Metabotyping and its application in targeted nutrition: an overview

Area of Science:

• Metabolic research
• Nutritional science
• Personalized medicine

Background:

• Individual metabolic diversity influences nutrient needs and responses to diet.
• Metabotyping aims to group metabolically similar individuals for tailored health recommendations.

Purpose of the Study:

• To review existing literature on human metabotyping.
• To explore metabotyping's potential for targeted dietary interventions in healthy and diseased populations.

Main Methods:

• Comprehensive literature search across PubMed, Google, and Google Scholar.
• Analysis of 34 human studies employing statistical methods for metabolic data clustering.
• Examination of variable clustering, statistical approaches, and metabotype definitions.

Main Results:

• Identified 34 human studies using statistical methods to define metabolic subgroups (metabotypes).
• Observed significant variability in study populations, clustering variables, statistical methods, and metabotype definitions.
• Metabotypes ranged from general fasting profiles to specific metabolic clusters and response groups.

Conclusions:

• Metabotyping effectively identifies subgroups with differential responses to nutritional interventions.
• Personalized dietary recommendations can be developed at the metabotype group level.
• Further research is needed to formalize metabotype definitions and validate them using metabolomics data.