Difference and clinical value of metabolites in plasma and feces of patients with alcohol-related liver cirrhosis (2024)

Study design and patients

This was a case-controlled observation study. We prospectively enrolled two groups of subjects: individuals with ALC and healthy controls (HCs). ALC subjects were selected from male inpatients admitted to the Liver Disease Center of Ditan Hospital (Beijing, China) from September 30, 2020 to December 31, 2021. The flow-chart demonstrates the selection of study participants (Figure ​(Figure1).1). According to the International Clinical Guidelines[11], individuals with ALC were diagnosed if they met the following criteria: (1) Regular drinking and have a prior or ongoing heavy alcohol intake of > 5 years (males >36 g alcohol/day); and (2) ultrasonography, computed tomography, or magnetic resonance imaging should exhibit liver cirrhosis. Additionally, we recruited age-matched healthy males as controls through recruitment advertisem*nts. These controls met the following criteria after clinical assessment and blood and imaging examinations: (1) Normal physical examination (heart rate, blood pressure, and respiratory rate) and body mass index (BMI) < 30 kg/m²; (2) no history of alcohol intake; (3) blood routine, liver function, kidney function, tumor maker, and abdominal ultrasound with normal limits; and (4) no sign of any disease, including liver and heart diseases. The following criteria were excluded for all participants: (1) Liver diseases except for alcohol etiologies, such as hepatitis B virus or hepatitis C virus infection, drug-induced liver diseases, and autoimmune hepatitis; (2) Complications except for ascites, such as hepatic encephalopathy, hepatorenal syndrome, hepatopulmonary syndrome, and esophageal and gastric variceal bleeding; (3) human immunodeficiency virus infection; (4) other digestive diseases (colitis, pancreatitis, and cholecystitis); (5) kidney, heart, lung, neurological, and metabolic diseases (diabetes, thyroid, and adrenal diseases); (6) all types of cancers; and (7) systematically using antibiotics, probiotics, and proton pump inhibitors within 1 mo before enrollment.

Clinical information, including demographic indicators, was collected. Moreover, the plasma and feces of all participants were collected and frozen at -80 °C for subsequent biochemical and hematological indexes tests and untargeted metabolomics detection.

This research scheme was approved by the Ethics Committee of Ditan hospital based on the ethical principles of the Declaration of Helsinki and registered at http://www.chictr.org.cn/ (ChiCTR-2000038216). Written informed consent was obtained from all the participants.

Biochemical and hematological indexes examination

White blood cell, neutrophilic granulocyte percentage, red blood cell (RBC), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), and platelet (PLT) levels were detected on automatic blood routine analyzers (Sysmex, Kobe, Japan). Alanine aminotransferase (ALT), aspartate transaminase (AST), total bilirubin (TBil), albumin (ALB), albumin/globulin (A/G), g-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP), cholinesterase (CHE), total bile acid (TBA), total cholesterol, urea nitrogen, and creatinine (Cr) were detected on automatic biochemical analyzers (Hitachi, Tokyo, Japan). Prothrombin time (PT) was detected by automatic coagulation analyzers (Werfan, Barcelona, Spain), and alpha-fetoprotein (AFP) was detected by chemiluminescence immunoassay analyzer (Abbott, Chicago, IL, United States).

Untargeted metabolomics detection

Plasma or feces sample was mixed with an extract buffer and hom*ogenized in an ice-water bath. The hom*ogenization and sonication cycle was repeated three times. The supernatant was collected by centrifugation of the hom*ogenates and analyzed by liquid chromatography-mass spectrometry (LC-MS/MS) on a UHPLC system (Thermo Fisher Scientific, Waltham, MA, United States) with a UPLC BEH Amide column coupled to Q Exactive HFX mass spectrometer from Thermo Fisher Scientific. The QE HFX mass spectrometer was used for its ability to acquire MS/MS spectra in information-dependent acquisition (IDA) mode using the acquisition software (Thermo Fisher Scientific, Waltham, MA, United States). The acquisition software continuously evaluates the full scan MS spectrum in this mode. The quality control (QC) sample was prepared by mixing an equal aliquot of the supernatants from all the samples.

Statistical analysis

The raw data were converted to the mzXML format using ProteoWizard and processed with an in-house program developed using R and based on XCMS for peak detection, extraction, alignment, and integration. Then, an in-house MS2 database (Biotree DB, Shanghai, China) was applied to metabolite annotation. The cutoff for the annotation was set at 0.3. All variables were normalized in MetaboAnalyst 5.0 (https://www.metaboan-alyst.ca) after preprocessing using the Masshunter Profinder software. R script can view details and download them from the website of MetaboAnalyst (https://www.metaboanalyst.ca/MetaboAnalyst/docs/RTutorial.xhtml). The results of the patients’ clinical and biochemical characteristics for continuous variables are expressed as median (upper quartile, lower quartile), and categorical variables are expressed as numbers and percentages. The between-group comparisons were made with Kruskal-Wallis H test. All statistical analyses were performed using SPSS 26.0 software. The significance level for all statistical tests was set at 0.05. Based on the metabolome profile, the phenotype-associated fingerprint metabolites were projected to one eigen metabolite by the first principal component in the unsupervised principal component analysis (PCA). Fold change (FC): the quantitative ratio of the two groups of experimental substances by comparing the ALC group to the HC group. Furthermore, we analyzed significant differences (P < 0.05 and FC > 2 or P < 0.05 and FC < 0.5) by comparing the ALC and HC groups and marked them as ALC/HC. We also detected the metabolic pathways of phenotype-associated fingerprint metabolites. PCA was conducted on the MetaboAnalyst website based on R. Furthermore, the P- and FC values were obtained from the MetaboAnalyst website. Then, the original variables were narrowed down to fingerprint based on P-value and FC.

Clinical characteristics of the study cohort

The clinical characteristics and laboratory tests of participants are shown in Table ​Table1.1. A total of 51 subjects were included in this study, including 24 HCs and 27 patients with ALC, and all subjects were males. No significant differences were detected in age and BMI between the two groups. The levels of ALT, AST, TBil, GGT, ALP, TBA, and PT were higher in the ALC group than in the HC group, while the number of RBCs and the levels of HGB, HCT, ALB, CHE, PLT, and T-CHO were lower in ALC than in HCs. Compared to HCs, there were no significant differences in UREA, CREA, AFP, and GLU in ALC patients.

Amino acids and derivatives were the most common metabolites in the plasma and feces of patients with ALC

More than 8000 plasma metabolites and more than 10,000 fecal metabolites were detected in this study. HMDB identified > 300 types of co-metabolites in plasma and feces. Amino acids and their derivatives accounted for the largest proportion of co-metabolites in plasma and feces. The remaining metabolites included bile acids, dicarboxylic acids, hydroxy fatty acids, saturated fatty acids, and unsaturated fatty acids (Figure ​(Figure2A).2A). Principal component analysis on the plasma and feces of patients with ALC showed (Figure ​(Figure2B2B and ​andC)C) that the metabolites in the plasma were clearly distinguished between the two groups based on the first and second principal component scores, while the metabolites in the feces were discretely distributed within the two groups, rendering difficulty in distinguish them between the groups. These findings indicated obvious individual differences in the types and content of fecal metabolites in ALC or HCs.

Differences in bile acid and amino acid metabolism pathways in plasma and feces of patients with ALC

The common metabolites were analyzed in plasma and feces, respectively, and compared to HCs after t-test and difference analysis. A total of 150 differential metabolites in plasma and 106 differential metabolites in the feces of patients with ALC were screened based on FC > 2 or FC < 0.5. The results showed that 46 metabolites were upregulated and 16 were downregulated in plasma, while 38 metabolites were upregulated and 14 were downregulated in feces (Table ​(Table2,2, SupplementaryTables 1 and 2). Similarly, metabolic pathway analysis showed that the metabolites in plasma and feces of patients with ALC were involved in porphyrin metabolism, phospholipid biosynthesis, inositol metabolism, sugar and starch metabolism, catecholamine biosynthesis, thyroid hormone synthesis, estrone metabolism, betaine metabolism, beta-oxidation of very long chain fatty acids, fatty acid metabolism, caffeine metabolism, and phosphatidylcholine synthesis. The bile acid metabolism pathway was significantly enriched in plasma, while the porphyrin metabolism pathway was significantly enriched in feces (Figures ​(Figures3A3A and ​andB).B). Strikingly, the amino acid metabolism was significantly enriched in both plasma and feces. However, the types of amino acids involved in plasma and feces were different. The metabolic pathways of methionine, phenylalanine, tyrosine, glycine, and serine were significantly enriched in both plasma and feces; however, the metabolic pathways of tryptophan, taurine, hypotaurine, D-arginine, and D-ornithine were enriched in the plasma, while alanine, valine, leucine, isoleucine, arginine, and proline metabolic pathways were significantly enriched in feces. Various substances were involved in the above-mentioned enrichment pathways (Supplementary Tables 3 and 4). Differential analysis and screening of FC > 2 or FC < 0.5 with P < 0.05 showed that nine substances, such as cholic acid (CA), taurocholic acid (TCA), DCA (DCA), and L-methionine were upregulated in plasma, and three metabolites, such as formylanthranilic acid, were down-regulated in ALC. Thirteen metabolites, such as glycine, L-threonine, L-phenylalanine, L-valine, L-tyrosine were upregulated, and betaine was downregulated in feces (Figure ​(Figure3C3C and ​andD,D, Supplementary Table 5).

Common and differential bile acids, amino acids, and related metabolites in the plasma and feces of patients with ALC

Compared to HCs, metabolites involved in the above metabolic pathways differed in the plasma and stool of ALC (Table ​(Table3).3). In the bile acid metabolism pathway, primary bile acids, including CA, TCA, and glycocholic acid (GCA), increased in the plasma of patients with ALC, while CA, TCA, and GCA did not change significantly in feces. Additionally, secondary bile acid DCA decreased in feces but did not differ in plasma. In the amino acid metabolism pathway, L-threonine, L-phenylalanine, L-tyrosine, and choline increased in both plasma and feces, while betaine decreased concomitantly. GCA, L-methionine, and taurine increased, while sarcosine decreased only in plasma. Glycine elevated only in feces, while L-valine decreased in plasma but increased in feces.

For patients with ALC, the differential enrichment analysis of bile acid and amino acid metabolites in plasma and feces revealed that the distribution of the above-mentioned metabolites differed in the plasma and feces. In the plasma of patients with ALC, betaine, TCA, GCA, sarcosine, and taurine were significantly enriched, while CA, DCA, and formylanthranilic acid, L-threonine, L-phenylalanine, L-tyrosine, L-methionine, and glycine were markedly enriched in feces (Figure ​(Figure44).

Association of amino acid or bile acid in plasma or feces with clinical features

Spearman’s correlation analysis revealed that plasma amino acids L-methionine, L-phenylalanine, and L-tyrosine in patients with ALC were positively correlated to TBA, MDF, TBil, and PT and negatively correlated with CHE and ALB. L-methionine had the highest correlation with TBA, and L-phenylalanine had the highest correlation with MDF, TBil, and PT. Nonetheless, no correlation was established between L-methionine, L-phenylalanine, L-tyrosine and TBA, MDF, TBil, PT in feces (Figure ​(Figure55).

Primary bile acids GCA and TCA in plasma exhibited a positive correlation with TBil in clinical practice to varying degrees, with GCA having the strongest correlation with a coefficient of 0.86. However, no correlation was established between DCA in feces and clinical TBil. The levels of both GCA and TCA in plasma showed a positive correlation with TBil, MDF, and PT and a negative correlation with CHE and ALB. Conversely, DCA in feces demonstrated a negative correlation with TBil, MDF, and PT but a positive correlation with CHE and ALB. Compared to a single index, the ratio of all primary bile acids in the blood, including GCA and TCA to DCA in the feces (recorded as P/S BA ratio) showed a higher correlation with MDF, TBil, CHE, PT, and ALB (Figure ​(Figure55).

Research background

Alterations in plasma and intestinal metabolites contribute to the pathogenesis and progression of alcohol-related liver cirrhosis (ALC).

Research motivation

Metabolites in enterohepatic circulation play an important role in cross-talk between liver and gut. The metabolites in the blood or the feces may have hom*ogeneity but may also have specific characteristics related to their respective environments. The correlation between intestinal and plasma metabolites in patients with ALC, which might be helpful for the diagnosis and treatment of ALC, has not yet been elucidated.

Research objectives

To explore the common and different metabolites in the plasma and feces of patients with ALC and evaluate their clinical implications.

Research methods

This was a case-controlled observation study. We prospectively enrolled two groups of subjects: individuals with ALC and healthy controls (HCs). We recruited age-matched healthy males as controls through recruitment advertisem*nts. According to the inclusion and exclusion criteria, 27 patients with ALC and 24 HCs were selected. The plasma and feces samples were collected. Liver function, blood routine, and other indicators were detected with automatic biochemical and blood routine analyzers. Liquid chromatography-mass spectrometry was used to detect the plasma and feces metabolites. Also, the association of metabolites with clinical features was analyzed.

Research results

More than 8000 plasma and more than 10000 fecal metabolites of patients with ALC were detected. Among them, More than 300 metabolites were found both in the plasma and feces. Enrichment analysis showed that these common metabolites are enriched in bile and amino acid metabolic pathways. Moreover, patients with ALC had a higher level of glycocholic acid (GCA) and taurocholic acid (TCA) in plasma and a lower level of deoxycholic acid (DCA) in the feces, while L-threonine, L-phenylalanine, and L-tyrosine increased simultaneously in plasma and feces. These results was consistent with previous studies and have indeed confirmed the disorder of bile acid and amino acid metabolism in patients with ALC. Besides, GCA, TCA, L-methionine, L-phenylalanine, and L-tyrosine in plasma were positively correlated with total bilirubin (TBil), prothrombin time (PT), and maddrey discriminant function score (MDF) and negatively correlated with cholinesterase (CHE) and albumin (ALB). The DCA in feces was negatively correlated with TBil, MDF, and PT and positively correlated with CHE and ALB. AP/S BA ratio of plasma primary bile acid (GCA and TCA) to fecal secondary bile acid (DCA), which was relevant to TBil, PT, and MDF score was established, which may be used as a biomarker of the severity of ALC.

Research conclusions

Bile acid and amino acid metabolism play a very important role in the progression of ALC. The enrichment of GCA, TCA, L-phenylalanine, L-tyrosine, and L-methionine in the plasma of patients with ALC and the reduction of DCA in feces was related to the severity of ALC. The P/S BA ratio of plasma primary bile acids (GCA and TCA) to fecal secondary bile acid (DCA), was relevant to TBil, PT, and MDF score.

Research perspectives

Integrating intestinal microbiomics and metabolomics analysis is essential to decipher the molecular mechanisms underlying ALC. Since LC-MS analysis used in this study has limitations, further comparisons with other detection methods and complementary studies are required.

Institutional review board statement: This research scheme was reviewed and approved by the Ethics Committee of Ditan hospital (Beijing, China) and registered at http://www.chictr.org.cn/ (ChiCTR2000038216).

Informed consent statement: Written informed consent was obtained from all study participants or their legal guardian prior to study enrollment.

Conflict-of-interest statement: There are no conflicts of interest to report.

STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Corresponding Author's Membership in Professional Societies: Society of Hepatology, Chinese Association of Integrative Medicine; the Microecology Professional Committee of Beijing Association of Preventive Medicine; the Infectious Disease Branch of Chinese Preventive Medicine Association; Chinese Society of Hepatology, Chinese Medical Association; the Microecology Branch of Chinese Preventive Medicine Association; the Hepatology Branch of the Beijing Medical Association.

Peer-review started: February 26, 2023

First decision: March 10, 2023

Article in press: May 4, 2023

Specialty type: Gastroenterology and hepatology

Country/Territory of origin: China

Peer-review report’s scientific quality classification

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P-Reviewer: Kotlyarov S, Russia; Manautou JE, United States S-Editor: Yan JP L-Editor: A P-Editor: Chen YX

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