Abstract
Purpose
To investigate the response of a targeted fraction of (168 metabolites) of the resting serum metabolome to 9 sessions of sprint interval training (SIT).
Methods
Thirty-four recreationally active males provided resting blood samples before (baseline) and 48–72 h after (post) a short-term (9 sessions) cycle ergometer-based SIT intervention. A targeted analysis of 168 metabolites was performed on serum using liquid chromatography mass spectrometry (LC–MS). 160 distinct metabolites were identified and combined with 4 calculated metabolite sums and 3 calculated metabolite ratios creating a panel of 167 individual factors. Data were analysed using principal component analysis and univariate testing of all factors classified into 5 metabolite subgroups.
Results
SIT improved anaerobic capacity measured by average power output during a Wingate test (p < 0.01; mean difference = 38 W, 95% confidence interval [26, 51]) and aerobic capacity measured by average power output in a 20 min cycling test (p < 0.01; 17 W [12, 23]). Limited separation was discernible in the targeted serum metabolome between baseline and post-intervention when projected on the first and second principal component(s). However, univariate testing identified 11 fatty acids that had lower concentrations (false discovery rate < 0.05) in post-intervention samples.
Conclusions
These findings demonstrate that this short-term SIT intervention had limited effect on the serum metabolome at rest, but a subfraction of fatty acids are potentially sensitive to short-term exercise training.
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Data availability
The data analysed in this study is available at the following link: https://osf.io/xj7ag/?view_only=2941ce2488a347ceb685afbc5ff8d2ce.
Abbreviations
- ANOVA:
-
Analysis of variance
- FDR:
-
False-discovery rate
- 1H-NMR:
-
1H-nuclear magnetic resonance spectroscopy
- LC–MS:
-
Liquid chromatography mass spectrometry
- PCA:
-
Principal component analysis
- SIT:
-
Sprint interval training
References
Aird TP, Farquharson AJ, Bermingham KM, O’Sulllivan A, Drew JE, Carson BP (2021) Divergent serum metabolomic, skeletal muscle signaling, transcriptomic, and performance adaptations to fasted versus whey protein-fed sprint interval training. Am J Physiol-Endocrinol Metab 321:E802–E820. https://doi.org/10.1152/ajpendo.00265.2021
Belhaj MR, Lawler NG, Hoffman NJ (2021) Metabolomics and lipidomics: expanding the molecular landscape of exercise biology. Metabolites 11:151. https://doi.org/10.3390/metabo11030151
Burgomaster KA, Hughes SC, Heigenhauser GJF, Bradwell SN, Gibala MJ (2005) Six sessions of sprint interval training increases muscle oxidative potential and cycle endurance capacity in humans. J Appl Physiol 98:1985–1990. https://doi.org/10.1152/japplphysiol.01095.2004
Burgomaster KA, Cermak NM, Phillips SM, Benton CR, Bonen A, Gibala MJ (2007) Divergent response of metabolite transport proteins in human skeletal muscle after sprint interval training and detraining. Am J Physiol-Regulat Integr Comparat Physiol 292:R1970–R1976. https://doi.org/10.1152/ajpregu.00503.2006
Burke LM, Gollan RA, Read RS (1991) Dietary intakes and food use of groups of elite Australian male athletes. Int J Sport Nutr 1:378–394. https://doi.org/10.1123/ijsn.1.4.378
Darragh IAJ, O’Driscoll L, Egan B (2021) Exercise training and circulating small extracellular vesicles: appraisal of methodological approaches and current knowledge. Front Physiol 12:1894. https://doi.org/10.3389/fphys.2021.738333
Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL (2011) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40:387–426. https://doi.org/10.1039/B906712B
Ekblom B (2017) The muscle biopsy technique. Historical and methodological considerations. Scand J Med Sci Sports 27:458–461. https://doi.org/10.1111/sms.12808
Emwas A-HM (2015) The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. In: Bjerrum JT (Eds) Metabonomics, methods in molecular biology. Springer New York, New York, NY, pp. 161–193. https://doi.org/10.1007/978-1-4939-2377-9_13
Feunang YD, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, Fahy E, Steinbeck C, Subramanian S, Bolton E, Greiner R, Wishart DS (2016) ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform 8:61. https://doi.org/10.1186/s13321-016-0174-y
Gibala MJ, Little JP, MacDonald MJ, Hawley JA (2012) Physiological adaptations to low-volume, high-intensity interval training in health and disease. J Physiol 590:1077–1084. https://doi.org/10.1113/jphysiol.2011.224725
Gika H, Virgiliou C, Theodoridis G, Plumb RS, Wilson ID (2019) Untargeted LC/MS-based metabolic phenotyping (metabonomics/metabolomics): the state of the art. J Chromatogr B 1117:136–147. https://doi.org/10.1016/j.jchromb.2019.04.009
Gist NH, Fedewa MV, Dishman RK, Cureton KJ (2014) Sprint interval training effects on aerobic capacity: a systematic review and meta-analysis. Sports Med 44:269–279. https://doi.org/10.1007/s40279-013-0115-0
Hazell TJ, MacPherson REK, Gravelle BMR, Lemon PWR (2010) 10 or 30-s sprint interval training bouts enhance both aerobic and anaerobic performance. Eur J Appl Physiol 110:153–160. https://doi.org/10.1007/s00421-010-1474-y
Helge JW, Wu BJ, Willer M, Daugaard JR, Storlien LH, Kiens B (2001) Training affects muscle phospholipid fatty acid composition in humans. J Appl Physiol 90:670–677. https://doi.org/10.1152/jappl.2001.90.2.670
Jeukendrup AE, Craig NP, Hawley JA (2000) The bioenergetics of world class cycling. J Sci Med Sport 3:414–433. https://doi.org/10.1016/S1440-2440(00)80008-0
Kaiser HF, Rice J (1974) Little Jiffy Mark IV. Educ Psychol Measur 34:111–117. https://doi.org/10.1177/001316447403400115
Liquet B, Cao K-AL, Hocini H, Thiébaut R (2012) A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC Bioinform 13:325. https://doi.org/10.1186/1471-2105-13-325
Little JP, Safdar A, Wilkin GP, Tarnopolsky MA, Gibala MJ (2010) A practical model of low-volume high-intensity interval training induces mitochondrial biogenesis in human skeletal muscle: potential mechanisms. J Physiol 588:1011–1022. https://doi.org/10.1113/jphysiol.2009.181743
López-Hernández Y, Monárrez-Espino J, Oostdam A-SH, Delgado JEC, Zhang L, Zheng J, Valdez JJO, Mandal R, González F de LO, Moreno JCB, Trejo-Medinilla FM, López JA, Moreno JAE, Wishart DS (2021) Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19. Sci Rep 11:14732. https://doi.org/10.1038/s41598-021-94171-y
Lund J, Rustan AC, Løvsletten NG, Mudry JM, Langleite TM, Feng YZ, Stensrud C, Brubak MG, Drevon CA, Birkeland KI, Kolnes KJ, Johansen EI, Tangen DS, Stadheim HK, Gulseth HL, Krook A, Kase ET, Jensen J, Thoresen GH (2017) Exercise in vivo marks human myotubes in vitro: training-induced increase in lipid metabolism. PLoS ONE 12:e0175441. https://doi.org/10.1371/journal.pone.0175441
MacInnis MJ, Gibala MJ (2017) Physiological adaptations to interval training and the role of exercise intensity: training adaptations and the nature of the stimulus. J Physiol 595:2915–2930. https://doi.org/10.1113/JP273196
Mashek DG, Wu C (2015) Mufas. Adv Nutr 6:276–277. https://doi.org/10.3945/an.114.005926
McKay AKA, Stellingwerff T, Smith ES, Martin DT, Mujika I, Goosey-Tolfrey VL, Sheppard J, Burke LM (2022) Defining training and performance caliber: a participant classification framework. Int J Sports Physiol Perform 1:1–15. https://doi.org/10.1123/ijspp.2021-0451
Monnerat G, Sánchez CAR, Santos CGM, Paulucio D, Velasque R, Evaristo GPC, Evaristo JAM, Nogueira FCS, Domont GB, Serrato M, Lima AS, Bishop D, Campos de Carvalho AC, Pompeu FAMS (2020) Different signatures of high cardiorespiratory capacity revealed with metabolomic profiling in elite athletes. Int J Sports Physiol Perform. https://doi.org/10.1123/ijspp.2019-0267
Nicholson JK, Lindon JC (2008) Metabonomics. Nature 455:1054–1056. https://doi.org/10.1038/4551054a
Nikolaidis MG, Mougios V (2004) Effects of exercise on the fatty-acid composition of blood and tissue lipids. Sports Med 34:1051–1076. https://doi.org/10.2165/00007256-200434150-00004
Pechlivanis A, Kostidis S, Saraslanidis P, Petridou A, Tsalis G, Veselkov K, Mikros E, Mougios V, Theodoridis GA (2013) 1 H NMR study on the short- and long-term impact of two training programs of sprint running on the metabolic fingerprint of human serum. J Proteome Res 12:470–480. https://doi.org/10.1021/pr300846x
Perry CGR, Heigenhauser GJF, Bonen A, Spriet LL (2008) High-intensity aerobic interval training increases fat and carbohydrate metabolic capacities in human skeletal muscle. Appl Physiol Nutr Metab 33:1112–1123. https://doi.org/10.1139/H08-097
Petersen A-K, Krumsiek J, Wägele B, Theis FJ, Wichmann H-E, Gieger C, Suhre K (2012) On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies. BMC Bioinform 13:120. https://doi.org/10.1186/1471-2105-13-120
Petridou A, Nikolaidis MG, Matsakas A, Schulz T, Michna H, Mougios V (2005) Effect of exercise training on the fatty acid composition of lipid classes in rat liver, skeletal muscle, and adipose tissue. Eur J Appl Physiol 94:84–92. https://doi.org/10.1007/s00421-004-1294-z
Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, Young N, Xia J, Knox C, Dong E, Huang P, Hollander Z, Pedersen TL, Smith SR, Bamforth F, Greiner R, McManus B, Newman JW, Goodfriend T, Wishart DS (2011) The human serum metabolome. PLoS ONE 6:e16957. https://doi.org/10.1371/journal.pone.0016957
Rohart F, Gautier B, Singh A, Cao K-AL (2017) mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol 13:e1005752. https://doi.org/10.1371/journal.pcbi.1005752
Sakaguchi CA, Nieman DC, Signini EF, Abreu RM, Catai AM (2019) Metabolomics-based studies assessing exercise-induced alterations of the human metabolome: a systematic review. Metabolites 9:164. https://doi.org/10.3390/metabo9080164
Schranner D, Schönfelder M, Römisch-Margl W, Scherr J, Schlegel J, Zelger O, Riermeier A, Kaps S, Prehn C, Adamski J, Söhnlein Q, Stöcker F, Kreuzpointner F, Halle M, Kastenmüller G, Wackerhage H (2021) Physiological extremes of the human blood metabolome: a metabolomics analysis of highly glycolytic, oxidative, and anabolic athletes. Physiol Rep 9:e14885. https://doi.org/10.14814/phy2.14885
Scorpio R, Rigsby RL, Thomas DR, Gardner BD (1984) Regulation of fatty acid biosynthesis in rats by physical training. J Appl Physiol 56:1060–1064. https://doi.org/10.1152/jappl.1984.56.4.1060
Shojaee-Moradie F, Baynes KCR, Pentecost C, Bell JD, Thomas EL, Jackson NC, Stolinski M, Whyte M, Lovell D, Bowes SB, Gibney J, Jones RH, Umpleby AM (2007) Exercise training reduces fatty acid availability and improves the insulin sensitivity of glucose metabolism. Diabetologia 50:404–413. https://doi.org/10.1007/s00125-006-0498-7
Talanian JL, Holloway GP, Snook LA, Heigenhauser GJF, Bonen A, Spriet LL (2010) Exercise training increases sarcolemmal and mitochondrial fatty acid transport proteins in human skeletal muscle. Am J Physiol-Endocrinol Metab 299:E180–E188. https://doi.org/10.1152/ajpendo.00073.2010
Xia J, Psychogios N, Young N, Wishart DS (2009) MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res 37:W652–W660. https://doi.org/10.1093/nar/gkp356
Zheng J, Zhang L, Johnson M, Mandal R, Wishart DS (2020) Comprehensive targeted metabolomic assay for urine analysis. Anal Chem 92:10627–10634. https://doi.org/10.1021/acs.analchem.0c01682
Funding
This research was supported by Carbery Food Ingredients Ltd grant (7867835.4) to B.P.C, which supported a studentship and subsequent fellowship for T.P.A.
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ID—analysed the data and wrote the manuscript; TPA—conceptualised the study, collected data and contributed to the manuscript; AOS—assisted with data analysis and contributed to the manuscript; BE—contributed to the manuscript; BPC—conceptualised the study and contributed to the manuscript.
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Darragh, I.A.J., Aird, T.P., O’Sullivan, A. et al. The resting serum metabolome in response to short-term sprint interval training. Eur J Appl Physiol 123, 867–876 (2023). https://doi.org/10.1007/s00421-022-05115-x
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DOI: https://doi.org/10.1007/s00421-022-05115-x