A biomarker for predicting the risk of gout attack and its application

Abstract

The present invention provides a biomarker for predicting the risk of gout attack and its application, which relates to the field of biomedical technology. The present invention provides a new biomarker for predicting the recurrence of gout, which can realize the prediction of gout attack and help to carry out preventive treatment.

Description

Technical Field

[0001] The present application relates to the field of biomedical technology, and in particular to a biomarker for predicting the risk of gout attacks and its application. Background Art

[0002] Gout is a progressive, inflammatory and metabolic disease. Gout attacks can easily lead to adverse outcomes, such as cardiovascular events and venous thromboembolism. Repeated attacks of gout may also progress to chronic gouty arthritis, manifested as tophi and joint damage, leading to poor quality of life and even severe disability. In gout patients, there are different trajectories of gout attacks over time. Therefore, early diagnosis and accurate prediction of gout development are beneficial for strengthening gout management. At present, metabolomics has become an effective tool for the diagnosis and prediction of gout. However, there are currently few studies on the prediction of gout attacks.

[0003] Currently, serum uric acid values ​​are often used as a reference value for gout. Elevated serum uric acid is indeed associated with an increased risk of gout attacks, but it cannot fully predict subsequent attacks in individual patients. Identifying predictors of recurrent attacks is clinically important because this may allow more targeted anti-inflammatory preventive treatment, especially when initiating uric acid-lowering therapy. To date, matrix metalloproteinase (MMP) activity, glycoprotein CA72-4, and the metabolite glycoprotein acetyl have become potential biomarkers for gout attacks and recurrences. In addition, increasing evidence shows that different metabolomic features exist at different stages of the disease, suggesting that metabolic biomarkers have the clinical potential to improve the risk stratification of gout patients.

[0004] Therefore, in order to promptly inhibit the progression of gout, it is necessary to identify individuals at risk of onset in order to optimize treatment strategies to achieve early detection and early treatment. Summary of the invention

[0005] In response to the above problems, the purpose of the present invention is to provide a biomarker for predicting the risk of gout attacks and its application. Specifically, the relative abundance of metabolites is determined by non-targeted liquid chromatography-mass spectrometry, the correlation between each metabolite and recurrent attacks is analyzed, and a machine learning model is performed to determine the biomarkers related to attack prediction.

[0006] On the one hand, the present application provides a biomarker for predicting the risk of gout attack, wherein the biomarker includes fructose-6-phosphate (Beta-D-Fructose 6-phosphate), 5-methoxytryptamine (5-Methoxytryptamine), S-adenosylhomocysteine ​​(S-Adenosylhomocysteine), glutathione (Glutathione), and N-acetylneuraminic acid (N-Acetylneuraminic acid).

[0007] Furthermore, the biomarkers also include: salicylic acid, UDP-4-deoxy-4-formamido-β-L-arabinopyranose, 3-oxalylmalic acid, 2,3-diphosphoglyceric acid, inosine triphosphate, propyl phenylalanine, LPC 15:0-d5, UDP-3-glucosamine, and riboic acid.

[0008] In a preferred embodiment, the biomarkers include: fructose-6-phosphate (Beta-D-Fructose6-phosphate), 5-methoxytryptamine (5-Methoxytryptamine), S-adenosylhomocysteine ​​(S-Adenosylhomocysteine), glutathione (Glutathione), N-acetylneuraminic acid (N-Acetylneuraminic acid).

[0009] On the other hand, the present application also provides the use of the biomarker in the preparation of a kit for predicting the risk of gout attacks.

[0010] On the other hand, the present application also provides a kit for predicting the risk of gout attacks, wherein the kit includes reagents for specifically detecting the content data of the biomarkers.

[0011] In a preferred embodiment, the content data refers to the relative percentage content of the metabolite in the sample, measured in mass percentage.

[0012] In a preferred embodiment, non-targeted liquid chromatography-mass spectrometry is used to determine the relative content of metabolites. It can be understood by those skilled in the art that the content of biomarkers in a sample can be determined by any known method to obtain its content data, and this is not limited here.

[0013] On the other hand, the present application also provides a model for predicting the risk of gout attack, the model comprising: a detection module, at least for obtaining the content data of the biomarker in the sample; a calculation module, at least for substituting the content data of the biomarker into the judgment formula for calculation to obtain a score; a judgment module, at least for comparing the score with a threshold to judge the risk of gout attack, if the score is higher than the threshold, it is judged as high risk, and if the score is lower than the threshold, it is judged as low risk.

[0014] Furthermore, the judgment formula is as follows:

[0015] Score

[0016] Among them, logit(P)=0.68-0.13×[content data of fructose-6-phosphate]+0.04×[content data of 5-methoxytryptamine]-0.09×[content data of S-adenosylhomocysteine]-0.11×[content data of glutathione]+0.04×[content data of N-acetylneuraminic acid].

[0017] Furthermore, the threshold is 0.5.

[0018] Furthermore, the prediction period of the risk of gout attack is 0 to 1 year.

[0019] The biomarkers in this application are mainly used to predict gout attacks within one year.

[0020] In a preferred embodiment, when the score is greater than 0.5, it can be considered that the risk of gout attack within 1 year is high, and when the score is less than 0.5, it can be considered that the risk of gout attack within 1 year is low.

[0021] In a preferred embodiment, the sample is a liquid sample, and the biomarker is obtained from the liquid sample, and the liquid sample is blood of a mammal and other liquid samples of biological origin, such as peripheral blood, serum, plasma, ascites, urine, cerebrospinal fluid, sputum, saliva, etc.; preferably, peripheral blood and serum. The mammals include rats, mice and humans; preferably, humans; preferably, the humans have a history of gout.

[0022] On the other hand, the present application also provides an information data processing terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor executes the computer program to calculate the model.

[0023] The information data processing terminal includes: a processor, a memory, and a communicator; the memory is used to store computer programs, the communicator is used to communicate with external devices, and the processor is used to execute the computer program stored in the memory so that the terminal executes the model.

[0024] On the other hand, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which is executed by a processor to calculate the model.

[0025] The computer program may be stored in a computer-readable storage medium. When the program is executed, the model is executed; the computer-readable storage medium may include, but is not limited to, a floppy disk, an optical disk, a CD-ROM (Compact Disc Read Only Memory), a magneto-optical disk, a ROM (Read Only Memory), a RAM (Random Access Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), a magnetic card or an optical card, a flash memory, or other types of media / machine-readable media suitable for storing machine-executable instructions. The computer-readable storage medium may be a product that is not connected to a computer device, or a component that is connected to a computer device for use.

[0026] In specific implementations, the computer program is a routine, program, object, component, data structure, etc. that performs specific tasks or implements specific abstract data types.

[0027] On the other hand, the present application also provides an apparatus, comprising an input device and a computing device, wherein the input device is used to input the biomarker content data, and the computing device is used to calculate the model.

[0028] The present invention has the following beneficial effects:

[0029] This application provides new biomarkers for predicting recurrent gout attacks, which can predict gout attacks and help accelerate treatment progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0031] Figure 1 This is a schematic diagram of the PCA principal component analysis results of metabolites in the seizure group and the non-seizure group in the embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of the OPLSDA analysis results of metabolites in the seizure group and the non-seizure group in the embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of the volcano plot analysis results of metabolites in the seizure group and the non-seizure group in the embodiment of the present invention;

[0034] Figure 4 This is a schematic diagram of a metabolite difference network in an embodiment of the present invention;

[0035] Figure 5 This is a schematic diagram of the metabolite enrichment pathway analysis results of the seizure group and the non-seizure group in the embodiment of the present invention;

[0036] Figure 6 This is a result schematic diagram of the relationship between the number of metabolite variables and AUC in the attack group and the non-attack group in the embodiment of the present invention;

[0037] Figure 7 Schematic diagram of the AUC curve of the marker machine learning model of the present invention in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to more clearly explain the overall concept of the present application, the following is described in detail in conjunction with the accompanying drawings of the specification by way of embodiment. In the following description, a large number of specific details are provided 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.

[0039] If no specific conditions are specified in the examples, the experiments were carried out according to conventional conditions or 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 art.

[0042] Subjects in the inter-gout period were collected and followed up prospectively for one year to distinguish between the attack group (at least one attack within one year) and the non-attack group. Clinical information and serum samples were collected at the first visit. The relative abundance of metabolites was determined by non-targeted liquid chromatography-mass spectrometry. The differential metabolites in the two groups were counted, the correlation between each metabolite and recurrent attacks was analyzed, and a machine learning model was performed to determine the biomarkers associated with attack prediction.

[0043] All subjects met the 2015 American College of Rheumatology (ACR) gout classification criteria, and the inclusion criteria were: (a) age between 18 and 70 years, (b) no malignant disease, and (c) estimated glomerular filtration rate (eGFR) > 15 mL / min / 1.73 m 2 , (d) normal liver function (alanine aminotransferase or total bilirubin <2 times the normal value), (e) experienced at least one attack and was currently in an intermittent period (a symptom-free period after or between gout attacks). All patients received conventional uric acid-lowering therapy (ULT) and were given colchicine or nonsteroidal anti-inflammatory drugs (no more than one month) when a gout attack occurred. During the 1-year follow-up, patients in the critical interval were required to undergo face-to-face visits when an attack occurred to record the number of attacks, excluding cases of poor compliance and long-term use of anti-inflammatory drugs. All subjects provided written informed consent, and this protocol was approved by the Ethics Committee of the Affiliated Hospital of Qingdao University.

[0044] During the follow-up period, the cases in the attack group had at least one acute attack within 1 year, and the cases in the non-attack group had no gout attack.

[0045] Subject data including demographics, physical examination (including subcutaneous administration), medications, and serum samples were collected at the first visit. 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 Roche automatic biochemical analyzer in Germany. Serum samples were processed according to the procedure before metabolite analysis.

[0046] The sample processing method involved in the following embodiment is as follows:

[0047] Mix 50L of serum sample with 200L of low-temperature methanol (containing fluorouracil internal standard) at 4°C and incubate for 15 minutes to precipitate protein. Collect the supernatant and store at -80°C until analysis. Divide the serum sample into equal parts to prepare quality control samples.

[0048] Non-targeted LC-MS processing conditions:

[0049] Data were collected in data-dependent acquisition (DDA) mode using UHPLC (Nexera UHPLC LC-30A, SHIMADZU Technologies, Japan) coupled to a quadruple time-of-flight (QTOF) mass spectrometer (AB 6600TripleTOF, SCIEX, CANADA). The sample (2 μL) was eluted on a Waters ACQUITY UPLC BEH Amide column (100 mm) at 25 °C and 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. The sample was eluted with the following gradient: 0-0.5 min, 95% B; 0.5-7 min, 95% B~65% B; 7-8 min, 65% B~40% B; 8-9 min, 40% B; 9-9.1 min, 40% B~95% B; 9.1-12 min, 95% B. The mass spectrometer was operated in information-dependent acquisition (IDA) mode with a certain 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℃; 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.

[0050] The raw data (.wiff files) were converted into mzML format using ProteoWizard software (version 3.0.23132, https: / / proteowizard.sourceforge.io / ). Metabolomics analysis was performed using the R package “XCMS”. According to MSI (Metabolomic Standards Initiative), MS / MS spectra of each MS feature were extracted and mapped using public databases including the Human Metabolome Database (HMDB), MassBank of North America (MoNA), Massbank, and the Global Natural Products Society Molecular Network (GNPS). Features with QC coefficient of variation > 30% were excluded.

[0051] Example 1 Screening of biomarkers and construction of models

[0052] In this example, the intermittent subjects were followed up to determine the different metabolites between the gout attack group and the one-year no-attack group. The clinical variables and metabolite content data of the individuals were specifically collected. The metabolite content data were determined by non-targeted liquid chromatography-mass spectrometry, and the specific data analysis method was as follows:

[0053] For clinical variables, data were compared by ANOVA, t-test, or rank-sum test.

[0054] For metabolite content data, unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLSDA) were performed using the R software package “MixOmics” and “ropls”. To identify differential metabolites, a linear model-based test was performed using the R software package “DEP” with false discovery rate (FDR) correction.

[0055] Biomarker selection was performed by the R software package "MUVR". A partial least squares (PLS) regression model was used to distinguish gout recurrence groups from non-attack groups by metabolites, and 10-fold cross-validation was performed in model training to avoid model overfitting. In brief, all samples were randomly divided into 20 external replicates, and in each external replicate, the sample replicates were randomly divided into 70% training sets and 30% test sets for model training and parameter adjustment 200 internal replicates. The optimal model was evaluated, and 70% of the variables were selected based on their importance, and the selected variables were continuously entered for another iteration. For each replicate, a PLS model was constructed and parameters were automatically adjusted. Model performance was evaluated using the area under the receiver operating characteristic (AUC) curve, and variable importance was ranked for comparison between replicates. Metabolite intensities were normalized to a range of 0-1 to construct the model. All statistical analyses were performed in R (4.3.1).

[0056] The results of clinical variable testing and analysis are shown in Table 1 .

[0057] Table 1 Characteristics of gout at each stage

[0058]

[0059] For categorical variables, data are presented as n (%). For continuous variables, values ​​are presented as mean (SD) or median (IQR) as appropriate. BMI: body mass index; FBG: fasting blood glucose; ALT: alanine aminotransferase; AST: aspartate aminotransferase; eGFR: estimated glomerular filtration rate. * indicates p < 0.05

[0060] As shown in Table 1, there were no significant differences in biomarkers between the two groups, including serum glucose, lipids, creatinine, and eGFR. Among the 127 patients in the borderline group, 48 had gout attacks within 1 year of follow-up, and the remaining 79 did not have gout attacks. Compared with patients without attacks, patients in the attack group had more frequent acute attacks in the past year and increased serum uric acid levels.

[0061] The untargeted platform identified a total of 2070 and 1490 metabolites of known structure in positive and negative ion modes, respectively. We observed that the individual metabolic features in PCA ( Figure 1 ) and OPLSDA( Figure 2 ). For the case of recurrent gout attacks, 127 intermittent gout cases were eligible, of which 48 had at least one attack during the 1-year follow-up. A total of 146 metabolites were significantly altered between the attack group and the non-attack group (FDR < 0.05), as shown in Table 2.

[0062] Table 2 Metabolite composition

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] In Table 2, S-adenosylhomocysteine ​​(SAH) and fructose-6-phosphate were significantly increased by 15- and 12-fold, respectively. Metabolites related to reactive oxygen species, receptor activation, amino sugar, and nucleotide sugar metabolism comprised the majority of significant changes ( Figure 3 The interaction between central carbon metabolism and carbon metabolism, glyoxylate and dicarboxylic acid metabolism, are both major energy sources and become the subnetwork with the largest changes ( Figure 4 ).

[0072] In order to select metabolites and establish a prediction model, a multivariate selection algorithm based on PLS regression, MUVR, was applied to screen potential biomarkers. After multiple trainings, the prediction performance of metabolites was good, and the more variables, the higher the AUC ( Figure 5 ). Based on the variable importance ranking, the top 5 predictors (Table 3) were used to construct prediction models using random forest, least absolute shrinkage and selection operator (LASSO) regression, and support vector machine (SVM). The LASSO model performed well in the cohort, and it is worth noting that most traditional clinical measurements made little contribution to these prediction models ( Figure 6) For the seizure prediction model, fructose-6-phosphate, 5-methoxytryptamine, S-adenosylhomocysteine, glutathione, and N-acetylneuraminic acid performed well in identifying subjects at risk of seizures, with an AUC of 0.760 (95% CI 0.663-0.857) (e.g. Figure 7 ).

[0073] Table 3

[0074] Compound Rank Beta-D-fructose 6-phosphate 3.036904762 Glutathione 16.1035714 N-Acetylneuraminic acid 25.0404762 5-Methoxytryptamine 10.0166666666667 S-adenosylhomocysteine 12.4619047619048

[0075] The best model for detecting the risk of gout attacks was obtained as follows:

[0076]

[0077] logit(P)=0.68-0.13×[Beta-D-Fructose6-phosphate]+0.04×[5-Methoxytryptamine]-0.09×[S-Adenosylhomocysteine]-0.11×[Glutathione]+0.04×[N-Acetylneuraminic acid]

[0078] When the prediction score is >0.5, the subject has a higher probability of belonging to the non-attack group (no gout attack within one year), while when the prediction score is <0.5, the subject will be classified as the attack group (gout attack within one year).

[0079] In summary, in this application, unique metabolites associated with gout recurrence were identified in serum samples of patients in the intermittent period, mainly involved in oxidative stress pathways and glucose metabolism. It is worth noting that S-adenosylhomocysteine ​​(SAH), an amino acid derivative, is a regulator in multiple metabolic pathways. SAH has also been found to be a risk factor for cardiovascular disease, suggesting that epigenetic reprogramming occurs. It suggests that epigenetic modifications are associated with frequent attacks during the development of gout. In fact, gout is described as a disease affected by trained immunity, in which exposure to urate crystals can alter the response of innate immune cells. In addition, 1,6-digalloyl-β-D-pyranose, UDP-3-ketoglucose, (Z,Z,Z)-3,6,9-dodecatriene-1-ol, and primisulfuron-methyl sodium levels, which are involved in glucose and lipid metabolism, are candidate biomarkers for distinguishing patients in the attack group from those in the non-attack group. Metabolites reflect the endometrial metabolic activity of the underlying pathophysiological process and also help to affect the clinical phenotype. We believe that the application of these useful metabolites could contribute to improved risk stratification of gout patients and more precise flare prevention.

[0080] In this example, through strict variable selection, abundant metabolites were screened as biomarkers, and then a diagnostic model based on a machine learning algorithm was established to achieve effective prediction in the cohort. From the priority ranking of β-d-fructose 6-phosphate, glucose homeostasis was significantly associated with gout inflammation. In addition, the attack risk prediction model confirmed that metabolic characteristics predicted clinical outcomes. Among them, we emphasized the role of S-adenosylhomocysteine ​​and glutathione, which are essential for maintaining immune homeostasis.

[0081] In conclusion, this prospective population-based study demonstrates that abundant metabolites are associated with gout pathogenesis and recurrent flares. These findings may provide insights into metabolic reprogramming pathways in gout and suggest new biomarkers that may predict acute flares or serve as new therapeutic targets.

[0082] Example 2 Application of biomarkers and models

[0083] In this example, non-targeted liquid chromatography-mass spectrometry was used to detect the contents of fructose-6-phosphate, 5-methoxytryptamine, S-adenosylhomocysteine, glutathione, and N-acetylneuraminic acid in the sample, and the sample was brought into the optimal model of gout attack risk in Example 1 for risk prediction. The prediction results were compared with the actual case conditions of the sample, and the results are shown in Table 4.

[0084] The calculation formulas for sensitivity, specificity and accuracy are as follows:

[0085] Sensitivity = (number of samples with positive test results and no gout attack within one year) / total number of samples with no gout attack within one year;

[0086] Specificity = (number of samples that tested negative for mutation and had gout attacks within one year) / total number of samples that had gout attacks within one year;

[0087] Accuracy = (the number of samples that tested positive for mutation and had no gout attacks within one year + the number of samples that tested negative for mutation and had gout attacks within one year) / total number of samples.

[0088] Table 4

[0089]

[0090] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A biomarker for predicting the risk of gout attack, characterized in that: The biomarkers include fructose-6-phosphate, 5-methoxytryptamine, S-adenosylhomocysteine, glutathione, and N-acetylneuraminic acid.

2. The biomarker according to claim 1, characterized in that The biomarkers also include: salicylic acid, UDP-4-deoxy-4-formamido-β-L-arabinopyranose, 3-oxalylmalic acid, 2,3-diphosphoglyceric acid, inosine triphosphate or one or more thereof.

3. Use of the biomarker according to claim 1 or 2 in the preparation of a kit for predicting the risk of gout attack.

4. A device for predicting the risk of gout attack, characterized in that: The device comprises: a detection module, at least for obtaining the content data of the biomarker according to claim 1 or 2 in the sample, wherein the content data is the relative percentage content of the biomarker in the sample in terms of mass percentage; a calculation module, at least for substituting the content data of the biomarker into a judgment formula for calculation to obtain a score; a judgment module, at least for comparing the score with a threshold value to judge the risk of gout attack, wherein a score higher than the threshold value is judged as a high risk, and a score lower than the threshold value is judged as a low risk, wherein the threshold value is 0.5; The judgment formula is as follows: Score ; Among them, logit(P)=0.68-0.13×[content data of fructose-6-phosphate]+0.04×[content data of 5-methoxytryptamine]-0.09×[content data of S-adenosylhomocysteine]-0.11×[content data of glutathione]+0.04×[content data of N-acetylneuraminic acid].

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