A combined marker for detecting crohn's disease and use thereof

Abstract

The application discloses a combined marker for detecting Crohn's disease and application thereof, and relates to the technical fields of biology and medicine. The combined marker is composed of the following components: goose deoxycholic acid, lithocholic acid, ursodeoxycholic acid, glycocholic acid, glycolithocholic acid, glyco-ursodeoxycholic acid, taurine goose deoxycholic acid, taurine lithocholic acid, taurine ursodeoxycholic acid, cholic acid, glycocholic acid, taurine cholic acid, glyco-deoxycholic acid, taurine deoxycholic acid, glycolithocholic acid sulfate and isolithocholic acid. The combined marker can be used for clinically diagnosing or monitoring Crohn's disease. The application explores a metabolomics combined marker that can be used for diagnosing Crohn's disease, and creates a diagnosis and identification method that is simple in detection, low in cost, easy to be accepted by patients, can be well used for replacing a traditional diagnosis mode, and is of great significance to clinical diagnosis and treatment of Crohn's disease, and has popularization value.

Description

Technical Field

[0001] This invention relates to the fields of biotechnology and pharmaceutical technology, and in particular to a combination biomarker for detecting Crohn's disease and its application. Background Technology

[0002] Crohn's disease (CD) is a type of inflammatory bowel disease (IBD), an immune-mediated chronic nonspecific inflammatory disease of the digestive tract. Crohn's disease is characterized by difficult diagnosis, protracted illness, challenging treatment, and a high rate of disability, severely impacting patients' quality of life and prognosis, and imposing a heavy emotional and economic burden on families and society.

[0003] Furthermore, the current classification of Crohn's disease is based on the Montreal classification, which categorizes it according to disease behavior into non-stenotic, non-penetrating type B1, stenotic type B2, and penetrating type B3. The pathogenesis of Crohn's disease is not yet fully understood, and effective diagnostic and differential diagnostic tools are lacking. Currently, diagnosis relies primarily on endoscopic and CT / MR imaging to assess for irreversible stenosis, fistulas, or abscesses in the intestine. Endoscopy is an invasive procedure with poor patient tolerance and is not suitable for frequent monitoring; CT scans involve radiation and are not suitable for frequent re-examination; moreover, the test results require physicians to rely heavily on experience for comprehensive judgment, which introduces a degree of subjectivity.

[0004] Metabolites, as direct manifestations of an organism's phenotype, are a crucial foundation for elucidating biological processes and mechanisms. Studies have shown that Crohn's disease (CD) patients exhibit significant gut microbiota imbalance and metabolic disorders, characterized by an increase in facultative anaerobes and a decrease in obligate anaerobes, accompanied by dysregulation of multiple metabolic pathways, including short-chain fatty acid, bile acid, and carnitine metabolic pathways. These metabolites play a vital role in the development and progression of CD. The development of biomarkers for active Crohn's disease is of great importance for the diagnosis and treatment guidance of the disease. Summary of the Invention

[0005] To address the above shortcomings, this invention provides a combined biomarker for detecting Crohn's disease and its application, solving the problems of complex, costly, and difficult-to-accept tests in traditional Crohn's disease diagnostic methods. The specific technical solution is as follows: A composite biomarker for detecting Crohn's disease, the composite biomarker comprising the following components: chenodeoxycholic acid (CDCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycochenodeoxycholic acid (GCDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), taurochenodeoxycholic acid (TCDCA), taurochenodeoxycholic acid (TLCA), taurochenodeoxycholic acid (TUDCA), cholic acid (CA), glycocholic acid (GCA), taurochelic acid (TCA), glycodeoxycholic acid (GDCA), taurochenodeoxycholic acid (TDCA), glycolithocholic acid sulfate (GLCA_3S), and isolithocholic acid (isoLCA).

[0006] The present invention also provides the use of the combined biomarkers in the preparation of clinical diagnostic or monitoring kits for Crohn's disease.

[0007] The present invention also provides a kit for the clinical diagnosis of Crohn's disease, comprising reagents for detecting the expression levels of a combination of biomarkers, said biomarkers being composed of the following components: chenodeoxycholic acid (CDCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycochenodeoxycholic acid (GCDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), taurochenodeoxycholic acid (TCDCA), taurochenodeoxycholic acid (TLCA), taurochenodeoxycholic acid (TUDCA), cholic acid (CA), glycocholic acid (GCA), taurochelic acid (TCA), glycodeoxycholic acid (GDCA), taurochenodeoxycholic acid (TDCA), glycolithocholic acid sulfate (GLCA_3S), and isolithocholic acid (isoLCA). The kit determines whether a subject has Crohn's disease by detecting the expression level of the combined biomarkers in the subject's serum.

[0008] Furthermore, the bile acid disorder index (BDI) was constructed using the combined biomarkers. When the subject's bile acid disorder index (BDI) increased, it indicated that the subject had Crohn's disease. The formula for calculating the bile acid disorder index (BDI) is: BDI = ln(geometric mean of BAM2 specific characteristics) - ln(geometric mean of BAM1 specific characteristics). The specific characteristics of BAM2 are: the percentage of total bile acids (TDCA_p) of glycodeoxycholic acid (GDCA), glycolithocholic acid (GLCA), taurideoxycholic acid (TDCA), glycolithocholic acid sulfate (GLCA_3S), isolithocholic acid (isoLCA), and taurideoxycholic acid (TDCA). The specific characteristics of BAM1 are the percentage of non-12α-hydroxy bile acids and the total percentage of primary bile acids; The percentage of non-12α-hydroxy bile is the percentage of the total non-12α-hydroxy bile acids to the total bile acids; the non-12α-hydroxy bile includes: chenodeoxycholic acid (CDCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycochenodeoxycholic acid (GCDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), taurochenodeoxycholic acid (TCDCA), taurochenodeoxycholic acid (TLCA), and taurochenodeoxycholic acid (TUDCA). The percentage of the total primary bile acids is the percentage of the total primary bile acids to the total bile acids; the primary bile acids are: cholic acid (CA), chenodeoxycholic acid (CDCA), glycocholic acid (GCA), glycocholic acid (GCDCA), taurocholic acid (TCA) and taurocholic acid (TCDCA).

[0009] Furthermore, the reagent for detecting the expression level of the combined biomarkers includes a reagent for detecting the expression level of the combined biomarkers based on liquid chromatography-mass spectrometry detection technology.

[0010] Furthermore, the Crohn's disease is penetrating Crohn's disease.

[0011] The present invention also provides a clinical diagnostic system for Crohn's disease, the system comprising: a diagnostic indicator input module, a data processing module, and a Crohn's disease status assessment module; The diagnostic indicator input module includes at least the expression levels of the combined biomarkers as described in claim 1; The data processing module includes at least receiving the expression levels of the combined biomarkers as described in claim 1, inputting them into a diagnostic model, and calculating the bile acid disorder index (BDI) as described in claim 4. The Crohn's disease status assessment module outputs the assessment results of the subject's Crohn's disease status based on the calculation results obtained by the data processing module and the preset cutoff value.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention involves following up on cases meeting the inclusion criteria, collecting patient biosamples, isolating and identifying bile acids in serum samples, classifying bile acids, and constructing the Bile Acid Disorder Index (BDI). This reveals the levels of bile acid metabolic disorders and intestinal inflammation in Crohn's disease (CD) patients, as well as the efficacy for penetrating CD. It explores a combination of metabolomics biomarkers that can be used for the diagnosis of Crohn's disease and establishes a convenient, low-cost, and patient-acceptable diagnostic and differential diagnosis method. This method can effectively replace traditional diagnostic models that rely on endoscopic and CT / MR imaging examinations and physician experience, and has significant implications for the clinical diagnosis and treatment of CD, making it worthy of widespread application.

[0013] 2. This invention focuses on bile acid metabolism and its application value in the diagnosis of Crohn's disease (CD). It proposes for the first time a bile acid metabolism subtype (BAM1 and BAM2), which can accurately distinguish CD patients from healthy individuals and patients with other intestinal diseases, and is significantly correlated with CD penetrating disease behavior. This not only deepens the theoretical understanding of the role of bile acid metabolism in the pathogenesis of CD, but also provides an important tool for CD diagnosis and differential diagnosis, which is of great significance to the clinical treatment of CD. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 The figures shown are a silhouette coefficient diagram (A) showing the clustering effect as a function of the number of clusters K and a PCA cluster diagram (B) showing the differentiation of different bile acid subtypes in the embodiments. Figure 2 The PCA cluster diagram (A) shows the differentiation of patients with different bile acid subtypes of enteropathy, and the PCA cluster diagram (B) shows the differentiation of patients with different bile acid subtypes of CD. Figure 3 This is a radar chart showing the differences in bile acids between the BAM1 and BAM2 populations in the example. Figure 4 This is a bar chart showing the distribution of the proportion of two bile acid metabolism types in different populations in the examples; Figure 5 This is a bubble volcano plot showing the differences in clinical and laboratory indicators between BMA1 and BMA2 type CD patients in the example. Figure 6 This is a forest plot showing the association between clinical and laboratory indicators and bile acid metabolism subtypes in the examples; Figure 7 This is a graph showing the relationship between the number of bile acid features and model performance in the examples; Figure 8 The violin used in the example comparing the BDI indices of the BMA1 and BMA2 groups; Figure 9 This is a violin plot comparing the BDI indices of different population groups in the example; Figure 10 This is a PCA cluster diagram comparing bile acid characteristics of BMA1 type CD, BMA2 type CD, and HC patients in the examples. Figure 11This is a heatmap of regression coefficients showing the correlation between BDI and the characteristics of various bile acids in the examples. Figure 12 A violin plot showing the comparison of BDI levels in CD patients with different disease behaviors in the examples; Figure 13 This is a forest plot showing the association between various clinical and laboratory indicators and BDI in the examples; Figure 14 The figures show a comparison of receiver operating characteristic (ROC) curves for distinguishing pCD using the CRP model alone and the CRP+BDI model (A) and a comparison of ROC curves for distinguishing pCD using the CRP+BDI model (B) in the examples. Figure 15 This is a violin plot comparing the baseline BDI levels of patients who eventually progressed to pCD with those who had no progression and subsequently developed stenotic CD in the examples. Figure 16 This is a comparison of receiver operating characteristic curves (ROCs) of the CRP model alone and the CRP+BDI model in the examples, predicting the progression of patients to pCD. Detailed Implementation

[0016] The specific embodiments of the present invention are described in detail below, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments. Unless otherwise defined, all technical terms used below have the same meaning as commonly understood by those skilled in the art. The technical terms used herein are for the purpose of describing specific embodiments only and are not intended to limit the scope of protection of the present invention. Unless otherwise specifically stated, all raw materials, reagents, instruments, and equipment used in the present invention are commercially available or can be prepared by existing methods.

[0017] Example 1. Research Subjects The inclusion criteria were: (1) being diagnosed with “Crohn’s disease (CD),” “ulcerative colitis (UC),” “intestinal tuberculosis (TB),” “intestinal Behcet’s disease (BD),” “ischemic colitis,” “infectious enteritis,” “functional gastrointestinal disorders (FGID),” or “Ileitis of unknown cause” based on clinical symptoms, endoscopic findings, imaging and histological features (patients with enteritis other than CD and UC were classified as “nonIBD”); (2) having a serum sample; (3) not having taken antibiotics or probiotics in the three months prior to sample collection; and (4) having complete clinical data and being regularly followed up after discharge until diagnosis.

[0018] Exclusion criteria were: (1) serious diseases of organs such as the heart, lungs, liver, kidneys, brain, and blood; (2) malignant tumors; and (3) pregnant or lactating women. Healthy volunteers were recruited through open recruitment. Volunteers underwent routine blood tests and C-reactive protein (CRP) tests. If any abnormal blood indicators were found, the volunteers were not included in the cohort. Ultimately, four independent cohorts were formed.

[0019] Cohort 1 was the discovery cohort, comprising 1255 patients with enteropathic diseases (CD) and 154 healthy controls (HC) from the First Affiliated Hospital of Sun Yat-sen University. The enteropathic patients included 908 with CD, 134 with UC, 73 with TB, 46 with BD, 11 with ischemic colitis, 24 with infectious enteritis, 15 with functional gastrointestinal disorders, and 44 with endileomycosis. Cohorts 2, 3, and 4 were validation cohorts. Cohort 2 included 62 CD patients, 61 patients with diarrhea-predominant irritable bowel syndrome (IBS-D), and 112 HC patients from the First Affiliated Hospital of Sun Yat-sen University, as well as 55 colorectal cancer (CRC) patients from Zhujiang Hospital of Southern Medical University. Cohort 3 was a multi-omics cohort previously published by our team, including 72 CD patients and 96 HC patients. Cohort 4 consisted of 56 CD patients from Sir Run Run Shaw Hospital of Zhejiang University and 30 CD patients from the Sixth Affiliated Hospital of Sun Yat-sen University.

[0020] 2. Sample Collection Fasting blood samples were collected from the subjects before breakfast and sent to the laboratory for serum separation within 2 hours. The process was as follows: the serum was centrifuged at 3000 rpm for 15 minutes at 4°C, the supernatant serum was collected, aliquoted and stored in specimen cryopreservation tubes, and then transferred to a -80°C freezer for subsequent bile acid targeted detection.

[0021] 3. Targeted quantitative detection of bile acids 3.1 Preparation of Standard Products This detection method requires the preparation of 18 bile acid standards (external standards) and 16 deuterated isotopic bile acid standards (internal standards), as detailed in Table 1-1. Accurately weigh 1 mg of each bile acid standard, dissolve it thoroughly in 1 ml of methanol, and then serially dilute to prepare bile acid stock solutions of 1 mg / ml, 100 μg / ml, and 10 μg / ml, and store them at -20°C. The isotopic bile acid standards are prepared as a mixed internal standard solution at a concentration of 0.5 μg / ml and stored at -20°C for later use. Dilute the 18 bile acid standards to prepare a mixed external standard solution, and then serially dilute it 4-fold with methanol to obtain mixed external standard solutions at 7 concentration levels for the standard curve, stored at -20°C for later use. All bile acid standards were purchased from Shanghai Zhenzhun Technology Co., Ltd., China. Methanol was of mass spectrometry grade and purchased from Thermo Fisher Scientific, USA.

[0022] 3.2 Sample Pretreatment When preparing the test sample, thaw the serum or standard solution on ice, accurately pipette 20 μl of serum or external standard solution into an EP tube containing 80 μl of mixed internal standard solution of 0.5 μg / ml, vortex thoroughly for 5 minutes, let stand in a -20℃ freezer for 30 minutes, then centrifuge at 23000g for 10 minutes at 4℃, carefully pipette 60 μl of supernatant into a glass chromatographic vial with an inner liner, and store in a 4℃ freezer until analysis.

[0023] 3.3 Detection using liquid chromatography-mass spectrometry (1) Quality control: To assess the stability and batch effect of the detection system, we added the same quality control (QC) sample to each batch. The QC sample was an equal mixture of serum samples after pretreatment. One QC sample was added between every 10 test samples for testing.

[0024] (2) Bile acids in serum samples were separated and ion collected using a SCIEX TripleQuad™ 7500 liquid chromatography-triple quadrupole mass spectrometry system at the Metabolic Platform of the Institute of Precision Medicine, First Affiliated Hospital of Sun Yat-sen University. The precursor or daughter ions of each bile acid were obtained using multiple reaction monitoring (MRM) in negative ion mode. The types of bile acids were identified using standards and isotopic internal standards. The absolute concentrations of bile acids were then calculated based on the mass-to-charge ratio and standard curves. The integration and analysis of bile acid ion peaks were performed using SCIEX OS software.

[0025] 4. Preprocessing of bile acid data The following data preprocessing operations follow the blinding principle, and all samples are randomly numbered.

[0026] (1) Establishing a standard curve: The concentrations of the eight external standards are used as the abscissa, and the ratio of the detected external standard peak area to the corresponding internal standard peak area is used as the ordinate. A linear regression equation is fitted to obtain the standard curve of the corresponding bile acid. Then, the bile acid concentration of the serum sample is calculated based on the standard curve.

[0027] (2) Batch effect and missing value handling: Based on the detection order of QC samples and serum samples, SERRF (Systematic Error Removal Using Random Forest) was used to handle the batch effect of all bile acid concentrations, and missing values ​​were replaced with the lowest concentration of that type of bile acid detected.

[0028] (3) Calculation of bile acid abundance and ratio: The percentage (i.e., abundance) of each bile acid in the total bile acids was calculated by the concentration of 18 bile acids. The bile acid ratio was calculated based on the biological processes of bile acid synthesis, decomposition and metabolism to reflect the metabolic activity of various metabolic enzymes. We finally obtained 90 bile acid-related indicators. The meaning of each indicator and its calculation formula are shown in Tables 1-2, 1-3 and 1-4.

[0029] (4) Data normalization and transformation: The bile acid abundance is transformed using the centered log ratio transformation (clr), that is, each index is replaced with the logarithm of the ratio of the abundance value to the geometric mean of the sample; other bile acid indices are converted to the logarithm with base 2. This normalizes the data distribution for subsequent data analysis.

[0030] 5. Data Analysis Bile acid typing: K-means clustering was used to identify bile acid typing, and the silhouette coefficient was used as a method to evaluate the clustering quality. The silhouette coefficient combines the cluster compactness (distance of a sample to other points in its cluster) and separation (distance of a sample to all points in its nearest different cluster) to measure the clustering performance. The coefficient ranges from [-1, 1], where positive values ​​indicate good clustering results and negative values ​​indicate poor clustering results. Clusters with the largest silhouette coefficient exhibit the best clustering performance.

[0031] BDI Feature Selection: First, 90 features related to bile acids (BA) were standardized by normalizing the mean to 0 and the standard deviation to 1 (Z-score standardization). The performance of the logistic regression model in distinguishing between two bile acid subtypes (Bile Acid Metabolism Type 1, BAM1 and Bile Acid Metabolism Type 2, BAM2) was evaluated by progressively increasing the number of bile acid features. The optimal number of features for BDI was determined to be 8. When the number of features exceeded 8, the model performance significantly decreased (AUC = 0.996; supplementary data is needed). Figure 2 These eight characteristics include two BAM1-specific markers: percentage of non-12α-hydroxybile acids (nonOH2_p) and percentage of total primary bile acids (PriBA_p), and six BAM2-specific markers: GDCA, GLCA, TDCA, GLCA_3S, isoliCCA, and TDCA_p (the percentage of TDCA in total bile acids). The BDI index reflects the directional imbalance between BAM1 and BAM2-specific characteristics and is calculated as: BDI = ln(geometric mean of BAM2-specific characteristics) - ln(geometric mean of BAM1-specific characteristics). This index was calculated for all individuals in the discovery cohort. To assess robustness and universality, the same characteristics and formula were used for validation in three independent validation cohorts.

[0032] 6. Experimental Data Table 1-1 Bile acid standards and their isotopic internal standards Table 1-2 Meaning and Calculation Formulas of Bile Acid Related Indicators Table 1-3 Meaning and Calculation Formulas of Bile Acid Related Indicators Table 1-4 Meaning and Calculation Formulas of Bile Acid Related Indicators 7. Results and Analysis 7.1 Unsupervised cluster analysis identified two bile acid subtypes. K-means cluster analysis was performed on all samples, and the number of clusters with the largest silhouette coefficient was found to be 2. Therefore, the population was divided into two clusters, defined as Bile acids Metabolism Type 1 (BAM1) and Bile acids Metabolism Type 2 (BAM2) (see...). Figure 1 A: Profile coefficient plot showing clustering performance as a function of cluster number K). PCA shows that this clustering method can effectively divide all samples into two groups (see...). Figure 1 B: PCA clustering diagrams distinguishing different bile acid subtypes. To eliminate the influence of healthy controls on the clustering results, PCA visualization was performed on all patients with enteropathy and on patients with CD alone. The results showed that this subtyping method consistently and reliably divided the population into two groups (see...). Figure 2 A: PCA cluster diagram differentiating patients with different bile acid subtypes of enteropathy; B: PCA cluster diagram differentiating patients with different bile acid subtypes of CD. The levels of TDCA, GDCA, DCA, GLCA_3S, isoLCA, and LCA in BAM1 type were significantly higher than in BAM2 type, reflected in the enrichment of secondary bile acids DCA and LCA and their derivatives (see...). Figure 3 (Radar graph showing the differences in bile acids between BAM1 and BAM2 individuals).

[0033] 7.2 Distribution of bile acid types in healthy individuals and various types of enteritis This study analyzed the distribution of bile acids BAM1 and BAM2 in HC (healthy control, HC) and various types of enteritis (see [link to study]). Figure 4 (Bar chart showing the distribution of the proportion of two bile acid metabolism types in different populations). Among them, patients with enteritis were divided into four categories: CD, UC and non-IBD patients. It was found that most HC patients were of type BAM1, while BAM1 and BAM2 were more evenly distributed among various enteritis patients.

[0034] 7.3 Bile acid typing indicates the level of intestinal inflammation in CD patients Patients with BAM2 type CD had higher CDAI scores than those with BM1, but the difference was not statistically significant. Systemic inflammatory markers such as serum amyloid A (SAA) and C-reactive protein (CRP) were significantly higher in BAM2 type CD patients (see [link to article]). Figure 5 (Bubble volcano plot showing the differences in clinical and laboratory indicators between BMA1 and BMA2 type CD patients).

[0035] 7.4 Bile acid typing is closely related to penetrating CD. Multivariate logistic regression analysis showed that, after adjusting for confounding factors such as age, sex, and BMI, BAM2 was more likely to exhibit penetrating disease behavior than BAM1 (OR=1.85, 95%CI: 1.27-2.72, P=0.001) (see [link to analysis]). Figure 6 Forest plot showing the association between clinical and laboratory indicators and bile acid metabolism subtypes.

[0036] 7.5 The Bile Acid Disorder Index (BDI) indicates a state of bile acid metabolism disorder. To quantify bile acid metabolism abnormalities in individual patients, we developed a bile acid disorder index (BDI). In a discovery cohort study, we screened eight bile acid-related features that distinguish BAM1 from BAM2 to construct the BDI. This index effectively distinguishes BAM2 patients, with an area under the curve (AUC) of 0.996. Figure 7 (Graph showing the relationship between the number of bile acid features and model performance), and the number of features in the BAM2 group was significantly higher than that in the BAM1 group (P<0.001). Figure 8 (Violin plot comparing BDI indices in the BMA1 and BMA2 groups). To assess the generalizability of BDI, we validated it in three independent cohorts, finding that BDI levels in CD patients were significantly higher than in the HC group, and also higher than in CRC and IBSD patients. Figure 9 (Violin plot comparing BDI indices of different populations). In addition, BAM1-enriched bile acids (DCA, GDCA, TDCA; see...) Figure 10 PCA clustering plot comparing bile acid characteristics in BMA1-type CD, BMA2-type CD, and HC patients) showed a negative correlation between bile acid concentration and BDI. Figure 11 (Regression coefficient heatmap showing the correlation between BDI and characteristics of various bile acids). Regarding bile acid composition, the percentages of primary bile acids, conjugated bile acids, and glycosylated chenodeoxycholic acid (GCDCA) were positively correlated with BDI (FDR < 0.05), while the percentages of secondary bile acids, lithocholic acid (GLCA), and DCA were negatively correlated with BDI (FDR < 0.05). Figure 11 ).

[0037] 7.6 The role of the bile acid disorder index (BDI) in the diagnosis of penetrating CD. In all four cohorts, penetrating CD (pCD) and BDI showed a significant positive correlation (univariate regression analysis, β = 0.42–0.68, FDR < 0.05). Specifically, comparative analysis of the four independent cohorts showed that pCD had a significantly higher BDI level compared to the Montreal B1 and B2 subtypes, while no significant difference was observed between the B1 and B2 subtypes. Figure 12 (Violin plot comparing BDI levels in CD patients with different disease behaviors). Further linear regression analysis revealed that even after adjusting for variables such as sex, age, BMI, and CRP, penetrating CD remained significantly correlated with BDI (β = 0.500–3.178), and this correlation was statistically significant in all four cohorts. Figure 13 Forest plots showing the association between various clinical and laboratory indicators and BDI. We attempted to construct a pCD diagnostic model using XGBoost, which was trained on 80% of CD patients in the discovery cohort and validated on the remaining 20%. Results showed that incorporating BDI into a diagnostic model that only included CRP significantly improved the model's ability to distinguish between pCD and non-pCD patients (AUC increased from 0.607 to 0.712, DeLong test P=0.009). Figure 14 A: Comparison of receiver operating characteristic curves (ROCs) for distinguishing pCD between the CRP-only model and the CRP+BDI model. Similar results were observed in all three validation cohorts (AUC values ​​0.614–0.702). Figure 14 B: Receiver operating characteristic curves for distinguishing pCD using the CRP+BDI model.

[0038] 7.7 The role of the bile acid disorder index (BDI) in predicting progression of CD patients to penetrating CD. A prospective follow-up of 552 non-pCD patients in the cohort was conducted for a median of 19 months. Compared with patients without progression-free disease (P=0.025) and those who subsequently developed stenotic CD (P=0.026), patients who eventually progressed to pCD had significantly higher baseline BDI levels (two-sided Wilcoxon test, P=0.022). Figure 15 A violin plot comparing baseline BDI levels in patients who eventually progressed to pCD with those who had no progression and subsequently developed stenotic CD. Furthermore, BDI was superior to CRP in predicting pCD (AUC = 0.672 vs 0.495, Delong test P = 0.032). Figure 16 (Comparison of receiver operating characteristic curves for predicting patient progression to pCD using the CRP-only model and the CRP+BDI model).

[0039] Based on the above research findings, this invention proposes to use chenodeoxycholic acid (CDCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycochenodeoxycholic acid (GCDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), taurocene chenodeoxycholic acid (TCDCA), taurocene lithocholic acid (TLCA), taurocene ursodeoxycholic acid (TUDCA), cholic acid (CA), glycocholic acid (GCA), taurocene cholic acid (TCA), glycolithocholic acid (GDCA), taurocene lithocholic acid (TDCA), glycolithocholic acid sulfate (GLCA_3S), and isolithocholic acid (isoLCA) as combined biomarkers for the clinical diagnosis or monitoring of Crohn's disease.

[0040] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A combination biomarker for detecting Crohn's disease, characterized in that, The combined biomarker consists of the following components: chenodeoxycholic acid (CDCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycochenodeoxycholic acid (GCDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), taurocene chenodeoxycholic acid (TCDCA), taurocene lithocholic acid (TLCA), taurocene ursodeoxycholic acid (TUDCA), cholic acid (CA), glycolithocholic acid (GCA), taurocene lithocholic acid (TCA), glycolithocholic acid (GDCA), taurocene lithocholic acid (TDCA), glycolithocholic acid sulfate (GLCA_3S), and isolithocholic acid (isoLCA).

2. The use of the combined biomarker as described in claim 1 in the preparation of a clinical diagnostic or monitoring kit for Crohn's disease.

3. A reagent kit for the clinical diagnosis of Crohn's disease, characterized in that, The invention includes reagents for detecting the expression levels of a combination of biomarkers, which consist of the following components: chenodeoxycholic acid (CDCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycochenodeoxycholic acid (GCDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), taurocenechenodeoxycholic acid (TCDCA), taurocenelithocholic acid (TLCA), taurocenelithocholic acid (TUDCA), cholic acid (CA), glycolithocholic acid (GCA), taurocenecholic acid (TCA), glycolithocholic acid (GDCA), taurocenelithocholic acid (TDCA), glycolithocholic acid sulfate (GLCA_3S), and isolithocholic acid (isoLCA). The kit determines whether a subject has Crohn's disease by detecting the expression level of the combined biomarkers in the subject's serum.

4. The reagent kit according to claim 3, characterized in that, The bile acid disorder index (BDI) was constructed using the combined biomarkers. When the bile acid disorder index (BDI) of a subject was elevated, it indicated that the subject had Crohn's disease. The formula for calculating the bile acid disorder index (BDI) is: BDI = ln(geometric mean of BAM2 specific characteristics) - ln(geometric mean of BAM1 specific characteristics). The specific characteristics of BAM2 are: the percentage of glycodeoxycholic acid (GDCA), glycolithocholic acid (GLCA), taurideoxycholic acid (TDCA), glycolithocholic acid sulfate (GLCA_3S), isolithocholic acid (isoLCA), and taurideoxycholic acid (TDCA) in total bile acids. The specific characteristics of BAM1 are the percentage of non-12α-hydroxy bile acids and the total percentage of primary bile acids; The percentage of non-12α-hydroxy bile is the percentage of the total non-12α-hydroxy bile acids to the total bile acids; the non-12α-hydroxy bile includes: chenodeoxycholic acid (CDCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycochenodeoxycholic acid (GCDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), taurochenodeoxycholic acid (TCDCA), taurochenodeoxycholic acid (TLCA), and taurochenodeoxycholic acid (TUDCA). The percentage of the total primary bile acids is the percentage of the total primary bile acids to the total bile acids; the primary bile acids are: cholic acid (CA), chenodeoxycholic acid (CDCA), glycocholic acid (GCA), glycocholic acid (GCDCA), taurocholic acid (TCA) and taurocholic acid (TCDCA).

5. The reagent kit according to claim 3, characterized in that, The reagents for detecting the expression levels of the combined biomarkers include: reagents for detecting the expression levels of the combined biomarkers based on liquid chromatography-mass spectrometry (LC-MS) detection technology.

6. The reagent kit according to any one of claims 3-5, characterized in that, The Crohn's disease mentioned is penetrating Crohn's disease.

7. A clinical diagnostic system for Crohn's disease, characterized in that, The system includes: a diagnostic indicator input module, a data processing module, and a Crohn's disease status assessment module; The diagnostic indicator input module includes at least the expression levels of the combined biomarkers as described in claim 1; The data processing module includes at least receiving the expression levels of the combined biomarkers as described in claim 1, inputting them into a diagnostic model, and calculating the bile acid disorder index (BDI) as described in claim 4. The Crohn's disease status assessment module outputs the assessment results of the subject's Crohn's disease status based on the calculation results obtained by the data processing module and the preset cutoff value.

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