Challenges in Personalized Nutrition and Health

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.