Maintaining a healthy diet throughout life helps prevent all forms of malnutrition, thereby reducing the risk of non-communicable diseases (NCDs) and related conditions [...]
This study examines the impact of technological progress on food price dynamics and supply stability across the 27 European Union Member States during 2011–2024. Using a balanced panel dataset, the analysis explores four dependent indicators—consumer food prices, food price inflation, price volatility, and food supply variability—while controlling for trade openness, GDP per capita growth, and population. Technological progress is estimated through panel least squares regression with fixed effects. The results reveal that technological advancement significantly reduces food prices and inflation, suggesting that innovation-driven productivity and efficiency gains stabilize consumer markets. However, its influence on food price volatility and supply variability is statistically insignificant, indicating that innovation alone cannot fully mitigate systemic risks in the European food system. The results provide policy-relevant evidence supporting the integration of technological innovation into food system governance across the European Union. They underline the need for targeted investment and regulatory coordination to translate innovation gains into tangible resilience outcomes, thus offering practical guidance for policymakers and stakeholders involved in implementing the European Green Deal and the Farm to Fork Strategy.
ObjectiveThis study examines the association between serum vitamin D levels and the prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD) in adolescents, along with potential modifying factors.MethodsData from 950 adolescents aged 12–18 years in the National Health and Nutrition Examination Survey (NHANES) 2017–2018 were analyzed. MAFLD was defined using hepatic steatosis and metabolic dysfunction criteria. Serum 25(OH)D levels were measured, and weighted logistic regression and restricted cubic spline models were applied to assess their association with MAFLD risk. Stratified analyses were also conducted.ResultsLower serum 25(OH)D levels were significantly associated with higher MAFLD risk (p < 0.001), showing a nonlinear dose-response relationship. Adolescents with 25(OH)D ≥ 75 nmol/L had a 57% lower risk of MAFLD compared to those with levels < 50 nmol/L. Stratified analysis indicated that the protective effect of vitamin D was more evident in individuals with higher retinol levels, though retinol alone was not significantly associated with MAFLD.ConclusionVitamin D deficiency is significantly associated with MAFLD in adolescents, with a nonlinear dose-response relationship modulated by retinol status. These findings underscore the potential role of vitamin D in MAFLD prevention and provide a basis for further prospective or intervention studies.
Dayne R. Freudenberg, Daniel G. Haughian, Mitchell A. Klusty
et al.
Accurate nutritional assessment is critical for public health, but existing profiling systems require detailed data often unavailable or inaccessible from colloquial text descriptions of food. This paper presents a machine learning pipeline that predicts the comprehensive Food Compass Score 2.0 (FCS) from text descriptions. Our approach uses multi-headed neural networks to process hybrid feature vectors that combine semantic text embeddings, lexical patterns, and domain heuristics, alongside USDA Food and Nutrient Database for Dietary Studies (FNDDS) data. The networks estimate the nutrient and food components necessary for the FCS algorithm. The system demonstratedstrong predictive power, achieving a median R^2 of 0.81 for individual nutrients. The predicted FCS correlated strongly with published values (Pearson's r = 0.77), with a mean absolute difference of 14.0 points. While errors were largest for ambiguous or processed foods, this methodology translates language into actionable nutritional information, enabling scalable dietary assessment for consumer applications and research.
Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF$^2$) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware visual feature fusion module combines ingredient features and visual representation to achieve accurate nutritional prediction. During testing, ingredient predictions are refined using large multimodal models by data augmentation and majority voting. Our experiments on both FastFood and Nutrition5k datasets validate the effectiveness of our proposed method built in different backbones (e.g., Resnet, InceptionV3 and ViT), which demonstrates the importance of ingredient information in nutrition estimation. https://huiyanqi.github.io/fastfood-nutrition-estimation/.
Thorsten Ruprechter, Marion Garaus, Ivo Ponocny
et al.
Deriving nutritional information from online food posts is challenging, particularly when users do not explicitly log the macro-nutrients of a shared meal. In this work, we present an efficient and straightforward approach to approximating macro-nutrients based solely on the titles of food posts. Our method combines a public food database from the U.S. Department of Agriculture with advanced text embedding techniques. We evaluate the approach on a labeled food dataset, demonstrating its effectiveness, and apply it to over 500,000 real-world posts from Reddit's popular /r/food subreddit to uncover trends in food-sharing behavior based on the estimated macro-nutrient content. Altogether, this work lays a foundation for researchers and practitioners aiming to estimate caloric and nutritional content using only text data.
IntroductionThe relationship between serum uric acid (SUA) and cervical cancer is inconclusive. This study aims to investigate the causal relationship between SUA levels and cervical cancer incidence, and to evaluate the potential role of nutritional interventions in cervical cancer prevention.MethodsWe conducted a two-sample bidirectional Mendelian randomization (MR) analysis using genetic instruments from publicly available genome-wide association studies (GWASs) of individuals of predominantly European ancestry. Methods such as inversevariance weighted, weighted-median, weighted model, and MR-Egger were applied. Sensitivity tests, including leave-one-out, MR-PRESSO, and Cochran’s Q test, assessed heterogeneity and pleiotropy.ResultsOur findings revealed that a high SUA concentration significantly increased the risk of malignant cervical cancer: a 1 mg/mL increase in SUA was associated with a 71% higher risk (OR = 1.71, 95% CI = 1.10–2.67; p = 0.018). Stratification by histological type showed a significant causal effect on cervical adenocarcinoma risk (OR = 2.56, 95% CI = 1.14–5.73; p = 0.023). However, no clear evidence was found for a causal effect of cervical cancer on SUA levels.ConclusionThis study identified a causal relationship between elevated SUA levels and the risk of malignant cervical cancer, particularly cervical adenocarcinoma. These findings provide novel insights into the mechanisms of cervical carcinogenesis and suggest that managing SUA levels could be a potential strategy for cervical cancer prevention through dietary management.
Angel Alois Osorio Manyari, Azucena Lirio Armas Alvarez, Joel Davis Osorio Manyari
et al.
Background: The effect of metabolic surgery on long-term diabetes remission in Asian patients with a body mass index (BMI) < 30 kg/m2 has not been widely reported. Methods: We conducted a systematic review of the PubMed and Cochrane Library databases from inception to June 2024. All clinical trials and observational studies involving the effect of metabolic surgery in Asian patients with type 2 diabetes mellitus and BMI <30 kg/m2 were considered. The quality of the studies was assessed using the Newcastle-Ottawa scale. Results: Of the 1175 studies screened, 21 studies (11 prospective and 10 retrospective), including 1005 patients, were selected. Only one study had a control group. The longest follow-up was 60 months. The results showed significant improvement in glycated hemoglobin (HbA1c), fasting blood glucose (FBG), 2-h plasma glucose (2hPG), homeostasis model assessment for insulin resistance index (HOMA-IR), fasting C-peptide, triglycerides, total cholesterol, and a reduction in the use of oral hypoglycemic agents/insulin at 12, 24, 36, and 60 months after metabolic surgery. The most common surgical complications observed were anemia (2.1 %–33 %), marginal ulcer (4.2 %–17.3 %), gastrointestinal bleeding (1.9 %–12 %), anastomotic leak (2.1 %–3.5 %), anastomotic stenosis (2.1 %–3.5 %), reoperation (1.18 %), and a mortality rate of zero. Conclusions: Long-term diabetes remission, along with improvements in HbA1c, 2hPG, FBG, and HOMA-IR, with an acceptable rate of complications, was observed in Asian patients with BMI <30 kg/m2 after metabolic surgery. Future research with controlled studies should focus on preoperative patient selection criteria beyond just the BMI cutoff.
Alicja Kucharska, Beata Irena Sińska, Mariusz Panczyk
et al.
IntroductionDietary fiber is a key component of a healthy diet, associated with a reduced risk of cardiovascular disease, obesity, type 2 diabetes, certain cancers, chronic inflammation, or depression. The aim of the study was to perform an in-depth analysis of dietary fiber intake in the Polish population, taking account of the consumption of groups of products that are fiber sources and identify any age-related differences in the dietary fiber intake of the subjects.MethodsWe analyzed data obtained from two representative cross-sectional studies on the diet and nutritional status of adult Polish residents including the total of 4,000 individuals aged 19 years and more. Two 24-h recalls were used per individual to assess the diet using the computer-assisted personal interview (CAPI) technique. Total fiber content and fiber contained in cereal products, vegetables, fruits, legumes, nuts and seeds were calculated. Fiber intake was compared to the recommendations: 25 g/d for adults up to 65 years of age and 20 g/d for those aged 66 years and older. All statistical analyses, including the Pearson’s chi-squared test, the Student’s t-test, and the Analysis of Variance (ANOVA), were conducted using STATISTICA™ version 13.3, with the results being adjusted for demographic distribution biases to enhance the representativeness.ResultsThe average daily fiber intake was 17.83 ± 0.14 g/day (78% of the recommended intake), with 20.5% of respondents meeting the requirement. More men than women (27.05% vs. 14.3%;) met the requirement and men were characterized by a higher average intake (19.34 ± 0.20 g/day) than women (16.43 ± 0.19 g/day). The main fiber sources were cereals (44.1%), vegetables (23.6%), and fruits (16.0%). As regards men, the sources included refined bread (25.8%), vegetables (23.1%), and fruits (10.2%) and for women, they were vegetables (24.0%), fruits (17.2%), and refined bread (16.3%). Although refined bread is not recommended as a primary fiber source due to its lower fiber content compared to whole grain bread, its high consumption significantly contributed to the total fiber intake.ConclusionThe prevalence of widespread dietary fiber deficiency calls for the intensification of educational efforts that address the health advantages and sources of dietary fiber, as well as methods for its inclusion in daily meals.
Abstract This study analyzes the changes that have occurred in food logistics in the three years since the emergence of the COVID-19 pandemic and the one year since the war in Ukraine commenced. Food logistics companies are highly sensitive to demand shocks, energy prices, and staff availability. In this study, “first-hand” information was collected in the Iberian Peninsula, and it showed a process of Schumpeterian transformation. This crisis environment in which food logistics companies have been operating has opened a unique opportunity to renew operating procedures and seek new solutions, products, and markets. Therefore, food logistics companies have developed more effective communication strategies and innovative, profitable, and forward-looking commercial strategies to adapt to the new needs of their clients, applied more efficient transport planning and management methods, implemented new technologies to increase automation and digitization in warehouses, transport platforms, and trucks, and boosted market concentration and investment in infrastructure. Therefore, public authorities and top executives must focus on promoting and facilitating these improvements.
Nutrition. Foods and food supply, Agricultural industries
Thomas Cherico Wanger, Estelle Raveloaritiana, Siyan Zeng
et al.
China is the leading crop producer and has successfully implemented sustainable development programs related to agriculture. Sustainable agriculture has been promoted to achieve national food security targets such as food self-sufficiency through the well-facilitated farmland construction (WFFC) approach. The WFFC is introduced in Chinas current national 10-year plan to consolidate farmlands into large and simplified production areas to maximise automation, and improve soil fertility and productivity. However, research suggests that diversified and smaller farms faciliate ecosystem services, can improve yield resilience, defuse human health threats, and increase farm profitability. Currently, WFFC has not considered ecological farmland improvements and it may miss long-term environmental benefits including ecosystem service preservation conducive to yields. Moreover, the nutritional status in China has changed in recent decades with undernutrition being dramatically reduced, but the prevalence of overweight, obesity, and chronic diseases being increased. While a strategic choice and management of crop and livestock species can improve nutrition, the environmental and production benefits of agricultural diversification are currently not well interlinked with Chinas food and nutrition security discussions. Lastly, the role of agricultural technology for socioeconomic benefits and the link with diversified agricultural production may provide vast benefits for food security. Here, we focus on the opportunities and co-benefits of agricultural diversification and technology innovations to advance food and nutrition security in China through ecosystem service and yield benefits. Our applied five-point research agenda can provide evidence-based opportunities to support China in reaching its ambitious food security targets through agricultural diversification with global ramifications.
Elena M. Martinez, Nicole Tichenor Blackstone, Parke E. Wilde
et al.
Background: Transitions towards healthier, more environmentally sustainable diets would require large shifts in consumption patterns. Cost and affordability can be barriers to consuming healthy, sustainable diets. Objective: This study provides the first worldwide test of how retail food prices relate to empirically estimated environmental footprints and nutritional profile scores between and within food groups. Methods: We use 48,316 prices for 860 retail food items commonly sold in 181 countries during 2011 and 2017, matched to estimated carbon and water footprints and nutritional profiles, to test whether healthier and more sustainable foods are more expensive between and within food groups. Results: Prices, environmental footprints, and nutritional profiles differ between food groups. Within almost all groups, more expensive items have significantly larger carbon and water footprints. Associations are strongest for animal source foods, where each 10% increment in price is associated with 21 grams higher carbon footprint and 5 liters higher water footprint per 100kcal of food. There is no such gradient for price and nutritional profile, as more expensive items are sometimes healthier and sometimes less healthy depending on the food group, price range, and nutritional attribute of interest. Conclusions: Our finding that higher-priced items have larger environmental footprints is contrary to expectations that a more sustainable diet would be more expensive. Instead, we find that within each food group, meeting dietary needs with lower environmental footprints is possible by choosing items with a lower unit price. These findings are consistent with prior observations that higher-priced items typically use more resources, including energy and water, but may or may not be healthful as measured by nutrient profile scores.
With diet and nutrition apps reaching 1.4 billion users in 2022 [1], it's not surprise that popular health apps, MyFitnessPal, Noom, and Calorie Counter, are surging in popularity. However, one major setback [2] of nearly all nutrition applications is that users must enter food data manually, which is time-consuming and tedious. Thus, there has been an increasing demand for applications that can accurately identify food items, analyze their nutritional content, and offer dietary recommendations in real-time. This paper introduces a comprehensive system that combines advanced computer vision techniques with nutritional analysis, implemented in a versatile mobile and web application. The system is divided into three key concepts: 1) food detection using the YOLOv8 model, 2) nutrient analysis via the Edamam Nutrition Analysis API, and 3) personalized meal recommendations using the Edamam Meal Planning and Recipe Search APIs. Preliminary results showcase the system's effectiveness by providing immediate, accurate dietary insights, with a demonstrated food recognition accuracy of nearly 80%, making it a valuable tool for users to make informed dietary decisions.
The profound impact of food on health necessitates advanced nutrition-oriented food recommendation services. Conventional methods often lack the crucial elements of personalization, explainability, and interactivity. While Large Language Models (LLMs) bring interpretability and explainability, their standalone use falls short of achieving true personalization. In this paper, we introduce ChatDiet, a novel LLM-powered framework designed specifically for personalized nutrition-oriented food recommendation chatbots. ChatDiet integrates personal and population models, complemented by an orchestrator, to seamlessly retrieve and process pertinent information. The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content. The orchestrator retrieves, synergizes and delivers the output of both models to the LLM, providing tailored food recommendations designed to support targeted health outcomes. The result is a dynamic delivery of personalized and explainable food recommendations, tailored to individual user preferences. Our evaluation of ChatDiet includes a compelling case study, where we establish a causal personal model to estimate individual nutrition effects. Our assessments, including a food recommendation test showcasing a 92\% effectiveness rate, coupled with illustrative dialogue examples, underscore ChatDiet's strengths in explainability, personalization, and interactivity.
The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. However, the scarcity of high-quality data and limited collaboration between industry and academia have constrained progress in this field. Building on recent advancements in 3D reconstruction, we host the MetaFood Workshop and its challenge for Physically Informed 3D Food Reconstruction. This challenge focuses on reconstructing volume-accurate 3D models of food items from 2D images, using a visible checkerboard as a size reference. Participants were tasked with reconstructing 3D models for 20 selected food items of varying difficulty levels: easy, medium, and hard. The easy level provides 200 images, the medium level provides 30 images, and the hard level provides only 1 image for reconstruction. In total, 16 teams submitted results in the final testing phase. The solutions developed in this challenge achieved promising results in 3D food reconstruction, with significant potential for improving portion estimation for dietary assessment and nutritional monitoring. More details about this workshop challenge and access to the dataset can be found at https://sites.google.com/view/cvpr-metafood-2024.
The main aim of the paper is to create a trust and transparency in the food supply chain system, ensuring food safety for everyone with the help of Blockchain Technology. Food supply chain is the process of tracing a crop from the farmer or producer to the buyer. With the advent of blockchain, providing a safe and fraud-free environment for the provision of numerous agricultural necessities has become much easier. Because of the globalization of trade, the present supply chain market today includes various companies involving integration of data, complex transactions and distribution. Information tamper resistance, supply-demand relationships, and traceable oversight are all difficulties that arise as a result of this. Blockchain is a distributed ledger technology that can provide information that is resistant to tampering. This strategy can eliminate the need for a centralized trusted authority, intermediaries, and business histories, allowing for increased production and security while maintaining the highest levels of integrity, liability, and safety. In order to have an integrity and transparency in food supply chain in the agricultural sector, a framework is proposed here based on block chain and IoT.
Alicia Sandall, Leanne Smith, Erika Svensen
et al.
AbstractObjective:Ultra-processed foods (UPF), including those containing food additive emulsifiers, have received research attention due to evidence implicating them in the pathogenesis of certain diseases. The aims of this research were to develop a large-scale, brand-level database of UPF in the UK food supply and to characterise the occurrence and co-occurrence of food additive emulsifiers.Design:A database was compiled sampling all products from the food categories contributing to energy intake from UPF in the UK from the National Diet and Nutrition Survey (2008–2014). Every food in these categories were identified from online supermarket provision from the ‘big four’ supermarkets that dominate the market share in the UK, comprising Tesco, Sainsbury’s, Asda and Morrisons.Setting:Major supermarkets in the UK.Results:A total of 32 719 food products in the UK supermarket food supply were returned in searches. Of these, 12 844 products were eligible and manually reviewed for the presence of emulsifiers. Emulsifiers were present in 6642 (51·7 %) food products. Emulsifiers were contained in 95·0 % of ‘Pastries, buns and cakes’, 81·9 % of ‘Milk-based drinks’, 81·0 % of ‘Industrial desserts’ and 77·5 % of ‘Confectionary’. Fifty-one per cent of all emulsifier-containing foods contained multiple emulsifiers. Across emulsifier-containing foods, there were a median of two emulsifiers (IQR 2) per product. The five most common emulsifiers were lecithin (23·4 % of all products), mono- and diglycerides of fatty acids (14·5 %), diphosphates (11·6 %), and xanthan gum and pectin (8·0 %).Conclusions:Findings from this study are the first to demonstrate the widespread occurrence and co-occurrence of emulsifiers in UPF in the UK food supply.
EFSA Panel on Contaminants in the Food Chain (CONTAM), Dieter Schrenk, Margherita Bignami
et al.
Abstract The European Commission requested EFSA to provide an assessment of the processing conditions which make Ambrosia seeds non‐viable in feed materials and compound feed. This assessment also includes information on a reliable procedure to verify the non‐viability of the seeds. Ambrosia seeds are known contaminants in feed with maximum levels set in the Directive 2002/32/EC. The manufacturing processes and processing conditions applied to the feed may affect the viability of the Ambrosia seeds. Therefore, the CONTAM Panel compared these conditions with conditions that have been shown to be sufficient to render Ambrosia seeds non‐viable. The Panel concluded with a certainty of 99–100% that solvent extraction and toasting of oilseed meals at temperatures of 120°C with steam injection for 10 min or more will make Ambrosia seeds non‐viable. Since milling/grinding feed materials for compound feed of piglets, aquatic species and non‐food producing animals would not allow particles of sizes ≥1 mm (the minimum size of viable Ambrosia seeds) passing the grinding process it was considered very likely (with ≥ 90% certainty) that these feeds will not contain viable Ambrosia seeds. In poultry, pig, and possibly cattle feed, particle sizes are ≥ 1 mm and therefore Ambrosia seeds could likely (66–90% certainty) survive the grinding process. Starch and gluten either from corn or wheat wet milling would not contain Ambrosia seeds with 99–100% certainty. Finally, ensiling fresh forages contaminated with A. artemisiifolia seeds for more than 3 months is very likely to render all seeds non‐viable. The Panel concluded that a combination of the germination test and a subsequent triphenyl‐tetrazolium‐chloride (TTC) test will very likely (with ≥ 90% certainty) verify the non‐viability of Ambrosia seeds. The Panel recommends that data on the presence of viable Ambrosia seeds before and after the different feed production processes should be generated.
Nutrition. Foods and food supply, Chemical technology