Molecular Big Data in Sports Sciences: State-of-Art and Future Prospects of OMICS-Based Sports Sciences

Abstract

Together with environment and experience (that is to say, diet and training), the biological and genetic make-up of an athlete plays a major role in exercise physiology. Sports genomics has shown, indeed, that some DNA single nucleotide polymorphisms (SNPs) can be associated with athlete performance and level (such as elite/world-class athletic status), having an impact on physical activity behavior, endurance, strength, power, speed, flexibility, energetic expenditure, neuromuscular coordination, metabolic and cardio-respiratory fitness, among others, as well as with psychological traits. Athletic phenotype is complex and depends on the combination of different traits and characteristics: as such, it requires a “complex science,” like that of metadata and multi-OMICS profiles. Several projects and trials (like ELITE, GAMES, Gene SMART, GENESIS, and POWERGENE) are aimed at discovering genomics-based biomarkers with an adequate predictive power. Sports genomics could enable to optimize and maximize physical performance, as well as it could predict the risk of sports-related injuries. Exercise has a profound impact on proteome too. Proteomics can assess both from a qualitative and quantitative point of view the modifications induced by training. Recently, scholars have assessed the epigenetics changes in athletes. Summarizing, the different omics specialties seem to converge in a unique approach, termed sportomics or athlomics and defined as a “holistic and top-down,” “non-hypothesis-driven research on an individual’s metabolite changes during sports and exercise” (the Athlome Project Consortium and the Santorini Declaration) Not only sportomics includes metabonomics/metabolomics, but relying on the athlete’s biological passport or profile, it would enable the systematic study of sports-induced changes and effects at any level (genome, transcriptome, proteome, etc.). However, the wealth of data is so huge and massive and heterogenous that new computational algorithms and protocols are needed, more computational power is required as well as new strategies for properly and effectively combining and integrating data.

Blood Metabolomics Analysis Identifies Differential Serum Metabolites in Elite and Sub-elite Swimmers

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Big data-driven intelligent governance of college students' physical health: System and strategy

“…The third is students' sports behavior data—that is, the behavior data left by students on the social platform, including evaluations, text, photos, videos, and so on. However, the volume of college students' physical health data is so huge, massive, and heterogeneous that new computing algorithms and protocols, more computing power, and new strategies are needed to properly and effectively combine and integrate data ( 17 ). The systematic construction of intelligent service capability is also inseparable from the support of relevant science and technology.…”

Editorial: Insights in Sport and Exercise Nutrition: 2021

“…This Research Topic focused on new insights in sports nutrition, with an emphasis on novel developments, future directions, and challenges. Sports nutrition research and practice have already started down the path of utilizing sophisticated multiomics measurements to formulate nutrition and training guidance more precisely at the individual athlete level (Guest et al, 2019;Sellami et al, 2022).…”