TABLE 3.

Research needs and future directions¹

Study design needs

Well-controlled intervention studies are needed to address individual differences in response to dietary exposures, food bioactives, and dietary patterns, including timing, duration, and dose-response. Studies specifically designed to target high-risk populations for prevalent health conditions (obesity, CVD, T2D, cancer, cognitive decline, and AD) are needed. Moreover, it is essential to determine the applicability of the findings to real-world settings.

Develop and test novel intervention strategies to help people change their dietary intake patterns over the long term and test if changing one's diet significantly affects disease risk and outcome. Considerable efforts are needed to help people 1) know what their dietary pattern is, 2) effectively modify their dietary pattern and be more “adherent” to a healthier diet, and 3) maintain these dietary and behavioral changes over time, including over a lifetime.

Studies that account for factors that cross all the different relevant scales (e.g., genetics, physiology, behavior, social networks, environment, economics).

Studies that utilize systems approaches and methods (e.g., maps and models) that can help better elucidate and bring together different components, factors, and mechanisms.

Hybrid approaches that combine different types of study design approaches (e.g., integrating systems models with intervention studies) to work synergistically.

Technologies and methods

Develop and validate accurate and precise objective measures of dietary intake, including real-time monitoring of food intake, postprandial response, and noninvasive biological responses.

Develop tools and methods to standardize, harmonize and improve interoperability of nutrition and food data.

Develop robust methods to integrate data from the genome, epigenome, microbiome, metabolome, and the exposome (i.e., single or multi-nutrient diet components, dose and timing of dietary modulations, and health behaviors (e.g., physical activity and sleep) into the precision nutrition framework.

Develop standardized and harmonized study procedures and data collection to control for and/or at least assess factors that can influence precision nutrition outcomes, including sleep and circadian biology (274).

Identify biomarkers for diet-related cancers and CVD is prioritized to more quickly elucidate the underlying basis of interindividual variability in diet and disease risks.

New methods and technologies to extract insights from existing data and sources (e.g., natural language-processing techniques to mine text for information).

Develop methods to extract and collapse larger data sources, including Big Data sources, into more refined datasets in ways that do not introduce bias.

Develop methods and tools to fill in missing data in ways that do not introduce bias.

Develop new AI/ML algorithms that can draw insights from datasets in ways that do not introduce bias.

Develop systems modeling methods that can better represent the actual mechanisms that affect and are affected by nutrition.

Develop mathematical and computational methods that help cross different scales (e.g., genetics, physiology, behavior, social networks, environment, economics).

Knowledge gaps

Develop more in-depth and precise knowledge of foods, food composition and groups and eating patterns, and related biomarkers.

Identify individual nutrigenomic/behavioral/lifestyle differences in chronic diet-related disease (e.g., CVD, neurodegenerative disease, cancer, T2D) and risk factors in order to personalize approaches for primary and/or secondary prevention of disease over the life course.

Collect data using objective measures of dietary intake episodically and prospectively over longer periods of time to learn if dietary patterns among the same individuals are reliable/repeatable and to what extent changes in dietary patterns affect disease risk and outcome.

Determine the predictive role of metabolomics and microbiome data in precision nutrition and chronic disease interrelations.

Determine the contribution and mechanisms of sleep and circadian effects in precision nutrition research and interventions based on chronobiological insights.

Quantify the effect of food policy, the food environment, socioeconomic and other personal factors, and industry on peoples’ dietary intake. Identify ways to change policy, the food environment, and industry to improve people's diets and presumably their health.

Better understand how complex systems are involved, affect, and are affected by nutrition.

Needs related to training in precision nutrition

Fill gaps in the implementation and dissemination of scientific research for evidence-based precision nutrition strategies and medical nutrition to reduce chronic diseases.

Develop a diverse workforce that has training in AI/nutrition science.

Develop a new generation of truly interdisciplinary researchers able to cross different content areas of nutrition and different new methodological areas such as mathematical and computer modeling and other types of AI.

Train more people well versed in systems, mathematical, and computational methods.

Train people to better recognize and address bias.

Train people to be better versed in social determinants.

¹AD, Alzheimer disease; AI, artificial intelligence; CVD, cardiovascular disease; ML, machine learning; T2D, type 2 diabetes.