Abstract
Most sports nutrition guidelines are based on group average responses and professional opinion. Precision nutrition for athletes aims to improve the individualization of nutrition practices to optimize long-term performance and health. This is a 2-step process that first involves the acquisition of individual-specific, science-based information using a variety of sources including lifestyle and medical histories, dietary assessment, physiological assessments from the performance lab and wearable sensors, and multiomics data from blood, urine, saliva, and stool samples. The second step consists of the delivery of science-based nutrition advice, behavior change support, and the monitoring of health and performance efficacy and benefits relative to cost. Individuals vary widely in the way they respond to exercise and nutritional interventions, and understanding why this metabolic heterogeneity exists is critical for further advances in precision nutrition. Another major challenge is the development of evidence-based individualized nutrition recommendations that are embraced and efficacious for athletes seeking the most effective enhancement of performance, metabolic recovery, and health. At this time precision sports nutrition is an emerging discipline that will require continued technological and scientific advances before this approach becomes accurate and practical for athletes and fitness enthusiasts at the small group or individual level. The costs and scientific challenges appear formidable, but what is already being achieved today in precision nutrition through multiomics and sensor technology seemed impossible just two decades ago.
Highlights
North Carolina Research Campus, Human Performance Laboratory, Department of Biology, Appalachian State University, Boone, NC, United States
Individuals vary widely in the way they respond to exercise and nutritional interventions, and understanding why this metabolic heterogeneity exists is critical for further advances in precision nutrition
The National Institutes of Health (NIH) and other agencies are focused on investigations to improve scientific understanding of these individual differences, adding momentum to the paradigm shift toward personalized nutrition [5, 6]
Summary
A core tenet of the 2020–2025 Dietary Guidelines for Americans (DGAs) is that a healthy dietary pattern is a customizable framework that supports tailored individual choices to meet personal preferences [1]. Novel precision nutrition programs to manage obesity have been designed with personalized macronutrient compositions that vary based on the individual’s genotype, enterotype, and other related factors [17] To refine this approach, comprehensive data sets from large groups are needed that include demographics, anthropometry, diet intake, physical activity, genomics, transcriptomics, epigenetics, proteomics, metabolomics, and environmental exposure [3, 11, 12]. Comprehensive data sets from large groups are needed that include demographics, anthropometry, diet intake, physical activity, genomics, transcriptomics, epigenetics, proteomics, metabolomics, and environmental exposure [3, 11, 12] With machine learning, these types of data sets can be modeled to improve understanding of interindividual variation and the development of more accurate precision nutrition recommendations. Remarkable progress has been made, multiomics-based solutions and elucidations remain a work in progress, with high expectations that this will be a successful, albeit costly initiative [6, 13]
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