Biomarkers for identifying acute stage and intermittent stage of gout and application of biomarkers

Abstract

The invention provides a group of biomarkers for identifying acute stage and intermittent stage of gout and application of the biomarkers, and relates to the technical field of biomedicine. The invention provides a new biomarker for identifying the acute stage and the intermittent stage of gout and a distinguishing system constructed by applying the biomarker, so that the acute attack stage and the intermittent stage can be distinguished, timely symptomatic treatment is facilitated, and the treatment process is accelerated.

Description

[0001] Cross - reference to related applications

[0002] This application claims the priority of a Chinese patent application with the application number 202410959296.3 filed on July 17, 2024. The entire content of the above - mentioned patent application is hereby incorporated by reference as part of this application. Technical field

[0003] This application relates to the field of biomedical technology, and particularly to a group of biomarkers for distinguishing acute and intercritical stages of gout and their applications. Background art

[0004] Gout is a progressive, inflammatory and metabolic disease. Gout attacks can easily lead to adverse outcomes such as cardiovascular events and venous thromboembolism. After repeated attacks, gout may also progress to chronic gouty arthritis, specifically manifested as tophi and joint damage, resulting in poor quality of life and even severe disability. Acute gouty arthritis, intercritical gout, and chronic gout are the recognized staging systems of gout. In line with the principle of "treating the symptoms in acute cases and the root cause in chronic cases", the clinical medications in different periods are also different. The treatment focus in the acute stage is to administer gout anti - inflammatory drugs as soon as the signs of acute attack appear and continue until the inflammation completely subsides. The treatment purpose in the intercritical stage is to prevent acute attacks. For those with only 1 - 2 acute gout attacks per year and normal serum uric acid levels and renal function after the attack, generally no preventive medication is required.

[0005] Therefore, in order to effectively treat gout, it is necessary to timely monitor the conditions of patients with acute - stage gout, promptly detect acute gout attacks, optimize treatment strategies, and achieve symptomatic medication. Currently, most of the markers for identifying the acute and intercritical stages of gout are inflammatory indicators, including C - reactive protein (CRP) and erythrocyte sedimentation rate (ESR), and the diagnostic accuracy is poor. Therefore, there is an urgent need for a new marker that can accurately identify the acute and intercritical stages of gout. Summary of the invention

[0006] Aiming at the above - mentioned problems, the purpose of the present invention is to provide a group of biomarkers for detecting the acute stage of gout and their applications. Specifically, the relative abundances of metabolites are determined by non - targeted liquid chromatography - mass spectrometry, the differential metabolites between the acute stage and the intercritical stage are compared, and a machine - learning system is used to determine the biomarkers related to the acute stage.

[0007] On the one hand, the present application provides a set of biomarkers for identifying the acute phase and the intermission phase of gout. The biomarkers include 5-Methoxytryptamine, Beta-D-Fructose6-phosphate, Citric acid, beta-Alanyl-L-arginine, and biotin. Preferably, the biomarkers further include one or more of glycine, L-Serine, S-Adenosylhomocysteine, and 2,3-Diphosphoglyceric acid.

[0008] On the other hand, the present application also provides the application of the aforementioned biomarkers in the preparation of kits, instruments, and systems for identifying the acute attack phase and the intermission phase.

[0009] On the other hand, the present application also provides a kit for identifying the acute attack phase and the intermission phase. The kit includes reagents for detecting the biomarkers. Preferably, it includes reagents for qualitatively or quantitatively analyzing the biomarkers.

[0010] Furthermore, the quantification of the biomarkers refers to obtaining the relative concentrations of the respective biomarker components.

[0011] Furthermore, the reagents include at least one of the following:

[0012] 1) Reagents for detecting the content of the aforementioned biomarkers;

[0013] 2) Reagents for extracting the aforementioned biomarkers from the sample.

[0014] In a preferred embodiment of the present invention, the sample is processed by the protein precipitation method to extract the biomarkers; the relative content of the biomarkers is determined by non-targeted liquid chromatography-mass spectrometry. Preferably, the numerical value of the relative content of the biomarkers needs to be subjected to 0-1 normalization processing. The 0-1 normalization formula is: X (normalized value) = [X (original value) - X (minimum value)] / [X (maximum value) - X (minimum value)].

[0015] Those skilled in the art can understand that the content of the biomarkers in the sample can be extracted or determined by any known method, and then the data of their relative content can be obtained. No excessive limitation is imposed here.

[0016] On the other hand, the present application also provides a system for identifying the acute attack stage and the remission stage of gout. The system includes: a detection module, at least for qualitatively and / or quantitatively detecting the content data of the biomarker; a calculation module, at least for calculating the quantitative data obtained by the detection module according to a judgment formula to obtain a score; a judgment module, at least for comparing the score with a threshold value to judge the gout attack stage. If the score is higher than the threshold value, it is determined as the remission stage, and if the score is lower than the threshold value, it is determined as the acute attack stage of gout; an output module, at least for outputting the judgment result. Preferably, the calculation module is used to obtain the relative concentration of each biomarker.

[0017] Further, the judgment formula is as follows:

[0018] Score

[0019] where logit(P) = 0.73 - 0.06×[5-methoxytryptamine] - 0.06×[β-D-fructose-6-phosphate] + 0.07×[citric acid] + 0.06×[β-alanyl-L-arginine] + 0.03×[biotin].

[0020] Further, the threshold value is 0.5.

[0021] In a preferred embodiment, when the score > 0.5, the probability that the subject belongs to the remission stage of gout is relatively high, and when the score < 0.5, the subject will be classified as acute gout.

[0022] In a preferred embodiment, the sample is a biological fluid sample, and the biomarker is obtained from the sample. The sample is blood, saliva, ascites, urine, cerebrospinal fluid, sputum, tissue fluid, etc.; preferably, blood samples such as venous blood, peripheral blood, peripheral blood, serum, plasma, dried blood spots, etc. In the embodiments of the present invention, the sample is serum.

[0023] On the other hand, the present application also provides 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. When the processor executes the computer program, the processor executes the computer program to calculate the system.

[0024] The information data processing terminal includes: a processor, a memory, and a communicator; the memory is used to store a computer program, the communicator is used to communicate with an external device, and the processor is used to execute the computer program stored in the memory so that the terminal executes the system.

[0025] On the other hand, the present application also provides a computer-readable storage medium storing a computer program which, when executed by a processor, calculates the system described above.

[0026] The computer program may be stored in a computer-readable storage medium. When executed, it executes the system; the computer-readable storage medium may include, but is not limited to, floppy disks, optical disks, CD-ROMs (Compact Disc Read-Only Memories), magneto-optical disks, ROMs (Read-Only Memories), RAMs (Random Access Memories), EPROMs (Erasable Programmable Read-Only Memories), EEPROMs (Electrically Erasable Programmable Read-Only Memories), magnetic cards or optical cards, flash memories, or other types of media / machine-readable media suitable for storing machine-executable instructions. The computer-readable storage medium may be a product not connected to a computer device or a component already connected to and used by a computer device.

[0027] In specific implementation, the computer program is routines, programs, objects, components, data structures, etc. for performing specific tasks or implementing specific abstract data types.

[0028] On the other hand, the present application also provides a device including an input device and a computing device. The input device is used to input the biomarker content data, and the computing device is used to calculate the system described above.

[0029] The present invention has the following beneficial effects:

[0030] 1. In the present application, a new set of biomarkers for identifying the acute and intercritical phases of gout is given, which can quickly and accurately identify the acute attack and intercritical phases of gout, helping to timely adjust the treatment strategy and achieve symptomatic treatment.

[0031] 2. The system for identifying the acute attack and intercritical phases of gout established by the present invention based on biomarkers has established a practical and feasible quantification method for identifying the acute attack and intercritical phases of gout through the system formula.

[0032] 3. The present invention also includes technical solutions such as corresponding diagnostic kits, detection systems, computing systems, etc. based on biomarkers. Through these methods, it makes the identification of the acute attack and intercritical phases of gout a practical and feasible operation.

[0033] 4. The accuracy of the biomarkers and identification system of the present invention is over 80%, and it can accurately identify the acute attack and intercritical phases of gout. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0035] Figure 1 It is a schematic diagram of the results of the principal component analysis of metabolites PCA during the intermittent period and the acute period in the embodiments of the present invention;

[0036] Figure 2 It is a schematic diagram of the results of the OPLSDA analysis of metabolites during the intermittent period and the acute period in the embodiments of the present invention;

[0037] Figure 3 It is a schematic diagram of the metabolite difference network in the embodiments of the present invention;

[0038] Figure 4 It is a schematic diagram of the results of the relationship between the number of metabolite variables and AUC during the intermittent period and the acute period in the embodiments of the present invention;

[0039] Figure 5 It is the ranking of metabolite importance in the embodiments of the present invention;

[0040] Figure 6 It is a schematic diagram of the AUC curve of the biomarker machine learning system in the embodiments of the present invention. Detailed implementation manners

[0041] In order to more clearly explain the overall concept of the present application, the following will be described in detail by way of examples 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 technical features well known in the art are not described.

[0042] For those not specified in the examples, they are carried out according to conventional conditions or the conditions recommended by the manufacturer.

[0043] Unless otherwise specified, in the following embodiments, for reagents or instruments whose manufacturers are not indicated, they are all conventional products that can be obtained through commercial purchase.

[0044] Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in the present invention all adopt the conventional technologies in the fields of microbiology, biochemistry, analytical chemistry, cell culture and related fields in the technical field.

[0045] Clinically, the course of gout can be roughly divided into the acute attack phase and the remission phase. Clinical information and serum samples were collected at the first visit. The relative abundances of metabolites were determined by non-targeted liquid chromatography-mass spectrometry. Statistical analyses such as PCA and OPLSDA were used to analyze the differential metabolites between the two groups, and KEGG pathway enrichment was performed. A machine learning system was used to identify biomarkers related to the acute attack phase and remission phase of gout.

[0046] Subjects had to meet the following criteria: a) aged between 18 and 70 years, (b) no malignant diseases, (c) estimated glomerular filtration rate (eGFR) > 15 mL / min / 1.73 m2, (d) normal liver function (alanine aminotransferase or total bilirubin < 2 times the normal value), (e) acute attack phase or remission phase.

[0047] According to the previous definition, the stage of gout was determined by two rheumatologists at the first clinical visit. The acute phase of gout was defined as a clinically obvious acute inflammatory attack, manifested as joint pain, swelling or tenderness. Intermittent gout was the asymptomatic period after or between gout attacks.

[0048] Subject data were collected at the first visit, including demographic data, physical examination data (including subcutaneous administration), medications, and serum samples. Patients with atrial fibrillation used a visual analogue scale (VAS). Serum uric acid, fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and serum creatinine (Scr) were measured by enzymatic colorimetry on a German Roche automatic biochemical analyzer. Serum samples were processed according to the procedure before metabolite analysis.

[0049] The sample processing method involved in the following examples is as follows:

[0050] Sample preparation by protein precipitation method: 50 μL of serum sample was mixed with 200 μL of cold methanol (containing fluorouracil internal standard) at 4°C, incubated for 15 min, and the supernatant was collected and stored at -80°C for analysis. Serum samples were aliquoted to prepare quality control samples.

[0051] Non-targeted liquid chromatography-mass spectrometry processing conditions:

[0052] Data were acquired in positive and negative ionization modes in data-dependent acquisition (DDA) mode using UHPLC (Nexera UHPLC LC-30A, SHIMADZU Technologies, Japan) coupled with a quadrupole time-of-flight (QTOF) mass spectrometer (AB 6600 TripleTOF, SCIEX, CANADA). Samples (2 μL) were eluted on a Waters ACQUITY UPLC BEH Amide column (100 mm) at 25 °C with a flow rate of 0.5 mL / min. Mobile phase A was ultrapure water containing 25 mM ammonium hydroxide (NH4OH) and 25 mM ammonium acetate (NH4OAc), and mobile phase B was pure ACN. Samples were eluted according to the following gradient: 0 - 0.5 min, 95% B; 0.5 - 7 min, 95% B to 65% B; 7 - 8 min, 65% B to 40% B; 8 - 9 min, 40% B; 9 - 9.1 min, 40% B to 95% B; 9.1 - 12 min, 95% B. The mass spectrometry was operated in information-dependent acquisition (IDA) mode with a acquisition rate of 4 spectra / s, and the TOF mass range was set to m / z 60 - 1200 Da. The source parameters were set as follows: GAS1 60; GAS2 60; CUR, 30; TEM, 650 °C; ISVF was 5000 v (positive) and -4000 v (negative). The product ion scan parameters were set to m / z 25 - 1200, collision energy 30 eV, and accumulation time 30 ms. The raw data (.wiff files) were converted to 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 QC coefficient of variation > 30%. According to the requirements of MSI (Metabolomics Standards Initiative), 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 priority of MSI and products was considered to select the most reliable annotation.

[0053] Example 1 Screening of Biomarkers and Construction of the System

[0054] In this example, clinical variables and metabolite content data of individuals were specifically collected for the acute attack period and the intermittent period of gout. The metabolite content data were determined by non-targeted liquid chromatography-mass spectrometry, and the specific data analysis methods are as follows:

[0055] For clinical variables, data were compared using ANOVA, t-test, or rank sum test (if applicable).

[0056] 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". To identify differential metabolites, a linear system-based test was performed using the R package "DEP", and false discovery rate (FDR) correction was carried out.

[0057] Biomarker selection was performed using the R package "caret". The partial least squares (PLS) regression system was used to distinguish gout stages by metabolites, and 10-fold cross-validation was performed during system training to avoid overfitting. Briefly, all samples were randomly divided into 20 external replicates. In each external replicate, the samples were randomly divided into a 70% training set and a 30% test set for 200 internal replicates of system training and parameter adjustment. The optimal system was evaluated, and 70% of the variables were selected based on their importance and then continuously input for another iteration. For each replicate, a PLS system was constructed and the parameters were automatically adjusted. The performance of the system was evaluated using the area under the receiver operating characteristic (AUC) curve, and the variable importance was ranked for comparison between replicates. The metabolite intensities were normalized to the 0-1 range to construct the system. All statistical analyses were performed in R (4.3.1).

[0058] The results of clinical variable detection and analysis are shown in Table 1.

[0059] Table 1 Comparison of clinical characteristics between the acute phase and intercritical phase cohorts

[0060]

[0061]

[0062] As shown in the results of Table 1, there were no significant differences in clinical indicators between the two groups, including serum glucose, lipids, creatinine, and eGFR. Compared with patients in the intercritical phase, patients in the acute phase of gout had a higher proportion of acute attacks in the past year and higher serum uric acid levels.

[0063] The untargeted platform identified a total of 2070 and 1490 metabolites with known structures in the acute attack and intercritical phases, respectively, in positive and negative ion modes. Table 2 shows partial results of the differential metabolites.

[0064] Table 2 Partial results of differential metabolites between the acute attack and intercritical phases

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] We observed individual metabolic features in PCA ( Figure 1 ) and OPLSDA ( Figure 2 ). In particular, S-adenosylhomocysteine (SAH) and fructose-6-phosphate were significantly increased 15- and 12-fold in pre-attack cases. Metabolites related to reactive oxygen species, receptor activation, amino sugar, and nucleotide sugar metabolism constituted most of the significant changes. Central carbon metabolism and glyoxylate and dicarboxylate metabolism are both major energy metabolism pathways, and their mutual influence became the sub-network with the greatest changes (as shown in Figure 3 ). To select metabolites and establish a system for identifying acute gout attacks to distinguish between the acute and intercritical phases of gout, the multivariate selection algorithm MUVR based on PLS regression was applied to screen for potential biomarkers. After multiple trainings, the recognition performance of the metabolites was good, and the more variables, the higher the AUC (as shown in Figure 4 ). Among the variable metabolites, the top 10 metabolites that can be used as biomarkers are: 5-methoxytryptamine, β-D-fructose-6-phosphate, citric acid, β-alanyl-L-arginine, biotin, glycine, L-serine, S-adenosylhomocysteine, 2,3-diphosphoglycerate. According to the variable importance ranking (as shown in Table 3, Figure 5 ), the first 5 variables were used to construct the recognition system, and random forest, least absolute shrinkage and selection operator (LASSO) regression, and support vector machine (SVM) were used. It is noteworthy that the LASSO system performed well in the cohort, and most traditional clinical measurements contributed little to these recognition systems. For the system for identifying acute gout attacks and the intercritical phase, β-D-fructose 6-phosphate, citric acid, β-alanyl-L-arginine, 5-methoxytryptamine, and biotin performed well in determining subjects with acute and intercritical gout, with an AUC of 0.785 (95% CI 0.731-0.84) (as shown in Figure 6 ).

[0073] Table 3 Ranking of the importance of each biomarker

[0074] compound rank 5-methoxytryptamine 2.91785714 β-D-fructose-6-phosphate 3.69047619 citric acid 12.1071429 β-alanyl-L-arginine 15.5845238 biotin 20.0595238 glycine 25.0547619 L-serine 28.1 S-adenosylhomocysteine 29.01309524 2,3-diphosphoglyceric acid 31.28690476

[0075] Finally, the best calculation formula for identifying the acute and intercritical phases of gout is as follows:

[0076] Among them, logit(P) = 0.73 - 0.06×[5-methoxytryptamine] - 0.06×[β-D-fructose-6-phosphate] + 0.07×[citric acid] + 0.06×[β-alanyl-L-arginine] + 0.03×[biotin]. When the score > 0.5, the probability that the subject belongs to the intercritical phase of gout is higher, and when the score < 0.5, the subject will be classified as acute gout.

[0077] Example 2 Practical applications of biomarkers and systems

[0078] There were 43 and 118 subjects in the acute gout attack group and the intercritical group respectively. After serum extraction, the biomarkers and systems of the present invention were used for identification. The results of the detection are shown in Table 4.

[0079] Among them, the subjects must meet the following conditions: a) aged between 18 and 70 years old, (b) without malignant diseases, (c) estimated glomerular filtration rate (eGFR) > 15 mL / min / 1.73 m2, (d) normal liver function (alanine aminotransferase or total bilirubin < 2 times the normal value), (e) acute attack phase or intercritical phase.

[0080] The calculation formulas for sensitivity, specificity, and accuracy are as follows:

[0081] Sensitivity = (number of intercritical phase samples detected as positive) / total number of intercritical phase samples;

[0082] Specificity = (number of acute attack phase samples detected as negative) / total number of acute attack phase samples;

[0083] Accuracy = (number of intercritical phase samples detected as positive + number of acute attack phase samples detected as negative) / total number of samples.

[0084] Table 4 Detection results of the biomarkers and systems of the present invention

[0085]

[0086] The accuracy of the biomarkers and systems of the present invention in identifying the acute attack phase and intercritical phase of gout reaches 83.2%.

[0087] Specific numerical examples of a subject: A patient in the intercritical period of gout, 65 years old, with the relative content of 5-methoxytryptamine in the metabolite detection result being -0.0012, β-D-fructose-6-phosphate being 0.058, citric acid being 0.67, β-alanyl-L-arginine being 0.88, and biotin being 0.93. After calculation, the predicted risk score is 0.71, 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 of 0-1).

[0088] In summary, this population-based prospective study shows that abundant metabolites are associated with the pathogenesis and recurrent attacks of gout, beyond clinical variables. These findings may contribute to an in-depth understanding of the metabolic reprogramming pathways of gout and suggest new biomarkers to identify acute attack periods from intercritical periods or serve as new therapeutic targets.

[0089] The above are only examples of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A set of biomarkers for identifying acute and intercritical phases of gout, characterized in that, The biomarkers include 5-methoxytryptamine, β-D-fructose-6-phosphate, citric acid, β-alanyl-L-arginine, and biotin.

2. The biomarker according to claim 1, wherein The biomarkers further include one or more of glycine, L-serine, S-adenosylhomocysteine, and 2,3-diphosphoglyceric acid.

3. Use of the biomarker according to claims 1-2 in the preparation of a kit, an instrument, or a system for identifying the acute attack phase and the intermission phase of gout.

4. A kit for identifying the acute stage of gout, characterized in that, The kit includes reagents for detecting the relative content of the biomarker according to claim 1 or 2.

5. The kit according to claim 4, wherein The reagents include at least one of the following: 1) Reagents for detecting the content of the biomarker according to any one of claims 1-2; 2) Reagents for extracting the biomarker according to any one of claims 1-2 from a sample.

6. A system for identifying the acute stage of gout, characterized in that, The system includes: a detection module, at least for qualitatively and / or quantitatively detecting the content data of the biomarker according to claim 1; a calculation module, at least for calculating the quantitative data obtained by the detection module according to a judgment formula to obtain a score; a judgment module, at least for comparing the score with a threshold value to judge the gout attack stage, if the score is higher than the threshold value, it is judged as the intermission phase, and if the score is lower than the threshold value, it is judged as the acute attack phase of gout; an output module, at least for outputting the result of the judgment module.

7. The system according to claim 6, wherein The judgment formula is as follows: Score: wherein, logit(P) = 0.73 - 0.06×[5-methoxytryptamine] - 0.06×[β-D-fructose-6-phosphate] + 0.07×[citric acid] + 0.06×[β-alanyl-L-arginine] + 0.03×[biotin]; The threshold value is 0.

5.

8. 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, characterized in that, When the processor executes the computer program, the processor executes the computer program to calculate the system according to any one of claims 6-7.

9. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and is characterized in that the program is executed by a processor to calculate the system according to any one of claims 6-7.

10. A device, the device comprising an input device and a computing device, characterized in that, The input device is used for inputting the quantitative data of the biomarker according to claim 1, and the calculation device is used for calculating the system according to any one of claims 6-7.

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