Objective Caffeine supplementation is a commonly used nutritional practice. Exogenous metabolites from caffeine, such as paraxanthine, theobromine and theophylline, are eventually excreted through urine. Yet, it is less clear whether caffeine would induce endogenous metabolites altered during exercise. Urine metabolomics is non-invasive method, which mainly focus on alterations of endogenous metabolic profiles caused by diseases, drugs, and lifestyle and nutritional interventions as well. Therefore, the purpose of the present study was to examine the effects of supplementation with caffeine in a well-designed high intensity interval training (HITT). We identified significant alterations in urinary metabolite levels and revealed key metabolic pathways involved in caffeine supplementation in HITT. Methods We performed a randomized, double-blind, placebo- controlled crossover study. Twelve women basketball players (age:19.12 ± 2.64 years, mass: 174.73 ± 5.18 cm, height: 62 ± 5.09 kg, with 8.50±2.11 years training period for basketball) were randomized to placebo (PLA) or caffeine (CAF) with dosage of 3mg on the basis of body weight (kg) 45min before a field HITT test. The test was repeated after three days when players were crossed over to the alternate test. The test began with a 30 min warmup, followed by a high intensity intermittent exercise trail with incremental load for about 25min, and a cool-down. Players are familiar with the test program which included 55 sets of dribble shuttle-run, pass, shoot, and rebound with basketball with a distance of 1540m (55 × 28m), the interval between two sets was gradually reduced. Performance (completed time), heart rates immediate (HR0min) and 1 min (HR1min) after test, blood lactate (BLa), proteinuria and ratings of perceived exertion (RPE) were collected during each protocol. Urine samples were obtained before and 1 h after of the test. 1H-NMR spectra (Bruker AVANCE III HD 600MHz) were obtained and then processed by NMR spectra (MestReNova 9.0). The binning values of NMR spectra are imported into MATLAB, and the peaks are aligned with the icoshift algorithm. Then concentrations of the aligned metabolites were calculated by converting the integral area of proton signals with that of the TSP. Pattern recognition was performed to the processed NMR data, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Characteristic metabolites were identified that contribute most to the metabolic pattern between groups according to the OPLS-DA models. Finally, we analyzed the metabolic pathway by importing characteristic metabolites with concentrations into the Enrichment Analysis (MetaboAnalysis 3.0) to determine the metabolic pathways with the greatest disturbance related to caffeine during exercise. Moreover, the main effects of exercise, caffeine and the interaction between exercise and caffeine were determined by Repeated measure GLM analysis (Spss 22.0).
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