Expiratory gas metabolite combined marker for diagnosing asthma and screening method thereof

Abstract

The invention discloses an expiratory gas metabolite combined marker for diagnosing asthma and a screening method of the expiratory gas metabolite combined marker. The expiratory gas metabolite combined marker for diagnosing asthma consists of dimethylamine and amino styrene. The screening method comprises the following steps: respectively collecting an exhaled gas sample of a healthy person and an exhaled gas sample of an asthma patient; carrying out HPPI-TOFMS detection analysis on the exhaled gas sample to obtain an original fingerprint spectrogram of the metabolic components; the spectrogram data is preprocessed, the obtained data containing metabolite information is sequentially subjected to univariate analysis and multivariate analysis, differential metabolites between the healthy people and the asthma patients are screened out, and the optimal combination of the differential metabolites between the healthy people and the asthma patients is screened out through binary logistic regression analysis, namely the combined marker; the HPPI-TOFMS analysis technology and the metabonomics method are combined to be used for screening the expired gas metabolite combined marker of bronchial asthma for the first time, and the method is easy to operate and has important economic benefits and wide application prospects.

Description

Technical Field

[0001] The present invention relates to a combination biomarker of exhaled metabolites for diagnosing asthma and a screening method therefor. The biomarker and the screening method can quickly and accurately distinguish patients with bronchial asthma from healthy people, and belong to the field of analysis technology. Background Art

[0002] Bronchial asthma, simply referred to as asthma, is characterized by variable expiratory airflow limitation, and the typical manifestations are wheezing, shortness of breath, chest tightness or coughing. According to the results of the Global Burden of Disease Study: As of 2015, the number of asthma patients globally reached 358 million, and the number of deaths exceeded 400,000 per year. In China, the group of asthma patients is huge. Among the people aged 20 and above, there are approximately 45.7 million. Among them, a large number of patients have not received standardized medical treatment or have been misdiagnosed. 71.2% of the patients have not been clearly diagnosed before, and only 5.6% of the patients have received standardized treatment. One of the important reasons for this phenomenon is the inability to achieve homogeneous early screening and diagnosis of asthma. Precise early diagnosis has always been the fundamental goal pursued worldwide.

[0003] In China, the diagnostic technology and level of asthma have experienced stage-by-stage development with the revision of multiple bronchial diagnosis and treatment guidelines. The diagnostic criteria for bronchial asthma have gradually changed from focusing solely on clinical manifestations to emphasizing both clinical manifestations and objective examinations of variable airflow limitation; from being localized to gradually approaching and conforming to the Global Initiative for Asthma (GINA) diagnostic criteria; and from being single-faceted to being diversified to meet the needs of medical workers at different levels, aiming to achieve precise diagnosis of asthma. Nevertheless, due to the high concealment and high mortality rate of the initial symptoms of asthma, the debate on the best diagnostic and management strategies for it continues, and the diagnosis and treatment of asthma still face bottleneck problems.

[0004] Currently, asthma is mainly diagnosed clinically based on objective examinations of variable airflow limitation. Although the bronchial provocation test using histamine and methacholine as provoking drugs is the gold standard for diagnosing asthma in terms of lung function, its sensitivity is high while its specificity is relatively low; the test is relatively dangerous and has high requirements for the physical fitness of patients, and is not suitable for patients with poor lung function and acute asthma attacks; it has high requirements for equipment, is complex and time-consuming to operate, and is restricted by the technical development conditions and is difficult to popularize so far.

[0005] In addition to the components of ambient air, human exhaled breath also contains volatile metabolites from blood-gas exchange in the airways, gastrointestinal tract, and lungs. In particular, respiratory diseases, pathogen metabolism, and human inflammatory stress responses will produce unique metabolites, including some small-molecule inorganic gases (such as nitric oxide NO, carbon monoxide CO, etc.) and a large number of volatile organic compounds (VOCs), which are directly excreted from the body through exhaled breath. Some of these metabolites have been proven to be biomarkers for certain diseases. Volatile metabolites in exhaled breath have been successfully used in the diagnosis and research of various acute and chronic respiratory diseases. In a study on chronic obstructive pulmonary disease (COPD), researchers used mass spectrometry (MS) to detect fatty acids, aldehydes, and amino acids produced by lung muscle degradation in the exhaled breath of COPD patients, and based on these characteristic metabolites, they achieved the distinction between COPD patients and healthy controls with an accuracy of 89%, a sensitivity of 93%, and a specificity of 86%.

[0006] For the detection of VOCs in the exhaled breath of asthma patients, the commonly used methods are mainly electronic nose (e-Nose) based on pattern recognition and gas chromatography-mass spectrometry (GC-MS). e-Nose detects VOCs in the respiratory tract by imitating the sense of smell of mammals, but its resolution is not high, it is easily interfered by the complex matrix in the exhaled breath, it has poor versatility, and it is difficult to accurately quantify, which affects the accuracy of identification. GC-MS test results are considered to be the gold standard for VOCs component analysis, but the entire analysis process requires complex offline enrichment and pretreatment of exhaled breath samples, the analysis speed is slow, and the cost of single sample analysis is high, making it difficult to use for high-throughput detection of large clinical sample volumes.

[0007] Direct mass spectrometry based on "soft" ionization sources has developed rapidly in recent years. Soft ionization mass spectrometry technology obtains mass spectrometry data that are easy to quickly analyze qualitatively and quantitatively by ionizing the molecules of substances to obtain molecular ions or quasi-molecular ions with very little dissociation, so that it can be used for rapid qualitative and quantitative analysis of complex mixture samples such as exhaled breath, and has been successful in the online detection of small molecule volatile metabolites in human exhaled breath and urine. The present invention combines high-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) to develop a new high-throughput exhaled breath detection technology, which effectively expands the analysis range of the analyte and can achieve high-throughput analysis and detection of a variety of organic compounds. The technology has the advantages of high sensitivity, good versatility, fast detection speed, and is less affected by sample humidity conditions. Summary of the invention

[0008] In view of the deficiencies in the existing asthma diagnosis methods, such as port lag and causing pain to the tested population, and there is no simple, fast, and suitable large-scale diagnosis technology. The purpose of the present invention is to provide an exhaled metabolite combination biomarker for diagnosing asthma and its screening method. This method is simple to operate and has high accuracy. It can be used for asthma diagnosis and early screening of the population, which is convenient, fast, economical, and can avoid the pain of the tested population, and is suitable for popularization and application.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] An exhaled metabolite combination biomarker for diagnosing asthma, which is composed of dimethylamine (m / z 46.066) and aminostyrene (m / z 120.081).

[0011] The present invention also provides a screening method for an exhaled metabolite combination biomarker for diagnosing asthma. This screening method is an exhaled metabolomics screening method based on high-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS). This method respectively collects exhaled breath samples of healthy people and asthma patients, uses HPPI-TOFMS to analyze the exhaled breath samples, obtains the original mass spectrometry diagrams of the exhaled breath samples containing metabolite information, preprocesses the original mass spectrometry diagrams, and screens out the differential metabolite combination through statistical analysis methods. This metabolite combination is the exhaled metabolite combination biomarker for diagnosing asthma;

[0012] Specifically, it includes the following steps:

[0013] (1) Respectively collect the exhaled breath of healthy people and asthma patients as analysis samples;

[0014] (2) Use HPPI-TOFMS to analyze each exhaled breath sample to obtain the original mass spectrometry diagram of each exhaled breath sample;

[0015] (3) Preprocess the original mass spectrometry diagram of each exhaled breath sample to obtain data containing metabolite relative intensity information;

[0016] (4) Perform univariate and multivariate analyses on the data containing metabolite relative intensity information, and screen out the differential metabolites between healthy people and asthma patients according to the set thresholds (VIP>1 and FDR<0.05);

[0017] (5) Screen out the optimal combination of differential metabolites between healthy people and asthma patients through binary logistic regression analysis. This metabolite combination is the exhaled metabolite combination biomarker for diagnosing asthma.

[0018] The screening method can also establish a receiver operating characteristic curve discrimination model based on the optimal combination of differential metabolites, and obtain the discrimination formula and discrimination threshold for patients with bronchial asthma and healthy people.

[0019] Further, in step (1), during the collection of exhaled breath samples from healthy people and asthma patients,

[0020] 1) When collecting exhaled breath samples, the subjects are healthy people and asthma patients. The asthma patients are those who have been clearly diagnosed with bronchial asthma and have acute symptom attacks.

[0021] 2) Before collecting the exhaled breath of the subjects, they should fast, refrain from drinking water, smoking, and stay calm for at least 3 hours to minimize the influence of factors such as diet and smoking on the test results.

[0022] 3) It is required that when collecting exhaled breath, the subject first takes a deep nasal inhalation, then exhales completely through the mouth and is collected into a 3L Tedlar collection bag by an exhaled breath sampling device. Repeat the above collection process until the exhaled breath fills the collection bag. Set the exhaled air flow rate of the subject to 2800 - 3200 ml / min to eliminate the difference in metabolite concentration caused by inconsistent exhaled air flow rates. The Tedlar collection bag is sent to the laboratory within 24 hours for high-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) analysis.

[0023] 4) The exhaled breath samples of healthy people and asthma patients are both taken from people aged 20 - 60 years old; there are more than 10 exhaled breath samples of healthy people and more than 10 exhaled breath samples of disease patients.

[0024] When performing HPPI-TOFMS analysis on the to-be-tested exhaled breath samples in step (2), first place the gas bag in an oven and heat it to 50°C, then enter the TOFMS ionization analysis. The injection flow rate is 20 - 50 ml / min. The ionization source uses a vacuum ultraviolet Kr lamp as the light source, the ionization source pressure is 200 - 300 Pa, the temperature is set to 100°C, the ionization source repulsion voltage is 18V, and the MCP voltage is 4100V; the spectrum acquisition time is 120s, and the mass-to-charge ratio range for spectrum data acquisition is 0 - 400 Da.

[0025] The preprocessing of the original mass spectrometry diagram of the exhaled breath samples in step (3) refers to: using Matlab software to perform mass correction and signal peak intensity extraction on the obtained original mass spectrometry diagram, and then performing total normalization, data filtering, and logarithmic transformation on the data of each exhaled breath sample on the MetaboAnalyst website. Finally, a data table that can be used for statistical analysis is obtained: among them, the first row of the table is the metabolite mass number, the first column of the table is the exhaled breath sample number information, the second column is the exhaled breath sample grouping information, and each of the remaining columns is the relative signal intensity of each metabolite.

[0026] In step (4), univariate and multivariate analyses are performed on the data containing metabolite relative intensity information.

[0027] The univariate analysis refers to the Mann-Whitney U rank sum test, which is performed on the Multiple Experiment Viewer software. Metabolites with a false discovery rate (FDR) less than 0.05 are considered to be different between the two groups. The multivariate analysis refers to orthogonal partial least squares discriminant analysis, which is performed on the Simca-P software. The data is transformed by unit variance scaling, and metabolites with a variable importance in projection (VIP) greater than 1 are screened out. Finally, the metabolites common to the results of the univariate and multivariate analyses are taken as the differential metabolites between the healthy group and the disease patient group.

[0028] The screening process for the optimal combination of differential metabolites between healthy individuals and asthma patients is as follows: binary logistic regression analysis is performed on the obtained differential metabolites on the SPSS software. The dependent variable is set as the grouping information of healthy and disease patients, and the covariates are the relative intensities of different metabolites. The method is "Forward: Conditional". The combination ranked first among all the obtained combinations is the optimal differential metabolite combination, and this combination is the exhaled metabolite combination biomarker for diagnosing asthma.

[0029] Establishing a receiver operating characteristic curve discriminant model based on the differential metabolite combination means: binary logistic regression analysis is performed on the obtained differential metabolites on the SPSS software. The dependent variable is set as the grouping information of healthy and disease patients, and the covariates are the relative intensities of different metabolites. The method is "Forward: Conditional". The combination ranked first among all the obtained combinations is the optimal differential metabolite combination. Then, the method is set to "Enter", and the metabolites in the optimal combination are selected as covariates, and binary logistic regression analysis is performed again to obtain the prediction probability of this combination. Finally, receiver operating characteristic curve (ROC) analysis is performed on the obtained differential metabolite combination in the SPSS software. The prediction probability of the differential metabolite combination is set as the test variable, and the grouping information is the status variable, and the value of the status variable is the disease grouping. Finally, the ROC curve is obtained. The abscissa and ordinate are 1-specificity and sensitivity respectively. The value corresponding to the maximum sum of specificity and sensitivity is the discrimination threshold between healthy and disease patients. Those greater than the threshold are healthy individuals, and those less than or equal to the threshold are disease patients.

[0030] The present invention combines the HPPI-TOFMS technology with metabolomics methods for the first time in asthma diagnosis. This method is simple to operate, only requires the collection of exhaled breath, is non-invasive, low-cost, and has high accuracy. Compared with bronchial provocation tests, it has higher safety, avoids the pain of the tested population, is suitable for popularization and application, and has high scientific research and medical value. Brief Description of the Drawings

[0031] Figure 1 Schematic structural diagram of the sampling device for collecting exhaled breath samples.

[0032] Figure 2 . Score plot for orthogonal partial least squares discriminant analysis, where R2Y = 0.829 > 0.8 and Q2 = 0.659 > 0.5.

[0033] Figure 3 OPLS-DA modeling validation plot based on 200 random permutations.

[0034] Figure 4 Box plot of the relative concentrations of two differential metabolites, where AS represents asthma patients and CTL represents healthy individuals.

[0035] Figure 5 ROC curve of the differential metabolite combination, with an area under the curve of 0.985, and the sensitivity and specificity being 100% and 94.4% respectively.

[0036] Figure 6 ROC curve of the differential metabolite combination after 10-fold cross-validation of the subjects, with an area under the curve AUC = 0.902, and the sensitivity and specificity being 94.7% and 94.4% respectively. Detailed implementation method

[0037] A combination biomarker of exhaled breath metabolites for diagnosing asthma, which consists of dimethylamine (m / z46.066) and aminostyrene (m / z 120.081).

[0038] A screening method for a combination biomarker of exhaled breath metabolites for diagnosing asthma. The screening method is an exhaled breath metabolomics screening method based on high-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS). This method involves separately collecting exhaled breath samples from healthy individuals and asthma patients, using HPPI-TOFMS to analyze the exhaled breath samples to obtain the original mass spectra of the exhaled breath samples containing metabolite information, preprocessing the original mass spectra, and screening out the differential metabolite combination through statistical analysis methods. This metabolite combination is the combination biomarker of exhaled breath metabolites for diagnosing asthma;

[0039] Specifically includes the following steps:

[0040] (1) Separately collect the exhaled breath of healthy individuals and asthma patients as analysis samples;

[0041] (2) Use HPPI-TOFMS to analyze each exhaled breath sample to obtain the original mass spectrum of each exhaled breath sample;

[0042] (3) Preprocess the original mass spectrometry of each exhaled breath sample to obtain data containing metabolite relative intensity information;

[0043] (4) Perform univariate and multivariate analyses on the data containing metabolite relative intensity information, and screen out differential metabolites between healthy people and asthma patients according to the set thresholds (VIP > 1 and FDR < 0.05);

[0044] (5) Screen out the optimal combination of differential metabolites between healthy people and asthma patients through binary logistic regression analysis. This metabolite combination is the exhaled breath metabolite combination biomarker for diagnosing asthma.

[0045] The screening method can also establish a receiver operating characteristic curve discrimination model based on the differential metabolite combination, and obtain the discrimination formula and discrimination threshold for bronchial asthma patients and healthy people.

[0046] During step (1) of collecting exhaled breath samples from healthy people and asthma patients respectively,

[0047] 1) When collecting exhaled breath samples, the population for collection is healthy people and asthma patients. The asthma patients are those who have been clearly diagnosed with bronchial asthma and have acute symptom attacks;

[0048] 2) Before collecting the exhaled breath of the subjects, they should fast, abstain from smoking, and remain calm for at least 3 hours to minimize the influence of factors such as diet and smoking on the test results;

[0049] 3) It is required that when collecting exhaled breath, the subject first takes a deep breath through the nose, and then completely exhales through the mouth into a Tedlar collection bag with a volume of 3L by an exhaled breath off-line sampling device. Repeat the above collection process until the exhaled breath fills the collection bag. The exhaled air flow rate of the subject is 2800 - 3200 ml / min to eliminate the metabolite concentration difference caused by inconsistent exhaled air flow rates. The Tedlar collection bag is sent to the laboratory within 24 hours for high-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) analysis.

[0050] 4) The exhaled breath samples of healthy people and asthma patients are both taken from the population aged 20 - 60 years old; there are more than 10 exhaled breath samples of healthy people and more than 10 exhaled breath samples of disease patients.

[0051] When performing HPPI-TOFMS analysis on the exhaled breath sample to be tested, first place the gas bag in an oven and heat it to 50 °C, then enter the TOFMS ionization analysis. The injection flow rate is 20 - 50 ml / min. The ionization source uses a vacuum ultraviolet Kr lamp as the light source, the ionization source gas pressure is 200 - 300 Pa, the temperature is set at 100 °C, the ionization source repulsion voltage is 18 V, and the MCP voltage is 4100 V. The spectrum acquisition time is 120 s, and the mass-to-charge ratio range for spectrum data acquisition is 0 - 400 Da.

[0052] Preprocessing the original mass spectrum of the exhaled breath sample means: using Matlab software to perform mass correction and signal peak intensity extraction on the obtained original mass spectrum, and then performing total sum normalization, data filtering, and logarithmic transformation on the data of each exhaled breath sample on the MetaboAnalyst website. Finally, a data table that can be used for statistical analysis is obtained. Among them, the first row of the table is the metabolite mass number, the first column of the table is the exhaled breath sample number information, the second column is the exhaled breath sample grouping information, and each of the remaining columns is the relative signal intensity of each metabolite.

[0053] Performing univariate and multivariate analysis on the data containing metabolite relative intensity information means:

[0054] Univariate analysis refers to the Mann-Whitney U test, which is performed on the Multiple Experiment Viewer software. It is considered that the differential metabolites with a false discovery rate (FDR) less than 0.05 are different between the two groups. Multivariate analysis refers to orthogonal partial least squares discriminant analysis, which is performed on the Simca-P software. The data is transformed by unit variance scaling, and metabolites with a variable importance in the projection (VIP) greater than 1 are selected. Finally, the metabolites common to the results of univariate analysis and multivariate analysis are taken as the differential metabolites between the healthy group and the disease patient group.

[0055] The screening process for the optimal combination of differential metabolites between healthy people and asthma patients is to perform binary logistic regression analysis on the obtained differential metabolites on the SPSS software. Set the dependent variable as the grouping information of healthy people and disease patients, the covariates as the relative intensities of different metabolites, and the method as "Forward: Conditional". The combination ranked first among all the obtained combinations is the optimal combination of differential metabolites, and this combination is the exhaled breath metabolite combination biomarker for diagnosing asthma.

[0056] Establishing a receiver operating characteristic (ROC) curve discrimination model based on a combination of differential metabolites means: performing binary logistic regression analysis on the obtained differential metabolites using SPSS software. Set the dependent variable as the grouping information of healthy individuals and disease patients, and the covariates as the relative intensities of different metabolites. The method is "Forward: Conditional". The combination ranked first among all the obtained combinations is the optimal combination of differential metabolites. Then, set the method to "Enter", select the metabolites in the optimal combination as covariates, and perform binary logistic regression analysis again to obtain the predicted probabilities of this combination. Finally, perform ROC analysis on the obtained combination of differential metabolites in SPSS software. Set the predicted probabilities of the combination of differential metabolites as the test variable, and the grouping information as the status variable, with the status variable value being the disease grouping. Finally, obtain the ROC curve, where the horizontal and vertical coordinates are 1 - specificity and sensitivity respectively. The value corresponding to the maximum sum of specificity and sensitivity is the discrimination threshold between healthy individuals and disease patients. Those greater than the threshold are healthy individuals, and those less than or equal to the threshold are disease patients.

[0057] Example 1

[0058] Next, the present invention will be further explained through the following specific embodiments, and the advantages of the present invention will be further demonstrated.

[0059] Screening method for exhaled breath metabolite combination markers for diagnosing asthma:

[0060] 1. Research subjects

[0061] 18 asthma patients and 19 healthy individuals diagnosed in Zhongshan Hospital Affiliated to Dalian University, aged between 20 - 60 years.

[0062] 2. Exhaled breath metabolomics detection by high - pressure photoionization time - of - flight mass spectrometry

[0063] 2.1 Collection and pretreatment of exhaled breath samples

[0064] Collect the exhaled breath of healthy individuals and asthma patients respectively as analysis samples. It is required that the subjects should maintain fasting, no smoking, and a calm state for at least 3 hours before collecting exhaled breath to minimize the influence of factors such as diet and smoking on the test results. When collecting exhaled breath, the participants should first take a deep nasal inhalation, and then completely exhale through the mouth and collect it into a 3 - L Tedlar collection bag by an exhaled breath sampling device until the bag is filled with exhaled air. The exhalation flow rate of the participants is required to be 2800 - 3200 ml / min to eliminate the difference in metabolite concentration caused by inconsistent exhalation flow rates. The Tedlar collection bag should be sent to the laboratory within 24 hours. When performing HPPI - TOFMS analysis on the exhaled breath samples to be tested, first heat the gas bag in an oven to 50°C, and then enter the TOFMS for ionization analysis.

[0065] As shown Figure 1 in the figure, the exhaled breath sampling device adopted includes: a pressure measurement system, a flow measurement system, a mode switching system, and a cleaning system,

[0066] The pressure measurement system includes: a sensor holder 3, a saliva chamber 5, and a pressure sensor 18, where:

[0067] The sensor holder 3 has a hollow structure and is connected to the mouthpiece 1 through the handle 2. A saliva chamber 5 is arranged below the sensor holder 3, and a pressure sensor 18 is arranged above it. A high-pressure detection port 17 is arranged at the end of the sensor holder 3; a sealing screw 4 is arranged below the saliva chamber 5. When the exhaled breath online sampling device is in the cleaning mode, the sealing screw 4 is removed to clean the saliva chamber 5.

[0068] The flow measurement system includes: a gas resistance pipeline 6, a flow sensor 16, a high-pressure detection port 17, and a low-pressure detection port 15, where:

[0069] The gas resistance pipeline 6 is connected to the sensor holder 3 to block the exhaled breath passage and drive the exhaled breath to enter the flow sensor 16 through the high-pressure detection port 17; the flow sensor 16 is a differential pressure type flow sensor, with one end connected to the high-pressure detection port 17 and the other end connected to the low-pressure detection port 15.

[0070] The mode switching system includes: a solenoid valve holder 7, a sampling port 9, an exhaust port 10, and a two-position three-way solenoid valve 14, where:

[0071] The solenoid valve holder 7 has a hollow structure. The front end of the solenoid valve holder 7 is connected to the low-pressure detection port 15. The two-position three-way solenoid valve 14 is arranged inside the solenoid valve holder 7. The two-position three-way solenoid valve 14 includes two passages, NO and NC. In the detection mode, NO is closed and NC is opened to connect the sampling port 9. In the cleaning mode, NC is closed and NO is opened to connect the exhaust port 10;

[0072] The cleaning system includes: a three-way pipeline 11, a cleaning pump 13, and a normally open two-way solenoid valve 12, where:

[0073] The three ports of the three-way pipeline 11 are respectively connected to the exhaust port 10, the cleaning pump 13, and the normally open two-way solenoid valve 12; before sampling, the exhaled breath online sampling device evacuates the gas in the exhaled breath online sampling device through the normally open two-way solenoid valve 12;

[0074] The cleaning pump 13 is used to blow high-pressure gas from the cleaning system through the mode switching system, the flow measurement system, the pressure measurement system, the handle 2, and out of the mouthpiece 1 in the cleaning mode to complete the cleaning.

[0075] Before sampling, the NC of the two-position three-way solenoid valve 14 is closed and the NO is open, exhausting the gas in the exhaled breath on-line sampling device through the normally open two-way solenoid valve 12; then the NO of the two-position three-way solenoid valve 14 is closed and the NC is opened;

[0076] Blow air into the exhaled breath on-line sampling device through the mouthpiece 1. The exhaled air flows through the pressure sensor 18 to measure the exhaled breath pressure, and the saliva is concentrated and collected into the saliva bin 5;

[0077] The exhaled breath flows from the high-pressure detection port 17 to the low-pressure detection port 15, and the exhaled breath flow rate is measured by the flow sensor 16;

[0078] The exhaled breath flows from the low-pressure detection port 15 to the solenoid valve rack 7 and passes through the NO of the passage to the sampling port 9, and is collected by the sample container 8a. The sample container 8a is a 3L Tedlar collection bag.

[0079] Control the exhaled breath flow rate to be 2800 - 3200 ml / min through the flow measurement system.

[0080] 2.2 Detection of exhaled breath by high-pressure photoionization time-of-flight mass spectrometry

[0081] For mass spectrometry detection, a self-developed high-pressure photoionization time-of-flight mass spectrometer is used. Connect the collection port of the Tedlar collection bag to the injection port of the mass spectrometer. Send the exhaled breath sample into the mass spectrometer through the air pressure difference inside and outside the mass spectrometer. The injection flow rate is 40 ml / min. The ionization source uses a vacuum ultraviolet Kr lamp as the light source, the temperature is set at 100 °C, the ionization source air pressure is 200 - 300 Pa, the ionization source repulsion voltage is 18 V, and the MCP voltage is 4100 V; the spectrum acquisition time is 120 s, and the mass-to-charge ratio range for spectrum data acquisition is 0 - 400 Da.

[0082] 3. Data preprocessing

[0083] Use Matlab software to perform mass correction and signal peak intensity extraction on the obtained original mass spectrum, and then perform total sum normalization, data filtering and logarithmic transformation on the data of each exhaled breath sample on the MetaboAnalyst website. Finally, obtain the discriminant analysis score chart based on orthogonal partial least squares method, as Figure 1 shown. Among them, the number of asthma patients is 18, and the number of healthy people is 19.

[0084] 4. Screening of differential metabolites

[0085] Import the data table containing metabolite relative intensity information obtained into Simca software for orthogonal partial least squares discriminant analysis (OPLS-DA). The OPLS-DA score chart is asFigure 1 As shown, the differences and classification trends in the metabolic profiles of asthma patients and healthy individuals can be observed. In the OPLS-DA score plot, R2Y = 0.829 > 0.8 and Q2 = 0.659 > 0.5, indicating that the model is reliable and has good predictive ability.

[0086] In addition, 200 permutation validations were performed, and the results are as Figure 2 shown (R2 = 0.154, Q2 = -0.279), indicating that overfitting did not occur during the OPLS-DA analysis.

[0087] Then, the Mann-Whitney U test was performed using the Multiple Experiment Viewer software to screen for differential metabolites. In the OPLS-DA analysis, metabolites with a variable importance in the projection (VIP) greater than 1 were selected, and in the Mann-Whitney U test, metabolites with a false discovery rate (FDR) less than 0.05 were chosen.

[0088] Finally, 25 differential metabolites were screened out. As shown in Table 1, the 25 compounds include alcohols, aldehydes, ketones, amines, organic acids, esters, and aromatic hydrocarbon compounds.

[0089] Table 1 Data table containing metabolite information

[0090]

[0091]

[0092] 5. Screening of the optimal combination of differential metabolites and establishment of a differential metabolite combination model

[0093] Based on the 25 differential metabolites screened out, binary logistic regression analysis was performed using the logistic regression analysis function of SPSS software to select the optimal combination of differential metabolites. The dependent variable was set as the grouping information of healthy individuals and disease patients, the covariates were the relative intensities of different metabolites, and the method was "Forward: Conditional". The combination ranked first among all the combinations obtained was the optimal combination of differential metabolites. Then, the method was set to "Enter", and the metabolites in the optimal combination were selected as covariates, and binary logistic regression analysis was performed again to obtain the prediction probability of this combination. Finally, the combination of dimethylamine (m / z 46.066) and styrylamine (m / z 120.081) was the optimal combination of differential metabolites. The relative concentrations of the two metabolites between asthma patients and healthy individuals are as Figure 3 shown.

[0094] In SPSS software, receiver operating characteristic curve (ROC) analysis was performed on the obtained differential metabolite combination. The prediction probability of the differential metabolite combination was set as the test variable, the grouping information was the status variable, and the value of the status variable was the disease grouping. The results are asFigure 4 As shown, the area under the ROC curve (AUC) value is 0.985, the sensitivity and specificity are 100% and 94.4% respectively, and the accuracy can reach 93.7%. The threshold corresponding to the maximum sum of sensitivity and specificity is 0.33, that is, when the threshold is higher than 0.33, it is a healthy person, and when it is lower than 0.33, it is an asthma patient.

[0095] The discrimination formula is y = exp(x / (x + 1)), where x = -0.008 * I 二甲胺 -0.038 * I 氨基苯乙烯 + 18.567, where I represents the relative intensity of the metabolite and y represents the discrimination threshold.

[0096] The model was internally validated by the method of 10-fold cross-validation, and the results are as Figure 5 shown. The area under its ROC curve (AUC) = 0.902, the sensitivity and specificity are 94.7% and 94.4% respectively. This result further proves that the model is well established and can better distinguish between the asthma patient group and the healthy control group.

[0097] 6. Conclusion

[0098] It can be seen from the above validation that the model constructed by the method of the present invention has a good prediction effect. The combination of metabolic markers screened by the present invention can diagnose asthma through exhaled breath detection, with high sensitivity and specificity, and has great application potential.

Claims

1. An exhaled metabolite combination biomarker for diagnosing asthma, characterized in that, It consists of dimethylamine (m / z 46.066) and aminostyrene (m / z 120.081).

2. A screening method for the exhaled metabolite combination biomarker for diagnosing asthma according to claim 1, characterized in that the screening method is an exhaled metabolomics screening method based on high-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS); specifically includes the following steps: (1) Collect the exhaled breaths of healthy people and asthma patients respectively as analysis samples; (2) Analyze each exhaled breath sample using HPPI-TOFMS to obtain the original mass spectrometry diagram of each exhaled breath sample; (3) Pretreat the original mass spectrometry diagram of each exhaled breath sample to obtain data containing metabolite relative intensity information; (4) Perform univariate and multivariate analyses on the data containing metabolite relative intensity information, and screen out the differential metabolites between healthy people and asthma patients according to the set thresholds (VIP>1 and FDR<0.05); (5) Screen out the optimal combination of differential metabolites between healthy people and asthma patients through binary logistic regression analysis, and this combination is the exhaled metabolite combination biomarker for diagnosing asthma.

3. The method according to claim 2, characterized in that: The screening method can also establish a receiver operating characteristic curve discrimination model based on the optimal differential metabolite combination, and obtain the discrimination formula and discrimination threshold for asthma patients and healthy people.

4. According to the method described in claim 2, the characteristics are: 1) When collecting exhaled breath samples, the collection population is healthy people and asthma patients. The asthma patients are those who are clearly diagnosed with bronchial asthma and have acute symptom attacks; 2) The subjects should fast, refrain from drinking water, smoking, and stay calm for at least 3 hours before collecting exhaled breath; 3) It is required that when collecting exhaled breath, the subject first takes a deep breath through the nose, and then completely exhales through the mouth into a Tedlar collection bag with a volume of 3L by an exhaled breath sampling device. Set the exhaled air flow rate to 2800-3200 ml / min, and the Tedlar collection bag is sent to the laboratory within 24 hours for high-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) analysis. 4) The exhaled breath samples of healthy people and asthma patients are both taken from people aged 20-60; there are more than 10 exhaled breath samples of healthy people and more than 10 exhaled breath samples of disease patients.

5. The method according to claim 2, wherein: When performing HPPI-TOFMS analysis on the to-be-tested exhaled breath sample, first place the gas bag in an oven and heat it to 50°C, then enter the TOFMS ionization analysis. The injection flow rate is 20-50 ml / min. The ionization source uses a vacuum ultraviolet Kr lamp as the light source, the ionization source pressure is 200-300 Pa, the temperature is set to 100°C, the ionization source repulsion voltage is 18V, and the MCP voltage is 4100V; the spectrum acquisition time is 120s, and the mass-to-charge ratio range for spectrum data acquisition is 0-400 Da.

6. The method according to claim 2, characterized in that: Preprocessing the original mass spectrometry of exhaled breath samples refers to: using Matlab software to perform mass correction and signal peak intensity extraction on the obtained original mass spectrometry, and then performing total normalization, data filtering, and logarithmic transformation on the data of each exhaled breath sample on the MetaboAnalyst website. Finally, a data table for statistical analysis is obtained: among them, the first row of the table is the metabolite mass number, the first column of the table is the exhaled breath sample number information, the second column is the exhaled breath sample grouping information, and each of the remaining columns is the relative signal intensity of each metabolite.

7. The method according to claim 2, characterized in that: Performing univariate and multivariate analyses on the data containing metabolite relative intensity information refers to: Univariate analysis refers to the Mann-Whitney U rank sum test, which is performed on the MultipleExperimentViewer software. Differentially expressed metabolites with a false discovery rate (FDR) less than 0.05 are considered to be different between the two groups; multivariate analysis refers to orthogonal partial least squares discriminant analysis, which is performed on the Simca-P software. The data is transformed by unit variance scaling, and metabolites with a variable importance in the projection (VIP) greater than 1 are selected; finally, the metabolites common to the results of univariate analysis and multivariate analysis are taken as the differentially expressed metabolites between the healthy group and the disease patient group.

8. The method according to claim 2, wherein Screening for the optimal combination of differentially expressed metabolites between healthy individuals and asthma patients Performing binary logistic regression analysis on the obtained differentially expressed metabolites in SPSS software, setting the dependent variable as the grouping information of healthy individuals and disease patients, the covariate as the relative intensity of different metabolites, and the method as "Forward: Conditional". The combination ranked first among all the obtained combinations is the optimal combination of differentially expressed metabolites, and this combination of differentially expressed metabolites is the exhaled breath metabolite combination biomarker for diagnosing asthma.

9. The method according to claim 3, wherein Establishing a receiver operating characteristic curve discriminant model based on the combination of differentially expressed metabolites means: performing binary logistic regression analysis on the optimal combination of differentially expressed metabolites in SPSS software, setting the method as "Enter", setting the dependent variable as the grouping information of healthy individuals and disease patients, selecting the metabolites in the optimal combination as covariates, and performing binary logistic regression analysis to obtain the predicted probability of this combination; finally, performing receiver operating characteristic curve (ROC) analysis on the obtained combination of differentially expressed metabolites in SPSS software, setting the predicted probability of the combination of differentially expressed metabolites as the test variable, the grouping information as the status variable, and the value of the status variable as the disease grouping. Finally, an ROC curve is obtained, with the abscissa and ordinate being 1 - specificity and sensitivity respectively. The value corresponding to the maximum sum of specificity and sensitivity is the discrimination threshold between healthy individuals and disease patients. Those greater than the threshold are healthy individuals, and those less than or equal to the threshold are disease patients.

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