SEARCHNARRATIVE_REVIEWnutrition, food science

narrative_review·nutrition, food science, research methods, systematic review·PMC10201679

Perspective: A Comprehensive Evaluation of Data Quality in Nutrient Databases

Advances in Nutrition · 4 authors, 2 centres

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TL;DR

This narrative review evaluated the completeness and FAIRness of global food and nutrient databases.

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Using the USDA SR Legacy database as a surrogate gold standard, the authors found it was incomplete, with mean values of 84.9% for NFP nutrients and 70.3% for NASEM nutrients. Analysis of 175 worldwide data sources showed USDA databases were the predominant source, used in over half of the sources. The review identified widespread deficiencies in data FAIRness, such as the lack of persistent URLs and unique identifiers. The authors conclude that current nutrient databases are not truly comprehensive and propose that integrating data science principles is essential for advancing precision nutrition.

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narrative_review

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nutrition

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ORIGINAL PAPER

PMC10201679

Perspective: A Comprehensive Evaluation of Data Quality in Nutrient Databases

Advances in Nutrition

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ABSTRACT

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.

CITATION EXPORT

Zhaoping Li, Shavawn Forester, Emily Jennings-Dobbs et al.. Perspective: A Comprehensive Evaluation of Data Quality in Nutrient Databases. Advances in Nutrition. (2023). https://doi.org/10.1016/j.advnut.2023.02.005

AUTHORS

4 · 2 CENTRES

Zhaoping Li, Shavawn Forester, Emily Jennings-Dobbs, David Heber

1Center for Human Nutrition, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, United States
2Nutrient Institute LLC, Reno, NV, United States