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

## TL;DR

This study developed a tool to classify children's metabolism into three types, which could help detect future health risks and personalize interventions.

## Contribution

A novel targeted metabolomic tool was developed and validated for metabotype classification in children.

## Key findings

- Three distinct metabotypes (METBA, METLI, METAA) were identified in healthy children.
- The classification tool showed strong stability and accuracy in internal validation.
- Physical activity changed metabotype classifications in 55% of children.

## Abstract

Background: Metabolomic profiling can uncover metabolic differences among seemingly healthy children, providing opportunities for personalized medicine and early detection of risk biomarkers for future metabolic disorders. This study aimed to identify and internally validate metabotypes in apparently healthy schoolchildren using targeted serum metabolomics and to assess the external validity of this metabotype classification tool in two separate groups of children. Methods: Data from schoolchildren aged 6–11 years were analyzed in two phases. In the first phase, we developed and validated a classification tool using targeted serum metabolomics in healthy children. Metabotypes were identified through unsupervised clustering with a self-organizing map, followed by assessment of cluster stability and classification accuracy. In the second phase, we tested the tool’s consistency by applying it to two additional groups: the same children from phase 1 after a 10-month physical activity intervention, and a separate group diagnosed with metabolic syndrome. Results: Three metabotypes were identified in healthy children: METBA (balanced profile), METLI (high lipid and glucose levels), and METAA (high amino acid levels). Internal validation showed strong cluster stability (ARI = 0.79) and high classification accuracy (0.95). After the intervention, 55% of children were reclassified, indicating diverse metabolic responses to physical activity. Among children with metabolic syndrome, 83% were classified as METLI and 13% as METAA. Conclusions. This tool revealed serum metabolomic diversity, enabling classification of healthy children into three distinct metabotypes. It also detects changes in metabotype classification associated with a physical activity intervention and identifies the majority of children diagnosed with metabolic syndrome within two groups. This supports the potential use of metabotypes as biomarkers and eventually for personalized interventions.