CSIC, Madrid, Spain.

Frontiers in Nutrition
|March 30, 2022
PubMed

Insights

The GENYAL study identifies children at risk for obesity by analyzing genetic and environmental factors. This predictive model aims to guide interventions for preventing childhood obesity and its complications.

Area of Science:

  • Pediatric Obesity Research
  • Genetics and Environmental Health
  • Preventive Medicine

Background:

  • Childhood obesity is a growing public health concern with complex origins.
  • Identifying at-risk children early is crucial for effective prevention strategies.
  • Both genetic predispositions and environmental influences contribute to obesity development.

Purpose of the Study:

  • To design and validate a predictive model for childhood obesity.
  • To identify children who would benefit most from targeted obesity prevention interventions.
  • To integrate genetic and environmental factors into a comprehensive risk assessment.

Main Methods:

  • Cluster randomized clinical trial with a 5-year follow-up.
  • Annual collection of anthropometric, social, health, dietary, and physical activity data.
  • Assessment of 26 single nucleotide polymorphisms (SNPs) and development of Machine Learning models.

Main Results:

  • Baseline prevalence of excess weight ranged from 19.0% to 32.2% across different criteria.
  • Significant associations found between child's nutritional status and maternal BMI, school location, dairy intake, and 8 specific SNPs.
  • Preliminary data indicate the interplay of environmental and genetic factors in childhood obesity.

Conclusions:

  • Environmental and genetic factors are integral to the development of childhood obesity.
  • The GENYAL study aims to validate a predictive model as a novel strategy against obesity.
  • Early identification and intervention based on personalized risk assessment are key to combating childhood obesity.
Abstract

Keywords:
early interventionmachine learningnutritionpediatric obesitysingle nucleotide polymorphisms

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