ECSS Paris 2023: OP-PN07
INTRODUCTION: Metabolic phenotyping uses mass spectrometry to comprehensively profile hundreds to thousands of metabolites (the products of metabolism) in a small blood volume (~0.02-0.10 mL). Such information can provide a detailed overview of the metabolic responses to swimming sessions performed at difference exercise intensities. This study explored the metabolic responses associated with three swimming sessions performed in the moderate, heavy, and severe exercise intensity domains in high performance male and female swimmers. METHODS: Sixteen (9 males, 7 females, age: 16-24 years) Tier 3 swimmers performed a 12 × 25 m step test to determine their critical swimming speed. One week later, swimmers undertook three swimming sessions performed in the moderate (5 × 400 m on 6’ at A1/A2 speed), heavy [3 × (8 × 100m on 1’ 40 holding critical speed, 100 m recovery on 2’)], and severe exercise intensity domains [3 × (1 × 35 m dive max on 2’, 2 × 50 m dive max on 3’, 200 m recovery on 5’]. Each session was scheduled two days apart. 1.0 mL capillary whole blood samples were collected before and immediately following the cool down for each session, centrifuged and 0.1 mL of blood plasma was frozen for metabolic phenotyping using 1H-NMR and LC-MS spectrometry to give broad lipoprotein, lipidomic and amino acid coverage. RESULTS: Preliminary random forest classification analysis of pre- vs. post-exercise time points revealed that exercise in the severe domain altered metabolites involved in membrane structure and energy metabolism (triacylglycerol’s and phosphatidylethanolamine’s), whereas exercise in the heavy and moderate domain mainly altered free fatty acids involved in oxidative energy metabolism and the inflammatory response (e.g., eicosatrienoic acid). Analysis of the log2fold change of post/pre-exercise values between swimming sessions distinguished different fatty acid profiles involved in cell membrane structure and the inflammatory response between sessions performed in the moderate, heavy, and severe domains, respectively. However, the metabolomic profile of the swimming sessions performed in the moderate and heavy domains could not be discriminated from each other as they showed a similar metabolic response. CONCLUSION: Similar metabolic responses (altered fatty acid metabolism) were observed in the swimming sessions performed within the moderate and heavy domains, but changes in metabolites involved in cell membrane structure and the inflammatory response were key features of exercise performed in the severe intensity domain. Further research is needed to understand the variability and long-term metabolomic responses to routine swimming training in high performance swimmers.
Read CV Andrew GovusECSS Paris 2023: OP-PN07
INTRODUCTION: Both ultrarunning, an extreme sport pushing participants to their physiological limits, and the gut microbiota have gained enormous interest in recent years. Despite this growing interest, research on physiological responses during ultra-endurance events is limited, especially research on the relation with the gut microbiota1,2. Therefore, this study aims to fill this gap by exploring changes in the gut microbiota during a Backyard ultra-endurance running (BYR) event. Understanding these alterations is crucial for understanding their impact on energy metabolism, immune function and long-term health. METHODS: Eight male ultrarunners (41 ± 9 years old, BMI: 23.0 ± 1.1 kg/m²), with personal BYR records ranging from 100.5 km to 677 km, were monitored before, during and after a BYR event. Gut microbiome was examined before (pre), immediately after (post) and two weeks after the event (recovery). Athletes diets were tracked using Food Frequency Questionnaires (pre and recovery) and food diaries (during). Together with the diet, other gut related metadata (e.g. medicines, surgeries,…), as well as performance data during the event (e.g. heart rate, pace, lactate,…) were collected in order to correlate and/or correct for during the gut microbiome analysis. RESULTS: Preliminary results show differences in beta-diversity of the gut microbiome between pre, post and recovery samples. On phylum level, a shift in the Bacteroidetes/Firmicutes ratio was observed, with a decreased abundance of Bacteroidetes post event compared to pre event in 6 out of 8 athletes. Additionally, in 6 out of 8 recovery samples, an increased abundance of Bacteroidetes compared to post and pre event was shown. Consistently, on family level, Prevotellaceae exhibited lower abundance in the post and higher abundance in the recovery samples. For Bifidobacteriaceae a higher abundance was found post event for 5 athletes. Lastly, results indicate a high variance in Lachnospiraceae abundance across the different time points. CONCLUSION: Performing an ultra-endurance running event is associated with a temporary shift in bacterial composition in the gut and with the increased/decreased representation of some bacterial phyla and families immediately after the event and after recovery. Correcting for and/or correlating the gut microbiome data with the diet and metadata of the athletes, should confirm these results. Notwithstanding, the abundance of Prevotellaceae and Lachnospiraceae are typically associated with a higher fiber intake, which was lower during the event. Further analysis on species level, as well as metabolomic analysis should reveal further details on the effect of an ultra-endurance running event on the gut microbiome. 1. Grosicki, G. J. et al. (2019). Rapid gut microbiome changes in a world‐class ultramarathon runner. Phys. Rep., 7(24), e14313. 2. Sato, M. et al. (2022). Alterations in intestinal microbiota in ultramarathon runners. Sci. Rep., 12(1), 6984.
Read CV Toon AmpeECSS Paris 2023: OP-PN07
INTRODUCTION: The impact of low energy availability (LEA) on metabolic processes has been widely documented in the literature, with notable alterations observed in various metabolic, endocrine and physiological pathways, e.g., sex hormones as well as indicators of bone and iron metabolism. However, a comprehensive understanding of the metabolic perturbations associated with LEA remains elusive. Metabolomics, capable of analyzing a vast array of metabolites at once, provides a unique opportunity to uncover the potentially complex metabolic signature of LEA, which holds promise for improved detection and characterization of LEA status. METHODS: In this study, we employed nuclear magnetic resonance-based metabolomics to quantify 250 metabolites and metabolite ratios in post-intervention blood samples obtained following short-term exposure to LEA (15 kcal/kg fat-free mass (FFM)/day) and normal EA as control (CON; 40 kcal/kg FFM/day). Blood samples utilized in our analysis were sourced from two larger crossover design studies (n=13, 85% males, aged 23.2±3.5 years), one of which involved daily aerobic exercise across both conditions, expending 15 kcal/kg FFM/day. We used generalized estimating equations to evaluate the effects of LEA on metabolite concentrations, while employing multiple logistic regression to predict LEA status based on metabolic profiles. RESULTS: We observed significant condition effects in 120 out of 250 metabolites, independent of exercise. Notably, triglycerides (LEA vs. CON: 0.63±0.20 vs. 0.99±0.44 mmol/L, adjusted p<0.05), fatty acids (9.22±1.38 vs. 10.65±2.51 mmol/L, adjusted p<0.05), ketone bodies (0.30±0.25 vs. 0.03±0.02 mmol/L, adjusted p<.001) and very-low density lipoprotein (VLDL) sub-classes (adjusted p<0.05) exhibited significant differences. Furthermore, the stepwise inclusion of these variables into a logistic regression model demonstrated their ability in predicting LEA status (LEA ~ Acetoacetate + Total triglycerides + Ratio of saturated fatty acids to total fatty acids, AIC=18.3, p<.001). CONCLUSION: Our analysis revealed significant group differences across a broad spectrum of metabolites, indicative of a transition towards increased fat utilization, ketosis, VLDL lipolysis and lipid transfer to high-density lipoprotein particles. These findings underscore the potential of metabolomics for identifying the metabolic signature of LEA, which may in turn be used to identify individuals currently exposed to LEA.
Read CV Valentin NusserECSS Paris 2023: OP-PN07