A gout attack risk prediction system based on HDL-C

Abstract

The present invention provides a gout attack risk prediction system based on HDL-C, which relates to the field of bioinformatics. The system of the present invention combines the differential metabolites related to gout attacks and the variable data in the case data, and realizes the prediction of gout attack risk through a series of data processing processes such as sample detection, data calculation, data judgment and output, which helps to accelerate the treatment process.

Description

Technical Field

[0001] This application relates to the field of bioinformatics, and particularly to a gout attack risk prediction system based on HDL-C. Background Art

[0002] Gout is a chronic inflammatory disease caused by the deposition of monosodium urate (MSU) crystals, and elevated serum urate is the main risk factor for the development of gout. According to the 2017 Global Burden of Disease Study, 41 million people worldwide suffer from gout, more than twice as many as those with rheumatoid arthritis. Gout is characterized by recurrent acute arthritis called gout attacks. Gout attacks are an important problem for gout patients, and their harm is not limited to unbearable pain. In fact, a case-control study found that gout attacks are associated with a temporary increase in the incidence of cardiovascular events. After repeated attacks of gout, it may also progress to chronic gouty arthritis, specifically manifested as tophi and joint damage, resulting in poor quality of life and even severe disability. Among gout patients, there are different gout attack trajectories over time. Therefore, early diagnosis and accurate prediction of gout development are beneficial for strengthening gout management. Currently, metabolomics has become an effective tool for gout diagnosis and prediction. However, there are few related studies on gout attack prediction at present.

[0003] However, during the treatment of gout, timely prediction of acute gout attacks helps to take treatment measures in a timely manner and treat gout attacks more quickly and effectively. Therefore, there is an urgent need for a method that can predict gout attacks at present. Summary of the Invention

[0004] In order to achieve the above object, the present invention provides a gout attack prediction system based on serum high-density lipoprotein cholesterol (HDL-C), characterized in that the system includes:

[0005] A detection module for detecting the content or quantity data of markers, the markers including: differential metabolites and variable data in case data; a calculation module for at least calculating the content or quantity data obtained by the detection module according to a judgment formula to obtain a score; a judgment module for at least 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 that the gout attack risk is high, and if the score is lower than the threshold value, it is determined that the gout attack risk is lower; an output module for at least outputting the result of the judgment module.

[0006] It should be noted that when the body ingests exogenous substances or suffers from diseases, its metabolites will change, interfering with the body's homeostasis balance, and these changed metabolites are differential metabolites.

[0007] In one embodiment of the present invention, the differential metabolites include high-density lipoprotein cholesterol (HDL-C) and uric acid; the variable data in the case data includes the frequent attack frequency in the past year, subcutaneous tophi, and the course of gout onset. The detection module is used to obtain the relative concentration of the differential metabolites and / or obtain the variable data in the case data.

[0008] In an embodiment of the present invention, the judgment formula of the system is as follows:

[0009] Score

[0010] Wherein, logit(P) = 0.05 * (course of gout onset) + 1.015 * (frequent attack frequency in one year) + 0.698 * (subcutaneous tophi) + 0.345 * (uric acid) - 1.349 * (high-density lipoprotein cholesterol) - 2.852.

[0011] Wherein the threshold is 0.5.

[0012] In an embodiment of the present invention, the system further includes a data acquisition module, and the data acquisition module is used for sample extraction to obtain a sample liquid to be detected. In the embodiment of the present invention, the sample is a biological fluid sample. Preferably, it is selected from peripheral blood, venous blood, capillary blood, dried blood spot, ascites, serum, plasma, tissue fluid, body fluid, urine, saliva, etc. In the embodiment of the present invention, the sample is serum.

[0013] On the other hand, the present invention also provides the application of the system of the present invention in the preparation of instruments, devices or kits for predicting the risk of gout attack.

[0014] On the other hand, the present invention provides an information data processing terminal, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The characteristic is that when the processor executes the computer program, the processor executes the computer program to calculate the system of the present invention.

[0015] On the third aspect, the present invention provides a computer-readable storage medium, which stores a computer program. The characteristic is that the program is executed by the processor to calculate the system of the present invention.

[0016] The present invention has the following beneficial effects:

[0017] 1. The system of the present invention combines metabolite data with variable data in case data to jointly predict the risk of gout attack during the treatment of gout, and has higher prediction accuracy compared to a biomarker system based on a single differential metabolite.

[0018] 2. The system established by the present invention based on markers has established a practical quantification method for predicting acute gout attacks through formulas.

[0019] 3. The accuracy of the markers and the gout attack risk prediction system of the present invention reaches 89.5%, which can accurately predict the gout attack risk during the treatment of gout and provide assistance for the treatment of gout. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and form 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 of the present application. In the drawings:

[0021] Figure 1 It is a schematic diagram of the composition of a gout attack risk prediction system based on HDL-C in an embodiment of the present invention;

[0022] Figure 2 It is a schematic diagram of the ROC curve of the marker prediction system for gout attacks in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] 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.

[0024] For those not specified in the examples, they are carried out according to conventional conditions or conditions recommended by the manufacturer.

[0025] 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.

[0026] Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in the present invention all adopt the conventional techniques in the fields of microbiology, biochemistry, analytical chemistry, cell culture and related fields in the technical field.

[0027] In order to predict gout attacks based on serum high-density lipoprotein cholesterol (HDL-C), the embodiments of the present invention screened a high HDL-C group and a low HDL-C group from gout patients for experiments, so as to screen out differentially expressed metabolites and variable data in the cases with statistically significant differences, and further achieve the risk prediction of gout attacks. The risk prediction system in the present invention needs to detect metabolites through a biochemical analyzer to obtain the original data of metabolites, and then screen out differentially expressed metabolites; at the same time, screen out the variable data in the cases according to the patient case information, and finally analyze, calculate and judge through the differentially expressed metabolites and the variable data in the cases, and finally achieve the prediction of the risk of gout attacks and realize the function of the system of the present invention. As Figure 1 shown, the system of the present invention includes a detection module for detecting the content or quantity data of markers, where the markers include differentially expressed metabolites and variable data in case data; a calculation module for at least calculating the content or quantity data obtained by the detection module according to a judgment formula to obtain a score; a judgment module for at least comparing the score with a threshold to judge the gout attack stage, if the score is higher than the threshold, it is determined that the risk of gout attack is high, and if the score is lower than the threshold, it is determined that the risk of gout attack is low; an output module for at least outputting the result of the judgment module.

[0028] Preparation before the experiment: Recruit subjects among gout patients. All patients participating in this study need to undergo a two-week washout period. During this period, patients cannot take urate-lowering drugs and maintain a low-purine diet. The selected subjects are divided into two groups according to the serum HDL-C level at baseline. Patients with baseline HDL-C ≥ 1.16 mmol / L are included in the high HDL-C group, and patients with baseline HDL-C < 1.16 mmol / L are assigned to the low HDL-C group. All participants have a face-to-face visit every 4 weeks until the 12th week. The gout attack situation in the 4 weeks before the visit is recorded each time. Here, a gout attack is defined as the appearance of more than two signs / symptoms of redness, joint swelling and redness, and at least one of the following: ① acute attack pain, ② decreased joint mobility, ③ increased local skin temperature, ④ (participants and / or researchers) believe that anti-inflammatory drug treatment is needed. If a patient meets one of the following criteria, they will be excluded: having a history of malignant disease, brain tumor and kidney tumor, having a history of cerebral infarction or myocardial infarction, estimated glomerular filtration rate (eGFR) < 45 mL / min / 1.73m 2 , and the serum transaminase is greater than or equal to twice the upper limit of the normal range (ULN).

[0029] After screening, a total of 394 people completed the follow-up (203 people in the low HDL-C group; 191 people in the high HDL-C group).

[0030] After a two-week washout period, all participants took febuxostat 20 mg / day or benzbromarone 25 mg / day according to their regular medications until the end of the trial. If a gout attack occurred during the follow-up period, colchicine or non-steroidal anti-inflammatory drugs were prescribed for treatment, rather than prophylactic medications as usual. For patients with serum transaminases more than twice the upper limit of normal, hepatoprotective drugs were allowed to be taken.

[0031] At baseline and each follow-up, demographic data of the patients were obtained, including age, height, weight, body mass index (BMI), systolic blood pressure and diastolic blood pressure. In addition, some gout-related comorbidities were recorded, such as hypertension, diabetes, metabolic dysfunction-associated steatohepatitis (MASH), obesity, kidney stones and cardiovascular diseases. In addition, the medication conditions corresponding to these comorbidities were also important statistical indicators. Regarding the history of gout, we collected the patients' positive family history, age of gout onset, subcutaneous tophi and the number of gout attacks in the most recent year. Laboratory indicators included HDL-C, low-density lipoprotein cholesterol (LDL-C), serum urate, blood glucose, triglycerides, total cholesterol, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and estimated glomerular filtration rate (eGFR). Biochemical indicators were measured by an automatic biochemical analyzer (TBA-40FR, Toshiba Company, Japan). The baseline demographic and clinical characteristics are shown in Table 1. The baseline characteristics of the two groups of patients were compared. The patients in the low HDL-C group were younger than those in the high HDL-C group (39.0 years old vs. 45.0 years old, P = 0.003), and the former had a higher BMI (27.2 kg / m 2 to 26.3 kg / m 2 , P = 0.001). There were no differences in comorbidities such as hypertension, diabetes, MASH, obesity, kidney stones and cardiovascular diseases between the two groups, so the drug applications were comparable. Although most laboratory indicators were different between the low HDL-C group and the high HDL-C group, including elevated serum urate (9.3 mg / dL vs. 8.8 mg / dL, P < 0.001), elevated triglycerides (2.1 mmol / L vs. 1.4 mmol / L, P < 0.001). In addition, there were no statistical differences in the proportion of all attackers (85.2% vs. 80.1%, P = 0.029) and frequent gout patients (62.1% vs. 52.4%, P = 0.066) in the two groups in the past year.

[0032] Table 1 Baseline demographic and clinical characteristics

[0033]

[0034] Example 1: Extraction and processing process of samples

[0035] Sample extraction: Place the extracted venous blood in a centrifuge tube or ordinary test tube, put it into a centrifuge, centrifuge at 2000 - 2500 rpm for 1 - 2 min, and aspirate the supernatant to obtain serum.

[0036] Sample processing: Place 0.5 - 1 ml of the serum obtained by centrifugation in an automatic biochemical analyzer (TBA - 40FR, Toshiba Company, Japan), set the parameters to the default values, conduct the analysis, and obtain the off - machine data.

[0037] Example 2: Screening of markers and design of the system

[0038] For this invention, clinical variables and metabolite content data of individuals were collected for the markers for predicting gout attacks during uric acid - lowering treatment in this example; the results are shown in Table 1. The metabolite content data was measured by an automatic biochemical analyzer, and the specific data analysis method is as follows:

[0039] IBM SPSS 26.0, GraphPad Prism V.9, and R 4.3.2 were used to analyze the variable data in the statistically significant metabolites and case data (a P - value < 0.05 was considered statistically significant). Continuous variables were expressed as mean (standard deviation) or median (interquartile range), and categorical variables were expressed as numbers (percentages). The independent - samples t - test or Wilcoxon signed - rank test was used to compare continuous variables between two groups, and the chi - square test was used to compare categorical variables. The Kaplan–Meier curve was used to estimate the attack - free and recurrent gout attack - free survival rates of participants in different groups. Cox regression analysis was applied to determine the risk factors associated with gout attacks, and the results are shown in Table 2.

[0040] Table 2 Cox regression analysis of factors related to gout attacks

[0041]

[0042] As can be seen from Table 2: The P - values of high - density lipoprotein cholesterol (HDL - C), blood uric acid, frequency of frequent attacks in the past year, subcutaneous tophi, and gout onset duration were < 0.05, which was statistically significant, providing a reference basis for predicting gout attacks during uric acid - lowering treatment. Among them, high - density lipoprotein cholesterol (HDL - C) and blood uric acid need to calculate the relative content.

[0043] To evaluate the predictive effect of HDL - C selected from the Logistic regression analysis on gout attacks, receiver operating characteristic (ROC) curve analysis must be conducted, and the results are as Figure 2As shown, serum HDL-C can predict gout attacks, with an AUC of 0.62 (95% CI = 0.57 - 0.68). As a well-known factor related to gout attacks, serum uric acid can also predict a comparable AUC of 0.66 (95% CI = 0.61 - 0.72). When combining serum HDL-C with serum uric acid, the course of gout onset, subcutaneous tophi, and the frequency of frequent attacks (≥2) in the most recent year, the predictive performance is significantly improved, with an AUC of 0.75 (95% CI = 0.70 - 0.80).

[0044] A Logistic regression equation was established using Logistic regression analysis:

[0045] Formula

[0046] logit(P) = 0.05 * (disease duration) + 1.015 * (frequency of recurrent attacks within one year) + 0.698 * (palpable tophi) + 0.345 * (serum urate) - 1.349 * (high-density lipoprotein cholesterol) - 2.852.

[0047] Among them, if the score calculated according to the formula is higher than 0.5, it is determined that the risk of gout attack is high; if the score is lower than 0.5, it is determined that the risk of gout attack is low.

[0048] Example 3: Practical application of the system of the present invention

[0049] For 100 subjects in each of the low HDL-C group and the high HDL-C group, venous blood was drawn and identified using the markers and system of the present invention. The results are shown in Table 3 through detection.

[0050] Among them, the calculation formulas for sensitivity, specificity, and accuracy are as follows:

[0051] Sensitivity = (number of samples in the high HDL-C group detected as positive) / total number of samples in the high HDL-C group;

[0052] Specificity = (number of samples in the low HDL-C group detected as negative) / total number of samples in the low HDL-C group;

[0053] Accuracy = (number of samples in the high HDL-C group detected as positive + number of samples in the low HDL-C group detected as negative) / total number of samples.

[0054] Table 3 Detection results of the system of the present invention

[0055]

[0056] As can be seen from Table 3, the accuracy of the markers and system of the present invention in predicting the risk of gout attack reaches 89.5%.

[0057] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the embodiments of devices, apparatuses, and non-volatile computer storage media, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the relevant parts of the method embodiments for the relevant content.

[0058] The specific embodiments of this specification are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] The above description is only for one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, various modifications and changes can be made to one or more embodiments of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A gout acute attack risk prediction system based on serum high-density lipoprotein cholesterol (HDL-C) during uric acid-lowering treatment, characterized in that, The system includes: A data acquisition module for sample extraction to obtain a sample to be detected, where the sample is a biological fluid sample; a detection module for detecting the content or quantity data of markers, where the markers include differential metabolites and variable data in case data, and the differential metabolites are high-density lipoprotein cholesterol (HDL-C) and blood uric acid; the variable data in the case data are the frequent attack frequency within one year, subcutaneous tophi, and the course of gout onset, and the detection module is used to obtain the relative concentration of the differential metabolites and / or obtain the variable data in the case data of the patient; A calculation module for at least calculating the content or quantity data obtained by the detection module according to a judgment formula to obtain a score, and the judgment formula is as follows: Score Where logit(P) = 0.05 * (course of gout onset) + 1.015 * (frequent attack frequency within one year) + 0.698 * (subcutaneous tophi) + 0.345 * (blood uric acid) - 1.349 * (high-density lipoprotein cholesterol) - 2.852; A judgment module for at least comparing the score with a threshold to judge the gout attack stage. If the score is higher than the threshold, it is determined that the gout attack risk is high; if the score is lower than the threshold, it is determined that the gout attack risk is low, and the threshold is 0.5; An output module for at least outputting the result of the judgment module; The sensitivity of the system reaches 92%, the specificity reaches 87%, and the accuracy reaches 89.5%.

2. The system according to claim 1, wherein The system is used for preparing one or more of an instrument, a device, or a kit for predicting the risk of gout attack.

3. 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 claim 1.

4. A computer-readable storage medium storing a computer program, characterized in that, The program is executed by the processor to calculate the system according to claim 1.

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