1H NMR Based Metabolomics in Human Sepsis and Healthy Serum

Human blood metabolites and risk of sepsis: A Mendelian randomization investigation

BACKGROUND

Evidence supports the observational correlations between human blood metabolites and sepsis. However, whether these associations represent a causal relationship is unknown. In this study, we applied two-sample Mendelian randomization (MR) analyses to examine causality between genetically proxied 486 blood metabolites and sepsis risk.

METHODS

We used summary data from genome-wide association studies (GWAS) on 486 metabolites involving 7824 individuals as exposure and a sepsis GWAS including 11,643 cases and 474,841 controls as the outcome. The inverse-variance weighted (IVW) was the primary method to estimate the causal relationship between exposure and outcome, with MR-Egger and weighted median serving as supplements. Sensitivity analyses were implemented with Cochrane's Q test, MR-Egger intercept, MR-PRESSO and leave-one-out analysis. In addition, we performed replication MR, meta-analysis, Steiger test, linkage disequilibrium score (LDSC) regression and multivariable MR (MVMR) to thoroughly verify the causation.

RESULTS

We identified that genetically determined high levels of 1-oleoylglycerophosphoethanolamine (odds ratio (OR) = .52, 95% confidence interval (CI): .31-.87, p = .0122), alpha-glutamyltyrosine (OR = .75, 95% CI: .60-.93, p = .0102), heptanoate (7:0) (OR = .51, 95% CI: .33-.81, p = .0041) and saccharin (OR = .84, 95% CI: .74-.94, p = .0036) were causally associated with a lower risk of sepsis. MVMR analysis demonstrated the independent causal effect of these metabolites on sepsis.

CONCLUSIONS

These findings indicated that four blood metabolites have a protective impact on sepsis, thus providing novel perspectives into the metabolite-mediated development mechanism of sepsis by combining genomics and metabolomics.

Assessment of sepsis-associated encephalopathy by quantitative magnetic resonance spectroscopy in a rat model of cecal ligation and puncture

Proton magnetic resonance spectroscopy (1H-MRS) is the only non-invasive technique to quantify neurometabolic compounds in the living brain. We used 1H-MRS to evaluate the brain metabolites in a rat model of Sepsis-associated encephalopathy (SAE) established by cecal ligation and puncture (CLP). 36 male Sprague-Dawley rats were randomly divided into sham and CLP groups. Each group was further divided into three subgroups: subgroup O, subgroup M, and subgroup N. Neurological function assessments were performed on the animals in the subgroup O and subgroup N at 24 h, 48 h, and 72 h. The animals in the subgroup M were examined by magnetic resonance imaging (MRI) at 12 h after CLP. Compared with the sham group, the ratio of N-acetylaspartate (NAA) to creatine (Cr) in the hippocampus was significantly lower in the CLP group. The respective ratios of lactate (Lac), myo-inositol (mIns), glutamate and glutamine (Glx), lipid (Lip), and choline (Cho) to Cr in the CLP group were clearly higher than those in the sham group. Cytochrome c, intimately related to oxidative stress, was elevated in the CLP group. Neurofilament light (NfL) chain and glial fibrillary acidic protein (GFAP) scores in the CLP group were significantly higher than those in the sham group, while zonula occludens-1 (ZO-1) was downregulated. Compared with the sham group, the CLP group displayed higher values of oxygen extraction fraction (OEF), central venous-arterial partial pressure of carbon dioxide (P (cv-a) CO2), and central venous lactate (VLac). In contrast, jugular venous oxygen saturation (SjvO2) declined. In the present study, 1H-MRS could be used to quantitatively assess brain injury in terms of microcirculation disorder, oxidative stress, blood-brain barrier disruption, and glial cell activation through changes in metabolites within brain tissue.

Sepsis, Management & Advances in Metabolomics

Though there have been developments in clinical care and management, early and accurate diagnosis and risk stratification are still bottlenecks in septic shock patients. Since septic shock is multifactorial with patient-specific underlying co-morbid conditions, early assessment of sepsis becomes challenging due to variable symptoms and clinical manifestations. Moreover, the treatment strategies are traditionally based on their progression and corresponding clinical symptoms, not personalized. The complex pathophysiology assures that a single biomarker cannot identify, stratify, and describe patients affected by septic shock. Traditional biomarkers like CRP, PCT, and cytokines are not sensitive and specific enough to be used entirely for a patient's diagnosis and prognosis. Thus, the need of the hour is a sensitive and specific biomarker after comprehensive analysis that may facilitate an early diagnosis, prognosis, and drug development. Integration of clinical data with metabolomics would provide means to understand the patient's condition, stratify patients better, and predict the clinical outcome.

LASSO regression shows histidine and sphingosine 1 phosphate are linked to both sepsis mortality and endothelial damage

Sepsis is a major cause of death worldwide, with a mortality rate that has remained stubbornly high. The current gold standard of risk stratifying sepsis patients provides limited mechanistic insight for therapeutic targeting. An improved ability to predict sepsis mortality and to understand the risk factors would allow better treatment targeting. Sepsis causes metabolic dysregulation in patients; therefore, metabolomics offers a promising tool to study sepsis. It is also known that that in sepsis endothelial cells affecting their function regarding blood clotting and vascular permeability. We integrated metabolomics data from patients admitted to an intensive care unit for sepsis, with commonly collected clinical features of their cases and two measures of endothelial function relevant to blood vessel function, platelet endothelial cell adhesion molecule and soluble thrombomodulin concentrations in plasma. We used least absolute shrinkage and selection operator penalized regression, and pathway enrichment analysis to identify features most able to predict 30-day survival. The features important to sepsis survival include carnitines, and amino acids. Endothelial proteins in plasma also predict 30-day mortality and the levels of these proteins also correlate with a somewhat overlapping set of metabolites. Overall metabolic dysregulation, particularly in endothelial cells, may be a contributory factor to sepsis response. By exploring sepsis metabolomics data in conjunction with clinical features and endothelial proteins we have gained a better understanding of sepsis risk factors.

Metabolic fingerprint of patients showing responsiveness to treatment of septic shock in intensive care unit

OBJECTIVE

An early metabolic signature associated with the responsiveness to treatment can be useful in the better management of septic shock patients. This would help clinicians in designing personalized treatment protocols for patients showing non-responsiveness to treatment.

METHODS

We analyzed the serum on Day 1 (n = 60), Day 3 (n = 47), and Day 5 (n = 26) of patients with septic shock under treatment using NMR-based metabolomics. Partial least square discriminant analysis (PLS-DA) was performed to generate the list of metabolites that can be identified as potential disease biomarkers having statistical significance (that is, metabolites that had a VIP score > 1, and p value < 0.05, False discovery rate (FDR) < 0.05).

RESULTS

Common significant metabolites amongst the three time points were obtained that distinguished the patients being responsive (R) and non-responsive (NR) to treatments, namely 3 hydroxybutyrate, lactate, and phenylalanine which were lower, whereas glutamate and choline higher in patients showing responsiveness.

DISCUSSION

The study gave these metabolic signatures identifying patients' responsiveness to treatment. The results of the study will aid in the development of targeted therapy for ICU patients.

Plasma metabolomics and network pharmacology identified glutamate, glutamine, and arginine as biomarkers of depression under Shuganjieyu capsule treatment

Depression is a psychiatric disorder and confers an enormous burden on society. Mild to moderate forms of depression (MMD) are particularly common. Our previous studies showed that the Shuganjieyu (SGJY) capsule might improve depressive and cognitive symptoms in patients with MMD. However, biomarkers evaluating the efficacy of SGJY and the underlying mechanism remains unclear. The aim of the present study was to discover efficacy biomarkers and explore the underlying mechanisms of SGJY as antidepression treatment. Twenty-three patients with MMD were recruited and administered with SGJY for 8 weeks. Results showed that the content of 19 metabolites changed significantly in the plasma of patients with MMD, among which 8 metabolites improved significantly after SGJY treatment. Network pharmacology analysis showed that 19 active compounds, 102 potential targets, and 73 enzymes were related to the mechanistic action of SGJY. Through a comprehensive analysis, we identified four hub enzymes (GLS2, GLS, GLUL, and ADC), three key differential metabolites (glutamine, glutamate, and arginine), and two shared pathways (alanine, aspartate, and glutamate metabolism; and arginine biosynthesis). Receiver operating characteristic curve (ROC) analysis showed that the three metabolites had a high diagnostic ability. The expression of hub enzymes was validated using RT-qPCR in animal models. Overall, glutamate, glutamine, and arginine may be potential biomarkers for evaluating the efficacy of SGJY. The present study provides a new strategy for pharmacodynamic evaluation and mechanistic study of SGJY, and offers new information for clinical practice and treatment research.

Metabolomics-based study of potential biomarkers of sepsis

The purpose of our study was to explore potential characteristic biomarkers in patients with sepsis. Peripheral blood specimens from sepsis patients and normal human volunteers were processed by liquid chromatography-mass spectrometry-based analysis. Outlier data were excluded by principal component analysis and orthogonal partial least squares-discriminant analysis using the metabolomics R software package metaX and MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/home.xhtml ) online analysis software, and differential metabolite counts were identified by using volcano and heatmaps. The obtained differential metabolites were combined with KEGG (Kyoto Gene and Kyoto Encyclopedia) analysis to screen out potential core differential metabolites, and ROC curves were drawn to analyze the changes in serum metabolites in sepsis patients and to explore the potential value of the metabolites in the diagnosis of sepsis patients. By metabolomic analysis, nine differential metabolites were screened for their significance in guiding the diagnosis and differential diagnosis of sepsis namely: 3-phenyl lactic acid, N-phenylacetylglutamine, phenylethylamine, traumatin, xanthine, methyl jasmonate, indole, l-tryptophan and 1107116. In this study, nine metabolites were finally screened based on metabolomic analysis and used as potential characteristic biomarkers for the diagnosis of sepsis.

Distinct subsets of neutrophils crosstalk with cytokines and metabolites in patients with sepsis

Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Despite continued efforts to understand the pathophysiology of sepsis, no effective therapies are currently available. While singular components of the aberrant immune response have been investigated, comprehensive studies linking different data layers are lacking. Using an integrated systems immunology approach, we evaluated neutrophil phenotypes and concomitant changes in cytokines and metabolites in patients with sepsis. Our findings identify differentially expressed mature and immature neutrophil subsets in patients with sepsis. These subsets correlate with various proteins, metabolites, and lipids, including pentraxin-3, angiopoietin-2, and lysophosphatidylcholines, in patients with sepsis. These results enabled the construction of a statistical model based on weighted multi-omics linear regression analysis for sepsis biomarker identification. These findings could help inform early patient stratification and treatment options, and facilitate further mechanistic studies targeting the trifecta of surface marker expression, cytokines, and metabolites.

Metabonomic Analysis of Metabolites Produced by Escherichia coli in Patients With and Without Sepsis

AIM

To analyze the metabolites of the most common sepsis-related pathogen and their correlation with clinical indicators.

METHODS

Information of bacterial-infection patients in Huzhou Central hospital was retrospectively investigated and analyzed. The most common pathogen inducing sepsis was selected. Then, the metabolic profiles of pathogens from blood were detected by liquid chromatography/mass spectrometry. Cluster and classification analysis, KEGG pathway enrichment analysis, multidimensional OPLS-DA, Z scores, correlation analysis were used to analyze the metabolites.

RESULTS

Escherichia coli (E. coli) was the pathogen that caused the most infection (about 21%) and sepsis. Amino acids, peptides, terpene glycosides, carbohydrates were the main metabolites of E.coli and they were mainly digestive and endocrine-related compounds. Most of them were related to amino acids metabolism, cofactors and vitamins metabolism, biosynthesis of secondary metabolites, et al. Moreover, metabolites were involved in purine metabolism, neuroactive ligand-receptor interaction, ABC transporters, etc. Then, over 70 differential metabolites such as tyramine, tryptophan, 3- hydroxymalondialdehyde were screened in E.coli from nonseptic and septic patients. They were mainly involved in phenylalanine metabolism, tryptophan metabolism, protein digestion and absorption. Distribution of metabolites of E. coli from nonseptic and septic patients was obviously different. What is more, differential metabolites had evidently correlation with SOFA score, APPACHE II score, C-reactive protein, erythrocyte, platelet, aspartate aminotransferase, coagulation function, lactic acid (p < 0.01).

CONCLUSION

The different metabolic profile of E. coli from nonseptic and septic patients indicated that differential metabolites might be associated with sepsis.

Multi-omics association analysis reveals interactions between the oropharyngeal microbiome and the metabolome in pediatric patients with influenza A virus pneumonia

Children are at high risk for influenza A virus (IAV) infections, which can develop into severe illnesses. However, little is known about interactions between the microbiome and respiratory tract metabolites and their impact on the development of IAV pneumonia in children. Using a combination of liquid chromatography tandem mass spectrometry (LC-MS/MS) and 16S rRNA gene sequencing, we analyzed the composition and metabolic profile of the oropharyngeal microbiota in 49 pediatric patients with IAV pneumonia and 42 age-matched healthy children. The results indicate that compared to healthy children, children with IAV pneumonia exhibited significant changes in the oropharyngeal macrobiotic structure (p = 0.001), and significantly lower microbial abundance and diversity (p < 0.05). These changes came with significant disturbances in the levels of oropharyngeal metabolites. Intergroup differences were observed in 204 metabolites mapped to 36 metabolic pathways. Significantly higher levels of sphingolipid (sphinganine and phytosphingosine) and propanoate (propionic acid and succinic acid) metabolism were observed in patients with IAV pneumonia than in healthy controls. Using Spearman’s rank-correlation analysis, correlations between IAV pneumonia-associated discriminatory microbial genera and metabolites were evaluated. The results indicate significant correlations and consistency in variation trends between Streptococcus and three sphingolipid metabolites (phytosphingosine, sphinganine, and sphingosine). Besides these three sphingolipid metabolites, the sphinganine-to-sphingosine ratio and the joint analysis of the three metabolites indicated remarkable diagnostic efficacy in children with IAV pneumonia. This study confirmed significant changes in the characteristics and metabolic profile of the oropharyngeal microbiome in pediatric patients with IAV pneumonia, with high synergy between the two factors. Oropharyngeal sphingolipid metabolites may serve as potential diagnostic biomarkers of IAV pneumonia in children.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Liang-Ge decoction ameliorates acute lung injury in septic model rats through reducing inflammatory response, oxidative stress, apoptosis, and modulating host metabolism

Liang-Ge decoction (LG) has been used in the treatment of early stage of spesis and can ameliorate sepsis-associated lung injury. However, the mechanism of LG on sepsis-associated lung injury remains unknown. In this study, we established a rat model of sepsis-associated lung injury using the cecal ligation and puncture (CLP) method, and investigated the therapeutic effects of LG on lung injury in rats with sepsis. In addition, the anti-inflammatory, anti-oxidative and anti-apoptotic effects of LG on sepsis-associated lung injury model rats were evaluated. Besides, untargeted metabolomics was used to investigate the regulation of metabolites in rats with sepsis-associated lung injury after LG treatment. Our results showed that LG could decrease the wet/dry (W/D) ratio in lung and the total cell count and total protein concentration in bronchoalveolar lavage fluid (BALF) in septic model rats. Hematoxylin and eosin (HE) staining showed that LG reduced the infiltration of pro-inflammatory cells in lung. In addition, LG treatmment down-regulated the gene and protein expression of pro-inflammatory cytokins in lung tissue and BALF. The activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) were increased and the level of methane dicarboxylic aldehyde (MDA) was decreased in lung tissue homogenate in septic model rats after LG treament. Moreover, the numbers of apoptotic cells in lung were reduced and the activity of lactic dehydrogenase (LDH) in BALF was decreased in septic model rats after LG treament. Untargeted metabolomics analysis showed that LG treatment affected the levels of 23 metabolites in lung in septic model rats such as citric acid, methionine, threonine, alpha-ketoglutaric acid, and inositol, these metabolites were associated with the glycine, serine and threonine metabolism, cysteine and methionine metabolism, inositol phosphate metabolism and citrate cycle (TCA cycle) pathways. In conclusion, our study demonstrated the therapeutic effetcts of LG on sepsis-associated lung injury model rats. Moreover, LG could inhibit the inflammatory response, oxidative stress, apoptosis and regulate metabolites related to glycine, serine and threonine metabolism, cysteine and methionine metabolism, inositol phosphate metabolism and TCA cycle in lung in sepsis-associated lung injury model rats.

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.

Fibroblast Growth Factor 19 Improves LPS-Induced Lipid Disorder and Organ Injury by Regulating Metabolomic Characteristics in Mice

Sepsis is extremely heterogeneous pathology characterized by complex metabolic changes. Fibroblast growth factor 19 (FGF19) is a well-known intestine-derived inhibitor of bile acid biosynthesis. However, it is largely unknown about the roles of FGF19 in improving sepsis-associated metabolic disorder and organ injury. In the present study, mice were intravenously injected recombinant human FGF19 daily for 7 days followed by lipopolysaccharide (LPS) administration. At 24 hours after LPS stimuli, sera were collected for metabolomic analysis. Ingenuity pathway analysis (IPA) network based on differential metabolites (DMs) was conducted. Here, metabolomic analysis revealed that FGF19 pretreatment reversed the increase of LPS-induced fatty acids. IPA network indicated that altered linoleic acid (LA) and gamma-linolenic acid (GLA) were involved in the regulation of oxidative stress and mitochondrial function and were closely related to reactive oxygen species (ROS) generation. Further investigation proved that FGF19 pretreatment decreased serum malondialdehyde (MDA) levels and increased serum catalase (CAT) levels. In livers, FGF19 suppressed the expression of inducible NO synthase (iNOS) and enhanced the expression of nuclear factor erythroid 2-related factor 2 (NRF2) and hemeoxygenase-1 (HO-1). Finally, FGF19 pretreatment protected mice against LPS-induced liver, ileum, and kidney injury. Taken together, FGF19 alleviates LPS-induced organ injury associated with improved serum LA and GLA levels and oxidative stress, suggesting that FGF19 might be a promising target for metabolic therapy for sepsis.

Untargeted metabolomics analysis of the plasma metabolic signature of moderate-to-severe acne

BACKGROUND

Acne vulgaris is a chronic inflammatory disease of pilosebaceous units and sebaceous glands. This study aimed to find out metabolites and metabolite pathways abnormal in moderate-to-severe acne vulgaris patients.

METHODS

The plasma metabolites LC-MS/MS analysis was conducted on 30 moderate-to-severe acne patients and 32 healthy controls. Multivariate data analyses were applied to identify the distinguishing metabolites.

RESULTS

Totally, 63 significant differential metabolites and 40 metabolic pathways were significantly changed. The top 3 metabolites on the basis of their VIP scores obtained from the PLS-DA were 2-Oxoadipic acid, Myo-inositol and Citrate. In addition, four sphingolipid metabolites include sphinganine, sphingosine, O-Phosphoethanolamine, and sphingomyelin (d18:1/18:0) were identified. The most closely related metabolic pathways included ATP-binding cassette (ABC) transporters and sphingolipid signaling pathway in moderate-to-severe acne patients.

CONCLUSIONS

The observed difference in metabolic profiles between acne patients and healthy controls provides a new insight into the link between plasma metabolic changes and acne vulgaris.

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.

1H-NMR Based Metabolomics Technology Identifies Potential Serum Biomarkers of Colorectal Cancer Lung Metastasis in a Mouse Model

Background

Lung metastasis is a common metastasis site of colorectal cancer which largely reduces the quality of life and survival rates of patients. The discovery of potential novel diagnostic biomarkers is very meaningful for the early diagnosis of colorectal cancer with lung metastasis.

Methods

In the present study, the metabonomic profiling of serum samples of lung metastasis mice was analyzed by ¹H-nuclear magnetic resonance (¹H-NMR). Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to elucidate the distinguishing metabolites between different groups, and all achieved excellent separations, which indicated that metastatic mice could be differentiated from control mice based on the metabolic profiles at serum levels. Furthermore, during lung metastasis of colorectal cancer, metabolic phenotypes changed significantly, and some of metabolites were identified.

Results

Among these metabolites, approximately 15 were closely associated with the lung metastasis process. Pathway enrichment analysis results showed deregulation of metabolic pathways participating in the process of lung metastasis, such as synthesis and degradation of ketone bodies pathway, amino acid metabolism pathway and pyruvate metabolism pathway.

Conclusion

The present study demonstrated the metabolic disturbances of serum samples of mice during the lung metastasis process of colorectal cancer and provides potential diagnostic biomarkers for the disease.

Gender-specific association of oxidative stress and immune response in septic shock mortality using NMR-based metabolomics

Background: Sepsis and septic shock are still associated with a high mortality rate. The early-stage prediction of septic shock outcomes would be helpful to clinicians for designing their treatment protocol. In addition, it would aid clinicians in patient management by understanding gender disparity in terms of clinical outcomes of septic shock by identifying whether there are sex-based differences in sepsis-associated mortality. Objective: This study aimed to test the hypothesis that gender-based metabolic heterogeneity is associated with sepsis survival and identify the biomarkers of mortality for septic shock in an Indian cohort. Method: The study was performed in an Indian population cohort diagnosed with sepsis/septic shock within 24 hours of admission. The study group was 50 patients admitted to intensive care, comprising 23 females and 27 males. Univariate and multivariate analysis were performed to identify the biomarkers for septic shock mortality and the gender-specific metabolic fingerprint in septic shock-associated mortality. Results: The energy-related metabolites, ketone bodies, choline, and NAG were found to be primarily responsible for differentiating survivors and non-survivors. The gender-based mortality stratification identified a female-specific association of the anti-inflammatory response, innate immune response, and β oxidation, and a male-specific association of the pro-inflammatory response to septic shock. Conclusion: The identified mortality biomarkers may help clinicians estimate the severity of a case, as well as predict the outcome and treatment efficacy. The study underlines that gender is one of the most significant biological factors influencing septic shock metabolomic profiles. This understanding can be utilized to identify novel gender-specific biomarkers and innovative targets relevant for gender medicine.

A Metabolomics approach for the diagnosis Of SecondAry InfeCtions in COVID-19 (MOSAIC): a study protocol

Background

Critically ill patients with COVID-19 are at an increased risk of developing secondary bacterial infections. These are both difficult to diagnose and are associated with an increased mortality. Metabolomics may aid clinicians in diagnosing secondary bacterial infections in COVID-19 through identification and quantification of disease specific biomarkers, with the aim of identifying underlying causative microorganisms and directing antimicrobial therapy.

Methods

This is a multi-centre prospective diagnostic observational study. Patients with COVID-19 will be recruited from critical care units in three Scottish hospitals. Three serial blood samples will be taken from patients, and an additional sample taken if a patient shows clinical or microbiological evidence of secondary infection. Samples will be analysed using LC–MS and subjected to bioinformatic processing and statistical analysis to explore the metabolite changes associated with bacterial infections in COVID-19 patients. Comparisons of the data sets will be made with standard microbiological and biochemical methods of diagnosing infection.

Discussion

Metabolomics analyses may provide additional strategies for identifying secondary infections, which might permit faster initiation of specific tailored antimicrobial therapy to critically ill patients with COVID-19.

Research Progress of NMR in Natural Product Quantification

In the fields of medicine and health, traditional high-performance liquid chromatography or UV-visible spectrophotometry is generally used for substance quantification. However, over time, nuclear magnetic resonance spectroscopy (NMR) has gradually become more mature. Nuclear magnetic resonance spectroscopy has certain advantages in the quantitative analysis of substances, such as being nondestructive, having a high flux and short analysis time. Nuclear magnetic resonance spectroscopy has been included in the pharmacopoeiae of various countries. In this paper, the principle of nuclear magnetic resonance spectroscopy and the recent progress in the quantitative study of natural products by NMR are reviewed, and its application in the quantitative study of natural products is proposed. At the same time, the problems of using NMR alone to quantify natural products are summarized and corresponding suggestions are put forward.

An analysis of urine and serum amino acids in critically ill patients upon admission by means of targeted LC-MS/MS: a preliminary study

Sepsis, defined as a dysregulated host response to infection, causes the interruption of homeostasis resulting in metabolic changes. An examination of patient metabolites, such as amino acids, during the early stage of sepsis may facilitate diagnosing and assessing the severity of the sepsis. The aim of this study was to compare patterns of urine and serum amino acids relative to sepsis, septic shock and survival. Urine and serum samples were obtained from healthy volunteers (n = 15) once or patients (n = 15) within 24 h of a diagnosis of sepsis or septic shock. Concentrations of 25 amino acids were measured in urine and serum samples with liquid chromatography-electrospray mass spectrometry. On admission in the whole cohort, AAA, ABA, mHis, APA, Gly-Pro and tPro concentrations were significantly lower in the serum than in the urine and Arg, Gly, His, hPro, Leu, Ile, Lys, Orn, Phe, Sarc, Thr, Tyr, Asn and Gln were significantly higher in the serum than in the urine. The urine Gly-Pro concentration was significantly higher in septic shock than in sepsis. The serum Cit concentration was significantly lower in septic shock than in sepsis. The urine ABA, mHis and Gly-Pro, and serum Arg, hPro and Orn concentrations were over two-fold higher in the septic group compared to the control group. Urine and serum amino acids measured in septic patients on admission to the ICU may shed light on a patient’s metabolic condition during sepsis or septic shock.

Effects of dexmedetomidine, propofol, sevoflurane and S-ketamine on the human metabolome: A randomised trial using nuclear magnetic resonance spectroscopy

BACKGROUND

Pharmacometabolomics uses large-scale data capturing methods to uncover drug-induced shifts in the metabolic profile. The specific effects of anaesthetics on the human metabolome are largely unknown.

OBJECTIVE

We aimed to discover whether exposure to routinely used anaesthetics have an acute effect on the human metabolic profile.

DESIGN

Randomised, open-label, controlled, parallel group, phase IV clinical drug trial.

SETTING

The study was conducted at Turku PET Centre, University of Turku, Finland, 2016 to 2017.

PARTICIPANTS

One hundred and sixty healthy male volunteers were recruited. The metabolomic data of 159 were evaluable.

INTERVENTIONS

Volunteers were randomised to receive a 1-h exposure to equipotent doses (EC50 for verbal command) of dexmedetomidine (1.5 ng ml-1; n = 40), propofol (1.7 μg ml-1; n = 40), sevoflurane (0.9% end-tidal; n = 39), S-ketamine (0.75 μg ml-1; n = 20) or placebo (n = 20).

MAIN OUTCOME MEASURES

Metabolite subgroups of apolipoproteins and lipoproteins, cholesterol, glycerides and phospholipids, fatty acids, glycolysis, amino acids, ketone bodies, creatinine and albumin and the inflammatory marker GlycA, were analysed with nuclear magnetic resonance spectroscopy from arterial blood samples collected at baseline, after anaesthetic administration and 70 min postanaesthesia.

RESULTS

All metabolite subgroups were affected. Statistically significant changes vs. placebo were observed in 11.0, 41.3, 0.65 and 3.9% of the 155 analytes in the dexmedetomidine, propofol, sevoflurane and S-ketamine groups, respectively. Dexmedetomidine increased glucose, decreased ketone bodies and affected lipoproteins and apolipoproteins. Propofol altered lipoproteins, fatty acids, glycerides and phospholipids and slightly increased inflammatory marker glycoprotein acetylation. Sevoflurane was relatively inert. S-ketamine increased glucose and lactate, whereas branched chain amino acids and tyrosine decreased.

CONCLUSION

A 1-h exposure to moderate doses of routinely used anaesthetics led to significant and characteristic alterations in the metabolic profile. Dexmedetomidine-induced alterations mirror α2-adrenoceptor agonism. Propofol emulsion altered the lipid profile. The inertness of sevoflurane might prove useful in vulnerable patients. S-ketamine induced amino acid alterations might be linked to its suggested antidepressive properties.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02624401. URL: https://clinicaltrials.gov/ct2/show/NCT02624401.

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).

Integration of FGF21 Signaling and Metabolomics in High-Fat Diet-Induced Obesity

Sex differences in obesity have been well established, but the metabolic mechanism underlying these differences remains unclear. In the present study, we determined the expression levels of endogenous fibroblast growth factor 21 (FGF21) and its related receptors in male and female mice that were fed a high-fat diet (HFD) for 12 weeks. We also analyzed the metabolic changes in serum and livers using a nuclear magnetic resonance-based metabolomics approach. Reverse transcription polymerase chain reaction and western blotting results revealed that the levels of FGFR1, FGFR2, and co-factor β-klotho were upregulated in female mice to alleviate FGF21 resistance induced by HFD. The metabolomics results demonstrated that the serum and liver metabolic patterns of HFD-fed male mice were significantly separated from those of the female HFD-fed group and the normal diet group. Furthermore, low-density lipoprotein/very low density lipoprotein and betaine levels were associated with the resistance of exogenous HFD in female mice. These findings imply that sex-based differences in metabolism and susceptibility to obesity might be mediated by the FGF21 signaling pathway.

Serum metabolic profiles of septic shock patients based upon co-morbidities and other underlying conditions

Diagnosis and management of patients with septic shock is still a significant challenge for clinicians with its high mortality amongst hospitalized patients. Septic shock is a heterogeneous condition and is usually accompanied by various underlying disease conditions. Dissecting the specific metabolic changes induced by these underlying disease conditions through metabolomics has shown the potential to improve our understanding of the disease's relevant pathophysiological mechanisms, leading to improved treatment. This study has shown the metabolic alterations caused due to co-morbid conditions like diabetes, hypertension, CAD, and CKD in septic shock. It has also shown the distinct metabolic profiles of septic shock patients with underlying respiratory illnesses and encephalopathy. Metabolic profiling of sera obtained from 50 septic shock patients and 20 healthy controls was performed using high-resolution 1D ¹H CPMG and diffusion-edited NMR spectra. Univariate and multivariate statistical analyses were performed to identify the potential molecular biomarkers. Noted dysregulations in amino acids, carbohydrates, and lipid metabolism were observed in septic shock patients. Further stratification within the septic shock patients based on co-morbid conditions and primary diagnosis has shown their role in causing metabolic alterations. Evaluation of these compounds during treatment will help design a personalized treatment protocol for the patients, improving therapeutics.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

1H NMR-based metabolomics analyses in children with Helicobacter pylori infection and the alteration of serum metabolites after treatment

BACKGROUND AND AIMS

Helicobacter pylori (H. pylori) infection can occur in early childhood, without eradication therapies such infection can persist throughout life and cause many different diseases. This study investigated the metabolic characteristics and explored the underlying mechanism of children with H. pylori infection, and identified potential biomarkers for evaluating the efficacy of H. pylori eradication therapies.

METHODS

We performed ¹H NMR-based metabonomics coupled with multivariate analysis to investigate the metabolic profiling of serum samples between Children with and without H. pylori infection. In the same manner, we compared the alternations of metabolites in H. pylori-infected children before and after H. pylori eradication therapies.

RESULTS

21 metabolites from serum in H. pylori-infected and H. pylori-uninfected children were identified, which were mainly involved in energy, amino acid, lipid and microbial metabolism. We found that the serum levels of trimethylamine N-oxide and alanine were significantly higher in H. pylori-infected children compared to uninfected sera, whereas lactate was significantly lower. We also found that the levels of trimethylamine N-oxide and creatine in H. pylori-infected children was significantly decreased after H. pylori eradication therapies, whereas lactate and low-density lipoprotein/very low-density lipoprotein was significantly increased.

CONCLUSIONS

This is the first study using ¹H NMR-based metabolomics approach to explore the effects of H. pylori infection in children. Our results demonstrated that the disturbances of metabolism in energy, amino acids, lipids and microbiota could play an important role in the pathogenesis of gastrointestinal and extragastric diseases caused by H. pylori infection. Trimethylamine N-oxide and lactate might serve as potential serum biomarkers for evaluating the efficacy of H. pylori eradication therapies.