Challenges in Personalized Nutrition and Health

Suitability of Selected Apple Varieties for People with Allergies and Diabetes

The study aimed to select apple varieties suitable for allergy sufferers and people with diabetes. The total polyphenol content, sugar content, acidity, and antioxidant activity of the apple fruit juices were determined using spectrophotometric techniques. The allergenic content in the apple juices was also measured. The strength of sensitisation was assessed using the ELISA method. Given their minimal content of both profilins and Bet v 1 homologues, Koksa Pomarańczowa (4.24 ± 0.08 µg/g Bet v 1 and 4.49 ± 0.82 ng/g profilins) and Książę Albrecht Pruski (5.57 ± 0.07 µg/g Bet v 1 and 3.34 ± 0.09 ng/g profilins) were identified as suitable for people with allergies. For people with diabetes, the most suitable apple variety was found to be Jakub Lebel, providing large doses of antioxidants and polyphenols (41.10 ± 0.20 and 5.16 ± 0.42, respectively) and a relatively low sugar content (9.06 g/100 g).

Modeling Zinc Absorption in the Adult Population of Colombia: Insights for Nutritional Evaluation and Intervention Strategies

Zinc is a vital trace element, yet its deficiency is common in various populations. This study addresses the gap in understanding zinc intake and its relationship with key nutritional parameters in a Colombian population. We analyzed data from 12,987 individuals, focusing on the daily intake of zinc, phytate, protein, and calcium, and used the phytate/zinc molar ratio as an input parameter in the Miller et al. (2013) model. This model was employed to estimate the total absorbed zinc (TAZ) and the fractional absorption of zinc (FAZ). Our findings highlight a general trend towards insufficient intake compared to the standards of the Institute of Medicine (IOM) and Colombia, with a significant percentage of the population falling below the estimated average requirement (EAR) and recommended daily allowance (RDA) for zinc, underscoring the need for targeted nutritional strategies. Our study contributes to a broader understanding of zinc nutrition and public health implications in Colombia, providing a basis for future dietary guidelines and health interventions.

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.

Advances in Understanding the Interplay between Dietary Practices, Body Composition, and Sports Performance in Athletes

The dietary practices of athletes play a crucial role in shaping their body composition, influencing sports performance, training adaptations, and overall health. However, despite the widely acknowledged significance of dietary intake in athletic success, there exists a gap in our understanding of the intricate relationships between nutrition, body composition, and performance. Furthermore, emerging evidence suggests that many athletes fail to adopt optimal nutritional practices, which can impede their potential achievements. In response, this Special Issue seeks to gather research papers that delve into athletes' dietary practices and their potential impacts on body composition and sports performance. Additionally, studies focusing on interventions aimed at optimizing dietary habits are encouraged. This paper outlines the key aspects and points that will be developed in the ensuing articles of this Special Issue.

The relationship between nutritional facts and temperament of selected Iranians' frequent food items: a summative content analysis study

Introduction

Dietary intake is considered as a major determinant factor in health maintenance as well as primary and secondary prevention of diseases. The knowledge on the relationship between food nutritional facts and their temperament (Mizaj) can be helpful in the integrative Iranian medicine and modern nutrition approach to individualized diet planning.

Material and methods

This study was carried out in three phases using a summative content analysis method: 1) Extraction of the Iranians' frequent food items through an academic discussion panel of nutritionists and MDs, PhDs of Iranian medicine; 2) Determination of the extracted food items' temperament and nutritional facts; 3) Statistical analysis of the extracted data using SPSS software.

Results

Foods with warm temperament had higher mean levels of energy and polyunsaturated fatty acids as well as iron, zinc, and manganese. On the other hand, the mean values of total fatty acids, cholesterol, vitamin B12, and retinol were significantly higher in wet temperament foods. Additionally, the dryness of food items had a positive significant association with total carbohydrates, fiber, vitamin B6, calcium, iron, magnesium, potassium, copper, and manganese. Finally, wet foods had higher amounts of moisture and vitamin A.

Conclusions

The results of the present study revealed that warmness of food items is associated with higher amounts of macronutrients as well as cell growth and proliferation related micronutrients. Moreover, foods with dry temperament had higher amounts of minerals. Further studies, especially food analytical studies, are required to validate the accuracy of aforementioned findings.

Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living

The unique physiological and genetic characteristics of individuals influence their reactions to different dietary constituents and nutrients. This notion is the foundation of personalized nutrition. The field of nutrigenetics has witnessed significant progress in understanding the impact of genetic variants on macronutrient and micronutrient levels and the individual's responsiveness to dietary intake. These variants hold significant value in facilitating the development of personalized nutritional interventions, thereby enabling the effective translation from conventional dietary guidelines to genome-guided nutrition. Nevertheless, certain obstacles could impede the extensive implementation of individualized nutrition, which is still in its infancy, such as the polygenic nature of nutrition-related pathologies. Consequently, many disorders are susceptible to the collective influence of multiple genes and environmental interplay, wherein each gene exerts a moderate to modest effect. Furthermore, it is widely accepted that diseases emerge because of the intricate interplay between genetic predisposition and external environmental influences. In the context of this specific paradigm, the utilization of advanced "omic" technologies, including epigenomics, transcriptomics, proteomics, metabolomics, and microbiome analysis, in conjunction with comprehensive phenotyping, has the potential to unveil hitherto undisclosed hereditary elements and interactions between genes and the environment. This review aims to provide up-to-date information regarding the fundamentals of personalized nutrition, specifically emphasizing the complex triangulation interplay among microbiota, dietary metabolites, and genes. Furthermore, it highlights the intestinal microbiota's unique makeup, its influence on nutrigenomics, and the tailoring of dietary suggestions. Finally, this article provides an overview of genotyping versus microbiomics, focusing on investigating the potential applications of this knowledge in the context of tailored dietary plans that aim to improve human well-being and overall health.

Nutrition and Health in the Lesbian, Gay, Bisexual, Transgender, Queer/Questioning Community: A Narrative Review

Sexual and gender minorities have a higher risk for health and nutrition-related disparities across the life course compared to the heterosexual or cisgender population. Experiences of stigmatization and discrimination are associated with diminished mental health quality and psychological distress, which are risk factors for developing various eating disorders. Other nutrition disparities include increased risk for food insecurity, body dissatisfaction, and weight complications, such as those experienced by the transgender population in association with gender-affirming hormone therapies. Despite the need for tailored nutrition recommendations that address the unique needs of the lesbian, gay, bisexual, transgender, queer/questioning (LGBTQ+) community, there are currently no such guidelines in North America. The purpose of this review is to summarize major LGBTQ+ nutrition disparities and highlight the need for tailored recommendations. We examine the evidence on mental health and social disparities in this group, including vulnerabilities to disordered eating, food insecurity, and healthcare provider discrimination. Importantly, we identify a scarcity of literature on dietary concerns and nutrition care guidelines for LGBTQ+ groups, including studies that address intersectionality and differences among specific gender and sexual orientations. These gaps underline the urgency of prioritizing nutrition for LGBTQ+ health needs and for developing tailored public health nutrition recommendations for this underserved population. Our review suggests that future LGBTQ+ health and nutrition research agendas should include personalized and precision nutrition, social determinants of health, diet quality, body image, and healthcare provider cultural competency and responsiveness. Moreover, the current evidence on LGBTQ+ nutrition and health will be strengthened when research studies (including clinical trials) with robust methodologies amplify inclusion and representation of this community to elucidate health and nutrition disparities in sexual and gender minorities.

Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems (APNASs)

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.

Concept of an Intervention for Sustainable Weight Loss in Postmenopausal Women with Overweight-Secondary Analysis of a Randomized Dietary Intervention Study

INTRODUCTION

The approach of an individual diet has great potential for sustainable weight reduction. Social support, participation and empowerment are also key factors for high motivation and compliance. So, the impact of an individual diet in combination with group sessions on weight loss in postmenopausal women with overweight was investigated.

METHODS

In this reanalysis of a controlled intervention study, postmenopausal women (n = 54; BMI 30.9 ± 3.4 kg/m²; 59 ± 7 years) were recruited receiving an energy restricted diet for 12 weeks, followed by a six-month follow-up phase. The women received 51 individual meal plans based on their habits and were trained in four group sessions.

RESULTS

Forty-six women completed the intervention phase, and 29 completed the follow-up. Average weight loss was -5.8 ± 3.0 kg (p < 0.001) after 12 weeks and was still significant at follow-up (-4.9 ± 5.4 kg, p < 0.001). Also, decreases in fat-free mass (-1.1 ± 1.2 kg, p < 0.001) and resting energy expenditure (-1096 ± 439 kJ/24 h, p < 0.001) were observed.

CONCLUSIONS

The individual nutrition approach with a focus on nutritype in combination with group sessions was effective for long-lasting weight loss in postmenopausal women. An important factor is close individual and group support.

The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment

In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented.

On the determinants and the role of the payers in the uptake of genetic testing and data sharing in personalized health

Background

New health technologies and data offer tailored prevention and spot-on treatments, which can considerably reduce healthcare costs. In healthy individuals, insurers can participate in the creation of health capital through data and preventing the occurrence of a disease. In the onset of a disease, sequencing an individual's genome can provide information leading to the use of more efficient treatments. Both improvements are at the core of the "personalized health" paradigm. As a positive side effect, a reduction in healthcare costs is expected. However, the integration of personalized health in insurance schemes starts with a closer understanding of the demand drivers.

Methods

Using novel data from a survey carried out in Switzerland, we determine the factors influencing the uptake and sharing of data from genetic tests. In our regression analyses, we use five sets of socioeconomic, lifestyle, health insurance, sentiment, and political beliefs variables. Furthermore, two framings assess the willingness to undertake a test and the readiness to share results with an insurer when the costs of the test are borne by the insurer or the individual.

Results

We find that socioeconomic, lifestyle, or political belief variables have very little influence on the uptake of tests and the sharing of data. On the contrary, our results indicate that sentiment and insurance factors play a strong role. More precisely, if genetic tests are perceived as a mean to perform health prevention, this pushes individuals to take them. Furthermore, using the insurer's smartphone app leads to an increase of the likelihood to undergo a test and doubles the probability to share related data. Regarding insurance plans and deductible levels, there is no strong correlation neither with the willingness to take a test nor to share the data. Finally, individuals with complementary health insurance plans are less likely to share results. From the framings for the payment of genetic tests, our results indicate a positive effect of the insurer as a payer on the willingness to undertake tests as well as on data sharing.

Conclusion

Our results lay the ground for a deeper understanding of the role of payers on health decisions and sharing of health-related data. In particular, we find that it is relevant for health insurers to engage with their clients.

Examining the Individual Response to a Low-Sodium Diet in Patients with Hypertension: Protocol for a Pilot Randomized Controlled Trial

BACKGROUND

Excessive dietary sodium intake is an independent risk factor for hypertension and cardiovascular disease (CVD). Despite the large body of evidence concerning the effects of dietary interventions on blood pressure (BP) and CVD outcomes, trials have often reported low adherence to decreased sodium intake, likely due in part to heterogeneous BP responses. To address the challenges, recent clinical findings suggested a precise and personalized dietary approach that seeks to deliver more preventive and practical dietary advice than the "one-size-fits-all" guidelines and weighs the personal risk of developing specific diseases.

OBJECTIVE

The purpose of this pilot randomized controlled trial was to test the feasibility and preliminary efficacy of integrating the use of mobile technology and metabolomics with a low-sodium diet intervention in patients with hypertension to develop personalized low-sodium diet programs. Additionally, the study will examine the associations of urine metabolites with urinary sodium levels and BP control based on the hypothesis that targeted urine metabolites. In this report, we describe the design and protocol of the pilot trial.

METHODS

A total of 40 patients with hypertension will be randomly assigned to either a 8-week low-sodium diet group (n=20) or a standard care group (n=20). Each week, intervention participants went through individual sessions with an interventionist via videoconferencing to discuss low-sodium diet regimens, patients' food choices, and BP tracks on mobile apps. The control group followed their usual care for hypertension management. All participants in both groups monitored diet and BP using mobile apps for 8 weeks. A 24-hour urinary sodium excretion for the estimation of dietary sodium intake, systolic, and diastolic BPs were measured at the baseline and at 8 weeks. The primary outcomes of this study include the feasibility of conducting a randomized controlled trial (RCT) by reporting recruitment, retention, and completion statistics. The preliminary effects of intervention will be tested by a generalized estimating equation model.

RESULTS

This pilot RCT study was approved by the institutional review board at the University of Texas Health San Antonio in January 2021. The first participant was enrolled in April 2021, and currently, 26 participants were enrolled. All data collection is expected to conclude by March 2023, with data analysis and study results ready for reporting by December 2023. Findings from this pilot RCT will further guide the team in planning a future large-scale study.

CONCLUSIONS

The findings of this proposed study will establish a comprehensive knowledge base for future research and development of personalized dietary interventions to promote adherence to dietary strategies and self-management of chronic disease using the Precision Health approach for millions of Americans who are struggling with uncontrolled hypertension.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04764253; https://clinicaltrials.gov/ct2/show/NCT04764253.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/39058.

Effect of Personalized Nutrition on Dietary, Physical Activity, and Health Outcomes: A Systematic Review of Randomized Trials

Personalized nutrition is an approach that tailors nutrition advice to individuals based on an individual's genetic information. Despite interest among scholars, the impact of this approach on lifestyle habits and health has not been adequately explored. Hence, a systematic review of randomized trials reporting on the effects of personalized nutrition on dietary, physical activity, and health outcomes was conducted. A systematic search of seven electronic databases and a manual search resulted in identifying nine relevant trials. Cochrane's Risk of Bias was used to determine the trials' methodological quality. Although the trials were of moderate to high quality, the findings did not show consistent benefits of personalized nutrition in improving dietary, behavioral, or health outcomes. There was also a lack of evidence from regions other than North America and Europe or among individuals with diseases, affecting the generalizability of the results. Furthermore, the complex relationship between genes, interventions, and outcomes may also have contributed to the scarcity of positive findings. We have suggested several areas for improvement for future trials regarding personalized nutrition.

Validating Accuracy of an Internet-Based Application against USDA Computerized Nutrition Data System for Research on Essential Nutrients among Social-Ethnic Diets for the E-Health Era

Internet-based applications (apps) are rapidly developing in the e-Health era to assess the dietary intake of essential macro-and micro-nutrients for precision nutrition. We, therefore, validated the accuracy of an internet-based app against the Nutrition Data System for Research (NDSR), assessing these essential nutrients among various social-ethnic diet types. The agreement between the two measures using intraclass correlation coefficients was good (0.85) for total calories, but moderate for caloric ranges outside of <1000 (0.75) and >2000 (0.57); and good (>0.75) for most macro- (average: 0.85) and micro-nutrients (average: 0.83) except cobalamin (0.73) and calcium (0.51). The app underestimated nutrients that are associated with protein and fat (protein: −5.82%, fat: −12.78%, vitamin B12: −13.59%, methionine: −8.76%, zinc: −12.49%), while overestimated nutrients that are associated with carbohydrate (fiber: 6.7%, B9: 9.06%). Using artificial intelligence analytics, we confirmed the factors that could contribute to the differences between the two measures for various essential nutrients, and they included caloric ranges; the differences between the two measures for carbohydrates, protein, and fat; and diet types. For total calories, as an example, the source factors that contributed to the differences between the two measures included caloric range (<1000 versus others), fat, and protein; for cobalamin: protein, American, and Japanese diets; and for folate: caloric range (<1000 versus others), carbohydrate, and Italian diet. In the e-Health era, the internet-based app has the capacity to enhance precision nutrition. By identifying and integrating the effects of potential contributing factors in the algorithm of output readings, the accuracy of new app measures could be improved.

Targeting the gut and tumor microbiota in cancer

Microorganisms within the gut and other niches may contribute to carcinogenesis, as well as shaping cancer immunosurveillance and response to immunotherapy. Our understanding of the complex relationship between different host-intrinsic microorganisms, as well as the multifaceted mechanisms by which they influence health and disease, has grown tremendously-hastening development of novel therapeutic strategies that target the microbiota to improve treatment outcomes in cancer. Accordingly, the evaluation of a patient's microbial composition and function and its subsequent targeted modulation represent key elements of future multidisciplinary and precision-medicine approaches. In this Review, we outline the current state of research toward harnessing the microbiome to better prevent and treat cancer.

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.

Empowering consumers to PREVENT diet-related diseases through OMICS sciences (PREVENTOMICS): protocol for a parallel double-blinded randomised intervention trial to investigate biomarker-based nutrition plans for weight loss

INTRODUCTION

Personalised nutrition holds immense potential over conventional one-size-fits-all approaches for preventing and treating diet-related diseases, such as obesity. The current study aims to examine whether a personalised nutritional plan produces more favourable health outcomes than a standard approach based on general dietary recommendations in subjects with overweight or obesity and elevated waist circumference.

METHODS AND ANALYSIS

This project is a 10-week parallel, double-blinded randomised intervention trial. We plan to include 100 adults aged 18-65 years interested in losing weight, with body mass index ≥27 but<40 kg/m² and elevated waist circumference (males >94 cm; females >80 cm). Participants will be categorised into one of five predefined 'clusters' based on their individual metabolic biomarker profile and genetic background, and will be randomised in a 1:1 ratio to one of two groups: (1) personalised plan group that will receive cluster-specific meals every day for 6 days a week, in conjunction with a personalised behavioural change programme via electronic push notifications; or (2) control group that will receive meals following the general dietary recommendations in conjunction with generic health behaviour prompts. The primary outcome is the difference between groups (personalised vs control) in the change in fat mass from baseline. Secondary outcomes include changes in weight and body composition, fasting blood glucose and insulin, lipid profile, adipokines, inflammatory biomarkers, and blood pressure. Other outcomes involve measures of physical activity and sleep patterns, health-related quality of life, dietary intake, eating behaviour, and biomarkers of food intake. The effect of the intervention on the primary outcome will be analysed by means of linear mixed models.

ETHICS AND DISSEMINATION

The protocol has been approved by the Ethics Committee of the Capital Region, Copenhagen, Denmark. Study findings will be disseminated through peer-reviewed publications, conference presentations and media outlets.

TRIAL REGISTRATION NUMBER

NCT04590989.

Design issues in personalized nutrition advice systems (Preprint)

The current health status of the general public can substantially benefit from a healthy diet. Using a personalized approach to initiate healthy dietary behavior seems to be a promising strategy, as individuals differ in terms of health status, subsequent dietary needs, and their desired behavior change support. However, providing personalized advice to a wide audience over a long period is very labor-intensive. This bottleneck can possibly be overcome by digitalizing the process of creating and providing personalized advice. An increasing number of personalized advice systems for different purposes is becoming available in the market, ranging from systems providing advice about just a single parameter to very complex systems that include many variables characterizing each individual situation. Scientific background is often lacking in these systems. In designing a personalized nutrition advice system, many design questions need to be answered, ranging from the required input parameters and accurate measurement methods (sense), type of modeling techniques to be used (reason), and modality in which the personalized advice is provided (act). We have addressed these topics in this viewpoint paper, and we have demonstrated the feasibility of setting up an infrastructure for providing personalized dietary advice based on the experience of 2 practical applications in a real-life setting.

Using Biological Feedback to Promote Health Behavior Change in Adults: Protocol for a Scoping Review

Background

Many health conditions can be prevented, managed, or improved through behavioral interventions. As a component of health behavior change interventions, biological feedback is of particular interest given recent advances in wearable biosensing technology, digital health apps, and personalized health and wellness. Nevertheless, there is a paucity of literature to guide the design and implementation of interventions that incorporate biological feedback to motivate health behavior change.

Objective

The goal of this scoping review is to deeply explore the use of biological feedback as a component of health behavior change interventions that target adults. The objectives of the review include (1) mapping the domains of research that incorporate biological feedback and (2) describing the operational characteristics of using biological feedback in the context of health behavior change.

Methods

A comprehensive list of search terms was developed to capture studies from a wide range of domains. The studies to be included are randomized controlled trials published as primary research articles, theses, or dissertations targeting adults 18 years and older, who use biological feedback to change a health-related behavior. The following electronic databases were searched: Ovid MEDLINE, Embase, Cochrane Central Register of Controlled Trials, EBSCOhost, PsycINFO, and ProQuest Dissertations & Theses Global. The screening and data extraction process will be guided by the Joanna Briggs Institute Manual for Evidence Synthesis and conducted by trained reviewers.

Results

Database searches were completed in June 2021. A total of 50,459 unique records were returned after the removal of 48,634 duplicate records. The scoping review is planned for completion in 2022.

Conclusions

To our knowledge, this will be the first scoping review to map the literature that uses biological feedback as a component of health behavior change interventions targeting adults. The findings will be used to develop a framework to guide the design and implementation of future health behavior change interventions that incorporate biological feedback.

Trial Registration

OSF Registries OSF.IO/YP5WA; https://osf.io/yp5wa

International Registered Report Identifier (IRRID)

DERR1-10.2196/32579

Systematic Review of Nutritional Recommendation Systems

In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.

Perceptions of Factors Influencing Engagement With Health and Well-being Apps in the United Kingdom: Qualitative Interview Study

BACKGROUND

Digital health devices, such as health and well-being smartphone apps, could offer an accessible and cost-effective way to deliver health and well-being interventions. A key component of the effectiveness of health and well-being apps is user engagement. However, engagement with health and well-being apps is typically poor. Previous studies have identified a list of factors that could influence engagement; however, most of these studies were conducted on a particular population or for an app targeting a particular behavior. An understanding of the factors that influence engagement with a wide range of health and well-being apps can inform the design and the development of more engaging apps in general.

OBJECTIVE

The aim of this study is to explore user experiences of and reasons for engaging and not engaging with a wide range of health and well-being apps.

METHODS

A sample of adults in the United Kingdom (N=17) interested in using a health or well-being app participated in a semistructured interview to explore experiences of engaging and not engaging with these apps. Participants were recruited via social media platforms. Data were analyzed with the framework approach, informed by the Capability, Opportunity, Motivation-Behaviour (COM-B) model and the Theoretical Domains Framework, which are 2 widely used frameworks that incorporate a comprehensive set of behavioral influences.

RESULTS

Factors that influence the capability of participants included available user guidance, statistical and health information, reduced cognitive load, well-designed reminders, self-monitoring features, features that help establish a routine, features that offer a safety net, and stepping-stone app characteristics. Tailoring, peer support, and embedded professional support were identified as important factors that enhance user opportunities for engagement with health and well-being apps. Feedback, rewards, encouragement, goal setting, action planning, self-confidence, and commitment were judged to be the motivation factors that affect engagement with health and well-being apps.

CONCLUSIONS

Multiple factors were identified across all components of the COM-B model that may be valuable for the development of more engaging health and well-being apps. Engagement appears to be influenced primarily by features that provide user guidance, promote minimal cognitive load, support self-monitoring (capability), provide embedded social support (opportunity), and provide goal setting with action planning (motivation). This research provides recommendations for policy makers, industry, health care providers, and app developers for increasing effective engagement.

Decentralized vitamin C & D dual biosensor chip: Toward personalized immune system support

Combating the ongoing COVID-19 pandemic has put the spotlight on nutritional support of the immune system through consumption of vitamins C and D. Accordingly, there are urgent demands for an effective on-the-spot multi-vitamin self-testing platform that monitors the levels of these immune-supporting micronutrients for guiding precision nutrition recommendations. Herein, we present a compact bioelectronic dual sensor chip aimed at frequent on-the-spot simultaneous monitoring of the salivary vitamin C and D dynamics. The new bioelectronic chip combines a new electrocatalytic vitamin C amperometric assay along with competitive vitamin D immunoassay on neighboring electrodes, to perform selective and cross-talk free detection of both vitamins in a 10-μL saliva sample within 25 min. The distinct vitamin C or D temporal profiles obtained for different individuals after vitamin supplementation indicate the potential of the new bioelectronic chip strategy for enhancing personalized nutrition towards guiding dietary interventions to meet individual nutrition needs and promote immune system health.

Digital medicine and the curse of dimensionality

Digital health data are multimodal and high-dimensional. A patient's health state can be characterized by a multitude of signals including medical imaging, clinical variables, genome sequencing, conversations between clinicians and patients, and continuous signals from wearables, among others. This high volume, personalized data stream aggregated over patients' lives has spurred interest in developing new artificial intelligence (AI) models for higher-precision diagnosis, prognosis, and tracking. While the promise of these algorithms is undeniable, their dissemination and adoption have been slow, owing partially to unpredictable AI model performance once deployed in the real world. We posit that one of the rate-limiting factors in developing algorithms that generalize to real-world scenarios is the very attribute that makes the data exciting-their high-dimensional nature. This paper considers how the large number of features in vast digital health data can challenge the development of robust AI models-a phenomenon known as "the curse of dimensionality" in statistical learning theory. We provide an overview of the curse of dimensionality in the context of digital health, demonstrate how it can negatively impact out-of-sample performance, and highlight important considerations for researchers and algorithm designers.

At the Intersection of Gut Microbiome and Stroke: A Systematic Review of the Literature

Background: Ischemic and hemorrhagic stroke are associated with a high rate of long-term disability and death. Recent investigations focus efforts to better understand how alterations in gut microbiota composition influence clinical outcomes. A key metabolite, trimethylamine N-oxide (TMAO), is linked to multiple inflammatory, vascular, and oxidative pathways. The current biochemical underpinnings of microbial effects on stroke remain largely understudied. The goal of our study is to explore the current literature to explain the interactions between the human gut microbiome and stroke progression, recovery, and outcome. We also provide a descriptive review of TMAO.

Methods: A systematic literature search of published articles between January 1, 1990, and March 22, 2020, was performed on the PubMed database to identify studies addressing the role of the microbiome and TMAO in the pathogenesis and recovery of acute stroke. Our initial investigation focused on human subject studies and was further expanded to include animal studies. Relevant articles were included, regardless of study design. The analysis included reviewers classifying and presenting selected articles by study design and sample size in a chart format.

Results: A total of 222 titles and abstracts were screened. A review of the 68 original human subject articles resulted in the inclusion of 24 studies in this review. To provide further insight into TMAO as a key player, an additional 40 articles were also reviewed and included. Our findings highlighted that alterations in richness and abundance of gut microbes and increased plasma TMAO play an important role in vascular events and outcomes. Our analysis revealed that restoration of a healthy gut, through targeted TMAO-reducing therapies, could provide alternative secondary prevention for at-risk patients.

Discussion: Biochemical interactions between the gut microbiome and inflammation, resulting in metabolic derangements, can affect stroke progression and outcomes. Clinical evidence supports the importance of TMAO in modulating underlying stroke risk factors. Lack of standardization and distinct differences in sample sizes among studies are major limitations.

Understanding Choline Bioavailability and Utilization: First Step Toward Personalizing Choline Nutrition

Choline is an essential macronutrient involved in neurotransmitter synthesis, cell-membrane signaling, lipid transport, and methyl-group metabolism. Nevertheless, the vast majority are not meeting the recommended intake requirement. Choline deficiency is linked to nonalcoholic fatty liver disease, skeletal muscle atrophy, and neurodegenerative diseases. The conversion of dietary choline to trimethylamine by gut microbiota is known for its association with atherosclerosis and may contribute to choline deficiency. Choline-utilizing bacteria constitutes less than 1% of the gut community and is modulated by lifestyle interventions such as dietary patterns, antibiotics, and probiotics. In addition, choline utilization is also affected by genetic factors, further complicating the impact of choline on health. This review overviews the complex interplay between dietary intakes of choline, gut microbiota and genetic factors, and the subsequent impact on health. Understanding of gut microbiota metabolism of choline substrates and interindividual variability is warranted in the development of personalized choline nutrition.

Designing Consumer Health Information Technology to Support Biform and Articulation Work: A Qualitative Study of Diet and Nutrition Management as Patient Work

BACKGROUND

Diet and nutrition management is an integral component of Crohn disease (CD) management. This type of management is highly variable and individualized and, thus, requires personalized approaches. Consumer health information technology (CHIT) designed to support CD management has typically supported this task as everyday life work and, not necessarily, as illness work. Moreover, CHIT has rarely supported the ways in which diet and nutrition management requires coordination between multiple forms of patient work.

OBJECTIVE

The purpose of this study was to investigate diet and nutrition management as biform work, identify components of articulation work, and provide guidance on how to design CHIT to support this work.

METHODS

We performed a qualitative study in which we recruited participants from CD-related Facebook pages and groups.

RESULTS

Semistructured interviews with 21 individuals showed that diet and nutrition management strategies were highly individualized and variable. Four themes emerged from the data, emphasizing the interactions of diet and nutrition with physical, emotional, information, and technology-enabled management.

CONCLUSIONS

This study shows that the extent to which diet and nutrition management is biform work fluctuates over time and that articulation work can be continuous and unplanned. The design guidance specifies the need for patient-facing technologies to support interactions among diet and nutrition and other management activities such as medication intake, stress reduction, and information seeking, as well as to respond to the ways in which diet and nutrition management needs change over time.

Wearable and Mobile Sensors for Personalized Nutrition

While wearable and mobile chemical sensors have experienced tremendous growth over the past decade, their potential for tracking and guiding nutrition has emerged only over the past three years. Currently, guidelines from doctors and dietitians represent the most common approach for maintaining optimal nutrition status. However, such recommendations rely on population averages and do not take into account individual variability in responding to nutrients. Precision nutrition has recently emerged to address the large heterogeneity in individuals' responses to diet, by tailoring nutrition based on the specific requirements of each person. It aims at preventing and managing diseases by formulating personalized dietary interventions to individuals on the basis of their metabolic profile, background, and environmental exposure. Recent advances in digital nutrition technology, including calories-counting mobile apps and wearable motion tracking devices, lack the ability of monitoring nutrition at the molecular level. The realization of effective precision nutrition requires synergy from different sensor modalities in order to make timely reliable predictions and efficient feedback. This work reviews key opportunities and challenges toward the successful realization of effective wearable and mobile nutrition monitoring platforms. Non-invasive wearable and mobile electrochemical sensors, capable of monitoring temporal chemical variations upon the intake of food and supplements, are excellent candidates to bridge the gap between digital and biochemical analyses for a successful personalized nutrition approach. By providing timely (previously unavailable) dietary information, such wearable and mobile sensors offer the guidance necessary for supporting dietary behavior change toward a managed nutritional balance. Coupling of the rapidly emerging wearable chemical sensing devices-generating enormous dynamic analytical data-with efficient data-fusion and data-mining methods that identify patterns and make predictions is expected to revolutionize dietary decision-making toward effective precision nutrition.

Applying Contemporary Machine Learning Approaches to Nutrition Care Real-World Evidence: Findings From the National Quality Improvement Data Set

Using real-world data from the Academy of Nutrition and Dietetics Health Informatics Infrastructure, we use state-of-the-art clustering techniques to identify 2 phenotypes characterizing the episodes of nutrition care observed in the National Quality Improvement (NQI) registry data set. The 2 phenotypes identified from recorded Nutrition Care Process data in the NQI exhibit a strong correspondence with the clinical expertise of registered dietitian nutritionists. For one of these phenotypes, it was possible to implement state-of-the-art classification techniques to predict the nutrition problem-resolution status of an episode of care. Prediction results show that the assessment of nutrition history, number of recorded visits in the episode, and use of nutrition counseling interventions were significantly and positively correlated with problem resolution. Meanwhile, evaluations of nutrition history that were not within the desired ranges were significantly and negatively correlated with problem resolution. Finally, we assess the usefulness of the current NQI data set and data model for supporting the application of contemporary machine learning methods to the data set. We also suggest ways of enhancing the NQI since registered dietitian nutritionists are encouraged to continue to contribute patient cases in this and other registry nutrition studies.

A New Paradigm for Clinical Nutrition Services in the Era of the Fourth Industrial Revolution

The role of clinical nutrition services is emphasized in the care of chronic diseases; the prevalence of chronic diseases continues to increase due to the living environment change, westernized dietary life and the aging population in Korea. The effectiveness of clinical nutrition services in the treatment of diseases in inpatients has been demonstrated in several studies. However, in recent days, innovative changes are pursued in clinical nutrition services through a convergence with information and communication technology (ICT), a core technology of the fourth industrial revolution such as big data, deep learning, and artificial intelligence (AI). The health care environment is changing from a medical treatment-oriented service to a preventive and personalized paradigm. Furthermore, we live in an era of personalization where we can personalize dietary aspects including food choice, cooking recipes, and nutrition in daily life. In addition, ICT technology can build a personalized nutrition platform in consideration of individual patient's diseases, genetic trait, and environment, all of which can be technical means in personalized nutrition management services. Personalized nutrition based on ICT technology is able to provide more standardized and high-quality clinical nutrition services to the patients. The purpose of this review is to examine the core technologies of the fourth industrial revolution affecting clinical nutrition services, and ultimately discuss how clinical nutrition professional should respond to ICT technology-related fields in the era of the new technological innovations.

Precision nutrition: A systematic literature review

Precision Nutrition research aims to use personal information about individuals or groups of individuals to deliver nutritional advice that, theoretically, would be more suitable than generic advice. Machine learning, a subbranch of Artificial Intelligence, has promise to aid in the development of predictive models that are suitable for Precision Nutrition. As such, recent research has applied machine learning algorithms, tools, and techniques in precision nutrition for different purposes. However, a systematic overview of the state-of-the-art on the use of machine learning in Precision Nutrition is lacking. Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. Nine research questions were defined in this study. We retrieved 4930 papers from electronic databases and 60 primary studies were selected to respond to the research questions. All of the selected primary studies were also briefly discussed in this article. Our results show that fifteen problems spread across seven domains of nutrition and health are present. Four machine learning tasks are seen in the form of regression, classification, recommendation and clustering, with most of these utilizing a supervised approach. In total, 30 algorithms were used, with 19 appearing more than once. Models were through the use of four groups of approaches and 23 evaluation metrics. Personalized approaches are promising to reduce the burden of these current problems in nutrition research, and the current review shows Machine Learning can be incorporated into Precision Nutrition research with high performance. Precision Nutrition researchers should consider incorporating Machine Learning into their methods to facilitate the integration of many complex features, allowing for the development of high-performance Precision Nutrition approaches.

Appetite-Regulating Hormones Are Reduced After Oral Sucrose vs Glucose: Influence of Obesity, Insulin Resistance, and Sex

CONTEXT

Fructose compared to glucose has adverse effects on metabolic function, but endocrine responses to oral sucrose vs glucose is not well understood.

OBJECTIVE

We investigated how oral sucrose vs glucose affected appetite-regulating hormones, and how biological factors (body mass index [BMI], insulin sensitivity, sex) influence endocrine responses to these 2 types of sugar.

DESIGN

Sixty-nine adults (29 men; 23.22 ± 3.74 years; BMI 27.03 ± 4.96 kg/m2) completed the study. On 2 occasions, participants consumed 300-mL drinks containing 75 g of glucose or sucrose. Blood was sampled at baseline, 10, 35, and 120 minutes post drink for plasma glucose, insulin, glucagon-like peptide (GLP-1)(7-36), peptide YY (PYY)total, and acyl-ghrelin measures. Hormone levels were compared between conditions using a linear mixed model. Interaction models were performed, and results were stratified to assess how biological factors influence endocrine responses.

RESULTS

Sucrose vs glucose ingestion provoked a less robust rise in glucose (P < .001), insulin (P < .001), GLP-1 (P < .001), and PYY (P = .02), whereas acyl-ghrelin suppression was similar between the sugars. We found BMI status by sugar interactions for glucose (P = .01) and PYY (P = .03); obese individuals had smaller increases in glucose and PYY levels after consuming sucrose vs glucose. There were interactions between insulin sensitivity and sugar for glucose (P = .003) and insulin (P = .04), and a sex by sugar interaction for GLP-1 (P = .01); men demonstrated smaller increases in GLP-1 in response to oral sucrose vs glucose.

CONCLUSION

Sucrose is less efficient at signaling postprandial satiation than glucose, and biological factors influence differential hormone responses to sucrose vs glucose consumption.

Nutritional Orthopedics and Space Nutrition as Two Sides of the Same Coin: A Scoping Review

Since the Moon landing, nutritional research has been charged with the task of guaranteeing human health in space. In addition, nutrition applied to Orthopedics has developed in recent years, driven by the need to improve the efficiency of the treatment path by enhancing the recovery after surgery. As a result, nutritional sciences have specialized into two distinct fields of research: Nutritional Orthopedics and Space Nutrition. The former primarily deals with the nutritional requirements of old patients in hospitals, whereas the latter focuses on the varied food challenges of space travelers heading to deep space. Although they may seem disconnected, they both investigate similar nutritional issues. This scoping review shows what these two disciplines have in common, highlighting the mutual features between (1) pre-operative vs. pre-launch nutritional programs, (2) hospital-based vs. space station nutritional issues, and (3) post-discharge vs. deep space nutritional resilience. PubMed and Google Scholar were used to collect documents published from 1950 to 2020, from which 44 references were selected on Nutritional Orthopedics and 44 on Space Nutrition. Both the orthopedic patient and the astronaut were found to suffer from food insecurity, malnutrition, musculoskeletal involution, flavor/pleasure issues, fluid shifts, metabolic stresses, and isolation/confinement. Both fields of research aid the planning of demand-driven food systems and advanced nutritional approaches, like tailored diets with nutrients of interest (e.g., vitamin D and calcium). The nutritional features of orthopedic patients on Earth and of astronauts in space are undeniably related. Consequently, it is important to initiate close collaborations between orthopedic nutritionists and space experts, with the musculoskeletal-related dedications playing as common fuel.

The landscape of nutri-informatics: a review of current resources and challenges for integrative nutrition research

Informatics has become an essential component of research in the past few decades, capitalizing on the efficiency and power of computation to improve the knowledge gained from increasing quantities and types of data. While other fields of research such as genomics are well represented in informatics resources, nutrition remains underrepresented. Nutrition is one of the most integral components of human life, and it impacts individuals far beyond just nutrient provisions. For example, nutrition plays a role in cultural practices, interpersonal relationships and body image. Despite this, integrated computational investigations have been limited due to challenges within nutrition informatics (nutri-informatics) and nutrition data. The purpose of this review is to describe the landscape of nutri-informatics resources available for use in computational nutrition research and clinical utilization. In particular, we will focus on the application of biomedical ontologies and their potential to improve the standardization and interoperability of nutrition terminologies and relationships between nutrition and other biomedical disciplines such as disease and phenomics. Additionally, we will highlight challenges currently faced by the nutri-informatics community including experimental design, data aggregation and the roles scientific journals and primary nutrition researchers play in facilitating data reuse and successful computational research. Finally, we will conclude with a call to action to create and follow community standards regarding standardization of language, documentation specifications and requirements for data reuse. With the continued movement toward community standards of this kind, the entire nutrition research community can transition toward greater usage of Findability, Accessibility, Interoperability and Reusability principles and in turn more transparent science.

Artificial Intelligence in Nutrients Science Research: A Review

Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.

[Applicability of innovation in clinical nutrition]

Introduction

The health industry has experienced great innovation and will continue to do so in the coming years. The term innovation comes from "outside to inside" driven by the need for knowledge and research to truly translate into effective improvements (hence the sequence from Research and Development to Innovation: R+D+I); but it also comes from "bottom up" as a drive of the health organization (based, as few others, on knowledge as a fundamental asset) to give way to their creativity and their ability to find new solutions to old and new problems. The current health system must advance in the development of a more global and integrated philosophy of care, which allows dealing with the consequences of aging and the increase in chronic diseases and dependence, which represent an increase in the demand for care. In the medium-long term, a care logic based on individual characteristics from the molecular perspective should be promoted, which is known as 5P medicine (personalized, preventive, predictive, participatory and population), also called personalized medicine, a paradigm that has already initiated its entry, slow and uneven, in health systems. And it must also adapt to a society with more informed and participatory people in the management of their own health, which increasingly use technologies whose development speed grows exponentially. Taking into account these characteristics and objectives, in this article we seek to define the fundamental features of the intersection between innovation and clinical nutrition.

Personalized Nutrition and -Omics

With change in global concern toward food quality over food quantity, consumer concern and choice of healthy food has become a matter of prime importance. It gave rise to concept of “personalized or precision nutrition”. The theory behind personalization of nutrition is supported by multiple factors including advances in food analytics, nutrition based diseases and public health programs, increasing use of information technology in nutrition science, concept of gene-diet interaction and growing consumer capacity or concern by better and healthy foods. The advances in “omics” tools and related analytical techniques have resulted into tremendous scope of their application in nutrition science. As a consequence, a better understanding of underlying interaction between diet and individual is expected with addressing of key challenges for successful implementation of this science. In this chapter, the above aspects are discussed to get an insight into driving factors for increasing concern in personalized nutrition.

Malnutrition, Health and the Role of Machine Learning in Clinical Setting

Nutrition plays a vital role in health and the recovery process. Deficiencies in macronutrients and micronutrients can impact the development and progression of various disorders. However, malnutrition screening tools and their utility in the clinical setting remain largely understudied. In this study, we summarize the importance of nutritional adequacy and its association with neurological, cardiovascular, and immune-related disorders. We also examine general and specific malnutrition assessment tools utilized in healthcare settings. Since the implementation of the screening process in 2016, malnutrition data from hospitalized patients in the Geisinger Health System is presented and discussed as a case study. Clinical data from five Geisinger hospitals shows that ~10% of all admitted patients are acknowledged for having some form of nutritional deficiency, from which about 60-80% of the patients are targeted for a more comprehensive assessment. Finally, we conclude that with a reflection on how technological advances, specifically machine learning-based algorithms, can be integrated into electronic health records to provide decision support system to care providers in the identification and management of patients at higher risk of malnutrition.

Advancements in Personalized Nutrition Technologies: Guiding Principles for Registered Dietitian Nutritionists

Individualized nutrition counseling and care is a cornerstone of practice for registered dietitian nutritionists (RDNs). The term personalized nutrition (PN) refers to "individual-specific information founded in evidence-based science to promote dietary behavior change that may result in measurable health benefits." PN technologies, which include the "omics" approaches, may offer the potential to improve specificity of nutrition care through assessment of molecular-level data, such as genes or the microbiome, in order to determine the course for nutrition intervention. These technologies are evolving rapidly, and for many RDNs, it is unclear whether, when, or how these technologies should be incorporated into the nutrition care process. In order to provide guidance in these developing PN fields, International Life Sciences Institute North America convened a multidisciplinary panel to develop guiding principles for PN approaches. The objective of this article is to inform RDN practice decisions related to the implementation of PN technologies by examining the alignment of proposed PN guiding principles with the Code of Ethics for the Nutrition and Dietetics Profession, as well as Scope and Standards of Practice. Guiding principles are described as they apply to each stage of the nutrition care process and include identifying potential beneficiaries, communicating effects transparently, and protecting individual privacy. Guiding principles for PN augment standard guidance for RDNs to pose relevant questions, raise potential concerns, and guide evaluation of supporting evidence for specific PN technologies. RDNs have a responsibility to think critically about the application of PN technologies, including appropriateness and potential effectiveness, for the individual served.

Perspective: Guiding Principles for the Implementation of Personalized Nutrition Approaches That Benefit Health and Function

Personalized nutrition (PN) approaches have been shown to help drive behavior change and positively influence health outcomes. This has led to an increase in the development of commercially available PN programs, which utilize various forms of individual-level information to provide services and products for consumers. The lack of a well-accepted definition of PN or an established set of guiding principles for the implementation of PN creates barriers for establishing credibility and efficacy. To address these points, the North American Branch of the International Life Sciences Institute convened a multidisciplinary panel. In this article, a definition for PN is proposed: "Personalized nutrition uses individual-specific information, founded in evidence-based science, to promote dietary behavior change that may result in measurable health benefits." In addition, 10 guiding principles for PN approaches are proposed: 1) define potential users and beneficiaries; 2) use validated diagnostic methods and measures; 3) maintain data quality and relevance; 4) derive data-driven recommendations from validated models and algorithms; 5) design PN studies around validated individual health or function needs and outcomes; 6) provide rigorous scientific evidence for an effect on health or function; 7) deliver user-friendly tools; 8) for healthy individuals, align with population-based recommendations; 9) communicate transparently about potential effects; and 10) protect individual data privacy and act responsibly. These principles are intended to establish a basis for responsible approaches to the evidence-based research and practice of PN and serve as an invitation for further public dialog. Several challenges were identified for PN to continue gaining acceptance, including defining the health-disease continuum, identification of biomarkers, changing regulatory landscapes, accessibility, and measuring success. Although PN approaches hold promise for public health in the future, further research is needed on the accuracy of dietary intake measurement, utilization and standardization of systems approaches, and application and communication of evidence.

Microbiota-dependent and -independent effects of dietary fibre on human health

Dietary fibre, such as indigestible oligosaccharides and polysaccharides, occurs in many foods and has gained considerable importance related to its beneficial effects on host health and specific diseases. Dietary fibre is neither digested nor absorbed in the small intestine and modulates the composition of the gut microbiota. New evidence indicates that dietary fibre also interacts directly with the epithelium and immune cells throughout the gastrointestinal tract by microbiota-independent effects. This review focuses on how dietary fibre improves human health and the reported health benefits that are connected to molecular pathways, in (a) a microbiota-independent manner, via interaction with specific surface receptors on epithelial and immune cells regulating intestinal barrier and immune function, and (b) a microbiota-dependent manner via maintaining intestinal homeostasis by promoting beneficial microbes, including Bifidobacteria and Lactobacilli, limiting the growth, adhesion, and cytotoxicity of pathogenic microbes, as well as stimulating fibre-derived microbial short-chain fatty acid production. LINKED ARTICLES: This article is part of a themed section on The Pharmacology of Nutraceuticals. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v177.6/issuetoc.

Artificial Intelligence Applied to Flavonoid Data in Food Matrices

Increasing interest in constituents and dietary supplements has created the need for more efficient use of this information in nutrition-related fields. The present work aims to obtain optimal models to predict the total antioxidant properties of food matrices, using available information on the amount and class of flavonoids present in vegetables. A new dataset using databases that collect the flavonoid content of selected foods has been created. Structural information was obtained using a structural-topological approach called TOPological Sub-Structural Molecular (TOPSMODE). Different artificial intelligence algorithms were applied, including Machine Learning (ML) methods. The study allowed us to demonstrate the effectiveness of the models using structural-topological characteristics of dietary flavonoids. The proposed models can be considered, without overfitting, effective in predicting new values of Oxygen Radical Absorption capacity (ORAC), except in the Multi-Layer Perceptron (MLP) algorithm. The best optimal model was obtained by the Random Forest (RF) algorithm. The in silico methodology we developed allows us to confirm the effectiveness of the obtained models, by introducing the new structural-topological attributes, as well as selecting those that most influence the class variable.

Psychosocial information use for clinical decisions in diabetes care

OBJECTIVE

There are increasing efforts to capture psychosocial information in outpatient care in order to enhance health equity. To advance clinical decision support systems (CDSS), this study investigated which psychosocial information clinicians value, who values it, and when and how clinicians use this information for clinical decision-making in outpatient type 2 diabetes care.

MATERIALS AND METHODS

This mixed methods study involved physician interviews (n = 17) and a survey of physicians, nurse practitioners (NPs), and diabetes educators (n = 198). We used the grounded theory approach to analyze interview data and descriptive statistics and tests of difference by clinician type for survey data.

RESULTS

Participants viewed financial strain, mental health status, and life stressors as most important. NPs and diabetes educators perceived psychosocial information to be more important, and used it significantly more often for 1 decision, than did physicians. While some clinicians always used psychosocial information, others did so when patients were not doing well. Physicians used psychosocial information to judge patient capabilities, understanding, and needs; this informed assessment of the risks and the feasibility of options and patient needs. These assessments influenced 4 key clinical decisions.

DISCUSSION

Triggers for psychosocially informed CDSS should include psychosocial screening results, new or newly diagnosed patients, and changes in patient status. CDSS should support cost-sensitive medication prescribing, and psychosocially based assessment of hypoglycemia risk. Electronic health records should capture rationales for care that do not conform to guidelines for panel management. NPs and diabetes educators are key stakeholders in psychosocially informed CDSS.

CONCLUSION

Findings highlight opportunities for psychosocially informed CDSS-a vital next step for improving health equity.

Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools

Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macronutrient composition) and output (physical activity) to predict energy stores (body weight) as a way to evaluate their ability to identify potential phenotypes based on these parameters. From the Women’s Health Initiative Observational Study (WHI OS), carbohydrates, proteins, fats, fibers, sugars, and physical activity variables, namely energy expended from mild, moderate, and vigorous intensity activity, were used to predict current body weight (both as body weight in kilograms and as a body mass index (BMI) category). Several machine learning tools were used for this prediction. Finally, cluster analysis was used to identify putative phenotypes. For the numerical predictions, the support vector machine (SVM), neural network, and k-nearest neighbor (kNN) algorithms performed modestly, with mean approximate errors (MAEs) of 6.70 kg, 6.98 kg, and 6.90 kg, respectively. For categorical prediction, SVM performed the best (54.5% accuracy), followed closely by the bagged tree ensemble and kNN algorithms. K-means cluster analysis improved prediction using numerical data, identified 10 clusters suggestive of phenotypes, with a minimum MAE of ~1.1 kg. A classifier was used to phenotype subjects into the identified clusters, with MAEs <5 kg for 15% of the test set (n = ~2000). This study highlights the challenges, limitations, and successes in using machine learning tools on self-reported data to identify determinants of energy balance.