Nutrition Informatics (Nutri-informatics): A call for improved and integrative standards for nutrition research

Nutri-informatics aims to computationally integrate and analyze nutrition study datasets in order to disentangle the interactions between an organism and its nutritional environment. Fueled by an interest in how food, nutrients, and nutrition sociology impact health, and a recent push towards “big data”, nutri-informatics is essential to incorporating nutrition into computational biomedical sciences. 

Nutrition is one of the most integral components to human life, and it impacts individuals far beyond just nutrient provisions. For example, nutrition plays a role in cultural practices, interpersonal relationships, and body image. Despite this, integrated computational investigations have been limited due to challenges within nutri-informatics and nutrition data. 

Nutri-informatics suffers from a lack of standardization with a wide array of groups working on similar projects with no community-wide development principles to ensure interoperability and cohesion between nutri-informatics and other biomedical resources. While a large number of resources for nutri-informatics are available, much of nutrition is underrepresented. This may be due to how expansive and heterogeneous nutrition is as a field, increasing the difficulty of data modeling. Approaches to formalize nutrition research language and connect standardized terminologies across biomedical fields have been initiated through the use of biomedical ontologies and computational nutrition data resources. While a variety of nutrition-related ontologies such as Food Ontology and the Ontology for Nutritional Studies have been initiated, they are still in development and require further attention from nutrition researchers and biomedical ontologists.

Should nutrition data continue to be produced with no standardization of language, documentation specifications, or requirements for data reuse, nutri-informatics investigations will continue to struggle with incompatible data. In efforts to support nutri-informatics, the community must encourage standards for nutrition data production, reuse, and publication. Academic journals as well as members from nutrition research and biomedical ontology communities should promote standardization of language, data interoperability, and FAIR principles.