Dataset Information

Aligning nutrient profiling with dietary guidelines: modifying the Nutri-Score algorithm to include whole grains.

ABSTRACT:

Purpose

Whole grains, generally recognised as healthy choices, are not included in most nutrient profiling systems. We tested modifications to the Nutri-Score algorithm to determine whether including whole grains would provide an improved measure of food, and overall diet quality.

Methods

The whole-grain content of food, with a minimum cut-point of 25%, was added to the algorithm, following similar methods used to score other health-promoting components such as fibre. We applied and compared the original and the modified Nutri-Score to food composition and dietary intake data from Australia, France, the United Kingdom, and the United States.

Results

At the food level, correlations between whole-grain content and food nutritional score were strengthened using the modified algorithm in Australian data, but less so for the other countries. Improvements were greater in grain-specific food groups. The largest shift in Nutri-Score class was from B to A (best score). At the dietary intake level, whole-diet nutritional scores for individuals were calculated and compared against population-specific diet-quality scores. With modifications, correlations with diet-quality scores were improved slightly, suggesting that the modified score better aligns with national dietary guidelines. An inverse linear relationship between whole-diet nutritional score and whole-grain intake was evident, particularly with modifications (lower whole-diet nutritional score indicative of better diet quality).

Conclusion

Including a whole-grain component in the Nutri-Score algorithm is justified to align with dietary guidelines and better reflect whole grain as a contributor to improved dietary quality. Further research is required to test alternative algorithms and potentially other nutrient profiling systems.

SUBMITTER: Kissock KR

PROVIDER: S-EPMC8783881 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Publications

Aligning nutrient profiling with dietary guidelines: modifying the Nutri-Score algorithm to include whole grains.

Kissock Katrina R KR Vieux Florent F Mathias Kevin C KC Drewnowski Adam A Seal Chris J CJ Masset Gabriel G Smith Jessica J Mejborn Heddie H McKeown Nicola M NM Beck Eleanor J EJ

European journal of nutrition 20211124 1

<h4>Purpose</h4>Whole grains, generally recognised as healthy choices, are not included in most nutrient profiling systems. We tested modifications to the Nutri-Score algorithm to determine whether including whole grains would provide an improved measure of food, and overall diet quality.<h4>Methods</h4>The whole-grain content of food, with a minimum cut-point of 25%, was added to the algorithm, following similar methods used to score other health-promoting components such as fibre. We applied a ...[more]

PMID: 34817679

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Project description:Front-of-package nutrition labelling (FOPNL) is known as an effective tool that can encourage healthier food choices and food reformulation. A very interesting type of FOPNL is grading schemes. Our objective was to compare two market-implemented grading schemes-European Nutri-Score (NS) and Australian Health Star Rating (HSR), using large Slovenian branded foods database. NS and HSR were used for profiling 17,226 pre-packed foods and drinks, available in Slovenian food supply dataset (2020). Alignment between models was evaluated with agreement (% of agreement and Cohen's Kappa) and correlation (Spearman rho). The 12-month nationwide sales-data were used for sale-weighing, to address market-share differences. Study results indicated that both models have good discriminatory ability between products based on their nutritional composition. NS and HSR ranked 22% and 33% of Slovenian food supply as healthy, respectively. Agreement between NS and HSR was strong (70%, κ = 0.62) with a very strong correlation (rho = 0.87). Observed profiling models were most aligned within food categories Beverages and Bread and bakery products, while less aligned for Dairy and imitates and Edible oils and emulsions. Notable disagreements were particularly observed in subcategories of Cheese and processed cheeses (8%, κ = 0.01, rho = 0.38) and Cooking oils (27%, κ = 0.11, rho = 0.40). Further analysis showed that the main differences in Cooking oils were due to olive oil and walnut oil, which are favoured by NS and grapeseed, flaxseed and sunflower oil that are favoured by HSR. For Cheeses and cheese products, we observed that HSR graded products across the whole scale, with majority (63%) being classified as healthy (≥3.5 *), while NS mostly graded lower scores. Sale-weighting analyses showed that offer in the food supply does not always reflect the sales. Sale-weighting increased overall agreement between profiles from 70% to 81%, with notable differences between food categories. In conclusion, NS and HSR were shown as highly compliant FOPNLs with few divergences in some subcategories. Even these models do not always grade products equally high, very similar ranking trends were observed. However, the observed differences highlight the challenges of FOPNL ranking schemes, which are tailored to address somewhat different public health priorities in different countries. International harmonization can support further development of grading type nutrient profiling models for the use in FOPNL, and make those acceptable for more stake-holders, which will be crucial for their successful regulatory implementation.

Project description:IntroductionWhen considering health-related impacts of foods, nutrient profile and (ultra)processing are two complementary dimensions. The Nutri-Score informs on the nutrient profile dimension. Recently, mounting evidence linked ultraprocessed food consumption to various adverse health outcomes, independently of their nutrient profile. To inform consumers about each of these health-related dimensions of food, we tested, in a randomised controlled trial, if a graphically modified version 'Nutri-Score V.2.0', including a black 'ultraprocessed' banner, would improve the capacity of consumers to rank products according to their nutrient profile and to detect those ultra-processed, compared with a no-label situation.Methods21 159 participants included in the NutriNet-Santé web-cohort were randomly assigned to a control arm (no front-of-pack label) or an experimental arm (Nutri-Score 2.0) and were presented an online questionnaire with three sets of food products (cookies, breakfast cereals and ready-to-eat meals) to rank according to nutrient profile and to identify ultraprocessed foods. The primary outcome was objective understanding of nutrient profile and ultraprocessing, represented by a score of correct answers. Secondary outcomes were purchasing intentions and the healthiest-perceived product. Multinomial logistic regressions were performed.ResultsThe Nutri-Score V.2.0 increased the objective understanding of both the nutrient profile dimension (OR highest vs lowest score category=29.0 (23.4-35.9), p<0.001) and the ultraprocessing dimension (OR=174.3 (151.4-200.5), p<0.001). Trends were similar for cookies, breakfast cereals and ready-to-eat meals. The Nutri-Score V.2.0 had a positive effect on purchasing intentions and on the products perceived as the healthiest.ConclusionThis randomised controlled trial demonstrates the interest of a front-of-pack label combining the Nutri-Score (informing on the nutrient profile dimension) with an additional graphic mention, indicating when the food is ultraprocessed, compared with a no-label situation. Our results show that a combined label enabled participants to independently understand these two complementary dimensions of foods.Trial registration numberNCT05610930.

Dataset Information

Aligning nutrient profiling with dietary guidelines: modifying the Nutri-Score algorithm to include whole grains.

Purpose

Methods

Results

Conclusion

Publications

Aligning nutrient profiling with dietary guidelines: modifying the Nutri-Score algorithm to include whole grains.

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