A set of biomarkers for predicting alcoholic liver disease, reagents, kits and applications
By using fecal metagenomic sequencing and plasma metabolomics analysis, L-Threonic acid and specific microorganisms were identified as biomarkers, solving the problem of early identification and treatment of ALD, achieving accurate diagnosis and effective treatment of ALD, and reducing liver pathological damage.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- THE SECOND AFFILIATED HOSPITAL OF HAINAN MEDICAL UNIV
- Filing Date
- 2026-03-09
- Publication Date
- 2026-05-26
AI Technical Summary
Current technologies lack effective biomarkers for early identification and accurate staging of alcoholic liver disease, especially ALD, and there is a lack of targeted therapies, resulting in poor clinical management and prognosis.
By using fecal metagenomic sequencing and plasma metabolomics analysis, L-Threonic acid and specific microorganisms such as Microbacterium sp. T32 were identified as biomarkers. These biomarkers were then combined with pharmaceutically acceptable excipients to formulate reagents and kits for the diagnosis and treatment of ALD.
It enables early identification and accurate staging of ALD, improves the accuracy and specificity of diagnosis, significantly reduces liver pathological damage, and provides a safe and effective treatment option.
Smart Images
Figure CN122084884A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of biomarker technology, and more specifically, to a set of biomarkers, reagents, kits, and applications for predicting alcoholic liver disease. Background Technology
[0002] Alcoholic liver disease (ALD) is a spectrum of progressive liver damage caused by long-term excessive alcohol consumption. The core diagnostic criteria are a clear history of excessive alcohol intake, excluding other major hepatic risk factors. Alcohol and its metabolites drive liver pathology from early steatosis (alcoholic fatty liver) to alcoholic hepatitis, liver fibrosis, and ultimately alcoholic cirrhosis and hepatocellular carcinoma through complex mechanisms such as direct toxicity, induced oxidative stress, and inflammatory responses. Studies have shown that patients with alcoholic hepatitis (AH) have an extremely high short-term mortality rate. Their liver pathological damage typically manifests as significant ballooning degeneration, neutrophil infiltration, and Mallory body formation, resulting in a very poor clinical prognosis. The clinical manifestations of ALD are closely related to the severity and stage of liver damage, ranging from asymptomatic to end-stage complications such as jaundice, ascites, and hepatic encephalopathy, and often coexist with systemic complications such as malnutrition and electrolyte imbalances.
[0003] In clinical practice, the diagnosis of ALD primarily relies on a thorough assessment of alcohol consumption history, characteristic laboratory indicators (such as significantly elevated AST levels and a typical AST / ALT ratio >2), and imaging studies. Although liver biopsy remains the gold standard for confirming the diagnosis and assessing the degree of inflammation and fibrosis, its invasiveness has led to the increasing use of non-invasive diagnostic tools (such as transient elastography and serum biomarker panels). Currently, the cornerstone of ALD treatment is strict abstinence from alcohol and nutritional support. However, for moderate to severe alcoholic hepatitis, there is a lack of widely effective and approved targeted therapies besides glucocorticoids. Furthermore, effective biomarkers that can accurately predict disease progression, treatment response, and prognosis remain scarce. Therefore, early identification, accurate staging, and risk stratification of ALD are crucial for optimizing clinical management and improving patient outcomes.
[0004] With in-depth research into the role of the gut-liver axis in the pathogenesis of ALD, gut microbiota dysbiosis and its resulting impaired intestinal barrier function, endotoxin translocation, and liver inflammation activation are considered key links in the development and progression of ALD. This provides direction for exploring new intervention targets. In recent years, the rapid development of multi-omics technologies has created unprecedented opportunities for systematically elucidating the pathogenesis of ALD and discovering novel diagnostic and prognostic biomarkers in different biological samples. Studies have shown that metabolomics can reveal characteristic fingerprints closely related to disease activity and progression by systematically analyzing changes in small molecule metabolites related to alcohol metabolism, oxidative stress, and energy metabolism in the serum or urine of patients, thus providing possibilities for early warning and non-invasive monitoring. Summary of the Invention
[0005] The purpose of this invention is to provide a set of biomarkers, reagents, kits, and applications for predicting alcoholic liver disease (ALD). This includes identifying biomarkers for ALD diagnosis and / or risk prediction through fecal metagenomic sequencing and plasma metabolomics analysis; determining the role and pathways of key metabolites in alleviating ALD using an alcohol-induced ALD mouse model; and providing a theoretical basis and practical guidance for developing novel ALD diagnostic methods and therapeutic drugs.
[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: A set of biomarkers for predicting alcoholic liver disease, including plasma metabolites such as L-threonic acid, and reagents for detecting plasma metabolites can be used to prepare products for predicting alcoholic liver disease.
[0007] The present invention is further configured such that the product can be a reagent kit.
[0008] The present invention is further configured such that the plasma metabolite also includes 1-(2,3-dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone (1-(2,3-Dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone) (CAS Registry No.: 97073-12-6).
[0009] The present invention is further configured such that the plasma metabolite L-Threonic acid can be used alone as a biomarker for predicting alcoholic liver disease or in combination with 1-(2,3-Dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone.
[0010] The present invention is further configured such that, in addition to plasma metabolites, biomarkers also include microorganisms.
[0011] The present invention is further configured such that: plasma metabolites can be used alone as biomarkers for predicting alcoholic liver disease or in combination with microorganisms.
[0012] The present invention is further configured such that the microorganisms include one or more of the following: Microbacterium sp. T32 and Bradyrhizobium sp. 174.
[0013] The present invention further provides a reagent for predicting alcoholic liver disease, the reagent being capable of detecting the aforementioned biomarkers.
[0014] The present invention further provides a kit for predicting alcoholic liver disease, the kit comprising the above-mentioned reagents for detecting biomarkers.
[0015] The present invention further provides the application of the above-mentioned biomarkers for predicting alcoholic liver disease in the preparation of products for the diagnosis and / or risk prediction of alcoholic liver disease.
[0016] The present invention further provides the application of the above-mentioned biomarkers for predicting alcoholic liver disease in the preparation of drugs for treating and / or alleviating alcoholic liver disease, wherein drug intervention on the plasma metabolite L-Threonic acid can significantly reduce liver pathological damage in alcoholic liver disease.
[0017] The present invention is further configured such that the drug's effect is related to the synergistic upregulation of key pathways including autophagy, oxidative phosphorylation, and ribosomes.
[0018] The present invention is further configured such that: the drug includes pharmaceutically acceptable excipients, which are selected from at least one of pharmaceutically acceptable solvents, solubilizers, cosolvents, emulsifiers, osmotic pressure regulators, stabilizers, suspending agents, anti-adhesives, integrators, permeation enhancers, pH adjusters, buffers, surfactants, absorbents, diluents, filter aids, and sustained-release materials.
[0019] The present invention is further configured such that: the drug includes various acceptable dosage forms, and the dosage form of the drug preparation is any one of capsules, granules, tablets, powders, ointments, powders, pills or liquids.
[0020] In summary, the present invention has the following beneficial effects: 1. This invention utilizes non-target metabolomics to identify potential biomarkers and related metabolic pathways in the plasma of ALD patients and healthy controls. Furthermore, it identifies potential biomarkers in the microbiome of ALD patients through fecal metagenomic sequencing. This provides a scientific basis for the pathogenesis and early screening of ALD.
[0021] 2. Currently, ALD treatment mainly relies on lifestyle interventions and dietary control, and effective drug therapies are still lacking. This invention systematically evaluates the therapeutic effect of L-Threonic acid on alcoholic liver disease and delves into its molecular mechanisms. Using a chronic ethanol binge-drinking mouse model to simulate the disease progression of human ALD, the in vivo effects of L-Threonic acid intervention on key pathological phenotypes such as hepatic steatosis, inflammatory infiltration, hepatocyte ballooning degeneration, and fibrosis were assessed. Furthermore, liver transcriptomics was used to elucidate the key pathways through which L-Threonic acid exerts its hepatoprotective effect. The results of this study provide solid preclinical evidence and theoretical basis for L-Threonic acid as a safe and effective natural metabolite-derived drug for the treatment of alcoholic liver disease, opening new directions for the development of innovative ALD therapies. Attached Figure Description
[0022] Figure 1 Metagenomic analysis of gut bacteria in the ALD and HC groups (A: Alpha diversity analysis; B: Beta diversity analysis; C: comparison of species-level abundance of dominant bacterial communities). Figure 2 These are differentially expressed plasma metabolites between ALD patients and the HC group (A is the PCA score plot; B is the volcano plot; C is the top 20 key differentially expressed metabolites). Figure 3 It is a diagram of ALD diagnostic models (including single plasma metabolites, single microbiota, combined plasma metabolites, and ALD diagnostic models based on microbiota + combined plasma metabolites). Figure 4 L-Threonic acid alleviates liver lipid deposition and liver fibrosis in an ALD mouse model (HE staining and MASSON microscopy). Figure 5 This is a KEGG analysis of the liver transcriptome in an ALD mouse model treated with L-Threonic acid. Detailed Implementation
[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below.
[0024] A set of biomarkers for predicting alcoholic liver disease, including plasma metabolites such as L-threonic acid, and reagents for detecting plasma metabolites can be used to prepare products for predicting alcoholic liver disease.
[0025] By adopting the above technical solutions and using non-targeted metabolomics technology to systematically analyze the plasma of patients with alcoholic liver disease, it was discovered for the first time that L-threonine was significantly reduced in the plasma of patients, and it can be used as a single biomarker to effectively identify alcoholic liver disease.
[0026] In some embodiments, the product may be a kit.
[0027] In some embodiments, the plasma metabolite also includes 1-(2,3-dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone (CAS Registry No.: 97073-12-6).
[0028] By adopting the above technical solutions, this invention further enriches the plasma metabolic biomarker profile of ALD. Incorporating these biomarkers into the detection combination allows for the capture of ALD-related pathophysiological changes from different metabolic pathway dimensions, complementing L-threonic acid and laying the foundation for constructing a more stable and accurate diagnostic model.
[0029] In some embodiments, the plasma metabolite L-Threonic acid can be used alone as a biomarker for predicting alcoholic liver disease or in combination with 1-(2,3-Dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone.
[0030] By employing the above technical approach, combining L-threonic acid with 1-(2,3-dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone significantly improves the sensitivity and specificity of diagnosis. This combined biomarker reflects liver damage status from different metabolic pathways, and the complementary nature of multiple indicators effectively reduces the false negative and false positive rates of single biomarkers, demonstrating significantly superior performance compared to single-indicator detection.
[0031] In some embodiments, the biomarker includes microorganisms in addition to plasma metabolites; the microorganisms include one or two of the following: Microbacterium sp. T32 and Bradyrhizobium sp. 174.
[0032] In some embodiments, plasma metabolites may be used alone as biomarkers for predicting alcoholic liver disease or in combination with microorganisms.
[0033] By employing the above technical approach and integrating plasma metabolomics and gut metagenomics data, we discovered a close correlation between decreased L-threonate levels and reduced abundance of gut microbiota T32, suggesting that gut microbiota dysbiosis may participate in the pathogenesis of alcoholic liver disease by affecting host metabolite levels. This provides technical support for the development of comprehensive diagnostic products based on the microbiota-metabolic axis.
[0034] A reagent for predicting alcoholic liver disease, which can detect the aforementioned biomarkers.
[0035] In some embodiments, the kit contains the reagents described above for detecting biomarkers.
[0036] By adopting the above technical solutions, specific detection reagents can be developed for the aforementioned biomarkers. Based on technologies such as liquid chromatography-mass spectrometry, the levels of metabolites such as L-threonic acid in plasma can be quantitatively detected, or the abundance of gut microbiota can be quantitatively analyzed based on high-throughput sequencing technology.
[0037] Application of a biomarker for predicting alcoholic liver disease in the development of products for the diagnosis and / or risk prediction of alcoholic liver disease.
[0038] Drug intervention in the plasma metabolite L-Threonic acid significantly reduces liver pathological damage in alcoholic liver disease. The drug's action is associated with the synergistic upregulation of key pathways, including autophagy, oxidative phosphorylation, and ribosomes. The drug includes pharmaceutically acceptable excipients selected from at least one of the following pharmaceutically acceptable solvents, solubilizers, cosolvents, emulsifiers, osmolarity regulators, stabilizers, suspending agents, anti-adhesives, integrators, permeation enhancers, pH adjusters, buffers, surfactants, absorbents, diluents, filter aids, and sustained-release materials. The drug includes various acceptable dosage forms, with the dosage form being any one of capsules, granules, tablets, powders, ointments, granules, pills, or aqueous solutions.
[0039] By adopting the above technical solutions, the drug uses L-threonine as the active ingredient and is formulated into various dosage forms with pharmaceutically acceptable excipients, allowing for the selection of an appropriate route of administration based on the patient's condition and medication needs. Optimizing the formulation process improves the drug's bioavailability and liver targeting, ensuring that the active ingredient reaches therapeutic concentrations at the site of action, thereby achieving effective intervention and long-term management of alcoholic liver disease.
[0040] The following is in conjunction with the appendix Figure 1-5 The present invention will be described in further detail below.
[0041] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.
[0042] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.
[0043] Example
[0044] Example 1: Metagenomic analysis of gut bacteria in ALD patients
[0045] 1. Objects and methods
[0046] 1.1 Object
[0047] A total of 25 healthy controls (HC) and 50 patients with alcoholic liver disease (ALD) were recruited.
[0048] The diagnostic criteria for ALD used in this study are as follows: All patients in this group must meet the prerequisite of long-term heavy alcohol consumption: a history of chronic alcohol consumption, daily intake >40 grams, and a duration of ≥5 years. Under this premise, they are divided into two categories according to different disease stages: Diagnostic criteria for alcoholic fatty liver (AFL): ultrasound examination confirms hepatic steatosis (fatty liver). Elevated serum ALT and / or AST levels. Diagnostic criteria for alcoholic cirrhosis (ALC): cirrhosis confirmed by transient elastography (such as FibroScan®), showing a liver stiffness measurement >12.5 kPa; and / or clinical evidence of portal hypertension, such as esophageal varices, ascites, or splenomegaly.
[0049] Diagnostic criteria for the healthy control group (HC): Alcohol consumption: Daily alcohol intake <20 grams. Imaging examination: Normal liver ultrasound results. Blood biochemistry indicators: Normal liver biochemistry indicators, i.e., alanine aminotransferase (ALT) and aspartate aminotransferase (AST) both <40 U / L.
[0050] 1.2 Shotgun metagenomic sequencing of gut bacteria
[0051] Sequencing and bioinformatics analysis were completed using Shanghai Baiqu Biomedical Technology Co., Ltd. Specifically, this included the following: Fecal samples were collected, and fecal DNA was extracted by Novogene Bioinformatics (Beijing, China) using the SDS method. Finally, all samples were sequenced in pairs using the Illumina platform.
[0052] Metagenome samples were used for species identification and analysis using the MetaPhlAn analysis platform.
[0053] 2. Results of gut microbiota analysis in ALD and HC groups ( Figure 1 )
[0054] Alpha diversity analysis showed a decrease in ALD patients (Figure A). Principal coordinate analysis (PCoA) results showed a significant separation trend in the gut microbiota structure between the ALD patient group and the HC group, indicating a significant change in the gut microbiota composition of ALD patients (Figure B). Comparison of dominant microbiota abundance at the species level revealed a significant deficiency of Microbacterium_sp_T32 in the ALD patient group (Figure C).
[0055] Example 2: Plasma non-target metabolomics of ALD patients
[0056] 1. Experimental Methods
[0057] The detection and bioinformatics analysis of plasma non-target metabolism in the subjects of Example 1 were completed using Shanghai Baiqu Biomedical Technology Co., Ltd., as detailed below: 100 μL of plasma sample was transferred to an EP tube, then methanol extract was added, mixed, and allowed to stand. The mixture was then centrifuged for 15 minutes. The supernatant was collected and placed in a sample vial for analysis. Chromatographic separation was performed using a Vanquish ultra-high performance liquid chromatography system from Thermo Fisher Scientific, paired with a Waters ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm). Chromatographic and mass spectrometric analyses were then performed.
[0058] (1) Sample processing
[0059] The experimental procedure is as follows: First, 100 μL of serum sample was transferred to an EP tube, and 400 μL of methanol extraction buffer (containing an isotope-labeled internal standard mixture) was added; after vortexing for 30 seconds, the sample was thoroughly mixed; it was then sonicated in an ice-water bath for 10 minutes; and allowed to stand at -40℃ for 1 hour. The sample was then centrifuged at 4℃ and 12000 rpm (centrifugal force 13800×g, rotor radius 8.6 cm) for 15 minutes; the supernatant was collected and placed in a sample vial for testing; separately, equal amounts of supernatant were extracted from each sample and mixed to prepare a QC sample for detection.
[0060] (2) LC-MS detection
[0061] This study employed a Vanquish ultra-high performance liquid chromatography (UHPLC) system from Thermo Fisher Scientific, paired with a Waters ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm). The mobile phase consisted of phase A (aqueous phase containing 5 mmol / L ammonium acetate and 5 mmol / L acetic acid) and phase B (acetonitrile). During chromatographic analysis, the sample pan was maintained at 4°C, and 2 μL was injected each time. Mass spectrometry was performed on an Orbitrap Exploris 120 mass spectrometer, with the acquisition of primary and secondary mass spectrometry data controlled using Xcalibur software (version 4.4, Thermo). Mass spectrometer parameter settings: sheath gas flow rate 50 Arb, auxiliary gas flow rate 15 Arb, capillary temperature 320℃, full scan mode resolution 60000, MS / MS mode resolution 15000, collision energy (NCE mode) 10 / 30 / 60, positive ion mode spray voltage 3.8 kV, negative ion mode spray voltage -3.4 kV.
[0062] (3) Identification of metabolites
[0063] The raw data was converted to mzXML format using ProteoWizard software, and then the data processing workflow, including peak identification, extraction, alignment, and integration, was performed using the company's self-developed R language package (based on the XCMS core). Substance identification was completed by matching with a self-built secondary mass spectrometry library in BiotreeDB (V2.1), with the matching algorithm score threshold (Cutoff value) set to 0.3.
[0064] (4) Metabolomics data analysis
[0065] 1) Raw data preprocessing
[0066] To improve data reliability, the original dataset, including quality control and testing samples, underwent multi-step preprocessing: first, feature variables with a missing rate exceeding 15% within each group were removed; for missing values among the retained features, a median imputation strategy was adopted; then, data normalization was performed using the summation method; next, QC validation was performed to ensure that the relative standard deviation (RSD) was no greater than 30; finally, the data were transformed using the Log10 logarithm. After completing this series of preprocessing steps, a standardized dataset suitable for further statistical analysis was formed.
[0067] 2) Differential metabolite analysis
[0068] Comparative analysis of samples began with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to assess differences between groups and similarities within groups. A double screening was then performed: Student's t-test was used to calculate the significance of metabolite differences between the two groups, while fold changes in expression were analyzed. Key differentially expressed metabolites were identified based on two criteria: a variable significance projection value (VIP) greater than 1 generated by the orthogonal partial least squares discriminant analysis (OPLS-DA) model, and a t-test p-value less than 0.05. These differentially expressed metabolites were then imported into the KEGG database (https: / / www.kegg.jp / kegg / pathway.html) for metabolic pathway annotation. Finally, Spearman correlation analysis was used to assess the correlation between clinical parameters and the screened differentially expressed metabolites.
[0069] 2. Experimental Results
[0070] Scoring plots (PCA plots) were constructed based on data obtained from plasma extracts using LC-MS in both positive and negative ion modes. Metabolites were relatively concentrated in the samples from both the ALD and HC groups, showing a clear separation between the two groups (Figure A). Figure B is a volcano plot, indicating that, compared to HC, ALD patients upregulated 1562 metabolites and downregulated 724 metabolites. The top 20 differentially expressed metabolites are shown in Figure C, with significantly deficient metabolites being L-Threonic acid and 1-(2,3-Dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone.
[0071] Example 3: Validation of the diagnostic efficacy of microbial community and metabolomics analysis for ALD.
[0072] The receiver operating characteristics (ROC) of the differentially expressed microorganisms screened in Example 1 and the differentially expressed plasma metabolites screened in Example 2 were analyzed using the R package "pROC". ROC curves were plotted to evaluate the diagnostic efficacy of microbiome and metabolomics for ALD.
[0073] The metabolites with the highest AUC values for individual metabolites were L-Threonic acid and 1-(2,3-Dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone (see Table 1). The single bacterial strain with the highest AUC value was Microbacterium_sp_T32 (see Table 1). The combined metabolite ALD diagnostic model had a higher AUC value (0.917) than the single metabolite AUC value. Multi-omics data (microbial community + metabolite) showed better predictive value than single-omics data, with an AUC value reaching 0.967 (see Table 1). Figure 3).
[0074] The results showed that individual metabolites L-Threonic acid and 1-(2,3-Dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone, and the individual microbial community Microbacterium_sp_T32, could be used to diagnose ALD. Combined predictive models of metabolites and the combination of metabolites and microorganisms showed high accuracy in diagnosing ALD. The accuracy of combined metabolite prediction was superior to that of microbial community, and multi-omics data (combined metabolite + microbial community) had higher predictive value than single-omics data. In other words, plasma metabolomics and microbial communities can both serve as biomarkers for ALD diagnosis.
[0075] Table 1 Prediction Model
[0076] Example 4: Constructing an in vivo model to analyze the role and mechanism of L-Threonic acid in ALD mice.
[0077] I. Experimental Objective
[0078] To evaluate the hepatoprotective effect and mechanism of L-threonine on a mouse model of alcoholic liver disease (ALD).
[0079] II. Laboratory Animals and Grouping
[0080] Animal selection: C57BL / 6 male mice, 8 weeks old, weighing 20-25g; Group design (10 per group, 30 in total): Control group: Normal diet + water intake; Model group: ALD model; High-dose L-Threonic acid group: ALD model + L-Threonic acid 100mg / kg / day.
[0081] III. Establishment of an alcoholic liver disease model (Lieber-DeCarli liquid diet method)
[0082] Adaptation period (3 days): Standard feed; Model building period (4 weeks): Week 1: Liquid feed containing 2% alcohol, gradually increasing to 5%; Second to fourth week: Liquid feed containing 5% alcohol; Consume freely, and record your daily intake. Dosage regimen: During model establishment, the drug was administered via gavage once daily.
[0083] IV. Detection Indicators and Time Points
[0084] 1. Weekly monitoring
[0085] Weight changes; Feed / alcohol intake; General behavioral observation; 2. Experiment endpoint (weekend of week 4) Sample collection: Blood was drawn from the eye socket, and serum was separated. Obtaining liver tissue samples: Partially fixed in 4% paraformaldehyde (pathology) for liver tissue pathology; Some samples were cryopreserved at -80℃ (biochemical, molecular) for liver tissue transcriptomics. 3. Liver histopathology H&E staining: fatty degeneration, inflammatory infiltration, and necrosis score; Masson staining: degree of collagen deposition and fibrosis; Liver tissue transcriptomics; Transcriptomics was used to detect the expression levels of liver-related genes.
[0086] 4. Results
[0087] L-Threonic acid intervention significantly reduced liver pathological damage in mice with alcoholic liver disease (ALD). HE and MASSON staining showed significant improvement in hepatocyte degeneration and fibrosis (see [link to article]). Figure 4 Further liver transcriptomic analysis revealed that this protective effect may be related to the synergistic upregulation of key pathways such as autophagy, oxidative phosphorylation, and ribosomes (see [link to study]). Figure 5 ).
[0088] In summary, this invention, through an integrated metagenomics and metabolomics dual-omics analysis strategy, has for the first time systematically revealed the intrinsic association between the gut microbiota characteristics of patients with alcoholic liver disease and host plasma metabolic disorders. It successfully identified a multi-dimensional biomarker combination, including L-threonine and specific gut microbiota, providing novel molecular targets and theoretical basis for the early diagnosis and risk prediction of alcoholic liver disease. Compared with traditional diagnostic methods relying on alcohol history assessment and liver biopsy, this invention, based on non-invasive detection methods of plasma metabolites and gut microbiota, not only significantly improves diagnostic accuracy and patient compliance but also enhances diagnostic efficacy to an excellent level of AUC value of 0.967 through multi-omics joint modeling, achieving precise identification and dynamic monitoring of alcoholic liver disease. At the mechanistic level, this invention elucidates the molecular mechanism by which L-threonine exerts its hepatoprotective effect by regulating key pathways such as autophagy, oxidative phosphorylation, and ribosomes, confirming the significant efficacy of this natural metabolite derivative in reducing hepatic steatosis, inflammatory infiltration, and fibrosis.
[0089] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.
Claims
1. A set of biomarkers for predicting alcoholic liver disease, characterized by: The biomarkers include plasma metabolites, including L-threonine, and reagents for detecting the plasma metabolites can be used to prepare products for predicting alcoholic liver disease.
2. The biomarker for predicting alcoholic liver disease according to claim 1, characterized in that: The plasma metabolites also include 1-(2,3-dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone.
3. The biomarker for predicting alcoholic liver disease according to claim 2, characterized in that: The plasma metabolite L-threonic acid can be used alone as a biomarker for predicting alcoholic liver disease or in combination with 1-(2,3-dihydro-6,7-dimethyl-1H-pyrrolizin-5-yl)-2-hydroxy-1-propanone.
4. The biomarker for predicting alcoholic liver disease according to claim 1, characterized in that: The biomarkers also include microorganisms; The microorganisms include one or two of the following: Microbacterium genus T32 and Slow-growing Rhizobium genus 17_4.
5. The biomarker for predicting alcoholic liver disease according to claim 4, characterized in that: The plasma metabolites can be used alone as biomarkers for predicting alcoholic liver disease, or they can be used in combination with microorganisms.
6. A reagent for predicting alcoholic liver disease, characterized in that: The reagent can detect the biomarkers described in any one of claims 1-5.
7. A kit for predicting alcoholic liver disease, characterized in that: The kit comprises the reagent of claim 6.
8. The use of the biomarkers for predicting alcoholic liver disease according to any one of claims 1-5 in the preparation of products for the diagnosis and / or risk prediction of alcoholic liver disease.
9. The use of the biomarker for predicting alcoholic liver disease according to any one of claims 1-5 in the preparation of a medicament for treating and / or alleviating alcoholic liver disease, characterized in that: The drug is the plasma metabolite L-threonine, which can significantly reduce liver pathological damage in alcoholic liver disease.
10. The application according to claim 9, characterized in that: The drug's effects are associated with the synergistic upregulation of key pathways, including autophagy, oxidative phosphorylation, and ribosomes.
11. The application according to claim 9, characterized in that: The drug includes pharmaceutically acceptable excipients selected from at least one of pharmaceutically acceptable solvents, solubilizers, cosolvents, emulsifiers, osmotic pressure regulators, stabilizers, suspending agents, anti-adhesives, binding agents, penetration enhancers, pH adjusters, buffers, surfactants, absorbents, diluents, filter aids, and sustained-release materials.
12. The application according to claim 9, characterized in that: The drug includes various acceptable dosage forms, and the dosage form of the drug preparation is any one of capsules, granules, tablets, powders, ointments, powders, pills or liquids.