Exploring plasma metabolomic changes in sepsis: a clinical matching study based on gas chromatography-mass spectrometry

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.

Exploring the potential mechanism of Xuebijing injection against sepsis based on metabolomics and network pharmacology

Sepsis is a major contributor to the death of critically ill patients globally, in which metabolic disturbance is observed. Xuebijing injection (XBJ), a well-known traditional Chinese medicine, has received approval by the State Food and Drug Administration (SFDA) of China owing to its satisfactory clinical therapeutic effect. Nowadays, it has been applied clinically to the treatment of sepsis, but its effect on metabolic disorders remains unclear. In the present study, we sought to explore its underlying mechanism by employing a combination of network pharmacology and metabolomics. Initially, its protective effects were validated using a sepsis rat model created through cecal ligation puncture (CLP). Subsequently, the metabonomic strategy was utilized to discriminate the differential metabolic markers. Meanwhile, a comprehensive view of the potential ingredient-target-disease network was constructed based on a network pharmacology analysis. Next, the network diagram was constructed by integrating the results of network pharmacology and metabonomics. Finally, qRT-PCR together with Western blot was used to validate the expression levels of the associated genes. Based on our findings, we identified 34 differential metabolites in the sepsis group and 26 distinct metabolites in the XBJ group, with 8 common biological metabolites predominantly associated with arginine and proline metabolism. Through comprehensive analysis, we identified 21 genes that regulate metabolites, and qRT-PCR validation was conducted on six of these genes in both liver and kidney tissues. Additionally, XBJ demonstrated the capability to inhibit the activation of the NF-kB signaling pathway in both liver and kidney tissues, leading to a reduction in the occurrence of inflammatory responses. In summary, our study has validated the complexity of the natural compounds within XBJ and elucidated their potential mechanisms for addressing CLP-induced metabolic disturbances. This work contributes to our understanding of the bioactive compounds and their associated targets, providing insights into the potential molecular mechanisms involved.

Systemic Metabolomic Profiles in Adult Patients with Bacterial Sepsis: Characterization of Patient Heterogeneity at the Time of Diagnosis

Sepsis is a dysregulated host response to infection that causes potentially life-threatening organ dysfunction. We investigated the serum metabolomic profile at hospital admission for patients with bacterial sepsis. The study included 60 patients; 35 patients fulfilled the most recent 2016 Sepsis-3 criteria whereas the remaining 25 patients only fulfilled the previous Sepsis-2 criteria and could therefore be classified as having systemic inflammatory response syndrome (SIRS). A total of 1011 identified metabolites were detected in our serum samples. Ninety-seven metabolites differed significantly when comparing Sepsis-3 and Sepsis-2/SIRS patients; 40 of these metabolites constituted a heterogeneous group of amino acid metabolites/peptides. When comparing patients with and without bacteremia, we identified 51 metabolites that differed significantly, including 16 lipid metabolites and 11 amino acid metabolites. Furthermore, 42 metabolites showed a highly significant association with the maximal total Sequential Organ Failure Assessment (SOFA )score during the course of the disease (i.e., Pearson's correlation test, p-value < 0.005, and correlation factor > 0.6); these top-ranked metabolites included 23 amino acid metabolites and a subset of pregnenolone/progestin metabolites. Unsupervised hierarchical clustering analyses based on all 42 top-ranked SOFA correlated metabolites or the subset of 23 top-ranked amino acid metabolites showed that most Sepsis-3 patients differed from Sepsis-2/SIRS patients in their systemic metabolic profile at the time of hospital admission. However, a minority of Sepsis-3 patients showed similarities with the Sepsis-2/SIRS metabolic profile even though several of them showed a high total SOFA score. To conclude, Sepsis-3 patients are heterogeneous with regard to their metabolic profile at the time of hospitalization.

Exploring the Muscle Metabolomics in the Mouse Model of Sepsis-Induced Acquired Weakness

Background/Aim

We aimed to identify the differentially expressing metabolites (DEMs) in the muscles of the mouse model of sepsis-induced acquired weakness (sepsis-AW) using liquid chromatography-mass spectrometry (LC-MS).

Materials and Methods

Sepsis by cecal ligation puncture (CLP) with lower limb immobilization was used to produce a sepsis-AW model. After this, the grip strength of the C57BL/6 male mice was investigated. The transmission electron microscopy was utilized to determine the pathological model. LC-MS was used to detect the metabolic profiles within the mouse muscles. Additionally, a statistically diversified analysis was carried out.

Results

Compared to the sepsis group, 30 DEMs, including 17 upregulated and 13 down-regulated metabolites, were found in the sepsis-AW group. The enriched metabolic pathways including purine metabolism, valine/leucine/isoleucine biosynthesis, cGMP-PKG pathway, mTOR pathway, FoxO pathway, and PI3K-Akt pathway were found to differ between the two groups. The targeted metabolomics analysis explored significant differences between four amino acid metabolites (leucine, cysteine, tyrosine, and serine) and two energy metabolites (AMP and cAMP) in the muscles of the sepsis-AW experimental model group, which was comparable to the sepsis group.

Conclusion

The present work identified DEMs and metabolism-related pathways within the muscles of the sepsis-AW mice, which offered valuable experimental data for diagnosis and identification of the pathogenic mechanism underlying sepsis-AW.

Identification of biomarkers and the mechanisms of multiple trauma complicated with sepsis using metabolomics

Sepsis after trauma increases the risk of mortality rate for patients in intensive care unit (ICUs). Currently, it is difficult to predict outcomes in individual patients with sepsis due to the complexity of causative pathogens and the lack of specific treatment. This study aimed to identify metabolomic biomarkers in patients with multiple trauma and those with multiple trauma accompanied with sepsis. Therefore, the metabolic profiles of healthy persons designated as normal controls (NC), multiple trauma patients (MT), and multiple trauma complicated with sepsis (MTS) (30 cases in each group) were analyzed with ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS)-based untargeted plasma metabolomics using collected plasma samples. The differential metabolites were enriched in amino acid metabolism, lipid metabolism, glycometabolism and nucleotide metabolism. Then, nine potential biomarkers, namely, acrylic acid, 5-amino-3-oxohexanoate, 3b-hydroxy-5-cholenoic acid, cytidine, succinic acid semialdehyde, PE [P-18:1(9Z)/16:1(9Z)], sphinganine, uracil, and uridine, were found to be correlated with clinical variables and validated using receiver operating characteristic (ROC) curves. Finally, the three potential biomarkers succinic acid semialdehyde, uracil and uridine were validated and can be applied in the clinical diagnosis of multiple traumas complicated with sepsis.

Identifying potential biomarkers and therapeutic targets for dogs with sepsis using metabolomics and lipidomics analyses

Sepsis is a diagnostic and therapeutic challenge and is associated with morbidity and a high risk of death. Metabolomic and lipidomic profiling in sepsis can identify alterations in metabolism and might provide useful insights into the dysregulated host response to infection, but investigations in dogs are limited. We aimed to use untargeted metabolomics and lipidomics to characterize metabolic pathways in dogs with sepsis to identify therapeutic targets and potential diagnostic and prognostic biomarkers. In this prospective observational cohort study, we examined the plasma metabolomes and lipidomes of 20 healthy control dogs and compared them with those of 21 client-owned dogs with sepsis. Patient data including signalment, physical exam findings, clinicopathologic data and clinical outcome were recorded. Metabolites were identified using an untargeted mass spectrometry approach and pathway analysis identified multiple enriched metabolic pathways including pyruvaldehyde degradation; ketone body metabolism; the glucose-alanine cycle; vitamin-K metabolism; arginine and betaine metabolism; the biosynthesis of various amino acid classes including the aromatic amino acids; branched chain amino acids; and metabolism of glutamine/glutamate and the glycerophospholipid phosphatidylethanolamine. Metabolites were identified with high discriminant abilities between groups which could serve as potential biomarkers of sepsis including 13,14-Dihydro-15-keto Prostaglandin A₂; 12(13)-DiHOME (12,13-dihydroxy-9Z-octadecenoic acid); and 9-HpODE (9-Hydroxyoctadecadienoic acid). Metabolites with higher abundance in samples from nonsurvivors than survivors included 3-(2-hydroxyethyl) indole, indoxyl sulfate and xanthurenic acid. Untargeted lipidomic profiling revealed multiple sphingomyelin species (SM(d34:0)+H; SM(d36:0)+H; SM(d34:0)+HCOO; and SM(d34:1D3)+HCOO); lysophosphatidylcholine molecules (LPC(18:2)+H) and lipophosphoserine molecules (LPS(20:4)+H) that were discriminating for dogs with sepsis. These biomarkers could aid in the diagnosis of dogs with sepsis, provide prognostic information, or act as potential therapeutic targets.

Multi-Omics Techniques Make it Possible to Analyze Sepsis-Associated Acute Kidney Injury Comprehensively

Sepsis-associated acute kidney injury (SA-AKI) is a common complication in critically ill patients with high morbidity and mortality. SA-AKI varies considerably in disease presentation, progression, and response to treatment, highlighting the heterogeneity of the underlying biological mechanisms. In this review, we briefly describe the pathophysiology of SA-AKI, biomarkers, reference databases, and available omics techniques. Advances in omics technology allow for comprehensive analysis of SA-AKI, and the integration of multiple omics provides an opportunity to understand the information flow behind the disease. These approaches will drive a shift in current paradigms for the prevention, diagnosis, and staging and provide the renal community with significant advances in precision medicine in SA-AKI analysis.

Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response

Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.

Infection Biomarkers Based on Metabolomics

Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.

Delirium and Psychiatric Sequelae Associated to SARS-CoV-2 in Asymptomatic Patients With Psychiatric History and Mild Cognitive Impairment as Risk Factors: Three Case Reports

Human coronaviruses have neuroinvasive and neurotropic abilities that might explain psychiatric outcomes in affected patients. We hypothesized that delirium might be the sole clinical manifestation or even the prodrome of a psychiatric episode consistent with the mental history of a few infected patients with a preexisting diagnosed cognitive impairment. We examined three patients with preexisting mild cognitive impairment and delirium at admission for suspected SARS-CoV-2 infection. We diagnosed delirium using DSM-5 and Confusion Assessment Method (CAM) and measured consciousness level by the Glasgow Coma Scale. All the patients had no history of fever, respiratory complications, anosmia or ageusia, meningitis, and negative cerebrospinal fluid analysis for SARS-CoV-2. Our first patient had no psychiatric history, the second reported only a depressive episode, and the third had a history of bipolar disorder dated back to 40 years before. In the first patient, delirium resolved 2 days following the admission. The other two patients recovered in 4 and 14 days, and delirium appeared as the prodrome of a new psychiatric episode resembling past events. Clinicians should monitor the possibility that SARS-CoV-2 presence in the brain might clinically manifest in the form of delirium and acute psychiatric sequelae, even without other systemic symptoms. Psychiatric history and preexisting mild cognitive impairment are to be considered as predisposing factors for COVID-19 sequelae in delirium patients.

Immunometabolic signatures predict risk of progression to sepsis in COVID-19

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).