Are the Eatwell Guide and Nutrient Profiling Models Consistent in the UK?
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ABSTRACT:
SUBMITTER: Pinho-Gomes AC
PROVIDER: S-EPMC8401171 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Improving the nutrient density of processed foods is one way to bring the global food supply closer to the WHO Sustainable Development Goals. Nutrient profiling (NP) has emerged as the preferred method of monitoring the progress toward product innovation and reformulation. This paper presents PepsiCo Nutrition Criteria (PNC), a new internal NP model that was designed to guide and monitor improvements in nutrient density and overall nutritional quality of foods and beverages. The new PNC NP model assigns food products into four classes of increasing nutritional value, based on the content of nutrients to limit, along with nutrients and ingredients to encourage. The nutrient standards used for category assignment followed those developed by global dietary authorities. Standards are proposed for calories, sodium, added sugars, saturated, and industrially produced trans fats. Also included are minimum values for food groups to encourage, low-fat dairy, and for country-specific gap nutrients. Internal use of the NP model has spurred product changes that are consistent with WHO goals for industry transparency. An audited review of company products showed that 48% met added sugar, 65% met sodium, and 71% met saturated fat goals. By the end of 2020, in the top 26 regions in which products are sold, 48% of the total sales volume of global beverages had 100 kcal or less from added sugars per 355 ml serving representing 80% of beverage volume and over 90% of food volume sold globally. The PNC NP model is not consumer-facing but is specifically intended for internal use to motivate stepwise and incremental product innovation and reformulation. Transparent and published NP models further WHO goals of engaging industry stakeholders in the (re)formulation of processed foods and beverages consistent with public health goals.
| S-EPMC8733001 | biostudies-literature
Project description:It has been shown that identical deep learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and finance, where transparency and explainability is paramount, this can be a significant barrier to DL adoption. In this study we present a further analysis of explanation (in)consistency on 6 tabular datasets/tasks, with a focus on Electronic Health Records data. We propose a novel deep learning ensemble architecture that trains its sub-models to produce consistent explanations, improving explanation consistency by as much as 315% (e.g. from 0.02433 to 0.1011 on MIMIC-IV), and on average by 124% (e.g. from 0.12282 to 0.4450 on the BCW dataset). We evaluate the effectiveness of our proposed technique and discuss the implications our results have for both industrial applications of DL and explainability as well as future methodological work.
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Project description:In Australia, manufacturers can use two government-endorsed approaches to advertise product healthiness: the Health Star Rating (HSR) front-of-pack nutrition labelling system, and health claims. Related, but different, algorithms determine the star rating of a product (the HSR algorithm) and eligibility to display claims (the Nutrient Profiling Scoring Criterion (NPSC) algorithm). The objective of this study was to examine the agreement between the HSR and NPSC algorithms. Food composition information for 41,297 packaged products was extracted from The George Institute's FoodSwitch database. HSR and the NPSC scores were calculated, and the proportion of products in each HSR category that were eligible to display a health claim under the NPSC was examined. The highest agreement between the HSR scoring algorithm and the NPSC threshold to determine eligibility to display a health claim was at the HSR cut-off of 3.5 stars (k = 0.83). Overall, 97.3% (n = 40,167) of products with star ratings of 3.5 or higher were also eligible to display a health claim, and 94.3% (n = 38,939) of products with star ratings less than 3.5 were ineligible to display a health claim. The food group with greatest divergence was "edible oils", with 45% products (n = 342) with HSR >3.5, but 64% (n = 495) eligible to display a claim. Categories with large absolute numbers of products with HSR <3.5, but eligible to display a claim, were "yoghurts and yoghurt drinks" (335 products, 25.4%) and "soft drinks" (299 products, 29.7%). Categories with a large number of products with HSR ≥3.5, but ineligible to display a claim, were "milk" (260 products, 21.2%) and "nuts and seeds" (173 products, 19.7%). We conclude that there is good agreement between the HSR and the NPSC systems overall, but divergence in some food groups is likely to result in confusion for consumers, particularly where foods with low HSRs are eligible to display a health claim. The alignment of the NPSC and HSR scoring algorithms should be improved.
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Project description:Many ecological studies employ general models that can feature an arbitrary number of populations. A critical requirement imposed on such models is clone consistency: If the individuals from two populations are indistinguishable, joining these populations into one shall not affect the outcome of the model. Otherwise a model produces different outcomes for the same scenario. Using functional analysis, we comprehensively characterize all clone-consistent models: We prove that they are necessarily composed from basic building blocks, namely linear combinations of parameters and abundances. These strong constraints enable a straightforward validation of model consistency. Although clone consistency can always be achieved with sufficient assumptions, we argue that it is important to explicitly name and consider the assumptions made: They may not be justified or limit the applicability of models and the generality of the results obtained with them. Moreover, our insights facilitate building new clone-consistent models, which we illustrate for a data-driven model of microbial communities. Finally, our insights point to new relevant forms of general models for theoretical ecology. Our framework thus provides a systematic way of comprehending ecological models, which can guide a wide range of studies.
| S-EPMC7895417 | biostudies-literature
Project description:Nutrient profiling (NP) is a method for evaluating the healthfulness of foods. Although many NP models exist, most have not been validated. This study aimed to examine the content and construct/convergent validity of five models from different regions: Australia/New Zealand (FSANZ), France (Nutri-Score), Canada (HCST), Europe (EURO) and Americas (PAHO). Using data from the 2013 UofT Food Label Information Program (n15342 foods/beverages), construct/convergent validity was assessed by comparing the classifications of foods determined by each model to a previously validated model, which served as the reference (Ofcom). The parameters assessed included associations (Cochran-Armitage trend test), agreement (? statistic) and discordant classifications (McNemar's test). Analyses were conducted across all foods and by food category. On the basis of the nutrients/components considered by each model, all models exhibited moderate content validity. Although positive associations were observed between each model and Ofcom (all P trend<0·001), agreement with Ofcom was 'near perfect' for FSANZ (?=0·89) and Nutri-Score (?=0·83), 'moderate' for EURO (?=0·54) and 'fair' for PAHO (?=0·28) and HCST (?=0·26). There were discordant classifications with Ofcom for 5·3 % (FSANZ), 8·3 % (Nutri-Score), 22·0 % (EURO), 33·4 % (PAHO) and 37·0 % (HCST) of foods (all P<0·001). Construct/convergent validity was confirmed between FSANZ and Nutri-Score v. Ofcom, and to a lesser extent between EURO v. Ofcom. Numerous incongruencies with Ofcom were identified for HCST and PAHO, which highlights the importance of examining classifications across food categories, the level at which differences between models become apparent. These results may be informative for regulators seeking to adapt and validate existing models for use in country-specific applications.
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Project description:ObjectiveThis study aimed to apply the newly developed Chile Adjusted Model (CAM) nutrient profiling model (NPM) to the food supply in South Africa (SA) and compare its performance against existing NPMs as an indication of suitability for use to underpin food policies targeted at discouraging consumption of products high in nutrients associated with poor health.DesignCross-sectional analysis of the SA packaged food supply comparing the CAM to three other NPMs: SA health and nutrition claims (SA HNC), Chilean warning octagon (CWO) 2019, and Pan-American Health Organization (PAHO) NPM.SettingThe SA packaged food supply based on products stocked by supermarkets in Cape Town, SA.ParticipantsPackaged foods and beverages (N=6474) available in 2018 were analyzed.Results49% of products contained excessive amounts of nutrients of concern (considered non-compliant) according to the criteria of all four models. Only 10.9% of products were not excessive in any nutrients of concern (considered compliant) according to all NPMs evaluated. The CAM had an overall non-compliance level of 73.2%, and was comparable to the CWO 2019 for foods (71.2% and 71.1% respectively). The CAM was the strictest NPM for beverages (80.4%) due to the criteria of non-sugar sweeteners and free sugars. The SA HNC was the most lenient with non-compliance at 52.9%. This was largely due to the inclusion of nutrients to encourage, which is a criterion for this NPM.ConclusionFor the purpose of discouraging products high in nutrients associated with poor health in SA, the CAM is a suitable NPM.
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Project description:Vegetation restoration is assumed to enhance carbon (C) sequestration in terrestrial ecosystems, where plant producers and microbial decomposers play key roles in soil C cycling. However, it is not clear how the nutrient limitation patterns of plants and soil microbes might change during vegetation restoration. We investigated the nutrient limitations of the plant and microbial communities along a natural vegetation restoration chronosequence (1, 8, 16, 31, and 50 years) following farmland abandonment in Qinling Mountains, China, and assessed their relationships with soil factors. The result showed that following natural vegetation restoration, the nitrogen (N) limitation of plant and microbial communities was alleviated significantly, and thereafter, it began to shift to phosphorus (P) limitation at a later stage. Plants showed P limitation 50 years after restoration, while microbial P limitation appeared 31 years later. The changes in plant nutrient limitation were consistent with those in microbial nutrient limitation, but soil microbes were limited by P earlier than plants. Random forest model and partial least squares path modeling revealed that soil nutrient stoichiometry, especially soil C:N ratio, explained more variations in plant and microbial nutrient limitation. Our study demonstrates that the imbalanced soil C:N ratio may determine the soil microbial metabolic limitation and further mediate the variation in plant nutrient limitation during natural vegetation restoration, which provides important insights into the link between metabolic limitation for microbes and nutrient limitation for plants during vegetation restoration to improve our understanding of soil C turnover in temperate forest ecosystems.
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Project description:Government agencies and private companies have supported the development of nutrient profiling (NP) systems to facilitate the selection of nutrient-dense foods by consumers, promote nutritious food development, and limit excessive advertising of products with low nutritional value. While most NP models were developed to assess individual foods, the Ajinomoto Group Nutrient Profiling System (ANPS) was developed to assess the overall nutritional value of cooked dishes that are culturally specific to Japan. Based on the national dietary recommendations and nutritional surveys, target values were created for 13 dish categories, while considering the combinations of meal units. For the ANPS, the four evaluating elements were protein and vegetables, which should be encouraged, and sodium and saturated fatty acids, which should be limited. The ANPS algorithm for dishes was the sum of the scores of individual elements, with a maximum of 10 points per serving. The sum of scores was then multiplied by 2.5 to convert to the 100-point scale. Convergent validity was tested using the nutrient-rich food index (NRF) score of 6.3. In total, 1,089 popular Japanese dishes were evaluated using the ANPS, and the median score of ANPS was 70.0 points (interquartile range, 55-78.8), and the average score was 67.7 (standard deviation, 16.5) points. Since salt intake is a major health risk in Japan, this tool was designed to evaluate sodium content with high sensitivity, and low-salt dishes significantly improved sodium and ANPS scores compared with regular dishes. The Pearson's correlation coefficient between the total score of NRF 6.3 and ANPS in 1,089 dishes was r = 0.452 (p < 0.0001). This newly developed ANPS could be used to evaluate culture-specific cooked dishes per serving size. It can determine the nutritional values of dishes, with a high sensitivity to sodium content, a major Japanese nutritional issue. Further research is needed to determine the accuracy and usefulness of the ANPS as a system that would lead to changes in eating behavior nationwide.
| S-EPMC9372512 | biostudies-literature
Project description:Nutrient profiling ranks foods based on their nutrient composition, with applications in multiple aspects of food policy. We tested the capacity of a category-specific model developed for product reformulation to improve the average nutrient content of foods, using five national food composition datasets (UK, US, China, Brazil, France). Products (n = 7183) were split into 35 categories based on the Nestlé Nutritional Profiling Systems (NNPS) and were then classified as NNPS 'Pass' if all nutrient targets were met (energy (E), total fat (TF), saturated fat (SFA), sodium (Na), added sugars (AS), protein, calcium). In a modelling scenario, all NNPS Fail products were 'reformulated' to meet NNPS standards. Overall, a third (36%) of all products achieved the NNPS standard/pass (inter-country and inter-category range: 32%-40%; 5%-72%, respectively), with most products requiring reformulation in two or more nutrients. The most common nutrients to require reformulation were SFA (22%-44%) and TF (23%-42%). Modelled compliance with NNPS standards could reduce the average content of SFA, Na and AS (10%, 8% and 6%, respectively) at the food supply level. Despite the good potential to stimulate reformulation across the five countries, the study highlights the need for better data quality and granularity of food composition databases.
| S-EPMC5409745 | biostudies-literature
Project description:Designing genetic networks with desired functionalities requires an accurate mathematical framework that accounts for the essential mechanistic details of the system. Here, we formulate a time-delay model of protein translation and mRNA degradation by systematically reducing a detailed mechanistic model that explicitly accounts for the ribosomal dynamics and the cleaving of mRNA by endonucleases. We exploit various technical and conceptual advantages that our time-delay model offers over the mechanistic model to probe the behavior of a self-repressing gene over wide regions of parameter space. We show that a heuristic time-delay model of protein synthesis of a commonly used form yields a notably different prediction for the parameter region where sustained oscillations occur. This suggests that such heuristics can lead to erroneous results. The functional forms that arise from our systematic reduction can be used for every system that involves transcription and translation and they could replace the commonly used heuristic time-delay models for these processes. The results from our analysis have important implications for the design of synthetic gene networks and stress that such design must be guided by a combination of heuristic models and mechanistic models that include all relevant details of the process.
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