Dairy and Fruit Listed as Main Ingredients Improve NRF8.3 Nutrient Density Scores of Children's Snacks

Drewnowski, Adam; Richonnet, Céline

doi:10.3389/fnut.2020.00015

Cited by 9 publications

(13 citation statements)

References 20 publications

(37 reference statements)

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“…Included as a key component of dietary guidelines worldwide [1- 3,13], whole grains are not a part of most nutrient density metrics. A similar case can be made for including fruit and low-fat dairy in nutrient profiling schemes [17]. New NP models need to incorporate MyPlate food groups together with nutrients of public health concern, without forgetting about the need to limit saturated fat, added sugar, and salt.…”

Section: Introductionmentioning

confidence: 99%

The New Hybrid Nutrient Density Score NRFh 4:3:3 Tested in Relation to Affordable Nutrient Density and Healthy Eating Index 2015: Analyses of NHANES Data 2013–16

Drewnowski

Smith

Fulgoni³

2021

Nutrients

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Background: Hybrid nutrient density scores are based on both nutrients and selected food groups. Objective: To compare the new hybrid nutrient-rich food NRFh 4:3:3 score to other nutrient-rich food (NRF) scores, energy density, and energy cost and to model the impact on the Healthy Eating Index (HEI-2015) of partially replacing less nutrient-rich with more nutrient-rich foods. Methods: Analyses were based on 5870 foods and beverages in the Food and Nutrient Database for Dietary Studies and on 24 h dietary recalls from the National Health and Nutrition Examination Survey (NHANES 2013–16). The NRFh 4:3:3 model was based on four nutrients to encourage (protein fiber, potassium, MUFA + PUFA); three food groups to encourage (dairy, fruit, whole grains); and three nutrients to limit (saturated fat, added sugar, sodium). Ratings generated by NRFh 4:3:3 and by other NRF models were correlated with score components, energy density (kcal/100 g), and energy cost (USD/100 kcal). The impact on HEI-2015 of replacing foods in the lowest nutrient density tertile (T1) with top tertile (T3) foods at 10%, 20%, 30%, and 100% equicaloric replacement was modeled using NHANES 2013–16 dietary data by population subgroups. Results: The NRFh 4:3:3 model awarded higher scores to foods containing dairy, fruit, and whole grains and proportionately lower scores to vegetables when compared to the NRF 9.3 model. Higher NRF and NRFh nutrient density scores were linked to lower energy density and higher energy cost; however, both correlations were lower for the NRFh 4:3:3. Isocaloric replacement of bottom tertile with top tertile foods as rated by both models led to significantly higher HEI-2105 values, based on complete (100%) and on partial (10–30%) replacement. Conclusion: The new NRFh 4:3:3 model provides the basis for developing new metrics of affordable nutrient density. The model identified “best value” food categories that were both affordable and nutrient-rich. Total and partial replacement of low nutrient density with high nutrient density foods was associated with higher HEI-2015 scores, suggesting that even partial inclusion of more nutrient dense foods in the diet may have an important impact on total diet quality.

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Section: Introductionmentioning

confidence: 99%

The New Hybrid Nutrient Density Score NRFh 4:3:3 Tested in Relation to Affordable Nutrient Density and Healthy Eating Index 2015: Analyses of NHANES Data 2013–16

Drewnowski

Smith

Fulgoni³

2021

Nutrients

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“…An alternative approach is to use information provided on the back of pack ingredient panel. Dairy and fruit listed as first ingredients were associated with higher NRF8.3 scores (26). However, developing branded databases of quantitative food group information as well as incorporating detailed data on food group intake in national dietary surveys should be undertaken to provide a better estimate of food groups intakes, the contribution of specific foods to recommended food groups and to aid with food group claims modernization, such as the potential to updated FDA Healthy claim to include food groups (26).…”

Section: Discussionmentioning

confidence: 99%

“…A case can be made for an alternative hybrid approach to NP, one that would integrate selected nutrients with My Plate food groups (24,25). One recent study (26) pointed to the value of the ingredient list as a potential source of data for nutrient profiling. When listed in the first place, dairy and fruit were associated with higher NRF8.3 nutrient density scores (26).…”

Section: Introductionmentioning

confidence: 99%

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New Nutrient Rich Food Nutrient Density Models That Include Nutrients and MyPlate Food Groups

Drewnowski

Fulgoni

2020

Front. Nutr.

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Background: Dietary guidelines have shifted emphasis from single nutrients to food patterns, food groups, and dietary ingredients. Nutrient profiling models need to do the same. Methods: Dietary intake data for 23,643 persons aged >2 years came from the 2011-2016 National Health and Examination Survey (NHANES 2011-16). Healthy Eating Index HEI-2015 was the diet quality measure. The new Nutrient Rich Food hybrid score (NRFh) was based on three subscores. The subscore based on x nutrients to encourage was defined as NRx. The subscore based on y MyPlate food groups to encourage was MPy. The negative subscore based on z nutrients to limit was LIMz. The final algorithm was NRFh(x.y.z) = NRx + MPy − LIMz. The selection of NRFh model components from among 16 nutrients and five food groups was based on regression analyses. Results: We conducted a total of 2,162,720 iterative regression analyses against HEI-2015 diet quality scores. NRF scores based on 16 nutrients accounted for up to 66% of the variance, whereas scores based on 5 MP food groups accounted for 50%. The new NRFh3:4:3 score with six nutrients and four food groups (fiber, potassium, PUFA+MUFA; whole grains, dairy, fruit, nuts and seeds; saturated fat, added sugar, sodium) explained 72%. The new NRFh4:3:3 score with seven nutrients and three food groups (protein, fiber, potassium, PUFA+MUFA; whole grain, dairy, fruit; saturated fat, added sugar, sodium;) also explained 72%. In both NRFh models, regressions remained significant for each population subgroup examined. Conclusion: The NRFh3:4:3 and NRFh4:3:3 models correlated well with HEI-2015 scores, a measure of diet quality that tracks compliance with Dietary Guidelines. Hybrid NP models based on nutrients and food groups could become part of dietary guidance.

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“…NRF index scores are dietary quality indices based on the nutrient density of a food item, accounting for beneficial nutrients, nutrients to limit, or a combination of both. This index is estimated as NRn - LIM = NRFn.3, where n represents the number of nutrients to encourage and LIM represents three nutrients that should be limited ( 29 ). The number of qualifying nutrients to encourage has varied across different studies, from 6 (NRF 6.3) to 15 (NRF 15.3) ( 29 ).…”

Section: Methodsmentioning

confidence: 99%

Food-Level Analysis to Identify Dietary Choices With the Highest Nutritional Quality and Lowest Greenhouse Gas Emissions and Price

et al. 2022

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IntroductionFood systems are challenged to provide healthy, sustainable and affordable foods. From a consumer perspective, identifying healthy, sustainable and affordable choices based on individual food products rather than diets could promote better shopping choices.ObjectiveTo identify foods and drinks with the highest nutritional quality and lowest greenhouse gas emissions (GHGE) and price. We also assessed how a combination of these indicators (e.g., nutritional quality, GHGE and price) for food categories aligned with current United Kingdom dietary recommendations.Materials and MethodsWe performed a secondary analysis of the National Diet and Nutrition Survey (NDNS) nutrient databank year 11 (2018/2019). Spearman correlation coefficients were used to assess the strength of relationships between nutritional quality, environmental impact and/or prices per 100 kcal. In addition, we developed an optimized nutritional quality, GHGE and price score for each food or drink item based on the overall medians for each of these indicators.ResultsMedian nutritional value was highest for fruit and vegetables, whilst median GHGE and price was lower for starchy carbohydrates, fats and items of which consumption should be limited. The relative proportions of foods considered the most nutritious and with a low GHGE and price in each of the food categories, on a per 100 kcal basis, were comparable to the proportions in the Eatwell Guide, except for the proportion of fruits and vegetables being smaller and the proportion of potatoes, bread, rice, pasta, and other starchy carbohydrates being larger in our analysis.ConclusionPublic health efforts should consider the impact of dietary choices not only in terms of nutritional quality but also in terms of environmental and economic impact. Our food-based analysis shows a large variation in nutritional quality, GHGE and price within and across food categories, which provides consumers with opportunities for “food swaps” that are more nutritious and have lower GHGE and price.

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Dairy and Fruit Listed as Main Ingredients Improve NRF8.3 Nutrient Density Scores of Children's Snacks

Cited by 9 publications

References 20 publications

The New Hybrid Nutrient Density Score NRFh 4:3:3 Tested in Relation to Affordable Nutrient Density and Healthy Eating Index 2015: Analyses of NHANES Data 2013–16

The New Hybrid Nutrient Density Score NRFh 4:3:3 Tested in Relation to Affordable Nutrient Density and Healthy Eating Index 2015: Analyses of NHANES Data 2013–16

New Nutrient Rich Food Nutrient Density Models That Include Nutrients and MyPlate Food Groups

Food-Level Analysis to Identify Dietary Choices With the Highest Nutritional Quality and Lowest Greenhouse Gas Emissions and Price

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