Publications

Currently, there is no standard for the citation of food composition data. This leads to the questions: how are food and nutrient data cited in research papers, and are they presented in a way that allows studies to be reproduced? To answer these questions, we performed a review of the literature and quantified the accuracy and completeness of data citations from publications (January to December 2020) in the top 5 nutrition journals as ranked by the Scimago Journal Rankings. We then performed a review of citation guidelines currently in place in other disciplines. Similar to the requirement of completing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist for systematic reviews, we have developed a comprehensive data citation checklist, the Comprehensive Food Data Citation (CFDC) checklist. The CFDC checklist was developed through a benchmarking assessment against established data citation standards. Its purpose is to establish a standardized, best-practice approach for reporting food composition data. The CFDC checklist has been designed to cater to both publishers and authors, ensuring consistency and accuracy in food composition data reporting. The CFDC checklist is also available as an interactive citation generator to facilitate the adoption of consistent and comprehensive citation of food composition data and is available at https://www.nutrientinstitute.org/cfdc. Despite general agreement that accurate data citation is paramount, this is the first citation standard specifically developed to capture food composition data. Because food composition data are the foundation of nutrition research, our proposed guidelines aim to provide the field with a much-needed foundation for acknowledging and sharing data in a way that fosters reproducibility.

Food systems represent all elements and activities needed to feed the growing global population. Research on sustainable food systems is transdisciplinary, relying on the interconnected domains of health, nutrition, economics, society, and environment. The current lack of interoperability across databases poses a challenge to advancing research on food systems transformation. Crosswalks among largely siloed data on climate change, soils, agricultural practices, nutrient composition of foods, food processing, prices, dietary intakes, and population health are not fully developed. Starting with US Department of Agriculture FoodData Central, we assessed the interoperability of databases from multiple disciplines by identifying existing crosswalks and corresponding visualizations. Our visual demonstration serves as proof of concept, identifying databases in need of expansion, integration, and harmonization for use by researchers, policymakers, and the private sector. Interoperability is the key: ontologies and well-defined crosswalks are necessary to connect siloed data, transcend organizational barriers, and draw pathways from agriculture to nutrition and health.

The future of precision nutrition requires treating amino acids as essential nutrients. Currently, recognition of essential amino acid requirements is embedded within a generalized measure of protein quality known as PDCAAS (Protein Digestibility-Corrected Amino Acid Score). Calculating PDCAAS includes the FAO/WHO/UNU amino acid score (AAS), which is based on the limiting amino acid in a food, that is, the single amino acid with the lowest concentration compared to the reference standard. That “limiting” AAS is then multiplied by a bioavailability factor to obtain the PDCAAS which ranks proteins from 0.0 (poor quality) to 1.0 (high quality). However, PDCAAS has multiple limitations: it only allows for direct protein quality comparison between two proteins, and it is not scalable, transparent, or additive. We therefore propose that shifting the protein quality evaluation paradigm from the current generalized perspective to a precision nutrition focus treating amino acids as unique, metabolically active nutrients will be valuable for multiple areas of science and public health. We report the development and validation of the Essential Amino Acid-9 (EAA-9) score, an innovative, nutrient-based protein quality scoring framework. EAA-9 scores can be used to ensure that dietary recommendations for each essential amino acid are met. The EAA-9 scoring framework also offers the advantages of being additive and, perhaps most importantly, allows for personalization of essential amino acid needs based on age or metabolic conditions. Comparisons of the EAA-9 score with PDCAAS demonstrated the validity of the EAA-9 framework, and practical applications demonstrated that the EAA-9 framework is a powerful tool for precision nutrition applications.

Nutrient databases are a critical component of nutrition science and the basis of exciting new research in precision nutrition (PN). To identify the most critical components needed for improvement of nutrient databases, food composition data were analyzed for quality, with completeness being the most important measure, and for FAIRness, how well the data conformed with the data science criteria of findable, accessible, interoperable, and reusable (FAIR). Databases were judged complete if they provided data for all 15 nutrition fact panel (NFP) nutrient measures and all 40 National Academies of Sciences, Engineering, and Medicine (NASEM) essential nutrient measures for each food listed. Using the gold standard the USDA standard reference (SR) Legacy database as surrogate, it was found that SR Legacy data were not complete for either NFP or NASEM nutrient measures. In addition, phytonutrient measures in the 4 USDA Special Interest Databases were incomplete. To evaluate data FAIRness, a set of 175 food and nutrient data sources were collected from worldwide. Many opportunities were identified for improving data FAIRness, including creating persistent URLs, prioritizing usable data storage formats, providing Globally Unique Identifiers for all foods and nutrients, and implementing citation standards. This review demonstrates that despite important contributions from the USDA and others, food and nutrient databases in their current forms do not yet provide truly comprehensive food composition data. We propose that to enhance the quality and usage of food and nutrient composition data for research scientists and those fashioning various PN tools, the field of nutrition science must step out of its historical comfort zone and improve the foundational nutrient databases used in research by incorporating data science principles, the most central being data quality and data FAIRness.

Informed policy and decision-making for food systems, nutritional security, and global health would benefit from standardization and comparison of food composition data, spanning production to consumption. To address this challenge, we present a formal controlled vocabulary of terms, definitions, and relationships within the Compositional Dietary Nutrition Ontology (CDNO, www.cdno.info) that enables description of nutritional attributes for material entities contributing to the human diet. We demonstrate how ongoing community development of CDNO classes can harmonize trans-disciplinary approaches for describing nutritional components from food production to diet.