strategic plan nutrition research

2020-2030 Strategic Plan for NIH Nutrition Research

The first NIH-wide strategic plan for nutrition research emphasizes cross-cutting, innovative opportunities to advance nutrition research across a wide range of areas, from basic science to experimental design to research training. These opportunities complement and enhance ongoing research efforts across NIH to improve health and to prevent or combat diseases and conditions affected by nutrition. The plan was developed by the NIH Nutrition Research Task Force (NRTF) with extensive input from the broader external research community and the public. The NRTF provides updates on the plan’s implementation on their webpage .

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National Association for Research Nutrition

Strategic Plan for NIH Nutrition Research (SPNR)

The Strategic Plan for NIH Nutrition Research (SPNR) was released in 2020 to provide a vision for how the NIH expects to invest resources in research related to nutrition and health over the next 10 years. The Plan outlines goals for the NIH which will drive research efforts to answer the fundamental question of what to eat to stay healthy, summarized as “Precision Nutrition”. NARN members have an interest in understanding this Plan and promoting their alignment with such research.

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Introducing the First Strategic Plan for NIH Nutrition Research

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Strategic Plan for NIH Nutrition Research

The Strategic Plan lays a bold framework to help answer important nutrition research questions over the next 10 years.

The seemingly simple question “What should I eat to be healthy?” is not simple at all, nor is it the same for people of different ages or sizes, nor across food environments and microbiomes.

strategic plan nutrition research

The NIH recently published its first  Strategic Plan for NIH Nutrition Research to help answer this question and many others related to nutrition and diseases linked to poor diet. The plan emphasizes innovative opportunities to advance nutrition research. From basic science to experimental design to research training, these opportunities will complement and enhance ongoing research efforts across NIH to improve health and to prevent or combat diseases and conditions affected by nutrition.

The research supported by the plan will help develop more targeted and effective diet interventions that are more specific to individuals to improve and maintain health in an increasingly diverse U.S. population. 

More specifically, the plan calls for a multidisciplinary approach through expanded collaboration across NIH Institutes and Centers to explore and answer the following strategic goals and key questions.

  • Spur Discovery and Innovation through Foundational Research – What do we eat and how does it affect us?
  • Investigate the Role of Dietary Patterns and Behaviors for Optimal Health – What and when should we eat?
  • Define the Role of Nutrition Across the Lifespan – How does what we eat promote health across our lifespan?
  • Reduce the Burden of Disease in Clinical Settings – How can we improve the use of food as medicine?

The plan has five cross-cutting areas relevant to all these strategic goals,

  • Minority Health and Health Disparities
  • Health of Women
  • Rigor and Reproducibility
  • Data Science, Systems Science, and Artificial Intelligence
  • Training the Nutrition Scientific Workforce

Research proposals from all sources and on all aspects of nutrition research are welcome, and NIH will continue to fund meritorious research that addresses many important topics in nutrition research, not solely topics identified in the plan. Researchers and health care professionals are encouraged to refer to the plan to help guide their research efforts.

The NIH Nutrition Research Task Force helped to guide plan development with extensive input from the broader external research community and the public. Updates on the plan's progress will be posted on their website .

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strategic plan nutrition research

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Office of Strategic Coordination - The Common Fund

Nutrition for Precision Health, powered by the All of Us Research Program

Program snapshot.

The goal of the NIH Common Fund’s Nutrition for Precision Health, powered by the All of Us Research Program , is to develop algorithms that predict individual responses to food and dietary patterns. Nutrition plays an integral role in human development and in the prevention and treatment of disease. However, there's no such thing as a perfect, one-size-fits-all diet. The NPH program will build on recent advances in biomedical science including artificial intelligence (AI), microbiome research, as well as the infrastructure and large, diverse participant group of the All of Us Research Program . These advances provide unprecedented opportunities to generate new data to provide insight into personalized nutrition also referred to as precision nutrition.

In addition, the first ever Strategic Plan for NIH Nutrition Research emphasized opportunities to improve our understanding of how individual human biology and molecular pathways influence relationships among diet and environmental, social, and behavioral factors to influence health. Designed to implement aspects of the Strategic Plan, the Nutrition for Precision Health program will conduct a study nested in the All of Us Research Program to explore how individuals respond to different diets. The NPH study is the first ancillary study to leverage the All of Us infrastructure to answer scientific questions important to participants like understanding more about the role of nutrition in health.  High-quality nutrition studies such as the NPH study will help individuals and their health care providers create healthy, precise, and effective diet plans. 

The objectives of the study are:

  • To examine individual differences observed in response to different diets by studying the interactions between diet, genes, proteins, microbiome, metabolism and other individual contextual factors
  • To use artificial intelligence (AI) to develop algorithms to predict individual responses to foods and dietary patterns

strategic plan nutrition research

The Nutrition for Precision Health program includes several integrated components:

strategic plan nutrition research

Nutrition for Precision Health, powered by the All of Us Research Program is a service mark of the U.S. Department of Health and Human Services (HHS).

NPH Center and Site Locations

Click the image below to view the interactive location map, detailing the NPH Centers and Sites.

strategic plan nutrition research

Stay Connected Sign Up for the NPH listserv

Join our listserv to receive updated and announcement about the program.

Enrollment is Now Open for NPH

Participate in a study to help researchers predict how our bodies respond differently to food. More information can be found here .

Request for Applications  NEW!

The AIMINGS AI for Precision Nutrition Pilot Program, part of the NPH Consortium , released a new funding opportunity . As the AI Center for the NPH consortium, AIMINGS (Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems) is developing AI, machine learning (ML), Big Data methods, and other data science approaches to better understand and improve precision nutrition .

This call for proposals seeks research projects that develop and utilize AI and other computer-aided approaches to facilitate precision nutrition.

This is a great opportunity for investigators to receive funding for a project that can eventually enhance into a larger project or an R01 application. Awardees will be connected with a community of researchers working on AI for precision nutrition, a new and emerging field.

Proposals are welcome from applicants anywhere in the US from diverse backgrounds and at varying career levels. Applicants who are in the early stages of their careers and investigators that are underrepresented in the scientific workforce are strongly encouraged to apply. Applicants cannot be current members of the NPH consortium.

Interested applicants are encouraged to attend the applicant webinar prior to submitting. The webinar will be held on July 20th between 3:30pm and 4:30pm ET. Questions should be emailed to [email protected] by July 13th as the AIMINGS team will do their best to cover submitted questions during the webinar. Registration is required and can be found here . 

The deadline to apply to the RFA is October 12, 2023.

Announcements

NIH launches largest precision nutrition research effort of its kind.

Science News

Could Precision Nutrition Be a Game Changer for Health?

Diabetes Discoveries & Practice Blog Will Precision Nutrition Help Patients Prevent or Treat Diabetes?

Healio interview with NPH's leadership: NIH will help guide precision nutrition research.

Precision nutrition: Tailored diet assessments to become a mainstay in medical care by 2030.

Press Releases

NIH awards $170 million for precision nutrition study

Program Background

On September 11, 2020 the NIH Council of Councils approved the concept for a new NIH Common Fund program “Nutrition for Precision Health, powered by the All of Us Research Program .” This program will be refined by NIH for potential implementation in Fiscal Year 2022. The archived videocast of the Council of Councils meeting is publicly available and can be viewed here (Nutrition for Precision Health discussion begins at 1:54:00 ). Slides  and a brief write-up are also available.

This page last reviewed on July 9, 2024

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Precision Nutrition: Research Gaps and Opportunities Workshop

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Description

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

Precision Nutrition: Research Gaps and Opportunities Workshop (NIDDK) (labroots.com)

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.

Discussions

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.

Research 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.

Cross-Cutting Themes

  • 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.
  • 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.

Selected references

  • 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.

Workshop Participants:

  • 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

NIH Co-Leads

  • 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

Workshop Speakers/Moderators

  • 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

NIH Participants

  • 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
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News releases.

News Release

Thursday, January 20, 2022

NIH awards $170 million for precision nutrition study

The Nutrition for Precision Health study leverages NIH’s All of Us Research Program’s large, diverse data platform.

The Common Fund's Nutrition for Precision Health Powered by All of Us

The goal of the NIH Common Fund’s Nutrition for Precision Health, powered by the All of Us Research Program (NHP), is to develop algorithms that predict individual responses to food and dietary patterns. The NPH program will build on recent advances in biomedical science including artificial intelligence (AI), microbiome research, as well as the infrastructure and large, diverse participant group of the All of Us Research Program. These advances provide unprecedented opportunities to generate new data to provide insight into personalized nutrition also referred to as precision nutrition.

The National Institutes of Health is awarding $170 million over five years, pending the availability of funds, to clinics and centers across the country for a new study that will develop algorithms to predict individual responses to food and dietary routines. The Nutrition for Precision Health powered by the All of Us Research Program (NPH) will recruit a diverse pool of 10,000 participants who are part of the NIH’s All of Us Research Program to inform more personalized nutrition recommendations.

The initiative includes 11 new awards and provides additional funds to three existing NIH All of Us Research Program awards. NPH is a NIH-wide effort managed by the NIH Common Fund and the first independent study to invite diverse participants from the All of Us program.

“We know that nutrition, just like medicine, isn’t one-size-fits-all,” said Holly Nicastro, Ph.D., M.P.H., a coordinator of NPH. “NPH will take into account an individual’s genetics, gut microbes, and other lifestyle, biological, environmental, or social factors to help each individual develop eating recommendations that improve overall health.”

A major challenge in precision nutrition is the inability to combine the many factors that affect how individuals respond to diet into a personalized nutrition regimen. These potential factors include the microbiome–the community of microbes that live in our gut, metabolism, nutritional status, genetics, and the environment. The way these factors interact to affect health are still poorly understood.

To address these gaps, NPH will collect new data on multiple potential predictive factors and combine it with existing data in the All of Us database to develop a more complete picture of how individuals respond to different foods or dietary routines. By developing this large study of precision nutrition research, NPH will complement ongoing nutrition research efforts across NIH and implement components of the 2020-2030 Strategic Plan for NIH Nutrition Research . The NPH data will be integrated into the All of Us Researcher Workbench and made widely available to allow researchers to make discoveries that could improve health and prevent or treat diseases and conditions affected by nutrition.

“The All of Us Research Program was designed to support a wide range of studies by providing the infrastructure for a large, diverse data set that has been previously unavailable,” said Josh Denny, M.D., CEO of All of Us . “We’re delighted that All of Us has a role in advancing in-depth nutrition research and furthering precision nutrition by serving as a platform for this unique initiative.” 

All of Us will contribute existing data from participants who agree to participate in the NPH study, including genomics, linked electronic health records, and survey data, such as information on daily life experiences, family health, and more. These data linkages will power NPH to be one of the largest, most diverse precision nutrition studies to date.

The NPH program includes multiple institutes and centers within NIH, including the NIH Common Fund; All of Us Research Program; Office of Nutrition Research; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Eunice Kennedy Shriver National Institute of Child Health and Human Development; the National Cancer Institute; and the National Center for Advancing Translational Sciences.

The 14 awards will establish the NPH consortium including six clinical centers, a dietary assessment center, a metabolomics and clinical assays center, a microbiome and metagenomics center, a multimodal data modeling and bioinformatics center, a research coordinating center, and additional support to existing All of Us infrastructure.

Watch an overview video of the Nutrition for Precision Health, powered by the All of Us Research Program ® to learn more about this initiative. 

About the NIH Common Fund: The NIH Common Fund encourages collaboration and supports a series of exceptionally high-impact, trans-NIH programs. Common Fund programs are managed by the Office of Strategic Coordination in the Division of Program Coordination, Planning, and Strategic Initiatives within the NIH Office of the Director in partnership with the NIH Institutes, Centers, and Offices. More information is available at the Common Fund website: https://commonfund.nih.gov .

About the All of Us Research Program: The mission of the All of Us Research Program is to accelerate health research and medical breakthroughs, enabling individualized prevention, treatment, and care for all of us. The program will partner with one million or more people across the United States to build the most diverse biomedical data resource of its kind, to help researchers gain better insights into the biological, environmental, and behavioral factors that influence health. For more information, visit www.ResearchAllofUs.org , www.joinallofus.org , and https://www.allofus.nih.gov/ .

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

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NIH-supported implementation science and nutrition research: a portfolio review of the past decade

Susan vorkoper.

1 Fogarty International Center, National Institutes of Health, Bethesda, MD, United States

Ariella R. Korn

2 Cancer Prevention Fellowship Program, Implementation Science Team, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States

Padma Maruvada

3 Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States

Holly L. Nicastro

4 Office of Nutrition Research, National Institutes of Health, Bethesda, MD, United States

Scarlet Shi

5 Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States

Associated Data

This portfolio analysis aims to describe the scope of NIH-funded extramural research grants at the intersection of nutrition research and implementation science (IS) from 2011 to 2022 and to offer insights into future research opportunities relevant to the Strategic Plan for NIH Nutrition Research 2020–2030.

A portfolio analysis of funded grants using NIH reporting systems was conducted to identify nutrition research and IS awarded between fiscal years 2011 and 2022. The authors screened the titles and abstracts for inclusion criteria: research and career development awards involved a nutrition and/or dietary intervention and measured a stated implementation outcome or used an IS theory, model, or framework.

In total, 33 NIH-funded awards met the inclusion criteria. Almost half of the awards (48.5%) were investigator-initiated research projects compared to research career awards and cooperative agreements. While studies were predominantly conducted in the United States, 15.2% were conducted in low- and middle-income countries in Africa, Latin America, and Asia. Adults aged 19–64 years and children aged 2–11 years represented most of the study populations (45.5 and 15.2%, respectively). Studies provided nutrition/dietary guidelines and created culturally tailored interventions, which were then adapted in collaboration with community partners in schools, hospitals, and religious settings. The most cited IS outcomes were feasibility, costs, adoption, and acceptability. Sixteen awards (48.5%) used an IS theory, model, or framework to guide their work.

The findings show the breadth of NIH-funded nutrition and implementation research and highlight potential research opportunities.

Introduction

For nutrition and dietary research, implementation science (IS) holds promise for moving the investments in evidence-based interventions into practice (e.g., changing dietary behaviors and environments) ( 1 , 2 ), sustaining adherence to those changes over time, and implementing strategies to scale and disseminate these interventions ( 1 – 3 ). IS addresses barriers to effective implementation, tests innovative approaches to advancing health programming, and develops and tests implementation strategies to improve diet and nutrition interventions' reach, uptake, and scale-up, among other outcomes ( 4 – 6 ).

The intersection of biological, behavioral, psychosocial, sociocultural, and environmental factors may act additively or cumulatively to improve or impede individuals' chances to change their dietary behaviors and sustain those changes over time ( 7 ). Thus, interventions need to target many aspects of the food environment (e.g., food security, food systems, and advertising) while accounting for participants in the context of their environments to gain insights that could improve long-term healthy behaviors. By using multilevel or systems approaches and incorporating metrics related to implementation, IS not only focuses on traditional outcomes such as efficacy and effectiveness but also acknowledges the fundamental role the context plays in nutrition and dietary research ( 8 ). Therefore, using IS designs and methodologies is essential for understanding and promoting effective and ready-to-adopt nutrition interventions.

The U.S. National Institutes of Health (NIH) 2020–2030 Strategic Plan for Nutrition Research recognizes the innovative role that IS can play in advancing nutrition research and sets the priorities in the plan ( 9 ). A recent commentary by members of the NIH Implementation of Nutrition-related Programs, Practices, and Behaviors working group highlights opportunities to stimulate IS in nutrition research in alignment with this strategic plan across three areas: (a) advancing consideration of implementation and dissemination early in the design of interventions to facilitate opportunities for equitable scale-up and sustainability of evidence-based interventions, (b) developing and testing strategies for equitable implementation of nutrition and diet interventions in health care and community settings, and (c) building and strengthening the infrastructure, capacity, and expertise needed to increase use of IS in clinical and community nutrition research to swiftly move the research into practice ( 10 ).

To build on this effort, the NIH Implementation of Nutrition-related Programs, Practices, and Behaviors working group conducted a portfolio analysis of research on the implementation of nutrition interventions funded through the NIH Institutes, Centers, and Offices (ICO). Portfolio analyses of funded research are important tools to provide insights into current interests and potential needs, challenges, and future trends in a given field. This portfolio analysis descriptively examines the funded grant mechanisms for nutrition and IS grants, the characteristics of the diet and nutrition interventions and study populations, and the aspects of implementation research, including implementation outcomes and the use of established IS frameworks. We use the NIH definition of IS, which is the study of scientific methods that facilitate evidence-based research findings into practice and is inclusive of dissemination research ( 11 ). The goal of the analysis is to describe the extent, range, and nature of the funded NIH research in nutrition and IS in the past decade, identify research gaps, and consider how to support the opportunities outlined in the commentary ( 10 ).

We conducted a search to identify nutrition research that used an implementation research approach. We searched an NIH internal reporting system, Query View Report (QVR), using the NIH's Research, Condition, and Disease Categorization (RCDC) system definition of nutrition research. RCDC uses sophisticated text data mining algorithms in conjunction with NIH-wide definitions to create a “fingerprint” that matches projects to research spending categories. Within the nutrition research category, we further conducted a search using keywords to identify awards funded in Fiscal Years 2011–2022 (from October 1, 2010 to September 30, 2022). Search terms included implementation research, implementation science, implementation strategy, implementation trial, annual implementation plans, clinical implementation, dissemination of results, dissemination research, dissemination trial, program dissemination, prevention dissemination research, research data dissemination, and research dissemination. Only the funded awards were exported and downloaded.

We included research projects awarded by the NIH since 2010 a Fellowship Grant (Fs), a Career Development Award (Ks), a Research Project or Center Grant (P01s), a Research Grant (Rs), or a Cooperative Agreement (US). These funding mechanisms constitute most NIH-supported grants, including career development, exploratory, hypothesis testing/generating, and resource-generating projects. Training grants (e.g., D43, D71, and T32) were excluded. Further, the projects had to address some aspects of a nutrition and/or dietary intervention and include consideration and/or measurement of at least one stated implementation outcome (i.e., acceptability, adaptation, adoption, appropriateness, costs, feasibility, fidelity, penetration, reach, sustainability, and scale-up) ( 12 – 14 ) or use an established IS theory, model, or framework ( Appendix A , Codebook). The selected outcomes expand on Proctor's original Implementation Outcome Framework from 2011 to integrate ( 12 , 14 ) those identified by Reilly in 2020 ( 13 ) related to the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) Framework.

Seven reviewers (AB, ARK, PM, HLN, AO, SS, and SV) screened the publicly available titles and abstracts of the identified awards with the above inclusion criteria using a dual-independent approach (two reviewers per award). Disagreements in screening between two reviewers were resolved by a third reviewer. Six reviewers (ARK, PM, HLN, AO, SS, and SV) then extracted data from those that were included, again using a dual-independent approach. The data were extracted into Microsoft Excel using an established codebook ( Supplementary material A ). We collected the following information from each award: demographics (e.g., age) of the population studied; location, including country and different settings; nutrition and dietary-related behaviors; implementation framework or theory used (if applicable); IS research focus areas or phases (i.e., IS measurement development; pre-implementation; implementation process description; implementation strategy testing; dissemination strategy testing; de-implementation; sustainability; and scale-up) ( 15 ); implementation outcome(s); study design; implementation strategy; type of intervention; and health disparities addressed. Disparities were included if the research focus has any of the following: minority health that included racial and/or ethnic groups who are usually underrepresented in biomedical research; health disparities that addressed health differences that adversely affect disadvantaged populations, based on factors such as higher disease burden, risk factors, condition-specific symptoms, and/or other categories of health outcomes; or health equity, an intervention on a social determinant of health and/or to take an equity approach and/or include a health equity outcome and that addressed an NIH disparity population (i.e., racial/ethnic minority, underserved rural populations, socioeconomically disadvantaged, sexual and gender minority, and physically disabled) ( 16 ).

A single reviewer (SV) conducted an initial screening of all data to identify any discrepancies in the results. These disagreements were resolved by consensus among the reviewers. The review was conducted on data that are publicly available to the research community in the NIH RePORTER; specifically, we looked at the research application title, abstract, and the public health relevance statement of the grant application.

A total of 71 unique titles and abstracts were retrieved via a QVR search. Titles and abstracts of these awards were screened, of which 38 were excluded based on the above criteria. A total of 33 competing awards were included ( Appendix B , summary of included awards).

Grant types

Almost half of the awards (16/33, 48.5%) were investigator-initiated research, which included R01 Research Project Grants ( n = 8, 24.2%), R03 Small Grants ( n = 3, 9.1%), R21 Exploratory/Developmental Research Grants ( n = 4, 12.1%), and R34 Clinical Trial Planning Grants focused on exploratory and project planning ( n = 1, 3.0%) ( Figure 1 ). More than a quarter of the awards ( n = 9, 27.3%) supported research career programs through K funding mechanisms. Four awards were Research Dissemination and Implementation R18 grants ( n = 4, 12.1%), three were cooperative agreements (U awards) that were awarded in response to a funding solicitation ( n = 3, 9.1%), and one was an Individual Fellowship for PhD Students (F31, n = 1, 3.0%). The leading ICOs for nutrition and dietary IS research from FY 2011 to FY 2022 were the National Heart, Lung, and Blood Institute (NHLBI, n = 8, 24.2%) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK, n = 8, 24.2%), followed by the National Cancer Institute (NCI, n = 7, 21.2%), the National Institute of Minority and Health Disparities (NIMHD, n = 4, 12.1%), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD, n = 3, 9.1%), the National Institute of Mental Health (NIMH, n = 2, 6.1%), and the Fogarty International Center (FIC, n = 1, 3.0%). Figure 2 describes the distribution of funded awards by year. Although the inclusion criteria included awards funded in 2011, the search spanned awards from 2012 to 2022. Nearly 70% of the awards were funded between 2017 and 2022, with the last 3 years (2020–2022) accounting for more than a quarter of the grants awarded ( n = 10, 30.3%).

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Awards by grant type, 2011–2022 ( n = 33).

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Awards by year initiated, 2011–2022 ( n = 33).

While most awards were investigator-initiated research applications, four awards responded to a Request for Applications (RFAs), which represent a special interest by one or more ICOs. The first RFA is a funding opportunity for “Late-Stage Implementation Research Addressing Hypertension in Low- and Middle-Income Countries: Scaling Up Proven-Effective Interventions”, which uses a UG3/UH3 two-phase mechanism by NHLBI to facilitate implementation research on hypertension in low- and middle-income countries ( 17 ). To date, this RFA has funded three projects, two of which are nutrition and IS awards. The second RFA calls for pilot effectiveness trials for treatment, preventive, and service interventions by NIMH, which involves a clinical trial ( 18 ). In response to this funding opportunity, the award, “Adaptation of an Evidence-based Interactive Obesity Treatment Approach (iOTA) for Obesity Prevention in Early Serious Mental Illness” ( 19 ), proposes to use combined methods to adapt and pilot test an interactive obesity treatment approach for obesity prevention in early serious mental illness using a design-for-dissemination approach and including a randomized pilot and feasibility clinical trial. The third RFA is an NHLBI K01 mechanism for mentored career development awards to promote faculty diversity in biomedical research ( 20 ).

Research study sample characteristics

Almost half of the funded research ( n = 16, 48.5%) took place in the United States, while 15.2% was conducted in low- and middle-income countries (LMICs, n = 5) in Africa, Latin America, and Asia ( Table 1 ). More than a third of the awards ( n = 12, 36.4%) did not specify their location in the abstract. Interventions addressed the individual ( n = 5, 15.2%), organizational ( n = 9, 27.3%), and community ( n = 4, 12.1%) levels of the socio-ecological model, with most addressing multiple levels ( n = 11, 33.3%). Among the grant applications in this analysis, 45.5% focused on study populations comprising adults aged 19–64 years ( n = 15), while young and elementary school-aged children (2–11 years) accounted for 18.2% ( n = 6). Infants (0–2 years), adolescents (12–18 years), and older adults (65+ years) were included in research projects in conjunction with other age groups. More than half of the awards ( n = 18, 54.5%) addressed or specified a focus on health disparities, health equity, or minority health. Predominantly, study designs were experimental ( n = 12, 36.4%), which included randomized control trials (RCTs), pragmatic RCTs, dynamic wait-listed control designs, and cluster RCTs. Quasi-experimental designs ( n = 4, 12.1%) included any manipulation without randomization like interrupted time series, and mixed methods ( n = 4, 12.1%) were also used. Almost a quarter of the awards ( n = 7, 21.2%) employed more than one study design, including one award focused on measurement development and validation. Eighteen awards used effectiveness-implementation hybrid designs: Type 1 designs ( n = 7) that tested intervention effects on relevant nutrition or clinical outcomes and collected data on the implementation process; Type 2 designs ( n = 7) that tested the effects of interventions or implementation strategies with emphasis on both nutrition/clinical and implementation outcomes; and Type 3 designs ( n = 4) that tested an implementation strategy while observing intervention effectiveness ( 21 ).

Characteristics of NIH-funded Nutrition and Implementation Science Awards, 2011–2022 ( n = 33).

US1648.5%
Nigeria26.1%
Dominican Republic13.0%
Peru13.0%
Malawi, Uganda, Kenya, Burkina Faso, Bangladesh and Pakistan13.0%
Not stated in the abstract1236.4%
Young and elementary school children (2–11 years)618.2%
Adults (19–64 years)1545.5%
Across ages 39.1%
Not stated in the abstract927.3%
Experimental1236.4%
Quasi-experimental412.1%
Mixed methods412.1%
Observational39.1%
Pre-post13.0%
More than one design 721.2%
Not stated in the abstract26.1%
Healthcare721.2%
Childcare515.2%
Community412.1%
Digital39.1%
Faith-based26.1%
Retail26.1%
Workplace13.0%
School13.0%
More than one39.1%
Not stated in the abstract515.2%

* One each: children (0–11 years); children and adolescents (0–18 years); adults (19+ years).

# Experimental and mixed methods ( n = 2); quasi-experimental and mixed methods ( n = 1); mix of quasi-experimental, observational, and mixed methods ( n = 1); systems science modeling and comparative risk assessment model ( n = 1); markov model with monte carlo simulation (diet cancer outcome model) ( n = 1); experimental; mixed methods; multiphase optimization strategy ( n = 1).

Diet and nutrition intervention research characteristics

A wide range of evidence-based interventions included both face-to-face and digital components. Some interventions included the implementation of established guidelines, such as the WHO Best Buy SHAKE package for salt reduction (UG3HL152381) ( 22 ). Others developed novel interventions, one of which is exploring eating behaviors through pilot studies to develop interventions for improving the nutritional environment (K01HL147882). More recently, awards have taken a policy-focused approach by evaluating variations in the implementation of federal nutrition policies during the COVID-19 pandemic ( {"type":"entrez-nucleotide","attrs":{"text":"CA260023","term_id":"35949538","term_text":"CA260023"}} CA260023 ) or developing and testing an implementation strategy to support the adoption of stronger nutrition standards outlined in the federal Child and Adult Care Food Program ( {"type":"entrez-nucleotide","attrs":{"text":"DK125278","term_id":"187580793","term_text":"DK125278"}} DK125278 ). Culturally tailored interventions engaged community partners to develop and adapt their interventions. Studies most often took place in healthcare settings ( n = 7, 21.2%), childcare settings ( n = 5, 15.2%), and community settings ( n = 4, 12.1%).

Evidence-based activities related to nutrition and obesity were often part of larger, multicomponent interventions. More than half of the awards employed dietary and lifestyle interventions using tailored self-management approaches ( n = 17, 51.5%), whereas 24.2% of the awards focused on diet quality and healthy eating patterns ( n = 8). Few awards explored food insecurity ( n = 2, 6.1%), symptomatic management of cancer among survivors ( n = 2, 6.1%), and malnutrition ( n = 1, 3.0%). While nearly half of the awards focused on obesity ( n = 15, 45.5%), 15.2% of funded awards explored nutrition-related cardiovascular diseases ( n = 5) and 9.1% of awards focused on diabetes ( n = 3). Some of the awards explored more than one nutrition topic. For instance, one award looked at a lifestyle intervention to improve obesity and diabetes risk for immigrant workers at agricultural worksites (R18DK096429).

Implementation outcomes

Among the awards, most measured more than one IS outcome ( n = 21, 63.6%). Of the outcomes assessed, feasibility ( n = 16, 20.3%) was the most defined and measured implementation outcome, followed by costs ( n = 13, 16.5%) and adoption ( n = 12, 15.2%) ( Table 2 ). Scale-up was not examined in any of the awards. It is worth noting that three of the four Research Dissemination and Implementation grants (R18) exclusively measured costs, while the cooperative agreements included, on average, five separate implementation outcomes related to nutrition.

Implementation characteristics of NIH-funded Nutrition and Implementation Science awards ( n = 33), 2011–2022.

=
.
Feasibility1620.3%
Costs1316.5%
Adoption1215.2%
Acceptability1113.9%
Sustainability1012.7%
Fidelity911.4%
Adaptation33.8%
Reach22.5%
Penetration11.3%
Appropriateness11.3%
Scale-up00.0%
Not stated in the abstract 11.3%
=
Pre-implementation721.2%
Implementation process description824.2%
Implementation strategy/strategies testing1957.6%
Dissemination strategy testing00%
Sustainability39.1%
De-implementation13.0%
IS measurement development13.0%
Scale-up00%
=
.
Reach, effectiveness, adoption, implementation, maintenance (RE-AIM)836.4%
Consolidated framework for implementation research (CFIR)313.6%
Exploration, preparation, implementation, sustainment (EPIS)29.1%
Interactive systems framework29.1%
Practical, robust implementation and sustainability model (PRISM)14.5%
Quality implementation framework14.5%
Behavioral change wheel14.5%
Dynamic adaptation process framework14.5%
Intervention mapping14.5%
Theoretical domains framework14.5%
Dynamic sustainability framework14.5%
=
Healthcare721.2%
Childcare515.2%
Community412.1%
Digital39.1%
Faith-based26.1%
Retail26.1%
Workplace13.0%
School13.0%
More than one setting39.1%
Not stated in the abstract515.2%

* One study focused on an implementation determinant (organizational readiness) and was included due to use of an implementation science framework.

This review identified six unique implementation focus areas among all 33 awards: pre-implementation, implementation process description, implementation strategy testing, de-implementation, sustainability, and IS measurement development [( 15 ), Table 2 ]. Five awards (15.2%) addressed more than one focus area relating to different parts of their research projects. More than half of all awards were for testing implementation strategies ( n = 19, 57.6%). Seven awards (21.2%) focused on the pre-implementation phase that addressed the efficacy, effectiveness, and/or cost-effectiveness of an intervention or adapting an intervention ( 23 ). Awards describing the implementation process (including identifying barriers and facilitators) and considering aspects of intervention sustainability comprised 24.2% ( n = 8) and 9.1% ( n = 3) of the awards, respectively. Only one award was for developing an IS measurement (3.0%), and only one addressed de-implementation (3.0%).

Among the 16 awards that referenced an established IS theory, model, or framework to guide their work, the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework, either alone or in conjunction with another framework, was the most cited, accounting for over a third of the referenced frameworks ( n = 8, 36.4%). The Consolidated Framework for Implementation Research (CFIR, n = 3, 13.6%), the Exploration, Preparation, Implementation, and Sustainment Framework (EPIS, n = 2, 9.1%), and the Interactive Systems Framework ( n = 2, 9.1%) were also referenced by multiple awards. The remaining seven frameworks were utilized only once. Seventeen of the 33 award abstracts (51.5%) did not report an IS framework, model, or theory in the abstract.

This analysis of the NIH diet and nutrition research and IS grants portfolio funded between fiscal years 2011 and 2022 found that, while research grants made up the majority of awards when broken down by grant mechanism, there was a fairly even distribution of career development grants, R03s, R21s, and R34s that are more exploratory grant mechanisms of smaller budgets and shorter timeframes and 5-year R01s that are larger in scope and scale and testing a trial or hypothesis. This range of grant mechanisms indicates an opportunity for growth. The number of awards funded through implementation research funding announcements, specifically the PAR “Dissemination and Implementation Research in Health” funding opportunities, was much lower than the other grant mechanisms in this portfolio analysis ( 24 , 25 ). Funding for IS and nutrition research spiked in 2019 and 2020. Despite a general increase in funding since 2017, only two grants were awarded in FY 2018 and only one in FY 2021. The overall increase in funding to support IS and nutrition in 2017, 2019, and 2020 is encouraging, though the slow growth in 2021 and 2022 is concerning.

While the US hosted all the award types previously mentioned, the five awards that conducted studies in LMICs, which made up more than 15% of the nutrition and IS nutrition portfolio, were limited to career support awards (Ks, n = 2), a short-term research grant (R03, n = 1), and cooperative agreements (UG3, n = 2). None of these were the larger, long-term research projects, specifically research dissemination and implementation grants. In comparison, direct foreign awards only accounted for ~1% of NIH's overall grant portfolio in 2020. Unlike the specific foreign awards identified in this analysis, direct foreign awards across NIH tend to vastly favor long-term research, with less than a quarter of the awards for career development grants being much less likely to support short-term exploratory awards. This contrast between the short-term research and career development awards found in this analysis and the long-term research funding to direct foreign awards across the NIH highlights an area of growth for IS global nutrition.

Most of the grants studied adult populations, while fewer awards focused on children. This distribution likely reflects the scope of these research projects focused on nutrition and diet as part of chronic disease prevention and treatment.

Although specific dietary or nutritional behaviors were difficult to identify from publicly available abstracts, we observed that studies frequently examined obesity and nutrition in tandem. Notably, 18 (54.5%) of the awards were coded as addressing health disparities or health equity. These grants focused on outcomes like feasibility, adoption, and adaptation and most often included a culturally tailored nutrition or dietary intervention. Many of these awards focused on tailoring or targeting an evidence-based intervention for an underserved population, highlighting the researchers' importance in addressing health disparities, health equity, and minority health ( 26 , 27 ). This finding aligns with the IS field's growing emphasis on addressing health equity and social determinants of health through the use of equity-centered IS frameworks, approaches (including community-engaged research), and measures ( 26 – 29 ).

The majority of awards assessed feasibility, acceptability, cost, and adoption and few (a little more than 10%) assessed sustainability, but none addressed scale-up. Awards included in the portfolio in early-stage implementation more frequently included the following measures: feasibility and acceptability, which focus on ensuring that an intervention is suitable to the population of interest; cost, including assessments of cost-effectiveness and cost-benefits; and adoption, which measures the uptake of an intervention ( 14 ). In contrast, sustainability and scale-up are generally addressed later in the research translation or implementation process once there has been uptake and demonstrated effectiveness of an intervention. It is worth noting that “scale-up” is also a relatively new outcome not originally included in Proctor's Implementation Outcomes Framework ( 14 ), which may explain its absence from some of the older awards.

Less than half of the awards included an established IS framework to steer the work with the variability of determinants, evaluation, and other kinds of existing frameworks. RE-AIM, CFIR, and EPIS accounted for most of the referenced frameworks. IS frameworks are important tools for guiding research that is “intended to enhance the generalizability of findings by establishing common concepts and terminologies that can be applied across disparate research studies and settings” ( 30 ). The lack of frameworks in the majority of awards may indicate the nascent application of IS in nutrition research. In addition, there may be a need to develop and adapt theories, models, and frameworks for application in diverse settings, including in LMICs and among minority populations ( 31 ).

The emergence in the last few years of the NIH Implementation of Nutrition-related Programs, Practices, and Behaviors working group, the USG Global Nutrition Coordination Plan's global nutrition and IS technical working group ( 32 ), and the National Collaborative on Childhood Obesity Research IS interest group ( 33 ) along with three nutrition workshops held across multiple ICOs at the NIH that included a sustainable IS component ( 34 – 36 ) demonstrates the increasing interest in applying IS to nutrition across the federal funding agencies.

As nutrition and IS research become more transdisciplinary, we anticipate an increase in the number of relevant NIH-funded awards. The recent NIH commentary ( 10 ) identifies three scientific opportunities to stimulate IS in nutrition research in alignment with the 2020–2030 Strategic Plan for NIH Nutrition Research, calling for including implementation and dissemination early in the intervention design, developing and testing strategies for equitable implementation of nutrition and diet evidence-based, and building and strengthening capacity and expertise needed to increase the use of IS in nutrition research.

A few limitations are worth noting. First, the analysis was limited to publicly available abstracts. Owing to the limitation of abstracts, certain project details, such as the research location, targeted nutritional behaviors, and the priority population's racial and ethnic makeup, were not coded. Additional details could have been available in the full application (e.g., use of IS frameworks). Second, we limited IS outcomes to those that were specifically labeled, which may have eliminated potentially relevant awards that did not list the outcomes in the abstract.

The Strategic Plan for NIH Nutrition Research 2020–2030 highlights growing interest and opportunities in nutrition and IS research. Findings from this analysis describe the current funded portfolio of research on nutrition research and IS across the NIH. While the overall number of awards was low, there was a gradual increase in those focusing on evidence-based IS research in nutrition and obesity over time. Notably, several of these awards addressed health disparities. Potential opportunities for growth in the nutrition and IS research portfolio include the following areas: (1) greater use of established IS frameworks, (2) an increase in awards that examine scale-up as an implementation outcome, (3) research focused on adaptation, reach, and contextual influences on implementation across diverse populations to promote equitable implementation, and (4) building capacity to increase the use of IS in clinical and community nutrition research. Enhancing IS knowledge, practice, and scale-up can improve the translation of evidence-based nutrition/dietary interventions into effective practice and address health disparities specifically related to nutrition and diet.

Author contributions

All authors contributed to the development, data coding and analysis, and drafting of the manuscript and approved the submitted version.

Acknowledgments

The authors thank the NIH Implementation of Nutrition-Related Programs, Practices, and Behaviors Implementation Working Group for their support and encouragement. In particular, we thank Ms. Karen Regan for her guidance in gathering data and Drs. April Oh and Alison Brown for their engagement in developing this portfolio analysis and their help in the review.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Author disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the Fogarty International Center, the Office of Nutrition Research, the National Cancer Institute, the National Heart, Lung, and Blood Institute, the National Institute of Diabetes and Digestive and Kidney Diseases; or the National Institutes of Health and the U.S. Department of Health and Human Services.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1235164/full#supplementary-material

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    In May 2020, NIH announced its first agency-wide strategic plan for nutrition research, the goal of which is to further advance the science and address diet-related diseases across the lifespan. More recently, NIH has been considering whether nutrition research would be better served by upgrading ONR as a trans-NIH effort, and therefore ...

  10. SPNNR

    The Strategic Plan for NIH Nutrition Research (SPNR) was released in 2020 to provide a vision for how the NIH expects to invest resources in research related to nutrition and health over the next 10 years. The Plan outlines goals for the NIH which will drive research efforts to answer the fundamental question of what to eat to stay healthy ...

  11. Introducing the First Strategic Plan for NIH Nutrition Research

    The NIH recently published its first Strategic Plan for NIH Nutrition Research to help answer this question and many others related to nutrition and diseases linked to poor diet. The plan emphasizes innovative opportunities to advance nutrition research. From basic science to experimental design to research training, these opportunities will ...

  12. PDF National Institutes of Health Nutrition Research Report 2020-2021

    2020-2030 Strategic Plan for NIH Nutrition Research, with the goal of further advancing this area of science and addressing diet-related diseases across the lifespan. It emphasizes cross-cutting, innovative opportunities for advancing nutrition research across a wide range of areas, from basic science to experimental design to research training.

  13. Nutrition for Precision Health, powered by the All of Us Research

    In addition, the first ever Strategic Plan for NIH Nutrition Research emphasized opportunities to improve our understanding of how individual human biology and molecular pathways influence relationships among diet and environmental, social, and behavioral factors to influence health.

  14. News from NHLBI: Nutrition Research at the National Heart, Lung, and

    In May 2020, the NIH released its first NIH-wide strategic plan for nutrition research emphasizing innovative opportunities to advance nutrition research from basic since to experimental design and research training (12, 13). The plan was developed with the input from the external research community and the public, highlighting the growing ...

  15. Precision Nutrition: Research Gaps and Opportunities Workshop

    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 ...

  16. Opportunities to advance implementation science and nutrition research

    The first NIH-wide strategic plan for nutrition research was developed with extensive input from the nutrition science community, practitioners, the public, and others, and identifies cross-cutting and innovative opportunities to advance the field. The plan is organized around four Strategic Goals, with specific objectives tied to each goal ...

  17. Office of Nutrition Research (ONR)

    The Office of Nutrition Research: Coordinates implementation of the Strategic Plan for NIH Nutrition Research; Identifies research projects that deserve expanded effort and support by the NIH institutes and centers (ICs) Develops, leads, and manages NIH-wide nutrition research projects in collaboration with the ICs

  18. PDF NIH Strategic Plan and Roadmap to Accelerate Nutrition Research Over

    Listening sessions topics may provide input on emerging opportunities, challenges/barriers and potential solutions that could: Accelerate progress in foundational, preclinical and clinical nutrition research. Identify ways that NIH could better support nutrition research training, career development, and progression.

  19. NIH awards $170 million for precision nutrition study

    The National Institutes of Health is awarding $170 million over five years, pending the availability of funds, to clinics and centers across the country for a new study that will develop algorithms to predict individual responses to food and dietary routines. The Nutrition for Precision Health powered by the All of Us Research Program (NPH ...

  20. NIH-supported implementation science and nutrition research: a

    The Strategic Plan for NIH Nutrition Research 2020-2030 highlights growing interest and opportunities in nutrition and IS research. Findings from this analysis describe the current funded portfolio of research on nutrition research and IS across the NIH. While the overall number of awards was low, there was a gradual increase in those ...

  21. Implementation

    Implementing the Strategic Plan for NIH Nutrition Research. The Strategic Plan for NIH Nutrition Research was released in May 2020. An article by Drs. Francis S. Collins and Griffin P. Rodgers, Precision Nutrition—the Answer to "What to Eat to Stay Healthy," described NIH efforts to advance the field of precision nutrition, the core vision of the strategic plan, and introduced a large ...

  22. Strengthening national nutrition research: rationale and options for a

    The need and opportunities for strengthened federal nutrition research are clear, with specific identified options to help create the new leadership, strategic planning, coordination, and investment the nation requires to address the multiple nutrition-related challenges and grasp the opportunities before us.

  23. PDF Implementing the Strategic Plan for NIH Nutrition Research

    Actions. Transfer ONR and staff to DPCPSI. Oversee implementation of the Strategic Plan. NIH-wide voice for Nutrition Research. Key role in the Nutrition for Precision Health- Powered by All of Us. Task Force fulfilled its charge of 10/11/2016. Develop 10-year Strategic Plan. Appoint a senior leadership group to guide implementation of the plan ...