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Identification of a select metabolite panel for measuring metabolic perturbation in response to heavy exertion

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Abstract

Introduction and objective

Databases from three global metabolomics-based studies (N = 59) (PMID: 25409020, 26561314, 29566095) were evaluated for metabolite shifts following heavy exertion (75-km cycling) to generate a representative, select panel of metabolites identified by variable importance in projection (VIP) scores.

Methods and results

OPLS-DA was used to separate samples at pre- and post-exercise during the water-only trial in one of the studies (PMID: 26561314), and of 590 metabolites, 26 (all but one from the lipid pathway) had a VIP > 2 and were selected for the panel. A second OPLS-DA based on the 26 metabolites was performed to separate pre- and post-exercise samples, and this model performed as well as the one with 590 metabolites (Q2Y = 0.923, 0.925 respectively); this model also showed a complete separation using OPLS-DA plots between pre- and post-exercise samples for the other two studies. A latent variable t1 (a linear combination of the 26 metabolites), was generated and the metabolite data at each time point were projected to t1 with the relative distance on t1 and area under the curve (AUC) determined from the three databases. Acute carbohydrate compared to water-only ingestion was linked to a 28–47% reduction in AUCs following exercise depending on the carbohydrate source and recovery time period.

Conclusions

These data support that a panel of 26 metabolites can be used to represent global metabolite increases induced by prolonged, intensive exercise. This select panel includes metabolites primarily from the lipid super pathway, and exercise-induced increases are sensitive to the moderating effect of acute carbohydrate ingestion.

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References

  • Abdelmagid, S. A., Clarke, S. E., Nielsen, D. E., Badawi, A., El-Sohemy, A., Mutch, D. M., & Ma, D. W. (2015). Comprehensive profiling of plasma fatty acid concentrations in young healthy Canadian adults. PLoS ONE, 10, e0116195.

    Article  Google Scholar 

  • Daskalaki, E., Blackburn, G., Kalna, G., Zhang, T., Anthony, N., & Watson, D. G. (2015). A study of the effects of exercise on the urinary metabolome using normalization to individual metabolic output. Metabolites, 5, 119–139.

    Article  CAS  Google Scholar 

  • Foulon, V., Sniekers, M., Huysmans, E., Asselberghs, S., Mahieu, V., Mannaerts, G. P., et al. (2005). Breakdown of 2-hydroxylated straight chain fatty acids via peroxisomal 2-hydroxyphytanoyl-CoA lyase: A revised pathway for the alpha-oxidation of straight chain fatty acids. The Journal of Biological Chemistry, 280, 9802–98012.

    Article  CAS  Google Scholar 

  • Gall, W. E., Beebe, K., Lawton, K. A., Adam, K. P., Mitchell, M. W., Nakhle, P. J., et al. (2010). Alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE, 5, e10883.

    Article  Google Scholar 

  • Hodson, L., Skeaff, C. M., & Fielding, B. A. (2008). Fatty acid composition of adipose tissue and blood in humans and its use as a biomarker of dietary intake. Progress in Lipid Research, 47, 348–380.

    Article  CAS  Google Scholar 

  • Howe, C. C. F., Alshehri, A., Muggeridge, D., Mullen, A. B., Boyd, M., Spendiff, O., et al. (2018). Untargeted metabolomics profiling of an 80.5 km simulated treadmill ultramarathon. Metabolites, 8(1), 14.

    Article  Google Scholar 

  • Lackey, D. E., Lynch, C. J., Olson, K. C., Mostaedi, R., Ali, M., Smith, W. H., et al. (2013). Regulation of adipose branched-chain amino acid catabolism enzyme expression and cross-adipose amino acid flux in human obesity. American Journal of Physiology-Endocrinology and Metabolism, 304, E1175–E1187.

    Article  CAS  Google Scholar 

  • Leckey, J. J., Ross, M. L., Quod, M., Hawley, J. A., & Burke, L. M. (2017). Ketone diester ingestion impairs time-trial performance in professional cyclists. Frontiers in Physiology, 8, 806.

    Article  Google Scholar 

  • Lehmann, R., Zhao, X., Weigert, C., Simon, P., Fehrenbach, E., Fritsche, J., et al. (2010). Medium-chain acylcarnitines dominate the metabolite pattern in humans under moderate intensity exercise and support lipid oxidation. PLoS ONE, 5, e11519.

    Article  Google Scholar 

  • Lewis, G. D., Farrell, L., Wood, M. J., Martinovic, M., Arany, Z., Rowe, G. C., et al. (2010). Metabolic signatures of exercise in human plasma. Science Translational Medicine, 2, 33ra37.

    Article  Google Scholar 

  • Lustgarten, M. S., Price, L. L., Chalé, A., & Fielding, R. A. (2014). Metabolites related to gut bacterial metabolism, peroxisome proliferator-activated receptor-alpha activation, and insulin sensitivity are associated with physical function in functionally-limited older adults. Aging Cell, 13, 918–925.

    Article  CAS  Google Scholar 

  • Nieman, D. C., Gillitt, N. D., Henson, D. A., Sha, W., Shanely, R. A., Knab, A. M., et al. (2012). Bananas as an energy source during exercise: A metabolomics approach. PLoS ONE, 7, e37479.

    Article  CAS  Google Scholar 

  • Nieman, D. C., Gillitt, N. D., Knab, A. M., Shanely, R. A., Pappan, K. L., Jin, F., & Lila, M. A. (2013a). Influence of a polyphenol-enriched protein powder on exercise-induced inflammation and oxidative stress in athletes: A randomized trial using a metabolomics approach. PLoS. ONE, 8, e72215.

    Article  CAS  Google Scholar 

  • Nieman, D. C., Gillitt, N. D., Sha, W., Esposito, D., & Ramamoorthy, S. (2018). Metabolic recovery from heavy exertion following banana compared to sugar beverage or water only ingestion: A randomized, crossover trial. PLoS ONE, 13, e0194843.

    Article  Google Scholar 

  • Nieman, D. C., Gillitt, N. D., Sha, W., Meaney, M. P., John, C., Pappan, K. L., & Kinchen, J. M. (2015). Metabolomics-based analysis of banana and pear ingestion on exercise performance and recovery. Journal of Proteome Research, 14, 5367–5377.

    Article  CAS  Google Scholar 

  • Nieman, D. C., Meaney, M. P., John, C. S., Knagge, K. J., & Chen, H. (2016). 9- and 13-Hydroxy-octadecadienoic acids (9 + 13 HODE) are inversely related to granulocyte colony stimulating factor and IL-6 in runners after 2 h running. Brain, Behavior, and Immunity, 56, 246–252.

    Article  CAS  Google Scholar 

  • Nieman, D. C., & Mitmesser, S. H. (2017). Potential impact of nutrition on immune system recovery from heavy exertion: A metabolomics perspective. Nutrients, 9(5), 513.

    Article  Google Scholar 

  • Nieman, D. C., Scherr, J., Luo, B., Meaney, M. P., Dréau, D., Sha, W., et al. (2014a). Influence of pistachios on performance and exercise-induced inflammation, oxidative stress, immune dysfunction, and metabolite shifts in cyclists: A randomized, crossover trial. PLoS ONE, 9, e113725.

    Article  Google Scholar 

  • Nieman, D. C., Sha, W., & Pappan, K. L. (2017). IL-6 linkage to exercise-induced shifts in lipid-related metabolites: A metabolomics-based analysis. Journal of Proteome Research, 16, 970–977.

    Article  CAS  Google Scholar 

  • Nieman, D. C., Shanely, R. A., Gillitt, N. D., Pappan, K. L., & Lila, M. A. (2013b). Serum metabolic signatures induced by a three-day intensified exercise period persist after 14 h of recovery in runners. Journal of Proteome Research, 12, 4577–4584.

    Article  CAS  Google Scholar 

  • Nieman, D. C., Shanely, R. A., Luo, B., Meaney, M. P., Dew, D. A., & Pappan, K. L. (2014b). Metabolomics approach to assessing plasma 13- and 9-hydroxy-octadecadienoic acid and linoleic acid metabolite responses to 75-km cycling. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 307, R68–R74.

    Article  CAS  Google Scholar 

  • Scrima, R., Menga, M., Pacelli, C., Agriesti, F., Cela, O., Piccoli, C., Cotoia, A., De Gregorio, A., Gefter, J. V., Cinnella, G., & Capitanio, N. (2017). Para-hydroxyphenylpyruvate inhibits the pro-inflammatory stimulation of macrophage preventing LPS-mediated nitro-oxidative unbalance and immunometabolic shift. PLoS ONE, 12, e0188683.

    Article  Google Scholar 

  • Spriet, L. L. (2014). New insights into the interaction of carbohydrate and fat metabolism during exercise. Sports Medicine, 44(Suppl 1), S87–S96.

    Article  Google Scholar 

  • Sullivan, L. B., Gui, D. Y., Hosios, A. M., Bush, L. N., Freinkman, E., Heiden, V., M.G (2015). Supporting aspartate biosynthesis is an essential function of respiration in proliferating cells. Cell, 162, 552–563.

    Article  CAS  Google Scholar 

  • van Hall, G. (2015). The physiological regulation of skeletal muscle fatty acid supply and oxidation during moderate-intensity exercise. Sports Medicine, 45(Suppl 1), S23–S32.

    Article  Google Scholar 

  • Yew, T. C., Virtue, S., Murfitt, S., Roberts, L. D., Phua, Y. H., Dale, M., et al. (2015). Adipose tissue fatty acid chain length and mono-unsaturation increases with obesity and insulin resistance. Scientific Reports, 5, 18366.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Human Performance Laboratory, Appalachian State University, North Carolina Research Campus, Kannapolis, NC, 28081, USA

    David C. Nieman

  2. Dole Nutrition Research Laboratory, North Carolina Research Campus, Kannapolis, NC, USA

    Nicholas D. Gillitt

  3. Bioinformatics Services Division, University of North Carolina at Charlotte, North Carolina Research Campus, Kannapolis, NC, 28081, USA

    Wei Sha

Authors
  1. David C. Nieman
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  2. Nicholas D. Gillitt
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  3. Wei Sha
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Contributions

DCN helped organize the data analysis and wrote the first draft of the paper. NDG conceived the concept of a select metabolite panel for exercise-based studies, provided conceptual guidance for the analysis process, and edited the paper. WS conducted the statistical analysis, wrote the methods section, and edited the paper. All authors read and approved the manuscript.

Corresponding authors

Correspondence to David C. Nieman or Wei Sha.

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Conflict of interest

The authors declare not conflicts of interest.

Ethical approval

All procedures performed in previously published studies by the authors involving human participants used in this data analysis were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the previously published studies used in this analysis (Nieman et al. 2014a, 2015, 2018).

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Nieman, D.C., Gillitt, N.D. & Sha, W. Identification of a select metabolite panel for measuring metabolic perturbation in response to heavy exertion. Metabolomics 14, 147 (2018). https://doi.org/10.1007/s11306-018-1444-7

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