The National Institutes of Health’s (NIH) National Heart, Lung, and Blood Institute (NHLBI); National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); and Office of Disease Prevention (ODP) convened a virtual two-day workshop on “Precision Nutrition: Research Gaps and Opportunities” on January 11–12, 2021. The workshop was opened to the public; about 1,800 participants registered, and about 1,350 logged in during the course of the meeting. Forty-seven posters were presented.
The purpose of the workshop was to convene scientists with diverse expertise to explore best approaches to addressing the complex array of factors (e.g., genetics, dietary habits, nutrients, circadian rhythm, psychosocial characteristics, and microbiome) within the framework of precision nutrition, and to identify research gaps and opportunities. A presentation was made on the 2020-2030 Strategic Plan for NIH Nutrition Research, which emphasizes cross cutting and innovative opportunities to advance nutrition research across NIH. Presenters discussed diet-related chronic diseases (e.g., cardiovascular, cognitive decline, diabetes, and cancer) and contributors to interindividual variability (e.g., genetics, sensory and immune system function, social determinants of health, and neuropsychosocial factors). Discussions also included potential roles of artificial intelligence (AI) techniques that could be used to generate algorithms and individualized dietary recommendations. In addition, participants discussed opportunities for research and training of the next generation of precision nutrition scientists.
The NIH Office of Nutrition Research (ONR) presented the NIH Common Fund initiative Nutrition for Precision Health, powered by All of Us. The workshop co‑chairs, who are experts in nutrition research and/or data analysts, moderated the sessions, and several NIH staff served on the organizing committee and participated in the workshop.
Watch on Demand Virtual Meeting
The importance of nutrition for good health and disease prevention is well established and global Dietary Guideline recommendations are clearly defined to guide impactful policy. However, the practical questions of what, when, and how to eat to stay healthy and to guide individuals in their personal quest to optimize healthy dietary patterns are much more complex. Many key influencing factors, including individual differences in disease risk, socio-environmental and cultural factors, and biological, physiological, and psychosocial responses to dietary interventions require consideration. Precision nutrition aims to understand these complex interrelationships to optimize metabolic responses to diet, tailor dietary approaches, and ultimately make sustainable and targeted individual dietary recommendations to prevent and treat diseases and improve overall health and wellbeing.
After brief introductions and charge, the workshop focused on the following discussion topics:
Precision nutrition in diet-related chronic diseases
Participants discussed precision nutrition in four major chronic disease areas: 1) cardiovascular disease, 2) cognitive decline and Alzheimer’s disease, 3) type 2 diabetes and impaired glucose regulation, and 4) diet-related cancers. Presenters illustrated the marked interindividual variability in response to dietary modulations designed to impact health and disease. Examples include a) variability in blood pressure changes in response to the Dietary Approaches to Stop Hypertension (DASH) trial, and b) variability in cognition changes in response to omega 3 fatty acid intake in patients with Alzheimer’s who had the ApoE4 allele--such patients need larger doses of omega 3 fatty acids compared to those without ApoE4 allele, suggesting gene-diet interactions. Other examples include variability in weight loss and blood glucose levels in response to different types of interventions involving diverse eating patterns, macronutrient distribution, and order and timing (chronobiology) of meals. Variability in cancer prevention and treatment strategies, metabolite generation, and the microbiome were also discussed. The presentations were followed by panel discussions of research gaps and high-priority opportunities.
- Develop well-controlled intervention studies that address individual differences in response to dietary exposures; food bioactives, and dietary patterns, including timing, duration and dose-response. Studies specifically designed to target high-risk populations for prevalent health issues (obesity, cardiovascular diseases, diabetes, cancer, cognitive decline and Alzheimer’s disease) are needed. Moreover, it is essential to determine the applicability of the findings to real-world settings.
- Identify individual nutrigenomic/behavioral/lifestyle differences in chronic diet-related disease (e.g., CVD, neurodegenerative disease, cancer, type 2 diabetes) and risk factors in order to personalize approaches for primary and/or secondary prevention of disease over the life course. Special emphasis on identifying biomarkers for diet-related cancers and CVD is prioritized to more quickly elucidate the underlying basis of interindividual variability in diet and disease risks.
- Develop and validate accurate and precise objective measures of dietary intake, including real time monitoring of food intake, post-prandial response, and non-invasive biological responses.
- Develop more in-depth and precise knowledge of foods, food groups and eating pattern composition and related biomarkers.
- Determine the predictive role of metabolomics and microbiome data in precision nutrition and chronic disease inter-relationships.
- Develop robust methods to integrate data from the genome, epigenome, microbiome, metabolome and the exposome (i.e., single or multi-nutrient diet components), dose and timing of dietary modulations, and health behaviors (e.g., physical activity and sleep) into the precision nutrition framework.
- Fill gaps in implementation and dissemination science research for evidence-based precision nutrition strategies and medical nutrition with the goal of reducing chronic diseases.
Measuring potential contributors to interindividual variability in dietary responses
Participants discussed the factors that contribute to individual variability in response to dietary exposures. These include genetics, age, gender, lifestyle health behaviors (e.g., sleep/physical activity), nutritional status at the start of an intervention, circadian rhythms the immune system, social determinants of health and health disparities, and psychosocial and cultural factors. Other discussions include sensory nutrition, mixed meal challenges, physiological measures and individual responses to alcohol. The presentations were followed by panel discussions on research gaps and opportunities and approaches for measuring contributors to individual variability in response to dietary exposures.
- Include social determinants of health (SDoH) indicators in precision nutrition research. For example, (1) develop standardized definition and metrics for SDoH, (2) understand the complexity of interactions with social factors requiring advanced analytical technologies, (3) include data from diverse and vulnerable populations, (4) understand mechanistic pathways and protective social factors in precision nutrition.
- Develop techniques for implementing and disseminating precision nutrition findings to target relevant cohorts and measure sustainable changes in food intake and other nutrition-related health behaviors.
- Include assessment of nutritional status in research measures that span a broad set of nutrients to understand how short-term food intake is influenced by longer-term patterns of nutritional health.
- Develop techniques to assess temporal dynamics of dietary behaviors, for example, visual monitoring for field-based dietary studies.
- Determine the magnitude and predictive value of variation in sensory responses to foods as well as the role of taste receptors and hedonic properties of food throughout the gastrointestinal track to explain individual differences in food choice, nutrient metabolism, and disease risk.
- Determine the contribution and mechanisms of sleep and circadian effects in precision nutrition research and interventions based on chronobiological insights.
- Study innovative advances in reducing inflammation, re-establishing innate and adaptive immunity, enhancing biomarker capacity, and optimizing immuno-nutritional status that impact many diseases such as diabetes, CVD and cancers.
- Understand individual differences in meal responses as the human body restrains endogenous nutrient rate of release (e.g., liver glucose production or lipolysis), while it simultaneously disposes of and assimilates ingested nutrients.
Systems Science, Data Science, and Computational Analytics
Since precision nutrition requires understanding and addressing complex systems, computational approaches, methods, and tools can help to elucidate complex factors and mechanisms. However, these need to be applied with systems science approaches in mind. Otherwise, rather than clarifying the systems involved, computational approaches could lead to inappropriate and misleading findings and conclusions. After a brief introduction to systems science and computational/data analytics, the presenters gave examples of how social network analysis, computational modeling, AI and machine learning (ML) could facilitate precision nutrition. These were followed by presentations on how to interpret and translate findings from such computational approaches and how potential ethical issues should be considered and addressed. A wrap-up panel discussed the tremendous potential that new computational and data analytics have in furthering precision nutrition as long as systems science and appropriate ethical approaches are incorporated.
- Develop and use novel computational methods to collect and analyze nutrition-related data, and to better understand and address the complex system of factors (e.g., social determinants) that affect dietary behaviors and the resulting biological responses.
- Develop and use novel computational methods to better understand and address the complex systems that link nutritional intake with near- and long-term health, including various chronic conditions.
- Develop and use novel computational methods that can help guide the design and conduct of various nutrition-related studies.
- Develop and implement new ways to share nutrition-related data and ways to analyze and use such data.
- Establish platforms that can help connect different computational and data science approaches.
- Identify ways to translate and communicate nutrition-related information to a wide range of decision makers and the public.
- Evaluate and better understand the different possible legal and ethical issues involved in precision nutrition and the use of computational approaches.
- Methods in Precision Nutrition
- Validate and standardize metrics for human studies including: 1) diet assessment tools, 2) standards for effective interventions with high compliance, 3) biomarkers of dietary and nutrient intakes, 4) statistical power and data analysis criteria, and 5) biomarkers relevant to disease and health outcomes.
- Use AI and ML to more precisely measure and model social ecological exposures to better characterize the dynamic features of food environment exposure and access by integrating innovative types of data and using state-of-the-art data analytics. For example, use data on social behaviors and exposures, and network science to understand what people eat and the ‘under-the-skin’ processes related to dietary response (e.g., inflammatory status, gut microbial community).
- Include in the analytic framework on precision nutrition a diverse population, especially those understudied—Asians (including HMong, Philippino, Vietnamese), American Indian/Alaskan Natives, Latinx, African born Americans, Caribbean; and consider sex and gender differences, and cultural factors in dietary response.
- Develop processes to standardize and harmonize data and ensure data are FAIR (Findable, Accessible, Interoperable, and Re-useable). Such data may include sensory nutrition, sex/gender, race, ethnicity, sleep health, immune status, stress, physical activity, circadian patterns of eating, genetics, and the microbiome.
- Develop integrated measures to understand the nutritional exposome. For example, the socioecological model, which is useful for population nutrition, could be used as a framework to inform precision nutrition studies.
- Identify specific patterns of behavior that inform predictive precision nutrition algorithms, and validate the use of generalizable and customizable recommendations involving behavioral phenotypes or patterns.
- Training the Next Generation of Diverse Researchers in Precision Nutrition and Data Science
- Train pre- and postdoctoral scientists in nutrition for precision health modeled after the NIH Office of Behavioral and Social Sciences Research program in Advanced Data Analytics for Behavioral and Social Sciences Research.
- Provide skills development training for a multidisciplinary research team including women and underrepresented groups, basic scientists and clinicians to foster bench-to-bedside opportunity and practical implementation of precision nutrition.
- Train clinicians to use evidence-based nutrition science in clinical care of patients and foster better adherence to precision nutrition.
- Provide skills development training to researchers to foster interdisciplinary and cross-disciplinary training of scientists (e.g., hybrid AI and nutrition) for the implementation of precision nutrition in health promotion efforts.
- Train clinicians in how precision nutrition could be used to guide prescription for weight loss and dietary approaches to disease.
- Train the scientific workforce to understand racial and ethnic differences in dietary response.
- Rodgers GP and Collins F. Precision Nutrition-the Answer to “What to Eat to Stay Healthy.” JAMA. 2020;324(8):735-736. doi:10.1001/jama.2020.13601.
- Pronk NP, Dehmer SP, Hammond R, Halverson PK, Lee BY. Complex Systems Science and Modeling. Submitted to the Secretary of Health and Human Services. U.S. Department of Health and Human Services. Washington, D.C. March, 2019. https://www.healthypeople.gov/sites/default/files/HP2030_Committee-Combined-Issue%20Briefs_2019-508c.pdf. Accessed March 26, 2020.
- Lee, BY, Bartsch SM, Mui Y, Haidari LA, Spiker ML, Gittelsohn J. A systems approach to obesity. Nutrition Reviews. 2016;75(S1):94-106.
- Dashti HS, Scheer FAJL, Saxena R, Garaulet M. Timing of Food Intake: Identifying Contributing Factors to Design Effective Interventions. Adv Nutr. 2019 Jul 1;10(4):606-620.
- Bruce Y. Lee, M.D., M.B.A., City University of New York Graduate School of Public Health and Health Policy
- José M. Ordovás, Ph.D., Tufts University
- Elizabeth Parks, Ph.D., University of Missouri School of Medicine
- Kimberly Barch, NIH Office of the Director (OD), Division of Program Coordination, Planning, and Strategic Initiatives (DPCPSI)
- Christopher Lynch, Ph.D., NIH OD, DPCPSI
- Charlotte A. Pratt, Ph.D., RD, NHLBI
- Cheryl Anderson, Ph.D., M.P.H., M.S., University of California, San Diego
- Albert-László Barabási, Ph.D., Northeastern University Khoury College of Computer Sciences
- Steven Clinton, M.D., Ph.D., The Ohio State University
- Kayla de la Haye, Ph.D., Keck School of Medicine of the University of Southern California
- Valerie Duffy, Ph.D., RD, University of Connecticut
- Paul W. Franks, Ph.D., Lund University Diabetes Center, Sweden
- Gary H. Gibbons, M.D., Director, NHLBI
- Elizabeth Ginexi, Ph.D., NIH OD
- Susan Gregurick, Ph.D. NIH OD
- Kristian Hammond, Ph.D., Northwestern University McCormick School of Engineering
- Erin C. Hanlon, Ph.D., The University of Chicago
- Michael Hittle, M.S., Stanford University
- Emily Ho, Ph.D., Oregon State University
- Abigail Horn, Ph.D., Keck School of Medicine of the University of Southern California
- Richard Isaacson, M.D., Weill Cornell Medical College
- Patricia Mabry, Ph.D., HealthPartners Institute
- Susan Malone, Ph.D., M.S.N., New York University Rory Meyers College of Nursing
- Corby Martin, Ph.D., Pennington Biomedical Research Center
- Josiemer Mattei, Ph.D., M.P.H., Harvard T.H. Chan School of Public Health
- Jessica Mazerik, Ph.D. (NIH OD)
- Simin Nikbin Meydani, Ph.D., D.V.M., Tufts University
- Lorene Nelson, Ph.D., Stanford University
- Marian L. Neuhouser, Ph.D., RD, Fred Hutchinson Cancer Research Center
- Holly Nicastro, Ph.D., M.P.H., NIH OD
- Brendan Parent, J.D., New York University Grossman School of Medicine
- Grace Peng, Ph.D., National Institute of Biomedical Imaging and Bioengineering
- Nico Pronk, Ph.D., M.A., HealthPartners Institute
- Helen Roche, Ph.D., University College Dublin, School of Public Health
- Griffin P. Rodgers, M.D., MACP, Director, NIDDK
- Suchi Saria, Ph.D., M.Sc., Johns Hopkins University
- Frank A.J.L. Scheer, Ph.D., M.Sc., Brigham and Women’s Hospital; Harvard Medical School
- Eran Segal, Ph.D., Weizmann Institute of Science
- Shurjo Sen, Ph.D., National Human Genome Research Institute
- Mary Ann Sevick, Sc.D., New York University Grossman School of Medicine
- Tim Spector, M.D., M.Sc., M.B., FRCP, King’s College London
- Linda Van Horn, Ph.D., RD, Northwestern University Feinberg School of Medicine
- Krista Varady, Ph.D., University of Illinois at Chicago
- Saroja Voruganti, Ph.D., The University of North Carolina Gillings School of Global Public Health
- Josephine Boyington, Ph.D., M.P.H., NHLBI
- Andrew Bremer, M.D., Ph.D., NICHD
- Allison AGM Brown, Ph.D. M.S., NHLBI
- Jill Reedy, Ph.D., M.P.H., RD, NCI
- Karen Regan, M.S., RD. NIH OD
- Scarlet Shi, Ph.D., NHLBI
- Pothur Srinivas, Ph.D., M.P.H., NCI
- Ashley Vargas, Ph.D., M.P.H., RDN, NICHD