Metabolic Reprogramming of Endothelial-Related Pathways in COVID-19 Patients Treated with Hyperbaric Oxygen Therapy: A Randomized Clinical Trial

Metabolic Reprogramming of Endothelial-Related Pathways in COVID-19 Patients Treated with Hyperbaric Oxygen Therapy: A Randomized Clinical Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metabolic Reprogramming of Endothelial-Related Pathways in COVID-19 Patients Treated with Hyperbaric Oxygen Therapy: A Randomized Clinical Trial Natalia Jermakow, Klaudia Brodaczewska, Jacek Kot, Krzysztof Urbanowicz, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8643601/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract COVID-19, caused by SARS-CoV-2, is increasingly recognized as a systemic disorder with inflammation, endothelial dysfunction, and metabolic perturbations. This study aimed to characterize metabolic changes in COVID-19 patients undergoing hyperbaric oxygen therapy (HBOT). The clinical trial was registered in EudraCT (2020-002722-90, 3 May 2020), prior to patient enrollment. Thirty hospitalized patients were randomized to HBOT (n=14) or standard care (n=14). The HBOT group received five sessions at 2.5 ATA for 75 minutes. Serum metabolites were analyzed using high-resolution LC-MS. Significant changes were observed in metabolites related to arginine/NO metabolism, creatine turnover, phospholipid remodeling, and pterin derivatives. Pathway analysis highlighted the urea cycle, glycerophospholipid remodeling, niacin metabolism, and folate/pterin pathways. HBOT patients showed enhanced metabolic network connectivity. The findings suggest that HBOT induces systemic metabolic adaptations involving amino acid and lipid pathways, as well as redox-related metabolites, which may intersect with vascular and inflammatory regulation. Biological sciences/Biochemistry Health sciences/Diseases Health sciences/Medical research HBOT COVID-19 Arginine metabolism Pterin pathway Oxidative stress Endothelial function Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), first identified in December 2019, rapidly became a global pandemic, overwhelming healthcare systems and impacting economies. Initially linked to symptoms like fever, cough, and shortness of breath leading to pneumonia and Acute Respiratory Distress Syndrome (ARDS), understanding evolved to show effects on cardiovascular, gastrointestinal, and neurological systems [1, 2]. The disease involves both direct viral effects and immune damage. Now seen as a systemic illness, COVID-19 can trigger a cytokine storm – an overactive immune response releasing excessive cytokines like Interleukin 6 (IL-6) and Tumor Necrosis Factor α (TNF-α)– causing tissue injury, vascular disruption, and multi-organ failure [3 – 6]. Beyond immune dysregulation, COVID- 19 causes significant metabolic disturbances. Severe COVID- 19 involves a hyperinflammatory state often called a cytokine storm. In intensive care patients, metabolomic profiling revealed widespread changes in amino acid and lipid metabolism that matched systemic inflammation, highlighting the link between immune activation and metabolic reprogramming [8]. A well- known example is the kynurenine pathway. Thomas et al. showed that increased kynurenine levels, produced via indoleamine 2, 3- dioxygenase (IDO) activity, strongly correlated with IL- 6 levels and kidney dysfunction in COVID- 19 [9]. This directly ties a key pro- inflammatory cytokine to tryptophan breakdown, identifying kynurenine as both a marker and a mediator of blood vessel damage and immune imbalance. Integrated proteomic and metabolomic studies also revealed that cytokine storms coincide with disrupted arginine metabolism, mitochondrial energy production, and lipid signaling. Costanzo et al. pointed out that these metabolic changes are not just downstream effects of inflammation but active factors that sustain and intensify immune responses [10]. Similarly, D' Avila et al. noted that altered phospholipid and sphingolipid metabolism during SARS-CoV-2 infection spreads through extracellular vesicles carrying pro- inflammatory mediators, thereby amplifying cytokine activity throughout the body [11]. From a diagnostic point of view, Hasan et al. reviewed growing evidence that metabolic biomarkers—including those involved in kynurenine, arginine, and lipid pathways—can distinguish COVID- 19 severity and predict progression to critical illness [12]. This supports the idea that metabolic reprogramming is inseparable from immune imbalance, providing measurable signs that reflect the strength of the cytokine storm. Overall, these studies confirm that cytokine storms in COVID- 19 are both an immune and metabolic event. Increases in IL- 6 and TNF- α are consistently associated with changes in tryptophan–kynurenine metabolism, lipid and fatty acid shifts, and mitochondrial dysfunction. Rather than being passive by- products, these metabolic alterations are crucial to the development of severe illness, influencing immune responses, tissue damage, and clinical outcomes. The systemic inflammation and mitochondrial problems caused by SARS- CoV- 2 infection can disrupt normal metabolic processes essential for maintaining balance, leading to further health decline in the patient [13 – 15]. Hyperbaric oxygen therapy (HBOT) offers a potential solution to these issues. Commonly used for conditions like decompression sickness, chronic wound healing, and carbon monoxide poisoning [16], HBOT involves administering 100% oxygen at pressures above atmospheric levels. This increased pressure helps dissolve more oxygen into the bloodstream, allowing it to reach tissues even when blood flow is restricted or hemoglobin function is impaired [3, 16, 17]. This enhanced oxygen delivery can be especially beneficial in COVID-19, where compromised oxygen supply to tissues, particularly the lungs, is a major complication. Recent studies suggest that HBOT might provide significant therapeutic benefits in managing COVID-19. By boosting tissue oxygen levels, HBOT could help reduce oxidative stress, improve mitochondrial function, and modulate immune responses [17]. There is also growing evidence indicating that HBOT may influence metabolic pathways, helping restore balance in systems affected by the disease. Addressing metabolic dysregulation, such as normalizing amino acid and lipid metabolism, could lead to better clinical outcomes and lower symptom severity in COVID-19 patients [17, 18, 19]. Our earlier work suggests that HBOT may prevent the progression of COVID-19 in patients [3, 17]. In this ongoing series of articles, we aim to investigate the effects of HBOT on metabolic dysfunction associated with COVID-19. The purpose is to evaluate how HBOT influences metabolite profiles in the context of COVID-19, explore the mechanisms behind its potential therapeutic benefits, and examine the clinical implications of adding it to COVID-19 treatment. We also plan to identify areas that need further research to fully understand HBOT's potential in managing COVID-19. Considering the previous work, we hypothesize that HBOT modulates systemic metabolism in COVID-19 through pathways related to endothelial and redox homeostasis. Methods Study Design and Participants As in our previous works [3, 17], a total of 30 patients from the Military Medical Institute – National Research Institute in Warsaw, Poland, aged 24 to 78 (6 women; mean age 55 ± 13.4 years), hospitalized for SARS-CoV-2 infection between 1 March 2021, and 3 February 2022 were enrolled and randomized into two groups: an HBOT-treated group (n=15) and a control group (n=15) (Fig. 1). The clinical trial was registered on the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT; 2020-002722-90). Participants were enrolled based on predefined inclusion criteria, which required the absence of metabolic disorders, no ongoing pharmacological treatments affecting metabolism, and a stable lifestyle during the study period. The institutional review board obtained ethical approval for the study, and all participants provided written informed consent. Blinding of the intervention is not implemented due to the epidemiological challenges of simulating HBOT sessions in COVID-19 patients. However, laboratory and radiology personnel remain blinded to group assignments to reduce assessment bias. All participants received subcutaneous anticoagulants and corticosteroids as part of their standard treatment regimen. We excluded two patients because they did not meet the inclusion criteria. During the study, three deaths occurred in the control group, whereas no adverse events (AEs) leading to HBOT discontinuation were reported in the HBOT group. Additionally, twenty-seven patients (including two excluded from the study) received antibiotics, five received remdesivir (HBOT: n = 3; Control: n = 2), and one patient in the HBOT group was treated with tocilizumab (Table 1). This study aimed to assess metabolic alterations in response to hyperbaric oxygen therapy (HBOT) by evaluating key metabolite changes over multiple time points. At baseline, there were no significant differences between groups in age (HBOT 56.07 ± 14.02 vs. Control 52.8 ± 13.51 years; Mann–Whitney U = 83.5; P = 0.52), sex (χ² = 0; P > 0.99), and clinical severity (National Early Warning Score , NEWS) (median in both groups; Mann–Whitney U = 82.0; P = 0.42). C-Reactive Protein (CRP) and Procalcitonin (PCT) levels showed no significant differences between groups (CRP: P = 0.27; PCT: P = 0.22). Table 1 Baseline characteristics of study participants by treatment group . Group Control HBOT n 14 14 Age (mean ± SD) 56.1 ± 14.0 52.8 ± 12.8 Sex (F/M) 3/14 2/14 Antibiotics 13 12 Corticosteroids 14 14 Remdesivir 2 3 Tocilizumab 0 1 NEWS (Me ± IQR) 2 ± 1 2 ± 0 CRP (M ± SD) 4.8 ± 3.6 3.8 ± 4.4 PCT (M ± SD) 0.2 ± 0.2 0.1 ± 0.1 Patients were monitored daily by a researcher trained in anesthesiology, intensive care, and hyperbaric medicine. Eligible patients were informed about the study’s objectives and, upon signing written informed consent, were randomized into the HBOT or control groups. No biological samples were collected before obtaining patient consent. Unlike the control group, the HBOT group underwent five consecutive daily (day 1 – 5) hyperbaric sessions. Sessions were conducted at 2.5 Atmospheres Absolute (ATA) for 75 minutes, including 5 minutes of compression, 60 minutes of 100% oxygen breathing via individual oxygen helmets, and 10 minutes of decompression adjusted for medical personnel. Before and after each HBOT session, an arterial blood gas test was conducted, and vital signs were recorded. Additionally, blood samples were collected for comprehensive biochemical and hematological analyses performed on days 1, 5, and 10. A total of 28 patients were included in the present analysis, comprising 14 individuals in the control group and 14 in the hyperbaric oxygen therapy (HBOT) group (Fig. 1). Across the entire cohort, age ranged from 24 to 78, with a mean of 54.4 ± 13.3. The control group had a mean value of 56.07 ± 14.02, whereas the HBOT group exhibited a comparable mean of 52.08 ± 13.51 (no significant differences A leave-one-out analysis in the HBOT group tested whether tocilizumab treatment influenced results. Permutation tests with FDR correction showed no significant metabolite differences ( all q > 0.05 ). Sample Collection At each time point, 3 mL of peripheral blood was drawn by venipuncture into serum tubes (Vacutainer SST™ II Advance, BD, Warsaw, Poland) and allowed to clot for 30 minutes at room temperature (RT). Serum was obtained by centrifugation (2000–2500× g, 15 minutes, RT). The resulting supernatant was aliquoted, immediately frozen, and stored at −80 °C until further analysis. Metabolite Profiling Untargeted metabolomics were performed using a modified, previously reported protocol [20]. Briefly, metabolites were extracted from 50 µl of patient serum by deproteinization with ice-cold methanol in a 1:4 serum to methanol ratio. Samples were centrifuged at 5 °C and 18000 g for 11 minutes. The supernatant was vacuum dried at 50 °C and stored at -80 °C until analysis. Dried samples were redispersed in 50 µl of 0.1% formic acid and injected into a Vanquish ultra-high performance liquid chromatography (UHPLC) system coupled to an Exploris 480 Orbitrap high-resolution mass spectrometer (MS). Spectra were acquired at 240,000 mass resolution over the 70-1000 m/z range in positive ionization mode. Source voltage was set to 3200 V, inlet capillary temperature to 325 °C, vaporizer to 230 °C, sheath gas to 30, and aux gas to 8. RF lens was set to 60%, AGC target to 300% and maximum injection time to 100 ms. EASY-IC was enabled in scan-to-scan mode. A Kinetex F5 2.6 µm 100x2.1 mm 100 Å column was used for chromatography with a 250 µl/min flow rate. Column oven temperature was set to 28 °C. Phase A consisted of 0.1% ACS-grade formic acid (Sigma-Aldrich) in type 1 ultrapure water; phase B consisted of 0.1% formic acid in LC-MS-grade acetonitrile (Supelco, Sigma-Aldrich). Elution conditions changed linearly between the following points: 0 min, 0% B; 0.6 min, 0% B; 3.9 min, 97% B; 5 min, 97% B; 5.01 min, 0% B; 1.99 min equilibration for a total run time of 7 minutes. A Hitachi L-6200 pump (Merck) was used to deliver 0.05% formic acid in LC-MS grade methanol (VWR) post-column at a gradient flow rate from 100 µl/min to 0 µl/min, decreasing over 5.5 minutes. Autosampler temperature was set to 5 °C. 2 µl of the sample was injected for analysis. Quality Control (QC) samples were injected every 10 samples and were performed using Systematic Error Removal Using Random Forest [21]. Fragmentation data were acquired on pooled samples at 30,000 resolution in data-dependent acquisition mode using stepped normalized collision energies of 20%, 55%, and 80%. Data mining was performed in Compound Discoverer 3.3 SP3 (Thermo Scientific). Retention time tolerance was set to 0.2 min. Mass tolerance for feature reduction was set to 3 ppm. Compound annotation was performed at 2 ppm mass tolerance using the Human Metabolome Database (HMDB) (MS1) [22] and mzCloud (MS2) (Thermo Scientific) libraries. Data Processing and Transformation Compounds were retained after filtering for MS1-level annotation (at least one database hit) and peak quality (at least 5 peak rating in at least 25% of samples). For each metabolite, pairwise comparisons between the HBOT and control groups were performed at every sampling day (Day 1 pre, Day 1 post, Day 5, and Day 10) using the non-parametric Mann–Whitney U test. In parallel, effect sizes were expressed as log₂-fold change (> 0.5, P < 0.1), and multiple-testing correction was applied using both the Benjamini–Hochberg (FDR-BH) and the two-stage Benjamini–Hochberg (Storey–Tibshirani) procedures. After manual inspection for peak quality, isotopic pattern, and annotation consistency, 42 metabolites were curated as the final feature set for targeted modelling. For each of the 42 curated metabolites, longitudinal trajectories were analyzed using linear mixed effect (LME) models. Missing values were imputed using a last observation carried forward (LOCF) approach, ensuring consistency in time-series analysis. To verify linear mixed-effects model assumptions, we used the Shapiro-Wilk, Breusch-Pagan, and Random Effects Variance tests. Then, metabolite intensities were log-transformed using a natural log with pseudo count (log(1+x)) to stabilize variance and account for zero values, making the residuals closer to a normal distribution and reducing heteroscedasticity. Selected 10 metabolites were annotated manually. Pterins were putatively annotated at level 3 [23] confidence due to possible isomerism, phosphatidylserines were identified at level 3 with limited fragment ion support; nevertheless, both PS species were unique mass matches in the LIPID MAPS [24] database within our mass tolerances. The remaining 6 metabolites were identified at level 2 confidence. Statistical Analysis All statistical analyses were conducted using Python v3.12 (with statsmodels, sciPy, scikit-learn, networkx, igraph, seaborn libraries). The primary statistical approach included: Linear Mixed-Effects Modeling (LME): Metabolite levels were analyzed using LME models, with random intercept per patient, fixed effects for group, time, and their interaction (Group × Day) with patient ID as a random effect factor and Control as the reference (baseline) group, and HBOT as the contrasted group. P-values were adjusted for multiple testing using Storey’s q-value procedure. Metabolites with q < 0.05 were considered significant, resulting in a final panel of 10 metabolites. To address potential deviations from LME assumptions, we conducted permutation-based significance testing by shuffling group labels within subjects over 500 iterations. Permutation test (Perm_p) creates an empirical null distribution for the Group × Day interaction coefficients, allowing comparison with observed effect sizes. Permutation testing validates the robustness of observed associations and reduces false positives from model misspecification. Hierarchical Clustering, Correlation and Network Analysis: Pearson correlation matrices and Ward's hierarchical clustering were applied to identify metabolite clusters. Pairwise associations between metabolites were calculated using Spearman’s rank correlation coefficients within the HBOT and control groups separately. Only correlations with P 0.5 were retained to construct undirected weighted networks. To identify clusters of tightly co-regulated metabolites, we applied the Leiden community detection algorithm (resolution = 0.5).This graph-partitioning method optimizes modularity to reveal communities of nodes that share stronger internal than external connections, corresponding to functional or biochemical modules. Node colours in the network plots reflect these communities, which represent metabolite groups potentially involved in coordinated biochemical pathways.Pathway Analysis: Significantly altered metabolites were mapped to known biochemical pathways using HMDB [25], Kyoto Encyclopedia of Genes and Genomes (KEGG) [26] and Reactome [27] databases. Results Linear Mixed-Effects Model Results The LME model identified 10 metabolites with significant Group × Day interactions ( P < 0.05), indicating differential trajectories between HBOT and control groups. Patient ID was modeled as a random effect. Mixed-effects modeling revealed a distinct pattern of metabolic alterations associated with HBOT Importantly, all eleven metabolites that reached nominal significance ( P < 0.05) also remained significant after controlling multiple testing using Storey’s q-value method (q < 0.05), confirming the robustness of these associations. Among the significantly modulated metabolites, 7,8-dihydroxanthopterin (β = 0.201, 95% CI [0.092, 0.309], P < 0.001, q = 0.005), xanthopterin (β = 0.187, 95% CI [0.075, 0.300], P = 0.001, q = 0.005), and creatine riboside (β = 0.198, 95% CI [0.047, 0.350], P = 0.010, q = 0.028) demonstrated positive coefficients, indicating an increase in HBOT relative to controls. In contrast, several metabolites exhibited significant decreases, including DL-arginine (β = –0.158, 95% CI [–0.253, –0.063], P = 0.001, q = 0.005), homo-L-arginine (β = –0.190, 95% CI [–0.307, –0.072], P = 0.002, q = 0.005), glycocyamine (β = –0.210, 95% CI [–0.342, –0.078], P = 0.002, q = 0.006), Phosphatidylserine (O-20:0/0:0) (β = –0.213, 95% CI [–0.368, –0.058], P = 0.007, q = 0.030), Phosphatidylserine (O-18:0/0:0) (β = –0.204, 95% CI [–0.354, –0.055], P = 0.007, q = 0.030), L-threonine (β = –0.116, 95% CI [–0.207, –0.026], P = 0.012, q = 0.018), and Alpha-Glycerylphosphorylcholine (Alpha-GPC; β = –0.113, 95% CI [–0.214, –0.012], P = 0.029, q = 0.030). Permutation tests confirmed the robustness of our mixed-effects results. Nine out of eleven metabolites remained significant at p_perm < 0.05, while (Alpha-GPC reached borderline empirical significance (p_perm ≈ 0.05). These findings indicate that most observed interaction effects are unlikely to arise from chance, even when relaxing distributional assumptions, thereby strengthening confidence in HBOT-associated metabolic alterations Arginine-related metabolites (DL-Arginine, Homo-L-Arginine) decreased in the HBOT group, contrasting with a stable increase in controls. Pterin derivatives (7,8-Dihydroxanthopterin, Xanthopterin) exhibited pronounced increases, particularly evident by day 10 in HBOT patients. Creatine pathway intermediates, including Creatine Riboside also displayed upward trends under HBOT, whereas no consistent change was observed in the control group. Phosphatidylserine [PS (O-18:0/0:0), PS (O-20:0/0:0)] demonstrated marked elevations in both groups. L-Threonine presented a transient increase followed by stabilization, while Alpha-GPC showed a delayed but distinct rise on day 10. Together, these patterns highlight robust time-dependent metabolic reprogramming associated with HBOT, absent in the control group (Fig. 2). Data are shown as mean ± 95% CI across study days (1-pre, 1-post, day 5, day 10). Purple lines denote the HBOT group and orange lines denote the control group. Network Analysis Correlation and network analysis demonstrated that HBOT markedly increased both the number and strength of metabolite–metabolite associations compared with controls (Fig. 3). In the HBOT group (Fig. 3A), highly significant positive correlations were observed within the pterin pathway, with 7,8-dihydroxanthopterin and xanthopterin showing an almost perfect association (r = 0.95, P < 0.001). This strong coupling extended to creatine riboside with xanthopterin (r = 0.71, P < 0.05), suggesting crosstalk between folate/pterin metabolism and creatine biosynthesis under hyperbaric conditions. A second dense module was identified around the arginine–creatine axis, where DL-arginine showed a strong correlation with glycocyamine (r = 0.77, P < 0.001), homo-L-arginine (r = 0.62, P < 0.001), and L-threonine (r = 0.65, P < 0.001). These associations point toward coordinated regulation of nitrogen handling, creatine turnover, and methylation reactions. DL-arginine also negatively correlated with 7,8-dihydroxanthopterin and xanthopterin (r = -0.62 and -0.61, P < 0.001). Additionally, phosphatidylserine species (PS O-18:0/0:0 and PS O-20:0/0:0) displayed a robust intra-class correlation (r = 0.89, P < 0.001), with both lipids showing significant cross-links to creatine riboside (r = -0.59 and -0.62, P < 0.00 and < 0.001). This indicates tighter integration between phospholipid remodeling and energy metabolism during HBOT. In contrast, the control group (Fig. 2B) exhibited a sparser and less modular network (Fig. 4). Although the pterin correlation between 7,8-dihydroxanthopterin and xanthopterin remained strong (r = 0.91, P < 0.001), most other associations were considerably weaker or absent. For example, DL-arginine and glycocyamine displayed correlation (r = 0.54, P < 0.001) and a non-significant correlation with pterins. Links between phosphatidylserine and creatine metabolites were either missing or weaker than in HBOT patients (r ≤ 0.52). Hierarchical clustering (Ward’s distance) of significantly altered metabolites revealed highly consistent groupings between HBOT and control subjects (Fig. 3A and 2B). Compounds associated with pterin metabolism (7,8-dihydroxanthopterin and xanthopterin) clustered together with creatine riboside in both groups, while Alpha-GPC was consistently paired with DL-arginine. Similarly, glycocyamine grouped with PS (O-20:0/0:0), and DL-arginine and alpha-GPC, homo-L-arginine, PS (O-20:0/0:0), L-threonine create one cluster. These clusters were preserved across conditions, indicating that HBOT does not reorganize the underlying network structure of metabolic associations. To formally assess the similarity of correlation structures, we performed a Mantel test comparing the pairwise metabolite correlation matrices between the HBOT group and the control group. The analysis demonstrated a very strong correspondence (Mantel r = 0.929, P = 0.001), confirming that the overall topology of metabolite–metabolite relationships was nearly identical across groups. To further assess the interdependencies among significantly modulated metabolites, we constructed correlation-based metabolic networks separately for the HBOT and control groups. Community detection using the Leiden algorithm revealed a distinct modular organization of metabolites in both groups (Fig. 3, panels C–D). The HBOT network was denser, comprising 41 significant edges compared with 28 in controls, indicating enhanced coordination of metabolic pathways under hyperbaric oxygen exposure. Within the HBOT network, a highly connected community was formed by DL-arginine, homo-L-arginine, L-threonine, and glycocyamine, reflecting tight interrelationships within arginine and amino acid metabolism. Lipid derivatives, including PS (O-18:0/0:0), PS (O-20:0/0:0), and Alpha-GPC, established an additional Leiden module that remained interconnected with the central amino acid cluster. Moreover, the pterin metabolites 7,8-dihydroxanthopterin and xanthopterin showed a strong positive correlation (r ≈ 0.95), serving as a hub-like axis of the HBOT network. In contrast, the control network displayed fewer connections, with metabolites largely organized into smaller, less integrated communities. Although the strong correlation between 7,8-dihydroxanthopterin and xanthopterin persisted, its surrounding interactions were markedly reduced compared with HBOT. Lipid metabolites also appeared more isolated, with weaker integration into the global network. Overall, Leiden clustering emphasized that HBOT promotes a tighter and more cohesive modular organization of metabolic pathways, particularly within arginine- and amino acid–related metabolites, while the control group retained a fragmented, less coordinated structure. Permutation-based Network Comparison Test confirmed that these structural differences were statistically significant (diff = 0.189, P = 0.018), supporting the conclusion that HBOT induces a profound reorganization of the metabolic correlation architecture. Pathway Enrichment Analysis Metabolites that were significantly altered in the LME model were mapped to curated pathways using KEGG, Reactome, and HMDB references. Key associations include: DL-Arginine, Homo-L-Arginine: Arginine and Proline Metabolism (KEGG), Urea Cycle (Reactome). Creatine Riboside, Glycocyamine: Creatine Biosynthesis and Guanidino Compound Metabolism (HMDB). Alpha-GPC : Choline Metabolism (HMDB). Xanthopterin, 7,8-Dihydroxanthopterin: Pterin (HMDB) and Folate (KEGG) Pathways. For pathway-level comparisons, enrichment scores were calculated as the mean log-transformed values of metabolites assigned to each pathway. Group differences between HBOT and control patients were assessed at individual study days using the non-parametric Mann–Whitney U test, as the data did not conform to normal distribution assumptions. This approach allowed visualization of pathway-level enrichment trends and facilitated statistical inference without assuming homoscedasticity or normality. Pathway analysis demonstrated that metabolites significantly affected by HBOT were enriched in: Arginine and Proline Metabolism: P = 0.0017 Choline Metabolism: P = 0.0300 Niacin Metabolism: P = 0.0132 Pterin and Folate Pathways: P = 0.0334 These findings suggest that HBOT modulates metabolic processes linked to cellular bioenergetics, lipid signaling, and oxidative stress adaptation (Fig. 4). Discussion This study provides one of the first systematic descriptions of metabolomic alterations in COVID-19 patients undergoing HBOT. The results demonstrate temporal changes in amino acid, pterin, and phospholipid pathways, together with a reorganization of metabolic network connectivity. These observations should be interpreted primarily as biochemical signatures. While they may suggest processes of potential relevance for vascular regulation, mitochondrial adaptation, and inflammatory balance, their translational significance requires confirmation in outcome-oriented studies. We identified ten metabolites with significant Group × Day interactions, reflecting divergent trajectories between HBOT and control patients. The most consistent increases were observed in pterin derivatives (7,8-dihydroxanthopterin, xanthopterin) and creatine riboside, whereas decreases were noted in DL-arginine, homoarginine, glycocyamine, phosphatidylserines [PS (O-20:0/0:0), PS (O-18:0/0:0)], and threonine. Glycocyamine decreased after HBOT, consistent with lower flux through creatine biosynthesis (guanidinoacetate methyltransferase) and potential coupling to methylation/energy pathways. Alpha-GPC also showed a transient elevation at baseline in HBOT patients, followed by a decline by day 10. These changes are consistent with metabolic axes previously implicated in COVID-19 severity. Reduced arginine availability and perturbations in nitric oxide (NO) metabolism have been linked to endothelial dysfunction and hyperinflammation [9–16]. In our cohort, lower DL-arginine and homo-L-arginine in HBOT patients coincided with reduced IL-6 and CRP in the same group [3, 17]. This pattern may suggest altered arginine utilization, though causality cannot be inferred without larger mechanistic studies. Alterations in phospholipid metabolism provide additional mechanistic hypotheses. Decreases in phosphatidylserines and alpha-GPC suggest remodeling of membrane composition and choline metabolism, disturbances also reported in sepsis and ARDS [28]. The transient increase and subsequent normalization of alpha-GPC in HBOT patients could reflect adaptive membrane remodeling under hyperoxic stress, though its physiological implications remain unclear. Beyond individual metabolites, HBOT was associated with a pronounced reorganization of metabolic interactions. Mantel testing confirmed stronger overall correlation structures in HBOT patients (r = 0.929, P = 0.001), and network analysis demonstrated a denser topology with 41 significant correlations versus 28 in controls ( P = 0.018). In the extended correlation maps (see Supplementary Fig. S1A–B), HBOT produced a broader, more integrated pattern of positive associations linking arginine-derived guanidino compounds, pterins, phosphatidylserines, and acylcarnitines—pathways central to endothelial nitric-oxide signaling, mitochondrial energetics, and membrane remodeling. The corresponding HBOT network (Supplementary Fig. S1C) displays tighter clustering among these metabolites, in contrast to the fragmented and sparsely connected control network (Supplementary Fig. S1D). However, this reorganization is not uniformly beneficial. The HBOT network also exhibits several new inverse correlations – particularly between redox-active or methylation-related metabolites (e.g., between phospholipids and arginine derivatives) – suggesting that part of the enhanced connectivity reflects metabolic compensation or redox strain rather than purely homeostatic regulation. Such bidirectional correlations likely capture transient redistribution of cofactors nicotinamide adenine dinucleotide oxidized to reduced (NAD⁺/NADH), and S-adenosylmethionine to S-adenosylhomocysteine (SAM/SAH) under repeated hyperoxic exposure. The pathway-level comparisons (Supplementary Fig. S2) complement these findings. At baseline, pathway means did not differ between groups, but by Day 10 significant changes emerged within arginine/urea-cycle, creatine, and phospholipid modules, all up-regulated under HBOT. These alterations mirror the main dataset and support coordinated late-phase metabolic adaptation. Conversely, pathways linked to tryptophan/indole metabolism, histidine/imidzazole intermediates, and bile-acid signaling remained largely unchanged, indicating that HBOT selectively engages energy- and membrane-related systems while leaving peripheral amino-acid and microbiota-derived metabolism relatively unaffected. It should be emphasized that, in the absence of a healthy control group, these HBOT-related changes reflect adaptive and redox-regulated processes that accumulate throughout the treatment period in hospitalized COVID-19 patients, rather than universal markers of recovery or normalization toward healthy physiology. Taken together, these results show that HBOT promotes a more cohesive but selectively remodeled metabolic network, one that strengthens crosstalk between nitric-oxide synthesis, membrane phospholipid turnover, and mitochondrial fuel utilization, yet does so under increased oxidative and methylation pressure. This dual nature of the response, adaptive but metabolically demanding, fits within the broader context of hyperoxia-induced hormesis, where transient reactive oxygen species (ROS) production and redox perturbation trigger long-term cellular resilience Such remodeling may also intersect with inflammatory and oxidative pathways. COVID-19 is characterized by cytokine surge, endothelial dysfunction, and mitochondrial stress; HBOT has been shown to mitigate cytokine release and improve oxygenation [3, 17, 29]. Controlled ROS generation during hyperoxia can induce antioxidant defenses, including upregulation of superoxide dismutase and catalase [30, 31]. HBOT-driven improvements in tissue oxygenation could further stabilize mitochondrial respiration and Adenosine Triphosphate (ATP) production, enhancing redox balance [32]. Experimental data support increased mitochondrial biogenesis and efficiency following HBOT [33, 34]. The observed network tightening around niacin- and threonine-related metabolites—both connected to NAD⁺ metabolism – aligns with these mitochondrial mechanisms. Nevertheless, the coexistence of strengthened coordination and inverse coupling among oxidative-stress markers suggests that the HBOT response involves both adaptive and compensatory elements, warranting validation in larger, outcome-based cohorts. The metabolic signatures observed here overlap with those described in other hyperinflammatory syndromes, including sepsis and ARDS. Sepsis has been associated with remodeling of amino acid metabolism and the nitrogen cycle [35], while ARDS has shown alterations in phospholipid metabolism and tryptophan catabolism [28,36]. Our results support these findings, involving arginine/NO, phospholipid, and pterin pathways. Whereas sepsis and severe COVID-19 typically display arginine depletion and kynurenine accumulation [37], HBOT in this study was associated with patterns suggesting partial rebalancing. These processes may hold particular importance for vascular health. COVID-19 is now recognized to increase the risk of ischemic stroke, including young adults without conventional vascular risk factors. Large-vessel occlusions have been reported in patients under 50 years of age [38, 39]. Comparative studies found stroke incidence to be higher in COVID-19 compared with influenza [40, 41], while registry data confirmed cerebrovascular events across diverse populations [42]. Case reports have even described ischemic stroke in adolescents [43]. The proposed mechanisms – cytokine storm, IDO1 activation and kynurenine pathway upregulation, endothelial dysfunction, and coagulopathy [4] – closely intersect with the metabolic pathways observed in our study. Among the pathways highlighted in this study, arginine metabolism stands out as particularly important, given its pivotal role in nitric oxide production, which is the primary regulator of vascular tone and endothelial homeostasis. Alterations in arginine and its downstream derivatives may therefore represent a mechanistic link between HBOT and improved vascular function. In this context, HBOT-related changes in arginine- and pterin-associated metabolites are likely to intersect with nitric oxide bioavailability and endothelial signaling. Such reprogramming could point toward a stabilizing effect on the vascular system, though this remains speculative and requires confirmation in larger cohorts with dedicated vascular endpoints. Independent evidence supports the possibility of vascular effects of HBOT. Preclinical and clinical studies suggest improvements in endothelial function, enhanced nitric oxide–dependent vasodilation, and reductions in oxidative stress [16, 32, 34]. Schottlender et al. demonstrated that HBOT supports mitochondrial respiration and limits oxidative stress in endothelial cells [32]. Our previous work reported no evidence of endothelial injury in patients receiving HBOT, with reductions in arginine derivatives [16]. Batinac et al. further highlighted HBOT as a potential emerging modality in cardiovascular disease through its impact on vascular reactivity and endothelial repair [34]. Together, these findings underscore the importance of linking metabolic data to vascular outcomes in future studies. Such research will be essential to determine whether HBOT-related biochemical reprogramming may contribute to the mitigation of COVID-19–associated vascular complications. Limittions The most important limitation is the absence of a healthy control cohort. While HBOT is clinically established for wound and fracture healing, enrolling age-matched, virus-negative healthy volunteers during the COVID-19 pandemic was not ethically or logistically feasible. Therefore, our conclusions are confined to treatment-related metabolic adaptations observed in hospitalized patients rather than universal recovery markers. The cohort size was small, limiting generalizability, and the study population included only patients in the acute phase of the disease; therefore, the findings cannot be directly extrapolated to the chronic form of long-COVID-19. The precise molecular targets of HBOT remain incompletely defined, and untargeted metabolomics could reveal additional pathways. Treatment parameters—including pressure, duration, and session frequency – require standardization. Accurate identification of pterins is particularly challenging due to their structural isomerism, which may limit the interpretability of changes observed within this pathway. Finally, while HBOT appears to modulate systemic metabolism, integration with established immunomodulatory therapies such as IL-1/IL-6 blockade or JAK inhibition should also be considered [34, 45]. Future research should incorporate targeted redox and oxidative lipidomic assays to validate and quantify specific oxidative-stress biomarkers. In addition, future studies including healthy, age- and sex-matched controls will be crucial to determine whether the observed metabolic trajectories reflect broader processes of physiological restoration beyond treatment-related adaptations. These complementary analyses will facilitate a more detailed mechanistic understanding of HBOT-induced redox adaptation and its association with endothelial recovery. Integrating metabolomics with transcriptomic and proteomic methodologies is recommended to elucidate the regulatory mechanisms underlying the effects of HBOT. Additionally, longitudinal studies that include vascular and inflammatory endpoints are essential to determine whether the observed biochemical reprogramming provides protective benefits for patient outcomes. Conclusion HBOT was associated with systemic metabolic adaptations in COVID-19 patients, including increases in pterin derivatives and creatine riboside, decreases in arginine-related metabolites, phospholipids, and glycocyamine, as well as strengthened metabolic network connectivity. These signatures overlap with those previously reported in sepsis and ARDS, suggesting that HBOT may modulate biochemical axes common to hyperinflammatory syndromes. Overall, these results support the hypothesis that HBOT induces systemic metabolic adaptations, particularly within amino acid and lipid metabolism pathways. Future studies should explore the mechanistic underpinnings of these findings and their potential therapeutic implications. While such patterns may point toward a potential protective influence on vascular function, they should be interpreted as adaptive metabolic responses occurring during HBOT in hospitalized COVID-19 patients. In the absence of healthy controls, these changes cannot be considered general recovery markers but rather reflect treatment-period adaptations. Confirmation of their clinical and physiological significance will require larger, longitudinal studies including healthy, age-matched cohorts and explicit clinical endpoints. Declarations Acknowledgments The authors are especially indebted to all employees of the designated COVID-19 clinics of the Military Medical Institute – National Research Institute involved in the implementation of this clinical trial. Author Contributions All authors contributed to the study and met ICMJE guidelines . Conceptualization, : J.S., K.B. and J.K.; methodology, J.S., K.B., N.J., J.K., K.U. and J.T.; software, N.J.; formal analysis, N.J. and K.U.; investigation, J.S., J.K., K.B., A.L. and K.K.; resources, J.S., K.B., A.L., K.K. and R.T.S.; data curation: N.J., K.B. and J.T.; writing—original draft preparation, N.J. and J.S.; writing—review and editing, N.J., J.S., K.B., J.K., A.L., K.K., K.U, J.T and R.T.S.; visualization, N.J; supervision, J.S. and J.K.; funding acquisition, J.S. and J.K. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethical approval The study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethics Committee of Military Institute of Medicine – National Science Institute (no. 25/WIM/2020 approved on 17 Jun 2020) for studies involving humans. Informed consent was obtained from all subjects involved in the study. Conflicts of Interest The authors declare no conflict of interest. Funding The study was funded by the Polish Medical Research Agency (grant 2020/ABM/COVID19/0043). References Oliveira, L. B., Mwangi, V. I., Sartim, M. A., et al. Metabolomic profiling of plasma reveals differential disease severity markers in COVID-19 patients. Front. Microbiol. 13 , 844283 (2022). doi:10.3389/fmicb.2022.844283. Gorenstein, S. A., Castellano, M. L., Slone, E. S., et al. Hyperbaric oxygen therapy for COVID-19 patients with respiratory distress: treated cases versus propensity-matched controls. Undersea Hyperb. Med. 47 , 405–13 (2020). Siewiera, J., Brodaczewska, K., Jermakow, N., Lubas, A., Kłos, K., Majewska, A., et al. Effectiveness of hyperbaric oxygen therapy in SARS-CoV-2 pneumonia: the primary results of a randomised clinical trial. J. Clin. Med. 12 , 8 (2023). doi:10.3390/jcm12010008. Mangalmurti, N., Hunter, C. A. 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Metabolomics: an emerging potential approach to decipher critical illness including ARDS and sepsis. Comp. Biochem. Physiol. B. 246 , 110442 (2020). doi:10.1016/j.cbpb.2020.110442. Oxley, T. J., Mocco, J., Majidi, S., et al. Large-vessel stroke as a presenting feature of COVID-19 in the young. N. Engl. J. Med. 382 , e60 (2020). doi:10.1056/NEJMc2009787. Fifi, J. T., Mocco, J. COVID-19 related stroke in young individuals. Lancet Neurol. 19 , 713–15 (2020). doi:10.1016/S1474-4422(20)30272-6. Merkler, A. E., Parikh, N. S., Mir, S., et al. Risk of ischemic stroke in patients with coronavirus disease 2019 (COVID-19) vs patients with influenza. JAMA Neurol. 77 , 1366–72 (2020). doi:10.1001/jamaneurol.2020.2730. Modin, D., Claggett, B., Sindet-Pedersen, C., et al. Acute COVID-19 and the risk of ischemic stroke: a nationwide study. Sci. Rep. 14 , 23713 (2024). doi:10.1038/s41598-024-23713-2. Siegler, J. E., Cardona, P., Arenillas, J. F., et al. Cerebrovascular events and outcomes in hospitalized patients with COVID-19: the SVIN COVID-19 multinational registry. Stroke. 52 , 409–18 (2021). doi:10.1161/STROKEAHA.120.031668. AlKandari, S., AlSuwaidan, S., AlKandari, H., et al. Post COVID-19 ischemic stroke in a 15-year-old patient. Case Rep. Neurol. Med. 2023 , 9987635 (2023). doi:10.1155/2023/9987635. Cavalli, G., Larcher, A., Tomelleri, A., et al. Interleukin-1 and interleukin-6 inhibition compared with standard management in patients with COVID-19 and hyperinflammation: a cohort study. Lancet Rheumatol. 3 , e253–61 (2021). doi:10.1016/S2665-9913(21)00012-6. Chen, C. X., Wang, J. J., Li, H., et al. JAK-inhibitors for coronavirus disease-2019 (COVID-19): a meta-analysis. Leukemia. 35 , 2616–20 (2021). doi:10.1038/s41375-021-01266-6. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8643601","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578358786,"identity":"cf99b956-9904-41da-bb91-3d7580967203","order_by":0,"name":"Natalia Jermakow","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFCCAyAigYGBvQFIG1iQooUHxDCQINoqoBaJBBCDCC3mjIePfbpRkyZncPP51Q0/CiQY+Nu7E/BqsWw4ljw751iOscHtnLKbPUCHSZw5uwGvFoMDZ4yZc9gqErfdzkm7wQPUYiCRS0jL+c/MOf+AWm6eSbv5hzgtZ5iZc9tyErfdYD92m0hbjhkz5/alGdufyWG7LWMgwUPYLzcOP2bO+ZYsJ9l+/NnNN39s5Pjbe/FrYZA4AGPxGIBJ/MpBgL8BxmJ/QFj1KBgFo2AUjEgAAMmTTWmaDH+FAAAAAElFTkSuQmCC","orcid":"","institution":"Military Institute of Medicine - 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Group differences (HBOT vs Control) at individual timepoints were assessed using non-parametric permutation tests (25,000 permutations). Significance levels are indicated above the brackets: \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.05 (*\u003cem\u003e), P \u0026lt; 0.01 (**), P \u0026lt; 0.001 (***\u003c/em\u003e); “ns” denotes non-significant.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8643601/v1/ff5367d9d629864947d4137d.png"},{"id":100876599,"identity":"645407bd-c6c4-469e-86f1-9b94b90a589c","added_by":"auto","created_at":"2026-01-22 10:26:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2090450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation structure and community organization of significant metabolites in COVID-19 patients.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8643601/v1/ebc2709f93f0e80e246095ad.png"},{"id":100876597,"identity":"139b9586-ab76-48eb-a430-f8d1d37090db","added_by":"auto","created_at":"2026-01-22 10:26:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":617933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway enrichment analysis at study day 1 (A), day 5 (B), and day 10 (C).\u003c/strong\u003e \u003cbr\u003e\nData are presented as violin plots with individual patient values overlaid (black dots). Pathway enrichment scores were calculated as the mean log-transformed and scaled metabolite levels within each pathway. Statistical comparisons between HBOT and control groups were performed using the Mann – Whitney U test. Significance levels are denoted as \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.05 (*), \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.01 (**), ns = not significant.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8643601/v1/6cbdcb5ef570614ebfdf3fc0.png"},{"id":105224280,"identity":"a5f521f8-27fd-44f2-99ee-4d5bf9404fc9","added_by":"auto","created_at":"2026-03-23 16:13:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3980393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8643601/v1/0cc4d115-acb0-469b-94cd-2efc13bf7530.pdf"},{"id":100876600,"identity":"1fe3854a-067c-4503-a00d-8b3b4b149b1b","added_by":"auto","created_at":"2026-01-22 10:26:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3819783,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8643601/v1/a850490608bf314bda0b7664.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic Reprogramming of Endothelial-Related Pathways in COVID-19 Patients Treated with Hyperbaric Oxygen Therapy: A Randomized Clinical Trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCOVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), first identified in December 2019, rapidly became a global pandemic, overwhelming healthcare systems and impacting economies. Initially linked to symptoms like fever, cough, and shortness of breath leading to pneumonia and Acute Respiratory Distress Syndrome (ARDS), understanding evolved to show effects on cardiovascular, gastrointestinal, and neurological systems [1, 2]. The disease involves both direct viral effects and immune damage. Now seen as a systemic illness, COVID-19 can trigger a cytokine storm \u0026ndash; an overactive immune response releasing excessive cytokines like Interleukin 6 (IL-6) and Tumor Necrosis Factor \u0026alpha; (TNF-\u0026alpha;)\u0026ndash; causing tissue injury, vascular disruption, and multi-organ failure [3 \u0026ndash; 6].\u003c/p\u003e\n\u003cp\u003eBeyond immune dysregulation, COVID- 19 causes significant metabolic disturbances. Severe COVID- 19 involves a hyperinflammatory state often called a cytokine storm. In intensive care patients, metabolomic profiling revealed widespread changes in amino acid and lipid metabolism that matched systemic inflammation, highlighting the link between immune activation and metabolic reprogramming [8]. A well- known example is the kynurenine pathway. Thomas et al. showed that increased kynurenine levels, produced via indoleamine 2, 3- dioxygenase (IDO) activity, strongly correlated with IL- 6 levels and kidney dysfunction in COVID- 19 [9]. This directly ties a key pro- inflammatory cytokine to tryptophan breakdown, identifying kynurenine as both a marker and a mediator of blood vessel damage and immune imbalance.\u003c/p\u003e\n\u003cp\u003eIntegrated proteomic and metabolomic studies also revealed that cytokine storms coincide with disrupted arginine metabolism, mitochondrial energy production, and lipid signaling. Costanzo et al. pointed out that these metabolic changes are not just downstream effects of inflammation but active factors that sustain and intensify immune responses [10]. Similarly, D\u0026apos; Avila et al. noted that altered phospholipid and sphingolipid metabolism during SARS-CoV-2 infection spreads through extracellular vesicles carrying pro- inflammatory mediators, thereby amplifying cytokine activity throughout the body [11].\u003c/p\u003e\n\u003cp\u003eFrom a diagnostic point of view, Hasan et al. reviewed growing evidence that metabolic biomarkers\u0026mdash;including those involved in kynurenine, arginine, and lipid pathways\u0026mdash;can distinguish COVID- 19 severity and predict progression to critical illness [12]. This supports the idea that metabolic reprogramming is inseparable from immune imbalance, providing measurable signs that reflect the strength of the cytokine storm.\u003c/p\u003e\n\u003cp\u003eOverall, these studies confirm that cytokine storms in COVID- 19 are both an immune and metabolic event. Increases in IL- 6 and TNF- \u0026alpha; are consistently associated with changes in tryptophan\u0026ndash;kynurenine metabolism, lipid and fatty acid shifts, and mitochondrial dysfunction. Rather than being passive by- products, these metabolic alterations are crucial to the development of severe illness, influencing immune responses, tissue damage, and clinical outcomes. \u0026nbsp;The systemic inflammation and mitochondrial problems caused by SARS- CoV- 2 infection can disrupt normal metabolic processes essential for maintaining balance, leading to further health decline in the patient [13 \u0026ndash; 15].\u003c/p\u003e\n\u003cp\u003eHyperbaric oxygen therapy (HBOT) offers a potential solution to these issues. Commonly used for conditions like decompression sickness, chronic wound healing, and carbon monoxide poisoning [16], HBOT involves administering 100% oxygen at pressures above atmospheric levels. This increased pressure helps dissolve more oxygen into the bloodstream, allowing it to reach tissues even when blood flow is restricted or hemoglobin function is impaired [3, 16, 17]. This enhanced oxygen delivery can be especially beneficial in COVID-19, where compromised oxygen supply to tissues, particularly the lungs, is a major complication.\u003c/p\u003e\n\u003cp\u003eRecent studies suggest that HBOT might provide significant therapeutic benefits in managing COVID-19. By boosting tissue oxygen levels, HBOT could help reduce oxidative stress, improve mitochondrial function, and modulate immune responses [17]. There is also growing evidence indicating that HBOT may influence metabolic pathways, helping restore balance in systems affected by the disease. Addressing metabolic dysregulation, such as normalizing amino acid and lipid metabolism, could lead to better clinical outcomes and lower symptom severity in COVID-19 patients [17, 18, 19].\u003c/p\u003e\n\u003cp\u003eOur earlier work suggests that HBOT may prevent the progression of COVID-19 in patients [3, 17]. In this ongoing series of articles, we aim to investigate the effects of HBOT on metabolic dysfunction associated with COVID-19. The purpose is to evaluate how HBOT influences metabolite profiles in the context of COVID-19, explore the mechanisms behind its potential therapeutic benefits, and examine the clinical implications of adding it to COVID-19 treatment. We also plan to identify areas that need further research to fully understand HBOT\u0026apos;s potential in managing COVID-19. Considering the previous work, we hypothesize that HBOT modulates systemic metabolism in COVID-19 through pathways related to endothelial and redox homeostasis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs in our previous works [3, 17], a total of 30 patients from the Military Medical Institute \u0026ndash; National Research Institute in Warsaw, Poland, aged 24 to 78 (6 women; mean age 55 \u0026plusmn; 13.4 years), hospitalized for SARS-CoV-2 infection between 1 March 2021, and 3 February 2022 were enrolled and randomized into two groups: an HBOT-treated group (n=15) and a control group (n=15) (Fig. 1). The clinical trial was registered on the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT; 2020-002722-90). Participants were enrolled based on predefined inclusion criteria, which required the absence of metabolic disorders, no ongoing pharmacological treatments affecting metabolism, and a stable lifestyle during the study period. The institutional review board obtained ethical approval for the study, and all participants provided written informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBlinding of the intervention is not implemented due to the epidemiological challenges of simulating HBOT sessions in COVID-19 patients. However, laboratory and radiology personnel remain blinded to group assignments to reduce assessment bias. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants received subcutaneous anticoagulants and corticosteroids as part of their standard treatment regimen. We excluded two patients because they did not meet the inclusion criteria. During the study, three deaths occurred in the control group, whereas no adverse events (AEs) leading to HBOT discontinuation were reported in the HBOT group. Additionally, twenty-seven patients (including two excluded from the study) received antibiotics, five received remdesivir (HBOT: n = 3; Control: n = 2), and one patient in the HBOT group was treated with tocilizumab (Table 1). This study aimed to assess metabolic alterations in response to hyperbaric oxygen therapy (HBOT) by evaluating key metabolite changes over multiple time points. At baseline, there were no significant differences between groups in age (HBOT 56.07 \u0026plusmn; 14.02 vs. Control 52.8 \u0026plusmn; 13.51 years; Mann\u0026ndash;Whitney U = 83.5; \u003cem\u003eP =\u003c/em\u003e 0.52), sex (\u0026chi;\u0026sup2; = 0; \u003cem\u003eP \u0026gt;\u003c/em\u003e 0.99), and clinical severity (National Early Warning Score , NEWS) (median in both groups; Mann\u0026ndash;Whitney U = 82.0; \u003cem\u003eP =\u003c/em\u003e 0.42). C-Reactive Protein (CRP) and Procalcitonin (PCT) levels showed no significant differences between groups (CRP: \u003cem\u003eP =\u003c/em\u003e 0.27; PCT:\u0026nbsp;\u003cem\u003eP =\u003c/em\u003e 0.22).\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Baseline characteristics of study participants by treatment group\u003c/strong\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"542\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBOT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003eAge (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e56.1 \u0026plusmn; 14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e52.8 \u0026plusmn; 12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003eSex (F/M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e3/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e2/14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003eAntibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003eCorticosteroids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003eRemdesivir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003eTocilizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003eNEWS (Me \u0026plusmn; IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e2 \u0026plusmn; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e2 \u0026plusmn; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003eCRP (M \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3808%;\"\u003e\n \u003cp\u003ePCT (M \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.6839%;\"\u003e\n \u003cp\u003e0.2 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9353%;\"\u003e\n \u003cp\u003e0.1 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients were monitored daily by a researcher trained in anesthesiology, intensive care, and hyperbaric medicine. Eligible patients were informed about the study\u0026rsquo;s objectives and, upon signing written informed consent, were randomized into the HBOT or control groups. No biological samples were collected before obtaining patient consent. Unlike the control group, the HBOT group underwent five consecutive daily (day 1 \u0026ndash; 5) hyperbaric sessions. Sessions were conducted at 2.5 Atmospheres Absolute (ATA) for 75 minutes, including 5 minutes of compression, 60 minutes of 100% oxygen breathing via individual oxygen helmets, and 10 minutes of decompression adjusted for medical personnel. Before and after each HBOT session, an arterial blood gas test was conducted, and vital signs were recorded. Additionally, blood samples were collected for comprehensive biochemical and hematological analyses performed on days 1, 5, and 10.\u003c/p\u003e\n\u003cp\u003eA total of 28 patients were included in the present analysis, comprising 14 individuals in the control group and 14 in the hyperbaric oxygen therapy (HBOT) group (Fig. 1). Across the entire cohort, age ranged from 24 to 78, with a mean of 54.4 \u0026plusmn; 13.3. The control group had a mean value of 56.07 \u0026plusmn; 14.02, whereas the HBOT group exhibited a comparable mean of 52.08 \u0026plusmn; 13.51 (no significant differences A leave-one-out analysis in the HBOT group tested whether tocilizumab treatment influenced results. Permutation tests with FDR correction showed no significant metabolite differences (\u003cem\u003eall q \u0026gt; 0.05\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt each time point, 3 mL of peripheral blood was drawn by venipuncture into serum tubes (Vacutainer SST\u0026trade; II Advance, BD, Warsaw, Poland) and allowed to clot for 30 minutes at room temperature (RT). Serum was obtained by centrifugation (2000\u0026ndash;2500\u0026times; g, 15 minutes, RT). The resulting supernatant was aliquoted, immediately frozen, and stored at \u0026minus;80 \u0026deg;C until further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolite Profiling\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUntargeted metabolomics were performed using a modified, previously reported protocol [20]. Briefly, metabolites were extracted from 50 \u0026micro;l of patient serum by deproteinization with ice-cold methanol in a 1:4 serum to methanol ratio. Samples were centrifuged at 5 \u0026deg;C and 18000 g for 11 minutes. The supernatant was vacuum dried at 50 \u0026deg;C and stored at -80 \u0026deg;C until analysis. Dried samples were redispersed in 50 \u0026micro;l of 0.1% formic acid and injected into a Vanquish ultra-high performance liquid chromatography (UHPLC) system coupled to an Exploris 480 Orbitrap high-resolution mass spectrometer (MS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpectra were acquired at 240,000 mass resolution over the 70-1000 m/z range in positive ionization mode. Source voltage was set to 3200 V, inlet capillary temperature to 325 \u0026deg;C, vaporizer to 230 \u0026deg;C, sheath gas to 30, and aux gas to 8. RF lens was set to 60%, AGC target to 300% and maximum injection time to 100 ms. EASY-IC was enabled in scan-to-scan mode. \u0026nbsp;A Kinetex F5 2.6 \u0026micro;m 100x2.1 mm 100 \u0026Aring; column was used for chromatography with a 250 \u0026micro;l/min flow rate. Column oven temperature was set to 28 \u0026deg;C. Phase A consisted of 0.1% ACS-grade formic acid (Sigma-Aldrich) in type 1 ultrapure water; phase B consisted of 0.1% formic acid in LC-MS-grade acetonitrile (Supelco, Sigma-Aldrich). Elution conditions changed linearly between the following points: 0 min, 0% B; 0.6 min, 0% B; 3.9 min, 97% B; 5 min, 97% B; 5.01 min, 0% B; 1.99 min equilibration for a total run time of 7 minutes. A Hitachi L-6200 pump (Merck) was used to deliver 0.05% formic acid in LC-MS grade methanol (VWR) post-column at a gradient flow rate from 100 \u0026micro;l/min to 0 \u0026micro;l/min, decreasing over 5.5 minutes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAutosampler temperature was set to 5 \u0026deg;C. 2 \u0026micro;l of the sample was injected for analysis. Quality Control\u0026nbsp;(QC) samples were injected every 10 samples and were performed using Systematic Error Removal Using Random Forest [21]. Fragmentation data were acquired on pooled samples at 30,000 resolution in data-dependent acquisition mode using stepped normalized collision energies of 20%, 55%, and 80%. Data mining was performed in Compound Discoverer 3.3 SP3 (Thermo Scientific). Retention time tolerance was set to 0.2 min. Mass tolerance for feature reduction was set to 3 ppm. Compound annotation was performed at 2 ppm mass tolerance using the\u0026nbsp;Human Metabolome Database\u0026nbsp;(HMDB) (MS1) [22] and mzCloud (MS2) (Thermo Scientific) libraries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Processing and Transformation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompounds were retained after filtering for MS1-level annotation (at least one database hit) and peak quality (at least 5 peak rating in at least 25% of samples). For each metabolite, pairwise comparisons between the HBOT and control groups were performed at every sampling day (Day 1 pre, Day 1 post, Day 5, and Day 10) using the non-parametric Mann\u0026ndash;Whitney U test. In parallel, effect sizes were expressed as log₂-fold change (\u0026gt; 0.5, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.1), and multiple-testing correction was applied using both the Benjamini\u0026ndash;Hochberg (FDR-BH) and the two-stage Benjamini\u0026ndash;Hochberg (Storey\u0026ndash;Tibshirani) procedures. After manual inspection for peak quality, isotopic pattern, and annotation consistency, 42 metabolites were curated as the final feature set for targeted modelling. For each of the 42 curated metabolites, longitudinal trajectories were analyzed using linear mixed effect (LME) models. Missing values were imputed using a last observation carried forward (LOCF) approach, ensuring consistency in time-series analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To verify linear mixed-effects model assumptions, we used the Shapiro-Wilk, Breusch-Pagan, and Random Effects Variance tests. Then, metabolite intensities were log-transformed using a natural log with pseudo count (log(1+x)) to stabilize variance and account for zero values, making the residuals closer to a normal distribution and reducing heteroscedasticity. Selected 10 metabolites were annotated manually. Pterins were putatively annotated at level 3 [23] confidence due to possible isomerism, phosphatidylserines were identified at level 3 with limited fragment ion support; nevertheless, both PS species were unique mass matches in the LIPID MAPS [24] database within our mass tolerances. The remaining 6 metabolites were identified at level 2 confidence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using Python v3.12 (with statsmodels, sciPy, scikit-learn, networkx, igraph, seaborn libraries). The primary statistical approach included:\u003c/p\u003e\n\u003cp\u003eLinear Mixed-Effects Modeling (LME): Metabolite levels were analyzed using LME models, with random intercept per patient, fixed effects for group, time, and their interaction (Group \u0026times; Day) with patient ID as a random effect factor and Control as the reference (baseline) group, and HBOT as the contrasted group. P-values were adjusted for multiple testing using Storey\u0026rsquo;s q-value procedure. Metabolites with q \u0026lt; 0.05 were considered significant, resulting in a final panel of 10 metabolites.\u003c/p\u003e\n\u003cp\u003eTo address potential deviations from LME assumptions, we conducted permutation-based significance testing by shuffling group labels within subjects over 500 iterations. Permutation test (Perm_p) creates an empirical null distribution for the Group \u0026times; Day interaction coefficients, allowing comparison with observed effect sizes. Permutation testing validates the robustness of observed associations and reduces false positives from model misspecification.\u003c/p\u003e\n\u003cp\u003eHierarchical Clustering, Correlation and Network Analysis: Pearson correlation matrices and Ward\u0026apos;s hierarchical clustering were applied to identify metabolite clusters. Pairwise associations between metabolites were calculated using Spearman\u0026rsquo;s rank correlation coefficients within the HBOT and control groups separately. Only correlations with \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.05 and\u0026nbsp;\u003cem\u003er\u003c/em\u003e \u0026gt; 0.5 were retained to construct undirected weighted networks.\u003cbr\u003eTo identify clusters of tightly co-regulated metabolites, we applied the Leiden community detection algorithm (resolution = 0.5).This graph-partitioning method optimizes modularity to reveal communities of nodes that share stronger internal than external connections, corresponding to functional or biochemical modules. Node colours in the network plots reflect these communities, which represent metabolite groups potentially involved in coordinated biochemical pathways.Pathway Analysis: Significantly altered metabolites were mapped to known biochemical pathways using HMDB [25], Kyoto Encyclopedia of Genes and Genomes (KEGG) [26] and Reactome [27] databases.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eLinear Mixed-Effects Model Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LME model identified 10 metabolites with significant Group \u0026times; Day interactions (\u003cem\u003eP \u0026lt;\u003c/em\u003e 0.05), indicating differential trajectories between HBOT and control groups. Patient ID was modeled as a random effect. Mixed-effects modeling revealed a distinct pattern of metabolic alterations associated with HBOT Importantly, all eleven metabolites that reached nominal significance (\u003cem\u003eP \u0026lt;\u003c/em\u003e 0.05) also remained significant after controlling multiple testing using Storey\u0026rsquo;s q-value method (q \u0026lt; 0.05), confirming the robustness of these associations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the significantly modulated metabolites, 7,8-dihydroxanthopterin (\u0026beta; = 0.201, 95% CI [0.092, 0.309], \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001, q = 0.005), xanthopterin (\u0026beta; = 0.187, 95% CI [0.075, 0.300], \u003cem\u003eP =\u003c/em\u003e 0.001, q = 0.005), and creatine riboside (\u0026beta; = 0.198, 95% CI [0.047, 0.350], \u003cem\u003eP =\u003c/em\u003e 0.010, q = 0.028) demonstrated positive coefficients, indicating an increase in HBOT relative to controls. In contrast, several metabolites exhibited significant decreases, including DL-arginine (\u0026beta; = \u0026ndash;0.158, 95% CI [\u0026ndash;0.253, \u0026ndash;0.063], \u003cem\u003eP =\u003c/em\u003e 0.001, q = 0.005), homo-L-arginine (\u0026beta; = \u0026ndash;0.190, 95% CI [\u0026ndash;0.307, \u0026ndash;0.072], \u003cem\u003eP =\u003c/em\u003e 0.002, q = 0.005), glycocyamine (\u0026beta; = \u0026ndash;0.210, 95% CI [\u0026ndash;0.342, \u0026ndash;0.078], \u003cem\u003eP =\u003c/em\u003e 0.002, q = 0.006), Phosphatidylserine (O-20:0/0:0) (\u0026beta; = \u0026ndash;0.213, 95% CI [\u0026ndash;0.368, \u0026ndash;0.058], \u003cem\u003eP =\u003c/em\u003e 0.007, q = 0.030), Phosphatidylserine (O-18:0/0:0) (\u0026beta; = \u0026ndash;0.204, 95% CI [\u0026ndash;0.354, \u0026ndash;0.055], \u003cem\u003eP =\u003c/em\u003e 0.007, q = 0.030), L-threonine (\u0026beta; = \u0026ndash;0.116, 95% CI [\u0026ndash;0.207, \u0026ndash;0.026], \u003cem\u003eP =\u003c/em\u003e 0.012, q = 0.018), and Alpha-Glycerylphosphorylcholine (Alpha-GPC; \u0026beta; = \u0026ndash;0.113, 95% CI [\u0026ndash;0.214, \u0026ndash;0.012], \u003cem\u003eP =\u003c/em\u003e 0.029, q = 0.030).\u003c/p\u003e\n\u003cp\u003ePermutation tests confirmed the robustness of our mixed-effects results. Nine out of eleven metabolites remained significant at p_perm \u0026lt; 0.05, while (Alpha-GPC reached borderline empirical significance (p_perm \u0026asymp; 0.05). These findings indicate that most observed interaction effects are unlikely to arise from chance, even when relaxing distributional assumptions, thereby strengthening confidence in HBOT-associated metabolic alterations\u003c/p\u003e\n\u003cp\u003eArginine-related metabolites (DL-Arginine, Homo-L-Arginine) decreased in the HBOT group, contrasting with a stable increase in controls. Pterin derivatives (7,8-Dihydroxanthopterin, Xanthopterin) exhibited pronounced increases, particularly evident by day 10 in HBOT patients. Creatine pathway intermediates, including Creatine Riboside also displayed upward trends under HBOT, whereas no consistent change was observed in the control group. Phosphatidylserine [PS (O-18:0/0:0), PS (O-20:0/0:0)] demonstrated marked elevations in both groups. L-Threonine presented a transient increase followed by stabilization, while Alpha-GPC showed a delayed but distinct rise on day 10. Together, these patterns highlight robust time-dependent metabolic reprogramming associated with HBOT, absent in the control group (Fig. 2). Data are shown as mean \u0026plusmn; 95% CI across study days (1-pre, 1-post, day 5, day 10). Purple lines denote the HBOT group and orange lines denote the control group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation and network analysis demonstrated that HBOT markedly increased both the number and strength of metabolite\u0026ndash;metabolite associations compared with controls (Fig. 3).\u003c/p\u003e\n\u003cp\u003eIn the HBOT group (Fig. 3A), highly significant positive correlations were observed within the pterin pathway, with 7,8-dihydroxanthopterin and xanthopterin showing an almost perfect association (r = 0.95, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001). This strong coupling extended to creatine riboside with xanthopterin (r = 0.71, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.05), suggesting crosstalk between folate/pterin metabolism and creatine biosynthesis under hyperbaric conditions. A second dense module was identified around the arginine\u0026ndash;creatine axis, where DL-arginine showed a strong correlation with glycocyamine (r = 0.77, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), homo-L-arginine (r = 0.62, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), and L-threonine (r = 0.65, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001). These associations point toward coordinated regulation of nitrogen handling, creatine turnover, and methylation reactions. DL-arginine also negatively correlated with 7,8-dihydroxanthopterin and xanthopterin (r = -0.62 and -0.61, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001). Additionally, phosphatidylserine species (PS O-18:0/0:0 and PS O-20:0/0:0) displayed a robust intra-class correlation (r = 0.89, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), with both lipids showing significant cross-links to creatine riboside (r = -0.59 and -0.62, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.00 and \u0026lt; 0.001). This indicates tighter integration between phospholipid remodeling and energy metabolism during HBOT.\u003c/p\u003e\n\u003cp\u003eIn contrast, the control group (Fig. 2B) exhibited a sparser and less modular network (Fig. 4). Although the pterin correlation between 7,8-dihydroxanthopterin and xanthopterin remained strong (r = 0.91, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), most other associations were considerably weaker or absent. For example, DL-arginine and glycocyamine displayed correlation (r = 0.54, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001) and a non-significant correlation with pterins. Links between phosphatidylserine and creatine metabolites were either missing or weaker than in HBOT patients (r \u0026le; 0.52).\u003c/p\u003e\n\u003cp\u003eHierarchical clustering (Ward\u0026rsquo;s distance) of significantly altered metabolites revealed highly consistent groupings between HBOT and control subjects (Fig. 3A and 2B). Compounds associated with pterin metabolism (7,8-dihydroxanthopterin and xanthopterin) clustered together with creatine riboside in both groups, while Alpha-GPC was consistently paired with DL-arginine. Similarly, glycocyamine grouped with PS (O-20:0/0:0), and DL-arginine and alpha-GPC, homo-L-arginine, PS (O-20:0/0:0), L-threonine create one cluster. These clusters were preserved across conditions, indicating that HBOT does not reorganize the underlying network structure of metabolic associations. To formally assess the similarity of correlation structures, we performed a Mantel test comparing the pairwise metabolite correlation matrices between the HBOT group and the control group. The analysis demonstrated a very strong correspondence (Mantel r = 0.929, \u003cem\u003eP =\u003c/em\u003e 0.001), confirming that the overall topology of metabolite\u0026ndash;metabolite relationships was nearly identical across groups.\u003c/p\u003e\n\u003cp\u003eTo further assess the interdependencies among significantly modulated metabolites, we constructed correlation-based metabolic networks separately for the HBOT and control groups. Community detection using the Leiden algorithm revealed a distinct modular organization of metabolites in both groups (Fig. 3, panels C\u0026ndash;D). The HBOT network was denser, comprising 41 significant edges compared with 28 in controls, indicating enhanced coordination of metabolic pathways under hyperbaric oxygen exposure. Within the HBOT network, a highly connected community was formed by DL-arginine, homo-L-arginine, L-threonine, and glycocyamine, reflecting tight interrelationships within arginine and amino acid metabolism. Lipid derivatives, including PS (O-18:0/0:0), PS (O-20:0/0:0), and Alpha-GPC, established an additional Leiden module that remained interconnected with the central amino acid cluster. Moreover, the pterin metabolites 7,8-dihydroxanthopterin and xanthopterin showed a strong positive correlation (r \u0026asymp; 0.95), serving as a hub-like axis of the HBOT network.\u003c/p\u003e\n\u003cp\u003eIn contrast, the control network displayed fewer connections, with metabolites largely organized into smaller, less integrated communities. Although the strong correlation between 7,8-dihydroxanthopterin and xanthopterin persisted, its surrounding interactions were markedly reduced compared with HBOT. Lipid metabolites also appeared more isolated, with weaker integration into the global network.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, Leiden clustering emphasized that HBOT promotes a tighter and more cohesive modular organization of metabolic pathways, particularly within arginine- and amino acid\u0026ndash;related metabolites, while the control group retained a fragmented, less coordinated structure. Permutation-based Network Comparison Test confirmed that these structural differences were statistically significant (diff = 0.189, \u003cem\u003eP =\u003c/em\u003e 0.018), supporting the conclusion that HBOT induces a profound reorganization of the metabolic correlation architecture.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolites that were significantly altered in the LME model were mapped to curated pathways using KEGG, Reactome, and HMDB references.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKey associations include:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cem\u003eDL-Arginine, Homo-L-Arginine:\u003c/em\u003e Arginine and Proline Metabolism (KEGG), Urea Cycle (Reactome).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eCreatine Riboside, Glycocyamine:\u003c/em\u003e Creatine Biosynthesis and Guanidino Compound Metabolism (HMDB).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eAlpha-GPC\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Choline Metabolism (HMDB).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eXanthopterin, 7,8-Dihydroxanthopterin:\u003c/em\u003e Pterin (HMDB) and Folate (KEGG) Pathways.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor pathway-level comparisons, enrichment scores were calculated as the mean log-transformed values of metabolites assigned to each pathway. Group differences between HBOT and control patients were assessed at individual study days using the non-parametric Mann\u0026ndash;Whitney U test, as the data did not conform to normal distribution assumptions. This approach allowed visualization of pathway-level enrichment trends and facilitated statistical inference without assuming homoscedasticity or normality. Pathway analysis demonstrated that metabolites significantly affected by HBOT were enriched in:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eArginine and Proline Metabolism: \u003cem\u003eP =\u003c/em\u003e 0.0017\u003c/li\u003e\n \u003cli\u003eCholine Metabolism: \u003cem\u003eP =\u003c/em\u003e 0.0300\u003c/li\u003e\n \u003cli\u003eNiacin Metabolism: \u003cem\u003eP =\u003c/em\u003e 0.0132\u003c/li\u003e\n \u003cli\u003ePterin and Folate Pathways: \u003cem\u003eP =\u003c/em\u003e 0.0334\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese findings suggest that HBOT modulates metabolic processes linked to cellular bioenergetics, lipid signaling, and oxidative stress adaptation (Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1769072771.png\" width=\"624\" height=\"283\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides one of the first systematic descriptions of metabolomic alterations in COVID-19 patients undergoing HBOT. The results demonstrate temporal changes in amino acid, pterin, and phospholipid pathways, together with a reorganization of metabolic network connectivity. These observations should be interpreted primarily as biochemical signatures. While they may suggest processes of potential relevance for vascular regulation, mitochondrial adaptation, and inflammatory balance, their translational significance requires confirmation in outcome-oriented studies.\u003c/p\u003e\n\u003cp\u003eWe identified ten metabolites with significant Group \u0026times; Day interactions, reflecting divergent trajectories between HBOT and control patients. The most consistent increases were observed in pterin derivatives (7,8-dihydroxanthopterin, xanthopterin) and creatine riboside, whereas decreases were noted in DL-arginine, homoarginine, glycocyamine, phosphatidylserines [PS (O-20:0/0:0), PS (O-18:0/0:0)], and threonine. Glycocyamine decreased after HBOT, consistent with lower flux through creatine biosynthesis (guanidinoacetate methyltransferase) and potential coupling to methylation/energy pathways. Alpha-GPC also showed a transient elevation at baseline in HBOT patients, followed by a decline by day 10.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese changes are consistent with metabolic axes previously implicated in COVID-19 severity. Reduced arginine availability and perturbations in nitric oxide (NO) metabolism have been linked to endothelial dysfunction and hyperinflammation [9\u0026ndash;16]. In our cohort, lower DL-arginine and homo-L-arginine in HBOT patients coincided with reduced IL-6 and CRP in the same group [3, 17]. This pattern may suggest altered arginine utilization, though causality cannot be inferred without larger mechanistic studies.\u003c/p\u003e\n\u003cp\u003eAlterations in phospholipid metabolism provide additional mechanistic hypotheses. Decreases in phosphatidylserines and alpha-GPC suggest remodeling of membrane composition and choline metabolism, disturbances also reported in sepsis and ARDS [28]. The transient increase and subsequent normalization of alpha-GPC in HBOT patients could reflect adaptive membrane remodeling under hyperoxic stress, though its physiological implications remain unclear.\u003c/p\u003e\n\u003cp\u003eBeyond individual metabolites, HBOT was associated with a pronounced reorganization of metabolic interactions. Mantel testing confirmed stronger overall correlation structures in HBOT patients (r = 0.929, \u003cem\u003eP =\u003c/em\u003e 0.001), and network analysis demonstrated a denser topology with 41 significant correlations versus 28 in controls (\u003cem\u003eP =\u003c/em\u003e 0.018). In the extended correlation maps (see Supplementary Fig. S1A\u0026ndash;B), HBOT produced a broader, more integrated pattern of positive associations linking arginine-derived guanidino compounds, pterins, phosphatidylserines, and acylcarnitines\u0026mdash;pathways central to endothelial nitric-oxide signaling, mitochondrial energetics, and membrane remodeling. The corresponding HBOT network (Supplementary Fig. S1C) displays tighter clustering among these metabolites, in contrast to the fragmented and sparsely connected control network (Supplementary Fig. S1D).\u003c/p\u003e\n\u003cp\u003eHowever, this reorganization is not uniformly beneficial. The HBOT network also exhibits several new inverse correlations \u0026ndash; particularly between redox-active or methylation-related metabolites (e.g., between phospholipids and arginine derivatives) \u0026ndash; suggesting that part of the enhanced connectivity reflects metabolic compensation or redox strain rather than purely homeostatic regulation. Such bidirectional correlations likely capture transient redistribution of cofactors nicotinamide adenine dinucleotide\u0026nbsp;oxidized to reduced (NAD⁺/NADH), and S-adenosylmethionine to S-adenosylhomocysteine (SAM/SAH) under repeated hyperoxic exposure.\u003c/p\u003e\n\u003cp\u003eThe pathway-level comparisons (Supplementary Fig. S2) complement these findings. At baseline, pathway means did not differ between groups, but by Day 10 significant changes emerged within arginine/urea-cycle, creatine, and phospholipid modules, all up-regulated under HBOT. These alterations mirror the main dataset and support coordinated late-phase metabolic adaptation. Conversely, pathways linked to tryptophan/indole metabolism, histidine/imidzazole intermediates, and bile-acid signaling remained largely unchanged, indicating that HBOT selectively engages energy- and membrane-related systems while leaving peripheral amino-acid and microbiota-derived metabolism relatively unaffected. It should be emphasized that, in the absence of a healthy control group, these HBOT-related changes reflect adaptive and redox-regulated processes that accumulate throughout the treatment period in hospitalized COVID-19 patients, rather than universal markers of recovery or normalization toward healthy physiology.\u003c/p\u003e\n\u003cp\u003eTaken together, these results show that HBOT promotes a more cohesive but selectively remodeled metabolic network, one that strengthens crosstalk between nitric-oxide synthesis, membrane phospholipid turnover, and mitochondrial fuel utilization, yet does so under increased oxidative and methylation pressure. This dual nature of the response, adaptive but metabolically demanding, fits within the broader context of hyperoxia-induced hormesis, where transient reactive oxygen species\u0026nbsp;(ROS) production and redox perturbation trigger long-term cellular resilience\u003c/p\u003e\n\u003cp\u003eSuch remodeling may also intersect with inflammatory and oxidative pathways. COVID-19 is characterized by cytokine surge, endothelial dysfunction, and mitochondrial stress; HBOT has been shown to mitigate cytokine release and improve oxygenation [3, 17, 29]. Controlled ROS generation during hyperoxia can induce antioxidant defenses, including upregulation of superoxide dismutase and catalase [30, 31]. HBOT-driven improvements in tissue oxygenation could further stabilize mitochondrial respiration and Adenosine Triphosphate\u0026nbsp;(ATP) production, enhancing redox balance [32]. Experimental data support increased mitochondrial biogenesis and efficiency following HBOT [33, 34]. The observed network tightening around niacin- and threonine-related metabolites\u0026mdash;both connected to NAD⁺ metabolism \u0026ndash; aligns with these mitochondrial mechanisms. Nevertheless, the coexistence of strengthened coordination and inverse coupling among oxidative-stress markers suggests that the HBOT response involves both adaptive and compensatory elements, warranting validation in larger, outcome-based cohorts.\u003c/p\u003e\n\u003cp\u003eThe metabolic signatures observed here overlap with those described in other hyperinflammatory syndromes, including sepsis and ARDS. Sepsis has been associated with remodeling of amino acid metabolism and the nitrogen cycle [35], while ARDS has shown alterations in phospholipid metabolism and tryptophan catabolism [28,36]. Our results support these findings, involving arginine/NO, phospholipid, and pterin pathways. Whereas sepsis and severe COVID-19 typically display arginine depletion and kynurenine accumulation [37], HBOT in this study was associated with patterns suggesting partial rebalancing.\u003c/p\u003e\n\u003cp\u003eThese processes may hold particular importance for vascular health. COVID-19 is now recognized to increase the risk of ischemic stroke, including young adults without conventional vascular risk factors. Large-vessel occlusions have been reported in patients under 50 years of age [38, 39]. Comparative studies found stroke incidence to be higher in COVID-19 compared with influenza [40, 41], while registry data confirmed cerebrovascular events across diverse populations [42]. Case reports have even described ischemic stroke in adolescents [43]. The proposed mechanisms \u0026ndash; cytokine storm, IDO1 activation and kynurenine pathway upregulation, endothelial dysfunction, and coagulopathy [4] \u0026ndash; closely intersect with the metabolic pathways observed in our study.\u003c/p\u003e\n\u003cp\u003eAmong the pathways highlighted in this study, arginine metabolism stands out as particularly important, given its pivotal role in nitric oxide production, which is the primary regulator of vascular tone and endothelial homeostasis. Alterations in arginine and its downstream derivatives may therefore represent a mechanistic link between HBOT and improved vascular function. In this context, HBOT-related changes in arginine- and pterin-associated metabolites are likely to intersect with nitric oxide bioavailability and endothelial signaling. Such reprogramming could point toward a stabilizing effect on the vascular system, though this remains speculative and requires confirmation in larger cohorts with dedicated vascular endpoints. Independent evidence supports the possibility of vascular effects of HBOT. Preclinical and clinical studies suggest improvements in endothelial function, enhanced nitric oxide\u0026ndash;dependent vasodilation, and reductions in oxidative stress [16, 32, 34]. Schottlender et al. demonstrated that HBOT supports mitochondrial respiration and limits oxidative stress in endothelial cells [32]. Our previous work reported no evidence of endothelial injury in patients receiving HBOT, with reductions in arginine derivatives [16]. Batinac et al. further highlighted HBOT as a potential emerging modality in cardiovascular disease through its impact on vascular reactivity and endothelial repair [34].\u003c/p\u003e\n\u003cp\u003eTogether, these findings underscore the importance of linking metabolic data to vascular outcomes in future studies. Such research will be essential to determine whether HBOT-related biochemical reprogramming may contribute to the mitigation of COVID-19\u0026ndash;associated vascular complications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimittions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe most important limitation is the absence of a healthy control cohort. While HBOT is clinically established for wound and fracture healing, enrolling age-matched, virus-negative healthy volunteers during the COVID-19 pandemic was not ethically or logistically feasible. Therefore, our conclusions are confined to treatment-related metabolic adaptations observed in hospitalized patients rather than universal recovery markers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cohort size was small, limiting generalizability, and the study population included only patients in the acute phase of the disease; therefore, the findings cannot be directly extrapolated to the chronic form of long-COVID-19. The precise molecular targets of HBOT remain incompletely defined, and untargeted metabolomics could reveal additional pathways. Treatment parameters\u0026mdash;including pressure, duration, and session frequency \u0026ndash; require standardization. Accurate identification of pterins is particularly challenging due to their structural isomerism, which may limit the interpretability of changes observed within this pathway. Finally, while HBOT appears to modulate systemic metabolism, integration with established immunomodulatory therapies such as IL-1/IL-6 blockade or JAK inhibition should also be considered [34, 45].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuture research should incorporate targeted redox and oxidative lipidomic assays to validate and quantify specific oxidative-stress biomarkers. In addition, future studies including healthy, age- and sex-matched controls will be crucial to determine whether the observed metabolic trajectories reflect broader processes of physiological restoration beyond treatment-related adaptations. These complementary analyses will facilitate a more detailed mechanistic understanding of HBOT-induced redox adaptation and its association with endothelial recovery. Integrating metabolomics with transcriptomic and proteomic methodologies is recommended to elucidate the regulatory mechanisms underlying the effects of HBOT. Additionally, longitudinal studies that include vascular and inflammatory endpoints are essential to determine whether the observed biochemical reprogramming provides protective benefits for patient outcomes.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHBOT was associated with systemic metabolic adaptations in COVID-19 patients, including increases in pterin derivatives and creatine riboside, decreases in arginine-related metabolites, phospholipids, and glycocyamine, as well as strengthened metabolic network connectivity. These signatures overlap with those previously reported in sepsis and ARDS, suggesting that HBOT may modulate biochemical axes common to hyperinflammatory syndromes. Overall, these results support the hypothesis that HBOT induces systemic metabolic adaptations, particularly within amino acid and lipid metabolism pathways. Future studies should explore the mechanistic underpinnings of these findings and their potential therapeutic implications.\u003c/p\u003e\n\u003cp\u003eWhile such patterns may point toward a potential protective influence on vascular function, they should be interpreted as adaptive metabolic responses occurring during HBOT in hospitalized COVID-19 patients. In the absence of healthy controls, these changes cannot be considered general recovery markers but rather reflect treatment-period adaptations. Confirmation of their clinical and physiological significance will require larger, longitudinal studies including healthy, age-matched cohorts and explicit clinical endpoints.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are especially indebted to all employees of the designated COVID-19 clinics of the Military Medical Institute \u0026ndash; National Research Institute involved in the implementation of this clinical trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study and met ICMJE guidelines\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eConceptualization, : J.S., K.B. and J.K.; methodology, J.S., K.B., N.J., J.K., K.U. and J.T.; software, N.J.; formal analysis, N.J. and K.U.; investigation, J.S., J.K., K.B., A.L. and K.K.; resources, J.S., K.B., A.L., K.K. and R.T.S.; data curation: N.J., K.B. and J.T.; writing\u0026mdash;original draft preparation, N.J. and J.S.; writing\u0026mdash;review and editing, N.J., J.S., K.B., J.K., A.L., K.K., K.U, J.T and R.T.S.; visualization, N.J; supervision, J.S. and J.K.; funding acquisition, J.S. and J.K. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethics Committee of Military Institute of Medicine \u0026ndash; National Science Institute\u0026nbsp;(no. 25/WIM/2020 approved on 17 Jun 2020) for studies involving humans.\u0026nbsp;Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the Polish Medical Research Agency (grant 2020/ABM/COVID19/0043).\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eOliveira, L. B., Mwangi, V. I., Sartim, M. A., et al. Metabolomic profiling of plasma reveals differential disease severity markers in COVID-19 patients. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 844283 (2022). doi:10.3389/fmicb.2022.844283.\u003c/li\u003e\n \u003cli\u003eGorenstein, S. A., Castellano, M. L., Slone, E. S., et al. Hyperbaric oxygen therapy for COVID-19 patients with respiratory distress: treated cases versus propensity-matched controls. \u003cem\u003eUndersea Hyperb. Med.\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 405\u0026ndash;13 (2020).\u003c/li\u003e\n \u003cli\u003eSiewiera, J., Brodaczewska, K., Jermakow, N., Lubas, A., Kłos, K., Majewska, A., et al.\u0026nbsp;Effectiveness of hyperbaric oxygen therapy in SARS-CoV-2 pneumonia: the primary results of a randomised clinical trial. \u003cem\u003eJ. Clin. 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JAK-inhibitors for coronavirus disease-2019 (COVID-19): a meta-analysis. \u003cem\u003eLeukemia.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 2616\u0026ndash;20 (2021). doi:10.1038/s41375-021-01266-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HBOT, COVID-19, Arginine metabolism, Pterin pathway, Oxidative stress, Endothelial function","lastPublishedDoi":"10.21203/rs.3.rs-8643601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8643601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"COVID-19, caused by SARS-CoV-2, is increasingly recognized as a systemic disorder with inflammation, endothelial dysfunction, and metabolic perturbations. This study aimed to characterize metabolic changes in COVID-19 patients undergoing hyperbaric oxygen therapy (HBOT). The clinical trial was registered in EudraCT (2020-002722-90, 3 May 2020), prior to patient enrollment. Thirty hospitalized patients were randomized to HBOT (n=14) or standard care (n=14). The HBOT group received five sessions at 2.5 ATA for 75 minutes. Serum metabolites were analyzed using high-resolution LC-MS. Significant changes were observed in metabolites related to arginine/NO metabolism, creatine turnover, phospholipid remodeling, and pterin derivatives. Pathway analysis highlighted the urea cycle, glycerophospholipid remodeling, niacin metabolism, and folate/pterin pathways. HBOT patients showed enhanced metabolic network connectivity. The findings suggest that HBOT induces systemic metabolic adaptations involving amino acid and lipid pathways, as well as redox-related metabolites, which may intersect with vascular and inflammatory regulation.","manuscriptTitle":"Metabolic Reprogramming of Endothelial-Related Pathways in COVID-19 Patients Treated with Hyperbaric Oxygen Therapy: A Randomized Clinical Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 10:26:07","doi":"10.21203/rs.3.rs-8643601/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-13T10:04:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T14:22:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T17:05:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249765111729069221717811557294376781181","date":"2026-02-11T16:43:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319703194090146123993789622656865122765","date":"2026-02-11T16:01:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-11T15:15:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-23T13:47:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-22T09:49:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-21T20:53:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-21T20:45:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b9037480-8b1e-4d8f-a019-0550954be024","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":61538134,"name":"Biological sciences/Biochemistry"},{"id":61538135,"name":"Health sciences/Diseases"},{"id":61538136,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-03-23T16:09:58+00:00","versionOfRecord":{"articleIdentity":"rs-8643601","link":"https://doi.org/10.1038/s41598-026-44520-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-18 15:58:49","publishedOnDateReadable":"March 18th, 2026"},"versionCreatedAt":"2026-01-22 10:26:07","video":"","vorDoi":"10.1038/s41598-026-44520-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-44520-6","workflowStages":[]},"version":"v1","identity":"rs-8643601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8643601","identity":"rs-8643601","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}