Cardiac Output and Performance during a Marathon Race in Middle-Aged Recreational Runners

Purpose. Despite the increasing popularity of marathon running, there are no data on the responses of stroke volume (SV) and cardiac output (CO) to exercise in this context. We sought to establish whether marathon performance is associated with the ability to sustain high fractional use of maximal SV and CO (i.e, cardiac endurance) and/or CO, per meter (i.e., cardiac cost). Methods. We measured the SV, heart rate (HR), CO, and running speed of 14 recreational runners in an incremental, maximal laboratory test and then during a real marathon race (mean performance: 3 hr 30 min ± 45 min). Results. Our data revealed that HR, SV and CO were all in a high but submaximal steady state during the marathon (87.0 ± 1.6%, 77.2 ± 2.6%, and 68.7 ± 2.8% of maximal values, respectively). Marathon performance was inversely correlated with an upward drift in the CO/speed ratio (mL of CO × m−1) (r = −0.65, P < 0.01) and positively correlated with the runner's ability to complete the race at a high percentage of the speed at maximal SV (r = 0.83, P < 0.0002). Conclusion. Our results showed that marathon performance is inversely correlated with cardiac cost and positively correlated with cardiac endurance. The CO response could be a benchmark for race performance in recreational marathon runners

Protein catabolism and high lipid metabolism associated with long-distance exercise are revealed by plasma NMR metabolomics in endurance horses.

International audienceDuring long distance endurance races, horses undergo high physiological and metabolic stresses. The adaptation processes involve the modulation of the energetic pathways in order to meet the energy demand. The aims were to evaluate the effects of long endurance exercise on the plasma metabolomic profiles and to investigate the relationships with the individual horse performances. The metabolomic profiles of the horses were analyzed using the non-dedicated methodology, NMR spectroscopy and statistical multivariate analysis. The advantage of this method is to investigate several metabolomic pathways at the same time in a single sample. The plasmas were obtained before exercise (BE) and post exercise (PE) from 69 horses competing in three endurance races at national level (130-160 km). Biochemical assays were also performed on the samples taken at PE. The proton NMR spectra were compared using the supervised orthogonal projection on latent structure method according to several factors. Among these factors, the race location was not significant whereas the effect of the race exercise (sample BE vs PE of same horse) was highly discriminating. This result was confirmed by the projection of unpaired samples (only BE or PE sample of different horses). The metabolomic profiles proved that protein, energetic and lipid metabolisms as well as glycoproteins content are highly affected by the long endurance exercise. The BE samples from finisher horses could be discriminated according to the racing speed based on their metabolomic lipid content. The PE samples could be discriminated according to the horse ranking position at the end of the race with lactate as unique correlated metabolite. As a conclusion, the metabolomic profiles of plasmas taken before and after the race provided a better understanding of the high energy demand and protein catabolism pathway that could expose the horses to metabolic disorders

Difference in mechanical and energy cost between highly, well, and nontrained runners

International audienceIntroduction: Recently it has been shown that endurance training decreases the variability in stride rate. This decrease would lead to a reduction in the mechanical and the energy cost of running.Purpose: This study therefore aimed to compare the mechanical and the energy cost of running according to the training status of the runner (highly, well, and nontrained endurance runners).Methods: The kinetic, potential, and internal mechanical costs (Cke, Cpe, and Cint) were measured with a 3D motion analysis system (ANIMAN3D). The energy cost of running (C) was measured from pulmonary gas exchange using a breath-by-breath portable gas analyser (Cosmed K4b2, Rome, Italy). All the parameters were measured on track, for a speed of 4.84 ± 0.36 m·s−1.Results: Highly trained runners did not exhibit significantly lower C compared with well or nontrained runners (4.46 ± 0.38; 4.33 ± 0.32; 4.46 ± 0.46 J·kg−1·m−1, respectively; P = 0.75). However, Cpe was significantly lower in highly and well-trained runners compared with nontrained runners (0.43 ± 0.07; 0.45 ± 0.05; 0.54 ± 0.08 J·kg−1·m−1, respectively; P < 0.05). In contrast, Cint was significantly higher in highly trained runners compared with well and nontrained runners (respectively, 0.80 ± 0.12; 0.60 ± 0.09; 0.59 ± 0.10 J·kg−1·m−1; P < 0.05).Conclusion: Although there is a significant difference in Cpe and in Cint between runners of various training status, there is no difference in C. Differences in Cpe and Cint may be associated with the same self-optimizing mechanism that contributes to a reduction in the impact loads during the initial portion of the support phase of the stride

Proton 1D NMR spectra of horse plasma sampled before the race (top spectrum) and after the race (bottom spectrum).

<p>The main metabolites are labeled as follows. 1 to 5: lipid fatty acid moieties, 1: methyl; 2: methylene, 3: methylene β ester, 4: methylene α ester, 5: acyl, 6: Branched chain amino acids (valine, leucine, isoleucine), 7: lactate, 8: alanine, 9: acetate, 10: N-acetyl moieties (glycoproteins), 11: glucose, 12: urea, 13: creatine 14: phosphocholine, 15: tyrosine, 16: exchangeable proton from carboxylic moieties, 17: creatinine, 18 : citrate. (The labels of metabolites appearing in both spectra are not repeated).</p

A: Score plot of the OPLS model computed with PE samples according to their creatinine content assessed by biochemistry.

<p>Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored according to their creatininemia. B: Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to metabolites which concentration increases when creatininemia increases. The buckets are labeled according to metabolite assignments of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090730#pone-0090730-g001" target="_blank">figure 1</a>.</p

Biochemical data obtained on the 45 samples drawn at PE.

<p>Biochemical data obtained on the 45 samples drawn at PE.</p

A: Score plot of the OPLS model computed with pre-exercise (BE) and post-exercise (PE) samples from the same horse.

<p>Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored in blue for BE and red for PE. B: Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to metabolites present at increased concentrations at PE. Conversely, negative signals correspond to metabolites present at increased concentrations at BE. The buckets are labeled according to metabolite assignments of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090730#pone-0090730-g001" target="_blank">figure 1</a>. C: projection of the unpaired sample spectra on the model A. Each new spectrum was projected in the score plot using the previously constructed model to enable prediction of BE or PE spectra.</p

Descriptive data of the paired set and the test set.

<p>Average speed and distance are given as mean values (standard deviations) calculated for the designated group of horses.</p