Diet and lifestyle are vital to population health, but their true contribution is difficult to quantify using traditional methods. Nutrient-health relations are typically based on epidemiological associations that are assessed at the population level, traditionally using self-reported dietary and lifestyle data. Unfortunately, such measures are inherently inaccurate. New technologies such as metabolomics can measure nutritional and micronutrient profiles in body fluids, providing objective evaluation of nutritional status. A critical step toward accurate health prediction models would be the building of integrated repositories of nutritional measures combining subjective methods of reporting with objective metabolomics profiles and precise phenotypic data. Here we outline a roadmap to achieve this goal and discuss both the advantages and risks of this approach. We also highlight the uncertain associations between the complexity of high-dimensional data generated in 'omics research (along with the public confusion this may engender) and the rapid adoption of 'omics approaches by nutrition and health companies to develop nutritional products and services.
Keywords: 'omics technologies; health status; metabolomics; nutrition; predictive modeling.
Copyright © American Society for Nutrition 2019.