Evidence map›Paper›PMID 40158698›Full record

ArticleThe American journal of clinical nutrition2025

Food Compass Score-10: validation of a method for evaluating the healthfulness of foods and beverages using ingredient list information.

Eden M Barrett, Frederick Cudhea, Erin Washbon, Zoe Levitan, Julia Reedy Sharib, Jeffrey B Blumberg, Renata Micha, Dariush Mozaffarian

Abstract readValidation Study
In one paragraph

Article in The American journal of clinical nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Eden M BarrettFood is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States; The George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia. Electronic address: ebarrett@georgeinstitute.org.au.
Frederick CudheaFood is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States.
Erin WashbonFood is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States.
Zoe LevitanFood is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States.
Julia Reedy SharibFood is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States.
Jeffrey B BlumbergFood is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States.
Renata MichaFood is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States; Department of Food Science and Nutrition, University of Thessaly, Volos, Greece.
Dariush MozaffarianFood is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States; Tufts University School of Medicine and Tufts Medical Center, Boston, MA, United States.

Funding

Cost-Effectiveness of Health System and State-Level Strategies to Improve Diet and Reduce Cardiometabolic DiseasesR01HL115189 · NHLBI · TUFTS UNIVERSITY BOSTON · PI DARIUSH MOZAFFARIAN · 2013 to 2026
$8.2M
NHLBI NIH HHS R01 HL115189
6 · The paper itself

Abstract

backgroundThe Food Compass, a novel food profiling system, provides a holistic, validated assessment of the healthfulness of foods, beverages, and meals using 54 attributes across 9 domains. However, information on several of these attributes is not commonly available.

objectivesWe aimed to develop and validate an approach, Food Compass Score-10 (FCS-10), to estimate FCSs using information commonly available on package labels.

methodsMissing attributes were calculated using weighted scores of each product's ingredients, derived from a dataset of ∼10,000 foods and beverages. The final FCS-10 was scaled from 1 (least healthful) to 10 (most healthful). As part of this validation study, diagnostic accuracy analysis was conducted to evaluate the performance of the FCS-10 compared with the original score. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated by comparing the FCS-10 recommendation categorizations with the FCS recommendation categorizations (≥7 for foods to encourage, 4-6 for foods to consume in moderation, ≤3 for foods to limit).

resultsFCS-10 produced scores within 1 unit of the original score (when rescaled 1-10 for comparison) for 89% of products (n = 481/538); none deviated >2 units. The correlation between FCS-10 and the original score was high (r = 0.93). FCS-10 also performed well in identifying products to encourage, moderate, or limit, with overall sensitivity and specificity of 87% and 93%, respectively.

conclusionsFCS-10 offers a practical approach for estimating the healthfulness of diverse packaged foods and beverages using readily available label data while maintaining the strengths of the original system.

Indexed as

BeveragesDiet, HealthyFoodFood IngredientsFood LabelingNutritive ValueHumansReproducibility of ResultsFood Ingredientsfood classificationFood Compassnutrient profilingnutritionvalidation study

Identifiers

PMID40158698
PMCPMC12226758

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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.