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

TitleIdentification of a select metabolite panel for measuring metabolic perturbation in response to heavy exertion.
Publication TypeJournal Article
Year of Publication2018
AuthorsNieman DC, Gillitt ND, Sha W
Date Published2018 Nov 01

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.

Alternate JournalMetabolomics
PubMed ID30830401