ECSS Paris 2023: OP-PN40
INTRODUCTION: Chronic hypoxia associated with high-altitude residence represents a major environmental constraint requiring profound biological adaptation. While adaptive mechanisms allow many highlanders to maintain physiological homeostasis, prolonged or extreme exposure can lead to maladaptive conditions such as chronic mountain sickness (CMS). The metabolic determinants underlying inter-individual variability in adaptation to hypoxia remain incompletely understood. This study aimed (i) to characterize systemic metabolic and erythrocyte adaptations to chronic hypoxia and (ii) to identify biological signatures associated with CMS severity. METHODS: We integrated untargeted blood (plasma and erythrocytes) metabolomic and lipidomic data from two independent human cohorts within the Expedition 5300 research program, including individuals living at sea level, moderate altitude (3,800 m), and very high altitude (5,100–5,300 m). The first expedition was used to model altitude- and CMS-associated biological signatures, while the second served as an external validation cohort. Multivariate hierarchical models (HPLS-DA), redundancy analyses, and machine-learning approaches were applied to jointly analyse metabolic, lipidomic, clinical, and hemorheological parameters. RESULTS: The plasma metabolome robustly discriminates the living altitude and reveals a marked remodelling of energy metabolism. Reduced availability of metabolites related to fatty acid β-oxidation, including L-carnitine and its precursor, is strongly associated with decreased oxygen availability (Spearman’s r = 0.53, p < 0.001). Concomitantly, increased circulating Nicotinamide and Lactate levels indicate a coordinated shift toward anaerobic glycolytic metabolism at the whole-organism level. CMS severity is specifically associated with reduced Nicotinamide levels among individuals living at very high altitudes, revealing a potential target to limit hypoxia-induced maladaptation. At the erythrocyte level, chronic hypoxia induces substantial remodelling of their membrane lipidome. Distinct lipid clusters are associated with blood viscosity, erythrocyte deformability and aggregation, and haemoglobin concentration. Notably, a specific phosphatidylserine species -PS(16:0/18:1)- is associated with increased haemoglobin concentration (Spearman’s r = 0.45, p < 0.01), suggesting altered erythrocyte turnover and potentially impaired eryptosis. CONCLUSION: Our results reveal systemic adaptations to chronic hypoxia involving coordinated modulation of energy metabolism and erythrocyte membrane composition. Nicotinamide availability and specific erythrocyte lipid signatures emerge as key determinants of successful adaptation or maladaptation to high altitude. These findings provide a mechanistic framework linking metabolic flexibility, red blood cell properties, and CMS severity, and suggest potential nutritional or therapeutic strategies to mitigate hypoxia-related pathologies.
Read CV David MartinECSS Paris 2023: OP-PN40
INTRODUCTION: The Tor des Géants is one of the most extreme mountain ultra-marathons worldwide (330 km, >24,000 m elevation gain). Athletes endure prolonged physical strain, sleep deprivation, nutritional stress, and harsh environmental conditions over several days, making this event a unique in vivo model to investigate the limits of human physiological and metabolic adaptation. Our objective was to characterize metabolomic and lipidomic adaptations during such extreme endurance exercise and to identify pre-race molecular signatures predictive of performance. METHODS: Forty-nine athletes (11 women, 38 men) were enrolled. Blood samples were collected pre-race, at mid-race (147 km), and at the finish (330 km). Plasma and red blood cells were isolated and stored at −80°C. Untargeted metabolomics and lipidomics were performed using liquid chromatography coupled to high-resolution mass spectrometry. In plasma, 363 metabolites and 277 lipid species were identified; 362 lipid species were characterized in red blood cells. Machine learning approaches (random forest, partial least squares regression) were applied to pre-race data to predict finishing status and race time. System-level adaptations were explored by mapping metabolites to biological pathways and functional subsystems and visualized as knowledge graphs. RESULTS: A pre-race signature of 11 plasma metabolites predicted finishing probability with 81% accuracy (P = 2.5 × 10⁻⁵; ROC AUC = 0.945, repeated 10-fold cross-validation). A second 13-metabolite signature predicted race time among finishers with an average error of 2.34 ± 0.34 %. Comparable predictive performance was obtained using selected plasma lipid species. Longitudinal analyses (PLS-discriminant analysis) revealed a marked metabolic shift from pre-race to mid-race, followed by a partial return toward baseline at the finish. Early adaptations (start to mid-race) involved oxidative stress defense, mitochondrial energy production, and gut microbiota-related metabolism. Later changes (mid-race to finish) included sterol and steroid metabolism, nucleotide metabolism, and signatures suggestive of epigenetic regulatory potential. Lipid metabolism, muscle metabolic tuning, and vascular regulation were modulated throughout the race. CONCLUSION: Extreme ultra-endurance exercise induces dynamic, non-linear metabolic reprogramming with distinct temporal phases. Pre-race metabolomic and lipidomic signatures show strong potential for predicting performance and may serve as tools to assess athlete preparedness. Validation is ongoing in an independent ultra-trail cohort (SwissPeaks700 & 380). The possibility of exercise-induced epigenetic modulation warrants further careful investigation. Omics coupling to clinic-physiological data is underway.
Read CV Jean-Charles MartinECSS Paris 2023: OP-PN40
INTRODUCTION: Exercise-induced adaptations are initiated by homeostatic perturbations (e.g., changes in metabolites) that activate intracellular signalling and transcriptional responses. The magnitude and type of adaptations are determined by the exercise prescription, with exercise intensity being a central factor. Although studies have demonstrated that certain metabolites and genes are sensitive to exercise intensity, the effects of exercise intensity on both the global metabolome and transcriptome in human skeletal muscle have never been reported. Furthermore, the mechanisms underlying the blunted transcriptional response to exercise after training continue to be debated. To address these knowledge gaps, we combined the latest ‘omics’ techniques with an innovative study design that required participants to exercise above and below their maximal lactate steady state (MLSS) before and after training designed to increase their MLSS. METHODS: 17 healthy and active males (30.9 ± 6.8 y; 36.9 ± 4.4 mL/min/kg; 78.4 ± 12.8 kg) first performed exercise below (Before Moderate; BM) and above (Before High; BH) their MLSS. Participants then underwent 8 weeks of high-intensity interval training (HIIT) before performing exercises below (After Moderate; AM) and above (After High; AH) their new MLSS. Muscle samples were collected pre-, immediately post-, and +4 hours post each exercise session. Metabolomics was performed on muscle samples collected pre- and immediately post-exercise, and RNA sequencing was performed on muscle samples collected pre- and +4 h post-exercise. RESULTS: Following BM, BH, AM, and AH, we identified 62, 71, 41, and 76 differentially expressed metabolites (DEMs) and 6165, 6247, 6271, and 3760 differentially expressed genes (DEGs), respectively. While there was considerable overlap, there were also distinct metabolic and transcriptomic signatures to moderate- and high-intensity exercise. An additional novel finding was that post-training, fewer DEMs but a similar number of DEGs were observed following moderate-intensity exercise (below the MLSS). In contrast, after 8 weeks of HIIT, there were a similar number of DEMs but almost half as many DEGs following high-intensity exercise (above the MLSS). CONCLUSION: In conclusion, exercise-induced metabolic and transcriptional responses in human skeletal muscle were modulated by exercise intensity and training status. For the first time, we have shown that HIIT reduces metabolic perturbations but not the transcriptional response to moderate-intensity exercise (below the MLSS). However, the same HIIT dramatically reduced the transcriptional response, but not metabolic perturbations, to high-intensity exercise (above the MLSS). Our novel experimental design has allowed us to uncover a disassociation between metabolic perturbations and transcriptional signatures in response to different exercise intensities.
Read CV Jia WeiECSS Paris 2023: OP-PN40