Gout tophi markers and uses thereof

Abstract

The application discloses a metabolic marker combination for predicting gouty tophi gout, comprising N-(p-toluenesulfonyl)-L-phenylalanine, [3,4,5-trihydroxy-6-(hydroxymethyl)oxacyclo-2-yl]10-[3-acetylamino-6-[[4,5-dihydroxy-3-(3,4,5-trihydroxyoxacyclo-2-yl)oxyoxacyclo-2-yl]oxymethyl]-4,5-dihydroxyoxacyclo-2-yl]oxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecahydrochrysen-4a-carboxylate, 8'-apo-beta-carotenol, 3,3,5-trimethyl-1,2,3,4-tetrahydro-gamma-carboline-1-spiro-4'-(1'-methyl)piperidine, dehydroepiandrosterone, quinolinic acid, 2'-deoxyguanosine 5'-monophosphate, tectoridin and C-13(2)-carboxyl pyrochlorophyllin a. The application also provides a kit containing the above marker detection reagent, a prediction model construction method, a prediction system, a terminal and a device. According to verification of clinical serum samples, the model AUC reaches 0.738, has good distinguishing ability and can realize non-invasive and efficient prediction.

Description

Technical Field

[0001] This invention belongs to the field of biomedical detection technology, specifically relating to tophi markers and their applications. Background Technology

[0002] Gout is a crystalline arthropathy caused by elevated blood uric acid levels due to purine metabolism disorders and / or impaired uric acid excretion, which in turn leads to the deposition of monosodium urate (MSU) crystals in joints and soft tissues. Based on disease progression, gout can be divided into acute attack phases, intercritical phases, and chronic tophaceous phases. Tophaceous gout is a severe stage of gout, typically characterized by the accumulation of urate crystals in joints, synovium, tendons, skin, and cartilage, forming subcutaneous nodules known as "gouty tophi."

[0003] Tophaceous gout not only leads to joint structural damage, deformity, and functional impairment, but also significantly increases the risk of cardiovascular events, chronic kidney disease, and death. Literature reports that the mortality risk of patients with tophaceous gout is significantly higher than that of patients without tophaceous gout. Furthermore, the formation of tophi often indicates a longer disease course, poor treatment adherence, or inadequate uric acid-lowering therapy, placing a heavy burden on patients' quality of life and the public health system.

[0004] Currently, the clinical diagnosis of tophaceous gout mainly relies on the following methods: (1) Physical examination: Observe or touch subcutaneous nodules, but this method can only identify tophi when they have formed and have a certain size, and lacks early warning capability; (2) Imaging techniques: such as dual-energy CT, ultrasound, and X-ray, although they can assist in diagnosis, have limitations such as expensive equipment, radiation exposure, and difficulty in large-scale screening. (3) Joint fluid aspiration: This is an invasive procedure with low patient acceptance and is not suitable for asymptomatic patients or those without obvious nodules.

[0005] Existing research indicates that advanced age, renal insufficiency, and diuretic use are clinical risk factors for tophi formation, but the underlying metabolic mechanisms (such as glycolysis and lipid metabolism reprogramming) and their association with tophi formation have not been systematically elucidated. In recent years, metabolomics technology has been used to explore metabolic differences between gout and hyperuricemia, but most existing studies have not distinguished between tophaceous and non-tophaceous gout, nor have they constructed machine learning models that can be used for clinical prediction.

[0006] Therefore, there is an urgent need to discover a combination of peripheral blood metabolic biomarkers that can be used to predict tophaceous gout and to construct a non-invasive, efficient, and quantifiable predictive model to achieve risk stratification of gout patients, guide early intervention, and delay or prevent the formation of tophi. Summary of the Invention

[0007] The purpose of this invention is to provide a set of metabolic biomarkers that can be used to predict whether gout patients will develop tophaceous gout, and to provide a reagent kit, a method for constructing a prediction model, a prediction system, a terminal, a storage medium and a device based on the biomarkers.

[0008] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a combination of metabolic biomarkers for predicting tophaceous gout, comprising the following nine metabolites: N-(p-Toluenesulfonyl)-L-phenylalanine; [3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]10-[3-acetamido-6-[[4,5-dihydroxy-3-(3,4,5-trihydroxyoxan-2-yl)oxyoxan-2-yl]oxymethyl]-4,5-dihydroxyoxan-2-yl]oxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecanoyl-4a-carboxylate ([3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]) 10-[3-acetamido-6-[[4,5-dihydroxy-3-(3,4,5-trihydroxyoxan-2-yl)oxyoxan-2-yl]oxymethyl]-4,5-dihydroxyoxan-2-y l]oxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecahydropicene-4a-carboxylate); 8'-apo-β-carotenol; 3,3,5-trimethyl-1,2,3,4-tetrahydro-gamma-carboline-1-spiro-4'-(1'-methyl)piperidine(2-d); dehydroepiandrosterone; quinolinic acid; 2'-deoxyguanosine 5'-monophosphate; Tecostanine; C-13(2)-Carboxypyropheophorbide a.

[0009] In a second aspect, the present invention provides a kit comprising reagents for detecting the relative amounts of the metabolic markers.

[0010] Furthermore, the reagent includes at least one of the following: (1) Reagents for detecting the content of the aforementioned metabolic markers; (2) A reagent for extracting the metabolic markers from the sample.

[0011] In a preferred embodiment of the present invention, the sample is treated by protein precipitation to extract the biomarker; the relative content of the biomarker is determined by non-targeted liquid chromatography-mass spectrometry.

[0012] Furthermore, the kit includes at least one of the following: a standard for each metabolic biomarker, a sample pretreatment reagent, a chromatographic separation reagent, a mass spectrometry detection reagent, and a quality control product.

[0013] In one specific embodiment of the present invention, the standard includes a high-purity standard for each metabolite (such as N-(p-toluenesulfonyl)-L-phenylalanine, dehydroepiandrosterone, quinolinic acid, etc.) and / or an isotopic internal standard for quantification (such as a deuterated or ¹³C-labeled analogue).

[0014] Optionally, the sample pretreatment reagent includes: Protein precipitants: methanol, acetonitrile, ammonium formate solution, etc. Extraction agents: methyl tert-butyl ether (MTBE), ethyl acetate, etc. (used for lipid-soluble metabolites such as carotene and dehydroepiandrosterone); Derivatization reagents (if it is necessary to improve detection sensitivity): such as dansyl chloride, Girard reagent, etc.

[0015] Optionally, the chromatographic separation reagent includes a mobile phase solution and a chromatographic column.

[0016] Optionally, quality control materials may include mixed control serum (low, medium, and high concentration samples of known concentration) and / or blank matrix (such as serum with metabolites removed).

[0017] Those skilled in the art will understand that the content of biomarkers in a sample can be extracted or measured by any known method to obtain data on their relative content, and no further limitations are imposed here.

[0018] In a third aspect, the present invention provides the application of the aforementioned combination of metabolic markers in the preparation of reagent kits, instruments, and systems for predicting tophaceous gout.

[0019] In a fourth aspect, the present invention provides a method for constructing a predictive model for tophaceous gout, comprising the following steps: Serum samples were collected from gout patients and divided into a tophaceous gout group and a non-tophaceous gout group. Metabolomics analysis was performed on serum samples to obtain metabolite profiles; Screen for metabolites with significantly different expression levels between the two groups; LASSO regression was used to screen key metabolites; Based on the screened metabolites, a prediction model was constructed using the XGBOOST algorithm; Evaluate model performance and calculate AUC value.

[0020] Furthermore, the metabolomics detection was performed using liquid chromatography-mass spectrometry (LC-MS).

[0021] In a fifth aspect, the present invention provides a system for predicting tophaceous gout, comprising: The data input module is used to input the expression levels of the metabolic biomarkers in the sample to be tested; The model calculation module is loaded with the XGBOOST prediction model; The results output module is used to output the risk probability of whether the patient has tophaceous gout.

[0022] Furthermore, the method for constructing the XGBOOST prediction model includes the following steps: Serum samples were collected from gout patients and divided into a tophaceous gout group and a non-tophaceous gout group. Metabolomics analysis was performed on serum samples to obtain metabolite profiles; Screen for metabolites with significantly different expression levels between the two groups; LASSO regression was used to screen key metabolites; Based on the screened metabolites, a prediction model was constructed using the XGBOOST algorithm; The area under the receiver operating characteristic curve (AUC) is calculated to evaluate model performance. In a preferred embodiment, the sample is a biological fluid sample from which the biomarker is obtained. The sample may be blood, saliva, ascites, urine, cerebrospinal fluid, sputum, tissue fluid, etc.; preferably, venous blood, peripheral blood, serum, plasma, dried blood spots, etc. In the embodiments of the present invention, the sample is serum.

[0023] In a sixth aspect, this application also provides an information data processing terminal, the information data processing terminal including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein 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 computer programs, the communicator is used to communicate with external devices, and the processor is used to execute the computer programs stored in the memory so that the terminal executes the system.

[0025] In a seventh aspect, this application also provides a computer-readable storage medium storing a computer program that is executed by a processor to compute the system.

[0026] The computer program may be stored in a computer-readable storage medium. When executed, the program executes the system. The computer-readable storage medium may include, but is not limited to, floppy disks, optical disks, CD-ROMs (Read-Only Optical Disk Memory), magneto-optical disks, ROMs (Read-Only Memory), RAMs (Random Access Memory), EPROMs (Erasable Programmable Read-Only Memory), EEPROMs (Electrically Erasable Programmable Read-Only Memory), magnetic cards or optical cards, 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 not connected to a computer device or a component used with a computer device.

[0027] In practice, the computer program is a routine, program, object, component, data structure, etc., that performs a specific task or implements a specific abstract data type.

[0028] In an eighth aspect, the present invention provides an apparatus comprising an input device and a computing device, characterized in that the input device is used to input quantitative data of the biomarker, and the computing device is used to calculate the system.

[0029] The beneficial effects of the present invention include, but are not limited to: This invention provides for the first time a combination of peripheral blood metabolic biomarkers specifically for predicting tophaceous gout, comprising nine metabolites. Based on these biomarkers, the prediction model constructed using XGBOOST achieved an AUC of 0.738 (95% CI: 0.592–0.884) on the validation set, demonstrating good discriminative ability. The test sample is serum, making it non-invasive, convenient, and suitable for large-scale screening and dynamic monitoring. A complete kit, prediction system, terminal, storage medium, and device are provided, showing promising prospects for industrial application. Attached Figure Description

[0030] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a volcano diagram of differential metabolites in an embodiment of the present invention.

[0031] Figure 2 This is a metabolomics data analysis of tophaceous gout in an embodiment of the present invention. Figure A shows the score of the orthogonal partial least squares discriminant analysis (OPLS-DA) model. Figure B is a schematic diagram of the pathway enrichment analysis results of tophaceous metabolites.

[0032] Figure 3 This is a schematic diagram of the network analysis results of significantly disrupted metabolic pathways between tophaceous gout and non-tophaceous gout in an embodiment of the present invention.

[0033] Figure 4 This is a schematic diagram of the LASSO coefficient path diagram results in an embodiment of the present invention. The horizontal axis is the logarithm of the regularization parameter, Log Lambda, and the vertical axis is the coefficient of each metabolite.

[0034] Figure 5 This is a cross-validation error graph in this embodiment of the invention. The horizontal axis is the logarithm of the regularization parameter, Log Lambda, and the vertical axis is the prediction error of the model. The vertical dashed lines indicate lambda.min and lambda.1se, which are used to select the optimal penalty parameters.

[0035] Figure 6 This is a schematic diagram of the model performance test results for diagnosing tophi using the first 9 metabolites in a cohort in an embodiment of the present invention. Detailed Implementation

[0036] The present invention is described in detail below with reference to the 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 all purchased through commercial channels.

[0037] Example 1: Collection and processing of serum samples 1.1 Research Subjects A total of 315 gout patients who visited a certain hospital were included in this study, of which 235 had non-tophagous gout and 80 had tophagous gout. All patients met the 1977 American College of Rheumatology (ACR) classification criteria for gout. The diagnostic criteria for tophagous gout were: physical examination revealed or palpable subcutaneous nodules, which were confirmed by dual-energy CT or ultrasound to be urate crystal deposits.

[0038] 1.2 Serum Sample Collection Example 2: Serum sample pretreatment (protein precipitation method) Take 50 µL of serum and mix it with 200 µL of ice-cold methanol containing an internal standard (fluorouracil) at 4°C. Incubate for 15 minutes to precipitate proteins. After vortexing, centrifuge at 14,000 rpm at 4°C, collect the supernatant, and store at -80°C for analysis. Prepare quality control samples by aliquoting the mixed serum samples.

[0039] Example 3: Detection by liquid chromatography-tandem mass spectrometry (LC-MS / MS) Ultra-high performance liquid chromatography (UHPLC) (Nexera UHPLC LC-30A) coupled with quadrupole time-of-flight mass spectrometry (AB6600 TripleTOF) was used to acquire data in both positive and negative ion modes under data-dependent acquisition mode. Liquid chromatography separation was performed using a Waters ACQUITY UPLC BEH Amide column (100 mm × 2.1 mm, 1.7 µm). Raw data (.wiff files) were converted to mzML format using ProteoWizard software (V.3.0.23132, https: / / proteowizard.sourceforge.io / ). Metabolomics analysis was performed using the R package "XCMS". MS / MS spectra of each MS feature were extracted and correlated, and identification was performed using a self-built metabolite database and public databases (including the Human Metabolomics Database, North American MassBank, MassBank, and the Global Natural Products Society Molecular Network) according to metabolomics standards initiatives. Features with a coefficient of variation greater than 30% in QC samples were removed.

[0040] Example 4: Data Preprocessing and Differential Metabolite Screening The clinical parameters of patients with tophaceous gout and non-tophaceous gout are summarized in Table 1. Patients with chronic tophaceous gout were significantly older and had a longer disease duration. Compared with the non-tophaceous gout group, the tophaceous gout group showed significant changes in the levels of 194 metabolites (Log2 fold change >0.25 or <-0.25, and false detection rate FDR <0.05). Figure 1 In the OPLS-DA model, we observed a separation of metabolic profiles (Figure 2). Furthermore, based on enrichment analysis, compared to patients with chronic non-tophagous gout, patients with chronic tophagous gout had significantly higher levels of pathways related to fructose and mannose metabolism and glycolysis in their serum. Figure 2 Significant changes occurred. These findings suggest that energy requirements in the inflammatory state of chronic gout during the tophi stage are different.

[0041] Table 1. Clinical parameters of patients with tophaceous gout and non-tophaceous gout

[0042] Note: For categorical variables, data are expressed as n (%). For continuous variables, values ​​are expressed as mean and standard deviation (SD) or median and interquartile range (IQR), as appropriate. BMI: Body Mass Index; FBG: Fasting Blood Glucose; ALT: Alanine Aminotransferase; AST: Aspartate Aminotransferase; eGFR: Estimated Glomerular Filtration Rate.

[0043] To further explore the interrelationships among metabolic pathways specifically disrupted at different stages of gout, we used the FELLA algorithm with functional enrichment and association annotation to identify metabolic networks. As shown in Figure 3, core carbon metabolism and the insulin signaling pathway mainly regulate the development of gout and are responsible for responding to inflammation induced by tophi (Figure 3). Since glycolysis is the main energy source for osteoblasts, it is speculated that activated glycolysis in different stages of gout promotes bone / joint erosion. On the other hand, fatty acids are also involved in bone metabolism, indicating that glucose and lipid reprogramming are fundamental to the pathogenesis of gout.

[0044] To screen metabolites and construct predictive models that distinguish different groups, this study employed the LASSO (Least Absolute Contraction and Selection Operator) method, using the parameter lambda to screen potential biomarkers. Figure 4 and 5Metabolites with non-zero coefficients selected by LASSO were used to construct a predictive machine learning model (XGBOOST model). Model parameters were set as follows: maximum tree depth of 4, learning rate of 0.1, minimum sample weight required for child nodes using the default value of 1, and row-to-column sampling ratio of 0.8; the optimal number of iterations was selected using 5-fold cross-validation. Nine metabolites were used (N-(p-toluenesulfonyl)-L-phenylalanine, [3,4,5-trihydroxy-6-(hydroxymethyl)oxehez-2-yl]). 10-[3-acetamido-6-[[4,5-dihydroxy-3-(3,4,5-trihydroxyoxetane-2-yl)oxyoxetane-2-yl]oxymethyl]-4,5-dihydroxyoxetane-2-yl]oxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecanoyl-4a-carboxylic acid ester, 8'-apo-β-carotene, 3,3,5-trimethyl-1,2,3,4-tetrahydro-γ-carboline-1-spiro-4'-(1'-methyl)piperidine (2-D), dehydroepiandrosterone, quinolinic acid, 2'-deoxyguanosine 5'-monophosphate, tekostanine, C-13(2)-carboxypyrophyllite a), the AUC obtained by the model was 0.738 (95% confidence interval: 0.592–0.884) Figure 6 The importance ranking is shown in Table 2.

[0045] Table 2 Ranking of the Importance of Nine Metabolites

[0046] Example 8: Practical Applications of Metabolic Markers and Systems In the non-gouty gout group and the tophaceous gout group, 57 and 39 subjects respectively, serum samples were collected and identified using the biomarkers and system of this invention. The results are shown in Table 3.

[0047] Subjects must meet the following criteria: diagnosed with gout, without malignant tumors, without severe liver or kidney dysfunction (eGFR>30 mL / (min·1.73 m²)), without acute infection, autoimmune disease, and have not used glucocorticoids recently.

[0048] The formulas for calculating sensitivity, specificity, and accuracy are as follows: Sensitivity = (Number of tophi samples that tested positive) / Total number of tophi samples; Specificity = (Number of non-tophagous samples that tested negative) / Total number of non-tophagous samples; Accuracy = (Number of positive tophi + Number of negative non-tophi samples) / Total number of samples.

[0049] Table 3. Detection results of the markers and system of this invention.

[0050] Example of specific values ​​for a test subject: The relative content of N-(p-toluenesulfonyl)-L-phenylalanine in a certain patient was 489.6504446, and [3,4,5-trihydroxy-6-(hydroxymethyl)oxehez-2-yl] The relative contents of 10-[3-acetamido-6-[[4,5-dihydroxy-3-(3,4,5-trihydroxyoxetane-2-yl)oxyoxetane-2-yl]oxymethyl]-4,5-dihydroxyoxetane-2-yl]oxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecanoyl-4a-carboxylic acid ester are 312915.6931, the relative contents of 8'-apo-β-carotene are 41687.5522, and 3,3,5-trimethyl-1,2,3,4-tetrahydro-γ-carboline-1-spiro-4'-(1'-methyl)piperidine (2-D) are 41687.5522. The relative contents of the following substances were 1058.815614, 179305.6086, 30099.41615, 5.764088841, 3836.958363, and 1421.395638. After calculation using the XGBOOST model, the probability of disease was 37.2%, which does not exceed the threshold of 0.50. Therefore, it was predicted to be negative (i.e., without tophi).

[0051] The above description is merely an embodiment of the present invention, and the scope of protection of the present invention is not limited to 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 modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the technical concept and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. A metabolic biomarker for predicting tophaceous gout, comprising the following nine metabolites: N-(p-Toluenesulfonyl)-L-phenylalanine; [3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]10-[3-acetamido-6-[[4,5-dihydroxy-3-(3,4,5-trihydroxyoxan-2-yl)oxyoxan-2-yl]oxymethyl]-4,5-dihydroxyoxan-2-yl]oxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecanoyl-4a-carboxylate ([3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]) 10-[3-acetamido-6-[[4,5-dihydroxy-3-(3,4,5-trihydroxyoxan-2-yl)oxyoxan-2-yl]oxymethyl]-4,5-dihydroxyoxan-2-y l]oxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecahydropicene-4a-carboxylate); 8'-apo-β-carotenol; 3,3,5-trimethyl-1,2,3,4-tetrahydro-γ-carboline-1-spiro-4'-(1'-methyl)piperidine (2-D); dehydroepiandrosterone; quinolinic acid; 2'-deoxyguanosine 5'-monophosphate; Tecostanine; C-13(2)-carboxypyropheophorbide a.

2. A reagent kit, characterized in that, The kit includes reagents for detecting the relative amounts of the metabolic markers of claim 1.

3. The reagent kit according to claim 2, characterized in that, The reagent includes at least one of the following: 1) A reagent for detecting the content of the metabolic markers described in claim 1; 2) A reagent for extracting the metabolic biomarker of claim 1 from a sample.

4. The application of the metabolic biomarker combination according to claim 1 in the preparation of reagent kits, instruments, and systems for predicting tophaceous gout.

5. A method for constructing a predictive model for tophaceous gout, comprising the following steps: Serum samples were collected from gout patients and divided into a tophaceous gout group and a non-tophaceous gout group. Metabolomics analysis was performed on serum samples to obtain metabolite profiles; Screen for metabolites with significantly different expression levels between the two groups; LASSO regression was used to screen key metabolites; Based on the screened metabolites, a prediction model was constructed using the XGBOOST algorithm; The area under the receiver operating characteristic curve (AUC) is calculated to evaluate model performance.

6. The method according to claim 5, characterized in that, The metabolomics detection was performed using liquid chromatography-mass spectrometry (LC-MS).

7. A system for predicting tophaceous gout, comprising: The data input module is used to input the expression level of the metabolic biomarker described in claim 1 in the sample to be tested; The model calculation module loads the XGBOOST prediction model constructed according to the method described in claim 3; The results output module is used to output the risk probability of whether the patient has tophaceous gout.

8. An information data processing terminal, the 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 according to claim 7.

9. A computer-readable storage medium storing a computer program, characterized in that, The program is executed by the processor to compute the system of claim 7.

10. An apparatus comprising an input device and a computing device, characterized in that, The input device is used to input quantitative data of the biomarker as described in claim 1, and the computing device is used to calculate the system as described in claim 7.

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