Consistency of the Initial and Updated Version of the Nutri-Score with Food-Based Dietary Guidelines: A French Perspective.

BACKGROUND: To help consumers to make healthier food choices, 7 European countries have implemented the front-of-pack nutrition label Nutri-Score. The algorithm was updated in 2022-2023 by the Nutri-Score European scientific committee, based on the current scientific knowledge. OBJECTIVES: The aim of this study was to investigate the consistency of the newly internationally developed algorithm with the French food-based dietary guidelines (FBDG) and compare the respective performances of the initial and updated algorithm. METHODS: Three complementary French food composition databases were used to access extensive coverage of the food supply in France (n = 46,752): the Oqali, OpenFoodFacts, and CIQUAL databases. Based on the French FBDG, a list of 41 criteria was defined by which the consistency between French FBDG and the Nutri-Score was assessed (eg, consumption of fresh vegetables is promoted in FBDG, thus the Nutri-Score should rate favorably such products). RESULTS: Of all criteria, the initial algorithm met 63% (26/41) of them, whereas the revised algorithm met 85% (35/41) of them. Improvements achieved by the updated version of the Nutri-Score in alignment with the FBDG were particularly observed for high-fat products (ie, fatty fish, nuts, and seeds), sweet products (ie, ice creams and sweet spreads), salty products (ie, savory snacks and salted nuts), dairy beverages, and beverages with artificial sweeteners. CONCLUSIONS: The Nutri-Score's updated nutrient profiling system appears to rate foods more consistently regarding the French dietary guidelines and improved the currently existing system. This work supports the implementation of the updated nutrient profiling system underlying Nutri-Score.

A randomized controlled trial to test the effects of displaying the Nutri-Score in food advertising on consumer perceptions and intentions to purchase and consume.

BACKGROUND: Some research shows that advertising for high-fat, sugar, or salt (HFSS) products is contributing to a shift in consumer preferences toward products of poor nutritional quality, leading to unhealthy nutritional intakes that increase the risk of obesity and chronic diseases. A strategy of displaying simple and understandable nutritional information (like the front-of-pack nutrition label Nutri-Score) in food messages could be an aid to help guide consumers' choice towards healthier products. METHODS: A randomized controlled experiment was conducted on 27,085 participants randomly assigned to two experimental conditions or a control condition. In both experimental conditions (independent variable: advertising messages with vs. without the Nutri-Score), participants were exposed to advertisements for diversified food products with contrasting nutritional quality and belonging to nine different food categories. Participants were then asked questions about their perception, affective evaluation, and intentions to purchase and consume the products. In the control condition, they were not exposed to the advertisements. RESULTS: Overall, interaction effects between the two variables (1) the messages with vs. without the Nutri-Score and (2) the nutritional quality of products, were significant for all dependent variables, with effect sizes between large and medium. Overall, the better the products' nutritional quality, the more positive their perceptions, affective evaluations, and intentions to buy and consume them. When the Nutri-score was displayed in advertising messages (vs. when it was not), perceptions, affective evaluation, and behavioral intentions: (1) became more positive for products of good nutritional quality (Nutri-score A and B), (2) became more negative for products of poor nutritional quality (Nutri-score D and E), (3) changed little or not at all for products of intermediate nutritional quality (Nutri-Score C). CONCLUSIONS: This research is the first in the literature to demonstrate that displaying the Nutri-Score in advertising messages assists consumers in directing their choices towards healthier foods. Regulations mandating the display of the Nutri-Score in food advertising could be an effective public health measure.

Differential association of dietary scores with the risk of type 2 diabetes by metabotype.

PURPOSE: We aimed to examine the association between dietary patterns and type 2 diabetes mellitus (T2DM) while considering the potential effect modification by metabolic phenotypes (metabotypes). Additionally, we aimed to explore the association between dietary scores and prediabetes. METHODS: A total of 1460 participants (11.8% with T2DM) from the cross-sectional population-based KORA FF4 study were included. Participants, classified into three metabotype subgroups, had both their FSAm-NPS dietary index (underpinning the Nutri-Score) and ultra-processed foods (UPF) intake (using NOVA classification) calculated. Glucose tolerance status was assessed via oral glucose tolerance tests (OGTT) in non-diabetic participants and was classified according to the American Diabetes Association criteria. Logistic regression models were used for both the overall and metabotype-stratified analyses of dietary scores' association with T2DM, and multinomial probit models for their association with prediabetes. RESULTS: Participants who had a diet with a higher FSAm-NPS dietary index (i.e., a lower diet quality) or a greater percentage of UPF consumption showed a positive association with T2DM. Stratified analyses demonstrated a strengthened association between UPF consumption and T2DM specifically in the metabolically most unfavorable metabotype (Odds Ratio, OR 1.92; 95% Confidence Interval, CI 1.35, 2.73). A diet with a higher FSAm-NPS dietary index was also positively associated with prediabetes (OR 1.19; 95% CI 1.04, 1.35). CONCLUSION: Our study suggests different associations between poorer diet quality and T2DM across individuals exhibiting diverse metabotypes, pointing to the option for stratified dietary interventions in diabetes prevention.

The role of Nutri-Score and NutrInform Battery in guiding the food choices of consumers with specific nutritional needs: A controlled study.

BACKGROUND AND AIMS: There is a significant knowledge gap regarding the effectiveness of front-of-pack labeling (FoPL) systems in helping food choices that meet specific nutritional requirements. This study sought to compare the effectiveness of the Nutri-Score (NS) and NutrInform Battery (NIB) labels in assisting consumers to select food products with low sodium or low saturated fat. METHODS AND RESULTS: Utilizing a controlled experimental design, a sample of 1512 participants (aged 18-70, 50.8% females) was exposed to scenarios through computer-assisted web interviewing, where they selected food products suitable for people with suboptimal blood pressure or plasma cholesterol levels, labeled with either NS, NIB, or no label (blind). The NIB proved significantly more efficacious than NS in guiding participants towards making selections in better agreement with nutritional needs, for blood pressure or cholesterol control. Furthermore, the NIB was generally viewed more favorably, with participants rating it as more trustworthy, informative, and useful for the required task. Notably, younger participants, those with higher nutritional knowledge, and those less inclined towards intuitive or spontaneous decision-making demonstrated a greater propensity for making health-congruent food choices when using the NIB. CONCLUSION: The findings suggest that the informative FoPL system, NIB, may offer greater advantages in addressing specific dietary requirements. This underscores the important role of FoPL systems in promoting public health and meeting diverse and specific consumer needs. Further research is needed to confirm these results in broader contexts and for additional health conditions.

The effects of health-related food taxes on the environmental impact of consumer food purchases: secondary analysis of data from a randomised controlled trial in a virtual supermarket.

OBJECTIVE: To measure the effects of health-related food taxes on the environmental impact of consumer food purchases in a virtual supermarket. DESIGN: This is a secondary analysis of data from a randomised controlled trial in which participants were randomly assigned to a control condition with regular food prices (n 152), an experimental condition with a sugar-sweetened beverage (SSB) tax (n 131) or an experimental condition with a nutrient profiling tax based on Nutri-Score (n 112). Participants were instructed to undertake their typical weekly grocery shopping for their households. Primary outcome measures were three environmental impact indicators: greenhouse gas (GHG) emissions, land use and blue water use per household per week. Data were analysed using linear regression analyses. SETTING: Three-dimensional virtual supermarket. PARTICIPANTS: Dutch adults (≥ 18 years) who were responsible for grocery shopping in their household (n 395). RESULTS: GHG emissions (-7·6 kg CO2-eq; 95 % CI -12·7, -2·5) and land use (-3·9 m2/year; 95 % CI -7·7, -0·2) were lower for the food purchases of participants in the nutrient profiling tax condition than for those in the control condition. Blue water use was not affected by the nutrient profiling tax. Moreover, the SSB tax had no significant effect on any of the environmental impact indicators. CONCLUSIONS: A nutrient profiling tax based on Nutri-Score reduced the environmental impact of consumer food purchases. An SSB tax did not affect the environmental impact in this study.

Complementarity between the updated version of the front-of-pack nutrition label Nutri-Score and the food-processing NOVA classification.

OBJECTIVE: To compare the initial and the updated versions of the front-of-pack label Nutri-Score (related to the nutritional content) with the NOVA classification (related to the degree of food processing) at the food level. DESIGN: Using the OpenFoodFacts database - 129,950 food products - we assessed the complementarity between the Nutri-Score (initial and updated) with the NOVA classification through a correspondence analysis. Contingency tables between the two classification systems were used. SETTINGS: The food offer in France. PARTICIPANTS: Not applicable. RESULTS: With both versions (i.e. initial and updated) of the Nutri-Score, the majority of ultra-processed products received medium to poor Nutri-Score ratings (between 77·9 % and 87·5 % of ultra-processed products depending on the version of the algorithm). Overall, the update of the Nutri-Score algorithm led to a reduction in the number of products rated A and B and an increase in the number of products rated D or E for all NOVA categories, with unprocessed foods being the least impacted (-3·8 percentage points (-5·2 %) rated A or B and +1·3 percentage points (+12·9 %) rated D or E) and ultra-processed foods the most impacted (-9·8 percentage points (-43·4 %) rated A or B and +7·8 percentage points (+14·1 %) rated D or E). Among ultra-processed foods rated favourably with the initial Nutri-Score, artificially sweetened beverages, sweetened plant-based drinks and bread products were the most penalised categories by the revision of Nutri-Score while low-sugar flavoured waters, fruit and legume preparations were the least affected. CONCLUSION: These results indicate that the update of the Nutri-Score reinforces its coherence with the NOVA classification, even though both systems measure two distinct health dimensions at the food level.

Comparison of the nutritional composition and calculated Nutri-score classifications of the Dutch food retail supply in 2018 and 2020.

OBJECTIVE: In the Netherlands, reformulation strategies have been established for several years, whereas Nutri-Score was implemented in 2024. Besides being a helpful tool for consumers to make healthier food choices, Nutri-Score also aims to stimulate food reformulation by food manufacturers. The present study investigates whether changes in food composition could have led to different calculated Nutri-Score classifications. DESIGN: Food compositions and Nutri-Score classifications were calculated using the updated Nutri-Score algorithm. Food groups with the largest change in the distribution of Nutri-Score classifications were analysed in-depth by plotting frequency distributions and calculating median contents for nutrient contents that relatively changed the most in 2020. SETTING: Food composition data were available from the Dutch Branded Food database in 2018 (n 38 295) and 2020 (n 48 091). PARTICIPANTS: Not applicable. RESULTS: In general, median nutrient contents and calculated Nutri-Score classifications were similar for 2018 and 2020. The median sugar and SFA contents were lower for some food groups (e.g. breakfast cereals, meat preserves, sweets and sweet goods) in 2020 compared to 2018. The median SFA content for meat preserves and sweets and sweet goods was relatively low in Nutri-Score classification A ascending towards higher median content in Nutri-Score classification E. CONCLUSIONS: Although food reformulation was not substantial in the Dutch food retail supply in 2018 and 2020, some differences in Nutri-Score classifications were observed. When implemented, Nutri-Score may encourage food manufacturers to increase their reformulation efforts. Repeated monitoring of food compositions and Nutri-Score classifications is recommended to establish reformulation efforts by food manufacturers.

Evidence on consumers' perceptions, understanding and uses of the Nutri-Score to improve communication about its update: a qualitative study with shopping observations in France.

BACKGROUND: The Nutri-Score is a front-of-pack nutrition label widely used in several European countries to improve the overall quality of consumers' diets. In the view of the upcoming update of the Nutri-Score algorithm, this study evaluates consumers' perceptions, understanding and uses of this label as well as their expectations or criticisms and opinions regarding the algorithm update. METHODS: Two complementary qualitative approaches were used in 2023. Six focus group discussions with a total of 51 participants as well as 20 individual shopping observations with real purchase conditions followed by in-depth interviews were conducted among French adults. The sessions were recorded, transcribed and then analysed using a thematic approach. RESULTS: Participants were familiar with the Nutri-Score but used it for food purchasing in a secondary way due to other more important criteria such as price or habits. They were aware that the label aimed to help them choose healthier products and to protect consumers. However, the lack of awareness about some aspects of the label such as the entity responsible for it or the calculation method of the score created a sense of mistrust about the Nutri-Score. Nevertheless, consumers did not report hearing criticism about the label in the media. Finally, they considered the updating of the label to be relevant. CONCLUSIONS: Participants had a rather positive image of the Nutri-Score and its forthcoming update. Notwithstanding, to improve consumers' trust in the label, this study recommends launching an information campaign to explain its calculation method and reassure them that the Nutri-Score is a government-endorsed scheme as part of the national public health nutrition policy.

The impact of nutritional labeling on adult snack choices: A controlled field experiment in a non-commercial professional setting.

Each year, 2.8 million people die because of comorbidities associated with being overweight. Snacking substantially contributes to people's calorie intake. One way to nudge consumers towards healthier alternatives is the implementation of nutritional labeling. This study reports on a controlled field experiment that evaluates the effect of two nutritional labels on free snack choices (n = 739). Participants at a conference could choose between nuts, cookies, and candy bars as a snack, presented at the bar at six different locations across 2 bar counters. The labels were set up in front of each snack in three conditions: no labeling (control), a calorie label, or a traffic light label (i.e., the Nutri-Score). The location of the snacks on the counter (Either side of the counter; Center, Right, Left) and the time-of-day (Morning (=reference) vs Afternoon) were statistically controlled for. The results show that calorie labels could not successfully nudge consumers toward healthier snack choices (nuts instead of candy bars or cookies). In contrast, the Nutri-Score label significantly increased the probability of choosing nuts over candy bars. The Nutri-Score also increased the chance of choosing nuts over cookies, but the difference was not significant. No prior studies to our knowledge have directly compared calorie labels to the Nutri-Score. This study suggests that the Nutri-Score label can be a more successful intervention than calorie labels to nudge consumers towards healthier choices in situations were free snacks are offered, like many modern workplaces. Changing snacking behavior is challenging and naturalistic field experiments like this one are needed to translate the theory from previous laboratory studies to real-life settings.

Understanding the influence of stakeholders on the implementation of front-of-pack nutrition labelling in the absence of public debate: the case of Portugal.

In the context of the European Commission's proposal for the harmonization of front-of-pack nutrition labels (FoPLs) across the European Union scheduled for 2023, Portugal's position on the preferred format to be adopted remains unclear. Our study aims to (1) describe the context of the political decision regarding FoPL's implementation in Portugal, (2) identify and characterize the actors involved in the debate and (3) evaluate their influence in the decision-making process and their position on the various formats of FoPLs considered. A document review complemented by a qualitative study with eight experts revealed the actors involved in the implementation of FoPLs in Portugal. The stakeholder theory was used to qualify their level of influence on the decision. To complement the experts' analysis, the views of 1014 Portuguese consumers on stakeholders' influence were collected via an online questionnaire. The results show a strong mobilization of the Portuguese Ministry of Health on the issue through the production of several technical and scientific reports. The background of the political decision related to FoPLs in Portugal highlights the commitment of some influential stakeholders in favour of the traffic light format, including one of the major Portuguese retailers, Continente. Nevertheless, in the face of public support for the Nutri-Score from a growing number of actors, Portugal has not yet adopted a specific format, partly due to some resistance from the agricultural sector. Portugal, by lack of consensus and agenda setting, is now awaiting formal harmonization of FoPLs by the European Commission. This study provides useful insights for public policy actors when promoting interventional measures.

Checkmating the Nutri-Score: Will Food Patriotism Prevent the Harmonization of Front-of-Pack Labeling Schemes within the EU?

Front-of-pack labeling schemes are an effective but contested regulatory approach to nudge consumers toward healthy food choices. The Nutri-Score, being implemented by eight European countries, is one of the most elaborated and evidence-based examples. Therefore, the Nutri-Score has been deemed as the front-runner within the European Commission's attempt to harmonize front-of-pack labeling among European Union (EU) member states under its Farm-to-Fork strategy by the end of 2022. However, the endeavor is on the brink of failure because of massive resistance by Mediterranean member states and parts of the food industry capitalizing on patriotic narratives (e.g., "Made in Italy"). This article investigates the Nutri-Score saga from a political and commercial determinants of health lens. It argues that an EU-wide rollout of the label hinges on the specific interplay between political structures and stakeholder agency. As shown, the EU's weak decision-making power has been exploited by the No-Nutri-Score alliance.

The influence of front-of-pack labelling on consumer decision-making: a comprehensive study using electroencephalography and FaceReader.

Front-of-pack labels (FOPL) can influence consumer behaviour in a variety of ways, and companies that prioritise accurate and transparent nutrition labels can see benefits in increased sales and brand loyalty. However, there are several types of FOPL on the market that differ in visual appeal and consumer clarity. The aim of the study was to reveal the impact of the nutrition label Nutri-Score as well as other factors on consumer decision-making when choosing healthier alternatives for selected food types. In addition to traditional research methods such as questionnaires, in-depth interviews, also emerging methods such as eye tracking, FaceReader and electroencephalography were used to collect implicit feedback. Research suggests that there are significant differences between implicit and explicit feedback when examining the impact of Nutri-Score on the decision-making process. Last but not least, the study presents ideas for future research in real-world settings using emerging methods.

Chemical Data and Relationships for a Scoring Algorithm of Extra Virgin Olive Oil's Nutritional Value.

Extra virgin olive oil (EVOO) is a valuable product and is highly appreciated by consumers for its great nutritional value. However, to date, there has been a lack of uniform systems capable of ranking the nutritional value of EVOO based on its chemical composition in terms of macro- and micronutrients (including phenolic compounds and tocopherols). The aim of this study was to propose a scoring algorithm to rank the nutritional value of EVOO samples, considering their chemical composition in macro- and micronutrients and their sensitivity to oxidation phenomena. Data from more than 1000 EVOO samples were used to assess the variability of the data, considering the selected negative parameters (free acidity, peroxide value, spectrophotometric indices) and positive components (composition in tocopherols via HPLC-DAD, phenolic compounds via HPLC-DAD, and fatty acids via GC-MS) so as to ensure the universal validity of the scoring algorithm. The dataset included samples from the main producing countries worldwide, in addition to Australia, across several production years; data were selected to represent different production realities. A mathematical model was set up for each chemical component, resulting in six variable values. By combining these values with a dimensionless constant value, the algorithm for computing the nutritional value score (NVS) was defined. It allows the nutritional value of an oil to be ranked on a scale of 0 to 100 based on its chemical composition. The algorithm was then successfully tested using chemical data from about 300 EVOO samples obtained from laboratories from different Italian regions. The proposed NVS is a simple and objective tool for scoring the nutritional value of an EVOO, easy to understand for both producers and consumers.

How does the updated Nutri-Score discriminate and classify the nutritional quality of foods in a Norwegian setting?

BACKGROUND: The Nutri-Score is a candidate for the harmonized mandatory front-of-pack nutrition label enabling consumers in the European Union to make healthier food choices. Nutri-Score classifies foods (including beverages) from A (high nutritional quality) to E (low nutritional quality) based on the foods' qualifying and disqualifying components. We aimed to evaluate the updated Nutri-Score for foods (2022) and beverages (2023) in a Norwegian setting by exploring its ability to discriminate the nutritional quality of foods within categories. Additionally, we assessed Nutri-Scores' ability to classify foods in accordance with the Norwegian food-based dietary guidelines (FBDGs). METHODS: The updated Nutri-Score was calculated for 1,782 foods in a Norwegian food database. The discriminatory ability of the updated Nutri-Score was considered by exploring the distribution of Nutri-Score within categories of foods using boxplots and frequency tables, and by examining which qualifying and disqualifying components that contributed most to the Nutri-Score class. Accordance with the Norwegian FBDGs was assessed by exploring Nutri-Score for foods specifically mentioned in the guidelines. RESULTS: Overall, the updated Nutri-Score seemed to discriminate the nutritional quality of foods within categories, in a Norwegian setting. The foods' content of salt and the beverages' content of sugar were components contributing the most to Nutri-Scores' discriminatory ability. Furthermore, in most cases the updated Nutri-Score classified foods in accordance with the Norwegian FBDGs. However, there were minor inconsistencies in how Nutri-Score classified certain foods, such as the inabilities to discriminate between full-fat and low-fat/leaner cheeses, cremes and processed meats (sausages), and between whole grain and refined pasta/rice. CONCLUSIONS: We observed an overall acceptable discriminatory performance of the updated Nutri-Score in a Norwegian setting and in most cases the updated Nutri-Score classified foods in accordance with the Norwegian FBDGs. However, minor inconsistencies were observed. Together with the FBDGs, the updated Nutri-Score could be a useful tool in guiding consumers towards healthier food choices in Norway, but consumer evaluations are warranted to fully assess the performance of the updated Nutri-Score in a Norwegian context.

Cross-sectional comparisons of dietary indexes underlying nutrition labels: nutri-score, Canadian 'high in' labels and Diabetes Canada Clinical Practices (DCCP).

PURPOSE: To assess the cross-sectional association between dietary indexes (DI) that underlie, respectively, the Nutri-score (NS), the proposed Canadian 'High In' Symbol (CHIL) and the Diabetes Canada Clinical Practice Guidelines (DCCP) with food consumption, nutrient intakes and metabolic markers. METHODS: 1836 adults (18-74 years) participating in the representative ESTEBAN study, conducted in mainland France in 2014-2016, were included in the analysis. Food consumption was assessed with three repeated 24 h dietary recalls. Anthropometric measurements and biomarkers of metabolic risk (cholesterol-total, LDL (Low Density Lipoprotein), HDL (High Density Lipoprotein)-triglycerides, glucose) were obtained through a clinical examination and fasting blood draw. The DI were assessed for their association with food consumption, dietary intakes and metabolic biomarkers as quintiles and continuous variables using multi-adjusted linear regression. Heathier diets were assigned to lower scores. RESULTS: Correlations between scores ranged from + 0.62 between CHIL-DI and NS-DI to + 0.75 between NS-DI and DCCP-DI. All DIs discriminated individuals according to the nutritional quality of their diets through food consumption and nutrient intakes (healthier diets were associated with lower intakes of energy, added sugars and saturated fat; and with higher intakes of fiber, vitamins and minerals). NS-DI was associated with blood glucose (adjusted mean in Q1 = 5 vs. Q5 = 5.46 mmol/dl, ptrend = 0.001) and DCCP-DI was associated with BMI (Q1 = 24.8 kg/m2 vs. Q5 = 25.8 kg/m2, ptrend = 0.025), while CHIL showed no significant association with any anthropometric measures or biomarkers. CONCLUSIONS: This study provides elements supporting the validity of the nutrient profiling systems underlying front-of-package nutrition labellings (FOPLs) to characterize the healthiness of diets.

Less favourable food consumption ratings in the Five-Color Nutri-Score are associated with incident frailty in older adults.

BACKGROUND: The Nutri-Score front-of-package labelling classifies food products according to their nutritional quality, so healthier food choices are easier when shopping. This study prospectively assesses the association of a diet rated according to the Nutri-Score system and incident frailty in community-dwelling older adults. METHODS: Cohort study with 1,875 individuals aged ≥60 recruited during 2008-2010 in Spain. At baseline, food consumption was assessed using a validated dietary history. Food was categorised into five Nutri-Score labels (A/green-best quality; B, C, D, E/red-worst quality) utilising an algorithm established in 2017 and currently in use. For each participant, a Five-Color Nutri-Score Dietary Index (5-CNS DI) in grams per day per kilogram was calculated. The 5-CNS DI sums up the grams per day of food consumed times their corresponding nutritional quality value (from A rated as 1 to E rated as 5) and divided by weight in kilograms. From baseline to December 2012, incident frailty was ascertained based on Fried's criteria. Statistical analyses were performed with logistic regression adjusted for main confounders. RESULTS: After a mean follow-up of 3.5 years, 136 cases of frailty were identified. The multivariable-adjusted odds ratios (95% confidence interval) of incident frailty across increasing quartiles of the 5-CNS DI were 1, 1.51 (0.86-2.68), 1.56 (0.82-2.98) and 2.32 (1.12-4.79); P-trend = 0.033. The risk of frailty increased by 28% (3-58%) with a 10-unit increment in this dietary index. Similar results were found with the Nutri-Score algorithm modified in 2022. CONCLUSIONS: consumption of a diet with less favourable Nutri-Score ratings doubles the risk of frailty among community-dwelling older adults.

High hopes for front-of-pack (FOP) nutrition labels? A conjoint analysis on the trade-offs between a FOP label, nutrition claims, brand and price for different consumer segments.

An increasing number of studies investigate the effect of front-of-pack (FOP) nutrition labels on consumer choice without considering differences in consumer preferences for product attributes. This study used a choice-based conjoint analysis to test consumers' preferences for four product attributes (5 levels of a FOP nutrition label, absence/presence of a nutrition claim, brand (unfamiliar, private label or premium) and 5 levels of price) when they coexist (n = 1156). As the consumer preferences showed distinct patterns (multimodality), consumers were subsequently clustered based on how a FOP nutrition label (Nutri-Score) influenced their food choices. Three consumer segments were identified, each valuing the Nutri-Score label differently. The label effectively seems to nudge one segment toward healthier choices (n = 456), while in contrast, another segment is unexpectedly steered toward unhealthier food choices by the label (n = 343). The third segment is only consistently nudged by the FOP label's extremes (n = 357). The segments also differ in their preferences for other product attributes (brand and price), health involvement, and self-reported understanding and use of the Nutri-Score, but not in the measured socio-demographic variables (age, sex, education, social class), dieting or smoking habits. In summary, consumers vary in their food label preferences, and studies that pool consumers may fail to capture these nuances, leading to biased results. This study shows that FOP labels do not steer all consumers toward healthier choices and may even have adverse effects for some. This suggests combining different policies and marketing strategies to reach all consumer segments.

Nutritional value of gluten-free products using the front-of-pack labeling nutri-score.

Background: Nutri-score is a useful and comprehensible system of extended nutrition labeling of food, which is intended to provide the consumer with simple guidance in choosing food products, taking into account the consumer's healthy diet. In several countries, in addition to the mandatory nutritional value of food indicated on the product packaging, the use of the so-called food traffic lights, which, based on a simple graphic display, make it easier for consumers to concentrate on choosing healthier food options. Objective: The aim of the work was to evaluate the nutritional composition of gluten-free food products based on the nutritional data indicated on the packaging of these products in order to find out how useful the use of Front-of-Pack labeling (FOPL) Nutri-score will be in distinguishing the nutritional value of products. Material and Methods: We analyzed 206 randomly selected gluten-free food products obtained from commercial retail chains (semi-finished products, other bakery products, biscuits, flour mixtures, porridges, pasta, muesli, snacks, confectionery, etc.) intended for celiacs. Based on the obtained data, we evaluated the composition of the products using a modified algorithm for calculating the Nutri-score. Results: We found that gluten-free products are a very rich source of energy, especially fats, carbohydrates and sugars, while the proportion of fiber and protein is very low. More than one third of the products had a nutritional score of category A or B, which are healthier variants, but over 40% of the analyzed products already fell into categories D or E. We found the lowest average energy value in the case of products classified in category B, the lowest average fat content and saturated fatty acids were found in products labeled A, the highest sugar content was found in products labeled D and E, the highest average protein content in products labeled A. The highest average salt content was found in products labeled C, fiber in products labeled B and A. Conclusions: Nutritional profiling can significantly contribute to several health-beneficial decisions, especially when choosing and buying healthier food options, including gluten-free foods.

Validation of the efficacy of the NUTRISCORE for the nutritional screening of cancer patients in China.

BACKGROUND: Malnutrition is common in cancer patients. The NUTRISCORE is a newly developed cancer-specific nutritional screening tool and was validated by comparison with the Patient-Generated Subjective Global Assessment (PG-SGA) and Malnutrition Screening Tool (MST) in Spain. We aimed to evaluate the performance of the NUTRISCORE, MST, and PG-SGA in estimating the risk of malnutrition in Chinese cancer patients. METHODS: Data from an open parallel and multicenter cross-sectional study in 29 clinical teaching hospitals in 14 Chinese cities were used. Cancer patients were assessed for malnutrition using the PG-SGA, NUTRISCORE, and MST. The sensitivity, specificity, and areas under the receiver operating characteristic curve were estimated for the NUTRISCORE and MST using the PG-SGA as a reference. RESULTS: A total of 1000 cancer patients were included. The mean age was 55.9 (19 to 92 years), and 47.5% were male. Of these patients, 450 (45.0%) had PG-SGA B and C, 29 (2.9%) had a NUTRISCORE ≥5, and 367 (36.7%) had an MST ≥ 2. Using the PG-SGA as a reference, the sensitivity, specificity, and area under the curve values of the NUTRISCORE were found to be 6.2, 99.8%, and 0.53, respectively. The sensitivity, specificity, and area under the curve values of the MST were 50.9, 74.9%, and 0.63, respectively. The kappa index between the NUTRISCORE and PG-SGA was 0.066, and that between the MST and PG-SGA was 0.262 (P < 0.05). CONCLUSIONS: The NUTRISCORE had an extremely low sensitivity in cancer patients in China compared with the MST when the PG-SGA was used as a reference.

Associations of patient-generated subjective global assessment (PG-SGA) and NUTRISCORE with survival in gastric cancer patients: timing matters, a retrospective cohort study.

BACKGROUND: The timing of nutritional assessment may be important to treat cancer patients and predict their prognosis. This study examined whether Patient-Generated Subjective Global Assessment (PG-SGA) and NUTRISCORE scores were associated with survival among gastric cancer patients who underwent surgery and chemotherapy and whether the timing of the assessment after surgery mattered. METHODS: A total of 952 gastric cancer patients (622 men and 330 women) were included in this retrospective cohort study. The PG-SGA and NUTRISCORE scores were calculated at 1 month (n = 952), 2 months (n = 657), and 3 months (n = 294) after surgery. Cox proportional hazards model was used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: The PG-SGA scores assessed at 1 month after gastrectomy were not associated with survival. However, high PG-SGA scores at 2 months after gastrectomy (median = 65 days) were associated with an increased risk of mortality; the HR (95% CI) was 2.26 (1.22-4.21) for 9-11 vs. ≤ 5 of PG-SGA scores. When we included patients who received all three consecutive consultations, HR (95% CI) was 2.56 (1.02-6.42) for ≥ 9 (malnutrition) vs. ≤ 8 of PG-SGA scores assessed at 3 months after surgery (median days = 98 days). Likewise, high NUTRISCORE scores assessed at the 3-month follow-up were associated with higher mortality; the HR (95% CI) was 3.84 (1.18-12.55) for ≥ 7 vs. ≤ 4 of NUTRISCORE scores. CONCLUSION: Malnutrition assessed with the PG-SGA and NUTRISCORE at 2 to 3 months after gastrectomy was associated with poor survival among gastric cancer patients. Our findings suggest that the timing of the nutritional evaluation may be important in identifying and treating malnutrition related to gastric cancer prognosis.