ReviewAdvances in nutrition (Bethesda, Md.)2023
Perspective: A Comprehensive Evaluation of Data Quality in Nutrient Databases.
Review in Advances in nutrition (Bethesda, Md.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 19 citations in OpenAlex.
- Laboratory-Analyzed Nutrient Values Diverge from Database Estimates in Research-Specific Diets (Notably for Protein, Iron, Sodium).Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems · 2026Article
- Application of the Total Nutrient Index as a Precision Nutrition Tool to Address Dietary Recommendations Across the Life Course.Journal of the Academy of Nutrition and Dietetics · 2026Article
- Estimating Nutrient Composition of Packaged Foods Using Natural Language Processing and Optimization Modeling.Current developments in nutrition · 2026Article
- Integration of modern technologies to advance dietary assessment.Nature food · 2026Review
- Dietary patterns associated with the new onset of chronic kidney disease using clustering algorithm.Scientific reports · 2025Article
- AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Sex-related differences in dietary phytochemical intake in the population of primary school children in urban setting.Frontiers in nutrition · 2025Article
- The state of food composition databases: data attributes and FAIR data harmonization in the era of digital innovation.Frontiers in nutrition · 2025Article
- Effect of Phytochemical-Rich Food Intake on Respiratory and Muscle Function in Middle-Aged Patients with COPD: A Cross-Sectional Study.Nutrients · 2024Article
- Polyphenols in Cereals: State of the Art of Available Information and Its Potential Use in Epidemiological Studies.Nutrients · 2024Review
- Development of a Comprehensive Food Data Citation Standard: A Surprising Gap in the Nutrition Research Literature.Current developments in nutrition · 2024Article
- Visualizing Data Interoperability for Food Systems Sustainability Research-From Spider Webs to Neural Networks.Current developments in nutrition · 2023Article
- Nutritional Composition of Hass Avocado Pulp.Foods (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.