Metabolomics-driven Molecular Source Analysis for personalized medicine in children

Introduction

While recent advent empowered the new reality of holistic metabolic phenotyping, we are currently still unable to accurately determine the source(s) of the underlying metabolites. This is undoubtedly the main stumbling block in inferring causality to new biomarkers for disease prevention, prediction, and prognosis, particularly given the rapidly increasing burden of metabolic diseases.

Challenges in Current Methods

At the same time, conventional metabolomics methods do not meet the requirements for adoption in clinical practice. MeMoSA will address these issues by unraveling the source hierarchy of the gastrointestinal metabolome, ultimately enabling effective personalized treatments through longitudinal source modulation and follow-up.

Development of Workflows

First, two workflows will be developed:

1. High-throughput comprehensive 2D metabolomics and lipidomics.
2. Rapid clinically applicable ambient ionization metabotyping.

Generating Molecular Fingerprints

Second, molecular fingerprints of our unique deeply phenotyped pediatric cohorts (1.5k children) will be generated. Advanced machine learning algorithms will be used to predict metabolite abundances based on their sources, including:

• Diet
• Lifestyle
• Anthropometrics
• Microbiome
• Drug intake
• Psychological factors
• Clinical markers

Understanding Source-Metabolite Causality

Third, a combination of in vitro digestions, in vivo humanized mice, and in silico experiments with selected source variables will be designed to contribute to our understanding of source-metabolite causality.

Future Implications

These mechanistic insights will be used to build dedicated intervention trials in children with specific source-dominated metabotypes. MeMoSA will lay the foundation for integrating metabolomics into personalized and preventive medicine in children through: i. Better prediction of individual metabotypes in relation to health. ii. In-depth insight into metabolite sources, which will foster a framework for biomarker qualification and unraveling disease etiology. iii. Greater treatment efficacy through dedicated metabolome-driven source modulation.