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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
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2

The Current Evidence Base and Limitations

The August 10 session of the workshop featured six presentations reviewing 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. Cindy Davis, Agricultural Research Service, U.S. Department of Agriculture, moderated the speaker presentations and an ensuing panel discussion.

HUMAN VARIABILITY: A BASIS FOR PRECISION AND PERSONALIZED NUTRITION

John Mathers, Newcastle University (United Kingdom), discussed human variability and how it serves as a basis for developing precision and personalized nutrition. He began by observing that people differ from each other in visible ways, such as height and body shape, as well as in ways that are less apparent, such as responses to specific foods and diets. To illustrate the latter, Mathers referenced two examples of people exhibiting different responses to the same nutrition intervention. The DIETFITS (Diet Intervention Examining the Factors Interacting with Treatment Success) study, which enrolled more than 600 adults in a 12-month weight loss trial, assessed changes in weight resulting from either a low-fat or a low-carbohydrate diet. In both diet groups, he reported, individual weight loss at the 12-month mark ranged from 25 or more kilograms to no loss, and some participants had even gained weight (up to 10 kilograms) (Gardner et al., 2018). He noted that interindividual differences have also been observed in response to

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

supplemental fish oil, a substance expected to lower the blood concentration of triacylglycerol: Among more than 300 people taking fish oil supplements, some experienced such decreases, but a considerable proportion did not and were deemed “nonresponders” (Madden et al., 2011).

Mathers moved on to discuss interindividual differences in glycemic responses to eating, which he identified as a useful way to begin exploring variation in individuals’ metabolic responses to foods. He stated that continuous monitoring of blood glucose concentration to estimate glycemic responses to food is easy and poses a low burden for participants, adding that differences in responses may be related to health outcomes. He highlighted a study in which changes in participants’ postmeal blood glucose concentrations were monitored continuously for 1 week, and large interindividual variation was observed in glycemic responses to several types of standardized meals (Zeevi et al., 2015).

Mathers raised the issue of how information on interindividual variation in responses to diet might be applied to the public health challenge of improving population-wide eating habits. Current public health approaches to changing diet are relatively ineffective, he contended, as they typically consist of similar advice for everyone (e.g., eat more fruits and vegetables). He speculated whether a precision or personalized nutrition approach, in which the individual is placed at the center, might lead to nutrition advice and support that would be more effective at improving population health relative to “one-size-fits-all” guidance.

Mathers shared proposed definitions for personalized nutrition and precision nutrition. He defined personalized nutrition as an approach that uses information on individual characteristics to develop targeted nutritional advice, products, or services. Precision nutrition, on the other hand, suggests the possibility of obtaining a sufficient quantitative understanding of the complex relationships among an individual, their food consumption, and their phenotype (including health) to offer nutritional intervention or advice that is known to be individually beneficial. Precision nutrition is more ambitious, Mathers clarified, as it demands much greater scientific certainty (Ordovas et al., 2018).

These definitions raise questions, Mathers continued, about the biological basis for interindividual variation and whether knowledge of that biological basis could be used to develop more effective personalized or precision nutrition. He outlined relevant biological features that influence an individual’s responses to food and ultimately long-term health—genotype, the epigenome,1 and the gut microbiome—and suggested that complex relationships exist between those features and psychological factors.

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1 The collection of genetic marks on DNA within a single cell that instruct the genome (NHGRI, 2020).

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

As an example, Mathers recounted a study in which participants underwent the same measurements on two occasions. First, they consumed a test meal while researchers assessed their satiety responses using an objective biological measure and a subjective, self-reported measure. Participants were then informed that they had either a high-risk or protective genotype for the development of obesity, based on random assignment (not their actual genotype). One week later, the same measurements were repeated following the same test meal. Among individuals who had been told that they had the high-risk gene, no differences were reported in satiety responses measured objectively or subjectively between the two test meals. In contrast, Mathers continued, individuals who had been told that they had the protective gene exhibited substantial increases in both objective and subjective measures of satiety (Turnwald et al., 2019). According to Mathers, these results suggest that learning one’s genetic risk changes physiology independently of actual genetic risk, a finding that, if replicated, has implications for using genetic information in the design of precision or personalized nutrition interventions.

Mathers appealed for consideration of a biopsychosocial model—which encompasses biological, psychosocial, and social factors that contribute to interindividual variation—when developing precision and personalized nutrition approaches. He explained that the way these different types of factors affect an individual’s response to diet depends on the time scale. For example, glycemic responses to eating manifest during the first 2–3 hours postmeal, with almost all interindividual variation in responses resulting from biological factors. Responses that manifest over longer periods of time (e.g., months, years), such as insulin resistance or development of diabetes, he pointed out, are likely influenced at least equally, if not more so, by psychological and social factors that drive eating behaviors.

Mathers proposed that differences in people’s health aspirations, general likes and dislikes, and food preferences are examples of factors to be considered when attempting to change eating behaviors using precision or personalized nutrition approaches. He also suggested that such approaches need to identify and account for barriers to and facilitators of dietary change for an individual.

Next, Mathers discussed inequity in diet and health. In the United Kingdom, he reported, the prevalence of obesity in children at ages 4 and 10 years is twice as high among those from the most deprived compared with the least deprived families. Prevalence increases steadily in both age groups, he added, as the level of deprivation2 increases (NHS Digital, 2014). As another example, he pointed to data showing that in the United States,

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2 Based on the English Indices of Deprivation, available here: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/6871/1871208.pdf (accessed October 11, 2021).

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

life expectancy has increased at the population level over the past several decades, but has done so to a lesser extent among the lowest-income individuals since 2000, an inequity that he stated is linked to socioeconomic differences (Chetty et al., 2016). Mathers asserted that the goal is to develop precision and personalized nutrition approaches that address inequities in dietary intake and health outcomes by improving opportunities for everyone.

Mathers then outlined challenges for precision and personalized nutrition at the individual and societal levels. For individuals, he emphasized the importance of making such approaches accessible, attractive, and acceptable so they create new, motivating opportunities to improve health. At the societal level, he argued for greater reach, affordability, and cost-effectiveness of precision and personalized nutrition approaches, as well as structures that sustain long-term behavior changes.

In closing, Mathers stated that personalized nutrition approaches have been shown to improve adults’ dietary intake (Celis-Morales et al., 2017; Jinnette et al., 2021). He suggested that the effectiveness of these approaches could be improved by taking a systems approach. Such an approach, he said, would link information on an individual’s characteristics (including barriers to and facilitators of change and aspirations) to a specific self-monitoring process that would feed back to those characteristics in a continuous cycle toward improved health.

PRECISION NUTRITION AT THE INTERSECTION OF HISTORY AND GENOMICS

Constance Hilliard, University of North Texas, began by pointing out that the genetic data on which precision approaches to health are based are often derived from people of European ancestry. The effectiveness of those approaches for improving health in other populations is thereby hindered, she maintained. She then elaborated on the need for precision in dealing with genetic populations by sharing a case study of the etiology of high rates of hypertension and kidney failure in African Americans of slave descent.

Hilliard observed that medical researchers have long searched for explanations for the high prevalence of salt-sensitive hypertension in this segment of the population, which she described as a “crisis situation” based on its linkage to kidney failure, cardiovascular disease, and other comorbidities. Research with inhabitants of West African coastal cities did not replicate the same high rates of hypertension observed in African Americans of slave descent, she recounted, suggesting that a key problem in explaining the latter phenomenon is the medical community’s lack of precise knowledge of the ecological niche from which these African Americans originated.

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

To fill that knowledge gap, Hilliard stated, it is necessary to address three fallacies related to the origins of this population. First, she explained, race is not a scientific concept, and is not suitable for use in medicine except for purposes related to addressing discrimination and past exclusion. Second, DNA ancestry provides a much sharper focus for medical research, she maintained, and understanding ecological niche populations further enhances precision. Third, she pointed out that many ancestors of Black Americans emanated from the deep interior of West Africa, and ended up on the coast only after having been kidnapped and marched up to 1,000 miles to reach waiting slave ships. This detail is critical, she stressed, because coastal West Africans and Europeans were genetically accustomed to consuming 5,000 mg/day of sodium, whereas subsistence farmers in the interior—one of the most sodium-deficient regions in the world—had become genetically adapted to diets providing only 200 mg/day. These farmers were in the lowest echelons of that society, Hilliard added, making them vulnerable to being kidnapped by slave traders.

Hilliard highlighted advances in genomics and genetics leading to the identification of two genetic variants that play a major role in sodium metabolism and are found almost exclusively in people whose ancestors originated in the West African interior. She reported that the presence of either of these variants has been associated with a 2- to 100-fold increased risk of developing kidney disease (NIH, 2017).

Hilliard went on to observe that, based on average sodium intakes of about 3,400 mg/day in the United States (USDA, 2020), African Americans of slave descent consume about 1,700 percent more sodium compared with their ancestors who had adapted to the low-sodium interior region of West Africa, whereas Americans of European ancestry and recent West African immigrants to the United States consume about 32 percent less sodium compared with their ancestors. To improve the health of two diverse genetic populations, then, Hilliard called for stratifying approaches not by race but by DNA ancestry and ecological niche.

On that note, Hilliard turned to a theoretical model of ancestral gene variants that she developed to illustrate that African Americans are admixed populations—for example, 75 percent Niger-Kordofanian West African and 25 percent northern European. This model, she elaborated, suggests that multiplying an individual’s percentage of genetic ancestry by the daily sodium consumption levels in healthy members of that population group and then adding the products yields an appropriate average daily sodium intake for the admixed individual. She explained that this model was translated into a theoretical equation (Figure 2-1) that can be used to calculate critical nutrient values in any ecological niche population (where Cadmixed is the healthy nutrient intake for each segment of admixed ancestry):

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
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FIGURE 2-1 The Hilliard-Wang ancestral gene variants theoretical equation for critical nutrient values in any ecological niche population.
SOURCE: Presented by Constance Hilliard on August 10, 2021.

According to Hilliard, the key takeaway is that 21st-century nutrition and health research continues to operate according to a “one-size-fits-all paradigm” that she characterized as outdated, yet powerful because it is invisible and unexamined. The Dietary Guidelines for Americans advises those aged 14 years and older to consume less than 2,300 mg/day of sodium, she pointed out, but this is substantially higher than the amounts consumed by the ancestors of African Americans of slave descent (USDA and HHS, 2020).

To conclude her presentation, Hilliard maintained that modern genomics and testing of DNA ancestry provide the opportunity to bring greater precision to sodium guidelines and other nutrient guidance for all Americans. From her perspective, these tools introduce the possibility of stratifying nutrition guidance by DNA ancestry instead of providing standardized guidance that she suggested disadvantages certain populations. Certain disease triggers vary from one genetic population to another, she stressed, and data available to provide precise guidance for one genetic population may be meaningless for other genetic populations. The idea of stratifying guidance implies the need to view Americans as not only multiethnic or multiracial, she argued, but also multigenomic.

INTEGRATING MICROBIOME AND DIETARY DATA

Abigail Johnson, University of Minnesota, discussed potential approaches for integrating information about the microbiome with data on dietary intake. She began by explaining that dramatic shifts in microbiome development and composition occur from infancy through early toddlerhood, corresponding with such dietary changes as initiation of breastfeeding or formula feeding and introduction of complementary foods. Microbiome composition reaches a more stable point by adulthood, she said, with some variation that can be attributed to seasonality, geographic location, movement between countries, exposure to antibiotics, and medical events, for example.

It is becoming increasingly apparent, Johnson emphasized, that the microbiome is shaped by the foods a person eats and is also an independent contributor to that person’s diet-related health outcomes. She explained

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

that nutrition scientists and researchers have long understood that dietary intake, with some variation from genetics, influences the phenotypes expressed and often is associated with health or disease conditions, but that the picture is clearer when the microbiome’s contribution is included. As an example, she pointed out that the microbiome modifies and produces dietary metabolites that were not previously considered but may have a role in the development of health outcomes (Figure 2-2).

Johnson described her postdoctoral research study, whose objective was to characterize day-to-day changes in the composition of the adult microbiome resulting from dietary intake. She explained that 34 study participants collected one microbiome (stool) sample daily for 17 days, and her research team analyzed the composition of each participant’s samples. One type of analysis reduced the complexity of the multiple species in each participant’s microbiome to two or three principal components, which captured the greatest possible variation among the microbiomes. In this type of analysis, each participant’s microbiome conformation was plotted as a single point on a coordinate plane. The farther apart the points, Johnson explained, the more distinct and different from each other were the microbiomes represented by the points, whereas overlapping points would indicate microbiomes that were essentially identical. This analysis revealed that participants had distinct microbiome conformations, she recounted, and that those conformations were relatively unchanged from day to day.

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FIGURE 2-2 The microbiome is a product of diet and an influence on diet-related health outcomes.
SOURCES: Presented by Abigail Johnson on August 10, 2021; Johnson et al., 2020. Reprinted with permission from Frontiers in Nutrition.
Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

The best way to integrate dietary and microbiome data was not immediately clear, Johnson acknowledged, and her team chose to collect participants’ dietary records for the 24 hours prior to each of the 17 daily microbiome samples. The researchers analyzed the dietary records in a way that enabled them to visualize the relative abundance of different food groups, which Johnson said made clear that dietary composition was more variable than corresponding microbiome composition from day to day. Dietary data were also analyzed using network visualization, a computational tool commonly used in microbiome research, to illustrate the number of people who ate each food reported in the dietary records. This visualization showed that some foods were consumed by many participants and others by only one or two. Another level of analysis determined the macronutrient and micronutrient composition of foods consumed by study participants, Johnson reported, confirming that nutrient intake was more stable than individual food intake during the study period. She described another computational tool common in microbiome research, Procrustes analysis, which was applied to assess how dietary intake influenced microbiome composition and showed that the participants’ microbiomes did not pair with the nutrients they consumed.

At this point, Johnson recounted, the team reconsidered its approach to assessing food intake, and the concept of “dietary dark matter” came to the fore. She described this as the myriad of biochemicals, such as polyphenols, flavors, and other compounds, that are present in a given food—many in minute amounts—and serve as substrates for bacteria, but are rarely quantified or studied to the same extent as essential nutrients. She pointed out that even small particles of food arrive in the lumen relatively intact even after digestion has occurred in the higher gastrointestinal tract, which suggests that they could have an impact on different communities of gut bacteria.

On that note, Johnson turned to discussing an additional approach to examining dietary intake. This approach, she said, incorporates the highly multivariate nature dietary of intake similarly to what has been done in microbiome analyses. Microbiome data can be organized in terms of specific species, she explained, and amounts of those species present in a sample can be quantified. Her team applied this approach to dietary intake by clustering the foods consumed into food groups and generating a phenetic tree to account statistically for similarities among foods with respect to their food group characteristics (Figure 2-3). It thereby became possible to apply additional tools from the microbiome space to explore diet, such as UniFrac, a distance metric used for comparing biological communities. Applied to dietary data, Johnson reported, UniFrac distance generated multivariate ordinations that revealed the distinct and highly variable nature of each study participant’s dietary patterns. When Procrustes analysis was applied to average microbiome composition and average food composition in the

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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FIGURE 2-3 A phenetic tree for use in accounting for food groups and their relatedness.
SOURCES: Presented by Abigail Johnson on August 10, 2021; Johnson et al., 2019. Reprinted with permission from Elsevier.
Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

ordination generated by the UniFrac distance method, microbiome composition successfully paired with food intake. Both grain fiber diversity 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.

According to Johnson, the ordination resulting from the UniFrac distance method also led the research team to determine that dietary patterns driven by consumption of alcohols, fats, poultry, grains, and cakes (i.e., similar to Western, high-fat diets) appeared to be associated with the abundance of specific bacterial species. Further analysis indicated that these dietary patterns explained a large amount of the variation in the microbiome and revealed a Bacteroidetes gradient.

Turning to the application of these findings to precision and personalized nutrition approaches, Johnson reported that the method applying UniFrac distance and Procrustes analysis identified significant agreement between diet and microbiome for 28 of the 34 study participants when the respective 17 days of data were considered longitudinally for each individual. Moreover, she continued, the research team found that most diet–microbe associations were personalized—i.e., a large number of significant relationships existed between foods and bacterial species within a person, but few of those relationships were repeated across people. For example, consumption of dark green vegetables was correlated with a decrease in a specific Bacteroidetes species in one person but an increase in the same Bacteroidetes species in another.

Johnson’s final point was that dietary diversity correlates with microbiome stability, an observation made possible by the availability of longitudinal data on dietary intake and microbial composition. She noted that this relationship has also been observed in infants (Homann et al., 2021), and suggested that further research will clarify potential health implications. Finally, she reiterated that foods and dietary patterns shape the composition and dynamics of the microbiome and that diet–microbiome relationships are individualized, and proposed that multivariate dietary data can be thought of as another “-ome” and integrated with multiomics data using computational tools from the microbiome space.

AN ENGINEERING PERSPECTIVE ON OPPORTUNITIES AND OBSTACLES IN PRECISION NUTRITION

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

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

Precision and personalized nutrition can absolutely be used to modulate health, Metallo asserted, but he maintained that more sophisticated understanding of the body’s robust, genetic biochemical engineering control mechanisms (i.e., metabolism) is needed to exploit those mechanisms therapeutically through diet. Static metabolite measurements have limited utility, he added, but can be enhanced with information about the location and dynamics of the processes by which the metabolites are derived.

Metallo presented a case study of macular telangiectasia (MacTel)—a disease of the macula, the part of the eye that is responsible for high visual resolution and accounts for about half of neural activity in the retina. MacTel is characterized by vascular abnormalities that are observed by fluorescein angiogram, he explained, and manifests in central vision loss and difficulties with such activities as driving and reading at a relatively young age (~40 years) in affected individuals. He noted that MacTel is a familial disease with a large genetic component, and an international group of scientists and clinicians was convened in 2005 to better understand its causes.

Since then, Metallo continued, genome-wide association studies (GWAS) have identified several specific genetic variations in MacTel patients that are associated with the serine (a nonessential amino acid) biosynthesis pathway, such that serine and glycine levels are lower in the plasma of MacTel patients compared with controls. Serine and glycine are involved in a number of metabolic pathways, he said, and give rise to numerous downstream metabolites.

Metallo’s team sought to understand serine’s role in the development of MacTel by examining how cells control flux of a substrate (serine) to different pathways. Because numerous enzymes compete for serine in cells, Metallo explained, evolution has attuned the most important enzymes to have a high affinity for serine so they can bind it when concentrations are low. The team identified serine palmitoyl transferase (SPT) as a key enzyme and recognized that it 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 if specific mutations are present in subunits of SPT. MacTel patients have higher levels of deoxysphingolipids relative to control patients, Metallo noted, and plasma levels of the metabolite 1-deoxysphinganine are also increased in patients with another type of hereditary neuropathy (some but not all of whom have MacTel) that is responsible for peripheral neuropathy and loss of thermal sensing.

Metallo’s team next examined whether dietary manipulation could drive a neurological phenotype similar to that observed in MacTel patients. They fed mice a serine/glycine-free diet and observed that after 10 months, the mice had lost thermal sensing and were exhibiting some retinal defect. The team concluded that the same phenotype as that driven by genetics in MacTel patients could also be caused by an atypical diet.

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

Metallo reported that several lines of genetic evidence are now available to clarify that MacTel is a multigenic disease of dysregulated amino acid metabolism, characterized by a metabolic phenotype in which levels of serine and glycine are low and levels of alanine are elevated. This discovery, he observed, has prompted additional questions about whether other factors, dietary or otherwise, influence this phenotype, and whether the phenotype is linked to more common diseases and comorbidities, such as type 2 diabetes. Metallo’s team explored this question by attempting to accelerate peripheral neuropathy via additional dietary manipulations. They found that a metabolic imbalance was triggered with a serine- and glycine-free low-fat diet, as well as with a serine- and glycine-adequate high-fat diet, but the combination of a serine- and glycine-free and 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, Metallo admitted, raising questions about how the metabolic pathway leading to peripheral neuropathy might function in individuals consuming a more typical diet.

Metallo then turned to another research question arising from the case study: whether there is a way to identify type 2 diabetes patients who are susceptible to peripheral neuropathy. This question arose, he explained, because it appears that metabolic syndrome steers metabolism in such a way as to reduce serine levels and cause peripheral neuropathy associated with serine deficiency in some patients. Specifically, the research team wondered whether they could identify an assay that could be used to confirm whether a person with diabetes is experiencing serine deficiency, similar to the way in which a glucose tolerance test reveals insulin resistance. A serine tolerance test could enable more direct quantification of serine/glycine disposal, he suggested, which is an indicator of whether serine supplementation may be an effective therapy for a particular patient with diabetes.

PSYCHOSOCIAL INFLUENCES ON EATING BEHAVIOR

Susan Carnell, Johns Hopkins University School of Medicine, discussed how psychosocial and behavioral research on eating behavior can enhance precision nutrition not only by determining an optimal diet for an individual but also by identifying the best strategies for supporting that individual in following the diet. To lay a foundation for her presentation, she referred to a biopsychosocial model of factors that influence the development of obesity (Figure 2-4) and highlighted eating behaviors in its psychological compartment.

Carnell began by defining the term “appetitive characteristics” as early-emerging, enduring dispositions toward food or eating styles that differ among individuals. As examples of appetitive characteristics, she cited food cue responsiveness (the degree to which a person responds to external

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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FIGURE 2-4 The biopsychosocial model as it relates to the development of obesity.
SOURCE: Presented by Susan Carnell on August 10, 2021.

food cues, such as the sight or smell of food) and satiety responsiveness (a person’s level of sensitivity to internal cues to stop eating, such as gut hormones or gastric distension) (Carnell and Wardle, 2008).

Appetitive characteristics can be measured with behavioral tests or questionnaires, Carnell continued, noting that the latter might be more useful for developing personalized nutrition plans for children. She described an instrument commonly used with children—the Child Eating Behavior Questionnaire (CEBQ), a 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) (Wardle et al., 2001). She added that a version of the questionnaire for infants—the Baby Eating Behavior Questionnaire (BEBQ)—assesses the same traits during the period in which a child consumes only breastmilk and/or infant formula (Llewellyn et al., 2011), and that a version for adults (AEBQ) was developed more recently (Hunot et al., 2016). With regard to the population prevalence of the traits measured by these questionnaires, Carnell shared unpublished data from a community survey of several hundred children (aged 2–12 years) and adults, indicating lower satiety responsiveness and higher food responsiveness and emotional overeating among adults compared with children.

Carnell next discussed relationships between appetitive characteristics and weight/adiposity. She started by highlighting a systematic review and meta-analysis of data on more than 36,000 children aged 1–14 years whose

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

eating behaviors were assessed using the BEBQ or CEBQ (Kininmoth et al., 2020). Based on the 46 cross-sectional studies included in the review, she reported, robust positive associations were found between food approach traits and adiposity, whereas negative associations were found between food avoidance traits and adiposity. The 11 longitudinal studies yielded similar results, Carnell added, noting that the dataset for the AEBQ is smaller than that for the BEBQ or CEBQ, but follows a similar pattern.

Moving to relationships between appetitive characteristics and diet, Carnell reported that, based on assessment with the CEBQ, higher satiety responsiveness appears to be associated with such outcomes as lower affinity for fruits and vegetables (Fildes et al., 2015), less fruit and vegetable intake as a percentage of overall intake (Carnell et al., 2016), less dietary variety (Vilela et al., 2018), and higher eating frequencies (i.e., perhaps more of a snacking pattern) (Vilela et al., 2019). Preliminary analyses of AEBQ data from Carnell’s recent community survey support similar patterns in adults, but to date are unpublished. According to Carnell, these findings indicate that both appetite and food preferences/habits are important considerations for developing personalized nutrition approaches.

Turning to the influence of genetics on eating behavior, Carnell presented data from a study that assessed satiety responsiveness and food enjoyment in pairs of twins aged 8–11 years. Correlations for both traits were much higher among monozygotic twins (those who share all of their genes) than dizygotic twins (those who share half their genes), with heritability calculations attributing 63 percent of the variation in satiety responsiveness and 75 percent of the variation in enjoyment of food to genetics. Carnell remarked further that traits of emotional overeating and undereating showed more environmental than genetic influence. She reported that data on heritability of appetitive characteristics among infants indicate that slowness in eating and satiety responsiveness are substantially influenced by genetics, as are enjoyment of food and food responsiveness (Llewellyn et al., 2010). According to Carnell, an implication of these findings is that because appetitive behaviors appear to emerge early in life and are genetically determined to some extent, it may be better to work with them than against them as they may be difficult to change (Carnell et al., 2008).

Carnell next discussed binge eating as another example of eating behavior, with some individuals meeting criteria for binge eating disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). According to that definition, binge eating disorder is characterized by recurrent (at least once per week) and persistent (≥3 months) binge eating episodes in the absence of compensatory behaviors, accompanied by marked distress (Hudson et al., 2007). According to Carnell, its lifetime prevalence in the United States is 2.8 percent. She explained that during an episode of binge eating, a large amount of food is consumed

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

in a short period of time, a behavior often triggered by stress and anxiety and occurring in the evening when an individual is alone. Some individuals may engage in recurrent binge eating episodes without meeting the criteria for binge eating disorder (a behavior termed subthreshold binge eating disorder), she pointed out, and still others may sometimes report subjective experiences of loss of control while eating a reportedly objectively large amount of food (termed binge eating). In adolescents, she added, loss-of-control eating may be present, in which individuals report subjective experiences of loss of control while eating irrespective of the reported amount of food consumed—a behavior reported by about one-third of children and adolescents with overweight or obesity (Tanofsky-Kraff et al., 2020). Carnell noted that the literature describes this type of eating behavior using a variety of terms (e.g., reward-based eating, hedonic eating, food addiction, disinhibited eating) that differ slightly, yet seem to point to an underlying latent trait of uncontrolled eating that is exhibited to varying degrees. Uncontrolled eating, she observed, is related to more general behavioral traits such as reward sensitivity, cognitive control, and negative affect, which she pointed out may all contribute to the expression of uncontrolled eating in an individual and lead to overeating and obesity.

Carnell next discussed the relationships between appetitive characteristics and individual responses to the food environment and to interventions designed to change eating behaviors. Acknowledging that few studies have examined these relationships, she cited one that examined children’s responses to variable portion sizes—100, 150, 200, and 250 percent of the recommended amount of food for a meal—and found that children with the highest satiety responsiveness were relatively unaffected by the portion size condition, whereas those with the lowest satiety responsiveness were most likely to increase food intake in response to increasing portion sizes (Mooreville et al., 2015). She also referenced a study of behavioral obesity treatment in which children participated in a family-based weight loss intervention, and children’s appetites were found to be associated with their outcomes: children with high satiety responsiveness had positive outcomes (i.e., decreases from baseline in body mass index z-score), even by the 18-month follow-up visit, whereas those with high food responsiveness or high emotional eating had begun regaining weight by the 6-month follow-up visit, suggesting that such children may have more challenges maintaining dietary changes (Boutelle et al., 2019). Finally, Carnell cited an intensive lifestyle intervention targeting weight loss in adults with diabetes, in which the group that displayed consistent binge eating behaviors had lost the least weight at the 4-year mark (Chao et al., 2017). According to Carnell, a key point from these studies is that when designing personalized nutrition plans, it is important to account for eating behaviors that could affect how individuals respond to certain recommendations.

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

Carnell then commented on the potential influence on eating behaviors of two factors associated with socioeconomic status—food insecurity and stress—and suggested that delay discounting may be involved. Delay discounting is the degree to which an individual is inclined to choose a reward (food or nonfood) that is smaller but delivered sooner as opposed to one that is larger but delivered later. In a study of food-related delay discounting, Carnell reported, women who were food insecure versus those who were food secure were more likely to choose smaller food rewards delivered sooner, suggesting that food insecurity may affect habitual food decisions (Rodriguez et al., 2021). Carnell cited another study, conducted at the beginning of the COVID-19 pandemic, in which psychosocial stress was found to influence the reinforcing value of food—that is, the motivation to obtain or work to obtain food. Motivation to obtain sweet snacks, fruit, and fast foods was greater than motivation to obtain savory snacks and vegetables, she observed, and higher COVID-19-related stress was associated with greater food motivation across all food categories (Smith et al., 2021). Thus, she suggested, psychosocial factors may be another important consideration in the context of personalized nutrition interventions.

Carnell closed by illustrating how eating behaviors could guide personalized nutrition approaches. In terms of eating plans and food environments, for example, someone with low satiety responsiveness might be more successful with portion-controlled meals than with buffet- or family-style meals, and foods with higher satiety value could be emphasized. In contrast, an individual with high satiety responsiveness might tend toward frequent snacking, so guidance for appropriately portioned, nutrient-dense snacks could be useful. If food responsiveness is high, Carnell continued, controlling the dietary environment is important, and those with binge or stress eating patterns might benefit more from maintaining a healthy home food environment to reduce temptation and stressors and to find alternative coping strategies. She suggested that such individuals might also seek treatment involving training in appetite awareness and cue exposure responsiveness (Boutelle et al., 2020).

INTEGRATION OF MULTIPLE OMICS

Michael Snyder, Stanford University, discussed the use of big data to support individualized profiling as a strategy for better managing health. He began by observing that it is increasingly becoming possible to quantify the factors that influence an individual’s health, such as one’s genome and lifestyle exposures, including stress, diet, physical activity, and environmental pathogens. Importantly, he stressed, the effects of these factors can also be quantified with detailed molecular and physiological measurements of an individual.

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

Snyder described his group’s longitudinal research on personal omics profiling, which involves using in-depth measures to gather information about a person’s 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 it means to be healthy and describing what a healthy profile looks like, how it changes over time and during phases of illness, and how it differs among individuals. The researchers have followed more than 100 people for about 8 years, he added, taking samples approximately every 3 months while participants are healthy, with additional samples being taken if they become sick or encounter other health challenges. Another objective is to determine whether advanced technologies such as genome sequencing and other deep biological profiling can make a measurable difference in better managing people’s health (Schüssler-Fiorenza Rose et al., 2019).

Snyder recounted nearly 50 occurrences of what he called “major health discoveries” during the first 3.5 years of profiling of study participants, including detection of cardiovascular, metabolic, hematological, or oncological abnormalities (such as gene mutations) that often precede disease. Early warning signs were detected by a variety of methods, including wearables, genome sequencing, imaging, and molecular measurements. Snyder noted that no single technology was common to all of the discoveries, but that in many cases, multiple measurements had indicated the anomalies. Importantly, he stressed, every health discovery was made before the affected individual had shown symptoms of the problem, enabling several participants to act early to treat underlying disease.

Snyder’s group is also monitoring participants’ transitions in status with respect to diabetes, and has observed an increase in the prevalence of diagnosed diabetes and prediabetes among participants over the course of the study. People followed different paths to developing diabetes, Snyder reported, such as weight gain, elevation of fasting blood glucose, or other triggers. This finding has led his group to believe that diabetes (particularly type 2) is a heterogeneous disease and that better understanding of its origins in a given person could lead to better management.

Another lesson learned from the study, Snyder continued, is that people age differently, as revealed by longitudinal molecular measures of various indicators of aging. He listed four general classes of aging molecules—relating to kidney, liver, metabolic, or immune functions and pathways—and suggested that people can be grouped into at least four “ageotypes” based on their rates of change in these pathways. People may age primarily in one category, he explained, or in two, three, or all four categories.

Snyder mentioned a company called Qbio that is scaling up the study’s concept of multiple omics profiling, emphasizing the use of longitudinal

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

measurements to detect changes over time and enhance understanding of an individual’s health trajectory. Snyder’s laboratory is using wearable sensors to collect a large amount of data each day on such biological indicators as heart rate, heart rate variability, respiration, skin temperature, blood oxygen, and blood pressure. Such physiological measures, he explained, can signal the onset of illness in an individual, and his research group was able to develop an algorithm for predicting illness based on changes in resting heart rate data.

Snyder then turned to the considerable potential of wearable devices to aid in rapid detection of illness, such as COVID-19. He gave as an example a smartwatch that can measure physiological events in real time and detect changes presymptomatically, citing it as a tool of interest in a study his group is conducting to examine how changes in heart rate can predict the development of COVID-19. In the latest version of the study, run over the past year, the group used smartwatch and smartphone alerts to notify people of changes in their resting heart rate, which are indicative of stress events, including respiratory viral infections. Snyder reported that the ongoing study successfully identified 80 percent of COVID-positive participants at or prior to symptom onset, and also detected asymptomatic cases. Elevations in heart rate prior to illness can be fairly subtle (e.g., the median difference being an extra 7 beats per minute) but can be detected with continuous measurements.

Snyder went on to point out that smartwatches can collect other clinical biomarkers, such as hemoglobin levels, red blood cell counts, and fasting glucose, which may be no substitute for diagnosis-grade measurements and consultation with a health care provider, but still can provide clues that something may be awry. He referred to a personal health dashboard that can integrate an individual’s wearable data and omics and microbiome data on a smartphone interface as they are collected over time, allowing the user to monitor these health measurements as often as desired.

Snyder ended his presentation by describing the example of a study that examined the effects of feeding with different types of dietary fibers. Among the 18 participants, he reported, a drop in low-density lipoprotein (LDL) cholesterol was observed after consumption of both arabinoxylan alone and a combination of arabinoxylan and inulin, but not after consumption of inulin alone. The literature is mixed regarding the effects of each of these fibers on cholesterol, he noted, but is more consistent in explaining the mechanism by which fiber is believed to lower cholesterol (i.e., by binding to it so that it is excreted along with the nondigestible fiber). But in this study, an increase in secondary bile acids was observed with consumption of arabinoxylan, which led researchers to develop a new theory for how that type of fiber lowers cholesterol. Of interest, Snyder observed, in one of the study participants, inulin but not arabinoxylan lowered LDL cholesterol, a finding he said highlights the importance of understanding people at the individual level to better manage their health.

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

PANEL DISCUSSION

Measures of Contributors to Interindividual Variation

An audience member asked whether it is better to frame interindividual variability as a reflection of the sensitivity of specific biological pathways to bidirectional, complex links to genomic, social, psychological, dietary, and other components instead of framing it in terms of separate biological, psychological, and social domains. According to Snyder, studying various components simultaneously is important because effects are not necessarily linear and can be synergistic. Modeling algorithms have been developed to support multiple inputs in studying nonlinear systems, he pointed out, and results suggest that the effects of individual inputs may differ when presented alone versus in combination with other inputs. According to Mathers, one of the challenges is to measure the variety of nonbiological factors that influence health, for which he suggested that fewer validated, scalable, inexpensive assessment tools exist compared with tools for measuring biological factors.

Interindividual Variation in Effects of Foods on Gut Microbial Species

Johnson proposed that variability among individuals in the effects of specific foods on gut microbial species could be attributed to the failure of dietary intake assessment tools to collect complete information about how a consumed food(s) was prepared. To illustrate this point, she noted that herbs and spices that have been added to a food could influence its effect on gut microbes. She also pointed out that even after controlling for differences in food preparation, variability among individuals in a food’s effect on gut microbial species can be attributed to gut microbes cross-feeding and interacting with each other and across kingdoms (i.e., fungal and bacterial cross-feeding networks), rather than acting alone to break down specific foods. Mathers suggested that from the perspective of viewing the gut microbiome as a complex ecosystem where different inhabitants play different roles, it is a “major task” to understand the influences of individual food components. Snyder remarked that more immune cells are present in the gut than anywhere else in the body, indicating that dietary intake is an important influence on immune function.

Communicating Differences in Nutrient Needs by Genetic Population

Asked whether nutrition labels should be changed to reflect variation in nutrient needs, Hilliard emphasized the value of stratifying dietary guidance according to genetic populations, but acknowledged that she was uncertain of the best approach for doing so. She suggested that because of

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

the small number of cases in which one ancestral population has a major genomic difference of consequence for a specific dietary variable, messaging and education might be a better approach than trying to provide multiple different percent daily intake values on a food label, for example, corresponding to different genetic populations. She reiterated that attempts to aggregate data from multiethnic population groups may be framed as diversifying, but contended that diversifying is inferior to stratifying if the goal is to obtain accurate data. Hilliard acknowledged that the diversity of genetic populations represented in the United States makes providing dietary guidance more difficult compared with countries consisting of one or two genetic populations.

Affordability of Individual Profiling Technology

Snyder observed that collecting multiple omics from an individual is expensive—several thousand dollars for the Qbio version—but maintained that its utility for preventing major health outcomes, such as heart attack, is extremely cost-saving, particularly for at-risk groups. He believes that in the future, inexpensive home tests will be available for simple biochemical measurements such as cholesterol and glucose, and reiterated that wearable devices costing as little as $60 each could be distributed at a global level to collect useful health information.

Drivers of Eating Behaviors

Asked whether genes or environments drive eating behaviors, Carnell replied that both are important. Evidence suggests, she said, that food preferences and appetite have genetic components, and also indicates that overconsumption of highly energy-dense foods may down-regulate brain dopamine receptors and cause individuals to be less responsive to those foods, which in turn may actually drive them to seek those foods. Carnell also pointed out that everyone is genetically predisposed to seeking energy-dense foods as a survival mechanism. At the same time, she continued, the availability of such foods also drives their consumption, and food preferences appear to be more influenced by environments than by genetics. Furthermore, she observed, the foods available in a given environment could lead to greater or lesser expression of an individual’s genetic propensity for seeking highly energy-dense foods.

Risks of Using Genomic Markers for Early Detection of Disease

Snyder recounted his experience with sequencing genomes of healthy people and making health predictions, noting that he has been cautioned

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

that this activity would lead to millions of dollars in follow-up tests and excessive overdiagnosis (i.e., disease that would not progress to pathology). The actual outcome was not that extreme (rather, a median cost of $700 per person), he recalled, and he contended that follow-up testing and costs are worthwhile when they have the potential to uncover indicators of disease. In terms of the potential for overdiagnosis, Snyder argued that an appropriate perspective on genomic markers of disease is to view them as a screening tool for potential risk factors. He also noted that the individuals involved in his study of multiple omics were enthusiastic participants who indicated that they had derived benefit and not experienced increased stress or anxiety from their participation and knowledge of their omics data.

Scalability of Metabolomics to Examine Disease Pathology

Metallo reminded participants that the MacTel project was made possible by a wealthy donor who was highly motivated to investigate the disease in new ways, but he also pointed out that studying the molecular mechanisms of this single disease led to understanding of different mechanisms that might impact the development of diabetes. He suggested that these kinds of incidental findings may be the most likely way of learning about more diseases via metabolomics because of the challenges of applying a MacTel approach to all major diseases.

Use of Microbiome Measures for Dietary Intake Assessment

Johnson affirmed that dietary intake assessment entails many challenges and predicted that future advances may make it possible to use microbiome data for insight into dietary intake. She referenced other colleagues’ efforts to collect data on microbiome responses to single foods and to examine microbial DNA metabarcodes3 from people on plant- versus animal-based diets to assess the diversity of foods consumed. Mathers suggested that metabolomic data on blood, urine, or saliva, which he said are directly related to individual foods, may be a more readily available tool for dietary assessment.

Effect of Metabolomic Differences on Interindividual Variability

Metallo pointed to the considerable interindividual variation for any given metabolite, which is why his group focused on a specific pathway and tried to examine the dynamics of how metabolite levels fluctuate.

___________________

3 Microbial DNA metabarcoding is a method of species identification that uses genetic markers to identify the DNA of a mixture of organisms (Taberlet et al., 2012).

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

According to Snyder, human data to inform the relationships among food, the microbiome, and metabolites are relatively scarce. Mathers noted that a critical component linking the genome to the metabolome is the Phase 2 enzyme system, which is involved in converting metabolites into urine solutes, the forms of which depend on genetic variation. Therefore, he suggested, understanding of genetic variation will enhance understanding of metabolomics variation. Snyder added that the epigenome is another critical component, referencing the effect of food intake on epigenetic expression, such as DNA methylation.

Biological Mechanisms of Ageotypes

Asked what biological mechanisms can explain the existence of a variety of ageotypes, Snyder responded that the ability to measure different ageotypes is the first step. The second, he continued, is to identify clinical markers associated with different ageotypes, such as hemoglobin A1c for metabolic aging and creatinine for kidney aging, and to learn how such exposures as dietary intake and statin use could affect levels of those markers. According to Hilliard, her research suggests that people digest staple foods from their ancestral diets most efficiently, a finding she believes may have implications for digestive aging when immigrants are brought into new locales where those staple foods are unavailable or less available.

Measuring Personalized Responses in Clinical Trials

Given the limitations of measuring mean response to pharmaceuticals, an audience member asked when the current paradigm of clinical trial design might change to enable personalized responses to be measurable and meaningful for conclusions about efficacy. The landscape is already changing in precision medicine, observed Mathers, where the individual appropriateness of pharmaceuticals is considered. The difficulty, he suggested, is that the current paradigm of clinical trial design has proven effective and is so entrenched in the research community that it is difficult to say when researchers might be willing to change it to address new needs. He predicted that in 10 years, measurement of personalized responses will be more common. Snyder agreed and urged more attention to the concept of distinguishing responders and nonresponders when designing endpoints for clinical trials instead of focusing solely on the average within the study population. Even drugs that have been removed from the market can be highly beneficial for some people, he contended, despite being removed because they were harmful for others. Johnson suggested that clinical trial designs could be enhanced by increasing the density of sampling and the number of time points for data collection. She also cited as a limitation of

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

distinguishing responders from nonresponders the need to define in advance what constitutes a response, as opposed to normal variation that may not be meaningful. In the context of obesity intervention, Carnell pointed to increasing attention to the need to integrate a consistent battery of psychological measures across studies so that individual differences in treatment response can be more fully understood.

Broadening the Accessibility of Precision Nutrition

In response to an audience member’s question about recruiting volunteers or attracting customers whose data can help in developing the evidence base for precision nutrition, Johnson observed that precision nutrition technologies are currently being used by the “worried well,” and raised the question of how to design approaches that are more widely accessible for people with fewer resources. Hilliard agreed and suggested that wider participation could help identify risk patterns involved in health outcomes for which racial and ethnic disparities exist. Snyder called for government funding, acknowledging that the private sector’s efforts are financed by affluent participants. Mathers added that inequity affects whole societies and that as a result, it is important for societies to invest in access to personalized or precision nutrition instead of expecting individuals to self-pay.

Final Thoughts

Davis invited the six speakers to offer final thoughts before concluding the panel discussion. Snyder reiterated the potential value of using multiple omics measurements to inform personalized and precision nutrition approaches to optimizing an individual’s health. Hilliard stressed that individuals are members of different genetic populations with variants that have the potential to influence health outcomes. Johnson appealed for making careful, deliberate progress in advancing the field of precision nutrition, instead of rushing to conclusions based on early results. Metallo agreed, and also expressed his excitement about advances that have allowed researchers to examine the molecular origins of disease. Mathers urged stakeholders to ask continually how advances in precision and personalized nutrition will address inequities and improve public health. Finally, Carnell appealed for evaluation of people’s psychological and behavioral responses to personal nutrition interventions, with attention to assessment of potential unintended consequences.

Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×

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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
×
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Suggested Citation:"2 The Current Evidence Base and Limitations." National Academies of Sciences, Engineering, and Medicine. 2022. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/26299.
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The Food Forum of the National Academies of Sciences, Engineering, and Medicine convened a virtual workshop, Challenges and Opportunities for Precision and Personalized Nutrition, on August 10-12, 2021. The workshop explored potential challenges and opportunities in the application of precision and personalized nutrition approaches to optimize dietary guidance and improve nutritional status. Workshops presenters discussed current precision and personalized nutrition research methodologies, limitations in data and design, adapting technologies for utilization, and policy and regulatory challenges. This Proceedings of a Workshop summarizes the presentations and discussions of the workshop.

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