Modernization of Research: Precision Nutrition

February 26, 2021

What is the best way to eat to stay healthy? This is a difficult question to answer. Studies show that individuals have profound differences in response to eating patterns and risk for disease. ¹ The 2020-2030 Strategic Plan for NIH Nutrition Research ² details a multidimensional approach based on precision nutrition. The plan includes strategic goals and research focus areas as steps to achieve the overarching goal of improving health and fighting and preventing diseases affected by nutrition.³

Precision nutrition considers the many factors which influence health including dietary habits, food environment, genetics, health status, microbiome, metabolism, physical activity, socioeconomics, environmental exposures and psychosocial characteristics. In the U.S., an increasingly diverse population warrants examining these factors to understand and develop effective dietary recommendations to improve and maintain health for all.²

The U.S. healthcare costs for managing cardiovascular disease and stroke, obesity, type 2 diabetes, and cancer add up to hundreds of billions of dollars every year.4 Nutrition plays a role in the prevention and management of these and other chronic diseases and conditions,4 which lends considerable credibility to use of precision nutrition methods to fine tune the understanding of the effects of food and other factors on the development of health conditions and diseases.¹ The 2020-2030 Strategic Plan aims to use innovation, investigation of dietary patterns, and examination of nutrition needs across the lifespan for healthy development and aging to ultimately help reduce the burden of disease in clinical settings. The plan focuses on four key questions:&sup1

  1. “What do we eat and how does it affect us?”
    This research ensures a strong foundation of nutrition science research and considers connections with bioinformatics, neurobiology and genomics.
  2. “What and when should we eat?”
    This research examines how specific dietary patterns uniquely influence health outcomes for individuals, subgroups and communities.
  3. “How does what we eat promote health across our lifespan?”
    This research will lead to a better understanding of how nutritional needs and eating behaviors change over time. The focus is on three understudied areas: pregnancy, infancy and toddlerhood, and older adulthood.
  4. “How can we improve the use of food as medicine?”²
    This research will expand knowledge about the role of nutrition in disease and grow evidence toward the development of medical nutrition therapies for improved health.

Some previous studies relied on self-reported dietary intake, and this approach has limitations even when adjustments are made for errors.5 Artificial intelligence (AI) and deep-learning techniques can detect specific dietary patterns in both new trials and existing studies to identify how variations in dietary intake affect health outcomes¹ while also considering eating behaviors and psychological processes. These techniques analyze the data collected and look for patterns, identify specific features of interest in the data and then find those same features in new data. Elements of interest in the data are labeled and the computer develops rules to identify the labeled data and ultimately, in nutrition science research, this information is assembled into trends that predict health outcomes.6 For example, AI and deep learning has been used to identify habits that are beneficial for managing stroke risk. This helps to establish real-life clinical guidance for at-risk individuals.7

Using these techniques leads to a better understanding of the diet as a whole, and when compared to benchmark statistical techniques used in the analysis of dietary patterns, this method is more accurate and can lead to valuable disease risk estimation.8 These patterns can be used to establish predictive tools and algorithms that healthcare practitioners can employ to help patients improve health outcomes.²

Precision Nutrition: Tailored Dietary Advice for Subgroups
Precision nutrition uses AI and deep-learning techniques to further individualize dietary advice. But it is not intended to provide unique, personalized prescriptions to individuals. The intention of precision nutrition is to stratify individuals into subgroups based on biomarkers and therapeutic efficacy. Simply put, precision nutrition can streamline nutrition advice to be more precise than current advice and improve dietary recommendations and interventions.9 The January 2021 issue of the Journal of the American College of Nutrition reflects on year-one of personalized nutrition and features four publications that explore precision nutrition topics and calls for researchers to determine how their future research may look deeper into precision nutrition.10

Precision Nutrition and Health Disparities
Certain populations defined by race, socioeconomic status, gender, ethnicity or geography are disproportionately affected by disease. One of the goals of precision nutrition is to examine ways to account for these disparities in a diverse U.S. population. Geography and socioeconomic status can make healthful foods inaccessible either due to cost or limited availability in food deserts.²

Pending specific criteria, some Orgain products qualify for reimbursement when using their respective Healthcare Common Procedure Coding System (HCPCS) codes through a Durable Medical Equipment provider. This makes Orgain more accessible to populations in need of better nutrition. Orgain’s shakes, bars and mixes are important sources of protein which is needed throughout life to build and maintain muscles, bones and skin.11 From both dairy and plant-based sources, Orgain products provide good, clean nutrition that promote good health.

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1 Rodgers GP. JAMA. 2020;324(8):735-736. doi:10.1001/jama.2020.13601
2 2020-2030 Strategic Plan for NIH Nutrition Research. A report of the NIH nutrition research task force. May 2020.
3 US Department of Health & Human Services; National Institutes of Health. 2020-2030 Strategic Plan for NIH Nutrition Research. Published May 2020. Accessed January 27, 2021. https://dpcpsi.nih.gov/onr/strategic-plan
4 Centers for Disease Control and Prevention (CDC), Health and Economic Costs of Chronic Diseases. Accessed January 27, 2021. https://www.cdc.gov/chronicdisease/about/costs/index.htm
5 Subar AF. Journal of Nutrition. 2015;145(12): 2639–2645. doi: 10.3945/jn.115.219634
6 National Institutes of Health. The NIH Catalyst – A Publication About NIH Intramural Research. July August 2018 Vol 26 Issue 4. Accessed January 28, 2021. https://irp.nih.gov/catalyst/v26i4/machine-learning
7 Jiang F. Stroke and Vascular Neurology. 2017;2:e000101. doi:10.1136/svn-2017-000101
8 Panaratos D. British Journal of Nutrition. 2018;120(3) 326 – 334. doi: https://doi.org/10.1017/S0007114518001150
9 Zeisel S. Annual Review of Food Science and Technology.2020;11:71–92. doi:10.1146/annurev-food-032519-051736
10 Kopec RE. J Am Coll Nutr. 2021 Jan;40(1):1-2. doi: 10.1080/07315724.2020.1852130.
11 U.S. National Library of Medicine. Medline Plus. Dietary Proteins. Accessed 1/29/2021. https://medlineplus.gov/dietaryproteins.html

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