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Identifying subgroups of childhood obesity by using multiplatform metabotyping
David Chamoso-Sanchez1, Francisco Rabadán Pérez2, Jesús Argente3,4,5
1Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain.
Frontiers in Molecular Biosciences
|January 4, 2024
Summary
This study used factor analysis to identify three distinct metabotypes in obese children, revealing subtle differences in lipids and insulin sensitivity for personalized treatment strategies.
Area of Science:
- Metabolomics and Genetics
- Obesity Research
- Personalized Medicine
Background:
- Obesity arises from complex genetic and environmental interactions.
- Current metabolomics struggles to differentiate obesity subtypes.
- Personalized obesity treatments require identifying individual characteristics.
Purpose of the Study:
- To develop a workflow for identifying metabotypes in obese children.
- To explore the utility of factor analysis in classifying obesity subtypes.
- To investigate potential links between genetic variants, metabolomics, and clinical features.
Main Methods:
- Studied 110 obese children, genotyped for leptin-melanocortin pathway genes.
- Collected anthropometric, clinical, and untargeted metabolomic data from serum.
- Applied factor analysis to a composite matrix from five analytical platforms.
Main Results:
- Genetic variants and clinical data did not define metabolomic subgroups.
- Factor analysis identified six factors and three distinct metabotypes.
- Metabotypes showed subtle differences in circulating lipids and insulin sensitivity.
Conclusions:
- Factor analysis enables the identification of obesity metabotypes.
- This approach can reveal distinct metabolic conditions within similar clinical diagnoses.
- Metabotyping holds potential for personalizing obesity treatment strategies.
