Biomarker for predicting preventive response of gout patient to colchicine and application of biomarker

Abstract

The invention discloses a group of biomarkers for predicting preventive response of gout patients to colchicine and application of the biomarkers, and relates to the technical field of biomedicine. The marker provided by the invention comprises Sm d42: 3, Cis-13-docosenic acid (erucic acid), Creatinine (creatinine), Gitoxin (hydroxydigitonin) and Trichosanic acid (tricosic acid). The invention also provides a kit and a detection system which are prepared by applying the marker and are used for predicting the preventive response of the gout patient to colchicine. Through the biomarker, the kit and the prediction system, whether the colchicine is effective in preventing gout attack can be accurately predicted, and more accurate preventive treatment is facilitated.

Description

[0001] Cross - reference to related applications

[0002] This application claims the priority of a Chinese patent application with the application number 202510043611.2 filed on January 10, 2025. The entire content of the above - mentioned patent application is hereby incorporated by reference as part of this application. Technical field

[0003] The present invention relates to the field of biomedical technologies, and particularly to a group of biomarkers for predicting the response of gout patients to colchicine and their applications. Background art

[0004] The main characteristics of a gout attack are pain, swelling, redness, and limited joint function, and most of them are unpredictable. After an attack, it is likely to lead to serious diseases such as cardiovascular diseases or venous thromboembolism. Colchicine is a commonly used anti - inflammatory drug and also a first - line anti - inflammatory preventive drug for early uric acid - lowering treatment. However, studies have found that only about 70% of patients respond to colchicine, and some patients are ineffective in using colchicine. This indicates the need for powerful biomarkers to predict the response of patients to colchicine, thereby improving treatment outcomes. Also, due to issues such as cost and side effects, more effective preventive treatments are preferred for certain individuals, such as preferentially choosing non - steroidal anti - inflammatory drugs (NSAIDs) or combined treatments. Currently, there are no reliable markers to predict the response to colchicine treatment. If patients who are ineffective in colchicine prevention can be identified in advance, the attacks of patients can be prevented more precisely.

[0005] Metabolic disorders can affect the regulation of the immune system, thus leading to the formation of diseases. For example, bile acid metabolism and purine metabolism are significantly different between the two groups of patients with frequent and infrequent gout attacks, but it is still unknown whether they are effective in predicting gout attacks. Metabolic changes represent the immune state of the body and may be related to the response to colchicine. Metabolites may be used to predict the effectiveness of colchicine. Summary of the invention

[0006] Aiming at the disadvantages and deficiencies of the prior art, the present invention provides a group of biomarkers for predicting the preventive response of gout patients to colchicine and their applications. The specific solutions of the present invention are as follows:

[0007] In the first aspect of the present invention, a set of biomarkers for predicting the preventive response of gout patients to colchicine is provided. The biomarkers include: Sm d42:3, Cis-13-docosenoic acid (erucic acid), Creatinine, Gitoxin, Tricosanoic acid. Among them, the preventive response to colchicine includes effective prevention of gout attacks by colchicine and ineffective prevention of gout attacks by colchicine. Effective is defined as no gout attacks during the treatment period, and ineffective is defined as at least one attack during the treatment period.

[0008] Furthermore, the biomarkers further include at least one of sarcosine, bilirubin, N2-trifluoroacetyl-l-glutamine, 3-hydroxydodecanoic acid, 3-methylxanthine.

[0009] Furthermore, the biomarkers further include the variable data in the case. Preferably, the variable data in the case includes at least one of age, body mass index, disease course, tophus, blood uric acid level, and attack frequency in the past year. Among them, age and gout disease course are in years; body mass index is in kg / m 2 units; tophus is divided into presence or absence; blood uric acid is in μmol / L; attack frequency in the past year is in times. In a specific embodiment of the present invention, the presence of tophus is recorded as 1, and the absence of tophus is recorded as 0.

[0010] In the second aspect of the present invention, there is provided the use of the aforementioned biomarkers in the preparation of a kit, instrument, and detection system for detecting the preventive response of a patient to colchicine.

[0011] In the third aspect of the present invention, there is provided a kit for predicting the preventive response of gout patients to colchicine. The kit includes reagents for detecting the content of the aforementioned biomarkers. Preferably, the content of the biomarkers is the relative content of the biomarkers. More preferably, the relative content of the biomarkers needs to be normalized.

[0012] The 0-1 normalization formula is: X (normalized value) = [X (original value) - X (minimum value)] / [X (maximum value) - X (minimum value)].

[0013] Furthermore, the reagents include at least one of the following:

[0014] 1) Reagents for detecting the content of the biomarker, preferably, the content of the biomarker is the relative content of the biomarker;

[0015] 2) Reagents for extracting the biomarker from serum.

[0016] Furthermore, the reagent includes at least one of the following:

[0017] (1) The mass spectrometry detection reagent includes a mobile phase reagent for mass spectrometry detection. Preferably, the mass spectrometry includes high performance liquid chromatography or time-of-flight mass spectrometry;

[0018] (2) The extraction reagent includes at least one of an alcohol reagent for protein precipitation and a reagent for salting out. Preferably, the alcohol reagent includes at least one of methanol and ammonium acetate.

[0019] Furthermore, the relative content of the biomarker needs to be normalized.

[0020] The 0-1 normalization formula is: X (normalized value) = [X (original value) - X (minimum value)] / [X (maximum value) - X (minimum value)].

[0021] In the fourth aspect of the present invention, a system for predicting the preventive response of gout patients to colchicine is provided. The system includes:

[0022] A detection module, at least for qualitatively and / or quantitatively detecting the content data of the biomarker described in claim 1, and the quantitative data is the relative content of the biomarker detected;

[0023] A calculation module, at least for calculating the quantitative data obtained by the detection module according to a judgment formula to obtain a score. The formula is:

[0024] Logit(P) = 1.992 + 0.038 * (relative content of creatinine) - 0.061 * (relative content of Sm d42:3) + 0.057 * (relative content of erucic acid) + 0.027 * (relative content of digitoxigenin) - 0.051 * (relative content of tricosanoic acid) + 0.001 * age - 0.008 * body mass index + 0.000045 * serum uric acid - 0.013 * disease course - 0.087 * tophus - 0.056 * attack frequency in the past year,

[0025] wherein, age and the gout disease course are in years; the body mass index is in kg / m 2in units; tophus is divided into presence or absence; blood uric acid is in units of μmol / L; the attack frequency in the past year is in units of times. In the specific embodiments of the present invention, the presence of tophus is recorded as 1, and the absence of tophus is recorded as 0. More preferably, the relative content of the biomarker needs to be subjected to 0-1 normalization processing; preferably, the 0-1 normalization formula is: X (normalized value) = [X (original value) - X (minimum value)] / [X (maximum value) - X (minimum value)];

[0026] A judgment module, at least used to compare the score with a threshold to judge the gout attack stage. If the score is higher than the threshold, it is determined as an invalid person, and if the score is lower than the threshold, it is determined as an effective person. The threshold is 0.5;

[0027] An output module, at least used to output the result of the judgment module.

[0028] An information data processing terminal, the information data processing terminal includes a memory, a processor, and a computer program stored on the memory and executable on the processor. It is characterized in that when the processor executes the computer program, the processor executes the computer program to calculate the described system.

[0029] A computer-readable storage medium, the computer-readable storage medium stores a computer program, and it is characterized in that the program is executed by a processor to calculate the described system.

[0030] The beneficial effects of the present invention include but are not limited to:

[0031] 1. The present invention provides a group of biomarkers for predicting the preventive response of gout patients to colchicine. The biomarkers can accurately predict the response of gout patients to colchicine. The AUC in the discovery cohort is 0.657 (95% CI 0.595 - 0.719). After adding traditional clinical variables such as age, body mass index, disease course, tophus, blood uric acid level, and attack frequency in the past year, the AUC reaches 0.768 (95% CI 0.711 - 0.825), greatly improving the accuracy of the prediction model. In addition, the AUC in the independent validation cohort is 0.715 (95% 0.592 - 0.838), and the model performs well. It can accurately identify the population ineffective in preventing gout with colchicine, which helps to adjust the treatment strategy in time and is more conducive to preventing gout attacks.

[0032] 2. The system for predicting the preventive response of gout patients to colchicine designed according to the biomarkers of the present invention has a sensitivity of 0.76, a specificity of 0.68, and an accuracy of 0.72. Description of the Drawings

[0033] The accompanying drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0034] Figure 1 is the metabolic difference spectrum between the effective and ineffective groups of colchicine in the embodiments of the present invention. (A) Principal component analysis (PCA) of patients in the effective and ineffective groups of colchicine; (B) Orthogonal partial least squares discriminant analysis (OPLSDA) based on the metabolic characteristics of the effective and ineffective groups of colchicine; (C) Expression of leukotriene B4; (D) Enrichment pathways of differential metabolites; (E) Network analysis of significantly disordered metabolic pathways between the effective and ineffective groups of colchicine.

[0035] Figure 2 is a schematic diagram of the results of differential metabolites between the effective and ineffective groups of colchicine in the embodiments of the present invention.

[0036] Figure 3 is the model performance of the top metabolites and clinical variables on the response to colchicine based on least absolute shrinkage and selection operator regression in the embodiments of the present invention. (A) Importance of the top-ranked metabolites. (B) AUC of the effective and ineffective groups of colchicine. AUC: Area under the curve. Discovery set: Discovery set. Validation set: Validation set. Metabolite: Five metabolites Sm d42:3, Cis-13-docosenoic acid (erucic acid), Creatinine, Gitoxin, Tricosanoic acid. Clinic includes age, body mass index BMI, gout duration, blood uric acid, tophus, number of attacks in the past year. Detailed implementation manners

[0037] The present invention will be described in detail below in conjunction with embodiments, but the present invention is not limited to these embodiments. Unless otherwise specified, the raw materials and catalysts in the embodiments of the present invention are purchased through commercial channels.

[0038] In order to more clearly illustrate the overall concept of the present application, the following will be described in detail in the form of embodiments in conjunction with the drawings of the specification. In the following description, a large number of specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some well-known technical features in the art are not described.

[0039] For those conditions not specified in the examples, they are carried out according to conventional conditions or the conditions recommended by the manufacturer.

[0040] Unless otherwise specified, in the following embodiments, the reagents or instruments used without indicating the manufacturer are all conventional products that can be purchased commercially.

[0041] Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in the present invention all adopt conventional techniques in microbiology, biochemistry, analytical chemistry, cell culture, and related fields conventional in the technical field.

[0042] The preventive response to colchicine included colchicine-effective prevention of gout attacks and colchicine-ineffective prevention of gout attacks. Effective was defined as no gout attacks during treatment, and ineffective was defined as at least one attack during treatment. Subjects must meet the following conditions: a) aged between 18 and 70 years, (b) no malignant disease, (c) estimated glomerular filtration rate (eGFR)>60mL / min / 1.73m 2 (d) Not in the acute phase.

[0043] Pre-experimental preparation: Participant data, including demographic data, physical examination (palpable tophi), medications, and serum samples were collected at enrollment. Serum biochemical indices, including serum uric acid (sUA), were measured by enzymatic colorimetry using an automated biochemical analyzer (Roche, Germany). All patients started uric acid-lowering therapy based on the treat-to-target principle with febuxostat or benzbromarone. Colchicine (0.5 mg, twice daily) was given for anti-inflammatory prevention for 3-6 months.

[0044] Participants were followed up every 2-4 weeks. The acute phase of gout was defined as a clinically evident acute inflammatory attack manifested by joint pain, swelling, or tenderness. Participants were divided into a colchicine-ineffective group (at least one gout attack during the follow-up period) and a colchicine-effective group (no gout attack).

[0045] The sample processing method involved in the following embodiment is as follows:

[0046] Sample preparation by protein precipitation: 50 μL of serum sample was mixed with 200 μL of ice-cold methanol at 4°C with internal standard, incubated for 15 min, centrifuged at 14,000 rpm at 4°C, and the supernatant was collected. The serum sample was divided into equal parts to prepare quality control samples.

[0047] UHPLC (Nexera UHPLC LC-30A, SHIMADZU Technologies, Japan) and a quadrupole time-of-flight (QTOF) mass spectrometer (AB 6600 TripleTOF, SCIEX, Canada) acquired data in positive and negative ionization modes in a data-dependent acquisition mode. Chromatographic separation was performed using a Waters ACQUITY UPLC BEH Amide column (100 mm × 2.1 mm, 1.7 μm).

[0048] The mass spectrometry conditions were as follows:

[0049] (1) Chromatographic conditions Samples were separated using a Vanquish LC ultra-high performance liquid chromatography (UHPLC) HILIC column; column temperature was 25 °C; flow rate was 0.5 mL / min; injection volume was 2 μL; the mobile phase composition was A: water + 25 mM ammonium acetate + 25 mM ammonia, B: acetonitrile; the gradient elution program was as follows: 0 - 0.5 min, 95% B; 0.5 - 7 min, B linearly changed from 95% to 65%; 7 - 8 min, B linearly changed from 65% to 40%; 8 - 9 min, B was maintained at 40%; 9 - 9.1 min, B linearly changed from 40% to 95%; 9.1 - 12 min, B was maintained at 95%; during the entire analysis process, the samples were placed in a 4 °C autosampler. To avoid the influence caused by fluctuations in the instrument detection signal, the samples were analyzed continuously in a random order. QC samples were inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of the experimental data.

[0050] (2) Orbitrap Exploris TM 480 mass spectrometry conditions After the samples were separated using a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system, mass spectrometry analysis was performed using an Orbitrap Exploris TM 480 mass spectrometer (Thermo), and electrospray ionization (ESI) positive and negative ion modes were used for detection respectively. The ESI source and mass spectrometry setting parameters were as follows: Nebulizing gas Auxiliary heating gas 1 (Gas1): 50, Auxiliary heating gas 2 (Gas2): 2, Ion source temperature: 350 °C, Spray voltage (ISVF) positive ion mode 3500 V, negative ion mode 2800 V; Primary mass-to-charge ratio detection range: 70 - 1200 Da, Resolution: 60000, Scan accumulation time: 100 ms, For the secondary stage, a segmented acquisition method was used, the scan range was 70 - 1200 Da, Secondary resolution: 60000, Scan accumulation time: 100 ms, Dynamic exclusion time: 4 s.

[0051] The raw data (.wiff files) were converted to the mzML format using ProteoWizard software (version 3.0.23132, https: / / proteowizard.sourceforge.io / ). Metabolomics analysis was performed using the R package "XCMS", excluding features with a QC coefficient of variation > 30%. According to the MSI (Metabolomics Standards Initiative) requirements, MS / MS spectra were extracted, and for each MS feature, the obtained MS / MS spectra were compared with the Human Metabolome Database (HMDB), MassBank of North America (MoNA), Massbank, and Global Natural Products Social Molecular Network (GNPS) for annotation. If a feature was identified by multiple annotations, the priorities of MSI and products were considered to select the most reliable annotation.

[0052] Example 1 Screening of Biomarkers and Construction of a Model

[0053] In this example, clinical variables and metabolite content data of individuals were specifically collected for the colchicine effective group and the ineffective group. The metabolite content data were determined by non-targeted liquid chromatography-mass spectrometry, and the specific data analysis methods are as follows:

[0054] For clinical variables, the data were compared using ANOVA, t-tests, or rank sum tests (where applicable).

[0055] For metabolite content data, unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLSDA) were performed using the R packages "MixOmics" and "ropls". The importance of each metabolite differential variable was measured using VIP and p-values. For metabolic network analysis, a KEGG-based metabolic network was constructed using the R package "FELLA". Network analysis was performed using differential metabolites, and nodes with p < 0.05 were filtered into sub-networks and imported into Cytoscape for visualization.

[0056] The experimental cohort in this study was divided into a discovery set and an independent validation set. Biomarker selection was performed using MUVR (Multivariate Unbiased Variable Selection Method). The model was trained and validated. Briefly, 10-fold cross-validation was performed to avoid overfitting of the model in the training set. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). The metabolite intensities were normalized to the range of 0 to 1 to build the model. All statistical analyses were performed using R (4.3.1) or SPSS software.

[0057] A total of 409 participants were involved in this study. In the discovery cohort (319 people), 104 participants had at least one gout attack (non - responder group), while 215 participants did not have an attack during the follow - up period (responder group). Compared with the responder group, the non - responder group showed more frequent gout attacks in the previous year (Table 1). There were no differences in most clinical characteristics or biochemical markers (including serum uric acid, lipids, and eGFR, etc.) between the two groups.

[0058] Table 1 Comparison of clinical characteristics between the responder and non - responder groups of colchicine

[0059]

[0060]

[0061] For categorical variables, data are presented as n (%). For continuous variables, values are presented as mean (SD) or median (IQR) where appropriate. BMI: body mass index; FBG: fasting blood glucose; eGFR: estimated glomerular filtration rate. Dyslipidemia includes hypertriglyceridemia and hypercholesterolemia. Liver disease is hepatic steatosis. * indicates p < 0.05.

[0062] Metabolic profiles between the responder and non - responder groups of colchicine

[0063] A total of 992 metabolites with known structures were identified by the non - targeted platform. We observed separate metabolic profiles between the responder and non - responder groups in PCA ( Figure 1 A) and OPLSDA ( Figure 1 B) analyses. A total of 49 metabolites showed significant changes (VIP > 1 and FDR < 0.05) between the two groups ( Figure 2 and Table 2). In the non - responder group, leukotriene B4 increased most significantly (2.8 - fold) ( Figure 1 C); amino acids - histidine, sarcosine, N - (phosphonoethyl) glycine, etc. also increased significantly; the most decreased were lipids. After mapping these metabolites to KEGG, the enriched pathways were the PPAR signaling pathway, beta - alanine metabolism, and protein digestion and absorption ( Figure 1 D). The metabolic network was mainly involved in changes in amino acid metabolism and inflammation - related signaling pathways ( Figure 1 E). These metabolic changes were related to the response to colchicine.

[0064] Table 2 Significantly different metabolites between the responder and non - responder groups of colchicine

[0065]

[0066]

[0067]

[0068] Biomarkers for predicting reactions

[0069] To select metabolites for predicting the effectiveness of colchicine, MUVR was used to screen for potential biomarkers. After multiple trainings, according to the variable importance ranking ( Figure 3 A), the top ten metabolites include: Creatinine, Smd42:3, Cis-13-docosenoic acid, Gitoxin, Tricosanoic acid, sarcosine, bilirubin, N2-trifluoroacetyl-l-glutamine, 3-hydroxydodecanoic acid, 3-methylxanthine. Prediction models (LASSO regression, random forest, and support vector machine) were constructed using the top five metabolites. The LASSO model performed the best. The metabolites (Creatinine, Smd42:3, Cis-13-docosenoic acid, Gitoxin, Tricosanoic acid) had an AUC of 0.657 (95% CI 0.595 - 0.719) for the non-effective group in the discovery cohort ( Figure 3 B). After adding traditional clinical variables such as age, body mass index (BMI), gout duration, tophus, serum uric acid level, and attack frequency in the past year, the AUC reached 0.768 (95% CI 0.711 - 0.825), greatly improving the accuracy of the prediction model. Additionally, in the independent validation cohort, the AUC was 0.715 (95% 0.592 - 0.838), and the model performed well.

[0070] A Logistic regression equation was established using Logistic regression analysis:

[0071] Formula:

[0072] Logit(P) = 1.992 + 0.038*(Relative content of creatinine) - 0.061*(Relative content of Sm d42:3) + 0.057*(Relative content of erucic acid) + 0.027*(Relative content of digitoxigenin) - 0.051*(Relative content of tricosanoic acid) + 0.001*Age - 0.008*Body mass index + 0.000045*Serum uric acid - 0.013*Disease course - 0.087*Tophus - 0.056*Attack frequency in the past year.

[0073] Among them, age and gout disease course are in years; body mass index is in kg / m 2 as the unit; tophus is divided into presence or absence; serum uric acid is in μmol / L; attack frequency in the past year is in times. In the specific embodiments of the present invention, the presence of tophus is recorded as 1 and the absence of tophus is recorded as 0.

[0074] The threshold is 0.5. If the score is higher than the threshold, it is determined as an invalid person, and if the score is lower than the threshold, it is determined as an effective person.

[0075] Example 2 Practical application of biomarkers and models

[0076] Randomly select 100 subjects from the effective group and the invalid group respectively. After collecting venous blood, use the biomarkers and system of the present invention to identify by the method of Example 1. The results are shown in Table 3 through detection.

[0077] The subjects must meet the following conditions: a) aged between 18 - 70 years old, (b) without malignant diseases, (c) estimated glomerular filtration rate (eGFR) > 60 mL / min / 1.73m 2 (d) not in the acute attack period.

[0078] Preparation before the experiment: Collect participant information at the time of enrollment, including demographic data, physical examination (palpable tophus), drug and serum samples. Use the enzyme colorimetric method to measure serum biochemical indexes, including serum uric acid (sUA), using an automatic biochemical analyzer (Roche, Germany). All patients started uric acid-lowering treatment based on the treatment-to-target principle with febuxostat or benzbromarone. Colchicine (0.5 mg, twice a day) was given for anti-inflammatory prophylaxis for a course of 3 - 6 months.

[0079] The participants were followed up every 2 - 4 weeks. The acute gout period was defined as a clinically obvious acute inflammatory attack, manifested as joint pain, swelling or tenderness. The participants were divided into the colchicine ineffective group (at least one gout attack during the follow-up period) and the colchicine effective group (no gout attack). Among them, the calculation formulas for sensitivity, specificity and accuracy are as follows:

[0080] Sensitivity = (Number of people detected as positive in the invalid group / Total number of people in the invalid group) * 100%;

[0081] Specificity = (Number of data detected as negative in the effective group / Total number of people in the effective group) * 100%;

[0082] Accuracy = [(Number of people detected as positive in the invalid group + Number of people detected as negative in the effective group) / Total number of test subjects] * 100%. Specific embodiments:

[0084] A patient in the invalid group, 66 years old, with a body mass index of 23.44 kg / m 2 , with a history of gout for 12 years, blood uric acid of 576 μmol / L, gout attacks once in the past year, and tophi. The relative content of creatinine in the metabolite test result is -0.94, Sm d42:3 is 0.74, erucic acid is -0.0058, digitoxigenin is -0.37, tricosanoic acid is 0.22. After calculation, the predicted risk score is 1.05, which is greater than 0.5, and the prediction of the present invention is accurate. (The values of the relative content of metabolites are processed by normalization from 0 to 1).

[0085] Table 3 Detection results of the system of the present invention

[0086]

[0087]

[0088] As mentioned above, only the embodiments of the present invention are given. The protection scope of the present invention is not limited by these specific embodiments, but is determined by the claims of the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the technical idea and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A set of biomarkers for predicting the prophylactic response of gout patients to colchicine, characterized in that: The markers include: Sm d42:3, Cis-13-docosenoic acid (erucic acid), Creatinine (creatinine), Gitoxin (hydroxydigoxin), and Tricosanoic acid (tricosanoic acid).

2. The biomarker according to claim 1, characterized in that The biomarkers also include at least one of sarcosine, bilirubin, N2-trifluoroacetyl-l-glutamine, 3-hydroxydodecanoic acid, and 3-methylxanthine.

3. The biomarker according to claim 1, characterized in that The biomarkers also include variable data within the case.

4. The biomarker according to claim 3, characterized in that The variable data in the case include at least one of age, body mass index, disease course, tophi, blood uric acid level and attack frequency in the past year, wherein age and gout disease course are in years; body mass index is in kg / m 2 The unit is μmol / L; the unit of tophi is presence or absence; the unit of blood uric acid is μmol / L; the frequency of attacks in the past year is times.

5. Use of the biomarker according to any one of claims 1 to 4 in the preparation of a kit, an instrument, or a detection system for detecting a patient's prophylactic response to colchicine.

6. A kit for predicting the prophylactic response of gout patients to colchicine, characterized in that: The kit comprises reagents for detecting the relative content of the biomarkers described in any one of claims 1-4.

7. The kit according to claim 6, characterized in that The reagents include at least one of the following: 1) A reagent for detecting the content of the biomarker according to any one of claims 1 to 4; 2) A reagent for extracting the biomarker according to any one of claims 1 to 4 from serum.

8. A system for predicting the prophylactic response of gout patients to colchicine, characterized in that: The system comprises: A detection module, at least for qualitative and / or quantitative detection of content data of the biomarker according to any one of claims 1 to 4; The calculation module is at least used to calculate the quantitative data obtained by the detection module according to the judgment formula to obtain a score, and the formula is: Logit(P)=1.992+0.038*(relative content of creatinine)-0.061*(relative content of Sm d42:3)+0.057*(relative content of erucic acid)+0.027*(relative content of hydroxydigoxin)-0.051*(relative content of tricosanoic acid)+0.001*age-0.008*body mass index+0.000045*(content of serum uric acid)-0.013*course of gout-0.087*tophus-0.056*frequency of attacks in the past year, Among them, age and gout duration are in years; body mass index is in kg / m 2 The unit is ; tophi is divided into presence or absence; blood uric acid is in μmol / L; the frequency of attacks in the past year is in times; the relative content of metabolites is normalized by 0-1; A judgment module, at least used to compare the score with a threshold value to judge the reaction to colchicine, if the score is higher than the threshold value, it is judged that the reaction to colchicine is ineffective, and if the score is lower than the threshold value, it is judged that the reaction to colchicine is effective, and the threshold value is 0.5; The output module is at least used to output the result of the judgment module.

9. An information data processing terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the processor executes the computer program to calculate the system of claim 8.

10. A computer-readable storage medium storing a computer program, characterized in that: The program is executed by a processor to generate a computer system as claimed in claim 8.

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