Food Compass is a nutrient profiling system using expanded characteristics for assessing healthfulness of foods

Mozaffarian, Dariush; El-Abbadi, Naglaa; O’Hearn, Meghan; Erndt‐Marino, Josh; Masters, William A.; Jacques, Paul F.; Shi, Peilin; Blumberg, Jeffrey B.; Micha, Renata

doi:10.1038/s43016-021-00381-y

Cited by 68 publications

(93 citation statements)

References 23 publications

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“…Our CFQS models are relatively simple, focusing only on those few elements of CF quality which are specific to the evidence-based recommendations provided by the 2020–2025 DGA. By contrast, some recent attempts at NP, such as the Food Compass Score, employ as many as 54 overlapping factors housed within 9 health-related domains [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

“…Bioactive food components tend to track with food group designation (e.g., flavonoids in fruits, glucosinolates in vegetables, and lectins and phytates in grains and legumes) [ 64 ] and could potentially be used as additional components in CFQS models to differentiate the CF quality of food groups. As a proof-of-concept, the flavonoid content for fruits has been included in NRF9f.3 scores to help capture the full dietary value of fruits in addition to their nutrient content [ 54 ], and both flavonoid content and carotenoid content have been included as part of the Food Compass Score for application to all foods and not just to CFs [ 55 ]. Interestingly, when the quality of foods is assessed by the Food Compass Score, the fruits, vegetables, and legumes groups are all ranked highest (i.e., most healthful), whereas grains tend to score much lower, even when they are high in fiber, potassium, and whole grains and when they are low in free sugars and sodium.…”

Section: Discussionmentioning

confidence: 99%

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A New Carbohydrate Food Quality Scoring System to Reflect Dietary Guidelines: An Expert Panel Report

et al. 2022

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Existing metrics of carbohydrate food quality have been based, for the most part, on favorable fiber- and free sugar-to-carbohydrate ratios. In these metrics, higher nutritional quality carbohydrate foods are defined as those with >10% fiber and <10% free sugar per 100 g carbohydrate. Although fiber- and sugar-based metrics may help to differentiate the nutritional quality of various types of grain products, they may not aptly capture the nutritional quality of other healthy carbohydrate foods, including beans, legumes, vegetables, and fruits. Carbohydrate food quality metrics need to be applicable across these diverse food groups. This report introduces a new carbohydrate food scoring system known as a Carbohydrate Food Quality Score (CFQS), which supplements the fiber and free sugar components of previous metrics with additional dietary components of public health concern (e.g., sodium, potassium, and whole grains) as identified by the Dietary Guidelines for Americans. Two CFQS models are developed and tested in this study: one that includes four dietary components (CFQS-4: fiber, free sugars, sodium, potassium) and one that considers five dietary components (CFQS-5: fiber, free sugars, sodium, potassium, and whole grains). These models are applied to 2596 carbohydrate foods in the Food and Nutrient Database for Dietary Studies (FNDDS) 2017–2018. Consistent with past studies, the new carbohydrate food scoring system places large percentages of beans, vegetables, and fruits among the top scoring carbohydrate foods. The whole grain component, which only applies to grain foods (N = 1561), identifies ready-to-eat cereals, oatmeal, other cooked cereals, and selected whole grain breads and crackers as higher-quality carbohydrate foods. The new carbohydrate food scoring system shows a high correlation with the Nutrient Rich Food (NRF9.3) index and the Nutri-Score. Metrics of carbohydrate food quality that incorporate whole grains, potassium, and sodium, in addition to sugar and fiber, are strategically aligned with multiple 2020–2025 dietary recommendations and may therefore help with the implementation of present and future dietary guidelines.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A New Carbohydrate Food Quality Scoring System to Reflect Dietary Guidelines: An Expert Panel Report

et al. 2022

View full text Add to dashboard Cite

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“…However, NP models do not always rate individual foods in a consistent manner ( 1 ). Existing NP models can be across-the-board, or category-specific; compensatory or not; balanced or not; and based on nutrients only, or based on some combination of nutrients, food groups, and dietary ingredients ( 7 – 9 ). Whereas NRF and now the bHNDS models are fully compensatory, the Nutri-Score and HSR are not.…”

Section: Discussionmentioning

confidence: 99%

“…The purpose of NP models is to “diagnose” or otherwise identify those foods that provide optimal nutritional value. Such foods have been variously described as healthful, wholesome, nutrient-dense, or nutrient-rich ( 3 , 4 , 7 , 9 ). Foods that are awarded A or B grades (Nutri-Score) or 4 or 5 stars (HSR) are generally viewed as conforming with dietary guidelines ( 29 ).…”

Section: Discussionmentioning

confidence: 99%

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Balanced Hybrid Nutrient Density Score Compared to Nutri-Score and Health Star Rating Using Receiver Operating Characteristic Curve Analyses

2022

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BackgroundNutrient profiling (NP) models that are used to assess the nutrient density of foods can be based on a combination of key nutrients and desirable food groups.ObjectiveTo compare the diagnostic accuracy of a new balanced hybrid nutrient density score (bHNDS) to Nutri-Score and Health Star Rating (HSR) front-of-pack systems using receiver operating characteristic (ROC) curve analyses. The diet-level bHNDS was first validated against Healthy Eating Index (HEI-2015) using data from the 2017–18 National Health and Nutrition Examination Survey (2017–18 NHANES). Food-level bHNDS values were then compared to both the Nutri-Score and HSR using ROC curve analyses.ResultsThe bHNDS was based on 6 nutrients to encourage (protein, fiber, calcium, iron, potassium, and vitamin D); 5 food groups to encourage (whole grains, nuts and seeds, dairy, vegetables, and fruit), and 3 nutrients (saturated fat, added sugar, and sodium) to limit. The algorithm balanced components to encourage against those to limit. Diet-level bHNDS values correlated well with HEI-2015 (r = 0.67; p < 0.001). Food-level correlations with both Nutri-Score (r = 0.60) and with HSR (r = 0.58) were significant (both p < 0.001). ROC estimates of the Area Under the Curve (AUC) showed high agreement between bHNDS values and optimal Nutri-Score and HSR ratings (>0.90 in most cases). ROC analysis identified those bHNDS cut-off points that were predictive of A-grade Nutri-Score or 5-star HSR. Those cut-off points were highly category-specific.ConclusionThe new bHNDS model showed high agreement with two front-of-pack labeling systems. Cross-model comparisons based on ROC curve analyses are the first step toward harmonization of proliferating NP methods that aim to “diagnose” high nutrient-density foods.

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Integrating Vision‐Language Models for Accelerated High‐Throughput Nutrition Screening

Ma,

Wu,

et al. 2024

Advanced Science

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Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision‐language models (VLMs) with chemical analysis techniques. A cutting‐edge VLM is unveiled, utilizing the expansive UMDFood‐90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro‐AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high‐throughput solution for nutritional analysis.

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Food Compass is a nutrient profiling system using expanded characteristics for assessing healthfulness of foods

Cited by 68 publications

References 23 publications

A New Carbohydrate Food Quality Scoring System to Reflect Dietary Guidelines: An Expert Panel Report

A New Carbohydrate Food Quality Scoring System to Reflect Dietary Guidelines: An Expert Panel Report

Balanced Hybrid Nutrient Density Score Compared to Nutri-Score and Health Star Rating Using Receiver Operating Characteristic Curve Analyses

Integrating Vision‐Language Models for Accelerated High‐Throughput Nutrition Screening

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