Challenges and Opportunities for Precision and Personalized Nutrition Proceedings of a Workshop—in Brief (2021) / Chapter Skim
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Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop - in Brief
Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop - in Brief
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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.
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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.
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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.
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They are also using data to inform an optimal interface for interaction and the format and frequency of information delivery, which Abrahams said contribute to consumer success with behavior change. Abrahams listed key market trends, including scientific advances driving new solutions, particularly around improved cardiometabolic health and microbiome composition; new entrants to the market, such as the pharmaceutical industry; wider applications of solutions, such as continuous glucose monitors for patients who do not have diabetes or elevated glucose but want to track their responses to foods; and demand for health care practitioner expertise in both understanding the science and technologies underlying personalized nutrition and translating the guidance into tailored individual behavior-change strategies.
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A fourth misconception is that genetic test results are too complex for people to understand, which El-Sohemy agreed is likely true if only raw data are provided but suggested that a health care practitioner translating the results can help provide practical strategies for behavior changes. A fifth misconception is that family history is more informative than individual genetic makeup, but families also share common environments, he pointed out, and without an individual's specific genetic information, it is not possible to know how each offspring has inherited specific genotypes that might affect the response to a dietary intervention.
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Longitudinal social network research with network analytic methods has shown that nutritional health and people's social networks are interdependent, de la Haye explained, because people tend to select and form social ties with those who are similar to themselves. She also described strong evidence that these networks influence people's health behaviors, including eating, through social influence mechanisms, such as mimicry, normative influence, and social support.
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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.
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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)
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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.
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2015. Personalized nutrition by prediction of glycemic responses.
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2021. Challenges and opportunities for precision and personalized nutrition: Proceedings of a workshop -- in brief.
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