Nutrition Science Research Articles

Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

The interplay between mood and eating episodes has been extensively researched within the fields of nutrition, psychology, and behavioral science, revealing a connection between the two. Previous studies have relied on questionnaires and mobile phone self-reports to investigate the relationship between mood and eating. In more recent work, phone sensor data has been utilized to characterize both eating behavior and mood independently, particularly in the context of mobile food diaries and mobile health applications. However, current literature exhibits several limitations: a lack of investigation into the generalization of mood inference models trained with data from various everyday life situations to specific contexts like eating; an absence of studies using sensor data to explore the intersection of mood and eating; and inadequate examination of model personalization techniques within limited label settings, a common challenge in mood inference (i.e., far fewer negative mood reports compared to positive or neutral reports). In this study, we examined the everyday eating and mood using two separate datasets from two different studies: i) Mexico (N \({}_{MEX}\) = 84, 1843 mood-while-eating reports with a label distribution of positive: 51.7%, neutral: 38.6% and negative: 9.8%) in 2019, and ii) eight countries (N \({}_{MUL}\) = 678, 329K mood reports, including 24K mood-while-eating reports with a label distribution of positive: 83%, neutral: 14.9%, and negative: 2.2%) in 2020, which contain both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models experience a decline in performance in specific contexts, such as during eating, highlighting the issue of sub-context shifts in mobile sensing. Moreover, we discovered that population-level (non-personalized) and hybrid (partially personalized) modeling techniques fall short in the commonly used three-class mood inference task (positive, neutral, negative). Additionally, we found that user-level modeling posed challenges for the majority of participants due to insufficient labels and data in the negative class. To overcome these limitations, we implemented a novel community-based personalization approach, building models with data from a set of users similar to the target user. Our findings demonstrate that mood-while-eating can be inferred with accuracies 63.8% (with F1-score of 62.5) for the MEX dataset and 88.3% (with F1-score of 85.7) with the MUL dataset using community-based models, surpassing those achieved with traditional methods.

Metatranscriptomics for Understanding the Microbiome in Food and Nutrition Science

Microbiome science has greatly expanded our understanding of the diverse composition and function of gut microorganisms over the past decades. With its rich microbial composition, the microbiome hosts numerous functionalities essential for metabolizing food ingredients and nutrients, resulting in the production of active metabolites that affect food fermentation or gut health. Most of these processes are mediated by microbial enzymes such as carbohydrate-active enzymes and amino acid metabolism enzymes. Metatranscriptomics enables the capture of active transcripts within the microbiome, providing invaluable functional insights into metabolic activities. Given the inter-kingdom complexity of the microbiome, metatranscriptomics could further elucidate the activities of fungi, archaea, and bacteriophages in the microbial ecosystem. Despite its potential, the application of metatranscriptomics in food and nutrition sciences remains limited but is growing. This review highlights the latest advances in food science (e.g., flavour formation and food enzymology) and nutrition science (e.g., dietary fibres, proteins, minerals, and probiotics), emphasizing the integration of metatranscriptomics with other technologies to address key research questions. Ultimately, metatranscriptomics represents a powerful tool for uncovering the microbiome activity, particularly in relation to active metabolic processes.

Potential of Process-Induced Modification of Potato Starch to Modulate Starch Digestibility and Levels of Resistant Starch Type III

Starch digestibility and the content of resistant starch (RS) play a crucial role in human health, particularly in relation to glycemic responses, insulin sensitivity, fat oxidation, and satiety. This study investigates the impact of processing methods on potato starch digestibility and RS content, focusing on two modification techniques: autoclaving and high hydrostatic pressure (HHP), followed by retrogradation at different temperatures. The research employs a comprehensive approach to characterize structural changes in starch samples using X-ray diffraction (XRD), attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, and scanning electron microscopy (SEM). In turn, semi-dynamic in vitro digestion experiments based on the INFOGEST protocol were conducted to assess starch digestibility, while RS content was evaluated through enzymatic digestion of the non-RS fraction. SEM, XRD, and FTIR measurements reveal thermal processing appreciably affected starch architectures while HHP had a marginal effect. Further, the FTIR 1045/1022R ratio was found to be correlated with RS content measurements while reducing rapidly digestible starch (RDS). The findings led to the stipulation that thermal processing facilitates amylose leaching and granular disruption. In turn, retrogradation enabled the deposition of the amylose onto the disrupted structures which delineated their subsequent liability to enzymatic digestion. Conversely, HHP had minimal effects on granular architectures and amylose leaching. Overall, this research provides valuable insights for processing starch-based food products with the goal of increasing RS content, which may have significant implications for the food industry and nutritional science.

Postprandial inflammation across the aging spectrum.

Postprandial inflammation post-high-fat meals may be linked to cardiovascular disease (CVD). CVD incidence increases with age; however, whether older adults experience greater postprandial inflammation remains unclear. We examined whether analyzing age categorically versus continuously influenced relationships between age and postprandial inflammatory measures. Cross-sectional. Laboratory for Applied Nutrition and Exercise Science at Oklahoma State University (Stillwater, OK, USA). 56 apparently healthy adults ages 20-69 years. We measured interleukin (IL)-1β, IL-6, IL-8, IL-10, and tumor necrosis factor (TNF)-α at baseline, 2-, 4-, and 6 -hs post-high-fat meal (9 kcal/kg; 70% fat). Data were examined in the full sample with paired t-tests (baseline to peak), by pre-defined age groups (i.e., 18-35, 36-49, 50-59, 60-69) using ANCOVA, and continuously using linear regression. Across the full sample, IL-1β, IL-6, and IL-8 increased after the high-fat meal (p's≤0.018). Cytokine differences post-high-fat meal by age category were generally not observed. However, age was positively associated with IL-6 incremental AUC when examined continuously (b = 0.029; p = 0.010). These data suggest increasing age is linked to a greater IL-6 response to a high-fat meal. Further, examining age continuously may have greater utility when studying aging and postprandial inflammation. N/A (secondary analysis).

Polyphenol-protein particles: A nutraceutical breakthrough in nutrition and food science

Polyphenol-protein particles: A nutraceutical breakthrough in nutrition and food science

Dietary fats and cardiometabolic health-from public health to personalised nutrition: 'One for all' and 'all for one'.

This paper provides a summary of the 2023 British Nutrition Foundation Annual Lecture by Professor Julie Lovegrove. Professor Lovegrove is the head of the Hugh Sinclair Unit of Human Nutrition at the University of Reading. Professor Lovegrove, who was nominated for the BNF Prize for her outstanding contribution to nutritional sciences has published over 300 scientific papers and made a major contribution to establishing the relevance of dietary fat quality in the development and prevention of cardiometabolic disease.

Neuro-Nutrition and Exercise Synergy: Exploring the Bioengineering of Cognitive Enhancement and Mental Health Optimization.

The interplay between nutrition, physical activity, and mental health has emerged as a frontier in bioengineering research, offering innovative pathways for enhancing cognitive function and psychological resilience. This review explores the neurobiological mechanisms underlying the synergistic effects of tailored nutritional strategies and exercise interventions on brain health and mental well-being. Key topics include the role of micronutrients and macronutrients in modulating neurogenesis and synaptic plasticity, the impact of exercise-induced myokines and neurotrophins on cognitive enhancement, and the integration of wearable bioelectronics for personalized monitoring and optimization. By bridging the disciplines of nutrition, psychology, and sports science with cutting-edge bioengineering, this review highlights translational opportunities for developing targeted interventions that advance mental health outcomes. These insights are particularly relevant for addressing global challenges such as stress, anxiety, and neurodegenerative diseases. The article concludes with a roadmap for future research, emphasizing the potential of bioengineered solutions to revolutionize preventive and therapeutic strategies in mental health care.

Artificial Intelligence in Guiding Nutrition and Health Science Communication

Artificial Intelligence in Guiding Nutrition and Health Science Communication

Process evaluation of Project Daire: a food environment intervention that impacted food knowledge, wellbeing and dietary habits of primary school children

BackgroundProject DAIRE was a randomised-controlled, factorial design trial which aimed to improve children’s health-related quality of life, wellbeing, food knowledge and dietary habits via two multi-component interventions: Nourish and Engage. Nourish was an intervention aiming to alter the school food environment, provide food-based experiences and expose pupils to locally produced foods. Engage was an age-appropriate cross-curricular food education intervention incorporating food, agriculture, nutrition science and related careers. The purpose of this study was to conduct a process evaluation to evaluate DAIRE implementation, mechanisms of impact (MOI) and context to elucidate trial results, and inform scalable implementation of the DAIRE approach for successful future rollout.MethodsThe Medical Research Council’s (MRC) framework for process evaluation was followed. Formal (questionnaires designed for process evaluation) and informal (researcher records and communications) methods were used to collect quantitative and qualitative data during the DAIRE trial in relation to process evaluation. Quantitative data were analysed using descriptive statistics and qualitative data via thematic analysis to identify key themes.ResultsFifteen schools and 983 pupils (n = 495 6–7 year olds/Year 3 and n = 488 10–11 year olds/Year 7) were recruited for the 6-month DAIRE intervention; a 100% retention rate was observed at the school level and the interventions had a high level of pupil and teacher acceptability. Nourish schools delivered a higher mean dose of intervention elements (61.4%) than Engage (50%) schools but, overall, mixed implementation of both interventions occurred. DAIRE produced change through four key MOI: social learning, experimental learning, interactive engaging content and real-life connections. Lack of time was the main contextual barrier to implementation and lack of financial cost to schools indicated as a potential facilitator.ConclusionsThis process evaluation helped to identify important findings related to implementation, MOI and context. The most effective elements of the interventions which should be maintained include provision of interactive and engaging intervention elements at no financial cost to the school. Findings also identified suggestions for improvement including provision of increased teacher training, support and planning time, content reduction to facilitate easy integration, and implementation across the full academic year. A sustainable funding and resourcing mechanism is required for successful future roll-out across the UK and beyond.Trial registrationsThe original trial referenced in this process evaluation is registered as follows: National Institute of Health (NIH) U.S. National Library of Medicine Clinical Trials.gov (ID: NCT04277312; retrospectively registered 11th February 2020).

Role of Scavenger Receptor B1 (SR-B1) in Improving Food Benefits for Human Health

Scavenger receptor class B member 1 (SR-B1) is a multiligand receptor with a broad range of functions spanning from the uptake of cholesteryl esters from high-density lipoproteins (HDLs) and transport of micronutrients such as fat-soluble vitamins and carotenoids across cell membranes to roles in tumor progression, pathogen recognition, and inflammatory responses. As a target of exposome factors such as environmental stressors and unhealthy lifestyle choices, as well as aging, dysregulated expression and activity of SR-B1 can negatively impact human health. Intriguingly, not only is SR-B1 a major determinant of nutrient homeostasis and, hence, metabolic health status, but these same nutrients and some phytochemicals have also demonstrated their ability to modulate SR-B1. Therefore, an integrated approach that, taking into account human health, nutrition, and food technology sciences, aims to produce foods with health-promoting effects should take advantage of the multifaceted properties of SR-B1. Improved functional foods and novel nanoparticle-based delivery systems, rich in nutrients and phytochemicals, with precise targeting to SR-B1 in specific tissues or structures could represent a strategic advance to improve human health and promote well-being.

Quantum food and nutrition: Subatomic approaches to nourishment for health and well-being.

Nutrition science has been represented as biomedical, environmental, societal and economic field, but quantum biology is sidestepped, thereby obscuring cognate problems and solutions. We are generally nourished for health, optimal well-being, longevity and personal security through sustainable livelihoods. Our nourish-ments include not only food and energy but also light from the sun, the firmament and the earth itself, along with information transmitted in subatomic particles and electromagnetic wave forms. We propose 'quantum nutrition' as an approach to reconcile quantum phenomena with nutritional biology. Appreciating quantum nutrition and recognizing its potential applications will provide opportunities for future health and well-being and for planetary habitability.

Diabetes-focused food recommender system (DFRS) to enabling digital health.

The integration of digital health technologies into diabetes management has shown the potential to improve patient outcomes by providing personalized dietary recommendations. This study aims to develop and evaluate the Diabetes-Focused Food Recommender System (DFRS), a system designed to assist individuals with diabetes in making informed food choices. Using a combination of advanced machine learning algorithms, nutrition science, and digital health technologies, DFRS generates personalized recommendations tailored to individual needs. The methodology involves data collection from diverse patient profiles and model development using Graph Neural Networks (GNN) and other machine learning techniques. Hyperparameter tuning and rigorous performance evaluation were conducted to optimize system accuracy. The results demonstrate that after optimization, GNN achieved an accuracy of 94 percent, significantly enhancing the precision of dietary recommendations. Clinical validation of the system showed a reduction in HbA1c levels, glycemic variability, and incidents of hyper- and hypoglycemia. Therefore, DFRS has proven to be an effective tool for improving dietary management in diabetes care, and its integration into clinical workflows offers the potential to enhance health outcomes and streamline healthcare delivery.

Antioxidant Assays in Phytonutrient Research: Translating Laboratory Innovations into Practical Applications

Background: There is great promise for improving nutrition and health thanks to phytonutrients' antioxidant qualities and health advantages. Their capacity to combat oxidative stress and associated illnesses emphasizes the importance of precisely evaluating their antioxidant characteristics. Objective: This study concludes by providing a comprehensive and critical critique of the current approaches to measure the antioxidant activity of phytonutrients. It dives into the fundamentals, benefits, drawbacks, and most recent developments of commonly used antioxidant assays, giving the reader a comprehensive grasp of the topic. This recapitulation of the review's goal in the end reinforces the reader's primary takeaway. Methods: Research on several antioxidant tests, such as FRAP, ORAC, DPPH, and ABTS, is consolidated in this review. It looks at each assay's performance traits, technological advances, and techniques. The review also assesses the incorporation of many assays to thoroughly examine phytonutrient potency and its uses in the food industry and nutritional science. Results: The review shows how antioxidant tests have advanced significantly, improving sensitivity, accuracy, and physiological relevance. It demonstrates how these tests can be used practically to guarantee food quality, create supplements, and offer nutritional advice. The paper also lists the difficulties today, including the intricacy of antioxidant mechanisms, test variability, and the requirement for assay standardization. The practical value of the research is emphasized by highlighting the significance of antioxidant tests for quality assurance, adulteration detection, and shelf life extension in the food business. Discussion: Scientists, doctors, and business experts interested in evaluating and applying phytonutrients will find this review helpful. It emphasizes how crucial it is to improve antioxidant testing to ascertain the possible health advantages and therapeutic uses of phytonutrients. The review highlights the need for increased test sensitivity, accuracy, and relevance while discussing the benefits and drawbacks of the available techniques. It draws attention to the importance of strong and trustworthy antioxidant tests to maximize the use of phytonutrients in food quality control and pharmaceutical research. Prospects: Future directions seek to address the challenges discovered through the development of multidisciplinary research and testing technologies. Novel approaches will advance our knowledge of phytonutrient potency and aid in developing medicines and nutraceuticals. This study highlights the significance of trustworthy assays for understanding and utilizing phytonutrients, providing academics and professionals in the business with vital insights.

Robust Dimensionality Reduction: A Bootstrap-Based Evaluation of PCA with Applications in Nutritional and Environmental Sciences

The complex structures of vast amounts of data provide a considerable challenge to researchers. Dimensional reduction methods are reliable and transform high-dimensional data into lower-dimensional representations while preserving most original information. Principal Component Analysis (PCA) is a commonly used approach for dimensionality reduction that transforms data into a lower-dimensional space while preserving important information. The variability within the sample may affect the stability and reliability of PCA results. The constraint of existing approaches compromises the accuracy of PCA stability assessments in practical data scenarios. These methodologies frequently depend on linear assumptions and encounter difficulties when addressing high-dimensional data. This study used the bootstrap method to assess the stability of PCA by assessing the variability of eigenvalues and principal components over several bootstrap iterations. We evaluate how stability metrics, particularly confidence intervals for eigenvalues and the proportion of variance clarified, can assist in determining the optimal number of principal components. The results indicate that the bootstrap provides a helpful framework for evaluating the robustness of PCA and guiding informed decisions on dimensionality reduction in many applications, including data compression, visualization, and classification. Moreover, the results illustrate the efficacy of this method in enhancing the reliability and interpretability of PCA findings among distinct data-driven research endeavors. This study enhances understanding of how principal component analysis (PCA) tackles data unpredictability while delivering valuable insights for professionals in several disciplines.

A New Mediterranean Lifestyle Pyramid for Children and Youth: A Critical Lifestyle Tool for Preventing Obesity and Associated Cardiometabolic Diseases in a Sustainable Context.

A New Mediterranean Lifestyle Pyramid for Children and Youth: A Critical Lifestyle Tool for Preventing Obesity and Associated Cardiometabolic Diseases in a Sustainable Context.

Bee Pollen Phytochemicals and Nutrients as Unequaled Pool of Epigenetic Regulators: Implications for Age-Related Diseases.

Bee pollen is characterized by an exceptional diversity and abundance of micronutrients and bioactive phytochemicals. This richness remains very sparsely investigated, but accumulating evidence strongly supports a promising future for bee pollen in human nutrition and medicine. Epigenetic regulation is among the most compelling biomedical topics that remain completely untapped in bee pollen and bee derivative research. In our current research, we identified numerous ubiquitous compounds that are consistently present in this matrix, regardless of its botanical and geographical origins, and that have been well studied and documented as epigenetic regulators in recent years. Given the relative newness of both bee pollen biomedical research and epigenetic studies within nutritional, pharmaceutical, and medical sciences, this review aims to bridge these valuable fields and advance related experimental investigations. To the best of our knowledge, this is the first work that has aimed to comprehensively investigate the epigenetic modulatory potential of bee pollen compounds. Our findings have also unveiled several intriguing phenomena, such as a dual effect of the same compound depending on the cellular context or the effect of some compounds on the cross-generational heritability of epigenetic traits. Although experimental studies of epigenetic regulation by bee pollen as a whole or by its extract are still lacking, our current study clearly indicates that this research avenue is very promising and worth further investigations. We hope that our current work constitutes a foundational cornerstone of future investigations for this avenue of research.

An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition.

Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture. This research explores the capabilities of the CNNs and transformers to build a robust classification model that can handle both short- and long-range dependencies with global features to accurately classify food images and enhance food image recognition for better nutritional analysis. Our approach, which combines CNNs and Vision Transformers (ViTs), begins with the RestNet50 backbone model. This model is responsible for local feature extraction from the input image. The resulting feature map is then passed to the ViT encoder block, which handles further global feature extraction and classification using multi-head attention and fully connected layers with pre-trained weights. Our experiments on five diverse datasets have confirmed a superior performance compared to the current state-of-the-art methods, and our combined dataset leveraging complementary features showed enhanced generalizability and robust performance in addressing global food diversity. We used explainable techniques like grad-CAM and LIME to understand how the models made their decisions, thereby enhancing the user's trust in the proposed system. This model has been integrated into a mobile application for food recognition and nutrition analysis, offering features like an intelligent diet-tracking system. This research paves the way for practical applications in personalized nutrition and healthcare, showcasing the extensive potential of AI in nutritional sciences across various dietary platforms.

Shell Noun Usage in Science Undergraduate Academic Writing: Insights from Mukuba University, Zambia

This study examined the use of shell nouns in the academic writing of science undergraduate students at Mukuba University, Zambia, focusing on their frequency, patterns, and functions across multiple disciplines. The topic was chosen due to the critical role of shell nouns in enhancing coherence, formality, and objectivity in academic writing, which are essential skills for scientific communication. Grounded in Halliday and Hasan’s theory of cohesion, the research aimed to (1) investigate the frequency of shell noun use across disciplines, (2) identify patterns in their usage across academic years, and (3) evaluate their functional contributions to coherence, formalization, and objectivity in student writing. A qualitative approach was employed, involving the analysis of written samples from disciplines including Biology, Chemistry, Public Health, Nutrition Science, Agricultural Science, Computer Science, Environmental and Climate Change, and Biomedical Science. The data set consisted of lab reports, research papers, and essays collected from first-year to final-year students. The data analysis involved frequency counts and thematic coding to identify trends and patterns in shell noun usage. The findings revealed notable disciplinary variations, with Chemistry and Biomedical Science demonstrating higher frequencies of shell noun use. Shell nouns played a vital role in maintaining textual coherence, formalizing tone, and enhancing objectivity in the students' writing. More advanced usage was evident among final-year students, suggesting a developmental progression in academic writing skills. However, challenges such as inconsistent use and difficulty applying shell nouns effectively in complex writing tasks were also identified. The study highlighted the importance of targeted pedagogical interventions, recommending the inclusion of explicit instruction on shell noun usage in writing curricula. Suggestions for future research included expanding the sample size, incorporating a broader range of genres, and adopting a longitudinal approach. By examining linguistic features in academic writing within a sub-Saharan African context, this research provided valuable insights for educators seeking to enhance writing instruction and support student success.

A new database to guide reference material selection for dietary supplement and nutrition science.

Rigorous research on the health effects of dietary supplements and related nutritional interventions requires thorough chemical characterization of complex matrices for their composition of macro- and micronutrients, botanical phytochemicals, and potential contaminants. Reference materials (RMs) with metrologically traceable values for these specific properties are ideal analytical tools to ensure requisite chemical measurements are reliable. However, identifying and comparing appropriate RMs for studying dietary ingredients and their metabolites is challenging, creating a barrier to reproducible regulatory testing and research. To address this challenge, the Office of Dietary Supplements at the National Institutes of Health coordinated the development of a new publicly available Reference Materials Search Tool ( https://odsrmst.od.nih.gov/ ) database featuring comprehensive data records and search and comparison functionality. The RMST significantly expands research capacities to discover and select fit for purpose reference materials for dietary supplement and related food and natural product chemical analysis.

The Natural Products Magnetic Resonance Database (NP-MRD) for 2025.

The Natural Products Magnetic Resonance Database (NP-MRD; https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction, and retrieval of nuclear magnetic resonance (NMR) data on natural products (NPs). The NP-MRD was initially established to support compound de-replication and data dissemination for the NP community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics, and nutrition science fields. Indeed, since its launch in 2022, the NP-MRD has expanded enormously in size, scope, and popularity. The current version of NP-MRD now contains nearly 7× more compounds (281 859 versus 40 908) and 7× more NMR spectra (5.5 million versus 817278) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11 000 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from 165 spectra per year to >10000 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20000 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, notable updates to the database's spectral search utilities and useful additions to support better NMR spectral analysis/prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD's database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.