Mohammad Mehdi Banoei

Traumatic brain injury (TBI) disrupts brain metabolism, which evolves over time and varies with the severity of the injury. Monitoring these metabolomic changes may reveal biomarkers indicating early damage, mechanisms of injury, and potentially help predict outcomes. This study used untargeted plasma metabolomics to investigate systemic time-dependent metabolic changes in mice exposed to controlled cortical impact (CCI) with or without replacement of a modified skull cap designed to reduce compensatory space for cerebral edema modelling a severe closed skull TBI, compared to sham controls. Male mice were subjected to CCI, CCI + CAP, or sham procedures comprised a scalp incision or a craniotomy. Plasma samples were collected at 4, 8, and 16 h, and 3 and 7 days after injury. Hydrophilic interaction liquid chromatography–mass spectrometry (HILIC-MS) was used to profile metabolites in all groups and time points, while ion-pair liquid chromatography–mass spectrometry (RPIPLC-MS) was used in CCI and sham mice at the early time points. The largest metabolic changes occurred at 8 h post-injury, distinguishing mice with CCI from sham controls. The early changes concerned metabolism of amino acids, energy, and nucleotide pathways, with metabolites such as succinate, phenylalanine, and cytidine showing significant changes. By 7 days, the metabolic patterns of the injured mice, especially CCI mice, had partially converged toward the sham state, although oxidative and mitochondrial disturbances persisted. The CCI + CAP mice had more pronounced and persistent metabolic disturbances compared to the CCI mice, which may reflect the effect of increased intracranial pressure post-injury. Plasma metabolomics can efficiently capture the evolving biochemical effects of TBI. The findings identified circulating metabolites that were associated with progression and severity of brain injury and provide a basis for future translational studies in human TBI.

Background: This study aimed to analyze metabolic changes in blood samples from patients with confirmed COVID-19 to explore the correlation between metabolomics and cytokines in survivors and non-survivors of SARS-CoV-2 infection. Understanding the complex biochemical and immunometabolic mechanisms underlying SARS-CoV-2 infection is essential for elucidating the pathophysiology and virulence of COVID-19. Methods: This study included 40 hospitalized COVID-19 patients and 40 healthy controls. Serum metabolic profiles were analyzed using ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS), and cytokine levels were measured using ELISA. Results: Our study defined three clear metabolic phenotypes among survivors and non-survivors of COVID-19 compared with healthy controls, which might be related to mortality, severity, and disease burden. A strong relationship was observed between certain inflammatory markers, including IL-1β, IL-2, IFN-β, IFN-γ, IL-17, and GM-CSF, as well as several metabolites, particularly in COVID-19 non-survivors, such as LysoPCs, 3-hydroxykynurenine, and serotonin. Different metabolite-cytokine correlation patterns were observed according to patient outcomes, indicating unique correlations between metabolic and immune responses in survivors and non-survivors. Metabolic phenotypes were associated with clinical outcomes, comorbidities, and sex-related differences. Kynurenine and related metabolites of tryptophan metabolism were closely correlated with COVID-19 severity, age, and mortality. Compared with survivors and healthy controls, non-survivors displayed higher IL-6, together with distinct metabolic changes. These included increased kynurenine through the IDO1 pathway, elevated glucose and lactate reflecting hyperglycolysis and energy stress, and higher xanthosine from purine turnover. Stronger cytokine–metabolite correlations in this group point to tightly linked immunometabolic activation. Conclusions: Metabolomic profiling revealed distinct metabolic phenotypes that could be associated with the severity and inflammation levels of COVID-19. Correlation analysis between metabolites and cytokines demonstrated strong intercorrelations between specific metabolites and cytokines, indicating a strong interrelationship between inflammatory markers and metabolic alterations. Specific metabolic pathways associated with cytokines and their clinical relevance may serve as potential therapeutic targets.

Background: Sarcoidosis is a granulomatous disease affecting multiple organ systems and poses a diagnostic challenge due to its diverse clinical manifestations and absence of specific diagnostic tests. Currently, blood biomarkers such as ACE, sIL-2R, CD163, CCL18, serum amyloid A, and CRP are employed to aid in the diagnosis and monitoring of sarcoidosis. Metabolomics holds promise for identifying highly sensitive and specific biomarkers. This study aimed to leverage metabolomics for the early diagnosis of sarcoidosis and to identify metabolic phenotypes associated with disease progression. Methods: Serum samples from patients with sarcoidosis (n = 40, including stage 1 to stage 4), were analyzed for metabolite levels by semi-untargeted liquid chromatography–mass spectrometry (LC-MS). Metabolomics data from patients with sarcoidosis were compared with those from patients with COVID-19 and healthy controls to identify distinguishing metabolic biosignatures. Univariate and multivariate analyses were applied to obtain diagnostic and prognostic metabolic phenotypes. Results: Significant changes in metabolic profiles distinguished stage 1 sarcoidosis from healthy controls, with potential biomarkers including azelaic acid, itaconate, and glutarate. Distinct metabolic phenotypes were observed across the stages of sarcoidosis, with stage 2 exhibiting greater heterogeneity compared with stages 1, 3, and 4. Conclusions: we explored immunometabolic phenotypes by comparing patients with sarcoidosis with patients with COVID-19 and healthy controls, revealing potential metabolic pathways associated with acute and chronic inflammation across the stages of sarcoidosis.

Background: In 2019, SARS-CoV-2, the novel coronavirus, entered the world scene, presenting a global health crisis with a broad spectrum of clinical manifestations. Recognizing the significance of metabolomics as the omics closest to symptomatology, it has become a useful tool for predicting clinical outcomes. Several metabolomic studies have indicated variations in the metabolome corresponding to different disease severities, highlighting the potential of metabolomics to unravel crucial insights into the pathophysiology of SARS-CoV-2 infection. Methods: The PRISMA guidelines were followed for this scoping review. Three major scientific databases were searched: PubMed, the Directory of Open Access Journals (DOAJ), and BioMed Central, from 2020 to 2024. Initially, 2938 articles were identified and vetted with specific inclusion and exclusion criteria. Of these, 42 articles were retrieved for analysis and summary. Results: Metabolites were identified that were repeatedly noted to change with COVID-19 and its severity. Phenylalanine, glucose, and glutamic acid increased with severity, while tryptophan, proline, and glutamine decreased, highlighting their association with COVID-19 severity. Additionally, pathway analysis revealed that phenylalanine, tyrosine and tryptophan biosynthesis, and arginine biosynthesis were the most significantly impacted pathways in COVID-19 severity. Conclusions: COVID-19 severity is intricately linked to significant metabolic alterations that span amino acid metabolism, energy production, immune response modulation, and redox balance.

Sepsis is the result of an uncontrolled host inflammatory response to infection that may lead to septic shock with multiorgan failure and a high mortality rate. There is an urgent need to improve early diagnosis and to find markers identifying those who will develop septic shock and certainly a need to develop targeted treatments to prevent septic shock and its high mortality. Herein, we explore metabolic alterations due to mesenchymal stromal cell (MSC) treatment of septic shock. The clinical findings for this study were already reported; MSC therapy was well-tolerated and safe in patients in this phase I clinical trial. In this exploratory metabolomics study, 9 out of 30 patients received an escalating dose of MSC treatment, while 21 patients were without MSC treatment. Serum metabolomics profiling was performed to detect and characterize metabolite changes due to MSC treatment and to help determine the sample size needed for a phase II clinical trial and to define a metabolomic response to MSC treatment. Serum metabolites were measured using ¹H-NMR and HILIC-MS at times 0, 24 and 72 h after MSC infusion. The results demonstrated the significant impact of MSC treatment on serum metabolic changes in a dose- and time-dependent manner compared to non-MSC-treated septic shock patients. This study suggests that plasma metabolomics can be used to assess the response to MSC therapy and that treatment-related metabolomics effects can be used to help determine the sample size needed in a phase II trial. As this study was not powered to detect outcome, how the treatment-induced metabolomic changes described in this study of MSC-treated septic shock patients are related to outcomes of septic shock in the short and long term will need to be explored in a larger adequately powered phase II clinical trial.

Background: Diagnosis and prognostication of severe traumatic brain injury (sTBI) continue to be problematic despite years of research efforts. There are currently no clinically reliable biomarkers, though advances in protein biomarkers are being made. Utilizing Omics technology, particularly metabolomics, may provide new diagnostic biomarkers for sTBI. Several published studies have attempted to determine the specific metabolites and metabolic pathways involved; these studies will be reviewed. Aims: This scoping review aims to summarize the current literature concerning metabolomics in sTBI, review the comprehensive data, and identify commonalities, if any, to define metabolites with potential clinical use. In addition, we will examine related metabolic pathways through pathway analysis. Methods: Scoping review methodology was used to examine the current literature published in Embase, Scopus, PubMed, and Medline. An initial 1090 publications were identified and vetted with specific inclusion criteria. Of these, 20 publications were selected for further examination and summary. Metabolic data was classified using the Human Metabolome Database (HMDB) and arranged to determine the ‘recurrent’ metabolites and classes found in sTBI. To help understand potential mechanisms of injury, pathway analysis was performed using these metabolites and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database. Results: Several metabolites related to sTBI and their effects on biological pathways were identified in this review. Across the literature, proline, citrulline, lactate, alanine, valine, leucine, and serine all decreased in adults post sTBI, whereas both octanoic and decanoic acid increased. Hydroxy acids and organooxygen compounds generally increased following sTBI, while most carboxylic acids decreased. Pathway analysis showed significantly affected glycine and serine metabolism, glycolysis, branched-chain amino acid (BCAA) metabolism, and other amino acid metabolisms. Interestingly, no tricarboxylic acid cycle metabolites were affected. Conclusion: Aside from a select few metabolites, classification of a metabolic profile proved difficult due to significant ambiguity between study design, sample size, type of sample, metabolomic detection techniques, and other confounding variables found in sTBI literature. Given the trends found in some studies, further metabolomics investigation of sTBI may be useful to identify clinically relevant metabolites.

Background: Prognostication is very important to clinicians and families during the early management of severe traumatic brain injury (sTBI), however, there are no gold standard biomarkers to determine prognosis in sTBI. As has been demonstrated in several diseases, early measurement of serum metabolomic profiles can be used as sensitive and specific biomarkers to predict outcomes. Methods: We prospectively enrolled 59 adults with sTBI (Glasgow coma scale, GCS ≤ 8) in a multicenter Canadian TBI (CanTBI) study. Serum samples were drawn for metabolomic profiling on the 1st and 4th days following injury. The Glasgow outcome scale extended (GOSE) was collected at 3- and 12-months post-injury. Targeted direct infusion liquid chromatography-tandem mass spectrometry (DI/LC–MS/MS) and untargeted proton nuclear magnetic resonance spectroscopy (¹H-NMR) were used to profile serum metabolites. Multivariate analysis was used to determine the association between serum metabolomics and GOSE, dichotomized into favorable (GOSE 5–8) and unfavorable (GOSE 1–4), outcomes. Results: Serum metabolic profiles on days 1 and 4 post-injury were highly predictive (Q² > 0.4–0.5) and highly accurate (AUC > 0.99) to predict GOSE outcome at 3- and 12-months post-injury and mortality at 3 months. The metabolic profiles on day 4 were more predictive (Q² > 0.55) than those measured on day 1 post-injury. Unfavorable outcomes were associated with considerable metabolite changes from day 1 to day 4 compared to favorable outcomes. Increased lysophosphatidylcholines, acylcarnitines, energy-related metabolites (glucose, lactate), aromatic amino acids, and glutamate were associated with poor outcomes and mortality. Discussion: Metabolomic profiles were strongly associated with the prognosis of GOSE outcome at 3 and 12 months and mortality following sTBI in adults. The metabolic phenotypes on day 4 post-injury were more predictive and significant for predicting the sTBI outcome compared to the day 1 sample. This may reflect the larger contribution of secondary brain injury (day 4) to sTBI outcome. Patients with unfavorable outcomes demonstrated more metabolite changes from day 1 to day 4 post-injury. These findings highlighted increased concentration of neurobiomarkers such as N-acetylaspartate (NAA) and tyrosine, decreased concentrations of ketone bodies, and decreased urea cycle metabolites on day 4 presenting potential metabolites to predict the outcome. The current findings strongly support the use of serum metabolomics, that are shown to be better than clinical data, in determining prognosis in adults with sTBI in the early days post-injury. Our findings, however, require validation in a larger cohort of adults with sTBI to be used for clinical practice.

Background: The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods: Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results: SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q² = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions: An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

Metabolomics is a tool that has been used for the diagnosis and prognosis of specific diseases. The purpose of this study was to examine if metabolomics could be used as a potential diagnostic and prognostic tool for H1N1 pneumonia. Our hypothesis was that metabolomics can potentially be used early for the diagnosis and prognosis of H1N1 influenza pneumonia. 1H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry were used to profile the metabolome in 42 patients with H1N1 pneumonia, 31 ventilated control subjects in the intensive care unit (ICU), and 30 culture-positive plasma samples from patients with bacterial community-acquired pneumonia drawn within the first 24 h of hospital admission for diagnosis and prognosis of disease. We found that plasma-based metabolomics from samples taken within 24 h of hospital admission can be used to discriminate H1N1 pneumonia from bacterial pneumonia and nonsurvivors from survivors of H1N1 pneumonia. Moreover, metabolomics is a highly sensitive and specific tool for the 90-day prognosis of mortality in H1N1 pneumonia. This study demonstrates that H1N1 pneumonia can create a quite different plasma metabolic profile from bacterial culture-positive pneumonia and ventilated control subjects in the ICU on the basis of plasma samples taken within 24 h of hospital/ICU admission, early in the course of disease.