A kit for assessing histologic inflammation in aih using non-invasive biomarkers

Abstract

The application discloses a kit for evaluating histological inflammation of AIH by using noninvasive biomarkers, relates to the technical field of biological detection, and discloses application of an sCD48-AIH-SI model in a kit for predicting histological inflammation of AIH patients. The application provides a kit for detecting sCD48 in serum and based on a prediction model sCD48-AIH-SI, can improve the prediction ability of histological inflammation of AIH patients, accurately reflects the severity of histological inflammation in the liver, and has good prompting value for drug use and scheme adjustment of AIH patients.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological detection technology, and particularly relates to a kit for evaluating histological inflammation of AIH by using non-invasive biomarkers. BACKGROUND

[0002] Autoimmune hepatitis (AIH) is an immune-mediated chronic inflammatory liver disease, which is characterized by elevated serum aminotransferase and immunoglobulin G (IgG) levels, serum autoantibody positivity, and typical histological features. In the clinical management of AIH patients, treatment options should be selected and adjusted individually based on the patient's liver histological status and treatment response to drugs in order to achieve the best efficacy while minimizing drug side effects. Currently, liver biopsy is still the gold standard for assessing liver histological status, but its application is often limited due to its invasive nature, high cost, and other characteristics, especially during long-term treatment follow-up of AIH patients.

[0003] Serum aminotransferase and IgG are the main biochemical indicators used in clinical practice to reflect the histological inflammation of AIH liver, but the predictive effect is not satisfactory, and it is generally believed that the remission of histological inflammation lags behind the remission of biochemical indicators. In addition, recent studies have found that the levels of serum WFA+-M2BP, Vitamin D, and GP73 can reflect the changes in liver histological inflammation to varying degrees. However, the number of AIH cases included in these studies is relatively small, and their predictive effects have not been verified. Therefore, it is necessary to explore non-invasive indicators that can accurately reflect the liver histological inflammation of AIH patients based on larger AIH cohorts.

[0004] CD48 is a member of the lymphocyte activation signal molecule family and can be expressed on the membrane of most hematopoietic cells. The subpopulation of cells stimulated by inflammation can up-regulate CD48 expression. In addition to the membrane expression form, there is also a soluble form of CD48 in serum (or plasma), and in immune-mediated inflammatory diseases such as asthma and Sjogren's syndrome, serum / plasma CD48 has been reported as a biomarker reflecting disease activity. However, there is no report on the involvement of CD48 in the pathogenesis of AIH, and accordingly, there is no report on the possible use of serum CD48 (sCD48) to predict histological inflammation in AIH patients.

[0005] For AIH patients, the commonly used blood biochemical indicators, aminotransferase and IgG, cannot accurately reflect the severity of histological inflammation in the liver, and have limited value in guiding drug use and regimen adjustment for AIH patients.

[0006] Therefore, the skilled in the art is committed to developing a non-invasive biomarker, kit and application for evaluating histological inflammation in autoimmune hepatitis. SUMMARY

[0007] In view of the above defects of the prior art, the technical problem to be solved by the present application is how to use non-invasive biomarkers for evaluating intrahepatic histological inflammation of autoimmune hepatitis and applying them to kits.

[0008] To achieve the above-mentioned object, the present application provides an application of sCD48-AIH-SI model in a kit for predicting histological inflammation of AIH patients.

[0009] Further, the method of the above-mentioned application comprises the following steps:

[0010] Step 1, determining the serum sCD48 concentration, IgG content and PLT count, and obtaining detection values respectively;

[0011] Step 2, substituting the detection values obtained in step 1 into the model sCD48-AIH-SI to obtain a judgment value;

[0012] Step 3, comparing the judgment value obtained in step 2 with a limit value, if the judgment value is higher than the limit value, it is predicted as severe histological inflammation, and if the judgment value is lower than or equal to the limit value, it is predicted as mild-moderate histological inflammation.

[0013] Further, step 1 further comprises:

[0014] Step 1.1, determining the serum sCD48 concentration by ELISA detection method;

[0015] Step 1.2, determining the IgG content by immunoturbidimetry;

[0016] Step 1.3, counting the platelet PLT value by indirect method.

[0017] Further, the step of counting the platelet by indirect method in step 1.3 comprises: after labeling the PLT with specific fluorescent antibody, detecting the ratio of red blood cells to PLT by flow cytometry, at the same time, accurately counting the red blood cells by semi-automatic cell counter based on single-channel impedance principle, and finally dividing the red blood cell count by the ratio of red blood cells to PLT to obtain the PLT count.

[0018] Further, step 1 further comprises: after blood sampling with a dry vacuum tube without additives, standing at room temperature for about 30 minutes, then centrifuging at 2800 revolutions per minute for 20 minutes under the condition of 4℃ to obtain the upper serum; the PLT count requires EDTA anticoagulant blood samples, and the sample should be placed at room temperature of 18-22℃ after blood sampling, and the platelet count detection should be completed within 4 hours after blood sampling.

[0019] Further, the model sCD48-AIH-SI in step 2 is:

[0020] 1 / (1+e 1.305–0.139×sCD48–0.122×IgG+0.01×PLT )

[0021] Further, the value defined in step 3 is 0.61.

[0022] Further, the sCD48 concentration is a non-invasive biomarker of AIH histological inflammation.

[0023] Further, the model sCD48-AIH-SI is a prediction model of AIH patient histological inflammation based on the sCD48 concentration.

[0024] Further, the blood sample of the patient is detected using a kit.

[0025] Further, a detection kit for the above-mentioned indexes, i.e., sCD48, IgG and PLT, is designed, which can accurately measure the values of the above-mentioned three indexes through the blood sample of the patient, and obtain the value of sCD48-AIH-SI through the model formula, and by comparing with the defined value obtained, the severity of the liver histological inflammation of the AIH patient can be predicted.

[0026] Further, CD48 can be expressed on a variety of immune cells, and interface hepatitis with portal tract lymphoplasmacytic infiltration is a major pathological feature of AIH. The results prove that in AIH patients, CD48 is up-regulated on the infiltrating inflammatory cells in the portal tract, and the expression amount is significantly positively correlated with the degree of liver inflammation. And previous studies have shown that soluble CD48 in serum can be cleaved from membrane-expressed CD48. Therefore, we speculate that in AIH patients, sCD48 is mainly derived from the up-regulated membrane-expressed CD48 in liver tissue, and thus can reflect the degree of liver histological inflammation.

[0027] Further, in the AIH patients in the exploration cohort (n=150), the areas under the receiver operating characteristic curve (AUC) of sCD48 and sCD48-AIH-SI for predicting severe inflammation (G3-4, Scheuer pathological scoring system) were 0.748 and 0.813, respectively, while the AUCs of the conventional indexes IgG, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were 0.677, 0.584 and 0.679, respectively. In the verification cohort (n=71), the AUCs of sCD48 and sCD48-AIH-SI for predicting severe inflammation were 0.724 and 0.753, respectively. Therefore, sCD48 and the detection kit based on the prediction model sCD48-AIH-SI improve the prediction ability of the histological inflammation of AIH patients.

[0028] In a preferred embodiment 1 of the present application, it is specified that the level of sCD48 in the serum of AIH patients is increased and the expression of CD48 in the liver tissue of AIH patients is up-regulated, and is related to the clinical and pathological characteristics of AIH patients;

[0029] In another preferred embodiment 2 of the present application, it is specified that sCD48 is an independent predictor of severe inflammation in AIH patients, and a prediction model of severe inflammation is constructed based on sCD48;

[0030] In another preferred embodiment 3 of the present application, it is specified that sCD48 and the prediction model sCD48-AIH-SI based on sCD48 have prediction value for severe histological inflammation in AIH patients;

[0031] In another preferred embodiment 4 of the present application, it is specified that the sCD48-AIH-SI model is applied in a kit for predicting histological inflammation in AIH patients.

[0032] Technical effects

[0033] The present application discloses that sCD48 has good prediction value for histological inflammation in AIH patients, which is a new non-invasive marker never reported in AIH patients, and the prediction model based on sCD48 constructed by multiple factor regression analysis further enhances the prediction effect of histological inflammation. For AIH patients, accurately reflecting the severity of histological inflammation in the liver has good prompting value for drug use and scheme adjustment of AIH patients.

[0034] The present application also discloses a detection kit for the above-mentioned indexes, i.e., sCD48, IgG and PLT, which can accurately measure the values of the above-mentioned three indexes through a blood sample of a patient, and obtain the value of sCD48-AIH-SI through a model formula, thereby improving the accuracy of predicting the severity of histological inflammation in the liver of AIH patients by comparing with the obtained limit value.

[0035] The concept, specific structure and generated technical effects of the present application will be further described below in combination with the drawings, so as to fully understand the purposes, features and effects of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 is the experimental result graph of the serum content of sCD48 in AIH patients and disease and healthy controls in a preferred embodiment 1 of the present application;

[0037] Figure 2 is the experimental result analysis graph of the correlation between the content of sCD48 in the serum of AIH patients and the inflammation grading and liver tissue expression amount in a preferred embodiment 1 of the present application;

[0038] Figure 3 Figure 2 is a sCD48 prediction chart of severe inflammation in AIH patients according to a preferred embodiment of the present application. DETAILED DESCRIPTION

[0039] The technical content of the present application will be more clearly and conveniently understood by reference to the following description of the preferred embodiments of the present application with reference to the accompanying drawings. The present application can be embodied in many different forms and the scope of the present application is not limited to the embodiments described herein.

[0040] Example 1 sCD48 levels in serum of AIH patients are elevated and CD48 expression in liver tissues of AIH patients is up-regulated, and is associated with clinical and pathological characteristics of AIH patients.

[0041] Experimental methods: The sCD48 levels in serum of AIH patients (n=150) in the above-mentioned exploratory cohort were detected by enzyme-linked immunosorbent assay (ELISA), and the serum sCD48 levels of 29 patients with non-alcoholic fatty liver disease (NAFLD), 50 patients with primary biliary cholangitis (PBC) and 39 healthy controls (HC) were also detected. The expression of CD48 in liver tissues of AIH, NAFLD and PBC patients and HC was detected by immunohistochemistry. The correlation between the serum sCD48 levels and the expression levels of CD48 in liver tissues of AIH patients and the histological inflammation grade was further analyzed.

[0042] Experimental results: As shown in Figure 1, Figure 1 Part A is the serum content of sCD48 in AIH and diseases and healthy controls, and it can be seen that the sCD48 level of AIH patients is significantly higher than that of NAFLD patients, PBC patients and HC; Part B is the content of sCD48 in serum of AIH patients with different inflammation grades, and the experimental results show that the sCD48 level of AIH patients with severe inflammation (G3-4) is significantly higher than that of AIH patients with mild-moderate inflammation (G1-2), suggesting that the sCD48 level can be used as a predictor of histological inflammation in AIH patients. Part C is the expression of CD48 in liver tissues of AIH and diseases and healthy controls, and similarly, the expression level of CD48 in liver tissues of AIH patients is significantly higher than that of NAFLD, PBC patients and HC.

[0043] Experimental analysis as follows: Figure 2As shown, part A is the correlation of CD48 expression in liver tissue of AIH patients with inflammation grading, and the results show that the expression level of CD48 in AIH patients is significantly correlated with inflammation grading (G); part B is the correlation of sCD48 content in serum of AIH patients with CD48 expression in liver tissue, and the results show that the expression level of CD48 in liver tissue is significantly correlated with sCD48 level, suggesting that the increase of sCD48 level in AIH patients may be related to the up-regulation of CD48 expression in liver. Therefore, we believe that sCD48 can be used as a predictor of histological inflammation in AIH.

[0044] Example 2 sCD48 is an independent predictor of severe inflammation in AIH patients, and a prediction model for severe inflammation is constructed based on sCD48.

[0045] Experimental method: We divided 150 AIH patients in the exploratory cohort into two groups, mild-moderate inflammation (G1-2, n=54) and severe inflammation (G3-4, n=96) according to histological inflammation grading. First, we compared the differences in sCD48 levels and other biochemical and clinical indicators between the two groups. Then, we further included the variables with differences into the multivariate regression analysis with severe inflammation as the outcome event to determine the variables with independent predictive effect on severe inflammation, and through multivariate logistic regression analysis, a regression equation was constructed based on the independent predictive factors as a model for predicting severe inflammation.

[0046] Experimental results: After single factor analysis, as shown in Table 1, the indicators with statistical differences (P<0.05) between the mild-moderate inflammation and severe inflammation groups included sCD48, AST, alkaline phosphatase (ALP), glutamyl transpeptidase (GGT), total bilirubin (TBIL), albumin (ALB), IgG and platelet (PLT) count. However, because ALP and GGT were missing in 5 patients, and these two indicators have limited clinical significance for AIH, these two indicators were not included in further multivariate analysis. As shown in Table 1, in the multivariate regression analysis including sCD48, AST, TBIL, ALB, IgG and PLT count, we found that sCD48 had independent predictive value for severe inflammation, and the other two variables with independent predictive effect were IgG and PLT. Based on these three independent predictive factors, we further constructed a prediction model for severe inflammation sCD48-AIH-SI: 1 / (1+e 1.305 –0.139×sCD48–0.122×IgG+0.01×PLT ), Note: In this model, the unit of sCD48 is ng / ml, the unit of IgG is g / L, and the unit of PLT count is 109 / L.

[0047] Table 1 Prediction analysis of severe inflammation in AIH patients in the exploratory cohort

[0048]

[0049]

[0050] Note: #: correlation statistics of multivariate analysis, including odds ratio (95% CI) and P value of multivariate analysis, only for independent variables with predictive effect; Abbreviations: CI, confidence interval; WBC, white blood cell; Hb, hemoglobin.

[0051] Example 3 Prediction value of sCD48 and sCD48-AIH-SI based prediction model on severe histological inflammation in AIH patients.

[0052] Experimental method: The prediction value of sCD48 and sCD48-AIH-SI on severe inflammation in AIH patients in the exploration cohort was evaluated, and verified in the verification cohort (n = 71) of AIH patients.

[0053] Experimental results: As shown in Figure 3 , part A is the prediction of sCD48 and sCD48-AIH-SI on severe inflammation in AIH patients in the exploration cohort. In the exploration cohort, the AUC of sCD48 for predicting severe inflammation in AIH patients was 0.748, and the optimal cutoff value was 11.54 ng / ml. Values above this value were predicted to be severe inflammation, and values below or equal to this value were predicted to be mild-moderate inflammation. The prediction sensitivity corresponding to this optimal cutoff value was 73.96%, and the specificity was 75.93%. The AUC of sCD48-AIH-SI for predicting severe inflammation was 0.813, and the optimal cutoff value was 0.61. Values above this value were predicted to be severe inflammation, and values below or equal to this value were predicted to be mild-moderate inflammation. The prediction sensitivity corresponding to this optimal cutoff value was 78.12%, and the specificity was 77.78%. The AUC of clinical routine indicators, IgG, ALT and AST for predicting severe inflammation was 0.677, 0.584 and 0.679, respectively. Part B is the prediction of sCD48 and sCD48-AIH-SI on severe inflammation in AIH patients in the verification cohort. In the verification cohort, the AUC of sCD48 for predicting severe inflammation was 0.724, and the AUC of sCD48-AIH-SI for predicting severe inflammation was 0.753. The AUC of IgG, ALT and AST for predicting severe inflammation was 0.553, 0.583 and 0.649, respectively. It can be seen that the sCD48-AIH-SI based prediction model has a better and more stable prediction value for severe inflammation than the conventional indicators, and 0.61 can be used as the cutoff value for predicting severe inflammation by sCD48-AIH-SI.

[0054] Example 4 Application of sCD48-AIH-SI model in the kit for predicting histological inflammation of AIH patients

[0055] 1. Instructions about the kit

[0056] 1) The kit detects three indexes in sCD48-AIH-SI model, i.e. serum sCD48 concentration, IgG content and PLT count. For sCD48 concentration, the detection method of ELISA is adopted; for IgG content, the immunoturbidimetry is adopted; and for PLT, the indirect method is adopted to count the platelets, i.e. after the PLT is labeled with specific fluorescent antibody, the ratio of red blood cells to PLT is detected by flow cytometry, and at the same time, the semi-automatic cell counter with single-channel impedance principle is used to accurately count the red blood cells, and finally the red blood cell count is divided by the ratio of red blood cells and PLT to obtain the PLT count.

[0057] 2) The detection kit needs to use the blood samples of patients for detection, and serum samples are needed for sCD48 concentration and IgG content detection: after blood is collected in a dry vacuum tube without additives, it is placed at room temperature for about 30 min, and then centrifuged at 2800 rpm per minute for 20 min under the condition of 4 degrees to obtain the upper serum; EDTA anticoagulant blood samples are needed for PLT count, and the samples are placed at room temperature of 18-22℃, and the detection of platelet count must be completed within 4 hours after blood collection.

[0058] 2. Detection steps

[0059] 1) The serum sCD48 concentration, IgG content and PLT count of AIH patients are detected respectively by using the kit according to the corresponding method;

[0060] 2) The detection values of the three indexes are substituted into the model sCD48-AIH-SI: 1 / (1+e1.305-0.139×sCD48-0.122×IgG+0.01×PLT) to calculate the corresponding value;

[0061] 3) The value calculated in the above step is compared with the predetermined value (0.61) by using the classification method of the predetermined value, if the calculated value is higher than 0.61, it is predicted as severe histological inflammation; if the calculated value is lower than or equal to 0.61, it is predicted as mild-moderate histological inflammation.

[0062] The above describes the preferred embodiments of the present application in detail. It should be understood that those skilled in the art can make many modifications and changes without creative labor according to the concept of the present application. Therefore, any technical solutions obtained by logical analysis, reasoning or limited experiments based on the prior art according to the concept of the present application should be within the protection scope determined by the claims.

Claims

1. Use of a sCD48-AIH-SI model for the preparation of a kit for predicting histological inflammation in AIH patients, characterized in that, The blood sample of a patient is detected using the kit, and the method for using the kit comprises the following steps: Step 1, determining the serum sCD48 concentration, IgG content and PLT count, and obtaining a detection value respectively; Step 2, substituting the detection value obtained in step 1 into the sCD48-AIH-SI model calculation to obtain a judgment value; Step 3, comparing the judgment value obtained in step 2 with a limit value, if the judgment value is higher than the limit value, it is predicted that the histological inflammation is severe; if the judgment value is lower than or equal to the limit value, it is predicted that the histological inflammation is mild to moderate; The step 1 further comprises: Step 1.1, determining the serum sCD48 concentration by using the detection method of ELISA; Step 1.2, determining the IgG content by using the immunoturbidimetry method; Step 1.3, determining the PLT count by using the indirect method for counting platelets; The sCD48-AIH-SI model in the step 2 is: 1 / (1+e 1.305–0.139×sCD48–0.122×IgG+0.01×PLT ), where sCD48 concentration is in ng / mL, IgG content is in g / L, and PLT count is in 10 9 / L. The limit value in the step 3 is 0.61; The sCD48 is a non-invasive biomarker of AIH histological inflammation; the sCD48-AIH-SI model is a prediction model of AIH patient histological inflammation based on the sCD48 concentration.

2. Use according to claim 1, wherein The step of the indirect method for counting platelets in the step 1.3 comprises: after the PLT is labeled with specific fluorescent antibodies, the ratio of red blood cells to the PLT is detected by using a flow cytometer, at the same time, the red blood cells are accurately counted by using a semi-automatic cell counter with single-channel impedance principle, and finally the count value of the red blood cells is divided by the ratio of the red blood cells to the PLT to obtain the PLT count.

3. The use according to claim 1, wherein In the step 1: the determination of the sCD48 concentration and the IgG content adopts a serum sample, after blood is collected in a dry vacuum tube without additives, it is placed at room temperature for about 30 minutes, and then centrifuged at 2800 revolutions per minute for 20 minutes under the condition of 4 DEG C to obtain the upper serum; the determination of the PLT count adopts an EDTA anticoagulated blood sample, after blood is collected, the sample is placed under the condition of room temperature of 18-22 DEG C, and the determination of the PLT count is completed within 4 hours after blood collection.

Images