CN111351870A - Irritable bowel syndrome serum metabolism marker combination and diagnostic kit thereof - Google Patents
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Abstract
The invention discloses a serum metabolic marker combination of irritable bowel syndrome, which can be used for in vitro diagnosis of the irritable bowel syndrome and can assist a clinician to judge whether a patient suffers from the irritable bowel syndrome, and hopefully applies the metabolic markers to a treatment target of the irritable bowel syndrome so as to improve the treatment effect of clinical patients. The invention also discloses a diagnostic kit for irritable bowel syndrome, which is constructed based on the principle that serum samples of positive control and negative control are subjected to liquid chromatography-mass spectrometry combined metabonomics analysis method to analyze abundance data, and the kit is verified to have higher detection rate and higher accuracy, can comprehensively and comprehensively represent the difference condition of serum metabolic markers of irritable bowel syndrome patients, can be used for early discovery of irritable bowel syndrome, strives for time for patients, starts treatment as early as possible and improves clinical treatment effect.
Description
Technical Field
The invention relates to the technical field of in-vitro diagnosis, in particular to a serum metabolic marker combination for irritable bowel syndrome and a diagnostic kit thereof.
Background
Irritable Bowel Syndrome (IBS) is one of the most common functional gastrointestinal disorder diseases in the world, and is a bowel disorder disease with abdominal pain, abdominal distension, flatulence, altered defecation habits, intermittent diarrhea and/or constipation as clinical manifestations, but lacking in gastrointestinal structural and biochemical abnormalities.
IBS patients are classified into three categories by the predominant bowel movement symptoms: constipation predominant IBS (IBS-C), diarrhea predominant IBS (IBS-D) and alternating diarrhea and constipation symptoms IBS (IBS-M). The total disease rate of IBS is 11%, the difference between different regions is large, the IBS reaches 12% -20% in developed countries, and the IBS accounts for over 50% of all dyspepsia patients. However, only a small fraction of patients will see a doctor. About 40% of the population that meet the diagnostic criteria for IBS have not received a formal diagnosis. In the united states, IBS accounts for 25% -50% of all cases of referral to the gastrointestinal disease department. In the uk IBS is associated with significant healthcare expenditures, estimated to cost approximately 3.2 billion pounds per year. The health care costs for IBS in the united states are estimated to be $ 80 million per year. IBS not only causes an increase in medical care costs, but also causes the second leading cause of absenteeism, and also seriously affects the quality of life of patients. Patients with IBS often have psychological abnormalities such as anxiety, tension, depression, etc. Meanwhile, mental stress can also induce or aggravate IBS symptoms, which shows that mental factors are closely related to IBS. It can be seen that IBS has formed a significant public health problem.
The increased visceral sensitivity and the increased digestive tract permeability are important pathophysiological mechanisms of IBS, but the exact pathogenic mechanisms are still unclear so far, and are considered to be related to factors such as colonic motility abnormality, visceral sensitivity increase, brain and intestinal axis dysfunction, inflammation, immunity, diet, intestinal flora imbalance and the like. The current diagnostic criteria, roman IV diagnostic criteria, for IBS is defined as repeated episodes of abdominal pain within the last 3 months, on average at least 1 day per week, with at least the following 2 features: abdominal pain is associated with defecation, with a change in frequency of defecation or with a change in the nature (appearance) of the feces. Provided that the patient does not have any structural or biochemical disease that may cause these symptoms. Thus, the standard requires a large history of disease and is only reliable if there is no abnormal bowel anatomy or metabolic process that would otherwise explain the symptoms. In addition, because the symptoms of IBS are similar or identical to many other intestinal disorders, this also increases the difficulty of differential diagnosis of IBS, preventing early detection and effective treatment of the disease. Since the exact pathogenic mechanism is still unclear to date, no specific biological, radiographic, endoscopic or physiological biomarkers can be used to identify the disease. The invention comprehensively and comprehensively embodies the serum metabolite difference condition of the irritable bowel syndrome patient by detecting the serum metabolism, can be used for early discovery of the irritable bowel syndrome, strives for time for the patient to start treatment as early as possible, and improves the clinical treatment effect.
Metabolomics is a scientific study of chemical processes involving metabolites, is a subject developed after genomics and proteomics, and is an important component of system biology. In contrast to genomics and proteomics, metabolomics is a study that involves studying the commonality, identity and regularity of each metabolic component. Despite the current focus on this goal, researchers are firmly believing that metabolomics is more closely linked to physiology than genomics and proteomics. The disease causes the change of the pathophysiology process of the organism, and finally causes the corresponding change of the metabolite, and a better disease diagnosis method is provided by analyzing certain metabolites and comparing with the metabolites of normal people to search the biomarkers of the disease.
Researches find that irritable bowel syndrome is closely related to bile acid metabolism, the metabolic spectrum of the irritable bowel syndrome is changed in a patient body, and the characteristic metabolites of the irritable bowel syndrome are found by utilizing the characteristic of amplification at the bottom of a metabolism group for modeling analysis, so that the diagnosis efficiency of the irritable bowel syndrome is improved, and a new thought is provided for in-vitro diagnosis of the irritable bowel syndrome. However, in the existing methods, the variables used in the metabonomics model construction method are all or a large number of variables obtained by high-throughput detection, and although the existing methods are comprehensive, the sensitivity and specificity are not enough.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a serum metabolic marker combination for irritable bowel syndrome. The combination is characterized in that the original metabolic fingerprint obtained by carrying out chromatography-mass spectrometry combined metabonomics analysis on serum samples of positive control and negative control can comprehensively and comprehensively represent the variation condition of serum metabolites between irritable bowel syndrome patients and healthy people, find a diagnostic marker of the irritable bowel syndrome and provide favorable technical support for the diagnosis and treatment of the irritable bowel syndrome.
Another object of the present invention is to provide a diagnostic kit for irritable bowel syndrome.
One of the purposes of the invention is realized by adopting the following technical scheme:
a irritable bowel syndrome serum metabolism marker combination is composed of at least 2 of serum differential metabolites of glyceryl phospho-N-arachidonic acid Ethanolamine (glycophospho-N-Arachidonoyl ethanoamine), Lauroyl coenzyme A (Lauroyl-CoA), 2E-tetradecyl coenzyme A (2E-Tetradecanoyl-CoA), myristoyl coenzyme A (Tetradecanoyl-CoA), Stearamide (Stearamide), glucose ceramide (gluconosylaceramide (d18:1/16:0)), Actinidine (quinidine), L-Arginine (L-Arginine), Dimethyltryptamine (N, N-Dimethyltryptamine), 1-Phenylethylamine (1-phenylethanolamine), Pyridoxamine-5' -Phosphate (pyrolidonate-5 ' -Phosphate), 4,7, 10-hexadecanoic-4-oxyphenamide (4, 13-oxyphenamide), arachidonic acid (1-phenylketonic acid), taurine (1, 13, 11-15), taurine-15-dihydrocholine (cholesterol-5 ' -Phosphate, 3-15, 3-Oxo-3-15), taurine (cholesterol-4, 7, 13-4, 13-Oxo-4, 13-4-arachidonic acid (cholesterol-7, 13, 15), taurine, 15, 3-Oxo-1-9-dihydroindole-2-one (cholesterol), and (testosterone-3, 9-3, 15).
The serum metabolism markers are determined by comparing the metabolite spectrum difference between healthy human control and irritable bowel syndrome patient by adopting rank sum test, multiple test correction and partial least square discriminant analysis in sequence.
Further, it is composed of at least two of the following serum differential metabolites, Dihydrouracil (Dihydrouracil), Guanine (Guanine), Pyridoxamine-5'-Phosphate (Pyridoxamine-5' -phophate), 4,7,10, 13-hexadecatrienoic acid (4,7,10, 13-hexadecanoenoic acid), Stearic acid (Stearic acid), Stearamide (Stearamide), 2-hydroxyalkynestradiol (2-hydroxyiminosylestradiol), PC (9:0), (9Z,11E,13E,15Z) -4-Oxo-9,11,13, 15-octadecenoic acid ((9Z,11E,13E,15Z) -4-oxooxooxoeicosanoic-9, 11,13, 15-octadecenoic acid), 10-Oxo-nonadecanoic acid (10-oxoeicosanoic-nocholinesterac acid), tetrahydrocholestyrol (tetrahydrocholestyrol-3527), tetrahydrocholestyrol-35 (tetrahydrocholestyrol-3527), tetrahydrocholestyrol-27 (tetrahydrocholestyrol-27) (glycopyrrol-3627), tetrahydrocholestyrol-27, tetrahydrocholestyrol-deoxycholic-3527, tetrahydrocholestyrol (tetrahydrocholestyrol-3527) and tetrahydrocholestyrol sulfate (tetrahydrocholestyrol-3627).
The preferable metabolite combination is to adopt a random forest model to further evaluate specific metabolites, screen more important metabolic markers and construct an irritable bowel syndrome discrimination model. On one hand, the interference of irrelevant variables is removed, the sensitivity and the specificity of the model are improved, on the other hand, the detection method of the verification set is simplified, only the screened variable combination needs to be detected, and the detection efficiency is improved.
Further, it is composed of a serum differential metabolite of Dihydrouracil (Dihydrouracil), Guanine (Guanine), Pyridoxamine-5'-Phosphate (Pyridoxamine-5' -Phosphate), 4,7,10,13-hexadecatetraenoic acid (4,7,10, 13-hexadecatetranenoic acid), Stearic acid (Stearic acid), Stearamide (Stearamide), 2-hydroxyestradiol (2-hydroxyiminosyldiol), PC (9:0), (9Z,11E,13E,15Z) -4-Oxo-9,11,13, 15-octadecenoic acid ((9Z,11E,13E,15Z) -4-oxooxodecanoic-9, 11,13, 15-octadecenoic acid), 10-Oxo-decanoic acid (10-Oxo-norcholedocosanoic acid), tetrahydrocholestyrol (tetrahydrocholestyrol-3527), tetrahydrocholestyrol-3-35 (tetrahydrocholestyrol-27), tetrahydrocholestyrol-35 (tetrahydrocholestyrol-3527), tetrahydrocholestyrol-27 (tetrahydrocholestyrol-3627) and tetrahydrocholestyrol (tetrahydrocholestyrol-27) (Pregnenolone-27, tetrahydrocholestyrol, and tetrahydrocholestyrol-27, and dihydrocholestyrol-27, and a pharmaceutically acceptable salt thereof, or a pharmaceutically acceptable salt of dihydrocholestyrol, such as a pharmaceutically acceptable salt of dihydrocholestyrol, for example.
The second purpose of the invention is realized by adopting the following technical scheme:
a diagnostic kit for irritable bowel syndrome, comprising a data analysis unit for analyzing abundance data of the above combination of irritable bowel syndrome serum metabolic markers in a serum sample.
Further, the data analysis unit is used for analyzing the relative abundance data of the irritable bowel syndrome serum metabolic marker combination in the blood serum sample to be tested relative to the blood serum sample of the positive control and the blood serum sample of the negative control.
Further, the serum samples of the negative control were derived from healthy normal physical examiners without a history of IBS disease, neurodegenerative disease, cardiovascular disease, metabolic disorders, gastrointestinal disease and a history of gastrointestinal surgery, and a history of cranial and cholecystectomy surgery. Preferably, the number of serum samples of the negative control n > 60.
Further, the serum sample of the positive control is derived from irritable bowel syndrome patients diagnosed with the roman IV diagnostic standard using the IBS diagnostic standard. Preferably, the number of serum samples of the positive control n > 200.
Further, the abundance data comprises an abundance data matrix including Retention Time (RT), mass-to-charge ratio (m/z) and response value obtained by liquid chromatography-mass spectrometry.
Further, a random forest classifier model is also included.
Further, the random forest classifier model classifies and judges the to-be-detected serum sample according to a classifier model established by serum metabolic marker abundance data of a positive control serum sample and a negative control serum sample.
The diagnostic kit for irritable bowel syndrome prepared according to the serum metabolism marker combination can comprehensively and comprehensively reflect the metabolic product difference condition of irritable bowel syndrome patients, can be used for early discovery of irritable bowel syndrome, strives for time for patients to start treatment as soon as possible, and improves clinical treatment effect.
Compared with the prior art, the invention has the beneficial effects that:
the irritable bowel syndrome serum metabolic marker provided by the invention can comprehensively and comprehensively represent the difference between irritable bowel syndrome patients and healthy people, the serum metabolic marker can obtain abundance data through liquid chromatography-mass spectrometry combined analysis, comprehensive comparison is carried out on the serum sample of a positive control and the corresponding serum metabolic marker combination in the serum sample of a negative control so as to obtain relative abundance data, and finally, the irritable bowel syndrome high-risk sample can be effectively screened out through a random forest classifier model, so that favorable technical support is provided for diagnosis and treatment of the irritable bowel syndrome.
The kit for diagnosing the irritable bowel syndrome is constructed based on the principle that serum samples of positive control and negative control are subjected to analysis of abundance data by a liquid chromatography-mass spectrometry combined metabonomics analysis method, and the kit is verified to have high detection rate and high accuracy and can be used for assessing the risk of irritable bowel syndrome.
Drawings
FIG. 1 is a boxplot of the differential serum metabolites obtained in example 3;
FIG. 2 is a bar graph of variable weight (VIP) values of the differential serum metabolites obtained in example 3 in a multidimensional PLS-DA model;
FIG. 3 is the importance scores of 18 serum metabolites obtained in example 4;
FIG. 4 is a ROC plot obtained for the training set serum samples of example 4;
FIG. 5 is a ROC plot from the validation set serum samples of example 4.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following are specific examples of the present invention, and raw materials, equipments and the like used in the following examples can be obtained by purchasing them unless otherwise specified.
In the following detailed description, the serum samples used as positive controls and the serum samples used as negative controls were obtained from the subsidiary colleges of TCM, Guangzhou university of TCM and hong Kong university. The serum samples of the negative control were derived from healthy normal examiners without a history of IBS disease, neurodegenerative disease, cardiovascular disease, metabolic disorder, gastrointestinal disease, and gastrointestinal surgery, and a history of cranial and cholecystectomy surgery. The serum samples of the positive control were derived from irritable bowel syndrome patients diagnosed with the roman IV diagnostic criteria using the IBS diagnostic criteria.
The serum metabolism marker is obtained by the following steps:
1) collecting a positive control serum sample and a negative control serum sample, wherein a training set is formed by not less than 200 positive control serum samples and not less than 60 negative control serum samples, and a verification set is formed by not less than 60 positive control serum samples and not less than 10 negative control serum samples;
2) analyzing and identifying metabolites of the serum sample of the training set by a chromatography-mass spectrometry combined metabonomics analysis method to obtain an original metabolic fingerprint;
3) preprocessing an original metabolic fingerprint map to convert the original metabolic fingerprint map into a metabolite abundance data matrix comprising Retention Time (RT), mass-to-charge ratio (m/z) and response values, and carrying out normalization and standardization processing to obtain preprocessed data;
4) obtaining differential metabolites from the preprocessed data under the selection standard that a variable weight VIP value of a cross validation partial least squares discriminant analysis (PLS-DA) model is larger than 1, a change multiple is less than 0.8 or more than 1.2 non-reference test and a Q value is less than 0.05 after FDR multiple test correction, searching secondary mass spectrum databases (HMDB, METLIN and Massbank databases) of the metabolites, comparing mass-to-charge ratios of mass spectra, and screening out the differential metabolites which can be annotated to the databases;
5) and further screening important differential metabolic markers by adopting a random forest method, constructing an irritable bowel syndrome distinguishing model, and performing model evaluation through a verification set.
Example 1: serum sample preparation
1) Training set:
259 positive control serum samples and 66 negative control serum samples; fasting serum samples were collected.
2) And (4) verification set:
84 positive control serum samples and 14 negative control serum samples; fasting serum samples were collected.
3) Sample pretreatment
Respectively taking 50 mu L of serum sample from the training set or the verification set, mixing with 200 mu L of cold methanol containing chlorophenylalanine (5 mu g/mL, water-soluble), performing vortex oscillation for 30s, and standing for 20 min; freeze-centrifuging at 13000rpm for 10min, and evaporating 200 μ L of supernatant to dryness; redissolved with 200. mu.L acetonitrile (98:2, v/v).
Example 2: abundance data determination
The abundance data of the serum samples obtained in example 1 were determined by liquid chromatography mass spectrometry (LC/MS) using an Agilent 1290 hplc, mass spectrometry using an Agilent 6543 quadrupole-time of flight tandem mass spectrometer, tuning the system to optimal sensitivity and resolution in positive ion electrospray ionization (ES +) and negative ion electrospray ionization (ES-) modes, respectively.
The liquid chromatography conditions were: agilent ACQUITY UPLC BEH C18Column (2.1 × 50mm,1.7 μm), flow rate 0.35mL/min, column temperature 30 deg.C, mobile phase A water (0.1% formic acid), B acetonitrile (0.1% formic acid), gradient elution program 0-1 min: 5% B, 2-12 min: 5% -100% B, 12-15 min: 100% B.
The time-of-flight mass spectrometry conditions were: ESI ion source, using positive and negative ion simultaneous scanning mode. Capillary voltage 3000V, data acquisition range: 100-1000 m/z.
Example 3: screening for differential metabolites
And (4) carrying out metabolite full-spectrum analysis and determination on the training set serum sample through LC-MS to obtain an original metabolic fingerprint. And preprocessing the original metabolic spectrogram by using an XCMS software package under an R software platform, converting the original metabolic spectrogram into a data matrix comprising retention time, a mass-to-charge ratio and a response value, and carrying out normalization and standardization processing for later statistical analysis. The qualitative method of the metabolite comprises the following steps: the secondary mass spectra databases of metabolites (HMDB, METLIN and Massbank databases) were searched and mass-to-charge ratios m/z of the mass spectra were compared.
As shown in FIG. 1 and FIG. 2, 33 different metabolites, i.e., Glycerophospho-N-arachidonic acid Ethanolamine (glycophospho-N-arylethanolamide), Lauroyl coenzyme A (Lauroyl-CoA), 2E-tetradecyl coenzyme A (2E-Tetradecanoyl-CoA), myristoyl coenzyme A (Tetradecadienoyl-CoA), Stearamide (Stearamide), Glucosylceramide (glucrylamide (d18:1/16:0)), sinosylactinide (L-Arginine), dimethyl tryptamine (N-tryptamine), pyridone (pyridone-D-7), pyridoxine (Pyridoxamine-13, N-acetylneuraminic acid), pyridone (pyridone-D-ethyl-1, N-acetylneuraminic acid), pyridone (pyridone-4, N-acetylneuraminic acid), pyridoxine (pyridone-7, 4-5-4, 15-5-4, pyridoxine (pyridoxine-5, 4-4, 15-5), pyridoxine (pyridoxine-4, 7-4-Oxo-5), pyridoxine (pyridone, 7-5, 7-4, 5-4, 7-4-Oxo-5), pyridoxine (pyridoxine-5, 7, 4-5, 4-5, 4-4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, and 7, 5, 4, 5, respectively.
Example 4: validation of differential metabolites
And finally determining the number of important metabolites incorporated into the model by using the number of variables required when the random forest selection error of the ten-fold cross validation is minimum. A random forest classifier is obtained through analysis of abundance data of the training set serum samples, and each irritable bowel syndrome suffering risk serum metabolic marker is scored in the classifier, and the result is shown in fig. 3. And calculating the sizes of different types of Proavailability values of the sample according to the abundance of the serum metabolic markers by the random forest model, so as to judge the irritable bowel syndrome morbidity risk of the sample, wherein the cut-off value is 0.5, and the Proavailability value larger than the cut-off value indicates that the sample subject suffers from the irritable bowel syndrome.
Among them, 18 metabolites, Dihydrouracil (Dihydrouracil), Guanine (Guanine), Pyridoxamine-5'-Phosphate (Pyridoxamine-5' -Phosphate), 4,7,10,13-hexadecatetraenoic acid (4,7,10, 13-hexadecatetranenoic acid), Stearic acid (Stearic acid), Stearamide (Stearamide), 2-hydroxyalkynestriol (2-hydroxyiminothiolanol), PC (9:0), (9Z,11E,13E,15Z) -4-Oxo-9,11,13, 15-octadecenoic acid ((9Z,11E,13E,15Z) -4-Oxo-9,11, 13-octenoic acid), 10-Oxo-nonadecanoic acid (10-Oxo-nonadecanoic acid), Bilirubin (bilirubicin), tetrahydrocholestyrol (dihydrochalcone) (tetrahydrocholestyrol-3556), tetrahydrocholestyrol sulfate (tetrahydrocholestyrol-3527)), and glycofuroxan (glycofuroxan-27) were found to be particularly effective as metabolites of Dihydrouracil (glycofuroxan-3, testosterone-27), testosterone (testosterone-27, and glycofuroxan (testosterone-27), cholesterol (testosterone-27), and cholesterol (testosterone-27), cholesterol (testosterone- α -fumarate), and cholesterol (testosterone-cholesterol, a metabolite of a combination of bovine cholesterol, a metabolite of the relevant metabolite of glucose-cholesterol (pgnocodarone).
The above-obtained 18 serum metabolism marker combinations were then evaluated on positive control serum samples of irritable bowel syndrome patients using a clinical diagnostic performance curve (ROC curve), and the results are shown in fig. 4. Satisfactory results were obtained using the ROC curve, AUC 0.9985 for the training set serum samples, 95% Confidence Interval (CIs) 0.9967-1.0000.
The serum samples of the validation set were used to validate the prediction probability parameter of irritable bowel syndrome by the 18 serum metabolic marker combinations obtained by the training set, and the results are shown in fig. 5 and table 1. The predicted probability was used to construct an ROC curve, and the obtained AUC was 0.9983 (95% CIs: 0.9943-1.0000).
TABLE 1 validation results of serum samples of the validation set against the predicted probability parameter
The prediction probability is more than 0.5 as a positive judgment standard, and the data of the verification set in table 1 show that only 1 case of 14 negative controls in the verification set has false positive, and in 84 cases of the serum samples of the positive control, only 1 case of false negative appears, namely the combination of 18 serum metabolic markers provided by the application can judge the height of the patient suffering from irritable bowel syndrome with higher detection rate and higher accuracy.
The combination of the 18 serum metabolic markers is a good irritable bowel syndrome diagnosis marker, can be used for clinical diagnosis, can assist a clinician to judge whether a patient suffers from irritable bowel syndrome, and can also be used for a treatment target of the irritable bowel syndrome, so that the treatment effect of the clinical patient is improved.
The embodiments of the present invention have been described in detail, but the embodiments are merely examples, and the present invention is not limited to the embodiments described above. Any equivalent modifications and substitutions to those skilled in the art are also within the scope of the present invention. Accordingly, equivalent changes and modifications made without departing from the spirit and scope of the present invention should be covered by the present invention.
Claims (10)
1. A combination of serum metabolism markers for irritable bowel syndrome consisting of at least two of the following serum differential metabolites glycerophospho-N-arachidonic acid ethanolamine, lauroyl-coa, 2E-tetradecanoyl-coa, stearamide, glucose ceramide, actinidine, L-arginine, dimethyltryptamine, 1-phenethylamine, pyridoxamine-5' -phosphate, 4,7,10,13-hexadecatetraenoic acid, taurochenodeoxycholate-3-sulfate, (9Z,11E,13E,15Z) -4-oxo-9,11,13,15-octadecatetraenoic acid, stearic acid, PGF2 α ethanolamide, guanine, arachidonic acid ethanolamine, bilirubin, carboxymethyl pyridine, choline phosphate, dihydropyrimidine, pregnenolone sulfate, tetrahydrodeoxycholone, gamma-tocotrienol, 27-norstanol, PC (9:0), 2-hydroxyestradiol, 10-oxo-nonadecanoic acid, dodecanedioic acid, oxalyl-methyl acetate, and 1-methyl-naphthaleneacetate.
2. The irritable bowel syndrome serum metabolism marker combination according to claim 1, consisting of at least two of the following serum differential metabolites, dihydrouracil, guanine, pyridoxamine-5' -phosphate, 4,7,10,13-hexadecatetraenoic acid, stearic acid, stearamide, 2-hydroxyethinylestradiol, PC (9:0), (9Z,11E,13E,15Z) -4-oxo-9,11,13, 15-octadecenoic acid, 10-oxo-nonadecanoic acid, bilirubin, tetrahydrodeoxycorticosterone, PGF2 α ethanolamide, pregnenolone sulfate, gamma-tocotrienol, 27-norcholestyrol, taurochenodeoxycholate-3-sulfate and glucuronamide.
3. The irritable bowel syndrome serum metabolism marker combination according to claim 1, consisting of serum differential metabolites of dihydrouracil, guanine, pyridyloxypentaphosphate, 4,7,10,13-hexadecatetraenoic acid, stearic acid, stearamide, 2-hydroxyalkynestriol, PC (9:0), (9Z,11E,13E,15Z) -4-oxo-9,11,13, 15-octadecenoic acid, 10-oxo-nonadecanoic acid, bilirubin, tetrahydrodeoxycorticosterone, PGF2 α ethanolamide, pregnenolone sulfate, gamma-tocotrienol, 27-norcholestanol, taurodeoxycholate-3-sulfate and glucosylceramide.
4. A diagnostic kit for irritable bowel syndrome, comprising a data analysis unit for analyzing data on abundance of the combination of serum metabolic markers for irritable bowel syndrome according to any one of claims 1 to 3 in a serum sample.
5. The irritable bowel syndrome diagnostic kit according to claim 4, wherein the data analysis unit is configured to analyze relative abundance data of the combination of the irritable bowel syndrome serum metabolic markers in the test serum sample relative to the positive control serum sample and the negative control serum sample.
6. The irritable bowel syndrome diagnostic kit of claim 5, wherein the serum sample of the negative control is derived from a healthy normal physical examiner without a history of IBS disease, neurodegenerative disease, cardiovascular disease, metabolic disorder, gastrointestinal disease and a history of gastrointestinal surgery, and a history of craniectomy and cholecystectomy.
7. The irritable bowel syndrome diagnostic kit according to claim 5, wherein the serum sample of the positive control is derived from the irritable bowel syndrome patient diagnosed with the Roman IV diagnostic standard using the IBS diagnostic standard.
8. The irritable bowel syndrome diagnostic kit of claim 5, wherein the abundance data comprises an abundance data matrix including Retention Time (RT), mass-to-charge ratio (m/z) and response values obtained by liquid chromatography-mass spectrometry.
9. The irritable bowel syndrome diagnostic kit of claim 5, further comprising a random forest classifier model.
10. The irritable bowel syndrome diagnostic kit of claim 9, wherein the random forest classifier model establishes a classifier model for classification judgment of the serum sample to be tested according to the serum sample of the positive control and the serum metabolic marker abundance data of the serum sample of the negative control.
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