A marker for diagnosing or evaluating the prognosis of children with epilepsy and its detection method and application

Abstract

The present invention discloses a marker for diagnosis or prognosis evaluation of children with epilepsy and its detection method and application, which belongs to the field of detection. The marker includes one or more of the following purine metabolites: uric acid, creatinine, xanthine, hypoxanthine, guanine, adenine, adenosine, adenosine triphosphate, adenosine diphosphate, and adenylic acid. The present invention reports the difference in blood / urine purine metabolite concentrations between children with epilepsy and healthy controls, and constructs a disease prediction model based on the difference in various blood / urine purine indicators, establishes a comprehensive scoring algorithm, facilitates the interpretation of test results, and provides a clinical basis for the diagnosis and prognosis evaluation of childhood epileptic seizures.

Description

Technical Field

[0001] The present invention relates to the field of biological detection, and in particular to a marker for diagnosing or evaluating the prognosis of children with epilepsy, and a detection method and application thereof. Background Art

[0002] Epilepsy is a common neurological disease characterized by recurrent symptoms and is a serious health problem worldwide. Epilepsy is a common neurological syndrome in children with complex etiology and recurrent attacks. The clinical manifestations of epilepsy are diverse, with both paroxysmal symptoms and chronic health problems, especially in childhood. Due to the rapid development of the brain, children with epilepsy often have developmental delays and cognitive impairments. Both children and adults can suffer from trauma caused by epileptic seizures, including car accidents and sudden death, which can have a negative impact on adults' education and employment.

[0003] Early and clear diagnosis and effective treatment can improve the quality of life and cognitive function. If it is not effectively treated, it may develop into refractory epilepsy in an average of 9.1 years (median 5 years), seriously affecting the quality of life of children. The premise of effective treatment is early and correct diagnosis. The diagnosis of epilepsy is often delayed due to various reasons. Between 1990 and 2019, the incidence and prevalence of epilepsy increased, and 3-5 out of every 1,000 newborns in my country were affected, and the mortality rate caused by neonatal epilepsy was as high as 23%. Epileptic seizures can aggravate acute injuries and lead to poor prognosis, including cerebral palsy, developmental delay, and mental defects.

[0004] The diagnosis of epilepsy relies on the description of epileptic seizure symptoms and electroencephalogram (EEG) examinations, but epileptic seizures are unpredictable and patients often find it difficult to recall seizure symptoms, and EEG examinations are difficult to perform immediately. Therefore, the diagnosis of epilepsy faces major clinical challenges, especially in environments where rapid diagnosis is required. To date, electroencephalogram (EEG) is the gold standard for the diagnosis of epilepsy. However, EEG is often difficult to record during epileptic seizures, and there are problems such as long time consumption, high cost, and the need for high-level professional technicians to interpret. Therefore, there is an urgent need for diagnostic methods that can quickly evaluate epileptic seizures in clinical practice, so new biomarkers for the diagnosis and prognosis of epileptic seizures are the current research focus.

[0005] Currently, many biomarkers have been confirmed to be associated with pathological changes that occur during epileptic seizures, including inflammatory markers in biological fluids, such as cytokines (such as interleukins), markers of neuronal damage (such as neuron-specific enolase) and markers of astrocyte response (such as glial fibrillary acid protein, protein S100β), and recently microRNA. However, these require difficult-to-obtain biological samples (such as cerebrospinal fluid), large amounts of blood or further blood processing, which may lead to sensitivity to hemolysis, molecular instability, lack of rapid and cost-effective analytical platforms, and thus lead to different results. Ideal epilepsy biomarkers should be as easy to obtain as possible (such as blood, urine), minimally invasive, do not require special processing, and be measured by a reproducible, easy-to-use and economically feasible analytical platform. At the same time, it should have high sensitivity and specificity. In recent years, the potential value of purines and their metabolites as blood biomarkers in children with epilepsy has received widespread attention.

[0006] Early research results have confirmed that adenosine accumulates outside brain cells in response to high levels of neuronal activity, thereby acting as an endogenous anticonvulsant. Adenosine can affect the occurrence, development and progression of epilepsy through receptor-dependent and -independent mechanisms. Studies have also pointed out that adenosine triphosphate and adenosine, as neuromodulators, play an important role in epileptic seizures. They play opposite roles between neuronal excitability and brain energy homeostasis, and affect the occurrence and development of epilepsy through different mechanisms. After an epileptic seizure, the concentration of purines in the blood and the expression of different metabolites of the purinergic signaling cascade may change significantly. Although Beamer et al. initially proposed that the blood levels of adenosine and its decomposition products can be used as biomarkers for epilepsy, the number of cases studied was small and only three metabolites with relatively high expression levels, inosine, xanthine and hypoxanthine, were detected. However, the inventors have verified that the diagnostic performance and accuracy of the combined diagnosis of epilepsy by these three metabolites are poor.

[0007] These research results indicate that purine and its metabolites may play a role in the occurrence and progression of epilepsy, but most of them are through animal models and superficial clinical studies. In addition, although blood and urine are relatively easy to obtain samples, there is currently a lack of systematic clinical research on the relationship between blood or urine markers and epileptic seizures and their application value. This aspect of research first requires a large number of clinically diagnosed children's samples as a basis; secondly, there are many types of metabolites related to epilepsy in existing studies, and a large number of experiments and data analysis are needed to screen out biomarkers with diagnostic value; finally, the content of metabolites related to epilepsy pathological changes in the blood or urine is generally not high, for example, the content of adenosine and its metabolites in the blood is relatively low, and the content of adenosine diphosphate is at the ng level. Improving the detection sensitivity of markers is also one of the technical difficulties.

[0008] Currently, there is still a lack of rapid diagnostic methods for children with epilepsy and a lack of biomarkers that are highly accurate and easy to detect. Summary of the invention

[0009] Purpose of the Invention

[0010] In order to overcome the above-mentioned deficiencies, such as the lack of rapid diagnostic methods for children with epilepsy and the lack of biomarkers with high accuracy and ease of detection, the purpose of the present invention is to provide a marker for diagnosis or prognosis evaluation of children with epilepsy and its detection method and application. The uric acid, creatinine, xanthine, hypoxanthine, guanine, adenine, adenosine, adenosine triphosphate, adenosine diphosphate, and adenylic acid of the present invention can be used as biomarkers for diagnosis and prognosis evaluation of epilepsy. The present invention reports the differences in the concentrations of blood / urine purine and its metabolites between children with epilepsy and healthy controls, and constructs a disease prediction model based on the changes in various indicators of blood / urine purine, establishes a comprehensive scoring algorithm, and facilitates the interpretation of test results, providing a clinical basis for the diagnosis and prognosis evaluation of childhood epileptic seizures.

[0011] Solution

[0012] To achieve the purpose of the present invention, the technical solution adopted by the present invention is as follows:

[0013] In a first aspect, the present invention provides a marker for diagnosis or prognosis assessment of epilepsy in children, including one or more of the following purine metabolites: uric acid, creatinine, xanthine, hypoxanthine, guanine, adenine, adenosine, adenosine triphosphate (ATP), adenosine diphosphate (ADP), and adenosine monophosphate (AMP).

[0014] In a second aspect, a marker for diagnosis or prognosis assessment of children with epilepsy is provided, which includes a combination of purine metabolites in urine: adenine and adenosine diphosphate.

[0015] In a third aspect, a marker for diagnosis or prognosis assessment of epilepsy in children is provided, which includes a combination of purine metabolites in the blood: uric acid, hypoxanthine, adenine, adenosine and adenylic acid.

[0016] In a fourth aspect, a method for detecting the concentration of purine metabolites in urine is provided for use in preparing a kit or an analysis system for diagnosing or evaluating the prognosis of children with epilepsy; wherein the combination of purine metabolites in urine includes adenine and adenosine diphosphate.

[0017] In a fifth aspect, a method for detecting the concentration of purine metabolites in the blood is provided for the preparation of a kit or an analysis system for diagnosing or evaluating the prognosis of children with epilepsy; wherein the combination of purine metabolites in the blood includes: uric acid, hypoxanthine, adenine, adenosine and adenylic acid.

[0018] In a sixth aspect, a method for detecting a marker for a non-diagnostic purpose is provided, including a method for detecting the concentration value of the marker described in the first, second or third aspect in blood or urine (or in the combination of markers described in the first, second or third aspect, or in the application described in the fourth or fifth aspect, the method for detecting the concentration value of the combination of purine metabolites in blood or urine includes): quantitative detection and analysis of purines by tandem mass spectrometry;

[0019] Optionally, the method for detecting the concentration value of the combination of purine metabolites in blood or urine comprises: performing tandem mass spectrometry analysis using parent ions and characteristic fragment ions optimized by purine metabolites and two isotope internal standards.

[0020] Optionally, the urine pretreatment method includes: adding acetonitrile to the urine, centrifuging to obtain the supernatant and spinning to dryness, adding 0.75% ammonia solution containing an internal standard to re-dissolve, and centrifuging to obtain the supernatant for sampling.

[0021] Optionally, serum is used as the blood sample, and the pretreatment method of the serum includes: adding acetonitrile containing an internal standard to the serum, centrifuging to obtain the supernatant and spinning to dryness, adding 0.75% ammonia solution to re-dissolve, and centrifuging to obtain the supernatant for sampling.

[0022] Quantitative analysis calculates the purine content by measuring the intensity of specific ion signals.

[0023] Liquid chromatography-tandem mass spectrometry (LC-MS / MS) combines the separation capability of liquid chromatography with the high selectivity and sensitivity of tandem mass spectrometry, and is particularly suitable for the analysis of trace components in complex biological samples.

[0024] The present invention adopts liquid chromatography-tandem mass spectrometry (LC-MS / MS) technology to establish a method for simply, quickly and accurately detecting purine and its metabolites in the blood or urine of children with epilepsy, and at the same time provides a reference for the detection of epileptic seizures and assisting doctors in treatment.

[0025] In the seventh aspect, an analysis system for diagnosing and evaluating children with epilepsy is provided, including a data analysis module, wherein the data analysis module is used to collect concentration values ​​of purine metabolite combinations of a target object to be predicted, using the concentration values ​​of the purine metabolite combinations as input features, and is also used to calculate a prediction value of whether the target object has epilepsy based on the concentration values ​​of the purine metabolite combinations; the input features are the markers described in the first, second or third aspects.

[0026] In an eighth aspect, an analysis system for diagnosing or evaluating the prognosis of children with epilepsy is provided, which uses the concentration value of the purine metabolite combination in urine as an input feature to calculate the predicted value of whether the target object has epilepsy, and the model prediction formula is:

[0027] pre = (-0.429~0.055) × adenine + (-17.546~9.210) × adenosine diphosphate;

[0028] Among them, adenine and adenosine diphosphate are the concentration values ​​of each indicator in urine. When the predicted value of the urine indicator is greater than 0.6904, it is considered to be positive for epilepsy, otherwise it is a healthy child.

[0029] In a ninth aspect, an analysis system for diagnosing or evaluating the prognosis of children with epilepsy is provided, which uses the concentration value of the combination of purine metabolites in the blood as an input feature to calculate the predicted value of whether the target object has epilepsy, and the model prediction formula is:

[0030] pre = (-0.004~0.002) × uric acid + (0.004~0.006) × hypoxanthine + (0.118~0.706) × adenine + (-0.077~0.497) × adenosine + (-21.207~-10.057) × adenylic acid;

[0031] Among them, uric acid, hypoxanthine, adenine, adenosine, and adenylic acid are the concentration values ​​of each indicator in the blood. When the predicted value of the blood indicator is greater than 0.5518, it is considered to be positive for epilepsy, otherwise it is a healthy child.

[0032] In the tenth aspect, a kit is provided for diagnosing childhood epilepsy, evaluating efficacy or prognosis, or evaluating related drugs, the kit comprising the markers and / or detection reagents described in the first, second or third aspect.

[0033] Beneficial Effects

[0034] (1) The present invention establishes a highly sensitive detection system for purine and its metabolites: The present invention provides a rapid, simple and effective detection system for purine and its metabolites, which can achieve detection at levels as low as ng / ml in urine and blood.

[0035] (2) This invention fills the gap in non-invasive epilepsy detection markers: This invention proposes 10 purine metabolite markers that may be used as non-invasive tools for epilepsy diagnosis. Through a large number of clinical diagnostic analyses, it has been proven that the combined diagnosis of uric acid, hypoxanthine, adenine, adenosine and adenylic acid in the blood and the combined diagnosis of adenine and adenosine diphosphate in urine can be used as non-invasive epilepsy detection markers.

[0036] (3) Another major innovation of the present invention is the concise result analysis system: the analysis system of the present invention is not the result analysis of a single indicator test (a single indicator has low diagnostic efficiency). The present invention imports the biomarker test results obtained by screening into a comprehensive scoring system formed by independent modeling, and performs a comprehensive scoring of the results of 2 / 5 types of purine metabolites detected in blood / urine, thus overcoming the problem of low diagnostic efficiency of a single indicator. It avoids the trouble caused by inconsistent single results in clinical interpretation, and the comprehensive interpretation makes the reliability and reference of the results more meaningful, which is more convenient for clinical promotion and application.

[0037] (4) The test of the present invention is simple to operate and can achieve the clinical goal of early diagnosis and early treatment. Currently, clinical EEG technology is difficult to perform instant detection and EEG interpretation is somewhat subjective. The present invention detects blood / urine purine and its metabolites as detection indicators, which helps to make an instant and objective assessment of epileptic seizures. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] One or more embodiments are exemplarily described by the pictures in the accompanying drawings, and these exemplary descriptions do not constitute limitations on the embodiments. The special word "exemplary" here means "used as an example, embodiment or illustrative". Any embodiment described as "exemplary" here is not necessarily interpreted as being superior or better than other embodiments.

[0039] Figure 1 It is a heat map of the correlation between the purine index of the present invention and the occurrence of epilepsy; wherein: A is a correlation matrix diagram, and B is a correlation heat map.

[0040] Figure 2 This is the ROC curve of the blood / urine single index and the combined prediction in epilepsy of the present invention (wherein: A is urine; B is blood).

[0041] Figure 3 The ROC curve of the existing literature using the concentrations of xanthine and hypoxanthine in the blood as biomarkers for epilepsy diagnosis is presented. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] In addition, in order to better illustrate the present invention, numerous specific details are provided in the specific embodiments below. It should be understood by those skilled in the art that the present invention can also be implemented without certain specific details. In some embodiments, raw materials, elements, methods, means, etc. well known to those skilled in the art are not described in detail, so as to highlight the main purpose of the present invention.

[0044] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0045] The raw materials used in the present invention are all commercially available.

[0046] The present invention provides a detection method for detecting the concentration of various purine indicators (uric acid, creatinine, xanthine, hypoxanthine, guanine, adenine, adenosine, adenosine triphosphate, adenosine diphosphate, and adenosine acid) in the blood and urine of epilepsy patients. The required reagents and detection steps include the following:

[0047] 1) Reagent and sample pretreatment:

[0048] Isotope internal standard: uric acid-1,3- 15 N2 (purchased from Aladdin, U471861-25mg), creatinine-(methyl-d3) (purchased from Aladdin, C471935-5mg).

[0049] Preparation method of internal standard stock solution and working solution:

[0050] UA- 15 N2 stock solution: Dissolve uric acid-1,3- 15 N2 solid powder was prepared into 5 mg / mL internal standard stock solution and stored in a -80℃ refrigerator.

[0051] Cr-D3 stock solution: Dissolve creatinine-(methyl-d3) solid powder in distilled water to prepare a 5 mg / mL internal standard stock solution and store in a -80°C refrigerator.

[0052] Mixed internal standard working solution (UA- 15 N2, 50μg / mL; Cr-D3, 100μg / mL): Take UA- 15 Add 100 μL of N2 stock solution and 200 μL of Cr-D3 stock solution to 9.7 mL of distilled water, mix well and set aside. Store in a -80℃ refrigerator.

[0053] 2) Urine sample extraction method:

[0054] Take 50 μL of urine sample, add 150 μL of acetonitrile, vortex for 30 seconds, and place in a 4°C refrigerator for 10 minutes. Centrifuge at 10000g for 10 minutes. Take 100 μL of supernatant, vacuum centrifuge and spin dry, add 50 μL of 0.75% ammonia solution (including internal standard) to reconstitute, vortex for 30 seconds, and centrifuge at 10000g for 10 minutes. Take 40 μL of supernatant for injection.

[0055] The preparation method of 0.75% ammonia solution (including internal standard) is as follows: 300 μL of ammonia solution (25%), mixed with internal standard working solution (UA- 15 N2, 50μg / mL; Cr-D3, 100μg / mL) 1mL, distilled water 8.7mL.

[0056] 3) Serum sample extraction method:

[0057] Take 50 μL of serum sample, add 160 μL of acetonitrile (including internal standard), vortex for 30 seconds, and place in a 4°C refrigerator for 10 minutes. Centrifuge at 10000g for 10 minutes. Take 120 μL of supernatant, vacuum centrifuge and spin dry, add 50 μL of 0.75% ammonia solution to reconstitute, vortex for 30 seconds, and centrifuge at 10000g for 10 minutes. Take 40 μL of supernatant for injection.

[0058] Among them, the preparation method of acetonitrile (including internal standard) is as follows: mixed internal standard working solution (UA- 15 N2, 50μg / mL; Cr-D3, 100μg / mL) 2mL, acetonitrile 30mL.

[0059] Among them, the preparation method of 0.75% ammonia solution is: 300μL of ammonia water (25%) and 9.7mL of distilled water.

[0060] 4) Liquid phase method

[0061] Acquisition system: Waters I Class LC System;

[0062] Chromatographic column: HSS T3 (100×2.1mm, 1.8um);

[0063] Mobile phase: A_water (5 mM ammonium formate); B_methanol:water (1:1, 5 mM ammonium formate);

[0064] Column temperature: 30°C;

[0065] Injection volume: 5 μl;

[0066] The program settings for gradient elution are shown in Table 1:

[0067] Table 1. Gradient elution program

[0068] time Flow rate (mL / min) %A (Water (5 mM Ammonium Formate)) %B (methanol:water (1:1, 5 mM ammonium formate)) initial 0.20 98.0 2.0 1.00 0.20 98.0 2.0 5.00 0.20 40.0 60.0 6.00 0.20 0.0 100.0 7.50 0.20 0.0 100.0 7.60 0.20 98.0 2.0 10.00 0.20 98.0 2.0 .

[0069] 5) Mass spectrometry detection method

[0070] Acquisition system: SCIEX Qtrap 4500 Mass System;

[0071] Ion source: ESI, pos;

[0072] Acquisition mode: MRM;

[0073] The optimized precursor ions and characteristic fragment ions of 10 components + 2 isotope internal standards are shown in Table 2 below.

[0074] Table 2. Optimized parent ions and characteristic fragment ions of 10 components + 2 isotope internal standards

[0075] Q1 Q3 ID DP CE 153 136 Xanthine 1 120 23 153 110 Xanthine 2 120 26 137 119 Hypoxanthine 1 100 30 137 110 Hypoxanthine 2 100 29 152.1 135 Guanine 1 110 26 152.1 110.1 Guanine 2 110 28 136 119 Adenine 1 130 30 136 92 Adenine 2 130 40 169.1 141.1 Uric acid 1 130 24 169.1 152.1 Uric acid 2 130 22 114 44 Creatinine 1 150 30 114 86 Creatinine 2 150 16 268 136 Adenosine 1 110 30 268 119.1 Adenosine 2 110 65 508 136 ATP-1 120 60 508 119 ATP-2 120 115 427.8 136 AMP-1 130 35 427.8 119 AMP-2 130 97 348 136 Adenosine 1 100 25 348 119 Adenosine 2 100 80 171 143.1 <![CDATA[Uric acid- 15 N2]]> 130 24 117 47 <![CDATA[Creatinine-d3]]> 150 30 .

[0076] 6) Data processing and analysis

[0077] The peak area was obtained by Analyst software from ScieX. The uric acid content was compared with uric acid- 15 The peak area ratio of the N2 isotope internal standard was used for calculation, and the other components were obtained by calculating the peak area ratio of the creatinine-d3 isotope internal standard.

[0078] 7) Real sample testing

[0079] Five samples from the case group and the control group were selected, and purine and its metabolites were detected using the sample processing method and detection method proposed by the present invention. The data processing and analysis proposed by us were used, and the test results are shown in Table 3 and Table 4:

[0080] Table 3. Detection results of 10 kinds of purines and their metabolites in urine

[0081] .

[0082] Table 4. Results of blood tests for 9 purines and their metabolites

[0083] .

[0084] According to Table 3 and Table 4, the detection method of the present invention can detect ng-level adenosine triphosphate and adenosine diphosphate in urine, and can detect ng-level adenosine diphosphate in blood, which has been improved from the traditional ug / mL detection level to ng / mL, overcoming the technical difficulty of detecting relatively low content, improving the detection sensitivity of purine and its metabolites, and providing strong support for the screening of markers.

[0085] Test example:

[0086] Establishment of a predictive analysis model and software for childhood epilepsy:

[0087] The present invention is based on a multivariate logistic regression algorithm and establishes a scoring model for epilepsy by calculating the binary classification probability.

[0088] First, prepare the data matrix to be analyzed, use the R language ggcorrplot and pheatmap tools, use the cor() function to calculate the correlation coefficient (that is, the correlation between each pair of purine indicators), and then use the corrplot package for visualization (such as Figure 1 ), heat map drawing (heat map drawing was for the purine indexes in the blood / urine of children in the control group and the case group), and correlation analysis between the groups.

[0089] Secondly, the data was preprocessed to check the integrity of the data. The purine index of blood / urine was used as the input, and whether the disease was the dependent variable. The original data was correctly coded and preliminarily screened. The data with a difference of more than 0.55 between the predicted value and the true value in the control were deleted as outliers. Then, the data set was divided into a training set and a test set using stratified sampling, with a ratio of 7:3. Then, a multivariate logistic regression model was constructed and trained. The trained model was used to calculate the probability of each sample belonging to the case group on the test set. The model performance was then evaluated according to different performance indicators, and the AUC value of the model was tested, that is, the area under the ROC curve. The AUC value was between 0.5 and 1, indicating that the performance of the prediction model was better than random guessing; the closer the AUC was to 1, the better. The prediction result of the model for each sample was a probability value. According to the ROC curve, an optimal critical point was obtained as the cutoff value to be found. The cutoff value found indicated that this model had at least one threshold that could accurately separate the data above and below the threshold.

[0090] 1. Sample processing

[0091] Blood and urine samples from 147 clinical samples (98 children with epilepsy and 49 healthy control children) who visited the Capital Institute of Pediatrics were collected using EDTA anticoagulation tubes. A total of 9 purine types in blood (uric acid, creatinine, xanthine, hypoxanthine, adenine, adenosine, adenosine triphosphate, adenosine diphosphate, and adenosine monophosphate) and 10 purine types in urine (uric acid, creatinine, xanthine, hypoxanthine, guanine, adenine, adenosine, adenosine triphosphate, adenosine diphosphate, and adenosine monophosphate) were tested. 50 μL of each sample was taken for blood and urine sample extraction for liquid chromatography and mass spectrometry detection.

[0092] 2. Clinical sample test results

[0093] The means and standard errors of urine purine indicators are shown in Table 5. There were no significant differences in urine uric acid, creatinine, xanthine, hypoxanthine, guanine, adenosine, adenosine triphosphate, and adenylic acid between the control group and the epilepsy case group, but there were significant differences in adenine and adenosine diphosphate.

[0094] Table 5. Statistical analysis of purine content in urine samples of case group and control group (Mean ± SEM)

[0095] Purine Index Control group (n=49) Case group (n=98) Z-value value Uric acid (ug / mL) 22.21±1.979 19.02±1.07 -1.072 0.284 Creatinine (ug / mL) 2.579±0.299 2.152±0.1672 -1.344 0.179 Xanthine (ug / mL) 1.773±0.3322 1.797±0.2317 -0.9 0.368 Hypoxanthine (ug / mL) 18.77±3.627 23.42±2.466 -1.75 0.08 Guanine (ug / mL) 1.268±0.2891 1.423±0.1885 -0.731 0.465 Adenine (ug / mL) 1.21±0.148 0.9389±0.0781 -2.178 0.029 Adenosine (ug / mL) 9.827±1.064 11.1±1.059 -0.316 0.752 Adenosine triphosphate (ng / mL) 11.57±1.915 8.357±1.057 -1.693 0.09 Adenosine diphosphate (ng / mL) 12.62±1.951 10.31±1.733 -2.227 0.026 Adenylate (ng / mL) 241.9±33.72 200.6±16.92 -0.518 0.605 .

[0096] The mean and standard error of each blood purine index are shown in Table 6. The difference analysis of each index is detailed in Figure 1 The results showed that there were no significant differences in blood creatinine, xanthine, adenosine triphosphate, and adenosine diphosphate between the control group and the epilepsy case group; there were significant differences in uric acid, adenine, and adenylic acid, and there were extremely significant differences in hypoxanthine and adenosine.

[0097] Table 6. Statistical analysis of purine content in blood samples of case group and control group (Mean±SEM)

[0098] Purine Index Control group (n=49) Case group (n=98) Z-value value Uric acid (ug / mL) 148.5±16.2 182.4±10.29 -2.379 0.017 Creatinine (ug / mL) 2.279±0.0674 2.163±0.0577 -1.81 0.07 Xanthine (ug / mL) 6.377±0.2457 6.998±0.5206 -0.228 0.82 Hypoxanthine (ug / mL) 611±26.37 882.5±29.5 -5.551 <0.001 Adenine (ug / mL) 2.816±0.2067 3.096±0.1111 -2.083 0.037 Adenosine (ug / mL) 7.291±0.2658 7.953±0.1331 -2.735 0.006 Adenosine triphosphate (ng / mL) 173.6±7.351 175.6±4.977 -0.144 0.886 Adenosine diphosphate (ng / mL) 11.51±0.8812 9.623±0.4623 -1.483 0.138 Adenylate (ng / mL) 114.6±12.64 83.96±4.897 -2.359 0.018 .

[0099] 3. Results Analysis

[0100] The analysis results of blood and urine tests on 147 subjects (98 children with epilepsy and 49 healthy children) showed that the purine content detection and scoring method for children's blood and urine described in the present invention has a high predictive value for the disease.

[0101] The multivariate logistic regression model formula obtained by the present invention is as follows:

[0102] Urine: pre = (-0.187) × adenine + (-4.168) × adenosine diphosphate;

[0103] Among them, adenine and adenosine diphosphate are the concentration values ​​of each indicator in urine.

[0104] Blood: pre = (-0.001) × uric acid + 0.005 × hypoxanthine + 0.412 × adenine + 0.210 × adenosine + (-15.632) × adenylic acid;

[0105] Among them, uric acid, hypoxanthine, adenine, adenosine, and adenylic acid are the concentration values ​​of each indicator in the blood.

[0106] In the urine sample purine test, adenine and adenosine diphosphate have predictive value for epilepsy (AUC>0.5). The ROC curve results are as follows: Figure 2 As shown in A, the AUC values ​​of adenine and adenosine diphosphate were 0.6095 and 0.5749, respectively. Multivariate logistic regression analysis calculated the probability of joint prediction of two urine purine indicators (adenine and adenosine diphosphate), and ROC analysis of the joint prediction value of each indicator showed that the AUC value of the combined two urine purine indicators (adenine and adenosine diphosphate) in the training set was 0.6074, with a sensitivity of 62.86% and a specificity of 66.67%. The AUC of the joint prediction of adenine and adenosine diphosphate in the test set was 0.6406, with a sensitivity of 60.71% and a specificity of 68.75%.

[0107] In the blood sample purine detection, the ROC curve analysis results are as follows Figure 2 As shown in Figure B, uric acid, hypoxanthine, adenine, adenosine, and adenylic acid as single indicators have certain predictive value for the diagnosis of epilepsy (AUC>0.5), and the AUC values ​​of uric acid, hypoxanthine, adenine, adenosine, and adenylic acid are 0.6177, 0.7719, 0.5861, 0.6416, and 0.6922, respectively. The AUC value of the combined diagnosis of the five blood purine indicators (uric acid, hypoxanthine, adenine, adenosine, and adenylic acid) in the training set is 0.8485, with a sensitivity of 85.71% and a specificity of 72.73%. The AUC value of the combined diagnosis of the five blood purine indicators (uric acid, hypoxanthine, adenine, adenosine, and adenylic acid) in the test set is 0.8371, with a sensitivity of 67.86.%, and a specificity of 87.50%, which has high diagnostic efficiency.

[0108] Compared with the combined diagnosis of two purine indicators in urine, the combined diagnosis of five purine indicators in blood (uric acid, hypoxanthine, adenine, adenosine, and adenylic acid) has a higher predictive value and better sensitivity and specificity for the diagnosis of epilepsy (especially childhood epilepsy).

[0109] According to the literature of Beamer et al., inosine, xanthine and hypoxanthine have the potential to be epilepsy markers, but due to the small sample size, it cannot accurately reflect whether they are effective in diagnosis. Since the inosine mentioned in the literature is not included in the indicators of the present invention, the above 147 clinical samples (98 cases and 49 control group samples) were used to verify the two indicators mentioned in the literature (xanthine and hypoxanthine in the blood). The results are as follows Figure 3 .

[0110] Figure 3 The results showed that the AUC of xanthine in blood 黄嘌呤=0.5116 (standard error = 0.0500; 95% confidence interval (0.4142-0.6089); P value = 0.8196; specificity 71.43%, sensitivity 40.82%), AUC of hypoxanthine in blood 次黄嘌呤 =0.7813 (standard error = 0.0384; 95% confidence interval (0.7062-0.8565); P value <0.0001; specificity 91.84%, sensitivity 52.04%), the AUC of the combined diagnosis of the two blood indicators (xanthine and hypoxanthine) 黄嘌呤+次黄嘌呤 =0.7878 (standard error = 0.0381; 95% confidence interval (0.7131-0.8625); P value <0.0001; specificity 95.92%, sensitivity 47.96%). Although the combined diagnosis of the two has a certain predictive value for the diagnosis of epilepsy, the sensitivity and diagnostic efficacy are relatively low, which is much lower than the AUC value (0.8485) and sensitivity and specificity of the combined diagnosis of the five purine indicators (uric acid, hypoxanthine, adenine, adenosine, adenylic acid) of the blood in this application.

[0111] The results of the above-mentioned single blood index or the combined prediction index of the two indexes are compared with the detection results of the present invention combined with five blood purine indexes (uric acid, hypoxanthine, adenine, adenosine, adenylic acid). The present invention combines five blood purine indexes to improve its detection sensitivity and specificity, and improves the diagnostic efficacy. The detection sensitivity of the present invention can reach the ng / mL level, while the detection sensitivity in the literature is only umol / L or ug / mL.

[0112] In summary, the present invention provides a convenient, rapid and effective means for detecting the purine content in children's blood and urine, and for the diagnosis and prognosis evaluation of childhood epilepsy.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An analysis system for diagnosing or evaluating the prognosis of children with epilepsy, characterized in that: The invention comprises a data analysis module, wherein the data analysis module is used to collect the concentration value of the purine metabolite combination of the target object to be predicted, and the concentration value of the purine metabolite combination is used as an input feature, and is also used to calculate the prediction value of whether the target object has epilepsy according to the concentration value of the purine metabolite combination; the input feature is the purine metabolite combination in urine or the purine metabolite combination in blood; wherein, When the concentration value of the purine metabolite combination in urine is used as the input feature, the prediction value of whether the target object has epilepsy is calculated, and the model prediction formula is: pre=(-0.429~0.055)×adenine+(-17.546~9.210)×adenosine diphosphate; adenine and adenosine diphosphate are the concentration values ​​of each index in urine. When the predicted value of the urine index is greater than 0.6904, it is considered to be epilepsy positive, otherwise it is a healthy child; When the concentration value of the purine metabolite combination in the blood is used as the input feature, the prediction value of whether the target object has epilepsy is calculated. The model prediction formula is: pre=(-0.004~0.002)×uric acid+(0.004~0.006)×hypoxanthine+(0.118~0.706)×adenine+(-0.077~0.497)×adenosine+(-21.207~-10.057)×adenosine; among them, uric acid, hypoxanthine, adenine, adenosine and adenosine are the concentration values ​​of each indicator in the blood. When the predicted value of the blood indicator is greater than 0.5518, it is considered to be positive for epilepsy, otherwise it is a healthy child.

2. The analysis system according to claim 1, characterized in that Also included is a kit for diagnosing childhood epilepsy, evaluating efficacy or prognosis, or evaluating related drugs, the kit comprising a combination of purine metabolites in urine or a combination of purine metabolites in blood, and / or a detection reagent thereof; wherein, The composition of purine metabolites in urine includes: adenine and adenosine diphosphate; The combination of purine metabolites in the blood includes: uric acid, hypoxanthine, adenine, adenosine and adenylic acid.

3. The analysis system according to claim 1, characterized in that The method for detecting the concentration value of the marker of the purine metabolite combination in urine or the purine metabolite combination in blood includes: using a tandem mass spectrometry method to quantitatively detect and analyze purine.

4. The analysis system according to claim 3, characterized in that The method for detecting the concentration value of the marker in blood or urine includes: performing tandem mass spectrometry analysis using parent ions and characteristic fragment ions optimized by purine metabolites and two isotope internal standards.

5. The analysis system according to claim 4, characterized in that The urine pretreatment method includes: adding acetonitrile to the urine, centrifuging to obtain the supernatant and spinning to dryness, adding 0.75% ammonia solution containing an internal standard to re-dissolve, and centrifuging to obtain the supernatant for sampling.

6. The analysis system according to claim 4, characterized in that Serum was used as the blood sample, and the pretreatment method of the serum included: adding acetonitrile containing an internal standard to the serum, centrifuging to obtain the supernatant and spinning to dryness, adding 0.75% ammonia solution to re-dissolve, and centrifuging to obtain the supernatant for sampling.

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