THE CURRENT EVIDENCE BASE AND LIMITATIONS

The August 10 session featured six presentations that reviewed the current evidence base for precision and personalized nutrition, including potential definitions for these terms, research designs and methodologies, limitations in designs and data, and future challenges and opportunities for the field.

John Mathers, Newcastle University, discussed human variability and how it forms a basis for developing personalized and precision nutrition. People differ in visible ways, such as height and body shape, and also in less apparent ways, such as responses to specific foods and diets. As an example, Mathers referenced a study that found large interindividual variation in glycemic responses to several types of standardized meals (Zeevi et al., 2015).

According to Mathers, current public health approaches to changing diet are relatively ineffective because they typically consist of “one-size-fits-all” guidance, but precision or personalized nutrition approaches might lead to advice and support that are more likely to promote behavior change. Mathers proposed defining “personalized nutrition” as an approach that uses information on individual characteristics to develop targeted nutritional guidance, whereas “precision nutrition” suggests the possibility of obtaining a sufficient quantitative understanding about the complex relationships among an individual, their food consumption, and their phenotype to offer nutritional intervention that is individually beneficial. Precision nutrition is more ambitious, he clarified, as it demands much greater scientific certainty.

Mathers requested considering a biopsychosocial model when developing both approaches, which encompasses biological (e.g., genotype, epigenome, and gut microbiome), psychological, and sociological factors that contribute to interindividual variation in responses to food. He also suggested that precision and personalized nutrition focus more on individual health aspirations, food preferences, and barriers/facilitators to behavior change. The goal, he proposed, is to improve opportunity for everyone by developing wide-reaching, affordable, cost-effective approaches that address inequities in dietary intake and health outcomes.

Constance Hilliard, University of North Texas, explained that the genetic data on which precision approaches to health are based are often derived from people of European ancestry. This hinders their effectiveness to improve health in other populations, she maintained. She elaborated on the importance of precision when working with genetic populations by sharing a case study of high rates of salt-sensitive hypertension in African Americans of slave descent.

This population derived from a unique ecological niche, Hilliard explained, which provides clues about the etiology of its high rates of hypertension. She emphasized the importance of addressing three fallacies related to this population’s origins. First, race is not a scientific concept and, in her view, is not suitable for use in medicine except to address discrimination and past exclusion. Second, DNA ancestry provides a much sharper focus for medical research, and understanding ecological niche populations further enhances precision. Third, many ancestors of African Americans came from the deep interior of West Africa, and as Hilliard described, ended up on the coast only after being kidnapped and marched up to 1,000 miles away. This detail is critical, she explained, because coastal West Africans and Europeans were genetically accustomed to consuming 5,000 milligrams/day of sodium, whereas subsistence farmers in the interior—one of the most sodium-deficient regions in the world—had adapted to only 200 milligrams/day. Hilliard highlighted the discovery of gene variants that play a major role in sodium metabolism and are found almost exclusively in people descended from the West African interior. The presence of either variant has been associated with a 2- to 100-fold increased risk of developing kidney disease (NIH, 2017).

Hilliard illustrated that modern genomics and testing of DNA ancestry allows for greater precision in sodium guidelines and other nutrient guidance for all U.S. residents. The Dietary Guidelines advise those 14 years and older to take in less than 2,300 mg/day sodium, but this is substantially higher than that consumed by these West African ancestors. To improve the health of diverse genetic populations, Hilliard called for stratifying dietary guidance by DNA ancestry, which implies viewing U.S. residents as not only multiethnic or multiracial but also multigenomic.

Abigail Johnson, University of Minnesota, discussed potential approaches for integrating information about the microbiome with dietary intake data. She recounted her postdoctoral research study, which aimed to characterize day-to-day changes in the composition of the adult microbiome resulting from dietary intake. Study participants collected one microbiome (stool) sample daily for 17 days, and analysis revealed that participants had distinct microbiome conformations that were relatively unchanged from day to day.

When the team integrated participants’ dietary intake data with their microbiome data, Johnson said it became clear that dietary composition was more variable than the corresponding microbiome composition from day to day. Dietary data analysis revealed that a person’s nutrient intake was more stable than individual food intake during the study period but also that nutrient intake did not pair with microbiome composition. At that point, the team clustered the foods into groups and generated a phenetic tree to statistically account for similarities in food group characteristics. The results indicated the distinct and highly variable nature of each participant’s dietary patterns, Johnson reported, and a successful pairing of microbiome composition and food intake. Both grain and fruit fiber diversity (a metric based on the diversity of fiber from grains and fruit, instead of the overall quantity of those food groups) also paired with microbiome composition using this method, which Johnson said was exciting.

Johnson reported that her team also applied computational tools to the study data to determine that certain dietary patterns appeared to be associated with the abundance of specific bacterial species and explained a large amount of the microbiome variation. Moreover, she continued, they found that most diet–microbe associations were personalized (many significant relationships existed between foods and bacterial species within a person, but very few of those relationships were repeated across people).

Christian Metallo, Salk Institute for Biological Studies, discussed applying metabolomics to studying nutrition and disease physiology, illustrated through a case study demonstrating how metabolic mechanisms affect the balance of nonessential amino acids and drive a disease state.

Macular telangiectasia (MacTel) is an eye disease characterized by vascular abnormalities resulting in central vision loss and difficulties in activities such as driving and reading at a relatively young age (around 40 years). Metallo reported that genome-wide association studies have identified several genetic variations that are associated with the biosynthesis pathway for serine, a nonessential amino acid, such that serine and glycine levels are lower in the plasma of MacTel patients compared with controls. Metallo’s team sought to understand serine’s role in the development of MacTel by examining how cells control its flux to different pathways. The team identified serine palmitoyl transferase (SPT) as a key enzyme and was able to determine that SPT normally uses serine to catalyze biosynthesis of sphingolipids (structural components of cell membranes) but can also use alanine to generate deoxysphingolipids if serine levels are low or specific mutations are present in subunits of SPT. MacTel patients have higher levels of deoxysphingolipids than controls, Metallo noted, and plasma levels of the metabolite 1-deoxysphinganine are increased in patients with a type of hereditary neuropathy, some but not all of whom have MacTel. Metallo’s team next fed mice a serine-/glycine-free diet and observed that it could produce a similar neurological phenotype as is observed in MacTel patients, where the phenotype is driven by genetics.

Several lines of genetic evidence are now available to clarify that MacTel is a multigenic disease of dysregulated amino acid metabolism, Metallo confirmed, characterized by a phenotype with low serine and glycine levels and elevated alanine levels. Metallo’s team also found that a metabolic imbalance was triggered with a serine- and glycine-free, low-fat diet and a serine- and glycine-adequate, high-fat diet, but a serine- and glycine-free, Western-style, high-fat (60 percent of energy intake from fat) diet triggered the “metabolic catastrophe” that led to peripheral neuropathy. This is a severe and extreme diet, he admitted, which raised questions about how this metabolic pathway might function in individuals with a more typical diet. Metallo suggested that more sophisticated understanding of the body’s robust, genetically evolved biochemical engineering control mechanisms is needed to exploit them therapeutically through diet.

Susan Carnell, Johns Hopkins University School of Medicine and Appetite Lab, discussed how psychosocial and behavioral research on eating behavior could enhance precision nutrition by determining not only an optimal diet for an individual but also identifying the best strategies to support that individual in following the diet.

Carnell began with a discussion of “appetitive characteristics”—early, emerging, enduring dispositions toward food or eating styles that differ between individuals. Examples include food cue responsiveness (the degree of response to external food cues, such as sight or smell) and satiety responsiveness (the level of sensitivity to internal cues to stop eating, such as gut hormones or gastric distension). Carnell highlighted the Child Eating Behavior Questionnaire as a commonly used, parental report measure of a child’s traits associated with food approach (e.g., food responsiveness, enjoyment of food, and emotional overeating) or food avoidance (e.g., satiety responsiveness, slowness in eating, food fussiness, and emotional undereating). Carnell said that because appetitive behaviors seem to emerge early in life and are somewhat genetically determined, it may be better to work with them, as they may be hard to change.

Carnell reviewed evidence indicating that appetitive characteristics are associated with a number of outcomes in children, including weight/adiposity, diet, and responses to the food environment and to interventions designed to change eating behaviors. For example, food approach and avoidance traits have been positively and negatively associated with adiposity, respectively, and different food intake responses are exhibited to variable portion sizes based on levels of satiety responsiveness. Carnell conveyed that these findings emphasize the importance of considering appetite, food preferences, and other aspects of eating behaviors when developing personalized nutrition plans.

Michael Snyder, Stanford University, discussed using “big data” to support individualized profiling as a strategy to better manage health. It is increasingly possible to quantify factors that influence individual health, he said, but more importantly, their effects can also be quantified with detailed molecular and physiological measurements of an individual.

Snyder described his group’s longitudinal research on personal omics profiling, which involves in-depth measures (“omics measurements”) to gather information about participants’ genome, epigenome, transcriptome, proteome, cytokines, metabolome, lipidome, and microbiome, among others. Questionnaires, basic and advanced clinical tests, and wearable biosensor devices provide additional information, he explained, with the goal of better characterizing what a healthy profile looks like, how it changes over time and during phases of illness, and how it differs between people. Snyder highlighted the importance of the longitudinal nature of measurements in order to detect changes and better understand a health trajectory.

He recounted nearly 50 occurrences of “major health discoveries” during the first few years of profiling participants, including of cardiovascular, metabolic, hematological, or oncological abnormalities that often precede disease. Every discovery was made before the person showed symptoms, he added, which enabled early action to treat underlying disease.

Snyder also pioneered the use of wearable sensors to collect daily data on biological indicators, such as heart rate and its variability, respiration, skin temperature, blood oxygen, and blood pressure. Smartwatches can collect other clinical biomarkers, such as hemoglobin levels, red blood cell counts, and fasting glucose, which he admitted are no substitute for diagnosis-grade measurements and health care consultation but can provide clues that something may be awry. For example, his research team developed an algorithm to detect infectious disease illness, such as Lyme and COVID-19, presymptomatically based on changes in resting heart rate data.

INNOVATIVE METHODOLOGIES AND TECHNOLOGIES

The August 11 session opened with an overview of the industry landscape followed by a review of a selection of innovative methodologies and technologies for personalized nutrition, which were grouped into four sets of scales—genetic, physiologic/microbiome, individual, and social-ecologic—each discussed by three speakers.

IMPLEMENTATION OF PRECISION AND PERSONALIZED NUTRITION

The August 12 session discussed the implementation of precision and personalized nutrition and included academic, regulatory, and industry perspectives on opportunities and challenges.

Christina Roberto, University of Pennsylvania, discussed challenges to precision nutrition’s potential to achieve greater health equity. The discussions tend to focus on individual-level factors that affect health, she observed, but broader thinking is important because those interact with many environmental- and systems-level forces that influence health behaviors.

Roberto pointed out that, according to data published in the Dietary Guidelines for Americans, 2020–2025, many people do not adhere to dietary recommendations, and she expressed her doubt that targeted nutrition advice will solve the problem. People have limited memories and information processing requires effort, she explained, but most food decisions are made quickly and automatically, using associations and emotions. Simple nutrition guidelines have tremendous recall advantages, Roberto maintained, relaying an example in which researchers found that personalized guidelines about the number of daily servings for each food group were less memorable and actionable than a generalized guideline to fill half the plate with fruits and vegetables. Technologies that remind people of advice reduce recall burden, Roberto acknowledged, but she expressed concern about increasing disparities by relying on them, given that mobile devices and health apps are most likely to be used by younger, educated women.

Roberto also raised concern about precision nutrition being exploited for profit. Online shopping could be a positive or negative source of personalized marketing, she suggested, noting that some retailers already organize products by dietary preferences and use customers’ browsing and purchasing data to recommend products. This could be beneficial if the products are healthy foods, she pointed out, but could exacerbate poor eating habits if not.

Roberto ended with a reference to a framework for achieving health equity, which she proposed incorporating into conversations about implementing precision nutrition in a way that does not widen disparities. The framework emphasizes the importance of developing interventions that aim to increase healthy options and/or reduce deterrents to unhealthy behaviors, she explained, while also incorporating considerations related to social disadvantage and social determinants of health (Kumanyika, 2019).

Peter Lurie, Center for Science in the Public Interest, cautioned that exciting science alone is not enough to improve public health. He proposed that current federal regulatory structures are inadequate for supporting the translation of personalized nutrition science into meaningful public health gains.

Lurie supported this hypothesis by describing what he called “an almost perfect storm of poor regulation,” characterized by high demand for personalized nutrition products (particularly among higher-income, “worried well” consumers); a largely unregulated market for laboratory-developed tests (LDTs), a subset of diagnostics; a policy for “general wellness” products that provides “safe harbor” for many devices; and claims on food products that could lead consumers to believe that their health-promoting potential exceeds what evidence suggests.

Lurie elaborated on his concerns with the U.S. Food and Drug Administration (FDA) oversight of LDTs, maintaining that it has infrequently exercised its enforcement authority. He listed resulting deficiencies in oversight, such as inadequate adverse event reporting and product labeling, unsupported manufacturer claims, and no premarket review of performance data.

Lurie described four types of FDA-permitted claims on food as another element of regulation relevant to precision nutrition. Different types of claims require different levels of supporting scientific evidence, he explained, but claim language sometimes blurs the line between the types and may be misconstrued by consumers. For example, health claims convey a relationship between an ingredient and a disease and require “significant scientific agreement” and preapproval, whereas structure/function claims describe the effect on normal body structure or function and require only “competent and reliable” evidence and no preapproval.

Lurie ended by highlighting that a challenge for personalized nutrition is to determine when it makes more sense than a population-based approach; he maintained that the choice between the two should be guided by which will have the most public health impact.

Robert Califf, Verily and Google Health, provided a regulatory perspective on challenges and opportunities for precision nutrition. FDA’s mission is to preserve and protect the public health, he began, and although it is science based, Califf emphasized that it is tasked with making decisions even when more research would be helpful to clarify an optimal choice.

Califf highlighted that the current environment offers real-time access to massive amounts of data and the ability to rapidly analyze data to gain insights and guidance. This environment enables integrating comprehensive health data, which he said is valuable for both assessing the entire human being and interacting components and generating evidence to inform public health decisions.

To build on his point about the value of comprehensive health data, Califf explained that individual biomarkers are unlikely to predict a food’s effect on health except in cases of specific nutritional deficiencies and that substantial evidence must exist for a biomarker of one measure to serve as a surrogate (i.e., a substitute for a clinical end point). The vast majority of biomarkers are not valid surrogates, he pointed out, because they exist in a milieu of biological complexity. The effect of food on health is a prime example of this complexity, he added, because many nutrients and substances are simultaneously interacting, each affecting multiple pathways and each with a potential impact on health. Biomarker tests need to be reliable, reproducible across multiple laboratories and clinical settings, and of adequate sensitivity and specificity before data based on them can be used for evaluation. Otherwise, Califf cautioned, personalized recommendations could cause harm that may not be apparent until many years later.

Josh Anthony, Nlumn, shared lessons learned from his experience in the personalized nutrition industry. As providers rush to understand how they can fit into the rapidly growing market’s ecosystem, he began, they often make the mistake of putting themselves and their brand at the center instead of starting with understanding consumer needs, behaviors, and values.

Consumers seeking personalized nutrition approaches hold widely different views about what health means, Anthony explained, with many embracing scientific controversies and seeking self-affirming information or groups aligned with their beliefs and a smaller segment seeking objective, data-driven personalization. Consumer expectations of personal nutrition’s delivery of solutions are broad and inconsistent, he continued, ranging from managing chronic disease risk to maintaining sustained energy and reducing stress levels.

Anthony described a model of personalized nutrition that supports creating and sustaining engagement with a wide variety of potential users. The model calls for first selecting the optimal health outcome of interest for the consumer, then collecting an objective, validated measure of health or function. The consumer’s result for that measure leads to personalized recommendations to improve health and lifestyle, which are intended to drive behavior change. The cyclical, continuous nature of the process supports reassessment and new advice, he said, as the consumer sets new outcome goals and/or health measures data change.

Regarding the state of the science of personalization, Anthony suggested that companies can differentiate themselves by committing to evidence-based practices, although he observed that most do not have published data to support their specific program and its purported outputs. He said personalized nutrition is most successful when the program delivers health and functional benefits that exceed benefits from “one-size-fits-all” approaches and are sustained over time. In his view, most of the programs that are more rooted in science are starting to show some health and functional benefits to users but have yet to demonstrate that their outcomes are sustainable and superior to those from broader approaches.

Martin Hahn, Hogan Lovells, discussed communicating personalized nutrition information to the public while maintaining compliance with the current regulatory framework. Hahn explained that FDA regulates food products based on intended use. That determines whether a product is regulated as a food or a dietary supplement, for example, and what type of claim it is permitted to carry. Hahn opined that tension exists between identifying claim language that effectively conveys the intended message while staying within regulatory guardrails.

Hahn underscored that messages to communicate the benefits of personalized and precision nutrition must be truthful and not misleading, supported by competent, reliable scientific evidence for both explicit and implied messages conveyed by claims. Hahn pointed out a paradox of structure/function claims, which is that many imply disease prevention. For instance, he recognized that the approved claims “calcium builds strong bones” and “whole oats support heart health” imply reduced risk for osteoporosis and coronary heart disease, respectively, potentially conveying a message that may not have been intended.

Hahn contended that the current regulatory system is not well suited to allow personalized nutrition technologies to communicate their benefits without risking allegations of misleading consumers or being regulated in a way that is inconsistent with their intended use. As an example, Hahn explained that a genetic analysis used to identify a diet that helps reduce the risk of developing diabetes could be communicated through an FDA-approved health claim. But if it is used to identify a diet that reduces insulin dependence for an individual with diagnosed diabetes, then it is communicating a treatment effect and warrants a disease claim, which would trigger filing as a drug. The new drug approval process is lengthy and expensive, he said, and could stifle innovation because it is not a viable option for many of the personalized nutrition technologies under way. The product could more safely make a structure/function claim, he added (e.g., that it supports blood glucose levels within normal ranges), but this would not communicate its full benefits and likely would not attract as much consumer interest.

Hahn espoused the value of a stakeholder dialogue to discuss a strategy for supporting a regulatory structure that allows personalized nutrition technologies to advance and, when they have a sufficient evidence base, to clearly and effectively communicate their benefits to target populations without risking unintended regulatory consequences. This could involve considering new legislation to give FDA the authority to develop a new regulatory framework for personalized nutrition solutions, he suggested, or adapting existing regulatory frameworks, such as those governing foods for special dietary uses or medical foods.

Patsy Brannon, Cornell University, discussed challenges and opportunities for incorporating precision nutrition into both public health guidelines and individualized guidelines. For context, she contrasted food- and nutrient-based guidelines for public health with precision nutrition–based recommendations for individuals in terms of the types of actions they support, the way they are implemented, and the strength of evidence required.

Brannon reviewed the expanded Dietary Reference Intakes (DRIs) model and highlighted opportunities for precision nutrition to help build stronger reference intake values. She listed 12 nutrients for which the distribution of need is not yet understood, primarily because no adequacy outcome has been identified. Precision nutrition could help build stronger reference intake values for these nutrients, she proposed, as research proceeds to identify biomarkers and their variance. When a DRI for chronic disease is warranted, precision nutrition could help identify surrogate outcomes for chronic disease.

Another opportunity, Brannon continued, is to determine population subgroups with specific nutrient needs, such as subgroups that have distinctly different distributions of requirements for a nutrient and responses to intake of that nutrient and subgroups of responders and nonresponders to a particular nutrient. Precision nutrition could also fill the research gap of defining a “healthy population” when the prevalence of chronic disease is high, which would promote better informed public health guidelines for nutrients and dietary patterns, Brannon explained, and enhance understanding of variance within a healthy population.

Brannon raised the issue of whether individual algorithms can be linked to public health guidelines so that a person’s specific nutrient needs could be used to tailor DRIs and Dietary Guidelines for them. The strength of evidence required for such tailoring remains to be determined, she noted, and individual guidelines based on precision nutrition also raise the issue of efficacy versus effectiveness. Brannon suggested that the effectiveness of some precision nutrition approaches may be related to participants’ self-selection bias. She encouraged the consideration of behavior-change theories, as they could provide insight into participant characteristics and the subgroup of participants who are successful. Last, she raised the questions of how to ensure equitable access for people at the highest risk of poor health and that practitioners receive an appropriate scope and depth of training to implement individual guidelines. ◆◆◆

REFERENCES

• Acosta A, Camilleri M, Abu Dayyeh B, Calderon G, Gonzalez D, McRae A, Rossini W, Singh S, Burton D, Clark MM. Obesity (Silver Spring). 4. Vol. 29. 2021. Selection of antiobesity medications based on phenotypes enhances weight loss: A pragmatic trial in an obesity clinic; pp. 662–671. [PMC free article: PMC8168710] [PubMed: 33759389]
• Adhikari S, Nice EC, Deutsch EW, Lane L, Omenn GS, Pennington SR, Paik Y-K, Overall CM, Corrales FJ, Cristea IM, Van Eyk JE, Uhlén M, Lindskog C, Chan DW, Bairoch A, Waddington JC, Justice JL, LaBaer J, Rodriguez H, He F. Nature Communications. 1. Vol. 11. 2020. A high-stringency blueprint of the human proteome; p. 5301. [PMC free article: PMC7568584] [PubMed: 33067450]
• Ben-Yacov O, Godneva A, Rein M, Shilo S, Kolobkov D, Koren N, Cohen Dolev N, Travinsky Shmul T, Wolf BC, Kosower N, Sagiv K, Lotan-Pompan M, Zmora N, Weinberger A, Elinav E, Segal E. Diabetes Care. 9. Vol. 44. 2021. Personalized postprandial glucose response-targeting diet versus Mediterranean diet for glycemic control in prediabetes; pp. 1980–1991. [PubMed: 34301736]
• Celis-Morales C, Livingstone KM, Marsaux CF, Macready AL, Fallaize R, O'Donovan CB, Woolhead C, Forster H, Walsh MC, Navas-Carretero S, San-Cristobal R, Tsirigoti L, Lambrinou CP, Mavrogianni C, Moschonis G, Kolossa S, Hallmann J, Godlewska M, Surwillo A, Traczyk I. International Journal of Epidemiology. 2. Vol. 46. 2017. Effect of personalized nutrition on health-related behaviour change: Evidence from the Food4Me European randomized controlled trial; pp. 578–588. [PubMed: 27524815]
• Fuzo CA, da Veiga U, Moco S, Cominetti O, Métairon S, Pruvost S, Charpagne A, Carayol J, Torrieri R, Silva WA Jr, Descombes P, Kaput J, Monteiro JP. Scientific Reports. 1. Vol. 11. 2021. Contribution of genetic ancestry and polygenic risk score in meeting vitamin B12 needs in healthy Brazilian children and adolescents; p. 11992. [PMC free article: PMC8184816] [PubMed: 34099811]
• Kumanyika SK. American Journal of Public Health. Vol. 109. 2019. A framework for increasing equity impact in obesity prevention; pp. 1350–1357. [PMC free article: PMC6727309] [PubMed: 31415203]
• NIH (National Institutes of Health). Story of discovery—APOL1 gene variants: Unraveling the genetic basis of elevated risk for kidney disease in African Americans. Washington, DC: National Institute of Diabetes and Digestive and Kidney Diseases; 2017. [September 27, 2021]. https://www​.niddk.nih​.gov/news/archive/2017​/story-variants-unraveling-genetic-basis-elevated-risk-kidney-disease-african-americans .
• Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalová L, Pevsner-Fischer M, Bikovsky R. Cell. 5. Vol. 163. 2015. Personalized nutrition by prediction of glycemic responses; pp. P1079–P1094. [PubMed: 26590418]

Suggested citation:

National Academies of Sciences, Engineering, and Medicine. 2021. Challenges and opportunities for precision and personalized nutrition: Proceedings of a workshop—in brief. Washington, DC: The National Academies Press. https://doi.org/10.17226/26407.