Metabolites In Serum Of Patients Research Articles

GALNT2 expression is associated with glucose control and serum metabolites in patients with type 2 diabetes

GALNT2 expression is associated with glucose control and serum metabolites in patients with type 2 diabetes

Investigating causal associations among gut microbiota, metabolites, and psoriatic arthritis: a Mendelian randomization study.

Currently, there has been observed a significant alteration in the composition of the gut microbiome (GM) and serum metabolites in patients with psoriatic arthritis (PsA) compared to healthy individuals. However, previous observational studies have shown inconsistent results regarding the alteration of gut microbiota/metabolites. In order to shed light on this matter, we utilized Mendelian randomization to determine the causal effect of GM/metabolites on PsA. We retrieved summary-level data of GM taxa/metabolites and PsA from publicly available GWAS statistics. Causal relationships between GM/metabolites and PsA were determined using a two-sample MR analysis, with the IVW approach serving as the primary analysis method. To ensure the robustness of our findings, we conducted sensitivity analyses, multivariable MR analysis (MVMR), and additional analysis including replication verification analysis, LDSC regression, and Steiger test analysis. Furthermore, we investigated reverse causality through a reverse MR analysis. Finally, we conducted an analysis of expression quantitative trait loci (eQTLs) involved in the metabolic pathway to explore potential molecular mechanisms of metabolism. Our findings reveal that eight GM taxa and twenty-three serum metabolites are causally related to PsA (P < 0.05). Notably, a higher relative abundance of Family Rikenellaceae (ORIVW: 0.622, 95% CI: 0.438-0.883, FDR = 0.045) and elevated serum levels of X-11538 (ORIVW: 0.442, 95% CI: 0.250-0.781, FDR = 0.046) maintain significant causal associations with a reduced risk of PsA, even after adjusting for multiple testing correction and conducting MVMR analysis. These findings suggest that Family Rikenellaceae and X-11538 may have protective effects against PsA. Our sensitivity analysis and additional analysis revealed no significant horizontal pleiotropy, reverse causality, or heterogeneity. The functional enrichment analysis revealed that the eQTLs examined were primarily associated with glycerolipid metabolism and the expression of key metabolic factors influenced by bacterial infections (Vibrio cholerae and Helicobacter pylori) as well as the mTOR signaling pathway. In conclusion, our study demonstrates that Family Rikenellaceae and X-11538 exhibit a strong and negative causal relationship with PsA. These particular GM taxa and metabolites have the potential to serve as innovative biomarkers, offering valuable insights into the treatment and prevention of PsA. Moreover, bacterial infections and mTOR-mediated activation of metabolic factors may play an important role in this process.

Serum Metabolomic Profiling for Colorectal Cancer using Machine Learning

Background: Colorectal cancer is one of the deadliest diseases with a high prevalence worldwide and is characterized by the appearance of adenomatous polyps in the colon mucosa which are at high risk of developing into colorectal cancer. This study aims to use serum metabolites as promising non-invasive biomarkers for colorectal cancer detection and prognostication. Differences in serum metabolites in patients with adenomatous polyps, colorectal cancer, and healthy controls are considered to be able to support the prognosis of colorectal cancer. Methods: Metabolite dataset is taken from the Metabolomic Workbench. Analysis and validation are carried out in silico using machine learning methods. Results: From a total of 234 samples, 113 metabolites were found and 5 metabolites; histidine, lysine, glyceraldehyde, linolenic acid, and aspartic acid were identified as the most significant in differentiating the sample groups. CTD analysis showed that aspartic acid and histidine are associated with the biological pathways of colorectal cancer progression and significant metabolites are associated with cancer-related phenotypes. Conclusion: The serum metabolites differ in colorectal cancer and healthy control. The significant metabolites can be used as a consideration in selecting colorectal cancer biomarkers, but improvisation is needed to obtain more accurate biomarkers.

Gastrointestinal symptoms of long COVID-19 related to the ectopic colonization of specific bacteria that move between the upper and lower alimentary tract and alterations in serum metabolites

BackgroundSince the coronavirus disease 2019 (COVID-19) outbreak, many COVID-19 variants have emerged, causing several waves of pandemics and many infections. Long COVID-19, or long-term sequelae after recovery from COVID-19, has aroused worldwide concern because it reduces patient quality of life after rehabilitation. We aimed to characterize the functional differential profile of the oral and gut microbiomes and serum metabolites in patients with gastrointestinal symptoms associated with long COVID-19.MethodsWe prospectively collected oral, fecal, and serum samples from 983 antibiotic-naïve patients with mild COVID-19 and performed a 3-month follow-up postdischarge. Forty-five fecal and saliva samples, and 25 paired serum samples were collected from patients with gastrointestinal symptoms of long COVID-19 at follow-up and from healthy controls, respectively. Eight fecal and saliva samples were collected without gastrointestinal symptoms of long COVID-19 at follow-up. Shotgun metagenomic sequencing of fecal samples and 2bRAD-M sequencing of saliva samples were performed on these paired samples. Two published COVID-19 gut microbiota cohorts were analyzed for comparison. Paired serum samples were analyzed using widely targeted metabolomics.ResultsMild COVID-19 patients without gastrointestinal symptoms of long COVID-19 showed little difference in the gut and oral microbiota during hospitalization and at follow-up from healthy controls. The baseline and 3-month samples collected from patients with gastrointestinal symptoms associated with long COVID-19 showed significant differences, and ectopic colonization of the oral cavity by gut microbes including 27 common differentially abundant genera in the Proteobacteria phylum, was observed at the 3-month timepoint. Some of these bacteria, including Neisseria, Lautropia, and Agrobacterium, were highly related to differentially expressed serum metabolites with potential toxicity, such as 4-chlorophenylacetic acid, 5-sulfoxymethylfurfural, and estradiol valerate.ConclusionsOur study characterized the changes in and correlations between the oral and gut microbiomes and serum metabolites in patients with gastrointestinal symptoms associated with long COVID-19. Additionally, our findings reveal that ectopically colonized bacteria from the gut to the oral cavity could exist in long COVID-19 patients with gastrointestinal symptoms, with a strong correlation to some potential harmful metabolites in serum.

Effect of Electroacupuncture with Different Current Intensities on the Serum Metabolomics of Functional Constipation.

The aim of the study is to investigate the serum metabolomics of electroacupuncture (EA) with different current intensities in the treatment of functional constipation (FC). The total number of FC patients was 19, (7, 6, 6, in the low current intensity group (LCI), high current intensity group (HCI), and mosapride citrate tablet control group (MC), respectively). Patients in the EA groups received 16 sessions of acupuncture treatments. Patients in the MC group were orally administered 5 mg mosapride citrate tablets 3 times daily, and serum samples were collected from the patients before and after treatment. Orthogonal partial least square-discriminant analysis (OPLS-DA) was used to assess the metabolic data. The significant differences before and after FC treatment are shown in the OPLS-DA score plot. Variable importance plots (VIPs) and T tests were used to identify significant metabolites. Among the three groups, the number of metabolites with VIP > 1 was 11, 7, and 21 (in LCI, HCI and MC groups, respectively). Compared with those before treatment, the serum metabolites of patients were characterized by increased levels of L-ornithine (p < 0.05) and glyceric acid in the LCI group (p < 0.05), increased levels of vanillic acid in the MC group (p < 0.05), and decreased levels of arabinonic acid in the MC group (p < 0.05). The effects of EA treatment on the serum metabolomics of FC may involve fatty acid and amino acid metabolism.

Comparison of Serum Metabolomics Pathways and Patterns between Patients with Major Depressive Disorder with and without Type 2 Diabetes Mellitus: An Exploratory Study.

A close relationship exists between major depressive disorder (MDD) and diabetes mellitus. The metabolomic difference and similarity between patients with and without diabetes mellitus have not been well studied in the context of MDD. We aimed to examine these differences and common serum metabolomics patterns, pathways and biomarkers that can comprehensively reflect the pathogenetic difference and similarity between these MDD groups. We performed a metabolomics analysis of serum samples of healthy controls (n = 6), patients with MDD and type 2 diabetes mellitus (n = 13), and patients with MDD without type 2 diabetes mellitus (n = 27). Metabolomics analysis was conducted using capillary electrophoresis Fourier transform mass spectrometry and a candidate compound was assigned to the 496 (290 cation, 206 anion) peaks. Moreover, we evaluated the sensitivity and specificity of the candidate biomarkers for distinguishing between MDD patients with or without type 2 diabetes mellitus. Principal component analysis revealed no clear distinction among the three groups, while naive partial least squares discriminant analysis yielded three relatively good and distinct populations based on the first principal component. Energy conversion by the tricarboxylic acid cycle represented the highest percentage among the top 30 positive factors of the first principal component, and glutamate metabolism and urea cycle represented the highest percentage among the top 30 negative factors of the first principal component. Synthesis and degradation of ketone bodies had high impact in MDD with type 2 diabetes mellitus group and taurine and hypotaurine metabolism had high impact in MDD without type 2 diabetes mellitus group for the pathway. Patterns of serum metabolites may be different among MDD with type 2 diabetes mellitus, MDD without type 2 diabetes mellitus, and healthy controls groups. Specifically, comorbid type 2 diabetes mellitus could affect metabolomics pathway and alter the distribution of serum metabolites in patients with MDD. These findings may shed light on the influence of the type 2 diabetes on the pathophysiology of MDD.

Metabolomic characterization of congenital microtia: a possible analysis for early diagnosis.

Although metabolic abnormalities have been deemed one of the essential risk factors for growth and development, the relationship between metabolic abnormalities and microtia is still unclear. In this study, we aimed to establish a cell model of microtia and the changes of serum metabolites in patients with microtia. After constructing a cell model of microtia with low expression of BMP5, we performed integrative metabolomics analysis. For the altered metabolites, the content of glycerophosphocholine (PC), triacylglycerol (TG), and choline in the serum of 28 patients (15 patients with microtia and 13 controls) with microtia was verified by enzyme-linked immunosorbent assay (ELISA). Detailed metabolomic evaluation showed distinct clusters of metabolites between BMP5-low expressing cells and normal control (NC) cells. The cell model of microtia had significantly higher levels of TG, PC, glycerophosphoethanolamine (PE), sphingomyelin, sulfatide, glycerophosphoglycerol, diacylglycerol, and glycosphingolipid. The main abnormal metabolites were mainly concentrated in the glycerophospholipid metabolism pathway, and PC and choline were closely related. In the serum of patients with microtia, the contents of PC, TG, and choline were significantly increased. The individual serum samples confirmed the different metabolites between patients with microtia and controls. In particular, we showed that a newly developed metabolic biomarker panel has a high sensitivity and specificity for separating patients with microtia from controls.

Integrating 16S RRNA gene sequencing and metabolomics to evaluate the association between gut microbiota and serum metabolites in patients with myositis.

Gut microbiota and metabolites have a profound impact on the maintenance of body health. In this study, we assessed the association between gut microbiota and serum metabolite changes in myositis using 16S rRNA gene sequencing and metabolomics to provide new ideas for screening and treating myositis. Blood and faecal samples were collected from 20 myositis patients and 20 healthy control subjects. Then, 16S rRNA gene sequencing, enzyme-linked immunosorbent assays and untargeted metabolomics study were performed to evaluate the relationship between gut microbiota and serum metabolites in patients with myositis. Compared to healthy control subjects, the blood samples from the patients with myositis had elevated levels of interleukin-4 (IL-4), tumour necrosis factor-α (TNF-α), and malondialdehyde (MDA) and decreased superoxide dismutase (SOD) levels. The increase in Bacteroidota (including Bacteroides and Parabacteroides, but not Prevotella) and the decrease in Firmicutes in the patients were accompanied by functional changes in amino acid and lipid metabolism. The gut microbiota (Bacteroides and Parabacteroides) were negatively correlated with the differential serum metabolites (glutamate and taurine). The differential serum metabolites (glutamate, pyrrolidonecarboxylic acid, and taurine) were also correlated with inflammatory factors (IL-4 and TNF-α) and oxidative stress indexes (MDA and SOD). Dysbiosis of gut microbiota in patients with myositis was accompanied by changes in inflammatory factors, oxidative stress indexes, and small molecule metabolites in serum. Blood and faecal biomarkers could be used for screening myositis.

Characterization of intestinal microbiota and serum metabolites in patients with mild hepatic encephalopathy.

Mild micro-hepatic encephalopathy (MHE) is a severe complication of cirrhosis. At present, there are differences in the consistency of detection strategies and treatment directions for MHE. The characteristic changes in intestinal microbiota and serum metabolites in MHE patients and the possible relevant interaction mechanisms would inevitably affect the developmental direction of MHE. Therefore, the changes in the characteristics of intestinal microbiota and serum metabolites of MHE patients were determined, and the possible interactions between them were analyzed. Stool and serum tests were performed on both the MHE patients and healthy individuals. The 16S rRNA gene high-throughput sequencing and bioinformatics analyses were used to analyze the differences in intestinal microbiota in MHE patients. The serum metabolites were detected using liquid LC-MS/MS (liquid chromatography-mass spectrometry) technology, and the differences in the metabolic networks of blood metabolites in MHE patients were analyzed. A comprehensive bioinformatics analysis approach was adopted to identify the composition and characteristics of microbiota and serum metabolites and the possible correlation between them. The main characteristics of the structural imbalance in the intestinal microbiota of MHE patients included a decrease in the number of beneficial bacteria at the levels of phylum, class, order, family, and genus and an increase in the pathogenic bacteria, resulting in substantial changes in the relative abundances of bacteria in the intestinal microbiota. The main predicted functions that showed significant differences included chromosome, amino acid-related enzymes, methane metabolism, and arginine and proline metabolism. The detection of serum metabolites resulted in 10 different metabolites, including taurocholic acid, citrulline, d-phenyl-lactic acid, l-tyrosine, benzoate, phenylalanine, linoleic acid, eicosapedienic acid, alpha-dimorphecolic acid, and dehydroepiandrosterone. The subsequent metabolite pathways analysis showed differences in the metabolism of linoleic acid, phenyl-propane, caffeine, arginine, proline, glycine, serine, threonine, tyrosine, and pyrimidine compared to the control group. In summary, it seems that the changes in the microbiome that we have identified have resulted in corresponding changes to the serum metabolome. In turn, this may represent changes in the absorption of metabolites from the gut or reflect the changed metabolic capacity of the MHE liver or both. There were characteristic changes in the intestinal microbiota and serum metabolites in the MHE patients. There might be a related interaction mechanism between the two, which would provide evidence and direction for the detection and treatment strategies of MHE.

Alterations in Gut Microbiota Are Correlated With Serum Metabolites in Patients With Insomnia Disorder.

This study aimed to investigate insomnia-related alterations in gut microbiota and their association with serum metabolites. A total of 24 patients with insomnia disorder and 22 healthy controls were recruited. The fecal and serum samples were collected. The 16s rRNA sequencing and bioinformatics analysis were conducted to explore insomnia-related changes in the diversity, structure, and composition of the gut microbiota. UPLC-MS was performed to identify insomnia-related serum metabolites. Spearman correlation analysis was used to investigate the correlations between insomnia-related gut bacteria and the serum metabolites. Despite the nonsignificant changes in the diversity and structure of gut microbiota, insomnia disorder patients had significantly decreased family Bacteroidaceae, family Ruminococcaceae, and genus Bacteroides, along with significantly increased family Prevotellaceae and genus Prevotella, compared with healthy controls. Genus Gemmiger and genus Fusicatenibacter were dominant in patients with insomnia disorder, whereas genus Coprococcus, genus Oscillibacter, genus Clostridium XI, and family Peptostreptococcaceae were dominant in healthy controls. The UPLC-MS analysis identified 97 significantly decreased metabolites and 74 significantly increased metabolites in the serum samples of patients with insomnia disorder, compared with those of healthy controls. KEGG enrichment analysis revealed 1 significantly upregulated metabolic pathway and 16 downregulated metabolic pathways in patients with insomnia disorder. Furthermore, Spearman correlation analysis unveiled significant correlations among the altered bacteria genus and serum metabolites. Patients with insomnia disorder have differential gut microbiota and serum metabolic profiles compared with healthy controls. The alterations in gut microbiota were correlated with specific serum metabolites, suggesting that some serum metabolites might mediate gut microbiota-brain communication in the pathogenesis of insomnia disorder.

The Effects of Moderate Alcohol Consumption on Circulating Metabolites and Gut Microbiota in Patients With Coronary Artery Disease.

Background: Epidemiological studies confirmed that moderate alcohol consumption was associated with a reduced risk of adverse cardiovascular events. It is increasingly recognized that the composition of gut microbiota and metabolites is involved in modulating the cardiovascular health of the host. However, the association of moderate alcohol consumption with serum metabolites and gut microbiome and its impact on coronary artery disease (CAD) is not fully investigated.Method: Serum untargeted metabolomics analysis and fecal 16S rRNA sequencing were performed on 72 male patients with CAD having various alcohol consumption (36 non-drinkers, 18 moderate drinkers, and 18 heavy drinkers) and 17 matched healthy controls. MetaboAnalyst and PICRUSt2 were utilized to analyze the possible involved metabolic pathways. Multi-omics analysis was achieved by Spearman correlation to reveal the interactions of alcohol consumption with gut microbiome and serum metabolites in patients with CAD.Results: We noted distinct differences between patients with CAD, with varying levels of alcohol consumption and healthy controls in aspects of serum metabolome and the gut microbiome. Moderate alcohol consumption significantly changed the lipidomic profiles, including reductions of sphingolipids and glycerophospholipids in moderate drinkers with CAD when compared with non and heavy drinkers with CAD. Moreover, we also found the reduction of microbial-derived metabolites in moderate drinkers with CAD, such as 2-phenylacetamide and mevalonic acid. To be noted, the gut microbiota of moderate drinkers with CAD tended to resemble that of healthy controls. Compared with non-drinkers, the relative abundance of genus Paraprevotella, Lysinibacillus was significantly elevated in moderate drinkers with CAD, while the genus Bifidobacterium, Megasphaera, and Streptococcus were significantly reduced in moderate drinkers with CAD. Multi-omics analysis revealed that specific metabolites and microbes associated with moderate alcohol consumption were correlated with the severity of CAD.Conclusions: Our study revealed that the impact of moderate alcohol consumption on serum metabolites and gut microbiota in patients with CAD seemed to be separated from that of heavy and non-alcohol consumption. Moderate drinking tended to have more positive effects on metabolic profiles and commensal flora, which may explain its beneficial effects on cardiovascular health. Overall, our study provides a novel insight into the effects of moderate alcohol consumption in patients with CAD.

UHPLC-MS/MS Analysis of Cannabidiol and Its Metabolites in Serum of Patients with Resistant Epilepsy Treated with CBD Formulations.

Cannabidiol (CBD) is a promising therapeutic agent with analgesic, myorelaxant, and anti-epileptic actions. Recently, a purified form of CBD (Epidiolex®) has been approved by the European Medicines Agency (EMA) for the treatment of two highly-refractory childhood-onset epilepsies (Dravet and Lennox-Gastaut syndrome). Given the interindividual response and the relationship between the dose administered and CBD blood levels, therapeutic drug monitoring (TDM) is a valuable support in the clinical management of patients. We herein report for the first time a newly developed and validated method using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC–MS/MS) to evaluate CBD and its metabolites (i.e., cannabidiol-7-oic acid (7-COOH-CBD), 7-hydroxycannabidiol (7-OH-CBD), 6-α-hydroxycannabidiol (6-α–OH–CBD) and 6-β-hydroxycannabidiol (6-β–OH–CBD)) in serum samples. The method reached the sensitivity needed to detect minimal amounts of analytes under investigation with limits of quantification ranging from 0.5 to 20 ng/mL. The validation results indicated in this method were accurate (average inter/intra-day error, <15%), precise (inter/intra-day imprecision, <15%), and fast (8 min run time). The method resulted to be linear in the range of 1–10,000 ng/mL for CBD-COOH, 1–500 ng/mL for 7-OH-CBD and CBD and 1–25 ng/mL for 6-α–OH–CBD and 6-β–OH–CBD. Serum levels of CBD (88.20–396.31 and 13.19–170.63 ng/mL) as well as of 7-OH-CBD (27.11–313.63 and 14.01–77.52 ng/mL) and 7-COOH-CBD (380.32–10,112.23 and 300.57–2851.82 ng/mL) were significantly higher (p < 0.05) in patients treated with GW pharma CBD compared to those of patients treated with galenic preparations. 6-α–OH–CBD and 6-β–OH–CBD were detected in the first group and were undetectable in the second group. 7-COOH-CBD was confirmed as the most abundant metabolite in serum (5–10 fold higher than CBD) followed by 7-OH-CBD. A significant correlation (p < 0.05) between the dose administrated and a higher bioavailability was confirmed in patients treated with a GW pharma CBD preparation.

Analysis of Serum Metabonomics in Patients with Multiple Myeloma Based on Liquid Chromatography-Mass Spectrometry

To observe the changes of serum metabolites in patients with multiple myeloma (MM) by metabonomics, and explore the potential biomarkers for diagnosis, prognosis, and progression of MM. Serum samples were collected from 26 patients with MM and 50 healthy controls. The data detected by liquid chromatography-mass spectrometry (LC-MS) was input into SIMCA-14.0 software for multivariate statistical analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the changes of metabolites. The metabolic change of uric acid and trans-vaccenic acid in serum samples of MM patients was 9.39 times and 2.77 times of these in healthy people, respectively, which were significantly higher than those of healthy people, and the difference was statistically significant(P<0.01). Uric acid and trans-vaccenic acid are expected to be important metabolic indicators for the diagnosis, prognosis, and efficacy evaluation of MM, thus providing some clues for the pathogenesis of MM.

Characterization of Intestinal Microbiota and Serum Metabolites in Patients with Mild Hepatic Encephalopathy

Characterization of Intestinal Microbiota and Serum Metabolites in Patients with Mild Hepatic Encephalopathy

Potential Metabolic Biomarkers for Early Detection of Oral Lichen Planus, a Precancerous Lesion.

Background: Oral lichen planus (OLP) is a T-cell-mediated chronic inflammatory disorder and precancerous oral lesion with high incidence. The current diagnostic method of OLP is very limited and metabolomics may provide a new approach for quantitative evaluation. Methods: The Ultra-Performance Liquid Chromatography-Quadrupole/Orbitrap High Resolution Mass Spectrometry (UHPLC-Q-Orbitrap HRMS) was applied to analyze the change of metabolites in serum of patients with OLP. A total of 115 OLP patients and 124 healthy controls were assigned to either a training set (n = 160) or a test set (n = 79). The potential biomarkers and the change of serum metabolites were profiled and evaluated by multivariate analysis. Results: Totally, 23 differential metabolites were identified in the training set between OLP group and healthy group. Three prominent metabolites in receiver operating characteristic (ROC) were selected as a panel to distinguish OLP or healthy individuals in the test set, and the diagnostic accuracy was 86.1%. Conclusions: This study established a new method for the early detection of OLP by analyzing serum metabolomics using UHPLC-Q-Orbitrap HRMS, which will help in understanding the pathological processes of OLP and identifying precancerous lesions in oral cavity.

Untargeted GC-MS investigation of serum metabolomics of coronary artery disease patients.

Recent advances in metabolomics provide tools to investigate human metabolome in order to establish new parameters to study different approaches towards diagnostics, diseases and their treatment. The present study focused on the untargeted identification of metabolites in serum of patients with coronary artery disease who were under treatment at the time of sample collection. AUCs (Area Under the Curves) from different peaks were considered for the analysis and comparison purposes. The metabolome was studied using GC–MS (Gas Chromatography Mass Spectrometry) and the metabolites were identified with NIST (The National Institute of Standards and Technology) and Wiley library matches. A total of 17 metabolites were identified and focused on to compare with the metabolome of healthy individuals. T test analysis found significant differences in alanine, malonic acid, ribitol, D-glucose, mannose (P < 0.001), acetohydroxamic acid, N-carboxyglycine, and aminobutyrate (P < 0.05). Principal Component Analysis of serum metabolites data found three components out of 17 metabolites; RC1 (Acetohydroxamic acid, alanine, D-glucose, malonic acid, mannose, N-carboxy glycine and ribitol), RC2 (Heptadecanoic acid, hexadecanoic acid, octadecanoic acid and Trans-9-octadecanoic acid), RC3 (Aminobutyrate, D-sorbit, gamma lactone, valine, benzene propanoic acid and lactic acid). No correlation was found among the components.

Abstract 3122: Metabolic dysregulation and risk of non-alcoholic fatty liver disease in Mexican adults

Abstract Background: Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease and the fastest growing cause of cirrhosis in the United States (U.S.). This is very concerning because NAFLD-related cirrhosis is associated with hepatocellular carcinoma (HCC), the most common form of liver cancer. Latinos have the highest prevalence of NAFLD in the U.S., and the second highest incidence of and mortality from liver cancer, after American Indians. Known risk factors for NAFLD include obesity, metabolic syndrome, diabetes, and insulin resistance. Less is known about how specific metabolites may contribute to increased liver cancer susceptibility among Latinos despite the disproportionate burden of disease in this population. Methods: Using existing clinical data and biospecimens obtained from a longitudinal study in Mexico, we examined the association between metabolic dysregulation and NAFLD in a sample of Mexican adults. A nested case-control analysis was conducted using 100 cases of NAFLD and 100 healthy controls. Risk of NAFLD was assessed using metabolic markers supplemented by clinical data. NAFLD cases were clinically confirmed by a hepatologist using elevated liver enzyme tests (ALT &amp;gt;40 UI/L or AST &amp;gt;40 UI/L) and ultrasound of the liver, after excluding heavy or binge drinking (&amp;gt;2 drinks per day for males, &amp;gt;1 drink per day for females). 159 targeted metabolites were detected with a Thermo Scientific Q Exactive mass spectrometer run with polarity switching on full scan mode with a 65-975 m/z range. TraceFinder Software 4.1 (Thermo Scientific) was used to quantify metabolites using retention time and accurate mass measurements (&amp;lt; 5 ppm). Data analysis was carried out using in-house R scripts. Principal component analysis was constructed from metabolites identified as significant (p&amp;lt;0.05) using a Multiple Analysis of Covariance comparing the healthiest controls in our cohort (without diabetes or metabolic syndrome, n=46) to the least healthy cases (with diabetes and metabolic syndrome, n=24). Results: Out of 159 metabolites assessed, we found 25 metabolites to be significantly different between the groups after adjusting for age and sex. After further Bonferroni correction for multiple comparisons, eight were significantly increased in cases: Aconitate, Gluconic Acid, Glucose, Hexose Phosphate, Malate, and Phosphate; while two had significantly lower levels: Guanine and Inosine. Our findings indicate distinguishable changes in the serum metabolites of patients with NAFLD, which appear to be associated with more metabolic dysregulation. Conclusions: Our results support the use of mass spectrometry-based metabolomics as an appropriate tool to identify serum metabolite signatures associated with NAFLD disease progression. This tool may be useful in developing targeted liver cancer prevention strategies. These findings should be validated in larger studies that examine the trafficking of these identified prognostic metabolites through the liver. Citation Format: Yvonne N. Flores, Gerardo J. Sanchez, Johanna ten Hoeve, Aryana T. Amoon, Thomas Graeber, Beth A. Glenn, Folasade P. May, Francisco Durazo, Zuo-Feng Zhang, Jorge Salmerón, Brad H. Pollock, Roshan Bastani. Metabolic dysregulation and risk of non-alcoholic fatty liver disease in Mexican adults [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3122.

Functional Microbiomics Reveals Alterations of the Gut Microbiome and Host Co-Metabolism in Patients With Alcoholic Hepatitis.

Alcohol‐related liver disease is a major public health burden, and the gut microbiota is an important contributor to disease pathogenesis. The aim of the present study is to characterize functional alterations of the gut microbiota and test their performance for short‐term mortality prediction in patients with alcoholic hepatitis. We integrated shotgun metagenomics with untargeted metabolomics to investigate functional alterations of the gut microbiota and host co‐metabolism in a multicenter cohort of patients with alcoholic hepatitis. Profound changes were found in the gut microbial composition, functional metagenome, serum, and fecal metabolomes in patients with alcoholic hepatitis compared with nonalcoholic controls. We demonstrate that in comparison with single omics alone, the performance to predict 30‐day mortality was improved when combining microbial pathways with respective serum metabolites in patients with alcoholic hepatitis. The area under the receiver operating curve was higher than 0.85 for the tryptophan, isoleucine, and methionine pathways as predictors for 30‐day mortality, but achieved 0.989 for using the urea cycle pathway in combination with serum urea, with a bias‐corrected prediction error of 0.083 when using leave‐one‐out cross validation. Conclusion: Our study reveals changes in key microbial metabolic pathways associated with disease severity that predict short‐term mortality in our cohort of patients with alcoholic hepatitis.

Study of Differential Serum Metabolites in Patients with Adenomatous Polyps of Colon and Yang-Deficiency Constitution Based on Ultra-Performance Liquid Chromatography-Mass Spectrometry.

To study the differences between the serum metabolites in patients with adenomatous polyps of the colon and yang-deficiency constitution and those without colon polyps and with balanced constitution, and look for biomarkers that can be used to distinguish between the two groups. General patient information was gathered, and Chinese medicine constitution were collected in 940 patients who underwent electronic colonoscopy. A total of 119 patients with adenomatous polyps of the colon and yang-deficiency constitution were included in the experimental group, and 150 patients without colon polyps and with balanced constitution were included in the control group. Metabolomics analysis was performed on the fasting venous blood obtained from each patient in both groups. Principal component analysis and orthogonal partial least squares discriminant analysis were performed on the detection results, potential biomarkers were screened, metabolic pathway changes were determined, and the metabolic processes involved were discussed. A total of 59 differential biomarkers between the experimental group and the control group were identified. The differential metabolites were found mainly in the glycerophospholipid metabolism pathway, and the bile acid 3-oxo-4,6-choladienoic acid was the biomarker that distinguished the experimental group from the control group. With the help of metabolomics analysis, the differential metabolites in patients with adenomatous polyps of the colon and yang-deficiency constitution and those in patients without colon polyps and with balanced constitution could be identified. The biomarker 3-oxo-4,6-choladienoic acid may have potential diagnostic value in patients with adenomatous polyp of the colon and yang-deficiency constitution. (Trial Registration No. NCT02986308).

Postoperative serum metabolites of patients on a low carbohydrate ketogenic diet after pancreatectomy for pancreatobiliary cancer: a nontargeted metabolomics pilot study

A ketogenic diet is a potential adjuvant cancer therapy that limits glucose availability to tumours while fuelling normal tissues with ketone bodies. We examined the effect of a low carbohydrate ketogenic diet (LCKD) (80% kcal from fat, ketogenic ratio 1.75:1, w/w) compared to a general hospital diet (GD) on serum metabolic profiles in patients (n = 18, ≥ 19 years old) who underwent pancreatectomy for pancreatobiliary cancer. Serum samples collected preoperatively (week 0) and after the dietary intervention (week 2) were analysed with a nontargeted metabolomics approach using liquid chromatography–tandem mass spectrometry. Serum β-hydroxybutyrate and total ketone levels significantly increased after 2 weeks of LCKD compared to GD (p < 0.05). Principal component analysis score plots and orthogonal partial least squares discriminant analysis also showed significant differences between groups at week 2, with strong validation. In all, 240 metabolites differed between LCKD and GD. Pathways including glycerophospholipid and sphingolipid metabolisms were significantly enriched in the LCKD samples. LCKD decreased C22:1-ceramide levels, which are reported to be high in pancreatic cancer, while increasing lysophosphatidylcholine (18:2), uric acid, citrulline, and inosine levels, which are generally low in pancreatic cancer. Postoperative LCKD might beneficially modulate pancreatic cancer-related metabolites in patients with pancreatobiliary cancer.