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从微观到宏观:运动与体育中的组学、多组学和运动组学方法。

From Microcosm to Macrocosm: The -Omics, Multiomics, and Sportomics Approaches in Exercise and Sports.

机构信息

Laboratory of Protein Biochemistry, The Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil.

Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, Canada.

出版信息

OMICS. 2023 Nov;27(11):499-518. doi: 10.1089/omi.2023.0169. Epub 2023 Nov 9.

Abstract

This article explores the progressive integration of -omics methods, including genomics, metabolomics, and proteomics, into sports research, highlighting the development of the concept of "sportomics." We discuss how sportomics can be used to comprehend the multilevel metabolism during exercise in real-life conditions faced by athletes, enabling potential personalized interventions to improve performance and recovery and reduce injuries, all with a minimally invasive approach and reduced time. Sportomics may also support highly personalized investigations, including the implementation of -of-1 clinical trials and the curation of extensive datasets through long-term follow-up of athletes, enabling tailored interventions for athletes based on their unique physiological responses to different conditions. Beyond its immediate sport-related applications, we delve into the potential of utilizing the sportomics approach to translate Big Data regarding top-level athletes into studying different human diseases, especially with nontargeted analysis. Furthermore, we present how the amalgamation of bioinformatics, artificial intelligence, and integrative computational analysis aids in investigating biochemical pathways, and facilitates the search for various biomarkers. We also highlight how sportomics can offer relevant information about doping control analysis. Overall, sportomics offers a comprehensive approach providing novel insights into human metabolism during metabolic stress, leveraging cutting-edge systems science techniques and technologies.

摘要

本文探讨了组学方法(包括基因组学、代谢组学和蛋白质组学)如何逐渐融入运动研究中,重点介绍了“运动组学”概念的发展。我们讨论了运动组学如何用于理解运动员在现实条件下运动中多层次的代谢,从而实现潜在的个性化干预,以提高运动表现和恢复能力并减少损伤,且所有这些都采用微创方法和缩短时间。运动组学还可以支持高度个性化的研究,包括实施“一对一”临床试验以及通过对运动员进行长期随访来整理广泛的数据集,从而可以根据运动员对不同条件的独特生理反应来为他们量身定制干预措施。除了其在运动相关应用方面的直接应用外,我们还探讨了利用运动组学方法将顶级运动员的大数据转化为研究不同人类疾病的潜力,尤其是通过非靶向分析。此外,我们介绍了生物信息学、人工智能和综合计算分析如何有助于研究生化途径,并促进各种生物标志物的寻找。我们还强调了运动组学如何为兴奋剂控制分析提供相关信息。总体而言,运动组学提供了一种全面的方法,利用先进的系统科学技术和技术,为代谢应激期间人体代谢提供了新的见解。

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