Serum Metabolic Changes Research Articles

Relationship between serum metabolic indexes and immune function in patients with insomnia and their mechanism

We conducted a study to examine how certain serum metabolic markers (reactive oxygen species (ROS), homocysteine (Hcy), and reduced glutathione (GSH)) impact the levels of IL-4 in patients with insomnia. The study involved 60 insomnia patients, including 20 with primary insomnia and 20 with somatopathy insomnia, aged 23-84 years with a mean age of 61.20 ± 12.59 years and a mean disease duration of 6.97 ± 8.45 years. There were 20 normal controls, 11 males and 9 females, aged 26-63 years, with an average age of 49.95 ± 10.52 years. We measured ROS levels using immunofluorescence, Hcy levels using enzyme-linked immunosorbent assay, and GSH levels using ELISA. The IL-4 level in serum was also detected by ELISA to assess the patient's immune function. Our analysis revealed that changes in ROS, Hcy, and GSH levels were associated with changes in IL-4 levels in serum. Therefore, early detection of serum metabolic changes in insomnia patients and proactive intervention can help reduce susceptibility to various infections and tumors.

Serum Metabolites as Potential Markers and Predictors of Depression-like Behavior and Effective Fluoxetine Treatment in Chronically Socially Isolated Rats.

Metabolic perturbation has been associated with depression. An untargeted metabolomics approach using liquid chromatography-high resolution mass spectrometry was employed to detect and measure the rat serum metabolic changes following chronic social isolation (CSIS), an animal model of depression, and effective antidepressant fluoxetine (Flx) treatment. Univariate and multivariate statistics were used for metabolic data analysis and differentially expressed metabolites (DEMs) determination. Potential markers and predictive metabolites of CSIS-induced depressive-like behavior and Flx efficacy in CSIS were evaluated by the receiver operating characteristic (ROC) curve, and machine learning (ML) algorithms, such as support vector machine with linear kernel (SVM-LK) and random forest (RF). Upregulated choline following CSIS may represent a potential marker of depressive-like behavior. Succinate, stachydrine, guanidinoacetate, kynurenic acid, and 7-methylguanine were revealed as potential markers of effective Flx treatment in CSIS rats. RF yielded better accuracy than SVM-LK (98.50% vs. 85.70%, respectively) in predicting Flx efficacy in CSIS vs. CSIS, however, it performed almost identically in classifying CSIS vs. control (75.83% and 75%, respectively). Obtained DEMs combined with ROC curve and ML algorithms provide a research strategy for assessing potential markers or predictive metabolites for the designation or classification of stress-induced depressive phenotype and mode of drug action.

Serum metabolic changes link metal mixture exposures to vascular endothelial inflammation in residents living surrounding rivers near abandoned lead–zinc mines

Metal exposure is associated with vascular endothelial inflammation, an early pathological phenotype of atherosclerotic cardiovascular events. However, the underlying mechanism linking exposure, metabolic changes, and outcomes remains unclear. We aimed to investigate the metabolic changes underlying the associations of chronic exposure to metal mixtures with vascular endothelial inflammation. We recruited 960 adults aged 20–75 years from residential areas surrounding rivers near abandoned lead–zinc mine and classified them into river area and non-river area exposure groups. Urine levels of 25 metals, Framingham risk score (FRS), and serum concentrations of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1), as biomarkers of vascular endothelial inflammation, were assessed. A “meet-in-the-middle” approach was applied to identify causal intermediate metabolites and metabolic pathways linking metal exposure to vascular endothelial inflammation in representative metabolic samples from 64 participants. Compared to the non-river area exposure group, the river area exposure group had significantly greater urine concentrations of chromium, copper, cadmium, and lead; lower urine concentrations of selenium; elevated FRS; and increased concentrations of ICAM-1 and VCAM-1. In total, 38 differentially abundant metabolites were identified between the river area and non-river area exposure groups. Among them, 25 metabolites were significantly associated with FRS, 8 metabolites with ICAM-1 expression, and 10 metabolites with VCAM-1 expression. Furthermore, fructose, ornithine, alpha-ketoglutaric acid, urea, and cytidine monophosphate, are potential mediators of the relationship between metal exposure and vascular endothelial inflammation. Additionally, the metabolic changes underlying these effects included changes in arginine and proline metabolism, pyrimidine metabolism, starch and sucrose metabolism, galactose metabolism, arginine biosynthesis, and alanine, aspartate, and glutamate metabolism, suggesting the disturbance of amino acid metabolism, the tricarboxylic acid cycle, nucleotide metabolism, and glycolysis. Overall, our results reveal biomechanisms that may link chronic exposure to multiple metals with vascular endothelial inflammation and elevated cardiovascular risk.

Clinical metabolomics investigation of rheumatoid arthritis patients receiving ayurvedic whole system intervention

BackgroundArthritis is a common clinical condition seen in Ayurveda clinics. Clinical trials have reported Ayurvedic interventions to be of benefits in many arthritic conditions including Rheumatoid Arthritis (RA). No mechanistic details however are available about how such interventions on their own or as a combination of whole system Ayurveda might be working. Objective: The study aims to evaluate simultaneously the clinical outcome of Ayurveda whole system (AWS) intervention in RA patients and identifying the serum metabolic signatures which could be useful for diagnosing the disease and monitoring treatment response. Material and methodsRA patients (n = 37) simultaneously diagnosed as Amavata fulfilling the specific inclusion and exclusion criteria were recruited in the study and were given Ayurveda whole system (AWS) intervention comprised of oral medicines, local therapy and dietary recommendation for 3 months. The clinical and serum metabolic changes were investigated for pre-treatment RA patients (baseline RA group, n = 37) and post-treatment RA patients (following treatment of 6-weeks (RA_F, n = 26) and three months (RA_T, n = 36). For comparative serum metabolomics analysis, 57 normal healthy control (HC) subjects were also involved and the serum metabolic profiles were measured at high-field 800 MHz NMR spectrometer. The serum metabolic profiles were compared using multivariate statistical analysis and discriminatory metabolic features were evaluated for diagnostic potential using receiver operating characteristic (ROC) curve analysis. ResultsA significant reduction in DAS-28 ESR, AAM Score, total swollen joints, total tender joints were observed following AWS intervention. The clinical outcomes were concordant with changes in metabolic profiles of RA patients as these were also shifting towards the normal levels following the intervention. Compared to healthy control (HC) subjects, the sera of baseline RA patients were characterised by increased circulatory level of succinate, lysine, mannose, creatine, and 3-Hydroxybutyrate (3-HB) and decreased levels of alanine. The present study also evaluated the serum metabolic ratios for their discriminatory and diagnostic potential and notably, six metabolic ratios (KHR, KThR, KVR, GHR, PTR and SHR) were found significantly altered (elevated) in baseline RA patients. However, in RA patients receiving AWS treatment, these metabolic changes showed marked convergence towards the metabolic signatures of healthy controls. ConclusionThis first of its kind study clearly shows the clinical efficacy of Ayurvedic Whole System (AWS) intervention in the management of Rheumatoid Arthritis (RA), as demonstrated by significant improvements in key clinical parameters. The intervention not only alleviated symptoms but also induced a profound metabolic shifting towards normalization; thus, underscoring the potential of AWS intervention to modulate cellular metabolism in a manner that facilitates a return to homeostasis in RA patients. However, future studies are imperative to confirm these preliminary observations and delineate the underlying mechanisms of action of intervention in cases of RA.

Metabolic Changes in Serum and Milk of Holstein Cows in Their First to Fourth Parity Revealed by Biochemical Analysis and Untargeted Metabolomics

The performance of dairy cows is closely tied to the metabolic state, and this performance varies depending on the number of times the cows have given birth. However, there is still a lack of research on the relationship between the metabolic state of Holstein cows and the performance of lactation across multiple parities. In this study, biochemical analyses and metabolomics studies were performed on the serum and milk from Holstein cows of parities 1–4 (H1, N = 10; H2, N = 7; H3, N = 9; H4, N = 9) in mid-lactation (DIM of 141 ± 4 days) to investigate the link between performance and metabolic changes. The results of the milk quality analysis showed that the lactose levels were highest in H1 (p = 0.036). The total protein content in the serum increased with increasing parity (p = 0.013). Additionally, the lipase activity was found to be lowest in H1 (p = 0.022). There was no difference in the composition of the hydrolyzed amino acids in the milk among H1 to H4. However, the free amino acids histidine and glutamate in the serum were lowest in H1 and highest in H3 (p < 0.001), while glycine was higher in H4 (p = 0.031). The metabolomics analysis revealed that 53 and 118 differential metabolites were identified in the milk and serum, respectively. The differential metabolites in the cows’ milk were classified into seven categories based on KEGG. Most of the differential metabolites in the cows’ milk were found to be more abundant in H1, and these metabolites were enriched in two impact pathways. The differential metabolites in the serum could be classified into nine categories and enriched in six metabolic pathways. A total of six shared metabolites were identified in the serum and milk, among which cholesterol and citric acid were closely related to amino acid metabolism in the serum. These findings indicate a significant influence of blood metabolites on the energy and amino acid metabolism during the milk production process in the Holstein cows across 1–4 lactations, and that an in-depth understanding of the metabolic changes that occur in Holstein cows during different lactations is essential for precision farming, and that it is worthwhile to further investigate these key metabolites that have an impact through controlled experiments.

Changes in the serum metabolomics of polycystic ovary syndrome before and after compound oral contraceptive treatment.

Polycystic ovary syndrome (PCOS) is both a common endocrine syndrome and a metabolic disorder that results in harm to the reproductive system and whole-body metabolism. This study aimed to investigate differences in the serum metabolic profiles of patients with PCOS compared with healthy controls, in addition to investigating the effects of compound oral contraceptive (COC) treatment in patients with PCOS. 50 patients with PCOS and 50 sex-matched healthy controls were recruited. Patients with PCOS received three cycles of self-administered COC treatment. Clinical characteristics were recorded, and the laboratory biochemical data were detected. We utilized ultra-performance liquid chromatography-high-resolution mass spectrometry to study the serum metabolic changes between patients with PCOS, patients with PCOS following COC treatment, and healthy controls. Patients with PCOS who received COC treatment showed significant improvements in serum sex hormone levels, a reduction in luteinising hormone levels, and a significant reduction in the levels of biologically active free testosterone in the blood. Differential metabolite correlation analysis revealed differences between PCOS and healthy control groups in N-tetradecanamide, hexadecanamide, 10E,12Z-octadecadienoic acid, and 13-HOTrE(r); after 3 months of COC treatment, there were significant differences in benzoic acid, organic acid, and phenolamides. Using gas chromatography-mass spectrometry to analyse blood serum in each group, the characteristic changes in PCOS were metabolic disorders of amino acids, carbohydrates, and purines, with significant changes in the levels of total cholesterol, uric acid, phenylalanine, aspartic acid, and glutamate. Following COC treatment, improvements in sex hormone levels, endocrine factor levels, and metabolic levels were better than in the group of PCOS patients receiving no COC treatment, indicating that COC treatment for PCOS could effectively regulate the levels of sex hormones, endocrine factors, and serum metabolic profiles.

High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 μL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983–1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Metabolomics Analysis of Mesenchymal Stem Cell (MSC) Therapy in a Phase I Clinical Trial of Septic Shock: An Exploratory Study.

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.

Abnormalities of serum lipid metabolism in patients with acute paraquat poisoning caused by ferroptosis

As the mechanism of paraquat (PQ) poisoning is still not fully elucidated, and no specific treatment has been developed in medical practice, the management of PQ poisoning continues to present a medical challenge. In this study, the objective was to investigate the early metabolic changes in serum metabolism and identify the key metabolic pathways involved in patients with PQ poisoning. Quantitative analysis was conducted to determine the relevant metabolites. Additionally, experiments were carried out in both plasma and cell to elucidate the mechanisms underlying metabolic disorder and cell death in PQ poisoning. The study found that polyunsaturated fatty acids (PUFAs) and their metabolites, such as arachidonic acid (AA) and hydroxy eicosatetraenoic acids (HETEs), were significantly increased by non-enzymatic oxidative reaction. Reactive oxygen species (ROS) production increased rapidly at 2 h after PQ poisoning, followed by an increase in PUFAs at 12 h, and intracellular glutathione, cysteine (Cys), and Fe2+ at 24 h. However, at 36 h later, intracellular glutathione and Cys decreased, HETEs increased, and the expression of SLC7A11 and glutathione peroxidase 4 (GPX4) decreased. Ultrastructural examination revealed the absence of mitochondrial cristae. Deferoxamine was found to alleviate lipid oxidation, and increase the viability of PQ toxic cells in the low dose. In conclusion, unsaturated fatty acids metabolism was the key metabolic pathways in PQ poisoning. PQ caused cell death through the induction of ferroptosis. Inhibition of ferroptosis could be a novel strategy for the treatment of PQ poisoning.

Compound Bacillus alleviates diarrhea by regulating gut microbes, metabolites, and inflammatory responses in pet cats

BackgroundPet cats frequently have diarrhea in their daily life. Bacillus has a protective role that has crucial beneficial functions on intestinal homeostasis. The aim of this research was to investigate the effects of the compound Bacillus on the prevention of diarrhea, microbiota and metabolism in pet cats. A total of 20 pet cats (1–2 years old, 3.91 ± 0.92 kg) were randomly divided into two groups and fed with a basal diet (Control group), or a basal diet supplemented with 3 × 109 CFU/kg compound Bacillus (Probiotics group). The experiment lasted 33 days.ResultsResults showed that the compound Bacillus significantly reduced the rate of soft stools and diarrhea in pet cats compared with the control group (P < 0.05, n = 10). Meanwhile, compared with the control group, the probiotics group significantly decreased the content of IL-1β and IL-6 and significantly increased IL-10 (P < 0.05, n = 6) in the serum. In addition, feeding probiotics significantly increased the abundance of p_Patescibacter and g_Plectosphaerella, decreased the abundance of p_Firmicutes, p_Gemmatimonadetes, g_Ruminococcaceae_UCG-005, g_Ascochytahe and g_Saccharomyces in the feces of the pet cats (P < 0.05, n = 6). And it also can significantly increase the content of total SCFAs, acetic acid and butyric acid in the feces (P < 0.05, n = 6). The fecal and serum metabolomics analyses revealed that most fecal and serum compounds were involved in metabolism, particularly in chemical structure transformation maps and amino acid metabolism. Also, eugenitol and methyl sulfate were the most significantly increased serum metabolites, and log2FC were 38.73 and 37.12, respectively. Pearson’s correlation analysis showed that changes in serum metabolism and fecal microbiota were closely related to immune factors. There was also a strong correlation between serum metabolites and microbiota composition.ConclusionsThe results of this research highlight the potential of the compound Bacillus as a dietary supplement to alleviate diarrhea in pet cats.

UPLC-Q-TOF/MS-based metabonomics reveals mechanisms for Holothuria leucospilota polysaccharides (HLP)-regulated serum metabolic changes in diabetic rats.

This study aimed to use metabolomic methods to explore how Holothuria leucospilota polysaccharides (HLP) improved metabolism disorders in the liver of Goto-Kakizaki (GK) rats with spontaneous type 2 diabetes. The results showed that HLP effectively improved the metabolic disorder. Based on KEGG functional analysis, five key biomarkers associated with bile acid metabolism were detected and screened (P<0.05). The results of serum total bile acid levels and liver damage in diabetic rats further showed the regulatory effects of HLP on bile acid metabolism. The results of bile acid-related gene expression in the liver showed that HLP inhibited liver farnesoidXReceptor - small heterodimer partner (FXR-SHP) signalling and increased the expression of bile acid synthesis genes (P <0.05). Our results explored the underlying mechanisms by which HLP accelerated cholesterol consumption to anti-hypercholesterolemia and anti-diabetic by inhibiting liver FXR-SHP signaling. HLP's effect on bile acid regulation provides insights into treating T2DM.

P31-021-23 Effect of Obesity and Metformin on Serum Metabolic Changes in Lean and Obese Zucker Rats

P31-021-23 Effect of Obesity and Metformin on Serum Metabolic Changes in Lean and Obese Zucker Rats

Role of kisspeptin in polycystic ovarian syndrome: A metabolomics study.

Polycystic ovary syndrome (PCOS) is a pathophysiological disease affecting reproductive and metabolic indicators. Research has shown that kisspeptin might be involved in the regulation of pituitary hormone secretion and energy metabolism. The aim of this study was to investigate the relationship between serum kisspeptin levels and abnormal metabolism in PCOS. Fifty patients with PCOS and 50 control patients were recruited for this study. Serum kisspeptin levels were measured via ELISA. High-performance liquid chromatography-tandem mass spectrometry metabolomics was used to study the changes in serum metabolism between the PCOS and control groups. Serum kisspeptin levels were significantly elevated in individuals with PCOS compared with those in healthy controls (p = 0.011) and positively correlated with LH, T, FFA, BA, and LEP levels (p < 0.05). Significantly dysregulated expression of several metabolites was observed in the intergroup comparisons of the high-kisspeptin PCOS, low-kisspeptin PCOS, and healthy control groups. These primarily consisted of lipid, amino acid, and carbohydrate metabolites, among which palmitic acid and N-formylkynurenine levels were lower in the high-kisspeptin group than in controls. Metabolite set enrichment analysis was also performed based on metabolites in the KEGG database. The results showed that owing to the differences in kisspeptin concentrations in individuals with PCOS, there was a significant difference in amino acid and pyruvate metabolism. Kisspeptin could be a potential biomarker for the diagnosis of PCOS and plays an important role in metabolic regulation in individuals with PCOS. In addition, metabolomics provides a promising method for the study of metabolic abnormalities in individuals with PCOS, which might contribute to our understanding of its mechanisms.

Nuclear Magnetic Resonance-Based Metabolomics Approach Revealed the Intervention Effect of Using Complementary and Alternative Medicine (CAM) by CKD Patients.

Chronic kidney disease (CKD) is the end point of a number of systemic chronic diseases. The prevalence of CKD is increasing worldwide and recent epidemiological studies are showing the high prevalence of renal failure in CKD patients using complementary and alternative medicines (CAMs). Clinicians believe that biochemical profiles of CKD patients using CAM (referred here to as CAM-CKD) may be different compared to those on standard clinical treatment and should be managed differently. The present study aims to explore the potential of the NMR-based metabolomics approach to reveal the serum metabolic disparity between CKD and CAM-CKD patients with respect to normal control (NC) subjects and if the differential metabolic patterns can provide rationale for the efficacy and safety of standard and/or alternative therapies. Serum samples were obtained from 30 CKD patients, 43 CAM-CKD patients, and 47 NC subjects. The quantitative serum metabolic profiles were measured using 1D 1H CPMG NMR experiments performed at 800 MHz NMR spectrometer. The serum metabolic profiles were compared using various multivariate statistical analysis tools available on MetaboAnalyst (freely available web-based software) such as partial least-squares discriminant analysis (PLS-DA) and random forest (a machine learning) classification method. The discriminatory metabolites were identified based on variable importance in projection (VIP) statistics and further evaluated for statistical significance (i.e., p < 0.05) using either Student t-test or ANOVA statistics. PLS-DA models were capable of clustering CKD and CAM-CKD with considerably high values of Q 2 and R 2. Compared to CAM-CKD patients, the sera of CKD patients were characterized by (a) elevated levels of urea, creatinine, citrate, glucose, glycerol, and phenylalanine and phenylalanine-to-tyrosine ratio (PTR) and (b) decreased levels of various amino acids (such leucine, isoleucine, valine, and alanine), high-density lipoproteins, lactate, and acetate. These changes suggested that CKD patients manifest severe oxidative stress, hyperglycemia (with dampened glycolysis), increased protein energy wasting, and reduced lipid/membrane metabolism. Statistically significant and strong positive correlation of PTR with serum creatinine levels suggested the role of oxidative stress in the progression of kidney disease. Significant differences in metabolic patterns between CKD and CAM-CKD patients were observed. With respect to NC subjects, the serum metabolic changes were more aberrant in CKD patients compared to CAM-CKD patients. The aberrant metabolic changes in CKD patients with manifestations of higher oxidative stress compared to CAM-CKD patients could explain clinical discrepancies between CKD and CAM-CKD patients and further advocate the use of different treatment strategies for CKD and CAM-CKD patients.

Metabolic remodeling of glycerophospholipids acts as a signature of dulaglutide and liraglutide treatment in recent-onset type 2 diabetes mellitus.

As metabolic remodeling is a pathological characteristic in type 2 diabetes (T2D), we investigate the roles of newly developed long-acting glucagon-like peptide-1 receptor agonists (GLP-1RAs) such as dulaglutide and liraglutide on metabolic remodeling in patients with recent-onset T2D. We recruited 52 cases of T2D and 28 control cases in this study. In the patient with T2D, 39 cases received treatment with dulaglutide and 13 cases received treatment with liraglutide. Using untargeted metabolomics analysis with broad-spectrum LC-MS, we tracked serum metabolic changes of the patients from the beginning to the end of follow-up (12th week). We identified 198 metabolites that were differentially expressed in the patients with T2D, compared to the control group, in which 23 metabolites were significantly associated with fasting plasma glucose. Compared to pre-treatment, a total of 46 and 45 differentially regulated metabolites were identified after treatments with dulaglutide and liraglutide, respectively, in which the most differentially regulated metabolites belong to glycerophospholipids. Furthermore, a longitudinal integration analysis concurrent with diabetes case-control status revealed that metabolic pathways, such as the insulin resistance pathway and type 2 diabetes mellitus, were enriched after dulaglutide and liraglutide treatments. Proteins such as GLP-1R, GNAS, and GCG were speculated as potential targets of dulaglutide and liraglutide. In total, a metabolic change in lipids existed in the early stage of T2D was ameliorated after the treatments of GLP-1RAs. In addition to similar effects on improving glycemic control, remodeling of glycerophospholipid metabolism was identified as a signature of dulaglutide and liraglutide treatments.

1H-NMR-Based Metabonomics Study to Reveal the Progressive Metabolism Regulation of SAP Deficiency on ApoE-/- Mice.

Atherosclerosis is the most common disease of the vascular system and the metabolic disorder is one of its important molecular mechanisms. SAP protein is found to be highly expressed in atherosclerotic blood vessels. Our previous study found that SAP deficiency can significantly inhibit the development of atherosclerosis. However, the regulatory effect of SAP deficiency on AS metabolism is unknown. Based on 1H-NMR metabonomics, this study investigated the serum metabolic changes in ApoE-/-;SAP-/- mice compared with ApoE-/- mice during the whole progression of atherosclerosis. The results showed that acetate, pyruvate, choline and VLDL + LDL were statistically regulated to the normal levels as in C57 mice by SAP deficiency in ApoE-/-;SAP-/- mice at 8 w (without obvious plaques). With the appearance and aggravation of atherosclerotic plaques (8 + 4 w and 8 + 8 w), the four metabolites of acetate, pyruvate, choline and VLDL + LDL were continuously regulated, which were denoted as the metabolic regulatory markers of SAP deficiency. We also found that the changes in these four metabolites had nothing to do with high-fat diet. Therefore, it was revealed that SAP deficiency regulated the metabolic disorders in ApoE-/- prior to the appearance of obvious atherosclerotic plaques, which is one of the important mechanisms leading to the inhibition of atherosclerosis, providing a new basis for the application of SAP in atherosclerosis.

The Aging Process: A Metabolomics Perspective.

Aging process is characterized by a progressive decline of several organic, physiological, and metabolic functions whose precise mechanism remains unclear. Metabolomics allows the identification of several metabolites and may contribute to clarifying the aging-regulated metabolic pathways. We aimed to investigate aging-related serum metabolic changes using a metabolomics approach. Fasting blood serum samples from 138 apparently healthy individuals (20-70 years old, 56% men) were analyzed by Proton Nuclear Magnetic Resonance spectroscopy (1H NMR) and Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS), and for clinical markers. Associations of the metabolic profile with age were explored via Correlations (r); Metabolite Set Enrichment Analysis; Multiple Linear Regression; and Aging Metabolism Breakpoint. The age increase was positively correlated (0.212 ≤ r ≤ 0.370, p &lt; 0.05) with the clinical markers (total cholesterol, HDL, LDL, VLDL, triacylglyceride, and glucose levels); negatively correlated (-0.285 ≤ r ≤ -0.214, p &lt; 0.05) with tryptophan, 3-hydroxyisobutyrate, asparagine, isoleucine, leucine, and valine levels, but positively (0.237 ≤ r ≤ 0.269, p &lt; 0.05) with aspartate and ornithine levels. These metabolites resulted in three enriched pathways: valine, leucine, and isoleucine degradation, urea cycle, and ammonia recycling. Additionally, serum metabolic levels of 3-hydroxyisobutyrate, isoleucine, aspartate, and ornithine explained 27.3% of the age variation, with the aging metabolism breakpoint occurring after the third decade of life. These results indicate that the aging process is potentially associated with reduced serum branched-chain amino acid levels (especially after the third decade of life) and progressively increased levels of serum metabolites indicative of the urea cycle.

Metabolomics profile in acute respiratory distress syndrome by nuclear magnetic resonance spectroscopy in patients with community-acquired pneumonia

BackgroundAcute respiratory distress syndrome (ARDS) is a challenging clinical problem. Discovering the potential metabolic alterations underlying the ARDS is important to identify novel therapeutic target and improve the prognosis. Serum and urine metabolites can reflect systemic and local changes and could help understanding metabolic characterization of community-acquired pneumonia (CAP) with ARDS.MethodsClinical data of patients with suspected CAP at the First Affiliated Hospital of Wenzhou Medical University were collected from May 2020 to February 2021. Consecutive patients with CAP were enrolled and divided into two groups: CAP with and without ARDS groups. 1H nuclear magnetic resonance-based metabolomics analyses of serum and urine samples were performed before and after treatment in CAP with ARDS (n = 43) and CAP without ARDS (n = 45) groups. Differences metabolites were identifed in CAP with ARDS. Furthermore, the receiver operating characteristic (ROC) curve was utilized to identify panels of significant metabolites for evaluating therapeutic effects on CAP with ARDS. The correlation heatmap was analyzed to further display the relationship between metabolites and clinical characteristics.ResultsA total of 20 and 42 metabolites were identified in the serum and urine samples, respectively. Serum metabolic changes were mainly involved in energy, lipid, and amino acid metabolisms, while urine metabolic changes were mainly involved in energy metabolism. Elevated levels of serum 3-hydroxybutyrate, lactate, acetone, acetoacetate, and decreased levels of serum leucine, choline, and urine creatine and creatinine were detected in CAP with ARDS relative to CAP without ARDS. Serum metabolites 3-hydroxybutyrate, acetone, acetoacetate, citrate, choline and urine metabolite 1-methylnicotinamide were identified as a potential biomarkers for assessing therapeutic effects on CAP with ARDS, and with AUCs of 0.866 and 0.795, respectively. Moreover, the ROC curve analysis revealed that combined characteristic serum and urine metabolites exhibited a better classification system for assessing therapeutic effects on CAP with ARDS, with a AUC value of 0.952. In addition, differential metabolites strongly correlated with clinical parameters in patients with CAP with ARDS.ConclusionsSerum- and urine-based metabolomics analyses identified characteristic metabolic alterations in CAP with ARDS and might provide promising circulatory markers for evaluating therapeutic effects on CAP with ARDS.

Multi-Timepoint Metabolic Fingerprinting of a Post-Episode Period of Hypoglycemia and Ketoacidosis Among Children With Type 1 Diabetes.

Background: Acute complications of type 1 diabetes mellitus such as diabetes ketoacidosis (DKA) and hypoglycemia (HG) are detrimental in a short- and long-term perspective. Restoration of normoglycemia and correction of pH do not mean that all metabolic disturbances caused by HG or DKA are immediately reversed. Aim: This study aimed to identify serum metabolic changes caused by an episode of DKA and HG that may indicate the mechanisms contributing to long-term consequences of DKA/HG. Materials and methods: Four groups of children with type 1 diabetes were recruited. The first two study groups included patients after an episode of DKA or HG, respectively. Additionally, two comparative groups were recruited—children with established type 1 diabetes (EDM) and patients with newly diagnosed diabetes without diabetes ketoacidosis (NDM). Serum samples were collected in three group-specific time points (since the hospital admission): HG 0h-12h–48h; DKA or NDM 0h-24h–72 h; and one random fasting sample from patients with EDM. Two batches of 100 samples each were created: for DKA batch 20 × 3 DKA patients, 10 × 3 NDM and 10 EDM; for HG batch: 10 × 3 HG patients, 25 EDM and 15 × 3 NDM. All patients within the batches were age and sex matched. Metabolic fingerprinting was performed with LC-QTOF-MS. Results: Four metabolites were associated with a DKA episode occurring in the preceding 72 h: three were found higher after the DKA episode versus comparative groups: lysophosphatidylcholine (LPC) (18:1), sphingomyelins (SM) (34:0 and d18:0/15:0), and one was found lower: LPC (18:0). Similarly, four metabolites were identified for the HG episode in the last 48 h: three were found higher after the HG episode versus comparative groups: two lysophosphatidylethanolamines (LPE) (18:2 and 20:3) and one LPC (18:2); and one was found lower after the HG episode: oxy-phosphatidylocholine (PC O-34:4). Conclusions: We found eight metabolites whose levels may be traced in the serum, indicating the DKA or HG episode for up to 72 h and 48 h, respectively. Acute complications of diabetes may cause persistent metabolic disturbances long after pH and glucose level normalization.

Identification of characteristic metabolic panels for different stages of prostate cancer by 1H NMR-based metabolomics analysis

BackgroundProstate cancer (PCa) is the second most prevalent cancer in males worldwide, yet detecting PCa and its metastases remains a major challenging task in clinical research setups. The present study aimed to characterize the metabolic changes underlying the PCa progression and investigate the efficacy of related metabolic panels for an accurate PCa assessment.MethodsIn the present study, 75 PCa subjects, 62 PCa patients with bone metastasis (PCaB), and 50 benign prostatic hyperplasia (BPH) patients were enrolled, and we performed a cross-sectional metabolomics analysis of serum samples collected from these subjects using a 1H nuclear magnetic resonance (NMR)-based metabolomics approach.ResultsMultivariate analysis revealed that BPH, PCa, and PCaB groups showed distinct metabolic divisions, while univariate statistics integrated with variable importance in the projection (VIP) scores identified a differential metabolite series, which included energy, amino acid, and ketone body metabolism. Herein, we identified a series of characteristic serum metabolic changes, including decreased trends of 3-HB and acetone as well as elevated trends of alanine in PCa patients compared with BPH subjects, while increased levels of 3-HB and acetone as well as decreased levels of alanine in PCaB patients compared with PCa. Additionally, our results also revealed the metabolic panels of discriminant metabolites coupled with the clinical parameters (age and body mass index) for discrimination between PCa and BPH, PCaB and BPH, PCaB and PCa achieved the AUC values of 0.828, 0.917, and 0.872, respectively.ConclusionsOverall, our study gave successful discrimination of BPH, PCa and PCaB, and we characterized the potential metabolic alterations involved in the PCa progression and its metastases, including 3-HB, acetone and alanine. The defined biomarker panels could be employed to aid in the diagnosis and classification of PCa in clinical practice.