Metabolomics reveals that fittest trail runners show a better adaptation of bioenergetic pathways.

OBJECTIVES: To analyze the effect in the blood metabolome of trail running, a demanding sport that takes place in the natural environment, places considerable strain on both muscles and joints. While metabolic responses to aerobic exercise have been analyzed in-depth, few studies have focused on trail running. DESIGN: Observational study to analyze changes in 35 different metabolites - representative of aerobic exercise-induced by a simulated 21-km trail race with an uphill gradient of 1400 m. METHODS: We performed a semiquantitative metabolomics study consisting of capillary blood microsampling and targeted screening with liquid chromatography and mass spectrometry to analyze, in 33 licensed athletes, changes concerning 35 metabolites. RESULTS: We observed significant changes in many metabolites, including increased acetyl-carnitine and taurine concentrations (false discovery rate-corrected paired t-test P value 1.63 × 10-13, and P value 5.021 × 10-12, respectively) and decreased carnitine and proline concentrations (P value 6.33 × 10-10, and P value 1.21 × 10-9, respectively). Metabolic responses to trail running were largely independent of sex but were influenced by the level of training, with runners with a higher level showing resistance to exercise-induced changes in taurine, 1-methyl histidine, acetyl-carnitine, and hypoxanthine concentrations. Performance (measured as race time) was inversely correlated with changes in specific metabolites (including taurine, serotonin, and hypoxanthine) and directly correlated with increases in glutathione. CONCLUSIONS: Our findings demonstrate the usefulness of metabolomics studies for analyzing exercise-induced physiological changes and show individual differences associated with the level of training and performance.

T-pattern detection and analysis for the discovery of hidden features of behaviour.

BACKGROUND: The behaviour of all living beings consists of hidden patterns in time; consequently, its nature and its underlying dynamics are intrinsically difficult to be perceived and detected by the unaided observer. METHOD: Such a scientific challenge calls for improved means of detection, data handling and analysis. By using a powerful and versatile technique known as T-pattern detection and analysis (TPA) it is possible to unveil hidden relationships among the behavioural events in time. RESULTS: TPA is demonstrated to be a solid and versatile tool to study the deep structure of behaviour in different experimental contexts, both in human and non human subjects. CONCLUSION: This review deepens and extends contents recently published by adding new concepts and examples concerning the applications of TPA in the study of behaviour both in human and non-human subjects.