A metabolite combination for evaluating refractory epilepsy in children and its application

Abstract

The present invention discloses a metabolite combination for evaluating refractory epilepsy in children and its application. For the first time, the present invention discovers that the metabolite markers 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide and / or arginine can be used in the diagnosis and / or early prediction of refractory epilepsy in children, and further discovers that the metabolite markers have extremely high diagnostic value for refractory epilepsy in children, and can diagnose and / or early predict with high accuracy, high sensitivity and high specificity whether a subject to be tested has refractory epilepsy in children or the risk of suffering from refractory epilepsy in children, effectively promoting the precise diagnosis and treatment of refractory epilepsy in children, and having broad clinical application prospects.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and specifically, relates to a metabolite combination for evaluating refractory epilepsy in children and its application. Background Art

[0002] Epilepsy is one of the most common neurological diseases in children, characterized by massive abnormal synchronous discharges of neurons, resulting in recurrent, episodic, and transient brain dysfunction. The incidence of epilepsy in children is about 15 times that of adults. 60 - 70% of children with epilepsy can achieve control or symptom remission after regular treatment, but there are still 20 - 30% of children who cannot be effectively controlled or relapse after treatment, developing into refractory epilepsy in children. Refractory epilepsy in children seriously endangers the physical and mental health of children. The course of refractory epilepsy in children 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, causing a heavy burden on families and society.

[0003] Compared with adult epilepsy patients, the etiology of epilepsy in children is complex, the pathogenesis is unclear, and the treatment methods are lacking, making the diagnosis and treatment of refractory epilepsy in children face major challenges. Currently, clinically, the diagnosis and treatment of refractory epilepsy in children are carried out through the child's gender, age, and clinical data within 6 months after epilepsy diagnosis, including the situation during maternal pregnancy and perinatal period, previous relevant medical history and relevant family history, age of onset, etiology, seizure type before treatment, seizure duration, seizure frequency, status epilepticus during the course of the disease, initial and post-treatment electroencephalogram results, imaging results, and the effect after treatment with antiepileptic drugs. Therefore, when clinically diagnosing and treating 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 during diagnosis and treatment. In addition, in the early stage of epilepsy in children, the clinical manifestations, biochemical indicators, clinical imaging, and histological characteristics of children overlap with those of other brain diseases, which further increases the tortuosity and complexity of the diagnosis process and easily leads to delays in the diagnosis and treatment of epilepsy in children.

[0004] When epilepsy attacks, multiple metabolites and metabolic pathways change, such as energy metabolism, amino acid metabolism, purine metabolism, tricarboxylic acid cycle, etc. In recent years, the research on metabolites and epilepsy in children has been relatively popular. However, at present, there is a lack of metabolic markers in this field 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 for evaluating refractory epilepsy in children and its application in this field, so as to provide guidance for future clinical work related to the disease of refractory epilepsy in children.

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

[0007] The first aspect of the present invention provides the use of metabolic markers in the preparation of products for diagnosing and / or early predicting refractory epilepsy in children.

[0008] Furthermore, the metabolic markers are 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 metabolic marker in a sample.

[0010] Furthermore, the reagent includes a reagent for detecting the content or concentration of the metabolic marker in a sample by targeted or non-targeted nuclear magnetic resonance method, 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 corresponding English names of 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-6 ceramide (CAS: 124753-97-5), respectively.

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

[0015] In a specific embodiment of the present invention, the present invention proves through experiments that the above metabolic markers have good diagnostic efficacy for the early diagnosis or prediction of refractory epilepsy in children, can be used in the screening and early diagnosis of refractory epilepsy in children, and maximize the prognosis of children with refractory epilepsy.

[0016] The metabolic markers, detection kits, detection chips, detection devices or detection systems provided by the present invention for diagnosing, screening and / or early predicting refractory epilepsy in children have the advantages of high detection throughput, high sensitivity, good specificity, etc. 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 spectrometry includes, but is not limited to: refractive index spectrometry, ultraviolet spectrometry, near-infrared spectrometry, and / or any combination thereof.

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

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

[0022] Furthermore, 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 metabolic marker in the sample by targeted or non-targeted nuclear magnetic resonance method, chromatography, mass spectrometry, and / or spectrometry;

[0024] Optionally, the product further includes 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 metabolic marker; the concentration of the metabolic marker is obtained by quantitatively measuring and calculating the concentration of the metabolic marker in a biological sample from a subject by liquid chromatography-tandem mass spectrometry (LC-MS / MS) method after processing the biological sample from the subject.

[0027] In some embodiments, the detection kit comprises a high performance liquid chromatography detection reagent for detecting the concentration of the metabolite marker and an isotope internal standard; the processing of the biological sample from the subject is to extract the metabolites in the biological sample from the subject using an extraction solution 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 extraction solution is an organic solvent, and the organic solvent includes but is not limited to: methanol, ethanol, propanol, ether or acetonitrile.

[0029] In some embodiments, the isotope internal standard is a metabolite labeled with a stable isotope, and an exemplary isotope internal standard is tritium labeling.

[0030] In some embodiments, the liquid chromatography can be any one 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 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 an additive, and the additive is 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 a silica gel packing column of C8 or C18, the column temperature is maintained at 25-50 °C, the flow rate is set at 0.2-0.6 mL / min, and the PH is maintained at 3-8; the detection conditions of the mass spectrometry include: data acquisition is carried out in the data-dependent acquisition mode (IDA) mode, the characteristic ion pair information of the metabolite is selected, and the information is confirmed and the detection method is established using a standard product. At the same time, an internal standard product is used for quantitative calibration to obtain the 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 of sample, mobile phase A being 100% water containing 25 mM ammonium acetate and 25 mM ammonium hydroxide monohydrate; mobile phase B being 100% acetonitrile, the chromatographic column being ACQUITY UPLC BEH Amide (1.7 μm, 2.1 mm * 100 mm), the column temperature being set at 50 °C, the flow rate being 0.5 mL / min, maintaining 5% of A and 95% of B from 0 - 0.5 min; from 0.5 - 7 min, linearly changing from 5% of A and 95% of B to 35% of A and 65% of B; from 7 - 8 min, linearly changing from 35% of A and 65% of B to 60% of A and 40% of B; from 8 - 9 min, maintaining 60% of A and 40% of B; from 9 - 12 min, linearly changing 60% of A and 40% of B to 5% of A and 95% of B.

[0033] In a specific embodiment of the present invention, the conditions of the mass spectrometry and the set mass spectrometry qualitative and quantitative detection modes include: performing mass spectrometry analysis using a Triple TOF 6600 mass spectrometer, adopting an electrospray ionization source (ESI), and selecting an ion scanning mode according to the response of the detected target compound; setting the ion source temperature at 600 °C, the spray voltage at ±5500 V, the nebulizing gas and auxiliary heating gas 1 (Gas1): 60, the auxiliary heating gas 2 (Gas2), and the curtain gas: 30; the detection range of the primary mass-to-charge ratio: 60 - 1000 Da, the detection range of the secondary mass-to-charge ratio: 25 - 1000 Da, the cumulative time of the primary mass spectrometry scan: 0.20 s / spectra, the cumulative time of the secondary mass spectrometry scan: 0.05 s / spectra; adopting the triple quadrupole mass spectrometry multiple reaction monitoring (MRM) mode for data acquisition, selecting the characteristic ion pair information of the metabolites, and using a standard product to confirm the information and establish the detection method, and at the same time using an internal standard product for quantitative calibration to obtain the accurate concentration values and related ratio values 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 childhood refractory epilepsy, improving the convenience of diagnosis and / or prediction, and promoting the standardization of diagnosis and / or prediction methods.

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

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

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

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

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

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

[0041] A model loading module for loading the 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 for obtaining the index value of each metabolite marker corresponding to a subject to be diagnosed according to the warning model for refractory epilepsy in children, where the metabolite markers are the metabolite markers described in the first aspect of the present invention;

[0043] An index value detection module for determining whether the index value of each metabolite marker corresponding to a subject to be diagnosed exceeds a preset normal value range;

[0044] A warning module for refractory epilepsy in children for outputting a warning message for refractory epilepsy in children of the subject to be diagnosed according to the judgment result.

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

[0046] Furthermore, the metabolite markers are the metabolite markers described in the first aspect of the present invention, and the system includes:

[0047] A detection device for detecting the levels of the metabolite markers in the samples of each reference subject and the subject to be tested in the reference group; the reference group consists of children with refractory epilepsy and healthy children without refractory epilepsy;

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

[0049] A comparison device, configured to receive information on the level of the metabolic marker in a sample of a subject to be tested output by a detection device and known grouping information output by a reference device, set the information on the level of the metabolic marker in the sample of the subject to be tested as unknown information, compare the unknown information with the dataset of the known grouping information, and determine whether the subject to be tested belongs to the group of children with refractory epilepsy or the group of non-children with refractory epilepsy.

[0050] In some embodiments, the device or system of the present invention is a method for distinguishing different components, elements, parts, portions or assemblies of different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions. Those skilled in the art of the present technical field are well aware that the present invention can be implemented as a device, a method or a computer program product. Therefore, the content disclosed by the present invention can be specifically implemented in the following forms, that is, it can be completely hardware, or completely software (including firmware, resident software, microcode, etc.), or 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 media contain computer-readable program codes.

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

[0052] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and 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 described in the present invention can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.

[0054] Further, more specific examples of the computer-readable storage medium include, but are not limited to: an electrical connection having one or more wires, 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), an 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 above.

[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 a sample to be tested;

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

[0058] Performing classification prediction based on the content data of the target metabolites to obtain a classification result as to whether the sample to be tested is a sample of refractory epilepsy in children;

[0059] The classification result is obtained based on a prediction model, and the construction method of the prediction model comprises: obtaining the content data of the target metabolites of a training set sample and the corresponding clinical features of the sample, the clinical features including children with refractory epilepsy and healthy children, extracting the content data of the target metabolites in the training set and inputting them into a machine learning model to construct a prediction model, obtaining the constructed prediction model and a threshold value;

[0060] If the content of the target metabolite is higher than the threshold value, obtaining a classification result that 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, obtaining a classification result that the sample to be tested is a non-refractory epilepsy in children sample.

[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 method, nuclear magnetic resonance method 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 blood of the subject 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 from fresh, frozen and / or preserved organ or tissue samples, biopsy tissue or aspirates; blood or any blood component; body fluid; cells from any time of an individual's 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, including humans and non-human animals. The non-human animals include all vertebrates, such as mammals, e.g., non-human primates (especially higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cows, and any domestic animals 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 class. For example, a logistic regression model outputs a probability value between 0 and 1. Usually, the default threshold is 0.5, that is, those with a probability greater than or equal to 0.5 are classified as the positive class, and those less than 0.5 are classified as the negative class.

[0067] In some embodiments, a Precision-Recall Curve can be used to select the optimal threshold. By plotting the Precision-Recall Curve, the impact of different thresholds on the model performance can be intuitively seen. Select a point on the curve such that the precision and recall reach the best balance, and this point is the optimal threshold.

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

[0069] In some embodiments, an ROC curve can be used to select the optimal threshold. The Receiver Operating Characteristic Curve (ROC) can also be used to select the optimal threshold. The performance of the model is evaluated by calculating the Area Under the Curve (AUC), and then the optimal threshold is determined.

[0070] In some embodiments, when the construction method of the above prediction model is determined, the prediction model contains the threshold, that is, when the prediction model is determined, the threshold is also determined. Based on the determined threshold, the classification result of whether the sample to be tested is a sample of childhood refractory epilepsy 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, the classification result that the sample to be tested is a sample of childhood refractory epilepsy is obtained; if the content of the target metabolite is lower than the threshold, the classification result that the sample to be tested is not a sample of childhood refractory epilepsy is obtained.

[0071] In some embodiments, the efficacy of the constructed prediction model can also be predicted, that is, another dataset containing the content data of the target metabolites corresponding to children with refractory epilepsy and healthy children is taken, and the efficacy of the constructed prediction model is verified in this dataset.

[0072] In addition, the present invention also provides a prediction system for children with refractory epilepsy, and the system includes:

[0073] A data acquisition unit that acquires metabolite content data of a sample to be tested;

[0074] A data extraction unit that extracts the content data of the target metabolites in the metabolite content data, and the target metabolites are 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and / or arginine;

[0075] A result prediction unit that performs classification prediction based on the content data of the target metabolites to obtain a classification result as to whether the sample to be tested is a sample of children with refractory epilepsy;

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

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

[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 method, nuclear magnetic resonance method, 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, which includes: a memory and a processor, the memory is used to store program instructions; the processor is used to call the 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, and when the computer program is executed by a processor, the prediction method for intractable epilepsy in children as described above is implemented.

[0083] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the following embodiments, and 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 illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, 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 displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be in 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 may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0085] Those of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by instructing relevant hardware through a program, and this program can be stored in a computer-readable storage medium. The storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0086] Those of ordinary skill in the art can understand that all or part of the steps in implementing the above - mentioned method can be completed by instructing relevant hardware through a program, and the said program can be stored in a computer - readable storage medium. The above - mentioned storage medium can be a read - only memory, a magnetic disk or an optical disc, etc.

[0087] In addition, the present invention also provides a method for diagnosing, screening and / or early predicting childhood refractory epilepsy, and the method includes: detecting the content or concentration of metabolite markers 3 - hydroxyvalproic acid, trans - 2 - octenoic acid, glutamate, C - 6 ceramide and / or arginine in a sample from a subject in need, and diagnosing, screening and / or early predicting whether the subject is a childhood refractory epilepsy patient or the risk of suffering from childhood refractory epilepsy based on the detection result.

[0088] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:

[0089] Through metabolomics analysis and research on childhood refractory epilepsy samples, the present invention first discovers that metabolite markers 3 - hydroxyvalproic acid, trans - 2 - octenoic acid, glutamate, C - 6 ceramide and / or arginine can be used in the diagnosis and / or early prediction of childhood refractory epilepsy, and further discovers that the said metabolite markers have extremely high diagnostic value for childhood refractory epilepsy, and can diagnose and / or early predict whether a subject to be tested has childhood refractory epilepsy or the risk of suffering from childhood refractory epilepsy with high sensitivity, high accuracy and high specificity, effectively promoting the precise diagnosis and treatment of childhood refractory epilepsy, and having broad clinical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 is the up - regulation multiple of 5 metabolites, namely 3 - hydroxyvalproic acid, trans - 2 - octenoic acid, glutamate, C - 6 ceramide, and arginine, in the training set of childhood refractory epilepsy group;

[0091] Figure 2 is a bar chart of the mean relative quantitative values of 5 metabolites, namely 3 - hydroxyvalproic acid, trans - 2 - octenoic acid, glutamate, C - 6 ceramide, and arginine, detected by targeted metabolomics in samples of children with refractory epilepsy (33 cases) and normal children (41 cases) in the training set;

[0092] Figure 3 is the ROC curve result chart of 5 metabolites, namely 3 - hydroxyvalproic acid, trans - 2 - octenoic acid, glutamate, C - 6 ceramide, and arginine, in the training set;

[0093] Figure 4It is a bar chart of the mean relative quantification values of 5 metabolites, namely 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamate, C6-ceramide, and arginine, in the samples of 31 children with refractory epilepsy and 32 normal children in the validation set detected by targeted metabolomics;

[0094] Figure 5 It is a ROC curve result chart of 5 metabolites, namely 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamate, C6-ceramide, and arginine, in the validation set. Detailed implementation manners

[0095] Through extensive and in-depth research, a large number of screenings, tests and validations, the inventors of the present invention have provided metabolite markers that can be used for accurate diagnosis and / or early prediction of childhood refractory epilepsy in the technical field of childhood refractory epilepsy diagnosis. The present invention for the first time discovers that the metabolite markers 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamate, C6-ceramide and / or arginine all have high diagnostic efficacy for childhood refractory epilepsy. Applying the metabolite markers to the diagnosis of childhood refractory epilepsy has extremely high diagnostic value, with high accuracy, sensitivity and specificity, and can be used in the effective diagnosis of childhood refractory epilepsy. On this basis, the present invention is completed.

[0096] In order to enable those skilled in the art to better understand the technical solutions of the present invention, some 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 well-known content in the art.

[0097] As used herein, the term "comprising" or "including" means that the compositions, methods and their corresponding components present in a given embodiment (i.e., it can be a closed type), but at the same time it can also be an open type, including unspecified elements.

[0098] As used herein, the term "metabolite marker" refers to an indicator compound suitable as an indicator of the presence and state of a certain disease or patient, and such a compound is a metabolite or metabolic compound that appears in the metabolic process in a mammalian body. In the present invention, the term "metabolite marker" refers to an indicator compound suitable as an indicator of the presence and state of childhood refractory epilepsy, and such a compound is a metabolite or metabolic compound that appears in the metabolic process in a mammalian body.

[0099] In some embodiments, the "metabolite marker" refers to any one or more of 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamate, C6-ceramide and / or arginine.

[0100] In some embodiments, the metabolic markers provided by the present invention can be used alone or in combination for the diagnosis and / or early prediction of refractory epilepsy in children, to evaluate the status of refractory epilepsy in children in a subject, where the status of refractory epilepsy in children includes the presence or absence of refractory epilepsy in children, the risk of occurrence of refractory epilepsy in children, and the detection of the disease progression of refractory epilepsy in children. In addition, based on the status of refractory epilepsy in children in a 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 (a child) has refractory epilepsy in children; the output of evaluating the risk of a subject having refractory epilepsy in children by 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 disorder. For example, through molecular characteristics (such as specific metabolites, proteins encoded by specific genes, specific microbial communities, specific genes, etc.), early differential diagnosis is made as to whether a subject has refractory epilepsy in children or the risk of having refractory epilepsy in children. In a specific embodiment of the present invention, the molecular characteristic is a specific metabolite (any one or more 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 evaluating whether a subject is at risk of having refractory epilepsy in children or not. In some embodiments, it should be evaluated whether the risk of the subject is at an increased risk or a decreased risk compared to the average risk of the population of subjects. The term "predicting refractory epilepsy in children" or "early predicting refractory epilepsy in children" as used herein means analyzing by the method of the present invention whether a subject is classified into a group of subjects at risk of having refractory epilepsy in children or a group of subjects not at risk of having refractory epilepsy in children.

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

[0105] As used herein, the term "sample" refers to a composition obtained from or derived from a target subject, which contains cellular 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 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 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 an individual's pregnancy or development; or plasma. The sample includes biological samples that have been processed in any way after their acquisition, such as treated with reagents, stabilized, or enriched 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 fluid, synovial fluid, sweat, saliva, tears, feces, urine, cerebrospinal fluid, cells, tissues, or organs, etc., from a subject. 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, and also refers to human and non-human animals. The non-human animals include all vertebrates, for example, mammals, such as non-human primates (especially higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cows, and any domestic or pet animals; 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. AUC measurement is useful for comparing the accuracy of classifiers across the entire data range. A classifier with a higher AUC has a higher ability to correctly classify unknowns between two or more target groups. The ROC curve is useful for depicting the performance of a specific feature (for example, any of the metabolic markers described herein and / or any entry of additional biomedical information) when differentiating between two populations.

[0109] Typically, feature data are selected across the entire population in ascending order based on the values 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 with a value higher than that of the feature and dividing by the total number of cases. The false positive rate is determined by counting the number of controls with a value higher than that of the feature and dividing by the total number of controls.

[0110] ROC curves can be generated for individual features or for other individual outputs. For example, combinations of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single sum value, and this single sum value can be plotted in an ROC curve. Additionally, any combination of multiple features whose combinations are derived from individual output values can be plotted in an ROC curve, and the ROC curve can be used to analyze the accuracy of a diagnosis.

[0111] The following describes the present invention in further detail with reference to specific embodiments. The specific embodiments are only for explaining the present invention and should not be construed as limiting the present invention. Those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. 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 those of ordinary skill in the art. Unless otherwise specified, they can all be obtained through commercial channels. The experimental methods without specific conditions noted in the present invention are generally carried out under conventional conditions or according to the conditions recommended by the manufacturer. In particular, the following embodiments are only for illustrating the present invention and should not limit the scope of the present invention in any way.

[0113] Example 1 Collection of Pediatric Whole Blood Samples

[0114] Referring to the "Venous Blood Sampling Guidelines" (People's Military Medical Press), strictly following the technical procedures for venous blood sampling in pediatric disease detection, venous blood from the arm was collected, and a medical heparin anticoagulant tube was used to store the collected whole blood samples.

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

[0116] Example 2 Targeted Metabolomics Screening for Potential Metabolic Biomarkers in Pediatric Refractory Epilepsy

[0117] After separation using an Agilent 1290 Infinity LC ultra-high performance liquid chromatography system (UHPLC), 300 metabolites including amino acids, organic acids, fatty acids, reducing sugars, bile acids, carnitines, phenyl or benzyl derivatives, indoles, etc. in the whole blood samples of the training set were quantitatively detected using a Triple TOF 6600 mass spectrometer.

[0118] The pretreatment method for the whole blood samples used is as follows: The frozen whole blood samples are slowly thawed at 4°C. For each whole blood sample, 100 μL is taken and 400 μL of ice-precooled methanol / acetonitrile solution (1:1, v / v) is added. After vortex mixing evenly, the sample is placed at -20°C for 30 min, and then centrifuged at 14000 g at 4°C for 20 min. The supernatant is taken and vacuum dried. When performing mass spectrometry analysis, 100 μL of acetonitrile aqueous solution (acetonitrile: water = 1:1, v / v) is added for reconstitution, vortexed, and centrifuged at 14000 g at 4°C for 15 min. The supernatant liquid is taken for injection analysis.

[0119] The liquid phase and mass spectrometry detection conditions used are as follows: The injection volume is 2 μL. 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 at 50°C, the flow rate is 0.5 mL / min. From 0 - 0.5 min, 5% of A and 95% of B are maintained; from 0.5 - 7 min, it linearly changes from 5% of A and 95% of B to 35% of A and 65% of B; from 7 - 8 min, it linearly changes from 35% of A and 65% of B to 60% of A and 40% of B; from 8 - 9 min, 60% of A and 40% of B are maintained; from 9 - 12 min, it linearly changes from 60% of A and 40% of B to 5% of A and 95% of B.

[0120] ProteoWizard and XCMS are used for conversion and statistical analysis of the detected metabolite data, and 5 differential metabolites are obtained. The data are shown in Table 1 and Figure 1 as follows. The results show that there are significant differential expressions of the 5 metabolites, namely 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine, between the group of children with refractory epilepsy and the healthy control group (P < 0.005).

[0121] Table 1 5 differential metabolites identified and related to childhood refractory epilepsy

[0122]

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

[0124] For the above 137 whole blood samples, after slowly thawing the frozen whole blood samples at a low temperature (4°C), 100 μL was taken respectively and added to 400 μL of ice-precooled methanol / acetonitrile solution (1:1, v / v). The samples were vortexed evenly by touch with a vortex mixer. After mixing evenly, the samples were placed at -20°C for 30 min, and then centrifuged at 14000 g at 4°C for 20 min. After centrifugation, the supernatant was taken and vacuum dried. When performing mass spectrometry analysis, 100 μL of acetonitrile aqueous solution (acetonitrile: water = 1:1, v / v) was added for reconstitution, vortexed, and centrifuged at 14000 g at 4°C for 15 min, and the supernatant was taken for mass spectrometry detection.

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

[0126] The liquid chromatography separation conditions and parameters were as follows: The injection volume was 2 μL, 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 at 50°C, the flow rate was 0.5 mL / min. From 0 - 0.5 min, 5% of A and 95% of B were maintained; from 0.5 - 7 min, it linearly changed from 5% of A and 95% of B to 35% of A and 65% of B; from 7 - 8 min, it linearly changed from 35% of A and 65% of B to 60% of A and 40% of B; from 8 - 9 min, 60% of A and 40% of B were maintained; from 9 - 12 min, it linearly changed from 60% of A and 40% of B to 5% of A and 95% of B.

[0127] The mass spectrometry detection parameters were: ProteoWizard and XCMS were used for conversion and statistical analysis of the metabolite data obtained from the detection.

[0128] Under the training set, the differential expression result graphs of the above 5 metabolites are as Figure 2 shown. The results showed that there were significant differential expressions of the 5 metabolites, 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine, between the children with refractory epilepsy patient group and the healthy control group.

[0129] Under the validation set, the differential expression result graphs of the above 5 metabolites are as Figure 4 shown. The results showed that there were also significant differential expressions of the 5 metabolites, 3-hydroxyvalproic acid, trans-2-octenoic acid, glutamic acid, C-6 ceramide, and arginine, between the children with refractory epilepsy patient group and the healthy control group.

[0130] Example 5 ROC Curve of Whole Blood Samples in the Training Set

[0131] In this embodiment, whole blood samples of 33 children with refractory epilepsy and 41 healthy children were used as the training set. The receiver operating characteristic curve (ROC) was plotted using the R package "pROC" to analyze the AUC value, sensitivity, and specificity of the 5 differentially expressed metabolites obtained through the above screening, either alone or in any combination, for the diagnosis of childhood refractory epilepsy, and to determine their diagnostic efficacy for childhood refractory epilepsy.

[0132] Among them, when evaluating the diagnostic efficacy of a single metabolite for childhood refractory epilepsy, the content of the metabolite was used for evaluation and analysis, and the point corresponding to the maximum Youden index was selected as its cutoff value, that is, the optimal division threshold was determined by the point with the maximum Youden index; when evaluating the diagnostic efficacy of the combined metabolites for childhood refractory epilepsy, first, a Logistics regression analysis was performed on the combined metabolites. In the Logistics regression analysis, the independent variable was the combined metabolite, and the dependent variable was the disease status of childhood refractory epilepsy. The probability of each individual having childhood refractory epilepsy or not could be calculated through the fitted regression curve, and different probability division thresholds could be determined to obtain the prediction results. The optimal probability division threshold was determined by the point with the maximum Youden index. According to the determined probability division threshold, the AUC value, sensitivity, specificity, etc. of the combined metabolite detection in the training set were calculated. The AUC values, sensitivity, and specificity of the obtained single metabolite and combined metabolite were analyzed to determine their diagnostic efficacy.

[0133] Under the training set, the ROC curve results diagram of the 5 metabolites is as Figure 3 shown, and the area under the ROC curve (AUC) of the training set is as follows:

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

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

[0136] Under the training set, the area under the curve (AUC) of glutamic acid = 0.957 (sensitivity: 87.7%; specificity: 100%);

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

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

[0139] ROC Curve of the Validation Set Whole Blood Samples in Example 6

[0140] In this example, whole blood samples from 31 children with refractory epilepsy and 32 healthy children were used as the validation set. The receiver operating characteristic curve (ROC) was plotted using the R package "pROC" to analyze the AUC values, sensitivities, and specificities of the 5 differentially expressed metabolites obtained through the above screening, either individually or in any combination, for the diagnosis of refractory epilepsy in children, and to judge their diagnostic efficacy for refractory epilepsy in children.

[0141] Among them, when evaluating the diagnostic efficacy of a single metabolite for refractory epilepsy in children, the content of the metabolite was used for evaluation and analysis, and the point corresponding to the maximum Youden index was selected as its cutoff value, that is, the optimal division threshold was determined by the point with the maximum Youden index; when evaluating the diagnostic efficacy of the combined metabolites for refractory epilepsy in children, first, a Logistics regression analysis was performed on the combined metabolites. In the Logistics regression analysis, the independent variable was the combined metabolite, and the dependent variable was the disease status of refractory epilepsy in children. The probability of each individual having refractory epilepsy or not could be calculated through the fitted regression curve, and different probability division thresholds could be determined to obtain the prediction results. The optimal probability division threshold was determined by the point with the maximum Youden index. According to the determined probability division threshold, the AUC value, sensitivity, specificity, etc. of the combined metabolite detection in the validation set were calculated. The AUC values, sensitivities, and specificities of the obtained single metabolites and combined metabolites were analyzed to judge their diagnostic efficacy.

[0142] Under the validation set, the ROC curve results diagram of the 5 metabolites is as Figure 5 shown, and the area under the ROC curve (AUC) of the validation set is as follows:

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

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

[0145] Under the validation set, the area under the curve (AUC) of glutamic acid is 0.921 (sensitivity: 76.8%; specificity: 100%);

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

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

[0148] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, which should all be regarded as belonging to 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 and / or C-6 ceramide.

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. The application of metabolic markers in constructing an early warning model for refractory epilepsy in children, characterized in that: The metabolite markers are 3-hydroxyvalproic acid, trans-2-octenoic acid and / or C-6 ceramide, and 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.

7. 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 6; 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 3-hydroxyvalproic acid, trans-2-octenoic acid and / or C-6 ceramide; 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.

8. A system for diagnosing and / or early predicting refractory epilepsy in children using metabolic markers, characterized in that: The metabolic markers are 3-hydroxyvalproic acid, trans-2-octenoic acid and / or C-6 ceramide, 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.

9. Application of metabolic markers in constructing a computational model 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 and / or C-6 ceramide.

Images