Metabolite composition for evaluating intractable epilepsy of children and application of metabolite composition

Abstract

The invention discloses a metabolite composition for evaluating children refractory epilepsy and application thereof, and finds that metabolic markers 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine can be applied to diagnosis and / or early prediction of children refractory epilepsy for the first time. Furthermore, it is found that the metabolic marker has an extremely high diagnostic value for children refractory epilepsy, and can diagnose and / or early predict whether a to-be-detected subject suffers from children refractory epilepsy or the risk of suffering from children refractory epilepsy in a high-accuracy, high-sensitivity and high-specificity manner, so that accurate diagnosis and treatment of children refractory epilepsy are effectively promoted; the clinical application prospect is wide.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology, and in particular, relates to a metabolite combination for evaluating refractory epilepsy in children and an application thereof. Background Art

[0002] Epilepsy is one of the most common neurological diseases in children, characterized by abnormal synchronous discharges of a large number of neurons leading to recurrent, paroxysmal and transient brain dysfunction. The incidence of childhood epilepsy is about 15 times that of adults. 60-70% of children with epilepsy can achieve control or symptom relief after regular treatment, but 20-30% of children still cannot be effectively controlled after treatment or experience relapses, developing into refractory childhood epilepsy. Refractory childhood epilepsy seriously endangers the physical and mental health of children. The course of refractory childhood epilepsy is long, the mental complications of children are 10 times higher than those of the general population, and the mortality rate is 3 times that of the general population, which has placed a heavy burden on families and society.

[0003] Compared with adult epilepsy patients, the etiology of childhood epilepsy is complex, the pathogenesis is unclear, and treatment methods are lacking, which makes the diagnosis and treatment of refractory epilepsy in children face major challenges. At present, clinical diagnosis and treatment of refractory epilepsy in children is based on the gender, age and clinical data within 6 months of the diagnosis of epilepsy, including maternal pregnancy and perinatal conditions, previous relevant medical history and relevant family history, age of onset, etiology, type of seizure before treatment, duration of seizures, number of seizures, status epilepticus during the course of the disease, initial and post-treatment EEG results, imaging results and the effect of anti-epileptic drug treatment. Therefore, in the clinical diagnosis and treatment of refractory epilepsy in children, the diagnosis and treatment of refractory epilepsy in children are delayed due to too many considerations, complex processes and low accuracy. In addition, in the early stages of childhood epilepsy, the clinical manifestations, biochemical indicators, clinical imaging and histological characteristics of children overlap with other brain diseases, which increases the tortuosity and complexity of the diagnostic process and easily leads to delays in the diagnosis and treatment of childhood epilepsy.

[0004] When epilepsy occurs, a variety of metabolites and metabolic pathways change, such as energy metabolism, amino acid metabolism, purine metabolism, tricarboxylic acid cycle, etc. In recent years, research on metabolites and childhood epilepsy has been quite popular. However, there is currently a lack of metabolic markers that can be used to accurately and effectively evaluate refractory epilepsy in children. Summary of the invention

[0005] In view of this, in order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a metabolite combination and application thereof for evaluating refractory epilepsy in children in the art, so as to provide guidance for future clinical work related to refractory epilepsy in children.

[0006] The above-mentioned object of the present invention is achieved through the following technical solutions:

[0007] The first aspect of the present invention provides the use of a metabolic marker in the preparation of a product for the diagnosis and / or early prediction of refractory epilepsy in children.

[0008] Furthermore, the metabolic marker is 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine.

[0009] Furthermore, the product includes a reagent for detecting the content or concentration of the metabolite marker in a sample.

[0010] Furthermore, the reagent includes a reagent for detecting the content or concentration of the metabolite marker in a sample by targeted or non-targeted nuclear magnetic resonance, chromatography, mass spectrometry and / or spectroscopy.

[0011] Furthermore, the sample is blood, serum and / or plasma.

[0012] Furthermore, the product includes a detection kit or a detection chip.

[0013] Furthermore, the English names of the 3-hydroxyvalproic acid, trans-2-octenoic acid and C-6 ceramide are 3-hydroxyvalproic acid (CAS: 58888-84-9), trans-2-Octenoic acid (CAS: 1871-67-6) and C-6ceramide (CAS: 124753-97-5), respectively.

[0014] Furthermore, the metabolite markers 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine are significantly highly expressed in children with intractable epilepsy. The metabolite markers are derived from biological samples of children with intractable epilepsy.

[0015] In a specific embodiment of the present invention, the present invention has experimentally demonstrated that the above-mentioned metabolic markers have good diagnostic efficacy for the early diagnosis or prediction of refractory epilepsy in children, and can be used for the screening and early diagnosis of refractory epilepsy in children, thereby maximizing the prognosis of children with refractory epilepsy.

[0016] The metabolic markers, detection kits, detection chips, detection devices or detection systems for diagnosing, screening and / or early prediction of refractory epilepsy in children provided by the present invention have the advantages of high detection throughput, high sensitivity, good specificity and the like in the diagnosis, screening and / or early prediction of refractory epilepsy in children.

[0017] In some embodiments, the chromatography includes, but is not limited to, high performance liquid chromatography, thin layer chromatography, gas chromatography, and / or any combination thereof.

[0018] In some embodiments, the mass spectrometry includes but is not limited to: matrix-assisted laser desorption ionization (MALDI)-time of flight (TOF) mass spectrometry, MALDI-TOF-TOF mass spectrometry, MALDI quadrupole-time of flight (Q-TOF) mass spectrometry, electrospray ionization (ESI)-TOF mass spectrometry, tandem mass spectrometry, ESI-Q-TOF, ESI-TOF-TOF, ESI-ion trap mass spectrometry, ESI triple quadrupole mass spectrometry, ESI Fourier transform mass spectrometry (FTMS).

[0019] In some embodiments, the spectroscopy includes, but is not limited to, refractive index spectroscopy, ultraviolet spectroscopy, near infrared spectroscopy, and / or any combination thereof.

[0020] In some embodiments, the reagent determines the content or concentration of the metabolite marker in the sample by any one or more of the following methods: nuclear magnetic resonance spectroscopy (NMR), mass spectrometry, chromatography, spectroscopy, fluorescence measurement, ultraviolet spectroscopy (UV), fluorescence analysis, radiochemical analysis, electrophoresis, immunohybridization, immunochemistry, immunoaffinity, near infrared spectroscopy (near IR), light scattering analysis (LS), turbidimetry.

[0021] A second aspect of the present invention provides a product for diagnosing and / or early predicting refractory epilepsy in children.

[0022] Further, the product includes a reagent for detecting the content or concentration of the metabolic marker described in the first aspect of the present invention in a sample;

[0023] Optionally, the reagent includes a reagent for detecting the content or concentration of the metabolite marker in a sample by targeted or non-targeted nuclear magnetic resonance, chromatography, mass spectrometry and / or spectroscopy;

[0024] Optionally, the product further comprises a pretreatment reagent for pretreating the sample;

[0025] Optionally, the product includes a detection kit or a detection chip.

[0026] In some embodiments, the detection kit is used to detect the concentration of the metabolite marker; the concentration of the metabolite marker is obtained by processing a biological sample derived from a subject, and then quantitatively measuring and calculating the concentration of the metabolite marker in the biological sample derived from the subject by a liquid chromatography-mass spectrometry (LC-MS / MS) method to obtain a corresponding result.

[0027] In some embodiments, the detection kit comprises a high performance liquid chromatography detection reagent and an isotope internal standard for detecting the concentration of the metabolite marker; the processing of the biological sample derived from the subject is to use an extraction liquid to extract the metabolites in the biological sample derived from the subject as a sample to be detected, the sample to be detected is an organic solvent dispersion system of the biological sample, and the biological sample is whole blood.

[0028] In some embodiments, the extract is an organic solvent, including but not limited to methanol, ethanol, propanol, ether or acetonitrile.

[0029] In some embodiments, the isotope internal standard is a stable isotope labeled metabolite, and an exemplary isotope internal standard is, for example, tritiated.

[0030] In some embodiments, the liquid chromatography can be any of high performance liquid chromatography (HPLC), gas chromatography (GC), ultra high performance liquid chromatography (UHPLC), size exclusion chromatography (SEC), ion exchange chromatography (IEC), affinity chromatography (AC), or reverse phase chromatography (RP-LC).

[0031] In some embodiments, the separation conditions of the liquid chromatography are as follows: mobile phase 1 is an aqueous solution containing additives, and the additives are selected from any one or a combination of formic acid, acetic acid, ammonium formate and ammonium acetate; mobile phase 2 is selected from one or a combination of methanol, ethanol, acetonitrile, propylene glycol and isopropanol; the stationary phase is selected from a C8 or C18 silica gel packing column, the column temperature is maintained at 25-50°C, the flow rate is set at 0.2-0.6mL / min, and the pH is maintained at 3-8; the detection conditions of the mass spectrometry include: using a data-dependent acquisition mode (IDA) mode for data acquisition, selecting characteristic ion pair information of metabolites, and using standards for information confirmation and detection method establishment, and using internal standards for quantitative calibration to obtain accurate concentration values ​​and related ratio values ​​of each metabolite in the biological sample.

[0032] The separation conditions of the liquid chromatography may include: injecting 2 μL, the mobile phase A is 100% water containing 25 mM ammonium acetate and 25 mM ammonia monohydrate; the mobile phase B is 100% acetonitrile, the chromatographic column is ACQUITY UPLC BEH Amide (1.7 μm, 2.1 mm*100 mm), the column temperature is set to 50° C., the flow rate is 0.5 mL / min, 0-0.5 min maintains 5% A and 95% B; 0.5-7 min, linearly changes from 5% A and 95% B to 35% A and 65% B; 7-8 min, linearly changes from 35% A and 65% B to 60% A and 40% B; 8-9 min, maintains 60% A and 40% B; 9-12 min, linearly changes from 60% A and 40% B to 5% A and 95% B.

[0033] In a specific embodiment of the present invention, the mass spectrometry conditions and the setting of the mass spectrometry qualitative and quantitative detection mode include: using a Triple TOF 6600 mass spectrometer for mass spectrometry analysis, selecting an electrospray ion source (ESI), and selecting an ion scanning mode according to the response of the target compound to be detected; the ion source temperature is set to 600°C, the spray voltage is set to ±5500V, the nebulizer gas auxiliary heating gas 1 (Gas1): 60, the auxiliary heating gas 2 (Gas2), and the curtain gas: 30; the primary mass-to-nuclear ratio detection range: 60-1000Da, the secondary mass-to-nuclear ratio detection range: 25-1000Da, the primary mass spectrometry scanning cumulative time: 0.20s / spectra, and the secondary mass spectrometry scanning cumulative time: 0.05s / spectra; using a triple quadrupole mass spectrometry multiple reaction monitoring (MRM) mode for data acquisition, selecting the characteristic ion pair information of the metabolite, and using a standard to confirm the information and establish the detection method, and using an internal standard for quantitative calibration to obtain the accurate concentration value and related ratio value of each metabolite in the biological sample.

[0034] In some embodiments, the detection kit can be used for the diagnosis and / or early prediction of refractory epilepsy in children, to improve the convenience of diagnosis and / or prediction, and to promote the standardization of diagnosis and / or prediction methods.

[0035] In some embodiments, the detection kit may include a solid substrate such as a chip, a slide, an array, etc., which has a reagent capable of detecting and / or quantifying one or more of the metabolites fixed at a predetermined position on the substrate. As an illustrative example, the chip may be provided with reagents fixed at discrete predetermined positions for detecting and quantifying the content or concentration of the metabolite markers, any amount thereof, or any combination thereof, in a sample derived from a subject.

[0036] In some embodiments, the detection chip has reagents capable of detecting and / or quantifying one or more of the metabolites fixed at predetermined positions on the substrate. As an illustrative example, the chip can be provided with reagents fixed at discrete predetermined positions for detecting and quantifying the content or concentration of the metabolite marker in a sample derived from a subject.

[0037] The third aspect of the present invention provides the use of the metabolic markers described in the first aspect of the present invention in constructing an early warning model for refractory epilepsy in children.

[0038] Furthermore, the early warning model for refractory epilepsy in children uses the content value or concentration value of the metabolite marker in the sample as an input variable.

[0039] A fourth aspect of the present invention provides an early warning device for refractory epilepsy in children based on metabolomics data.

[0040] Furthermore, the early warning device for refractory epilepsy in children comprises:

[0041] A model loading module, used to load the early warning model for refractory epilepsy in children constructed by the application described in the third aspect of the present invention;

[0042] An index value acquisition module, used to obtain the index value of each metabolite marker corresponding to the subject to be diagnosed according to the early warning model for refractory epilepsy in children, wherein the metabolite marker is the metabolite marker described in the first aspect of the present invention;

[0043] An index value detection module is used to determine whether the index value of each metabolic marker corresponding to the subject to be diagnosed exceeds a preset normal value range;

[0044] The childhood refractory epilepsy warning module is used to output the childhood refractory epilepsy warning information of the subject to be diagnosed based on the judgment results.

[0045] A fifth aspect of the present invention provides a system for diagnosing and / or early predicting refractory epilepsy in children using metabolic markers.

[0046] Furthermore, the metabolite marker is the metabolite marker described in the first aspect of the present invention, and the system comprises:

[0047] A detection device is used to detect the level of the metabolite marker in each reference person and the sample of the test subject in the reference group; the reference group is composed of children with intractable epilepsy and healthy people without intractable epilepsy;

[0048] A reference device, used for receiving the information of the levels of the metabolite markers in all the reference group samples output by the detection device, and setting the information of the levels of the metabolite markers in all the reference person samples in the reference group as the known grouping information;

[0049] The comparison device is used to receive the information on the level of the metabolite marker in the sample of the test subject output by the detection device and the known grouping information output by the reference device, set the information on the level of the metabolite marker in the sample of the test subject as unknown information, compare the unknown information with the known grouping information data set, and determine whether the test subject belongs to the childhood refractory epilepsy group or the non-childhood refractory epilepsy group.

[0050] In some embodiments, the device or system of the present invention is a method for distinguishing different components, elements, parts, parts or assemblies of different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions. It is well known to those skilled in the art that the present invention can be implemented as an apparatus, method or computer program product. Therefore, the content disclosed in the present invention can be specifically implemented in the following forms, that is, it can be complete hardware, it can also be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software. In addition, in some specific embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains a computer-readable program code.

[0051] The sixth aspect of the present invention provides the use of the metabolic markers described in the first aspect of the present invention in constructing a computational model for diagnosing and / or early predicting refractory epilepsy in children.

[0052] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the system described in the fifth aspect of the present invention is implemented.

[0053] In some embodiments, the computer-readable medium of the present invention may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0054] Further, more specific examples of computer-readable storage media include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk-read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0055] In addition, the present invention also provides a method for predicting refractory epilepsy in children, the method comprising:

[0056] Obtaining metabolite content data of the sample to be tested;

[0057] Extracting content data of target metabolites from the metabolite content data, wherein the target metabolites are 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine;

[0058] Perform classification prediction based on the content data of the target metabolite to obtain a classification result of whether the sample to be tested is a sample of pediatric refractory epilepsy;

[0059] The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: obtaining the content data of the target metabolite of the training set samples and the clinical characteristics corresponding to the samples, the clinical characteristics including children with refractory epilepsy and healthy children, extracting the content data of the target metabolite in the training set and inputting it into a machine learning model to construct a prediction model, and obtaining a constructed prediction model and threshold;

[0060] If the content of the target metabolite is higher than the threshold, the classification result of the sample to be tested is a sample of refractory childhood epilepsy; if the content of the target metabolite is lower than the threshold, the classification result of the sample to be tested is a sample of non-refractory childhood epilepsy.

[0061] In some embodiments, the machine learning model is a linear regression model, a logistic regression model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a naive Bayes model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model, or a CatBoost model.

[0062] In some embodiments, the metabolite content data is metabolite content data obtained by detecting the sample to be tested by mass spectrometry, chromatography, liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, enzyme-linked immunosorbent assay, fluorescence, nuclear magnetic resonance or spectroscopy.

[0063] In some embodiments, the sample to be tested refers to a sample to be tested derived from a subject to be tested, which can be obtained from the subject's blood and other fluid samples and tissue samples of biological origin, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample can be solid tissue, such as fresh, frozen and / or preserved organ or tissue samples, biopsy tissues or aspirates; blood or any blood component; body fluid; cells from any time of individual pregnancy or development; or plasma.

[0064] In some embodiments, the sample to be tested is a blood sample, a serum sample, a plasma sample, a tissue sample or a cell sample.

[0065] In some embodiments, the subject refers to any animal, and also refers to humans and non-human animals. The non-human animals include all vertebrates, for example, mammals, such as non-human primates (particularly higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cattle, and any livestock or pets; and non-mammals, such as chickens, amphibians, reptiles, etc. In a specific embodiment of the present invention, the subject is a human.

[0066] In some embodiments, the threshold in the machine learning model refers to the critical value used to convert the model output into a classification result. In a binary classification problem, the model usually outputs a real value, which represents the probability of belonging to a certain category. For example, a logistic regression model outputs a probability value between 0 and 1, and the default threshold is usually 0.5, that is, a probability greater than or equal to 0.5 is classified as a positive class, and a probability less than 0.5 is classified as a negative class.

[0067] In some embodiments, a precision-recall curve can be used to select the best threshold. By plotting the precision-recall curve, the impact of different thresholds on model performance can be intuitively seen. A point on the curve is selected so that the precision and recall rate reach the best balance, and this point is the best threshold.

[0068] In some embodiments, the optimal threshold can be selected using the F1 score, which is the harmonic mean of precision and recall, and the optimal threshold is selected by maximizing the F1 score.

[0069] In some embodiments, the ROC curve can be used to select the best threshold. The receiver operating characteristic curve (ROC) can also be used to select the best threshold, and the performance of the model is evaluated by calculating the area under the curve (AUC) to determine the best threshold.

[0070] In some embodiments, when the method for constructing the above-mentioned prediction model is determined, the prediction model includes the threshold value, that is, when the prediction model is determined, the threshold value is also determined. Based on the determined threshold value, the classification result of whether the sample to be tested is a sample of refractory epilepsy in children can be predicted. The specific judgment result based on the prediction model is: if the content of the target metabolite is higher than the threshold value, the classification result of the sample to be tested is a sample of refractory epilepsy in children; if the content of the target metabolite is lower than the threshold value, the classification result of the sample to be tested is a sample of non-refractory epilepsy in children.

[0071] In some embodiments, the effectiveness of the constructed prediction model can also be predicted, that is, another data set containing the content data of the target metabolite corresponding to children with refractory epilepsy and healthy children is obtained, and the effectiveness of the constructed prediction model is verified in the data set.

[0072] In addition, the present invention also provides a prediction system for refractory epilepsy in children, the system comprising:

[0073] A data acquisition unit, which acquires metabolite content data of the sample to be tested;

[0074] A data extraction unit, extracting content data of a target metabolite from the metabolite content data, wherein the target metabolite is 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine;

[0075] A result prediction unit, which performs classification prediction based on the content data of the target metabolite to obtain a classification result of whether the sample to be tested is a sample of pediatric refractory epilepsy;

[0076] The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: obtaining the content data of the target metabolite of the training set samples and the clinical characteristics corresponding to the samples, the clinical characteristics including children with refractory epilepsy and healthy children, extracting the content data of the target metabolite in the training set and inputting it into a machine learning model to construct a prediction model, and obtaining a constructed prediction model and threshold;

[0077] If the content of the target metabolite is higher than the threshold, the classification result of the sample to be tested is a sample of refractory childhood epilepsy; if the content of the target metabolite is lower than the threshold, the classification result of the sample to be tested is a sample of non-refractory childhood epilepsy.

[0078] In some embodiments, the machine learning model is a linear regression model, a logistic regression model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a naive Bayes model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model, or a CatBoost model.

[0079] In some embodiments, the metabolite content data is metabolite content data obtained by detecting the sample to be tested by mass spectrometry, chromatography, liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, enzyme-linked immunosorbent assay, fluorescence, nuclear magnetic resonance or spectroscopy.

[0080] In some embodiments, the sample to be tested is a blood sample, a serum sample, a plasma sample, a tissue sample or a cell sample.

[0081] In addition, the present invention also provides a computer prediction device for intractable epilepsy in children, the computer prediction device comprising: a memory and a processor, the memory is used to store program instructions; the processor is used to call program instructions, and when the program instructions are executed, the prediction method for intractable epilepsy in children as described above is implemented.

[0082] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for predicting intractable epilepsy in children as described above is implemented.

[0083] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the following embodiments, which will not be repeated here. In the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, each functional unit may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units.

[0085] A person skilled in the art can understand that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0086] A person skilled in the art will understand that all or part of the steps in the above method can be implemented by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0087] In addition, the present invention also provides a method for diagnosing, screening and / or early prediction of refractory epilepsy in children, the method comprising: detecting the content or concentration of metabolic markers 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamate, C-6 ceramide and / or arginine in a sample derived from a subject in need, and diagnosing, screening and / or early prediction of whether the subject is a patient with refractory epilepsy in children or is at risk of refractory epilepsy in children based on the test results.

[0088] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0089] The present invention conducts metabolomics analysis on samples of refractory epilepsy in children and finds for the first time that the metabolite markers 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine can be used for the diagnosis and / or early prediction of refractory epilepsy in children. It is further found that the metabolite markers have extremely high diagnostic value for refractory epilepsy in children, and can diagnose and / or early predict whether the subject to be tested suffers from refractory epilepsy in children or the risk of suffering from refractory epilepsy in children with high sensitivity, high accuracy and high specificity, which effectively promotes the precise diagnosis and treatment of refractory epilepsy in children and has broad prospects for clinical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 The up-regulation folds of five metabolites, 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine, in the pediatric refractory epilepsy group of the training set;

[0091] Figure 2 This is a bar graph showing the mean relative quantitative values ​​of five metabolites, 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine, detected in samples of children with refractory epilepsy (33 cases) and normal children (41 cases) in the training set using targeted metabolomics;

[0092] Figure 3 The ROC curve results for the five metabolites 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine in the training set are shown in FIG.

[0093] Figure 4This is a bar graph showing the mean relative quantitative values ​​of five metabolites, 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine, detected by targeted metabolomics in the validation set of children with refractory epilepsy (31 cases) and normal children (32 cases);

[0094] Figure 5 This is the ROC curve result corresponding to the five metabolites 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine in the validation set. DETAILED DESCRIPTION

[0095] The inventors of the present invention have conducted extensive and in-depth research, and through a large amount of screening, testing and verification, have provided metabolic markers that can be used for accurate diagnosis and / or early prediction of refractory epilepsy in children for the technical field of diagnosis of refractory epilepsy in children. The inventors of the present invention have discovered for the first time that the metabolic markers 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine all have high diagnostic efficacy for refractory epilepsy in children. The application of the metabolic markers in the diagnosis of refractory epilepsy in children has extremely high diagnostic value, high accuracy, sensitivity and specificity, and can be used for the effective diagnosis of refractory epilepsy in children. The present invention has been completed on this basis.

[0096] In order to enable those skilled in the art to better understand the technical solution of the present invention, some of the terms involved in the present invention are explained as follows. Unless otherwise specified, the following terms involved in the application documents of the present invention refer to the following contents that are well known in the art.

[0097] As used herein, the terms "comprising" or "including" refer to the compositions, methods and their corresponding components present in a given embodiment (ie, may be closed), but may also be open and include unspecified elements.

[0098] As used herein, the term "metabolic marker" refers to a compound suitable as an indicator of the presence and state of a disease or patient, which is a metabolite or metabolic compound that occurs during the metabolic process in a mammal. In the present invention, the term "metabolic marker" refers to a compound suitable as an indicator of the presence and state of refractory epilepsy in children, which is a metabolite or metabolic compound that occurs during the metabolic process in a mammal.

[0099] In some embodiments, the "metabolite marker" refers to any one or more of 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine.

[0100] In some embodiments, the metabolic markers provided by the present invention can be used alone for the diagnosis and / or early prediction of refractory epilepsy in children, or can be used in combination for the diagnosis and / or early prediction of refractory epilepsy in children to assess the refractory epilepsy status of a subject, including the presence or absence of refractory epilepsy in children, the risk of occurrence of refractory epilepsy in children, and the detection of disease progression of refractory epilepsy in children. In addition, based on the refractory epilepsy status of the subject, additional procedures can be indicated, including, for example, additional diagnostic tests or treatment procedures.

[0101] As used herein, the term "risk" refers to whether a subject (child) suffers from refractory childhood epilepsy; the output of assessing the risk of a subject suffering from refractory childhood epilepsy using the metabolic markers or methods provided by the present invention is an output of "yes" or "no" obtained by comparison with a pre-set standard value (i.e., the value of a normal child).

[0102] As used herein, the term "diagnosis" or "early diagnosis" refers to the identification or classification of a molecular or pathological state, disease or condition. For example, by molecular features (e.g., specific metabolites, proteins encoded by specific genes, specific microbial communities, specific genes, etc.), early differential diagnosis of whether a subject suffers from refractory epilepsy in children or the risk of refractory epilepsy in children. In a specific embodiment of the present invention, the molecular feature is a specific metabolite (any one or any multiple of 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine).

[0103] As used herein, the term "prediction" or "early prediction" refers to assessing whether a subject is at or not at risk of suffering from refractory epilepsy in children. In some embodiments, it should be assessed whether the subject's risk is at an increased risk or a reduced risk compared to the average risk of the subject population. The term "predicting refractory epilepsy in children" or "early prediction of refractory epilepsy in children" means herein that the subject is analyzed by the method of the present invention to be divided into a group of subjects at risk of suffering from refractory epilepsy in children, or into a group of subjects not at risk of suffering from refractory epilepsy in children.

[0104] According to the present invention, having a risk of developing refractory epilepsy in children preferably means an increased risk of developing refractory epilepsy in children (preferably falling within the prediction window). According to the present invention, a subject who is not at risk of developing refractory epilepsy in children preferably has a reduced risk of developing refractory epilepsy in children (preferably falling within the prediction window). A subject who is at risk of developing refractory epilepsy in children preferably has a risk of developing refractory epilepsy in children of 10-20% or more, more preferably 20% or more. A subject who is not at risk of developing refractory epilepsy in children preferably has a risk of developing refractory epilepsy in children of less than 10%, more preferably less than 5% or less.

[0105] As used herein, the term "sample" refers to a composition obtained from or derived from a target subject, which contains cell entities and / or other molecular entities to be characterized and / or identified, for example, based on physical, biochemical, chemical and / or physiological characteristics. In some embodiments, the "sample" can be obtained from the subject's blood and other fluid samples of biological origin and tissue samples, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample can be solid tissue, such as from fresh, frozen and / or preserved organ or tissue samples, biopsy tissues or aspirates; blood or any blood component; body fluids; cells from any time of individual pregnancy or development; or plasma. The sample includes biological samples that have been processed in any way after it is obtained, such as reagent treatment, stabilization, or enrichment for certain components (such as proteins or polynucleotides), or embedded in a semi-solid or solid matrix for sectioning purposes.

[0106] In some embodiments, the "sample" includes, but is not limited to, blood, plasma, serum, lymph, synovial fluid, sweat, saliva, tears, feces, urine, cerebrospinal fluid, cells, tissues or organs from a subject, etc. The techniques for obtaining the above different types of biological samples are well known in the art.

[0107] As used herein, the term "subject" refers to any animal, including humans and non-human animals. The non-human animals include all vertebrates, for example, mammals, such as non-human primates (particularly higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cattle, and any livestock or pets; and non-mammals, such as chickens, amphibians, reptiles, etc. In a specific embodiment of the present invention, the subject is preferably a human.

[0108] As used herein, the term "AUC" refers to the area under the curve of a receiver operating characteristic (ROC) curve, which is well known in the art. The AUC measurement is useful for comparing the accuracy of classifiers across the entire data range. A classifiers with higher AUCs have a higher ability to correctly classify unknowns between two or more target groups. ROC curves are useful for describing the performance of specific features (e.g., any metabolic markers described herein and / or any entry of additional biomedical information) when distinguishing between two populations.

[0109] Typically, feature data is selected across the entire population in ascending order based on the value of a single feature. Then, for each value of the feature, the true positive rate and false positive rate of the data are calculated. The true positive rate is determined by counting the number of cases above the value of the feature and dividing by the total number of cases. The false positive rate is determined by counting the number of controls above the value of the feature and dividing by the total number of controls.

[0110] ROC curves can be generated with respect to individual features or other individual outputs. For example, a combination of two or more features can be combined mathematically (e.g., added, subtracted, multiplied, etc.) to provide a single sum value, and the single sum value can be plotted in the ROC curve. In addition, any combination of multiple features in which the combination is derived from individual output values ​​can be plotted in the ROC curve, and the ROC curve can be used to analyze the accuracy of the diagnosis.

[0111] The present invention is further described below in conjunction with specific embodiments, which are only used to explain the present invention and cannot be understood as limiting the present invention. It can be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and purpose of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0112] The reagents and raw materials used in the present invention are easily obtained by ordinary technicians in the field and can be obtained from commercial channels unless otherwise specified. The experimental methods in the present invention that do not specify specific conditions are usually carried out under conventional conditions or under conditions recommended by the manufacturer. In particular, the following examples are only used to illustrate the present invention and should not limit the scope of the present invention in any way.

[0113] Example 1 Collection of whole blood samples from children

[0114] Refer to the "Guidelines for Venous Blood Collection" (People's Military Medical Publishing House), strictly follow the technical procedures for venous blood collection for children's disease detection, collect blood from the arm vein, and use medical heparin anticoagulant tubes to store the collected whole blood samples.

[0115] The whole blood samples collected by the present invention include a training set: 33 whole blood samples of children with refractory epilepsy and 41 whole blood samples of healthy children; a validation set: 31 whole blood samples of children with refractory epilepsy and 32 whole blood samples of healthy children. The design and implementation of this study were approved and supervised by the Medical Ethics Committee, and written informed consent was obtained from all patients.

[0116] Example 2 Targeted metabolomics screening of potential metabolic markers related to refractory epilepsy in children

[0117] After separation using an Agilent 1290 Infinity LC ultra-high performance liquid chromatography (UHPLC) system, a Triple TOF6600 mass spectrometer was used to quantitatively detect 300 metabolites in the training set whole blood samples, including amino acids, organic acids, fatty acids, reducing sugars, bile acids, carnitines, phenyl or benzyl derivatives, and indoles.

[0118] The whole blood sample pretreatment method used was as follows: the frozen whole blood samples were slowly thawed at 4°C, 100 μL of each whole blood sample was taken, 400 μL of ice-cold methanol / acetonitrile solution (1:1, v / v) was added, and the mixture was vortexed to mix evenly. The sample was placed at -20°C for 30 min, and then centrifuged at 14000g for 20 min at 4°C. The supernatant was taken and vacuum dried. For mass spectrometry analysis, 100 μL of acetonitrile aqueous solution (acetonitrile: water = 1:1, v / v) was added for reconstitution, vortexed, and centrifuged at 14000g for 15 min at 4°C. The supernatant was taken for sampling and analysis.

[0119] The liquid chromatography and mass spectrometry detection conditions adopted were as follows: 2 μL injection, mobile phase A was 100% water containing 25 mM ammonium acetate and 25 mM ammonia monohydrate; mobile phase B was 100% acetonitrile, the chromatographic column was ACQUITY UPLC BEH Amide (1.7 μm, 2.1 mm*100 mm), the column temperature was set to 50°C, the flow rate was 0.5 mL / min, 0-0.5 min maintained 5% A and 95% B; 0.5-7 min, linearly changed from 5% A and 95% B to 35% A and 65% B; 7-8 min, linearly changed from 35% A and 65% B to 60% A and 40% B; 8-9 min, maintained 60% A and 40% B; 9-12 min, linearly changed from 60% A and 40% B to 5% A and 95% B.

[0120] ProteoWizard and XCMS were used to convert and statistically analyze the metabolite data detected, and 5 differential metabolites were obtained. The data are shown in Table 1 and Figure 1 The results showed that the five metabolites, 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine, were significantly differentially expressed between the childhood refractory epilepsy group and the healthy control group (P < 0.005).

[0121] Table 1 Five differential metabolites associated with refractory epilepsy in children

[0122]

[0123] Example 3 Pretreatment of children's whole blood samples and extraction of diagnostic marker composition

[0124] For the above 137 whole blood samples, the frozen whole blood samples were slowly thawed in a low temperature environment (4°C), and 100 μL was taken respectively, and 400 μL of ice-cold methanol / acetonitrile solution (1:1, v / v) was added, and the samples were evenly mixed by vortexing. After evenly mixing, the samples were placed at -20°C for 30 min, and then centrifuged at 14000g for 20 min at 4°C. After centrifugation, the supernatant was taken and vacuum dried. When mass spectrometry analysis is required, 100 μL of acetonitrile aqueous solution (acetonitrile: water = 1:1, v / v) must be added for re-dissolution, vortexed, and centrifuged at 14000g for 15 min at 4°C, and the supernatant was taken for mass spectrometry detection.

[0125] Example 4 High Performance Liquid Chromatography-Mass Spectrometry Detection of Diagnostic Metabolic Composition

[0126] Liquid chromatography separation conditions and parameters are as follows: 2 μL injection, mobile phase A is 100% water containing 25 mM ammonium acetate and 25 mM ammonia monohydrate; mobile phase B is 100% acetonitrile, the chromatographic column is ACQUITY UPLC BEH Amide (1.7 μm, 2.1 mm*100 mm), the column temperature is set to 50°C, the flow rate is 0.5 mL / min, 0-0.5 min maintains 5% A and 95% B; 0.5-7 min, linearly changes from 5% A and 95% B to 35% A and 65% B; 7-8 min, linearly changes from 35% A and 65% B to 60% A and 40% B; 8-9 min, maintains 60% A and 40% B; 9-12 min, linearly changes from 60% A and 40% B to 5% A and 95% B.

[0127] The mass spectrometry detection parameters were as follows: ProteoWizard and XCMS were used to convert and statistically analyze the metabolite data obtained.

[0128] Under the training set, the differential expression results corresponding to the five metabolites are shown in the figure below: Figure 2 As shown, the results showed that the five metabolites 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine were significantly differentially expressed between the childhood refractory epilepsy group and the healthy control group.

[0129] In the validation set, the differential expression results of the five metabolites are shown in the figure below: Figure 4 As shown, the results showed that the five metabolites, 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine, were also significantly differentially expressed between the childhood refractory epilepsy group and the healthy control group.

[0130] Example 5 ROC curve of training set whole blood samples

[0131] In this example, whole blood samples of 33 children with refractory epilepsy and 41 healthy children were used as training sets. The R package "pROC" was used to draw the receiver operating curve (ROC), and the AUC values, sensitivity, and specificity of the five differentially expressed metabolites obtained by the above screening alone or in any combination for the diagnosis of refractory epilepsy in children were analyzed to determine their diagnostic efficacy for refractory epilepsy in children.

[0132] Among them, when evaluating and analyzing the diagnostic efficacy of a single metabolite for refractory epilepsy in children, the content of the metabolite is used for evaluation and analysis, and the point corresponding to the maximum Youden index is selected as its cutoff value, that is, the optimal partition threshold is determined by the point with the largest Youden index; when evaluating and analyzing the diagnostic efficacy of the combined metabolites for refractory epilepsy in children, the combined metabolites are first subjected to a Logistics regression analysis. In the Logistics regression analysis, the independent variable is the combined metabolite, and the dependent variable is the prevalence of refractory epilepsy in children. The probability of each individual suffering from refractory epilepsy in children can be calculated by fitting the regression curve, and the prediction result can be obtained by determining different probability partition thresholds. The optimal probability partition threshold is determined by the point with the largest Youden index. According to the determined probability partition threshold, the AUC value, sensitivity, specificity, etc. of the combined metabolite detection in the training set are calculated. The AUC values, sensitivity, and specificity of the obtained single metabolites and combined metabolites are analyzed to determine their diagnostic efficacy.

[0133] Under the training set, the ROC curve results corresponding to the five metabolites are shown in the figure below: Figure 3 As shown, the area under the ROC curve AUC of the training set is as follows:

[0134] In the training set, the area under the curve of 3-hydroxyvalproic acid AUC = 0.768 (sensitivity: 61.8%; specificity: 91.3%);

[0135] In the training set, the area under the ROC curve of trans-2-octenoic acid was AUC = 0.814 (sensitivity: 63.6%; specificity: 95.6%);

[0136] In the training set, the area under the glutamate curve AUC = 0.957 (sensitivity: 87.7%; specificity: 100%);

[0137] In the training set, the area under the curve of C-6 ceramide was AUC = 0.8 (sensitivity: 61.8%; specificity: 94.6%);

[0138] In the training set, the area under the curve of arginine AUC=0.757 (sensitivity: 62.1%; specificity: 89.8%).

[0139] Example 6 Validation set whole blood sample ROC curve

[0140] In this example, whole blood samples from 31 children with refractory epilepsy and 32 healthy children were used as validation sets. The R package "pROC" was used to draw the receiver operating curve (ROC) to analyze the AUC value, sensitivity, and specificity of the five differentially expressed metabolites obtained by the above screening alone or in any combination for the diagnosis of refractory epilepsy in children, and to determine their diagnostic efficacy for refractory epilepsy in children.

[0141] Among them, when evaluating and analyzing the diagnostic efficacy of a single metabolite for refractory epilepsy in children, the content of the metabolite is used for evaluation and analysis, and the point corresponding to the maximum Youden index is selected as its cutoff value, that is, the optimal partition threshold is determined by the point with the largest Youden index; when evaluating and analyzing the diagnostic efficacy of combined metabolites for refractory epilepsy in children, firstly, a Logistics regression analysis is performed on the combined metabolites. In the Logistics regression analysis, the independent variable is the combined metabolite, and the dependent variable is the prevalence of refractory epilepsy in children. The probability of each individual suffering from refractory epilepsy in children can be calculated by fitting the regression curve, and the prediction result can be obtained by determining different probability partition thresholds. The optimal probability partition threshold is determined by the point with the largest Youden index. According to the determined probability partition threshold, the AUC value, sensitivity, specificity, etc. of the combined metabolite detection in the validation set are calculated. The AUC values, sensitivity, and specificity of the obtained single metabolites and combined metabolites are analyzed to determine their diagnostic efficacy.

[0142] In the validation set, the ROC curve results corresponding to the five metabolites are shown in the figure below: Figure 5 As shown, the area under the ROC curve AUC of the validation set is as follows:

[0143] In the validation set, the area under the curve of 3-hydroxyvalproic acid AUC = 0.796 (sensitivity: 62.8%; specificity: 97.1%);

[0144] In the validation set, the area under the ROC curve of trans-2-octenoic acid was AUC = 0.837 (sensitivity: 64.1%; specificity: 98.6%);

[0145] In the validation set, the area under the glutamate curve AUC = 0.921 (sensitivity: 76.8%; specificity: 100%);

[0146] In the validation set, the area under the curve of C-6 ceramide was AUC = 0.875 (sensitivity: 69.8%; specificity: 98.4%);

[0147] In the validation set, the area under the curve of arginine AUC=0.75 (sensitivity: 61.8%; specificity: 91.8%).

[0148] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. Use of a metabolic marker in the preparation of a product for diagnosis and / or early prediction of refractory epilepsy in children, characterized in that: The metabolic markers are 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine.

2. The use according to claim 1, characterized in that: The product includes a reagent for detecting the content or concentration of the metabolite marker in a sample.

3. The use according to claim 2, characterized in that: The reagents include reagents for detecting the content or concentration of the metabolite marker in a sample by targeted or non-targeted nuclear magnetic resonance, chromatography, mass spectrometry and / or spectroscopy.

4. The use according to claim 2, characterized in that: The sample is blood, serum and / or plasma.

5. The use according to claim 1, characterized in that: The product includes a detection kit or a detection chip.

6. A product for diagnosing and / or early predicting refractory epilepsy in children, characterized in that: The product includes a reagent for detecting the content or concentration of the metabolic marker described in claim 1 in a sample; Optionally, the reagent includes a reagent for detecting the content or concentration of the metabolite marker in a sample by targeted or non-targeted nuclear magnetic resonance, chromatography, mass spectrometry and / or spectroscopy; Optionally, the product further comprises a pretreatment reagent for pretreating the sample; Optionally, the product includes a detection kit or a detection chip.

7. The use of the metabolic markers described in claim 1 in constructing an early warning model for refractory epilepsy in children, characterized in that: The childhood refractory epilepsy early warning model uses the content value or concentration value of the metabolite marker in the sample as an input variable.

8. A device for early warning of refractory epilepsy in children based on metabolomics data, characterized in that: The early warning device for refractory epilepsy in children comprises: A model loading module, used to load the early warning model for refractory epilepsy in children constructed by the application of claim 7; An index value acquisition module, used for obtaining the index value of each metabolite marker corresponding to the subject to be diagnosed according to the early warning model for refractory epilepsy in children, wherein the metabolite marker is the metabolite marker described in claim 1; An index value detection module is used to determine whether the index value of each metabolic marker corresponding to the subject to be diagnosed exceeds a preset normal value range; The childhood refractory epilepsy warning module is used to output the childhood refractory epilepsy warning information of the subject to be diagnosed based on the judgment results.

9. A system for diagnosing and / or early predicting refractory epilepsy in children using metabolic markers, characterized in that: The metabolite marker is the metabolite marker described in claim 1, and the system comprises: A detection device is used to detect the level of the metabolite marker in each reference person and the sample of the test subject in the reference group; the reference group is composed of children with intractable epilepsy and healthy people without intractable epilepsy; A reference device, used for receiving the information of the levels of the metabolite markers in all the reference group samples output by the detection device, and setting the information of the levels of the metabolite markers in all the reference person samples in the reference group as the known grouping information; The comparison device is used to receive the information on the level of the metabolite marker in the sample of the test subject output by the detection device and the known grouping information output by the reference device, set the information on the level of the metabolite marker in the sample of the test subject as unknown information, compare the unknown information with the known grouping information data set, and determine whether the test subject belongs to the childhood refractory epilepsy group or the non-childhood refractory epilepsy group.

10. Use of the metabolic markers described in claim 1 in constructing a computational model for diagnosis and / or early prediction of refractory epilepsy in children.

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