Precision nutrition: hype or hope for public health interventions to reduce obesity?

Economic Evaluation of a Personalized Nutrition Plan Based on Omic Sciences Versus a General Nutrition Plan in Adults with Overweight and Obesity: A Modeling Study Based on Trial Data in Denmark

BACKGROUND

Since there is no diet that is perfect for everyone, personalized nutrition approaches are gaining popularity to achieve goals such as the prevention of obesity-related diseases. However, appropriate choices about funding and encouraging personalized nutrition approaches should be based on sufficient evidence of their effectiveness and cost-effectiveness. In this study, we assessed whether a newly developed personalized plan (PP) could be cost-effective relative to a non-personalized plan in Denmark.

METHODS

Results of a 10-week randomized controlled trial were combined with a validated obesity economic model to estimate lifetime cost-effectiveness. In the trial, the intervention group (PP) received personalized home-delivered meals based on metabolic biomarkers and personalized behavioral change messages. In the control group these meals and messages were not personalized. Effects were measured in body mass index (BMI) and quality of life (EQ-5D-5L). Costs [euros (€), 2020] were considered from a societal perspective. Lifetime cost-effectiveness was assessed using a multi-state Markov model. Univariate, probabilistic sensitivity, and scenario analyses were performed.

RESULTS

In the trial, no significant differences were found in the effectiveness of PP compared with control, but wide confidence intervals (CIs) were seen [e.g., BMI (-0.07, 95% CI -0.51, 0.38)]. Lifetime estimates showed that PP increased costs (€520,102 versus €518,366, difference: €1736) and quality-adjusted life years (QALYs) (15.117 versus 15.106, difference: 0.011); the incremental cost-utility ratio (ICUR) was therefore high (€158,798 to gain one QALY). However, a 20% decrease in intervention costs would reduce the ICUR (€23,668 per QALY gained) below an unofficial gross domestic product (GDP)-based willingness-to-pay threshold (€47,817 per QALY gained).

CONCLUSION

On the basis of the willingness-to-pay threshold and the non-significant differences in short-term effectiveness, PP may not be cost-effective. However, scaling up the intervention would reduce the intervention costs. Future studies should be larger and/or longer to reduce uncertainty about short-term effectiveness.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov registry (NCT04590989).

Potential Mechanisms of Precision Nutrition-Based Interventions for Managing Obesity

Precision nutrition (PN) considers multiple individual-level and environmental characteristics or variables to better inform dietary strategies and interventions for optimizing health, including managing obesity and metabolic disorders. Here, we review the evidence on potential mechanisms-including ones to identify individuals most likely to respond-that can be leveraged in the development of PN interventions addressing obesity. We conducted a review of the literature and included laboratory, animal, and human studies evaluating biochemical and genetic data, completed and ongoing clinical trials, and public programs in this review. Our analysis describes the potential mechanisms related to 6 domains including genetic predisposition, circadian rhythms, physical activity and sedentary behavior, metabolomics, the gut microbiome, and behavioral and socioeconomic characteristics, i.e., the factors that can be leveraged to design PN-based interventions to prevent and treat obesity-related outcomes such as weight loss or metabolic health as laid out by the NIH 2030 Strategic Plan for Nutrition Research. For example, single nucleotide polymorphisms can modify responses to certain dietary interventions, and epigenetic modulation of obesity risk via physical activity patterns and macronutrient intake have also been demonstrated. Additionally, we identified limitations including questions of equitable implementation across a limited number of clinical trials. These include the limited ability of current PN interventions to address systemic influences such as supply chains and food distribution, healthcare systems, racial or cultural inequities, and economic disparities, particularly when designing and implementing PN interventions in low- and middle-income communities. PN has the potential to help manage obesity by addressing intra- and inter-individual variation as well as context, as opposed to "one-size fits all" approaches though there is limited clinical trial evidence to date.

Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems (APNASs)

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.

Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management of patients with obesity.

Methods

The perspectives of the authors were augmented by scientific support from published citations and integrated with information derived from search engines (i.e., Chrome by Google, Inc) and chatbots (i.e., Chat Generative Pretrained Transformer or Chat GPT).

Results

Artificial Intelligence (AI) is the technologic acquisition of knowledge and skill by a nonhuman device, that after being initially programmed, has varying degrees of operations autonomous from direct human control, and that performs adaptive output tasks based upon data input learnings. AI has applications regarding medical research, medical practice, and applications relevant to the management of patients with obesity. Chatbots may be useful to obesity medicine clinicians as a source of clinical/scientific information, helpful in writings and publications, as well as beneficial in drafting office or institutional Policies and Procedures and Standard Operating Procedures. AI may facilitate interactive programming related to analyses of body composition imaging, behavior coaching, personal nutritional intervention & physical activity recommendations, predictive modeling to identify patients at risk for obesity-related complications, and aid clinicians in precision medicine. AI can enhance educational programming, such as personalized learning, virtual reality, and intelligent tutoring systems. AI may help augment in-person office operations and telemedicine (e.g., scheduling and remote monitoring of patients). Finally, AI may help identify patterns in datasets related to a medical practice or institution that may be used to assess population health and value-based care delivery (i.e., analytics related to electronic health records).

Conclusions

AI is contributing to both an evolution and revolution in medical care, including the management of patients with obesity. Challenges of Artificial Intelligence include ethical and legal concerns (e.g., privacy and security), accuracy and reliability, and the potential perpetuation of pervasive systemic biases.

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Nutrigenomics: Perceptions of South African Dietitians and General Practitioners

INTRODUCTION

Although investigations into the emerging field of nutrigenomics are relatively limited and more research in this field is required, experts agree that there is potential for it to be incorporated into health care practice. If health care professionals can promote healthy dietary behavior based on nutrigenomic testing, it can assist in addressing the health consequences of poor diet and lightning the strain on the South African health care system.

METHODS

Registered dietitians (RDs) and general practitioners (GPs) registered with the Health Professions Council of South Africa (HPCSA) who obtained their qualification in South Africa (SA) were eligible to participate in this cross-sectional study. Participants were identified using convenience and snowball sampling. A self-administered electronic survey using EvaSys Software® was completed by those that agreed to participate.

RESULTS

Nearly all RDs (97.3%), but less than a third of GPs (30.4%), had heard of the term nutrigenomics. Approximately three-quarters of RDs (74.7%) and GPs (73.9%) had or would personally consider undergoing genetic testing. More than 40% (43.5%) of RDs ranked direct-to-consumer genetic testing companies as the most equipped, while 31.8% of GPs ranked RDs as the most equipped to provide patients with nutrigenomic services. Both RDs and GPs ranked similar reasons as "strongly agree" for why consumers were motivated to make use of nutrigenomic services, which included "motivated by a desire to prevent or manage disease" (56.7%), "prevent a disease based on family history" (65.9%), "control health outcomes based on family history" (54.9%), and "improve overall health-related quality of life" (48.6%). Cost concerns were reported as the greatest barrier to implementing nutrigenomic services (75.7%). Other barriers included confidentiality issues (47.8%) and moral concerns (37.3%). Greater individualization of diet prescription (66.5%), stronger foundations for nutrition recommendations (62.4%), and dietary prescriptions that would manage or prevent certain diseases more effectively (59.0%) were all perceived as benefits of including nutrigenomics in practice.

CONCLUSION

This study identified perceived consumer motivators and barriers that might affect the willingness to seek nutrigenomic services in SA. In addition, the need for more nutrigenomic training opportunities, including the planning of personalized diets based on genetic testing results and interpretation of results, was confirmed. However, both RDs and GPs felt that the emerging field of nutrigenomics needs further development before it can be applied effectively in routine private and public health care in SA.

Individual differences in the effects of the ACTION-PAC intervention: an application of personalized medicine in the prevention and treatment of obesity

There is an increased interest in the use of personalized medicine approaches in the prevention or treatment of obesity, however, few studies have used these approaches to identify individual differences in treatment effects. The current study demonstrates the use of the predicted individual treatment effects framework to test for individual differences in the effects of the ACTION-PAC intervention, which targeted the treatment and prevention of obesity in a high school setting. We show how methods for personalized medicine can be used to test for significant individual differences in responses to an intervention and we discuss the potential and limitations of these methods. In our example, 25% of students in the preventive intervention, were predicted to have their BMI z-score reduced by 0.39 or greater, while at other end of the spectrum, 25% were predicted to have their BMI z-score increased by 0.09 or more. In this paper, we demonstrate and discuss the process of using methods for personalized medicine with interventions targeting adiposity and discuss the lessons learned from this application. Ultimately, these methods have the potential to be useful for clinicians and clients in choosing between treatment options, however they are limited in their ability to help researchers understand the mechanisms underlying these predictions.

Perspective: Novel Approaches to Evaluate Dietary Quality: Combining Methods to Enhance Measurement for Dietary Surveillance and Interventions

Refining existing dietary assessment methods to reduce measurement error and facilitate the routine evaluation of dietary quality is essential to inform health policy. Notable advancements in technology in the past decade have enhanced the precision and transformation of dietary assessment methods with applications toward both population health and precision nutrition. Within population health, innovative applications of big data including use of automatically collected food purchasing data, quantitative measurement of food environments, and novel, yet simplified dietary quality metrics provide important complementary data to traditional self-report methods. Precision nutrition is similarly advancing with greater use of validated biomarkers for assessing dietary patterns and understanding individual variability in metabolism. Concurrently enhancing our understanding of diet-disease relations at the population health and precision nutrition levels provides tremendous potential to generate evidence needed to advance public health nutrition policy. This commentary highlights the importance of these advances toward progressing the field of dietary assessment and discusses the application of food purchasing data, data analytics, alternative dietary quality metrics, and -omics technology in population and clinical medicine.

Metabotyping for Precision Nutrition and Weight Management: Hype or Hope?

PURPOSE OF REVIEW

Precision nutrition requires a solid understanding of the factors that determine individual responses to dietary treatment. We review the current state of knowledge in identifying human metabotypes - based on circulating biomarkers - that can predict weight loss or other relevant physiological outcomes in response to diet treatment.

RECENT FINDINGS

Not many studies have been conducted in this area and the ones identified here are heterogeneous in design and methodology, and therefore difficult to synthesize and draw conclusions. The basis of the creation of metabotypes varies widely, from using thresholds for a single metabolite to using complex algorithms to generate multi-component constructs that include metabolite and genetic information. Furthermore, available studies are a mix of hypothesis-driven and hypothesis-generating studies, and most of them lack experimental testing in human trials. Although this field of research is still in its infancy, precision-based dietary intervention strategies focusing on the metabotype group level hold promise for designing more effective dietary treatments for obesity.

What is the promise of personalised nutrition?

Personalised nutrition (PN) is an emerging field that bears great promise. Several definitions of PN have been proposed and different modelling approaches have been used to claim PN effects. We tentatively propose to group these approaches into two categories, which we term outcome-based and population reference approaches, respectively. Understanding the fundamental differences between these two types of modelling approaches may allow a more realistic appreciation of what to expect from PN interventions presently and may be helpful for designing and planning future studies investigating PN interventions.

Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking

OBJECTIVES

Using a nationally-representative, cross-sectional cohort, we examined nutritional markers of undiagnosed type 2 diabetes in adults via machine learning.

METHODS

A total of 16429 men and non-pregnant women ≥ 20 years of age were analysed from five consecutive cycles of the National Health and Nutrition Examination Survey. Cohorts from years 2013-2016 (n = 6673) was used for external validation. Undiagnosed type 2 diabetes was determined by a negative response to the question "Have you ever been told by a doctor that you have diabetes?" and a positive glycaemic response to one or more of the three diagnostic tests (HbA1c > 6.4% or FPG >125 mg/dl or 2-hr post-OGTT glucose > 200mg/dl). Following comprehensive literature search, 114 potential nutritional markers were modelled with 13 behavioural and 12 socio-economic variables. We tested three machine learning algorithms on original and resampled training datasets built using three resampling methods. From this, the derived 12 predictive models were validated on internal- and external validation cohorts. Magnitudes of associations were gauged through odds ratios in logistic models and variable importance in others. Models were benchmarked against the ADA diabetes risk test.

RESULTS

The prevalence of undiagnosed type 2 diabetes was 5.26%. Four best-performing models (AUROC range: 74.9%-75.7%) classified 39 markers of undiagnosed type 2 diabetes; 28 via one or more of the three best-performing non-linear/ensemble models and 11 uniquely by the logistic model. They comprised 14 nutrient-based, 12 anthropometry-based, 9 socio-behavioural, and 4 diet-associated markers. AUROC of all models were on a par with ADA diabetes risk test on both internal and external validation cohorts (p>0.05).

CONCLUSIONS

Models performed comparably to the chosen benchmark. Novel behavioural markers such as the number of meals not prepared from home were revealed. This approach may be useful in nutritional epidemiology to unravel new associations with type 2 diabetes.

Perspective: Application of N-of-1 Methods in Personalized Nutrition Research

Personalized and precision nutrition aim to examine and improve health on an individual level, and this requires reconsideration of traditional dietary interventions or behavioral study designs. The limited frequency of measurements in group-level human nutrition trials cannot be used to infer individual responses to interventions, while in behavioral studies, retrospective data collection does not provide an accurate measure of how everyday behaviors affect individual health. This review introduces the concept of N-of-1 study designs, which involve the repeated measurement of a health outcome or behavior on an individual level. Observational designs can be used to monitor a participant's usual health or behavior in a naturalistic setting, with repeated measurements conducted in real time using an Ecological Momentary Assessment. Interventional designs can introduce a dietary or behavioral intervention with predictors and outcomes of interest measured repeatedly either during or after 1 or more intervention and control periods. Due to their flexibility, N-of-1 designs can be applied to both short-term physiological studies and longer-term studies of eating behaviors. As a growing number of disease markers can be measured outside of the clinic, with self-reported data delivered via electronic devices, it is now easier than ever to generate large amounts of data on an individual level. Statistical techniques can be utilized to analyze changes in an individual or to aggregate data from sets of N-of-1 trials, enabling hypotheses to be tested on a small number of heterogeneous individuals. Although their designs necessitate extra methodological and statistical considerations, N-of-1 studies could be used to investigate complex research questions and to study underrepresented groups. This may help to reveal novel associations between participant characteristics and health outcomes, with repeated measures providing power and precision to accurately determine an individual's health status.

Scientific challenges for precision public health

The notion of ‘precision’ public health has been the subject of much debate, with recent articles coming to its defence following the publication of several papers questioning its value.

Critics of precision public health raise the following problems and questionable assumptions: the inherent limits of prediction for individuals; the limits of approaches to prevention that rely on individual agency, in particular the potential for these approaches to widen inequalities; the undue emphasis on the supposed new information contained in individuals’ molecules and their ‘big data’ at the expense of their own preferences for a particular intervention strategy and the diversion of resources and attention from the social determinants of health.

In order to refocus some of these criticisms of precision public health as scientific questions, this article outlines some of the challenges when defining risk for individuals; the limitations of current theory and study design for precision public health; and the potential for unintended harms.

Perspective: Guiding Principles for the Implementation of Personalized Nutrition Approaches That Benefit Health and Function

Personalized nutrition (PN) approaches have been shown to help drive behavior change and positively influence health outcomes. This has led to an increase in the development of commercially available PN programs, which utilize various forms of individual-level information to provide services and products for consumers. The lack of a well-accepted definition of PN or an established set of guiding principles for the implementation of PN creates barriers for establishing credibility and efficacy. To address these points, the North American Branch of the International Life Sciences Institute convened a multidisciplinary panel. In this article, a definition for PN is proposed: "Personalized nutrition uses individual-specific information, founded in evidence-based science, to promote dietary behavior change that may result in measurable health benefits." In addition, 10 guiding principles for PN approaches are proposed: 1) define potential users and beneficiaries; 2) use validated diagnostic methods and measures; 3) maintain data quality and relevance; 4) derive data-driven recommendations from validated models and algorithms; 5) design PN studies around validated individual health or function needs and outcomes; 6) provide rigorous scientific evidence for an effect on health or function; 7) deliver user-friendly tools; 8) for healthy individuals, align with population-based recommendations; 9) communicate transparently about potential effects; and 10) protect individual data privacy and act responsibly. These principles are intended to establish a basis for responsible approaches to the evidence-based research and practice of PN and serve as an invitation for further public dialog. Several challenges were identified for PN to continue gaining acceptance, including defining the health-disease continuum, identification of biomarkers, changing regulatory landscapes, accessibility, and measuring success. Although PN approaches hold promise for public health in the future, further research is needed on the accuracy of dietary intake measurement, utilization and standardization of systems approaches, and application and communication of evidence.