Metabolic biomarker for predicting hepatic fibrosis progress and application thereof

Abstract

The invention discloses a metabolic biomarker for predicting the progress of hepatic fibrosis and application of the metabolic biomarker, a group of metabolic biomarkers with diagnostic value is screened out through a metabonomics method, and the metabolic biomarkers comprise acetic acid, alanine, arginine, citric acid, glucose, glycerol, lysine, ornithine, pyruvic acid, succinic acid and tyrosine. By using these biomarkers in combination, the accuracy and specificity of diagnosis can be further improved. A biological sample is collected from a tested object, the content of the biomarker is detected, and the content is compared with a preset threshold concentration for analysis, the detection means is nuclear magnetic resonance spectroscopy, and compared with the prior art, the sample pretreatment steps are simpler, the experiment operation is quicker, and the detection accuracy is higher. The problems that early hepatic fibrosis is not easy to perceive and a traditional diagnosis and treatment method is high in cost and long in consumed time are solved, and the accuracy of early hepatic fibrosis diagnosis is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of biological detection, and in particular relates to a metabolic biomarker for predicting the progression of liver fibrosis and an application thereof. Background Art

[0002] MAFLD (metabolic associated fatty liver disease) is a chronic liver disease caused by metabolic dysfunction. The prevalence of MAFLD is as high as 25-30%, and it is increasing with the rising incidence of obesity and type 2 diabetes. There is currently no specific drug for the treatment of MAFLD, and lifestyle intervention is still the first-line measure for the treatment of MAFLD. In summary, MAFLD not only affects liver health, but also increases the risk of multiple adverse liver outcomes, bringing a heavy burden to patients and society. Therefore, early intervention and treatment of liver fibrosis in MAFLD is crucial.

[0003] Liver fibrosis is the most common and complex liver pathological process in people of childbearing age and a wider age range, and is an important cause of chronic liver disease progression and liver function damage. However, early liver fibrosis is not easy to detect, which poses a great challenge to the accurate diagnosis and early screening of MAFLD.

[0004] The traditional diagnosis and treatment process requires a comprehensive evaluation of liver puncture biopsy, multiple biochemical index tests, and liver imaging examinations (such as B-ultrasound, CT, etc.). Although this multi-index combined detection method is still the main means of screening liver fibrosis, it is costly and time-consuming. Compared with this, biomarker screening has higher convenience and accessibility. Existing studies have confirmed that intestinal microbial metagenomics and metabolomics combined with age analysis can predict the progression of MAFLD to the middle and late stages, that is, the risk of cirrhosis. However, there is still a gap in microbial markers for the early liver fibrosis stage of MAFLD progression. In summary, exploring and establishing a simple, fast, highly sensitive and specific early liver fibrosis diagnosis technology has become an urgent clinical need.

[0005] Therefore, the purpose of conducting relevant research is to clarify the differences in serum metabolites between the control group and the liver fibrosis group, and to screen out metabolic biomarkers as a new method for assisting the diagnosis of liver fibrosis. Metabolomics, as a professional tool for discovering metabolic biomarkers, can help identify biomarkers associated with phenotypic changes in liver fibrosis, thereby serving as an auxiliary indicator for early diagnosis of the disease. Since metabolic biomarkers have high specificity and high sensitivity in diagnosis and monitoring, they provide sufficient evidence for the accurate diagnosis and screening of liver fibrosis. Summary of the invention

[0006] In view of the complex clinical manifestations of liver diseases and the difficulty in screening for early liver fibrosis, the present invention aims to provide a metabolic biomarker for predicting the progression of liver fibrosis and its application.

[0007] A metabolic biomarker for predicting the progression of liver fibrosis, comprising metabolites, wherein the metabolites are selected from one or more of the following metabolites: acetic acid (CID No.: 176), alanine (CID No.: 736104), arginine (CID No.: 614), citric acid (CID No.: 7442), glucose (CID No.: 757), glycerol (CID No.: 753), lysine (CID No.: 5962), ornithine (CID No.: 5924), pyruvate (CID No.: 1031), succinic acid (CID No.: 7507) and tyrosine (CID No.: 6057).

[0008] The biomarkers all have good diagnostic effects. The area under the receiver operating characteristic curve (AUC) is used to judge the strength of the disease recognition ability of a single metabolite. Any of the above compounds can be greater than 0.75.

[0009] By detecting specific biomarkers or other clinical indicators in the biological samples of the subjects, the normal group and the liver fibrosis group can be effectively distinguished. The selection of these biomarkers or clinical indicators can be based on different criteria, such as metabolic pathways (including carbohydrate metabolism, lipid metabolism, fatty acid metabolism, vitamin metabolism, hormone metabolism, etc.), metabolic organs (such as intestines, liver, kidneys, brain, etc.), or for the convenience of detection and cost reduction, such as classification according to the type of compound or detection method.

[0010] Based on this principle or foundation, a combination of multiple biomarkers can be selected to effectively distinguish healthy controls from liver fibrosis patients, so as to diagnose and screen liver fibrosis, or predict its risk of onset. These biomarkers can include, but are not limited to, any one or more combinations of the 11 biomarkers listed above (or with similar functions), as well as other biomarkers or clinical indicators selected according to the same principle.

[0011] The selection of multiple biomarkers can not only improve the accuracy of diagnosis, but also assist in the classification of different types of liver fibrosis. Therefore, when selecting biomarkers, in addition to considering the biomarkers listed above, other biomarkers or clinical indicators related to liver fibrosis can also be selected according to the specific disease characteristics and detection needs.

[0012] A use of a metabolic biomarker for predicting liver fibrosis progression in the preparation of a reagent for predicting liver fibrosis progression comprises the following steps: detecting the content of the biomarker in a biological sample obtained from a subject, and comparing and analyzing the biomarker with a preset threshold concentration.

[0013] Furthermore, the biological sample is detected by nuclear magnetic resonance spectroscopy, which is used to collect NMR data.

[0014] Further, the preset threshold concentration is the concentration of the biomarker obtained from a normal population or the concentration of the biomarker obtained from a population suffering from liver fibrosis.

[0015] Further, the biological sample is selected from plasma or serum.

[0016] A kit for detecting liver fibrosis, comprising at least two of the above metabolites.

[0017] The beneficial effects of the present invention are:

[0018] By detecting metabolites in biological samples as biomarkers of liver fibrosis, the accuracy and specificity of early diagnosis, screening and risk prediction of liver fibrosis can be improved, which is of great significance for the management and identification of patients with liver fibrosis. The detection method is nuclear magnetic resonance spectroscopy. Compared with existing detection methods, its advantages include simple sample pretreatment and fast test operation. Combined with Chenomx NMR Suite software, the absolute concentration of serum metabolites can be directly obtained. This feature greatly improves the convenience and accuracy of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Mouse liver appearance and H&E staining

[0020] Figure 2 This is a multivariate statistical analysis diagram of the metabolomics data of the model group and the normal group;

[0021] Figure 3 ROC curve for the predictive diagnostic efficacy of metabolites constructed based on characteristic metabolic biomarkers in animal models.

[0022] Figure 4 ROC curve of the diagnostic performance of metabolites predicted based on characteristic metabolic biomarkers in clinical data. DETAILED DESCRIPTION

[0023] The present invention will elaborate on the technical solution in detail through specific embodiments and drawings of the specification. It should be clear that the embodiments described below are only exemplary and do not exhaust all possible forms of the present invention. Without departing from the basic concept of the present invention, those skilled in the art may make various changes and improvements based on the embodiments of the present invention, and these changes and improvements should be included in the protection scope of the present invention.

[0024] Example: Construction of a mouse model of liver fibrosis and screening of differential biomarkers between the control group and the model group

[0025] 1. Construction and evaluation of liver fibrosis mouse model

[0026] Twenty-four 6-week-old C57BL / 6 male mice were selected and kept at a temperature of 22-24°C, a humidity of 50-60%, a light intensity of 20lx, a light-dark cycle of 12h / 12h, and noise controlled below 60dB.

[0027] Six mice in the control group were intraperitoneally injected with 200 μL olive oil and fed normally for 6 weeks. Six mice in each of the mild, moderate and severe groups were intraperitoneally injected with 10% CCl 4 (diluted with olive oil) 200 μL, mild group, moderate group, severe group were injected for 6, 8, 10 weeks, and then the mice were killed. After the animals were killed, part of the liver of each mouse was taken for HE staining ( Figure 1 ).like Figure 1 As shown in A, the livers of mice in the control group were full and triangular in shape, soft in texture, and without obvious protrusions and nodules. The liver was a uniform reddish brown color, with a smooth surface and a certain luster, and was in a healthy state. The overall shape of the liver in the mild group was slightly deformed, and no white nodules or hardening had appeared. Starting from the moderate lesion group, the overall morphology of the liver showed an accelerated trend of distortion and deformation; in the severe lesion group, the liver texture became significantly harder, the surface became rough, and the color turned dark red. These changes may reveal profound changes in the internal structure of the liver and the intensification of the fibrosis process. Secondly, white nodules of varying sizes appeared on the surface of the liver in the severe group, and the nodules were unevenly distributed, making the liver surface appear uneven. Furthermore, as Figure 1 As shown in B, HE staining showed that the liver cells in the control group had normal structure, neat arrangement, and no obvious lesions. A few septa were observed in the moderate group, and multiple septa were observed in the severe group, accompanied by lesion necrosis, disappearance of liver nucleus dissolution, intracellular fatty degeneration, interlobular fibrosis, and obvious inflammatory infiltration around the central vein. The results proved that the mouse model of liver fibrosis was successfully established.

[0028] 2. Based on 1 Metabolic phenotypes of mice with different degrees of liver fibrosis studied by H NMR metabolomics

[0029] Sample pretreatment: Take 350μL serum sample and put it into a 5mm NMR tube. At the same time, add 180μL serum buffer (0.05mM TMSP) into the 3mm NMR tube. Insert the 3mm NMR tube into the 5mm NMR tube and adjust the position so that the two tubes are coaxial. Wait for test.

[0030] NMR data acquisition: NMR data were collected on a Bruker AVANCE III HD 600 MHz at 310 K. Serum samples were subjected to CPMG (Carr-Purcell-Meibom-Gill) experiments using a CPMG pr1d [RD-90°-(τ-180°-τ) n -acq].

[0031] Identification of metabolite NMR signals: Collect typical 1 H NMR spectra were identified using Chenomx NMR Suit software to determine the absolute concentrations of metabolites in serum samples. A total of 32 endogenous metabolites were found (Table 1).

[0032] Table 1 32 metabolites identified in mouse serum samples

[0033]

[0034]

[0035]

[0036] 3. Screening and cluster analysis of differential metabolites

[0037] Multivariate statistical analysis of serum samples of mice in each group: The absolute concentrations of each metabolite were imported into SIMCA software for multivariate statistical analysis. PCA score chart ( Figure 2 A) in the figure shows that the control group, mild group, moderate group and severe group are well separated, and the sample points in each group are well clustered. PLS-DA can maximize the difference between groups, thus achieving better separation effect than PCA. PLS-DA score chart ( Figure 2 B) in the figure shows that there are significant differences among the four groups of samples. The permutation test of the model ( Figure 2 The results in C) show that the model is not overfitting. In addition, PLS-DA is applied to the analysis between two groups, and the PLS-DA score chart ( Figure 2D, E, and F in the figure show the significant differences between the control group and the mild group, the mild group and the moderate group, and the moderate group and the severe group, respectively. The R2Y between the control group and the mild group, the mild group and the moderate group, and the moderate group and the severe group (R2Y=0.855; R2Y=0.857; R2Y=0.995, respectively) is very close to 1, indicating that the established model is reliable and effective, consistent with the actual situation of the sample data, and without overfitting.

[0038] Search for biomarkers: In the validated PLS-DA discriminant model, we searched for significantly different metabolites between different groups as biomarkers according to VIP>1 and p<0.05. A total of 19 differential metabolites were found: 17 metabolites were significantly downregulated in the liver fibrosis model group, and 2 metabolites were significantly upregulated in the liver fibrosis model group (Table 2), P<0.05:*, P<0.01:**, P<0.001:***.

[0039] Table 2 Differential metabolites between the control group and the model group (serum)

[0040]

[0041]

[0042] In order to further evaluate the classification efficiency of the model, receiver operating characteristic (ROC) curve analysis was used, and according to the optimization principle of diagnostic sensitivity and specificity, the lower limit of the area under the curve (AUC) at the 95% confidence interval was set to be more than 0.75 as the standard for screening metabolites. Between the control group and the mild group, 14 significantly different metabolites were identified, including 3-hydroxybutyrate, 3-hydroxyisobutyrate, acetate, alanine, arginine, citric acid, dimethylamine, glucose, glycerol, lysine, ornithine, pyruvate, succinate, and tyrosine. When further comparing the mild group with the moderate group, 16 differential metabolites were found, which were 3-hydroxybutyrate, acetate, alanine, arginine, betaine, citric acid, creatinine, galactose, dimethylamine, glucose, glycerol, lysine, ornithine, pyruvate, succinate, and tyrosine. After this series of rigorous screening processes, 13 metabolites were identified in animal experiments using the metabolite content of the moderate group as the standard. These metabolites continued to show significant changes from the control group to the mild group and then to the moderate group, and were therefore considered potential biomarkers. This continuous monitoring is more beneficial for the correct diagnosis, screening and risk assessment of early liver fibrosis (see Figure 3 ).

[0043] Furthermore, this study collected a batch of serum samples including healthy controls and liver disease patients from a hospital. The healthy control group consisted of 22 individuals who had undergone a comprehensive physical examination, including 15 males and 7 females, with an age range of 21 to 48 years old; the patient group covered cases clinically diagnosed with decompensated hepatitis B cirrhosis, specifically 15 males and 4 females, with an age range of 37 to 80 years old. In order to deeply analyze these samples, this study used the BRUKER AVANCE IVDr system for metabolite detection. 13 metabolites that had been previously screened and confirmed in animal experiments were selected to further observe their performance in human serum samples. It is worth noting that, with the exception of 3-hydroxybutyric acid and dimethylamine, the remaining 11 metabolites also showed potential as disease screening markers in human samples (see Figure 4 ). Combining animal experiments and clinical data, 11 metabolites were finally identified as biomarkers. These biomarkers not only show high sensitivity, but also have excellent specificity, providing strong support for the early detection and diagnosis of diseases.

[0044] The optimal cutoff value was selected based on the Youden index (Youden index = sensitivity + specificity - 1), and the optimal cutoff value of each biomarker was determined. Based on this analysis, it is preliminarily inferred that when the acetate concentration in the organism is less than or equal to 0.1435mM, the alanine concentration is less than or equal to 0.48955mM, the arginine concentration is less than or equal to 0.8896mM, the citric acid concentration is less than or equal to 0.1553mM, the glucose concentration is less than or equal to 8.5032mM, the glycerol concentration is less than or equal to 0.3284mM, the lysine concentration is less than or equal to 0.3290mM, the ornithine concentration is less than or equal to 0.4019mM, the pyruvate concentration is less than or equal to 0.1428mM, the succinate concentration is less than or equal to 0.0169mM, and the tyrosine concentration is less than or equal to 0.0502mM, if two, three, four, five, six, seven, eight, nine, ten or all of the above conditions are met, the organism may be at risk of liver fibrosis.

[0045] The above is a preferred embodiment of the present invention, but the present invention should not be limited to the contents disclosed in the embodiment. Therefore, any equivalent or modification completed without departing from the spirit disclosed in the present invention shall fall within the scope of protection of the present invention.

Claims

1. A metabolic biomarker for predicting the progression of liver fibrosis, characterized in that: Included are metabolites selected from one or more of the following metabolites: acetate, alanine, arginine, citrate, glucose, glycerol, lysine, ornithine, pyruvate, succinate, and tyrosine.

2. A use of the metabolic biomarker for predicting the progression of liver fibrosis as claimed in claim 1 in the preparation of a reagent for predicting the progression of liver fibrosis, characterized in that: The method comprises the following steps: detecting the content of biomarkers in a biological sample obtained from a subject, and comparing and analyzing the content of the biomarkers with a preset threshold concentration.

3. Use of a metabolic biomarker for predicting the progression of liver fibrosis as claimed in claim 2 in the preparation of a reagent for predicting the progression of liver fibrosis, characterized in that: The biological sample is selected from plasma or serum.

4. Use of a metabolic biomarker for predicting the progression of liver fibrosis according to claim 3 in the preparation of a reagent for predicting the progression of liver fibrosis, characterized in that: Serum or plasma samples are tested using nuclear magnetic resonance spectroscopy to collect NMR data.

5. Use of a metabolic biomarker for predicting the progression of liver fibrosis according to claim 2 in the preparation of a reagent for predicting the progression of liver fibrosis, characterized in that: The preset threshold concentration is the concentration of the biomarker obtained from a normal population or the concentration of the biomarker obtained from a population suffering from liver fibrosis.

6. A kit for detecting liver fibrosis, characterized in that: Comprising at least two of the metabolites of claim 1.

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