Challenges and Opportunities for Precision and Personalized Nutrition Proceedings of a Workshop (2022) / Chapter Skim
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3 Innovative Methodologies and Technologies
3 Innovative Methodologies and Technologies
Pages 27-54
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In 2012, she began, limited scientific evidence was available to indicate the impact of giving personalized nutrition advice beyond that in existing medical nutrition therapy guidelines. The market consisted of a few niche players focused on the "worried well," she observed, who could afford personalized guidance.
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She added that companies are increasingly emphasizing behavior change techniques and adding services to their products, such as connections with registered dietitians, to help people implement personalized advice. She noted that the
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FIGURE 3-1 Breakdown of the personalized nutrition market by segment. SOURCE: Presented by Mariëtte Abrahams on August 11, 2021.
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Additionally, she pointed to the increasing availability of wider applications of some solutions, such as continuous glucose monitors for patients who do not have diabetes or elevated glucose but are interested in tracking their responses to foods. Lastly, she highlighted a demand for the expertise of health care practitioners both in understanding the science and technologies underlying personalized nutrition and in translating the guidance offered into tailored behavior change strategies for individual patients.
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THE GENETIC SCALE Denise Ney, University of Wisconsin–Madison, was the first of three speakers to discuss innovative methodologies and technologies focused on matching nutrition guidance to an individual's genetic makeup. Ney addressed the topic from the angle of population-wide newborn screening for inborn errors of metabolism using whole-exome sequencing (WES)
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, in which the researchers analyzed variants within an exome slice of 78 genes linked with 48 inborn errors of metabolism, ascertained by newborn screening in California using archived dried blood spots from 4.5 million births between 2005 and 2013. Ney emphasized the critical importance of using population-level newborn screening to identify all positive cases of inborn errors of metabolism, many of which are life-threatening, while minimizing false positives to reduce undue stress on families and unnecessary use of medical resources.
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. Although he characterized these results as important, Kaput said they lack the level of detail needed for application to personalized nutrition guidance.
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In conclusion, Kaput reiterated that assessing multiple genetic variants is more useful than assessing single SNPs for personalizing dietary guidance. It is also important to understand an individual's genetic architecture, he argued, and to adjust associations by incorporating other factors that may influence metabolite levels (e.g., age, sex, dietary intake)
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To illustrate this point, he explained that approximately 50 percent of the population are "slow" metabolizers of caffeine, and for them following the population-based recommendations for coffee consumption will increase their risk of heart attack, hypertension, and prediabetes. THE PHYSIOLOGICAL/MICROBIOME SCALE Sarah Berry, King's College London, was the first of three speakers to discuss innovative methodologies and technologies that focus on matching dietary guidance with an individual at the physiological/microbiome scale.
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They also logged all foods and beverages consumed along with their corresponding satiety levels in an app that was developed by the research team and monitored in real time by a team of nutrition experts who communicated with participants to ensure the provision of complete, high-quality dietary intake data. Berry illustrated the scale of the PREDICT 1 study data with a series of statistical results, such as 132,000 meals logged, 750,000 metabolomic measures, more than 2 million continuous glucose monitor readings, and 75 billion metagenomic reads.
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She described an approach to personalized nutrition guidance involving prediction of glycemic responses using clinical and microbiome features. Recounting a study conducted 9 years earlier, Rein explained that researchers continuously tracked glucose levels and multiple other clinical, lifestyle, diet, and microbiome measures among 1,000 healthy individuals.
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38 FIGURE 3-2 Differences in the relative contributions of exposures to the outcomes of blood levels of glucose (left) and triglycerides (right)
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Thirty percent of participants also self-reported reduced use of glucose-lowering medication after 3–4 months of intervention. The results of these three interventions, Rein said in summary, support the use of personalized dietary interventions for improving glycemic control and metabolic health.
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that are not beneficial. Banavar then illustrated Viome's use of glycemic response data plus microbiome pathway analysis to develop personalized dietary guidance, taking spinach as an example.
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THE INDIVIDUAL SCALE Andres Acosta, Mayo Clinic, was the first of three speakers to discuss innovative methodologies and technologies for developing personalized nutrition approaches tailored to an individual in terms of both physiology and behavior. He discussed the use of pathophysiological and behavioral phenotypes for guiding obesity management to enhance weight loss.
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Intake is driven mainly by hunger, satiation, satiety, and emotional eating, he elaborated, whereas expenditure is driven by resting energy expenditure, exercise, and nonexercise activity thermogenesis. The Mayo Clinic phenotypes patients with obesity based on their results on a series of tests measuring domains that influence energy intake and expenditure.
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Acosta stated that, since identifying the four main obesity phenotypes in 2015, the Mayo Clinic has completed multiple proof-of-concept, placebo-controlled trials to demonstrate that phenotypes can help enhance weight loss in patients with obesity. The trials have evaluated response to FIGURE 3-4 Distribution of obesity phenotypes in 450 patients.
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Kleinberg described her team's work on developing passive methods for tracking individual dietary intake, clarifying that perfect accuracy is neither attainable nor the end goal. Even active approaches to tracking dietary intake are associated with error, she said, arguing that passive approaches that were approximately as good as active, human-driven approaches would represent a valuable contribution.
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She added that the level of specificity needed for categorizing foods using these tools depends on the intended use of the data. For example, she said, if the goal is to understand food sensitivities without using an elimination diet, specific foods need to be identified, something that could be achieved by combining self-reported dietary intake with data from wearables.
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. When the sentiment data were combined with the survey's Likert-style question asking respondents to classify their self-perceived weight status, sentiment became more negative as the respondent's self-perceived weight status grew heavier, Thomas reported.
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In addition to these social selection processes, de la Haye cited strong evidence that social networks influence people's health behaviors, including eating behaviors, through such mechanisms of social influence as mimicry and normative influence, as well as by providing social support (Aral and Nicolaides, 2017; Fletcher et al., 2011; Trogdon et al., 2008; Valente et al., 2009; Zhang et al., 2018)
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She emphasized that these social exposures reinforce and influence nutritional health behaviors, and can be barriers to behavior change or targeted in network interventions. de la Haye next described two types of intervention approaches that use social networks to promote health behavior change.
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As an example, he described how when coaches engaged directly with members after noticing that they exhibited unconscious behavioral patterns, the behavior of meal tracking increased to almost the same level as that obtained through automatic nudges, and more weight loss was achieved. Duffy attributed the improvement to the human touchpoint and outreach of care and support.
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PANEL DISCUSSION Promoting Health Equity Responding to a question about how the personalized nutrition industry can avoid targeting a narrow segment of consumers at the cost of equity, Abrahams asserted that companies with values centered on improving the health of all consumers tend to partner with like-minded companies. They also seek inputs from health care professionals, she added, about incorporating equity at the front end of product and service designs.
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He proposed shifting the focus from the cost of the technology to its value in terms of health advantages it can provide. Reimbursement for Personalized Nutrition Solutions According to Kaput, it is necessary to apply machine learning to integrate more data across multiple scales (e.g., genetic, microbiome)
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Involving Social Networks and Communities in Research Designs de la Haye observed that community-based participatory research approaches are increasingly being used for social network and ecologicalfocused interventions. These approaches, she elaborated, draw on local community organizations and families as key networks of people who are open to helping with intervention planning and creating social and community-level change.
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INNOVATIVE METHODOLOGIES AND TECHNOLOGIES 53 includes enrolling a large-enough membership base to serve as the source for recruiting participants for large-scale RCTs. He also stressed that personalized nutrition guidance must be aligned with an individual's ability and willingness to change behavior, and suggested that technology could provide "digital trails" to facilitate behavior change.
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