Barrett Eden M, Cudhea Frederick, Washbon Erin, Levitan Zoe, Sharib Julia Reedy, Blumberg Jeffrey B, Micha Renata, Mozaffarian Dariush
Food 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.
Food is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States.
Am J Clin Nutr. 2025 Jun;121(6):1328-1334. doi: 10.1016/j.ajcnut.2025.03.015. Epub 2025 Mar 28.
The 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.
We aimed to develop and validate an approach, Food Compass Score-10 (FCS-10), to estimate FCSs using information commonly available on package labels.
Missing 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).
FCS-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.
FCS-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.
食品指南针（Food Compass）是一种新型的食品评估系统，它利用9个领域的54个属性，对食品、饮料和餐食的健康程度进行全面且经过验证的评估。然而，其中一些属性的信息并不常见。
我们旨在开发并验证一种方法，即食品指南针评分-10（FCS-10），利用包装标签上常见的信息来估算食品指南针评分（FCS）。
利用约10000种食品和饮料的数据集中得出的每种产品成分的加权分数来计算缺失的属性。最终的FCS-10从1（最不健康）到10（最健康）进行缩放。作为这项验证研究的一部分，进行了诊断准确性分析，以评估FCS-10与原始评分相比的性能。通过将FCS-10推荐分类与FCS推荐分类（鼓励食用的食品≥7分，适度食用的食品4-6分，限制食用的食品≤3分）进行比较，计算敏感性、特异性、阳性预测值和阴性预测值。
FCS-10对89%的产品（n = 481/538）得出的分数与原始分数相差在1个单位以内（重新缩放到1-10进行比较时）；没有偏差超过2个单位的情况。FCS-10与原始分数之间的相关性很高（r = 0.93）。FCS-10在识别鼓励食用、适度食用或限制食用的产品方面也表现良好，总体敏感性和特异性分别为87%和93%。
FCS-10提供了一种实用的方法，可利用现成的标签数据估算各种包装食品和饮料的健康程度，同时保持原始系统的优势。
