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Genetically Tailored Sports and Nutrition Actions to Improve Health

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11837))

Abstract

With the development of molecular biology techniques, genomics is broadly introduced to expound the individual difference and molecular mechanism. Effects of genetic diversity on sports performance have been more and more found by scientists. For instance, studies on association of gene polymorphisms and training response mainly intend to discover the effects of different genotypes on the effectiveness of aerobic exercise training to increase aerobic physical fitness. Genes related to training response primarily include PPARD, PPARGC1A, ACTN3, ACE, HBB, TFAM, NFR2, AR and etc. Gene polymorphisms of PPARD and PPARGC1A are shown to be associated with post-training individual anaerobic threshold. ACE is one of the earliest and most studied gene in genes related to endurance performance that ACE is a carboxypeptidase, the key enzyme in Renin-angiotensin system, it was found in many tissues in the body including skeletal muscle, associated to functions of degrading bradykinin and transferring angiotensin I to angiotensin II. Therefore, genetic testing can help people to know the precise information on how body uniquely responds to exercise. Based on the deeper understanding of a person’s genetics and physiology information, in this paper, we provide a platform with sports and nutrition actions that are tailored specifically for different people to optimize their sports performance and effects as well as improve their health.

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Acknowledgment

This work was partially supported by the Science Foundation of Beijing Language and Culture University (supported by “the Fundamental Research Funds for the Central Universities”) (19YJ040010, 17YJ0302, 15YJ030001, 18YJ030006)

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Correspondence to Jitao Yang .

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Yang, J. (2019). Genetically Tailored Sports and Nutrition Actions to Improve Health. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_25

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  • DOI: https://doi.org/10.1007/978-3-030-32962-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32961-7

  • Online ISBN: 978-3-030-32962-4

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