Challenges and Opportunities for Precision and Personalized Nutrition Proceedings of a Workshop (2022) / Chapter Skim
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2 The Current Evidence Base and Limitations
2 The Current Evidence Base and Limitations
Pages 3-26
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, 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.
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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.
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, 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.
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. 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.
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, 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.
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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.
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. 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.
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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.
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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.
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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.
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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)
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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)
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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.
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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.
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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)
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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 familystyle meals, and foods with higher satiety value could be emphasized.
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. 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)
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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.
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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.
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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.
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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.
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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.
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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.
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