Development and Validation of a Targeted Metabolomic Tool for Metabotype Classification in Schoolchildren

Area of Science:

• Metabolomics
• Biomarkers
• Personalized Medicine

Background:

• Metabolomic profiling reveals metabolic variations in healthy children.
• Identifies opportunities for personalized medicine and early risk detection of metabolic disorders.

Purpose of the Study:

• Identify and validate metabotypes in schoolchildren using targeted serum metabolomics.
• Assess the external validity of a metabotype classification tool in separate child cohorts.

Main Methods:

• Unsupervised clustering (self-organizing map) to identify metabotypes in children aged 6-11.
• Internal validation of cluster stability and classification accuracy.
• External validation in children post-physical activity intervention and with metabolic syndrome.

Main Results:

• Three metabotypes identified: METBA (balanced), METLI (high lipid/glucose), METAA (high amino acid).
• High internal validation: cluster stability (ARI=0.79), classification accuracy (0.95).
• Post-intervention: 55% reclassification; Metabolic syndrome group: 83% METLI, 13% METAA.

Conclusions:

• Serum metabolomic diversity allows classification into three distinct metabotypes.
• Tool detects metabotype changes with physical activity and identifies metabolic syndrome cases.
• Supports metabotypes as biomarkers for personalized interventions.