Challenges in Personalized Nutrition and Health

Personalized nutrition and approaches employed Personalized nutrition refers to tailored nutritional recommendations aimed at the promotion, maintenance of health and prevention against diseases (1). These recommendations take into account differential responses to certain individualized food-derived nutrients that arise due to the interaction between nutrients and biological processes (2). These include the interactions between internal factors such as genetics, microbiome, metabolome interactions as well as external factors such as dietary habits and physical activity (3). In contrast to precision medicine defined by the Precision Medicine Initiative (https://obamawhitehouse.archives.gov/node/333101) as an approach toward the treatment and prevention of disease for an individual, the goal of personalized nutrition is to promote the health and well-being through diet. A balanced diet promotes good health as it provides adequate amounts of energy, proteins, vitamins, minerals, essential fats, micro, and macronutrients for the metabolic needs of the body to function properly at each stage of the lifespan. The absence of balanced food and nutrition security leads to health problems such as diabetes, obesity, and malnutrition (4). Although, the importance of nutrition and beneficial effects of food are well established, the mechanisms underlying their role in disease prevention or health benefits are incompletely understood (5, 6). Further, there exists an inter-individual response to dietary intervention due to which a sub population may benefit more than others. This underlying variability can be attributed to genetics, age, gender, lifestyle, environmental exposure, gut microbiome, epigenetics, metabolism nutrition derived from diet, and foods. The inter-individual variability to treatments and nutritional recommendations is largely reflected in biomarker values (7). Reductionist approaches fail to demonstrate how the cellular and molecular responses due to food produce health benefits (6). Current approaches used to study the inter-individual response to diet include–omics technologies such as genomics, metabolomics, proteomics integrated with the systems biology programs. These approaches are focused on integrating and analyzing complex datasets generated during dietary intervention association studies (3, 8, 9). Systems biology approaches are impacting the field of nutrition (10–12) and immunology (13), however, significant challenges still remain in the translation and application of these advances to human studies (9). A comprehensive systems-wide mechanistic understanding of the interplay between nutrition and health benefits requires the knowledge of network dynamics in the context of health, pre-disease, and disease states. This requirement gives rise to the demand for new approaches and methods that could not only quantify the effects of dietary interventions in healthy individuals but also facilitate comparison to diseased patients (6).

[1]  Ting-Wei Hou,et al.  A clinical nutritional information system with personalized nutrition assessment , 2018, Comput. Methods Programs Biomed..

[2]  Brett K. Beaulieu-Jones,et al.  Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis , 2017, bioRxiv.

[3]  Melissa J. Morine,et al.  Clinical and Vitamin Response to a Short‐Term Multi‐Micronutrient Intervention in Brazilian Children and Teens: From Population Data to Interindividual Responses , 2018, Molecular nutrition & food research.

[4]  P. Finglas,et al.  Advancing food, nutrition, and health research in Europe by connecting and building research infrastructures in a DISH-RI: Results of the EuroDISH project , 2018 .

[5]  Shraddha Chakradhar,et al.  Predictable response: Finding optimal drugs and doses using artificial intelligence , 2017, Nature Medicine.

[6]  S. Steinhubl,et al.  High-Definition Medicine , 2017, Cell.

[7]  N. Schork,et al.  Single-Subject Studies in Translational Nutrition Research. , 2017, Annual review of nutrition.

[8]  J. Després,et al.  Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome , 2017, Nutrients.

[9]  Josep Bassaganya-Riera,et al.  Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection , 2017, Artif. Intell. Medicine.

[10]  D. Corella,et al.  Utilizing nutritional genomics to tailor diets for the prevention of cardiovascular disease: a guide for upcoming studies and implementations , 2017, Expert review of molecular diagnostics.

[11]  P. van 't Veer,et al.  [Accepted Manuscript] Concepts and procedures for mapping food and health research infrastructure: New insights from the EuroDISH project , 2017 .

[12]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[13]  F. Hu,et al.  One (small) step towards precision nutrition by use of metabolomics , 2017, The lancet. Diabetes & endocrinology.

[14]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[15]  J. Gonzalez,et al.  Personalised nutrition: What makes you so special? , 2016 .

[16]  J. Matthews,et al.  Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. , 2016, International journal of epidemiology.

[17]  Hannelore Daniel,et al.  A Dietary Feedback System for the Delivery of Consistent Personalized Dietary Advice in the Web-Based Multicenter Food4Me Study , 2016, Journal of medical Internet research.

[18]  Joseph L. Kannry,et al.  The Chief Clinical Informatics Officer (CCIO) , 2016, J. Am. Medical Informatics Assoc..

[19]  Raquel Hontecillas,et al.  Modeling-Enabled Systems Nutritional Immunology , 2016, Front. Nutr..

[20]  Dana C. Crawford,et al.  Unravelling the human genome–phenome relationship using phenome-wide association studies , 2016, Nature Reviews Genetics.

[21]  Sergio Guadarrama,et al.  Im2Calories: Towards an Automated Mobile Vision Food Diary , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[22]  E. Segal,et al.  Personalized Nutrition by Prediction of Glycemic Responses , 2015, Cell.

[23]  M. Kussmann,et al.  Human nutrition, environment, and health , 2015, Genes & Nutrition.

[24]  J. Bassaganya-Riera,et al.  Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection , 2015, PloS one.

[25]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[26]  M. Kussmann,et al.  The genomics of micronutrient requirements , 2015, Genes & Nutrition.

[27]  N. Schork Personalized medicine: Time for one-person trials , 2015, Nature.

[28]  R. Marting The cure for claims denials. , 2015, Family practice management.

[29]  Neel Joshi,et al.  Menu-Match: Restaurant-Specific Food Logging from Images , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[30]  Jose Lara,et al.  Conference on 'Changing dietary behaviour: physiology through to practice' Symposium 3: Novel methods for motivating dietary change Personalising nutritional guidance for more effective behaviour change , 2014 .

[31]  Lu Qi,et al.  Personalized nutrition and obesity , 2014, Annals of medicine.

[32]  L. Ohno-Machado,et al.  “Big Data” and the Electronic Health Record , 2014, Yearbook of Medical Informatics.

[33]  Michael Brauer,et al.  Genes, the environment and personalized medicine , 2014, EMBO reports.

[34]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[35]  Michael Muller,et al.  Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life , 2013, Genes & Nutrition.

[36]  B. Wells,et al.  Strategies for Handling Missing Data in Electronic Health Record Derived Data , 2013, EGEMS.

[37]  Deanna M. Church,et al.  ClinVar: public archive of relationships among sequence variation and human phenotype , 2013, Nucleic Acids Res..

[38]  M. Marathe,et al.  Predictive Computational Modeling of the Mucosal Immune Responses during Helicobacter pylori Infection , 2013, PloS one.

[39]  Melissa J. Morine,et al.  Network analysis of adipose tissue gene expression highlights altered metabolic and regulatory transcriptomic activity in high-fat-diet-fed IL-1RI knockout mice. , 2013, The Journal of nutritional biochemistry.

[40]  George Hripcsak,et al.  Next-generation phenotyping of electronic health records , 2012, J. Am. Medical Informatics Assoc..

[41]  Ross A. Hammond,et al.  A systems science perspective and transdisciplinary models for food and nutrition security , 2012, Proceedings of the National Academy of Sciences.

[42]  S. Brunak,et al.  Mining electronic health records: towards better research applications and clinical care , 2012, Nature Reviews Genetics.

[43]  Christopher D Fjell,et al.  A systems biology approach to nutritional immunology - focus on innate immunity. , 2012, Molecular aspects of medicine.

[44]  Ben van Ommen,et al.  Transcriptomic Coordination in the Human Metabolic Network Reveals Links between n-3 Fat Intake, Adipose Tissue Gene Expression and Metabolic Health , 2011, PLoS Comput. Biol..

[45]  Heather N. Watson,et al.  Use of electronic medical records (EMR) for oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data , 2011, Clinical epidemiology.

[46]  Bruce H Dobkin,et al.  The Promise of mHealth , 2011, Neurorehabilitation and neural repair.

[47]  Christopher G. Chute,et al.  Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience , 2011, J. Am. Medical Informatics Assoc..

[48]  N. Schork,et al.  The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? , 2011, Personalized medicine.

[49]  I. White,et al.  Multiple imputation using chained equations: Issues and guidance for practice , 2011, Statistics in medicine.

[50]  David S. Ebert,et al.  Volume estimation using food specific shape templates in mobile image-based dietary assessment , 2011, Electronic Imaging.

[51]  Isobel Claire Gormley,et al.  Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome , 2010, BMC Bioinformatics.

[52]  Raquel Hontecillas,et al.  Model of colonic inflammation: immune modulatory mechanisms in inflammatory bowel disease. , 2010, Journal of theoretical biology.

[53]  Marylyn D. Ritchie,et al.  PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations , 2010, Bioinform..

[54]  A. Travis,et al.  Challenges of molecular nutrition research 6: the nutritional phenotype database to store, share and evaluate nutritional systems biology studies , 2010, Genes & Nutrition.

[55]  M. Kenward,et al.  Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls , 2009, BMJ : British Medical Journal.

[56]  C. Drevon,et al.  The challenges for molecular nutrition research 2: quantification of the nutritional phenotype , 2008, Genes & Nutrition.

[57]  Michael E Phelps,et al.  Systems Biology and New Technologies Enable Predictive and Preventative Medicine , 2004, Science.

[58]  Ross A. Hammond,et al.  Solving Immunology? , 2017, Trends in immunology.

[59]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[60]  Raquel Hontecillas,et al.  Phase III Placebo-Controlled, Randomized Clinical Trial With Synthetic Crohn's Disease Patients to Evaluate Treatment Response , 2016 .