In a recent webinar hosted by the European Nutrition Leadership Platform (ENLP), Dr. Nicola Guess, an expert in the dietary management of type 2 diabetes, conducted a comprehensive analysis of personalized nutrition interventions. Dr. Guess emphasized the three main avenues through which personalized nutrition can be achieved: preferences, observable health traits (phenotype), and genomic, microbiomic, and metabolomic data (omics).
According to Dr. Guess, there is ample evidence to support the effectiveness of tailoring diets to an individual’s lifestyle and preferences. Adherence to personalized diets has been shown to improve overall health outcomes. Additionally, she highlighted the findings of the DASH study, which demonstrated the efficacy of personalizing diets based on phenotype to reduce blood pressure.
In today’s digital age, Dr. Guess believes that nutrition apps and artificial intelligence (AI) can play a significant role in helping individuals adhere to nutrition plans and make healthier choices. However, she expressed reservations about the value of gathering omics data to provide diet advice. Despite significant investments in research, numerous commercial products already claim to offer personalized diets based on omics data. Dr. Guess questioned whether these diets truly provide personalized recommendations and whether they deliver better outcomes compared to standard healthy diets.
To shed light on the less-discussed aspects of personalized nutrition studies, Dr. Guess reviewed several studies and raised thought-provoking points. She underscored the fact that algorithms used by CGI-based personalized nutrition services often lead to low-carb diets with higher protein and fat intake. However, she argued that these diets are not genuinely personalized, as certain diet characteristics, such as low-carb and high-protein, have universally recognized benefits.
Furthermore, Dr. Guess cautioned against the allure of statistically significant correlations derived from omics-driven services. She emphasized the need to differentiate between statistical significance and clinical significance, as correlations alone do not demonstrate causality. Moreover, she highlighted the considerable intra-person variation in glucose responses to meals, which can be influenced by previous meals, exercise, and other factors. Consequently, she cast doubt on the efficacy of using continuous glucose monitors (CGMs) to determine which foods should be avoided.
Dr. Guess also discussed the issue of control groups in personalized nutrition studies. She pointed out disparities in the level of support and guidance provided to the control group compared to the personalized group. To ensure a fair comparison, she suggested that the control group should receive equal levels of support and access to an app that promotes healthy choices, albeit without the personalized diet recommendations.
Beyond methodological concerns, Dr. Guess expressed concern about the potential risks associated with personalized nutrition. She emphasized the widespread issue of overconsumption of ultra-processed foods and cautioned against using personalized metabolism as a scapegoat for poor eating habits. Rather, she stressed the need for broader initiatives to address the underlying problem.
In response to an audience question about the potential negative effects of CGMs on the general population’s glucose control, Dr. Guess argued that focusing on glucose control is unnecessary for individuals without prediabetic or diabetic conditions. Instead, she highlighted the greater prevalence of issues like high cholesterol and blood pressure.
As the field of personalized nutrition continues to evolve, Dr. Guess’s critical analysis encourages further scrutiny regarding the evidence base, methodologies, and marketing claims associated with these interventions. The ultimate aim should be to promote broader, evidence-based strategies that prioritize overall health and well-being.
