Kayvan Khoramipour1,2, Øyvind Sandbakk3, Ammar Hassanzadeh Keshteli4, Abbas Ali Gaeini5, David S Wishart4,6, Karim Chamari7. 1. Physiology Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran. k.khoramipour@kmu.ac.ir. 2. Department of Physiology and Pharmacology, Medical Faculty, Kerman University of Medical Sciences, Blvd. 22 Bahman, Kerman, Iran. k.khoramipour@kmu.ac.ir. 3. Department of Neuromedicine and Movement Science, Centre for Elite Sports Research, Norwegian University of Science and Technology, Trondheim, Norway. 4. Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2E9, Canada. 5. Department of Exercise Physiology, University of Tehran, Tehran, Iran. 6. Department of Computing Science, University of Alberta, AB, T6G 2E9, Edmonton, Canada. 7. ASPETAR, Qatar Orthopaedic and Sports Medicine Hospital, Doha, Qatar.
Abstract
BACKGROUND: Metabolomics is a field of omics science that involves the comprehensive measurement of small metabolites in biological samples. It is increasingly being used to study exercise physiology and exercise-associated metabolism. However, the field of exercise metabolomics has not been extensively reviewed or assessed. OBJECTIVE: This review on exercise metabolomics has three aims: (1) to provide an introduction to the general workflow and the different metabolomics technologies used to conduct exercise metabolomics studies; (2) to provide a systematic overview of published exercise metabolomics studies and their findings; and (3) to discuss future perspectives in the field of exercise metabolomics. METHODS: We searched electronic databases including Google Scholar, Science Direct, PubMed, Scopus, Web of Science, and the SpringerLink academic journal database between January 1st 2000 and September 30th 2020. RESULTS: Based on our detailed analysis of the field, exercise metabolomics studies fall into five major categories: (1) exercise nutrition metabolism; (2) exercise metabolism; (3) sport metabolism; (4) clinical exercise metabolism; and (5) metabolome comparisons. Exercise metabolism is the most popular category. The most common biological samples used in exercise metabolomics studies are blood and urine. Only a small minority of exercise metabolomics studies employ targeted or quantitative techniques, while most studies used untargeted metabolomics techniques. In addition, mass spectrometry was the most commonly used platform in exercise metabolomics studies, identified in approximately 54% of all published studies. Our data indicate that biomarkers or biomarker panels were identified in 34% of published exercise metabolomics studies. CONCLUSION: Overall, there is an increasing trend towards better designed, more clinical, mass spectrometry-based metabolomics studies involving larger numbers of participants/patients and larger numbers of metabolites being identified.
BACKGROUND: Metabolomics is a field of omics science that involves the comprehensive measurement of small metabolites in biological samples. It is increasingly being used to study exercise physiology and exercise-associated metabolism. However, the field of exercise metabolomics has not been extensively reviewed or assessed. OBJECTIVE: This review on exercise metabolomics has three aims: (1) to provide an introduction to the general workflow and the different metabolomics technologies used to conduct exercise metabolomics studies; (2) to provide a systematic overview of published exercise metabolomics studies and their findings; and (3) to discuss future perspectives in the field of exercise metabolomics. METHODS: We searched electronic databases including Google Scholar, Science Direct, PubMed, Scopus, Web of Science, and the SpringerLink academic journal database between January 1st 2000 and September 30th 2020. RESULTS: Based on our detailed analysis of the field, exercise metabolomics studies fall into five major categories: (1) exercise nutrition metabolism; (2) exercise metabolism; (3) sport metabolism; (4) clinical exercise metabolism; and (5) metabolome comparisons. Exercise metabolism is the most popular category. The most common biological samples used in exercise metabolomics studies are blood and urine. Only a small minority of exercise metabolomics studies employ targeted or quantitative techniques, while most studies used untargeted metabolomics techniques. In addition, mass spectrometry was the most commonly used platform in exercise metabolomics studies, identified in approximately 54% of all published studies. Our data indicate that biomarkers or biomarker panels were identified in 34% of published exercise metabolomics studies. CONCLUSION: Overall, there is an increasing trend towards better designed, more clinical, mass spectrometry-based metabolomics studies involving larger numbers of participants/patients and larger numbers of metabolites being identified.
Authors: Elin Pohjanen; Elin Thysell; Pär Jonsson; Caroline Eklund; Anders Silfver; Inga-Britt Carlsson; Krister Lundgren; Thomas Moritz; Michael B Svensson; Henrik Antti Journal: J Proteome Res Date: 2007-04-12 Impact factor: 4.466
Authors: Nikolai B Nordsborg; Luke Connolly; Pál Weihe; Enzo Iuliano; Peter Krustrup; Bengt Saltin; Magni Mohr Journal: J Appl Physiol (1985) Date: 2015-05-28
Authors: Michael Nyberg; Matteo Fiorenza; Anders Lund; Magnus Christensen; Tue Rømer; Peter Piil; Morten Hostrup; Peter M Christensen; Simon Holbek; Thomas Ravnholt; Thomas P Gunnarsson; Jens Bangsbo Journal: Med Sci Sports Exerc Date: 2016-07 Impact factor: 5.411
Authors: David S Wishart; Carin Li; Ana Marcu; Hasan Badran; Allison Pon; Zachary Budinski; Jonas Patron; Debra Lipton; Xuan Cao; Eponine Oler; Krissa Li; Maïlys Paccoud; Chelsea Hong; An C Guo; Christopher Chan; William Wei; Miguel Ramirez-Gaona Journal: Nucleic Acids Res Date: 2020-01-08 Impact factor: 16.971
Authors: Luis C O Gonçalves; Anibal M Magalhães-Neto; Adriana Bassini; Eduardo Seixas Prado; Renan Muniz-Santos; Marcio V A Verli; Lukas Jurisica; Jaqueline S S Lopes; Igor Jurisica; Claudia M B Andrade; L C Cameron Journal: Sci Rep Date: 2022-05-17 Impact factor: 4.996
Authors: Gil Rodas; Eva Ferrer; Xavier Reche; Juan Daniel Sanjuan-Herráez; Alan McCall; Guillermo Quintás Journal: Front Physiol Date: 2022-09-30 Impact factor: 4.755