Perspective: Metabotyping-A Potential Personalized Nutrition Strategy for Precision Prevention of Cardiometabolic Disease

Abstract

Diet is an important, modifiable lifestyle factor of cardiometabolic disease risk, and an improved diet can delay or even prevent the onset of disease. Recent evidence suggests that individuals could benefit from diets adapted to their genotype and phenotype: that is, personalized nutrition. A novel strategy is to tailor diets for groups of individuals according to their metabolic phenotypes (metabotypes). Randomized controlled trials evaluating metabotype-specific responses and nonresponses are urgently needed to bridge the current gap of knowledge with regard to the efficacy of personalized strategies in nutrition. In this Perspective, we discuss the concept of metabotyping, review the current literature on metabotyping in the context of cardiometabolic disease prevention, and suggest potential strategies for metabotype-based nutritional advice for future work. We also discuss potential determinants of metabotypes, including gut microbiota, and highlight the use of metabolomics to define effective markers for cardiometabolic disease-related metabotypes. Moreover, we hypothesize that people at high risk for cardiometabolic diseases have distinct metabotypes and that individuals grouped into specific metabotypes may respond differently to the same diet, which is being tested in a project of the Joint Programming Initiative: A Healthy Diet for a Healthy Life.

Keywords: cardiometabolic diseases; gut microbiota; metabolomics; metabotyping; personalized nutrition; precision nutrition; targeted nutrition.

FIGURE 1

Metabotyping and group-based nutrition in the context of the conventional population-based guidelines and personalized nutrition.

FIGURE 2

The graphic illustration of potential determinants of metabotypes and a metabolomics-based strategy for the identification of biomarkers of metabotypes by using untargeted LC-MS metabolomics. Individuals are grouped into metabotypes based on potential determinants and their interactions, using certain statistical approaches, such as PC analysis. PC, principal component.