Cascade mass spectrometry-based expiration metabolite detection method and detection system

Abstract

The invention provides an expiration metabolite detection method and detection system based on tandem mass spectrometry. The method comprises the following steps: receiving a pre-exercise expiration sample and a post-exercise expiration sample of a subject; performing non-target mass spectrometric detection on the pre-exercise expiration sample and the post-exercise expiration sample to obtain pre-exercise expiration sample mass spectrometric data and post-exercise expiration sample mass spectrometric data; analyzing the pre-exercise expiration sample mass spectrum data and the post-exercise expiration sample mass spectrum data respectively; calculating an analysis result to obtain substance content change data; according to the method, the expiration sample is measured through the tandem mass spectrometry equipment, so that the variety and the quantity of metabolites in the expiration sample are tested and analyzed; according to the detection result, the change trend of the expiration metabolite along with the movement is consistent with the change trend of the metabolite in the blood, but the types of the metabolite changing along with the movement process are almost different, so that the lost metabolite information in the existing blood sample analysis process can be effectively made up.

Description

Technical Field

[0001] The present invention relates to the technical field of detection and analysis, and more specifically, to a breath metabolite detection method and a detection system based on tandem mass spectrometry. Background Art

[0002] As an advanced testing method, mass spectrometry equipment is widely used in the field of biological metabolite detection by measuring the chemical or physical properties of samples. It is used to accurately test or analyze the substance content, substance concentration and characteristics of samples.

[0003] Exercise plays a very important role in both human physiological and mental health, and exercise monitoring is an important prerequisite for ensuring moderate exercise. The exercise process will cause significant changes in the human body's internal environment. By detecting human indicators, the body's state can be monitored and evaluated, which has an indispensable guiding significance for training. Through scientific training monitoring and the exercise model built by data calculation, it is possible to maximize the development of human athletic ability while avoiding the occurrence of adverse symptoms such as sports injuries, overtraining and fatigue. Traditional monitoring indicators include perceived fatigue rate, blood lactate, hemoglobin, heart rate and muscle strength, which can assess the body's physical state at multiple levels and holistically. However, the process of sampling, testing and evaluating multiple indicators is cumbersome and time-consuming, and it is difficult to fully reflect the body's state in a short period of time.

[0004] Metabolomics can detect thousands of metabolites in a sample at the same time, thereby achieving a comprehensive analysis of the body's state and providing a more systematic perspective for us to understand the human body's response to exercise. In recent years, a new branch of exercise physiology that combines metabolomics with exercise research, "exercise metabolomics," is emerging. However, the biological samples used in traditional metabolomics mainly include blood, urine, and tissue samples, which have problems such as invasive collection process, limited sampling volume, complex pre-processing, and inability to perform real-time monitoring. Therefore, the development of a non-invasive real-time monitoring method for the exercise process will promote the further development of exercise physiology and provide humans with more accurate and personalized training guidance.

[0005] In recent years, breath metabolomics has received extensive attention, and researchers can explore the physiological and metabolic processes of the human body in a non-invasive way. At present, breath metabolomics has been widely studied in the fields of physiological state monitoring, disease diagnosis and course monitoring, and drug monitoring. Among the existing breath detection methods, real-time online breath mass spectrometry detection technology can effectively avoid the loss of breath components, and can instantly obtain rich metabolic information in the human body, which is expected to achieve non-invasive real-time monitoring of physical conditions during exercise. Targeted analysis studies based on proton transfer reaction mass spectrometry (PTR-MS) have initially found that acetone, isoprene, and acetaldehyde in breath change with exercise. Osswald et al. used secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) to screen for the first time from the perspective of breath metabolomics and found that multiple metabolites in glyoxylic acid and dicarboxylic acid, tricarboxylic acid cycle, and tryptophan metabolic pathways change during exercise. However, the exercise regimen of the above study lacks repeated experiments and control group verification; in addition, the qualitative method is only based on the first-level mass spectrometry results, and the accuracy of the qualitative method needs to be improved. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a breath metabolite detection method and detection system based on tandem mass spectrometry to overcome the disadvantages that the existing metabolite analysis process usually requires the collection of blood samples. This invasive detection method may cause discomfort or risks to the subject.

[0007] The above technical objectives of the present invention are achieved through the following technical solutions: A method for detecting exhaled metabolites based on tandem mass spectrometry, comprising: S1, receiving a pre-exercise breath sample and a post-exercise breath sample of a subject, wherein the pre-exercise breath sample is a gas sample exhaled by the subject before performing a predetermined exercise step, and the post-exercise breath sample is a gas sample exhaled by the subject after performing a predetermined exercise step; S2, transporting the pre-exercise breath sample and the post-exercise breath sample one by one at a predetermined flow rate into a tandem mass spectrometer for non-target mass spectrometry detection, and obtaining mass spectrometry data of the pre-exercise breath sample and mass spectrometry data of the post-exercise breath sample accordingly; S3, analyzing the mass spectrometry data of the breath sample before exercise to obtain the types of substances in the mass spectrometry data of the breath sample before exercise and the corresponding content of substances before exercise; analyzing the mass spectrometry data of the breath sample after exercise to obtain the types of substances in the mass spectrometry data of the breath sample after exercise and the corresponding content of substances after exercise; S4. Calculate the pre-exercise substance content and post-exercise substance content corresponding to each substance type to obtain substance content change data; the substance content change data specifically includes the content change value of each substance type and the change trend of each substance type following the exercise process.

[0008] In one embodiment, the analysis of the pre-exercise breath sample mass spectrometry data to obtain each substance type and the corresponding pre-exercise substance content in the pre-exercise breath sample mass spectrometry data specifically includes: converting the pre-exercise breath sample mass spectrometry data into a metabolite expression matrix; identifying the exhalation intervals in the metabolite expression matrix, and extracting the average peak height matrix and MS / MS spectrum corresponding to each exhalation interval; respectively comparing the similarity between each of the MS / MS spectra and each reference spectrum, and when the similarity is greater than a similarity threshold, correlating the MS / MS spectrum with the corresponding reference spectrum, and determining the substance type of the associated MS / MS spectrum based on the reference spectrum; and determining the pre-exercise substance content corresponding to each substance type based on the average peak height matrix.

[0009] In one embodiment, the identifying of the exhalation interval in the metabolite expression matrix specifically includes: calculating the total ion intensity corresponding to each sampling point in the metabolite expression matrix, generating a total ion intensity peak based on the total ion intensity of each sampling point; connecting the vertices of the total ion intensity peak in chronological order to obtain a total ion intensity curve; determining a baseline based on an input intensity threshold; obtaining all maximum points on the total ion intensity curve that are higher than the baseline; making a horizontal scan line downward from each maximum point according to a predetermined step distance until the baseline height, and both ends of the horizontal scan line extend to intersect with the exhalation characteristic curve; and determining the exhalation interval based on the overlapping area between the horizontal scan line and the baseline.

[0010] In one embodiment, the respectively comparing the similarity between each of the MS / MS spectra and each of the reference spectra specifically includes: using an inverse dot product spectrum similarity algorithm to calculate the similarity between each of the MS / MS spectra and each of the reference spectra.

[0011] In one embodiment, the number of the predetermined movement steps is three; a rest step of a predetermined length of time is provided after each predetermined movement step; the receiving of the subject's pre-exercise exhaled breath sample and post-exercise exhaled breath sample specifically includes: obtaining the subject's first pre-exercise exhaled breath sample before the subject performs the predetermined movement step for the first time; obtaining the subject's first post-exercise exhaled breath sample after the subject performs the predetermined movement step for the first time; obtaining the subject's second pre-exercise exhaled breath sample after the subject performs the first rest step and before the second execution of the predetermined movement step; obtaining the subject's second post-exercise exhaled breath sample after the subject performs the predetermined movement step for the second time; obtaining the subject's third pre-exercise exhaled breath sample after the subject performs the second rest step and before the third execution of the predetermined movement step; obtaining the subject's third post-exercise exhaled breath sample after the subject performs the predetermined movement step for the third time; obtaining the subject's fourth pre-exercise exhaled breath sample after the subject performs the third rest step.

[0012] In one embodiment, the calculation of the pre-exercise substance content and post-exercise substance content corresponding to each substance type to obtain substance content change data specifically includes: calculating the first pre-exercise substance content of each substance type in the first pre-exercise breath sample; calculating the first post-exercise substance content of each substance type in the first post-exercise breath sample; calculating the second pre-exercise substance content of each substance type in the second pre-exercise breath sample; calculating the second post-exercise substance content of each substance type in the second post-exercise breath sample; calculating the third pre-exercise substance content of each substance type in the third pre-exercise breath sample; calculating the third post-exercise substance content of each substance type in the third post-exercise breath sample; calculating the fourth pre-exercise substance content of each substance type in the fourth pre-exercise breath sample; using the first pre-exercise substance content, the first post-exercise substance content, the second pre-exercise substance content, the second post-exercise substance content, the third pre-exercise substance content, the third post-exercise substance content and the fourth pre-exercise substance content, to generate a change trend of each substance category following the exercise process.

[0013] In one embodiment, the change trends specifically include the following four types: The first pre-exercise substance content is greater than the first post-exercise substance content, the first post-exercise substance content is less than the second pre-exercise substance content, the second pre-exercise substance content is greater than the second post-exercise substance content, the second post-exercise substance content is less than the third pre-exercise substance content, the third pre-exercise substance content is greater than the third post-exercise substance content, and the third post-exercise substance content is less than the fourth pre-exercise substance content; The first pre-exercise substance content is less than the first post-exercise substance content, the first post-exercise substance content is greater than the second pre-exercise substance content, the second pre-exercise substance content is less than the second post-exercise substance content, the second post-exercise substance content is greater than the third pre-exercise substance content, the third pre-exercise substance content is less than the third post-exercise substance content, and the third post-exercise substance content is greater than the fourth pre-exercise substance content; The first pre-exercise substance content is greater than the first post-exercise substance content, the first post-exercise substance content is greater than the second pre-exercise substance content, the second pre-exercise substance content is greater than the second post-exercise substance content, the second post-exercise substance content is greater than the third pre-exercise substance content, the third pre-exercise substance content is greater than the third post-exercise substance content, and the third post-exercise substance content is greater than the fourth pre-exercise substance content; The first substance content before exercise is less than the first substance content after exercise, the first substance content after exercise is less than the second substance content before exercise, the second substance content before exercise is less than the second substance content after exercise, the second substance content after exercise is less than the third substance content before exercise, the third substance content before exercise is less than the third substance content after exercise, and the third substance content after exercise is less than the fourth substance content before exercise.

[0014] A breath metabolite detection system based on tandem mass spectrometry, comprising: A receiving unit, used to receive a pre-exercise breath sample and a post-exercise breath sample of a subject, wherein the pre-exercise breath sample is a gas sample exhaled by the subject before performing a predetermined exercise step, and the post-exercise breath sample is a gas sample exhaled by the subject after performing a predetermined exercise step; A detection unit, used for conveying the pre-exercise breath sample and the post-exercise breath sample one by one at a predetermined flow rate into a tandem mass spectrometer for non-target mass spectrometry detection, and correspondingly obtaining mass spectrometry data of the pre-exercise breath sample and the post-exercise breath sample; An analysis unit is used to analyze the mass spectrum data of the breath sample before exercise to obtain the types of substances in the mass spectrum data of the breath sample before exercise and the corresponding content of the substances before exercise; and to analyze the mass spectrum data of the breath sample after exercise to obtain the types of substances in the mass spectrum data of the breath sample after exercise and the corresponding content of the substances after exercise; The calculation unit is used to calculate the pre-exercise substance content and post-exercise substance content corresponding to each substance type to obtain substance content change data; the substance content change data specifically includes the content change value of each substance type and the change trend of each substance type following the exercise process.

[0015] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0016] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0017] In summary, the present invention has the following beneficial effects: a breath metabolite detection method based on tandem mass spectrometry, comprising: receiving a pre-exercise breath sample and a post-exercise breath sample of a subject; transporting the pre-exercise breath sample and the post-exercise breath sample one by one at a predetermined flow rate into a tandem mass spectrometry device for non-target mass spectrometry detection to obtain breath sample mass spectrometry data; analyzing the pre-exercise breath sample mass spectrometry data to obtain each substance type and the corresponding pre-exercise substance content in the pre-exercise breath sample mass spectrometry data; calculating the pre-exercise substance content and post-exercise substance content corresponding to each substance type to obtain substance content change data; the present invention measures the breath sample through a tandem mass spectrometry device to test and analyze the types and quantities of metabolites in the breath sample; obtaining breath samples as a non-invasive monitoring method has great potential in assessing the physical condition and exercise level of the subject; and according to the test results, the change trend of breath metabolites with exercise is consistent with the results in the blood, but the compounds that change with the exercise process are almost different, which can effectively make up for the volatile metabolite information lost in the existing blood sample analysis process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of a method for detecting breath metabolites based on tandem mass spectrometry; Figure 2 is a structural diagram of a breath metabolite detection system based on tandem mass spectrometry in an embodiment of the present invention; Figure 3 is an internal structure diagram of a computer in an embodiment of the present invention; Figure 4 It is a schematic diagram of the experimental design process of the present invention; Figure 5 A schematic diagram of using the Topological algorithm to identify the exhalation interval in an embodiment of the present invention; Figure 6 This is a schematic diagram of the results of using c-means clustering in an embodiment of the present invention; Figure 7 Schematic diagram of the equipment structure and gas flow rate in an embodiment of the present invention.

[0019] In the figure, 1, receiving unit; 2, detection unit; 3, analysis unit; 4, calculation unit. DETAILED DESCRIPTION

[0020] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. Several embodiments of the present invention are shown in the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0021] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0022] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0023] 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 aforementioned method embodiments and will not be repeated here.

[0024] In several embodiments provided in the present application, any function can be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for enabling a device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0025] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

[0026] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments. In order to facilitate understanding of the embodiments, the related technology is first described.

[0027] The exercise process is an extremely complex physiological activity, which involves the rapid reaction and conversion of many metabolites in the human body. In this process, the human body will experience the production and consumption of a large number of metabolites. These metabolites undergo rapid changes in a very short period of time, thereby providing the energy and physiological needs required for exercise. Such a metabolic process will be reflected in people's various samples as changes in metabolite concentrations. Substances with the two changing trends of "down-up repetitive type" and "up-down repetitive type" usually return to normal levels quickly through the body's self-regulatory mechanism within a period of time after the end of exercise. In traditional exercise monitoring, there are many indicators with similar changing trends to the "down-up repetitive type" and "up-down repetitive type", the most common of which is blood lactate. In blood samples, it was observed that blood lactate rose rapidly after exercise and returned to normal levels after a period of rest. However, as a non-real-time invasive test, blood testing will cause certain harm to the subjects, and the amount of volatile organic matter contained in the blood pretreatment process is lost, resulting in the influence of the types of metabolites detected.

[0028] Embodiment 1 In order to solve the above problems, the present invention provides a method for detecting breath metabolites based on tandem mass spectrometry, such as Figure 1 As shown, including: include: S1, receiving a pre-exercise breath sample and a post-exercise breath sample of a subject, wherein the pre-exercise breath sample is a gas sample exhaled by the subject before performing a predetermined exercise step, and the post-exercise breath sample is a gas sample exhaled by the subject after performing a predetermined exercise step; S2, transporting the pre-exercise breath sample and the post-exercise breath sample one by one at a predetermined flow rate into a tandem mass spectrometer for non-target mass spectrometry detection, and obtaining mass spectrometry data of the pre-exercise breath sample and mass spectrometry data of the post-exercise breath sample accordingly; S3, analyzing the mass spectrometry data of the breath sample before exercise to obtain the types of substances in the mass spectrometry data of the breath sample before exercise and the corresponding content of substances before exercise; analyzing the mass spectrometry data of the breath sample after exercise to obtain the types of substances in the mass spectrometry data of the breath sample after exercise and the corresponding content of substances after exercise; S4. Calculate the pre-exercise substance content and post-exercise substance content corresponding to each substance type to obtain substance content change data; the substance content change data specifically includes the content change value of each substance type and the change trend of each substance type following the exercise process.

[0029] In the present application, in order to explore the concentration changes of metabolites in people before and after exercise through breath samples, the present application proposes a breath metabolite detection method based on tandem mass spectrometry, by collecting breath samples of subjects before and after exercise respectively, and inputting the breath samples into the tandem mass spectrometry device in real time according to the predetermined flow rate requirements for non-target mass spectrometry detection, eliminating the process of sample pretreatment, and being able to retain volatile metabolites in breath samples. Non-target mass spectrometry detection can simultaneously analyze a large number of samples, quickly identify a variety of unknown substances therein, improve analysis efficiency, and can also comprehensively analyze metabolites in breath metabolites. By analyzing the mass spectrometry data, determining the substance category corresponding to each mass-to-charge ratio, and then determining the content of the metabolite according to the ion peak intensity corresponding to the mass-to-charge ratio, the content change of the metabolite before and after exercise is associated with the exercise process, the correlation between each substance and the exercise process can be determined, and according to the results of calculation and analysis, it can be further determined which metabolites can be used to characterize the human body's exercise state and exercise ability.

[0030] By adopting the technical solution of the present application, it is possible to perform online detection of metabolites in breath samples without taking blood from the subjects, and it is found that there are a large number of metabolites related to exercise and with the same trend as metabolites in the blood in the breath after exercise intervention. These compounds are closely related to energy metabolism and post-exercise body recovery, and can assist in the assessment of the athlete's physical condition without invasiveness. It helps to optimize the training and health management of athletes, and also provides necessary biomarkers for the future development of wearable devices for sports monitoring and health assessment. At the same time, the method of the present application can also be used in conjunction with the blood metabolome detection method to improve the coverage of metabolite detection, and can monitor the exercise process more comprehensively, providing a new perspective for exercise physiology and metabolic health research.

[0031] In one embodiment, the analyzing the mass spectrometry data of the pre-exercise breath sample to obtain the types of substances in the mass spectrometry data of the pre-exercise breath sample and the corresponding pre-exercise substance contents specifically includes: converting the mass spectrometry data of the pre-exercise breath sample into a metabolite expression matrix; Identifying the exhalation intervals in the metabolite expression matrix, and extracting the average peak height matrix and MS / MS spectra corresponding to each exhalation interval; Comparing the similarity between each of the MS / MS spectra and each of the reference spectra respectively, and when the similarity is greater than a similarity threshold, correlating the MS / MS spectrum with the corresponding reference spectrum, and determining the substance type of the correlated MS / MS spectrum according to the reference spectrum; The pre-exercise substance content corresponding to each substance type is determined according to the average peak height matrix.

[0032] Specifically, in mass spectrometry analysis, the collected raw data is usually stored in a raw file format proprietary to the mass spectrometer manufacturer. Mass spectrometers of different brands may use different file formats, which means that the data requires specific equipment to read and identify, reducing the readability of the data. Therefore, it is necessary to further convert the data into a more universal mzXML format to facilitate subsequent data processing. The mzXML data mainly includes the following three parts: 1. Metadata; 2. Mass spectrometry data (that is, the metabolite expression matrix in this application); 3. Hierarchical structure. The second part of the mass spectrometry data can be divided into the following three parts: ①. m / z and intensity values ​​(that is, the metabolite expression matrix in this application): store data points in the mass spectrum, describing the mass-to-charge ratio of the molecular ion and the corresponding ion intensity; ② Scanning information: including parameters such as scanning time, mass spectrometry level (MS1, MS2, etc.), acquisition mode, scanning range, etc.; ③ Binary data storage: to save space, m / z The and intensity values ​​are usually stored in binary encoding, and the file contains a Base64-encoded binary data part, which can be decoded into floating point numbers when parsing.

[0033] Since each subject performed multiple breath tests during the process of collecting breath samples, and there was some non-exhalation time between the breath samples, in order to obtain the breath phase data more accurately, it is first necessary to distinguish the exhalation interval and the non-exhalation interval in the data, extract the sample data of the exhalation interval, and remove the sample data of the non-exhalation interval. The sample data specifically includes: average peak height matrix and MS / MS spectrum. After extracting the mass spectrum data of the exhalation interval, the relevant data is analyzed to obtain the substance category and substance content in each breath sample. Among them, the peak height matrix is ​​the peak height matrix of the primary mass spectrum, which is a table used to characterize the mass-to-charge ratio of the substance and the ion intensity corresponding to the mass-to-charge ratio. The mass-to-charge ratio represents the mass number of a substance, which is used to distinguish different substance categories; the ion intensity represents the concentration (or quantity) of the substance corresponding to the mass-to-charge ratio in the breath sample. In order to further determine the type of substance, collision-induced dissociation of the parent ion is performed to generate a unique fragment ion spectrum (MS / MS spectrum). These fragment ions reflect the information of the internal structure of the molecule to obtain the MS / MS spectrum, and based on the matching of the MS / MS spectrum, the type of substance is further determined. In addition, since the length of exhalation time for each subject is different, it is not clear to count the total number of substances. In order to reduce the impact of exhalation time on the test results, when calculating the ion concentration, it is necessary to divide the total area of ​​the intensity peak in the exhalation phase by the total time of the intensity peak to obtain the average peak height matrix related to the ion concentration, and then the concentration of the metabolite ion can be inferred based on the average peak height matrix.

[0034] In one embodiment, the identifying the exhalation interval in the metabolite expression matrix specifically includes: Calculating the total ion intensity corresponding to each sampling point in the metabolite expression matrix, and generating a total ion intensity peak based on the total ion intensity of each sampling point; The vertices of the total ion intensity peaks are connected in chronological order to obtain a total ion intensity curve; Determine a baseline based on an input intensity threshold; obtain all maximum value points on the total ion intensity curve that are higher than the baseline; A horizontal scan line is sequentially drawn downward from each maximum point according to a predetermined step distance until the baseline height, and both ends of the horizontal scan line extend to intersect the exhalation characteristic curve; and the exhalation interval is determined based on the overlapping area between the horizontal scan line and the baseline.

[0035] In one embodiment, the intensity threshold is 10% of the global maximum value of the total ion intensity peak.

[0036] Specifically, the data output by the mass spectrometer is based on the sampling time point. A test is performed at each sampling time point, and the output data includes the mass-to-charge ratio value detected at the sampling time point and the ion intensity corresponding to the mass-to-charge ratio. In the process of identifying exhaled substances, since people cannot exhale all the time, but need to have intervals, when a mass spectrometer data is obtained, it is necessary to distinguish the exhaled part from the non-exhaled part from each other in the mass spectrometer data, and then perform qualitative and quantitative analysis on the substances in the exhaled part, identify the metabolite type and metabolite concentration of the exhaled gas, and then judge the physical condition of the test subject.

[0037] As more samples are delivered to the mass spectrometer during exhalation, the total ion concentration and the peak value of the total ion intensity peak will increase. Therefore, by specifying the baseline, the total ion intensity peak can be divided into exhaled data and non-exhaled data. In order to facilitate data processing in subsequent steps to find the type and concentration of metabolites from the mass spectrometry data, it is also necessary to summarize the exhaled data into the exhaled cycle according to the division results, so as to facilitate the statistics of the exhaled data. For example, in the process of calculating the concentration of metabolites, it is necessary to obtain the time period information of the exhaled cycle, and divide the total amount of metabolites by the time period information to obtain the concentration of the substance.

[0038] Since the start and end points of exhalation need to be accurately identified in the subsequent clustering and analysis process, a horizontal scan line is drawn in the total ion intensity curve by means of a horizontal scan line, and the intersection between the height position of the horizontal scan line at the baseline and the total ion intensity curve is obtained, which corresponds to the start and end points of the exhalation cycle. The exhalation data within the exhalation cycle all belong to the same exhalation cycle. In this process, although there is an intersection between the baseline and the total ion intensity curve, the exhalation cycle cannot be identified based on the direct intersection, such as Figure 5 As shown, the reason is that when two or more exhalation cycles exist at the same time, the area between the two intersections is not necessarily the exhalation cycle, but may also be the end point of the first exhalation cycle and the starting point of the second exhalation cycle. The area between the two intersections is the non-exhalation cycle. Therefore, the exhalation cycle can be directly determined by the method of drawing horizontal scan lines provided by the present application, and there will be no erroneous situation of identifying the non-exhalation cycle as the exhalation cycle.

[0039] In one embodiment, the respectively comparing the similarity between each of the MS / MS spectra and each of the reference spectra specifically includes: using an inverse dot product spectrum similarity algorithm to calculate the similarity between each of the MS / MS spectra and each of the reference spectra.

[0040] In one embodiment, the comparing the similarity between each of the MS / MS spectra and each of the reference spectra further includes: setting the mass deviation between the MS / MS spectrum and each reference spectrum to 0.05 Da.

[0041] In one embodiment, the similarity threshold is 0.8.

[0042] Specifically, the inverse dot product matrix calculates the similarity, which mainly includes the following steps: match the mass-to-charge ratio between the MS / MS spectrum and the reference spectrum with an allowable error of 0.05Da to obtain the corresponding ion intensity peak pair; calculate the product of the two ion intensity peaks in the example intensity peak pair, and then add all the products together to obtain a total value. The larger the sum, the more similar the MS / MS spectrum is to the reference spectrum. In order to ensure that the different intensities of different spectra do not affect the comparison results, this sum is also divided by the sum of the squares of the intensities of the two spectra to obtain a normalized score; the normalized score is corrected using a correction factor to obtain the final similarity score.

[0043] In this embodiment, only when the similarity score is greater than 0.8 can the MS / MS spectrum and the standard spectrum be considered to represent the same substance.

[0044] With respect to the above steps, this embodiment further lists an example for easier understanding: The MS / MS spectrum obtained in the experiment contains four characteristic peaks, namely:

[0045] The standard spectrum also contains four characteristic peaks, namely:

[0046] According to the two tables above, m / z The three intensity peaks at the mass-to-charge ratios of 100, 150, and 200 can match, and the fourth intensity peak in the two spectra is m / z =250, and m / z = No match was found at position 300.

[0047] For each successfully matched intensity peak pair above, calculate the product separately, including: m / z 100: ; m / z 150: ; m / z 200: ; Then add all the products together to get the final calculation result, including: ; During the normalization process, the sum of squares of each spectrum is first calculated, including: MS / MS spectrum: ; Standard spectrum: ; Calculate the normalized score, including: ; In order to avoid a few peaks being too large and resulting in a high score, a correction factor is also needed to correct the normalized score. The correction factor is calculated based on the ratio of the number of matched mass-to-charge ratio points to the total number of mass-to-charge ratio points in the reference spectrum. In this example, the number of matched mass-to-charge ratio points is 3, and the total number of mass-to-charge ratio points in the reference spectrum is 4. The calculation process of the correction factor is:

[0048] The normalized score is corrected using a correction factor to obtain the final similarity score, including: ; Since the similarity score between the two spectra is 0.5325, which is less than the predetermined threshold value of 0.8, the substances represented in the two spectra are not the same.

[0049] In one embodiment, the number of the predetermined motion steps is three; a rest step of a predetermined length of time is provided after each predetermined motion step; The receiving of the pre-exercise breath sample and the post-exercise breath sample of the subject specifically comprises: Before the subject performs the predetermined exercise steps for the first time, obtaining a first pre-exercise breath sample of the subject; After the subject performs the predetermined exercise steps for the first time, obtaining a first post-exercise exhaled breath sample of the subject; After the subject performs a first rest step and before the subject performs a second predetermined exercise step, obtaining a second pre-exercise exhaled breath sample from the subject; After the subject performs the predetermined exercise steps for the second time, obtaining a second post-exercise exhaled breath sample of the subject; After the subject performs the second rest step and before the subject performs the third predetermined exercise step, obtaining a third pre-exercise breath sample of the subject; After the subject performs the predetermined exercise steps for the third time, obtaining a third post-exercise exhaled breath sample of the subject; After the subject performs the third rest step, obtain the subject's fourth pre-exercise exhaled breath sample.

[0050] Specifically, in this embodiment, the predetermined exercise step is repeated three times, and after each exercise step, there is a rest step of a predetermined length. For example, after first performing 10 minutes of full-strength exercise, the subject rests for 60 minutes, then performs 10 minutes of full-strength exercise again, rests for 60 minutes, then performs 10 minutes of full-strength exercise for the third time, rests for 60 minutes, and repeats three times. The length of exercise and rest time needs to be moderate, giving the body enough time to recover so that some metabolites with faster changes can be fully adjusted. The rest time should not be too long to avoid excessive metabolism of metabolites affecting the trend judgment of metabolites.

[0051] In one embodiment, the calculation of the pre-exercise substance content and the post-exercise substance content corresponding to each substance type to obtain substance content change data specifically includes: Calculate the first pre-exercise substance content of each substance type in the first pre-exercise breath sample; calculate the first post-exercise substance content of each substance type in the first post-exercise breath sample; calculate the second pre-exercise substance content of each substance type in the second pre-exercise breath sample; calculate the second post-exercise substance content of each substance type in the second post-exercise breath sample; calculate the third pre-exercise substance content of each substance type in the third pre-exercise breath sample; calculate the third post-exercise substance content of each substance type in the third post-exercise breath sample; calculate the fourth pre-exercise substance content of each substance type in the fourth pre-exercise breath sample; The first substance content before exercise, the first substance content after exercise, the second substance content before exercise, the second substance content after exercise, the third substance content before exercise, the third substance content after exercise and the fourth substance content before exercise are used to generate the change trend of each substance category following the exercise process.

[0052] In one embodiment, the first pre-exercise substance content, the first post-exercise substance content, the second pre-exercise substance content, the second post-exercise substance content, the third pre-exercise substance content, the third post-exercise substance content and the fourth pre-exercise substance content are used to generate the change trend of each substance category following the exercise process, specifically including: C-means clustering is performed on each substance type in the first pre-exercise substance content, the first post-exercise substance content, the second pre-exercise substance content, the second post-exercise substance content, the third pre-exercise substance content, the third post-exercise substance content and the fourth pre-exercise substance content to obtain the change trend of each substance content following the exercise process.

[0053] Specifically, the object of c-means clustering is seven breath samples, that is, each mass-to-charge ratio needs to be divided into seven clusters. Using c-means clustering, first randomly specify the cluster center, then calculate the distance between each sample and the cluster center, and classify the sample to the nearest center to form the first distribution. After the first distribution, calculate the average value of all samples in each cluster, and re-specify seven cluster centers according to the position of the average value. The re-specified cluster center needs to be closest to the position of the average value after the last clustering. After re-specifying the cluster center, clustering is performed again with the re-specified cluster center. Repeat the above process until the clustering result does not change significantly. In this example, the distance between each mass-to-charge ratio point represents the interval between sampling time points. After clustering is completed, the average value of the mass-to-charge ratio intensity peak in each cluster is calculated, which can determine the trend of the substance following the change of the motion process.

[0054] In one embodiment, the change trends specifically include the following four types: The first pre-exercise substance content is greater than the first post-exercise substance content, the first post-exercise substance content is less than the second pre-exercise substance content, the second pre-exercise substance content is greater than the second post-exercise substance content, the second post-exercise substance content is less than the third pre-exercise substance content, the third pre-exercise substance content is greater than the third post-exercise substance content, and the third post-exercise substance content is less than the fourth pre-exercise substance content; The first pre-exercise substance content is less than the first post-exercise substance content, the first post-exercise substance content is greater than the second pre-exercise substance content, the second pre-exercise substance content is less than the second post-exercise substance content, the second post-exercise substance content is greater than the third pre-exercise substance content, the third pre-exercise substance content is less than the third post-exercise substance content, and the third post-exercise substance content is greater than the fourth pre-exercise substance content; The first pre-exercise substance content is greater than the first post-exercise substance content, the first post-exercise substance content is greater than the second pre-exercise substance content, the second pre-exercise substance content is greater than the second post-exercise substance content, the second post-exercise substance content is greater than the third pre-exercise substance content, the third pre-exercise substance content is greater than the third post-exercise substance content, and the third post-exercise substance content is greater than the fourth pre-exercise substance content; The first substance content before exercise is less than the first substance content after exercise, the first substance content after exercise is less than the second substance content before exercise, the second substance content before exercise is less than the second substance content after exercise, the second substance content after exercise is less than the third substance content before exercise, the third substance content before exercise is less than the third substance content after exercise, and the third substance content after exercise is less than the fourth substance content before exercise.

[0055] In this application, by clustering the concentrations of various substances, four substance concentration change trends can be obtained, such as Figure 6As shown in the figure, the concentration changes of the four types of substances are as follows: 1. The concentration drops immediately after the exercise process and recovers before the next exercise process; 2. The concentration rises immediately after the exercise process and recovers before the next exercise process; 3. Continuous decrease; 4. Continuous increase. According to the concentration test results of the metabolite blood lactate, the concentration change pattern of blood lactate conforms to Figure 6 The change law of the substances in the second group in the exercise can be summarized; taking the change trend of blood lactate concentration as the standard, the substances in the second group with the same change law as the blood lactate concentration can be summarized. Based on this change law, the opposite law can also be summarized, that is, the change law of the substances in the first group. In addition, according to the clustering results, the substances with the change law of the third group can be summarized, that is, the substances that continue to accumulate with the exercise process, and the substances with the change law of the fourth group can be summarized, that is, the substances that will continue to decrease with the exercise process.

[0056] Although the experimental process of this application only designs three exercise processes and three rest processes, setting more exercise processes or setting fewer exercise processes will not affect the data analysis and statistical process. Therefore, all processes that use the data analysis method recorded in this application to analyze exhaled metabolites should fall within the scope of protection of this application.

[0057] Embodiment 2 Furthermore, the present application also provides a specific experimental process embodiment to illustrate the analysis process of obtaining breath metabolite mass spectrometry data.

[0058] The first is the selection of subjects. During the experiment, the subjects were divided into three groups, including 13 athletes who had undergone long-term training (aged 18-24 years old), 10 non-athlete volunteers (aged 23-26 years old) and 6 people who did not exercise as a control group. The subjects were required to fast for 3 hours before sampling, not to exercise vigorously for 1 hour before the test, and not to drink beverages, alcohol, tea, coffee, etc. on the day of the test. Before the test, makeup, lipstick, lip gloss, perfume, etc. were not allowed to be applied, so as to minimize the impact of non-exercise factors on the samples.

[0059] The second is to limit the movement process: Figure 4 As shown, the exercise process of the present application has six stages, including three full-strength exercise stages and three rest stages, and each exercise stage and each rest stage are alternately arranged. Therefore, each subject collects 7 breath samples, including four pre-exercise samples and three post-exercise samples (in this embodiment, the sample collected after the last rest is the fourth pre-exercise sample. Such a division rule is convenient for dividing the types of samples, and when the number of exercises is increased, it can be connected with the subsequent exercise steps).

[0060] Each exercise stage includes the following steps: a 3-minute warm-up, followed by HIIT on a power bike in the laboratory. The power bike is set to 70 kg of resistance. Each subject rides at full strength for 30 seconds and rests for 30 seconds, completing a total of 6 rides, which means the exercise stage lasts about 9 minutes in total.

[0061] Each time the subjects completed an exercise session, they rested for one hour.

[0062] In order to provide comparative data for the breath samples, the experiment of the present application also tests the blood samples. Each time the breath sample is collected, the blood sample of the subject is also collected, and the type and quantity of each metabolite in the blood sample are analyzed.

[0063] In the process of collecting breath samples from subjects, the specific operating steps are as follows: Have each subject exhale 12 times using the mouthpiece, using voice prompts to guide the subject, with a time interval between exhalations ranging from 10 to 15 seconds; For the breath samples before and after exercise, the positive ion scanning mode was used to detect 6 breaths of the subject, and the negative ion scanning mode was used to detect 6 breaths of the subject.

[0064] In one embodiment, the pre-exercise breath sample and the post-exercise breath sample are transported one by one at a predetermined flow rate into a tandem mass spectrometer for non-target mass spectrometry detection, specifically including: controlling the pre-exercise breath sample and the post-exercise breath sample to be transported to the ionization chamber of the tandem mass spectrometer at a flow rate of 0.5 L / min for detection.

[0065] In one embodiment, the pre-exercise exhaled breath sample and the post-exercise exhaled breath sample are transported one by one at a predetermined flow rate into a tandem mass spectrometer for non-target mass spectrometry detection, and further includes: controlling the source region auxiliary gas to mix with the exhaled breath sample in an ionization chamber at a flow rate of 2 L / min and then transporting the mixed gas to the ionization chamber of the tandem mass spectrometer for detection.

[0066] In one embodiment, the step of transporting the breath sample to a tandem mass spectrometer at a predetermined flow rate for detection further includes: using a SESI source coupled to Q Exactive to perform real-time online non-target detection on the breath sample.

[0067] Specifically, in the actual breath sample collection process, a SESI source (secondary electrospray ionization source, manufacturer: Super SESI, Fossil Ion Technology, Spain) coupled to Q Exactive (high-resolution mass spectrometer, manufacturer: Thermo Fisher Scientific, Germany) was used for real-time online non-target analysis of breath. The subjects rinsed their mouths with water three times before officially starting to collect breath, and then used a special mouthpiece (MicroGardTM, Vyaire Medical, USA) to collect breath samples. A total of 12 breaths of the subjects were collected each time (6 times in positive and negative ion modes), and the interval between each breath was 10-15 s. Specifically, as Figure 7 As shown, during the process of collecting and analyzing the breath sample, the flow rate of the gas sample is controlled as follows: the subject exhales through a specific mouthpiece, and the blowing flow rate is controlled at 7 L / min by a flow sensor (manufacturer: Exhalion, Fossil Ion Technology, Spain); since the gas flow rate entering the ionization chamber is about 0.5 L / min, the breath sample with a flow rate of 6.5 L / min is discharged through the exhaust valve. The breath sample entering the ionization chamber also needs to be mixed with the auxiliary gas in the source area, and the auxiliary gas is used to entrain the breath sample, so that the ionized breath metabolites enter the mass spectrometer more concentratedly. Since the ionized sample is automatically inhaled by the mass spectrometer at a specific flow rate, the excess gas in the ionization chamber is discharged from the exhaust end in the form of exhaust gas, wherein the gas flow rate discharged from the exhaust end is about 0.9 L / min. In this embodiment, the flow rate of the source area auxiliary gas input into the ionization chamber is about 2 L / min.

[0068] In this embodiment, the overall device has two gas input ends, namely, the input end of the breath sample and the input end of the source area auxiliary gas. The overall device has three gas output ends, namely, the exhaust valve of the breath sample, the exhaust gas discharge end discharged from the ionization chamber, and the input port automatically inhaled by the mass spectrometer. Since the flow rate of the gas automatically inhaled by the mass spectrometer is automatically controlled by the mass spectrometer, after blocking the input end and the exhaust valve of the breath sample, the flow rate of the input source area auxiliary gas can be determined by measuring the flow rate of the exhaust gas discharge end. In the present application, the flow rate of the source area auxiliary gas discharged from the exhaust gas discharge end is 0.4 L / min, which means that it is 0.4 L / min more than the gas automatically inhaled by the mass spectrometer.

[0069] The parameter settings of the tandem mass spectrometer are as follows: Super SESI sets the sampling tube temperature to 130 ℃, the ionization chamber temperature to 90 ℃, the voltage to ±2.8 kV, the sheath gas pressure to 60, the auxiliary gas flow rate to 2, the ion transfer tube temperature to 275 ℃, and the electrospray solution to 0.1% (v / v) formic acid water, where the sheath gas pressure and the auxiliary gas flow rate are both set values ​​of the instrument and are two dimensionless units.

[0070] Q Exactive uses a data-dependent acquisition (DDA) approach in both positive and negative modes.

[0071] The Full MS parameters were set as follows: resolution 140000, AGC 1e6 (Automatic Gain Control), Maximum inject time 100 ms, scan range 70–1000 m / z .

[0072] Some parameters of MS2 are set as follows: resolution 35000, AGC 1e5, Maximum inject time 50 ms, TopN 5, scan range 50–750 m / z , Isolation window 1 m / z , NCE (normalized collision energy) 20, 30, 40 eV. In order to observe the changes of acetone in breath, three of the athletes collected breath samples with the FullMS scan range set to 50–750 m / z The mass axis of the QE was calibrated before the experiment using the commercial calibration solution Pierce™ LTQ Velos ESI (manufacturer: Thermo Fisher Scientific, Germany). The instrument was calibrated with -terpinene (Dalian Special Gases, China) standard gas to ensure that its response intensity was above 1E9.

[0073] Among them, AGC (Automatic Gain Control) is a mechanism in the mass spectrometer that controls the number of ions entering the analyzer. It dynamically adjusts the ion accumulation time to ensure that an appropriate amount of ions enter the analyzer during mass spectrometry analysis, which can avoid too low a signal caused by too few ions and prevent overload caused by too many ions.

[0074] Maximum Inject Time refers to the maximum ion accumulation time allowed by the mass spectrometer in a scan cycle. If the mass spectrometer fails to reach the target ion number set by AGC (such as AGC 1e5) within the set maximum injection time, the mass spectrometer will stop accumulating and proceed to the next scan.

[0075] Isolation window is a key parameter used by mass spectrometers to select specific precursor ions for fragmentation when performing MS / MS (tandem mass spectrometry) analysis. It defines the mass-to-charge ratio (m / z) of the precursor ion that the mass spectrometer isolates in MS / MS analysis. m / z )scope.

[0076] NCE (normalized collision energy) is used to control the collision energy used when precursor ions are fragmented in MS / MS (tandem mass spectrometry) analysis.

[0077] In order to provide a comparison sample for the exhaled metabolites, after each exhaled breath collection process, a blood sample of the subject is also collected synchronously. In this embodiment, the number of blood samples of each subject is also 7. After the blood sample is collected, the metabolite components in the blood sample need to be analyzed, and the specific analysis process is as follows: Immediately after breath sample collection, a disposable blood collection needle (Jiangxi Hongda Group, China) and a sodium heparin anticoagulant tube (Hebei Kangweishi Medical, China) were used to collect venous blood samples from the antecubital area of ​​the subjects’ arms. After blood sample collection, it was placed at room temperature for 30 min. After coagulation, it was centrifuged at 3000 rpm for 5 min at room temperature to obtain plasma, which was cooled with liquid nitrogen and stored in a −80 ℃ refrigerator.

[0078] Before testing on the machine, the plasma was pretreated. After the sample was thawed, 400 μL of the extract (acetonitrile: methanol = 1:1) was taken and vortexed with 100 μL of plasma, and then centrifuged at 4 °C and 15000 rpm for 5 min. The supernatant was taken and placed in a −80 °C refrigerator overnight to fully precipitate the insoluble matter. The stabilized sample was centrifuged at 4 °C and 15000 rpm for 10 min, and the supernatant was dried using a centrifugal concentrator and concentrated to 100 μL.

[0079] Non-targeted analysis of the plasma metabolome was performed using ultra-high performance liquid chromatography (Vanquish, Thermo Fisher Scientific, Germany) coupled to QExactive Plus (Thermo Fisher Scientific, Germany) with an injection volume of 5 μL. The positive ion mode was run at 30 °C and 0.5 mL / min using an ACQUITY UPLC HSS T3 column (2.1 × 100 mm, 3 µm, Waters, USA) with a mobile phase consisting of A (0.1% formic acid in water) and B (acetonitrile). The mobile phase gradient started with 1% B for 1 min, then gradually increased to 100% B from 1–8 min and maintained for 8–10 min, then decreased to 1% B from 10–10.5 min until the end of the run at 12.5 min. The negative ion mode used a HILIC column (2.1×150 mm, 5 µm, HILICON, Sweden) and was run at 30 °C and 0.2 mL / min. The mobile phase consisted of A (20 mM ammonium acetate, 10 mM ammonia, 5% acetonitrile, 95% water) and B (acetonitrile). The mobile phase gradient started with 85% B phase for 2 min, then gradually decreased to 35% B phase from 2–12 min, then decreased to 20% from 12–12.1 min, and maintained from 12.1–15.9 min, and then increased to 85% B phase from 15.9–16 min until the end of the 23 min run.

[0080] The HESI source voltage was set to +3.5 kV / −3.2 kV, the sheath gas was 50 and the auxiliary gas was 8 in the positive ion mode, the sheath gas was 30 and the auxiliary gas was 5 in the negative ion mode, and the ion transfer tube temperature was 350 °C.

[0081] QE Plus uses the DDA method in both positive and negative modes. The Full MS parameters are set as follows: resolution 70000, AGC 1e6, Maximum inject time 100 ms, scan range 70–1000 m / z The MS2 parameters are set as follows: resolution 17500, AGC 1e5, Maximum inject time 50 ms, TopN 15, Isolation window 1 m / z , NCE 20, 30, 40 eV.

[0082] UHPLC-ESI-HRMS / MS data of plasma samples were analyzed using MS-DIAL (version 4.9.22). m / z- Extraction of peak area matrix and qualitative analysis based on MS / MS. The mass deviation of MS1 in MS-DIAL was set to 0.001 Da, the mass deviation of MS2 was set to 0.05 Da, the minimum peak height of peak extraction was 10000, the retention time deviation between samples was 0.1 min, and the retention time and MS1 weight was 1:1. m / z C-means clustering is also used to screen substances with obvious change trends, and the substances are classified according to their change trends.

[0083] Conclusion of the embodiment: Through the above experimental process, a total of 5307 m / z (The other three only collected acetone metabolism signals in their breath). A total of 5,687 molecules were detected in the blood of the 12 athletes. m / z (One person's blood sample was excluded because of hemolysis) m / z C-means clustering was performed. According to the clustering results, the metabolites in exhaled breath and blood that were correlated with the three HIIT processes showed four main trends, including: 1. The concentration dropped immediately after the exercise process and recovered before the next exercise process; 2. The concentration rose immediately after the exercise process and recovered before the next exercise process; 3. Continuous decline; 4. Continuous rise. m / z Further screening of highly relevant sports processes according to the following requirements m / z :(1)More than 50% of the volunteers have the same trend of change;(2) m / z The missing values ​​in all samples should be less than 50%; (3) remove isotope peaks and low-intensity impurity peaks and background peaks. m / z The changes in the breath of 6 non-athletes were caused by exercise, not time and background substances. The control group followed the same experimental procedure, but did not exercise and collected breath samples every 1 hour. m / z Athletes with the same change trend and change multiple were excluded from the subsequent study. m / z , of which 133 in group 1 showed "the concentration dropped immediately after the exercise and recovered before the next exercise", 45 in group 2 showed "the concentration increased immediately after the exercise and recovered before the next exercise", 138 in group 3 showed "the concentration continued to drop", and 16 in group 4 showed "the concentration continued to rise". A total of 334 exercise-related parameters were obtained from the blood. m / z, among which group 1 had "concentrations immediately decreased after exercise and recovered before the next exercise" with 115, group 2 had "increases immediately after exercise and recovered before the next exercise" with 103, group 3 had "continuous decreases" with 41, and group 4 had "continuous increases" with 75. In summary, after exercise intervention, the metabolites that changed in exhaled breath and blood had similar trends and the same total number, but the proportions of metabolites with the four trends were different. For example, the proportion of "continuous decrease" in exhaled breath was the highest (41.57%), while the proportion of this type in the blood metabolome was the lowest (12.28%), indicating that the exhaled metabolome was different from the information carried by the blood metabolome, and the former further improved the information of the latter.

[0084] Embodiment 3 See also Figure 2 , a breath metabolite detection system based on tandem mass spectrometry, the system comprising: A receiving unit 1 is used to receive a pre-exercise breath sample and a post-exercise breath sample of a subject, wherein the pre-exercise breath sample is a gas sample exhaled by the subject before performing a predetermined exercise step, and the post-exercise breath sample is a gas sample exhaled by the subject after performing a predetermined exercise step; Detection unit 2, used for conveying the pre-exercise breath sample and the post-exercise breath sample one by one at a predetermined flow rate into a tandem mass spectrometer for non-target mass spectrometry detection, and correspondingly obtaining mass spectrometry data of the pre-exercise breath sample and the post-exercise breath sample; The analysis unit 3 is used to analyze the mass spectrum data of the breath sample before exercise to obtain the types of substances in the mass spectrum data of the breath sample before exercise and the corresponding content of substances before exercise; and to analyze the mass spectrum data of the breath sample after exercise to obtain the types of substances in the mass spectrum data of the breath sample after exercise and the corresponding content of substances after exercise; The calculation unit 4 is used to calculate the pre-exercise substance content and post-exercise substance content corresponding to each substance type to obtain substance content change data; the substance content change data specifically includes the content change value of each substance type and the change trend of each substance type following the exercise process.

[0085] For the specific definition of the breath metabolite detection system based on tandem mass spectrometry, please refer to the definition of the breath metabolite detection method based on tandem mass spectrometry above, which will not be repeated here. Each module in the above-mentioned breath metabolite detection system based on tandem mass spectrometry can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer in the form of hardware, or can be stored in the memory in the computer in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0086] Those skilled in the art will understand that Figure 2 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation of the scheme of the present application. A specific breath metabolite detection system based on tandem mass spectrometry may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0087] Embodiment 4 A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for detecting breath metabolites based on tandem mass spectrometry as described in Example 1 is implemented.

[0088] Embodiment 5 In one embodiment, an electronic device is provided. The electronic device may be a server, a computer, or a mobile communication terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer is used to provide computing and control capabilities. The memory of the computer includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. When the computer program is executed by the processor, a breath metabolite detection method based on tandem mass spectrometry is implemented.

[0089] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer to which the scheme of the present application is applied. The specific computer may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0090] In one embodiment, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented: S1, receiving a pre-exercise breath sample and a post-exercise breath sample of a subject, wherein the pre-exercise breath sample is a gas sample exhaled by the subject before performing a predetermined exercise step, and the post-exercise breath sample is a gas sample exhaled by the subject after performing a predetermined exercise step; S2, transporting the pre-exercise breath sample and the post-exercise breath sample one by one at a predetermined flow rate into a tandem mass spectrometer for non-target mass spectrometry detection, and obtaining mass spectrometry data of the pre-exercise breath sample and mass spectrometry data of the post-exercise breath sample accordingly; S3, analyzing the mass spectrometry data of the breath sample before exercise to obtain the types of substances in the mass spectrometry data of the breath sample before exercise and the corresponding content of substances before exercise; analyzing the mass spectrometry data of the breath sample after exercise to obtain the types of substances in the mass spectrometry data of the breath sample after exercise and the corresponding content of substances after exercise; S4. Calculate the pre-exercise substance content and post-exercise substance content corresponding to each substance type to obtain substance content change data; the substance content change data specifically includes the content change value of each substance type and the change trend of each substance type following the exercise process.

[0091] In one embodiment, the analyzing the mass spectrometry data of the pre-exercise breath sample to obtain the types of substances in the mass spectrometry data of the pre-exercise breath sample and the corresponding pre-exercise substance contents specifically includes: converting the mass spectrometry data of the pre-exercise breath sample into a metabolite expression matrix; Identifying the exhalation intervals in the metabolite expression matrix, and extracting the average peak height matrix and MS / MS spectra corresponding to each exhalation interval; Comparing the similarity between each of the MS / MS spectra and each of the reference spectra respectively, and when the similarity is greater than a similarity threshold, correlating the MS / MS spectrum with the corresponding reference spectrum, and determining the substance type of the correlated MS / MS spectrum according to the reference spectrum; The pre-exercise substance content corresponding to each substance type is determined according to the average peak height matrix.

[0092] In one embodiment, the identifying the exhalation interval in the metabolite expression matrix specifically includes: Calculating the total ion intensity corresponding to each sampling point in the metabolite expression matrix, and generating a total ion intensity peak based on the total ion intensity of each sampling point; The vertices of the total ion intensity peaks are connected in chronological order to obtain a total ion intensity curve; Determine a baseline based on an input intensity threshold; obtain all maximum value points on the total ion intensity curve that are higher than the baseline; A horizontal scan line is sequentially drawn downward from each maximum point according to a predetermined step distance until the baseline height, and both ends of the horizontal scan line extend to intersect the exhalation characteristic curve; and the exhalation interval is determined based on the overlapping area between the horizontal scan line and the baseline.

[0093] In one embodiment, the comparing the similarity between each of the MS / MS spectra and each of the reference spectra comprises: The inverse dot product spectrum similarity algorithm is used to calculate the similarity between each of the MS / MS spectra and each reference spectrum.

[0094] In one embodiment, the number of the predetermined motion steps is three; a rest step of a predetermined length of time is provided after each predetermined motion step; The receiving of the pre-exercise breath sample and the post-exercise breath sample of the subject specifically comprises: Before the subject performs the predetermined exercise steps for the first time, obtaining a first pre-exercise breath sample of the subject; After the subject performs the predetermined exercise steps for the first time, obtaining a first post-exercise exhaled breath sample of the subject; After the subject performs a first rest step and before the subject performs a second predetermined exercise step, obtaining a second pre-exercise exhaled breath sample from the subject; After the subject performs the predetermined exercise steps for the second time, obtaining a second post-exercise exhaled breath sample of the subject; After the subject performs the second rest step and before the subject performs the third predetermined exercise step, obtaining a third pre-exercise breath sample of the subject; After the subject performs the predetermined exercise steps for the third time, obtaining a third post-exercise exhaled breath sample of the subject; After the subject performs the third rest step, obtain the subject's fourth pre-exercise exhaled breath sample.

[0095] In one embodiment, the calculation of the pre-exercise substance content and the post-exercise substance content corresponding to each substance type to obtain substance content change data specifically includes: Calculate the first pre-exercise substance content of each substance type in the first pre-exercise breath sample; calculate the first post-exercise substance content of each substance type in the first post-exercise breath sample; calculate the second pre-exercise substance content of each substance type in the second pre-exercise breath sample; calculate the second post-exercise substance content of each substance type in the second post-exercise breath sample; calculate the third pre-exercise substance content of each substance type in the third pre-exercise breath sample; calculate the third post-exercise substance content of each substance type in the third post-exercise breath sample; calculate the fourth pre-exercise substance content of each substance type in the fourth pre-exercise breath sample; The first substance content before exercise, the first substance content after exercise, the second substance content before exercise, the second substance content after exercise, the third substance content before exercise, the third substance content after exercise and the fourth substance content before exercise are used to generate the change trend of each substance category following the exercise process.

[0096] In one embodiment, the change trends specifically include the following four types: The first pre-exercise substance content is greater than the first post-exercise substance content, the first post-exercise substance content is less than the second pre-exercise substance content, the second pre-exercise substance content is greater than the second post-exercise substance content, the second post-exercise substance content is less than the third pre-exercise substance content, the third pre-exercise substance content is greater than the third post-exercise substance content, and the third post-exercise substance content is less than the fourth pre-exercise substance content; The first pre-exercise substance content is less than the first post-exercise substance content, the first post-exercise substance content is greater than the second pre-exercise substance content, the second pre-exercise substance content is less than the second post-exercise substance content, the second post-exercise substance content is greater than the third pre-exercise substance content, the third pre-exercise substance content is less than the third post-exercise substance content, and the third post-exercise substance content is greater than the fourth pre-exercise substance content; The first pre-exercise substance content is greater than the first post-exercise substance content, the first post-exercise substance content is greater than the second pre-exercise substance content, the second pre-exercise substance content is greater than the second post-exercise substance content, the second post-exercise substance content is greater than the third pre-exercise substance content, the third pre-exercise substance content is greater than the third post-exercise substance content, and the third post-exercise substance content is greater than the fourth pre-exercise substance content; The first substance content before exercise is less than the first substance content after exercise, the first substance content after exercise is less than the second substance content before exercise, the second substance content before exercise is less than the second substance content after exercise, the second substance content after exercise is less than the third substance content before exercise, the third substance content before exercise is less than the third substance content after exercise, and the third substance content after exercise is less than the fourth substance content before exercise.

[0097] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0098] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for detecting exhaled breath metabolites based on tandem mass spectrometry, characterized in that: include: Receiving a pre-exercise breath sample and a post-exercise breath sample of the subject, wherein the pre-exercise breath sample is a gas sample exhaled by the subject before performing a predetermined exercise step, and the post-exercise breath sample is a gas sample exhaled by the subject after performing the predetermined exercise step; The pre-exercise breath sample and the post-exercise breath sample are transported one by one at a predetermined flow rate into a tandem mass spectrometer for non-target mass spectrometry detection, and mass spectrometry data of the pre-exercise breath sample and mass spectrometry data of the post-exercise breath sample are obtained accordingly; Analyze the mass spectrometry data of the breath sample before exercise to obtain the types of substances in the mass spectrometry data of the breath sample before exercise and the corresponding content of the substances before exercise; analyze the mass spectrometry data of the breath sample after exercise to obtain the types of substances in the mass spectrometry data of the breath sample after exercise and the corresponding content of the substances after exercise; The pre-exercise substance content and post-exercise substance content corresponding to each substance type are calculated to obtain substance content change data; the substance content change data specifically includes the content change value of each substance type and the change trend of each substance type following the exercise process.

2. The method for detecting breath metabolites based on tandem mass spectrometry according to claim 1, characterized in that: The analyzing the mass spectrometry data of the breath sample before exercise to obtain the types of substances in the mass spectrometry data of the breath sample before exercise and the corresponding pre-exercise substance contents specifically includes: converting the mass spectrometry data of the pre-exercise breath sample into a metabolite expression matrix; Identifying the exhalation intervals in the metabolite expression matrix, and extracting the average peak height matrix and MS / MS spectra corresponding to each exhalation interval; Comparing the similarity between each of the MS / MS spectra and each of the reference spectra respectively, and when the similarity is greater than a similarity threshold, correlating the MS / MS spectrum with the corresponding reference spectrum, and determining the substance type of the correlated MS / MS spectrum according to the reference spectrum; The pre-exercise substance content corresponding to each substance type is determined according to the average peak height matrix.

3. The method for detecting breath metabolites based on tandem mass spectrometry according to claim 2, characterized in that: The identifying the exhalation interval in the metabolite expression matrix specifically includes: Calculating the total ion intensity corresponding to each sampling point in the metabolite expression matrix, and generating a total ion intensity peak based on the total ion intensity of each sampling point; The vertices of the total ion intensity peaks are connected in chronological order to obtain a total ion intensity curve; Determine a baseline based on an input intensity threshold; obtain all maximum value points on the total ion intensity curve that are higher than the baseline; A horizontal scan line is sequentially drawn downward from each maximum point according to a predetermined step distance until the baseline height, and both ends of the horizontal scan line extend to intersect the exhalation characteristic curve; and the exhalation interval is determined based on the overlapping area between the horizontal scan line and the baseline.

4. The method for detecting breath metabolites based on tandem mass spectrometry according to claim 2, characterized in that: The comparing the similarity between each of the MS / MS spectra and each of the reference spectra specifically includes: The inverse dot product spectrum similarity algorithm is used to calculate the similarity between each of the MS / MS spectra and each reference spectrum.

5. The method for detecting breath metabolites based on tandem mass spectrometry according to claim 4, characterized in that: The number of the predetermined movement steps is three; after each predetermined movement step, a rest step of a predetermined length of time is provided; The receiving of the pre-exercise breath sample and the post-exercise breath sample of the subject specifically comprises: Before the subject performs the predetermined exercise steps for the first time, obtaining a first pre-exercise breath sample of the subject; After the subject performs the predetermined exercise steps for the first time, obtaining a first post-exercise exhaled breath sample of the subject; After the subject performs a first rest step and before the subject performs a second predetermined exercise step, obtaining a second pre-exercise exhaled breath sample from the subject; After the subject performs the predetermined exercise steps for the second time, obtaining a second post-exercise exhaled breath sample of the subject; After the subject performs the second rest step and before the subject performs the third predetermined exercise step, obtaining a third pre-exercise breath sample of the subject; After the subject performs the predetermined exercise steps for the third time, obtaining a third post-exercise exhaled breath sample of the subject; After the subject performs the third rest step, obtain the subject's fourth pre-exercise exhaled breath sample.

6. The method for detecting breath metabolites based on tandem mass spectrometry according to claim 5, characterized in that: The calculation of the pre-exercise substance content and the post-exercise substance content corresponding to each substance type to obtain substance content change data specifically includes: Calculate the first pre-exercise substance content of each substance type in the first pre-exercise breath sample; calculate the first post-exercise substance content of each substance type in the first post-exercise breath sample; calculate the second pre-exercise substance content of each substance type in the second pre-exercise breath sample; calculate the second post-exercise substance content of each substance type in the second post-exercise breath sample; calculate the third pre-exercise substance content of each substance type in the third pre-exercise breath sample; calculate the third post-exercise substance content of each substance type in the third post-exercise breath sample; calculate the fourth pre-exercise substance content of each substance type in the fourth pre-exercise breath sample; The first substance content before exercise, the first substance content after exercise, the second substance content before exercise, the second substance content after exercise, the third substance content before exercise, the third substance content after exercise and the fourth substance content before exercise are used to generate the change trend of each substance category following the exercise process.

7. The method for detecting breath metabolites based on tandem mass spectrometry according to claim 6, characterized in that: The change trends specifically include the following four types: The first pre-exercise substance content is greater than the first post-exercise substance content, the first post-exercise substance content is less than the second pre-exercise substance content, the second pre-exercise substance content is greater than the second post-exercise substance content, the second post-exercise substance content is less than the third pre-exercise substance content, the third pre-exercise substance content is greater than the third post-exercise substance content, and the third post-exercise substance content is less than the fourth pre-exercise substance content; The first pre-exercise substance content is less than the first post-exercise substance content, the first post-exercise substance content is greater than the second pre-exercise substance content, the second pre-exercise substance content is less than the second post-exercise substance content, the second post-exercise substance content is greater than the third pre-exercise substance content, the third pre-exercise substance content is less than the third post-exercise substance content, and the third post-exercise substance content is greater than the fourth pre-exercise substance content; The first pre-exercise substance content is greater than the first post-exercise substance content, the first post-exercise substance content is greater than the second pre-exercise substance content, the second pre-exercise substance content is greater than the second post-exercise substance content, the second post-exercise substance content is greater than the third pre-exercise substance content, the third pre-exercise substance content is greater than the third post-exercise substance content, and the third post-exercise substance content is greater than the fourth pre-exercise substance content; The first substance content before exercise is less than the first substance content after exercise, the first substance content after exercise is less than the second substance content before exercise, the second substance content before exercise is less than the second substance content after exercise, the second substance content after exercise is less than the third substance content before exercise, the third substance content before exercise is less than the third substance content after exercise, and the third substance content after exercise is less than the fourth substance content before exercise.

8. A breath metabolite detection system based on tandem mass spectrometry, characterized in that: include: A receiving unit, used to receive a pre-exercise breath sample and a post-exercise breath sample of a subject, wherein the pre-exercise breath sample is a gas sample exhaled by the subject before performing a predetermined exercise step, and the post-exercise breath sample is a gas sample exhaled by the subject after performing a predetermined exercise step; A detection unit, used for conveying the pre-exercise breath sample and the post-exercise breath sample one by one at a predetermined flow rate into a tandem mass spectrometer for non-target mass spectrometry detection, and correspondingly obtaining mass spectrometry data of the pre-exercise breath sample and the post-exercise breath sample; An analysis unit is used to analyze the mass spectrum data of the breath sample before exercise to obtain the types of substances in the mass spectrum data of the breath sample before exercise and the corresponding content of the substances before exercise; and to analyze the mass spectrum data of the breath sample after exercise to obtain the types of substances in the mass spectrum data of the breath sample after exercise and the corresponding content of the substances after exercise; The calculation unit is used to calculate the pre-exercise substance content and post-exercise substance content corresponding to each substance type to obtain substance content change data; the substance content change data specifically includes the content change value of each substance type and the change trend of each substance type following the exercise process.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting breath metabolites based on tandem mass spectrometry according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the processor executes the computer program, the method for detecting breath metabolites based on tandem mass spectrometry according to any one of claims 1 to 7 is implemented.

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