A comparative analysis of nutritional quality, amino acid profile, and nutritional supplementations in plant-based products and their animal-based counterparts in the UK.

Plant-based (PB) food products have surged in popularity over the past decade. Available PB products in the UK market were extracted from NielsenIQ Brandbank and compared with animal-based (AB) counterparts in their nutrient contents and calculated Nutri-Scores. The amino acid contents of four beef products and their PB alternatives were analysed by LC-MS/MS. PB products consistently exhibited significantly higher fibre content across all food groups. Protein was significantly higher in AB products from all food groups except beef and ready meals. PB products were more likely to have higher Nutri-Scores compared to AB counterparts, albeit with greater score variability within each food group. Nutrient fortifications were primarily focused on dairy and ready meals; the most supplemented nutrient was vitamin B12 (found in 15% of all products). A higher proportion of EAAs in relation to total protein content was observed in all beef products.

Nutri-Score of Meat, Fish, and Dairy Alternatives: A Comparison between the Old and New Algorithm.

Nutri-Score is a front-of-pack label that visualizes the nutritional quality of food products from most healthy (A, dark green) to least healthy (E, red). However, concerns have been raised about discrepancies between Nutri-Score labels and dietary recommendations. Therefore, the Nutri-Score algorithm has recently been adapted. To investigate the effect of the new algorithm, the Nutri-Score of plant-based meat, fish, and dairy alternatives (n = 916) was calculated with the old and new algorithms. In addition, the nutritional values of meat and milk alternatives with Nutri-Score labels A and B were compared under the old and new conditions and subsequently assessed for alignment with the criteria of Dutch dietary guidelines. The new algorithm resulted in a reduction in the number of products with labels A and B, ranging from 5% (cold cuts alternatives) to 55% (milk alternatives). The nutritional composition of products with labels A and B improved for meat alternatives (lower energy and saturated fatty acid contents; higher protein content) and milk alternatives (lower energy, salt, and sugar contents; higher protein and fiber contents). Overall, the new Nutri-Score algorithm is more in line with the Dutch dietary guidelines for plant-based meat and dairy alternatives, though challenges remain with respect to micronutrient (iron, calcium, vitamin B12), salt, and protein contents.

A Randomized Controlled Study to Test Front-of-Pack (FOP) Nutrition Labels in the Kingdom of Saudi Arabia.

One common strategy for governments to tackle the non-communicable disease (NCD) epidemic is front-of-package (FOP) nutrition labeling. The Kingdom of Saudi Arabia (KSA) is considering implementing a new FOP label that is based on either France's Nutri-Score (NS), which labels all foods (A = healthiest to E = least healthy) based on overall nutritional quality, or the Chilean warning label (WL) approach, which identifies foods to avoid based on select nutritional characteristics. Using a fully functional online grocery store, this study aimed to test these two promising FOP strategies by randomizing 656 KSA adults into one of the three versions of the store to complete a hypothetical grocery shop: no-label (control), NS, and WL. The NS was modified with a sugar percentage tag given that reducing sugar consumption is one of KSA's public health goals. We found that both modified NS labels and Chilean warning labels positively influenced food and beverage choices among KSA participants, but there were differential effects across the two labels. Relative to the control, NS improved the overall diet quality of the shopping baskets, measured by the weighted (by the number of servings) average NS point (ranging from 0, least healthy, to 55, healthiest), by 2.5 points [95% CI: 1.7, 3.4; p < 0.001], whereas results for WL were not statistically significant (0.6 points [95% CI: -0.2,1.5]). With respect to each nutritional attribute, we found that NS reduced sugar intake per serving, whereas WL was effective at decreasing energy and saturated fat intake per serving from food and beverages purchased. Our results suggest that the NS approach that identifies the healthiness of all foods using a holistic approach appears preferable if the purpose of the label is to improve overall diet quality as opposed to addressing select nutrients to avoid.

Examining the diet quality of Canadian adults and the alignment of Canadian front-of-pack labelling regulations with other front-of-pack labelling systems and dietary guidelines.

Introduction: Canada promulgated mandatory front-of-pack labelling (FOPL) regulations in 2022, requiring pre-packaged foods meeting and/or exceeding recommended thresholds for nutrients-of-concern (i.e., saturated fat, sodium, sugars) to display a "high-in" nutrition symbol. However, there is limited evidence on how Canadian FOPL (CAN-FOPL) regulations compare to other FOPL systems and dietary guidelines. Therefore, the objectives of the study were to examine the diet quality of Canadians using the CAN-FOPL dietary index system and its alignment with other FOPL systems and dietary guidelines. Methods: Nationally representative dietary data from the 2015 Canadian Community Health Survey-Nutrition survey (n = 13,495) was assigned dietary index scores that underpin CAN-FOPL, Diabetes Canada Clinical Practice (DCCP) Guidelines, Nutri-score, Dietary Approaches to Stop Hypertension (DASH) and Canada's Food Guide (Healthy Eating Food Index-2019 [HEFI-2019]). Diet quality was examined by assessing linear trends of nutrient intakes across quintile groups of CAN-FOPL dietary index scores. The alignment of CAN-FOPL dietary index system compared with other dietary index systems, with HEFI as the reference standard, was examined using Pearson's correlations and к statistics. Results: The mean [95% CI] dietary index scores (range: 0-100) for CAN-FOPL, DCCP, Nutri-score, DASH, and HEFI-2019 were 73.0 [72.8, 73.2], 64.2 [64.0, 64.3], 54.9 [54.7, 55.1], 51.7 [51.4, 51.9], and 54.3 [54.1, 54.6], respectively. Moving from the "least healthy" to the "most healthy" quintile in the CAN-FOPL dietary index system, intakes of protein, fiber, vitamin A, vitamin C, and potassium increased, while intakes of energy, saturated fat, total and free sugars, and sodium decreased. CAN-FOPL showed moderate association with DCCP (r = 0.545, p < 0.001), Nutri-score (r = 0.444, p < 0.001), and HEFI-2019 (r = 0.401, p < 0.001), but poor association with DASH (r = 0.242, p < 0.001). Slight to fair agreement was seen between quintile combinations of CAN-FOPL and all dietary index scores (к = 0.05-0.38). Discussion: Our findings show that CAN-FOPL rates the dietary quality of Canadian adults to be healthier than other systems. The disagreement between CAN-FOPL with other systems suggest a need to provide additional guidance to help Canadians select and consume 'healthier' options among foods that would not display a front-of-pack nutrition symbol.

Front-of-pack labels: "Directive" versus "informative" approaches.

Front-of-pack labels (FOPLs) aim at communicating to consumers the health value of food items in support of public health policies. Two main types can be discerned: directive and semidirective FOPLs using color schemes (e.g., Nutri-Score) and informative FOPLs (e.g., Nutrinform Battery). Directive approaches tend to show a "wear-out effect" and, additionally, they tend to have various underlying conceptual problems. Usually, their nutritional scores are calculated using changing, arbitrary algorithms and involve a reductionist set of parameters of debatable validity. Thus, they overstate the effects of selected nutritional factors, such as saturated fat and energy, while overlooking the food matrix and the more holistic aspects of nourishment. Moreover, they do not reflect the portion that is consumed, ignore the preparation steps at home, and fail to serve as a useful basis for composing a healthy diet. Also, so long as the nutritional formulations match the algorithmic standards, they tend to allow ultra-processed products. Thus, this might confuse and mislead consumers. Overconfidence in green-colored labels could even result in unbalanced dietary choices, whereas avoidance of red products may eliminate certain foods from the diet that are rich in essential nutrients (e.g., cheese), leading to opposite results than aimed for. The latter is particularly relevant to vulnerable populations, such as the young, pregnant women, and older adults, or for individuals with specific needs. Taken together, directive FOPLs such as Nutri-Score contradict the declared intent of the European Commission to empower consumers to undertake healthy and balanced diets based on easily accessible and robust information. Although informative systems usually also keep the focus on a few selected nutritional parameters, they have are less paternalizing and obviate the need to classify foods as healthy or unhealthy. They also focus attention on the individual portions that are consumed (even if the definition of portion size remains contentious). Given the importance of dietary patterns, rather than individual foods or nutrients, directiveFOPLs of the Nutri-Score type represent a regretful case of nutritionism. Finally, attempts to associate the adoption of a FOPL with an improvement in the health status are few and mainly applied in virtual settings; none of which are longitudinal, nor have they been able to identify a causal link.

Fantastic Foods and Where to Find Them-Advantages and Disadvantages of Nutri-Score in the Search for Healthier Food.

The composition and quality of food rations and the intake of nutrients with the diet are undoubtedly reflected in our health. In order to help the consumer choose the right food product, food manufacturers use front-of-pack labels, which are designed to convey concise information about the nutritional value of the product. Such labels include the Nutri-Score system. In this paper, we have critically analyzed this system. As the available data indicate, this system does not take into account a number of factors that affect the quality of a food product, including the size of the package, the contents of vitamins, minerals, and other selected health-promoting ingredients, the degree of processing, or the fatty acid profile of the product, and it discriminates against regional products, organic products, and juices and nectars. This system, although intuitive and created with good intentions, still has quite a few flaws that must be addressed before it can be considered to correctly indicate the nutritional value of food products.

Perspective: How to Develop Nutrient Profiling Models Intended for Global Use: A Manual.

Nutrient profiling (NP) models aim to assess the nutritional quality of individual foods, according to their energy content and nutrient composition. NP models, initially created to prevent obesity in high-income countries, have tended to penalize dietary energy by giving lower ratings to foods containing excessive calories, fat, sugar, and salt. Energy-driven NP models may need to be reconceptualized for use in low- and middle-income countries (LMIC) where hunger, undernutrition, and micronutrient deficiencies continue to be issues of public health concern. Consistent with the position of the WHO that the purpose of NP methods is to address an identified public health problem, NP models intended for use in LMIC ought to address inadequate intakes of vitamin A, B vitamins, folate, calcium, iron, iodine, and zinc and the frequent lack of high-quality protein. Those models of nutrient density that feature beneficial nutrients (high-quality protein, vitamins, minerals, and trace elements) may be better suited to LMIC needs than are some current NP models that are wholly based around nutrients to limit. NP models intended for LMIC and global use will also need to take food fortification into account. The challenge for LMIC public health agencies is how to balance the future risk of excess "empty" calories against the continuing danger of inadequate nutrients and micronutrient deficiencies that persist at the population level.