Method for detecting symbolic expiration data in different motion states based on tandem mass spectrometry

Abstract

The invention discloses a method for detecting symbolic expiration data under different motion states based on tandem mass spectrometry. The method comprises the following steps: acquiring expiration sample data and blood sample data based on tandem mass spectrometry analysis; the expiration sample data and the blood sample data are sample data acquired in different motion states when a predetermined motion process is executed; and determining standard metabolites in the blood sample data, and screening expiration metabolites in the expiration sample data according to the change trends of the standard metabolites in different motion states to obtain symbolic expiration data. The method for comparing the change trends of substances in an expiration sample and a blood sample effectively proves that the motion state and the motion intensity of a subject can be diagnosed and identified in real time through expiration, and support is provided for monitoring of athletic ability and personalized motion guidance.

Description

Technical Field

[0001] The present invention relates to the technical field of biological data detection, and in particular to a method for detecting characteristic exhalation data under different motion states based on tandem mass spectrometry. Background Art

[0002] As people pay more attention to sports health, monitoring the changes in human metabolic status during exercise is crucial for personalized sports guidance and health management. By monitoring the changing trends of metabolites, we can better understand the impact of exercise on the human body, adjust exercise intensity, and prevent sports injuries. However, most traditional exercise monitoring methods rely on collecting biological samples such as blood and urine samples, which have the following problems: 1. Invasive collection: Traditional blood sample collection methods usually require invasive operations, such as drawing venous blood, which may not only cause discomfort to the subjects, but also limit the realization of real-time monitoring. Although blood samples provide rich metabolic information, their invasive operation becomes a limitation.

[0003] 2. Lack of real-time performance: Currently, most blood and urine sample tests are performed offline. They can capture changes in metabolites during exercise, but because they require offline testing, information on the changes cannot be obtained immediately.

[0004] During exercise, the collection of breath samples is simple and non-invasive, and breath metabolites can be detected in real time through mass spectrometry technology, which is expected to replace blood samples or urine samples for exercise monitoring. However, how to effectively screen out exercise-related breath metabolites and ensure that these metabolites can reflect exercise intensity and exercise status are problems that need to be solved in current breath monitoring technology. Summary of the invention

[0005] Based on the above deficiencies in the prior art, the present invention provides a method for detecting iconic exhalation data under different exercise states based on tandem mass spectrometry, which screens out iconic exhalation data related to exercise and realizes non-invasive monitoring of exercise intensity and exercise state.

[0006] In order to solve the above technical problems, the present invention discloses a method for detecting characteristic exhalation data under different motion states based on tandem mass spectrometry, comprising the following steps: Acquire breath sample data and blood sample data based on tandem mass spectrometry analysis; the breath sample data and blood sample data are sample data collected during a predetermined motion process and under different motion states; The standard metabolites in the blood sample data are determined, and the exhaled metabolites in the exhaled sample data are screened according to the change trends of the standard metabolites under different motion states to obtain the iconic exhaled data.

[0007] In some embodiments, screening the breath sample data according to the change trend of the standard metabolite comprises: Screen exhaled breath metabolites with consistent changing trends with standard metabolites.

[0008] In some embodiments, determining a standard metabolite in blood sample data comprises: The blood metabolites in the blood sample data are clustered, and the standard metabolites are determined based on the clustering results.

[0009] In some embodiments, the number of the standard metabolite is at least one; the change trend of the standard metabolite under different motion states includes: Recovery within one hour after ascent, recovery within one hour after descent, continuous ascent and / or continuous descent.

[0010] In some embodiments, obtaining breath sample data and blood sample data based on tandem mass spectrometry analysis further includes: The blood metabolites in the blood sample data and the breath metabolites in the breath sample data are qualitatively characterized according to qualitative rules.

[0011] In some embodiments, the qualitative rules include: When MS / MS data of exhaled metabolites exist, the structures of exhaled metabolites are confirmed and the exhaled metabolites are qualitatively identified through the MS / MS data of exhaled metabolites. When there is no MS / MS data for the exhaled metabolites, the structure of the exhaled metabolites is determined by the mass-to-charge ratio of the positive ions and negative ions of the exhaled metabolites. The exhaled metabolites are qualitatively identified by combining the known metabolite structures in the database and the water-gas partition coefficient ratio of the exhaled metabolites.

[0012] In some embodiments, before obtaining blood sample data and breath sample data based on tandem mass spectrometry analysis, the method further includes: Acquire raw blood data and raw exhaled breath data, and analyze the raw blood data and raw exhaled breath data by a tandem mass spectrometer to obtain blood sample data and exhaled breath sample data; The blood original data and the exhalation original data both include the first blood original data and the first exhalation original data collected before the first execution of the predetermined exercise, The second blood raw data and the second exhalation raw data collected after the first scheduled exercise is performed, The third blood raw data and the third exhalation raw data collected after the first rest step and before the second scheduled exercise are performed, the fourth blood raw data and the fourth exhalation raw data collected after the second execution of the predetermined exercise, The fifth blood raw data and the fifth exhalation raw data collected after the second rest step and the third scheduled exercise are collected. The sixth blood raw data and the sixth exhalation raw data collected after the second scheduled exercise, The seventh blood raw data and the seventh exhalation raw data collected after the third rest step.

[0013] In some embodiments, the blood raw data is analyzed by a tandem mass spectrometry device to obtain blood sample data, including: Acquiring raw blood data, extracting a mass-to-charge ratio-peak area matrix of metabolites from the raw blood data, and screening blood metabolites and their mass-to-charge ratios; Data clustering was used to determine the changing trends of blood metabolites during exercise; A qualitative analysis of the blood metabolites is performed according to the qualitative rules, and a quantitative analysis of the blood metabolites is performed according to the peak areas to obtain blood sample data.

[0014] In some embodiments, the breath raw data is analyzed by a tandem mass spectrometry device to obtain breath sample data, including: Convert exhaled breath raw data into 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 breath metabolite of the correlated MS / MS spectrum according to the reference spectrum; The substance content of the exhaled metabolites was determined based on the first-order average peak height corresponding to the exhaled metabolites.

[0015] In some embodiments, the tandem mass spectrometry analysis is performed by ultra-high performance liquid chromatography coupled to a high-resolution mass spectrometer; Ultra-high performance liquid chromatography was combined with high-resolution liquid chromatography-mass spectrometry for non-target analysis of the plasma metabolome. The injection volume was 5 μL. An ACQUITY UPLC HSS T3 column was used in positive ion mode at 30 °C and 0.5 mL / min. Mobile phase A was 0.1% formic acid in water and mobile phase B was acetonitrile. The mobile phase gradient started with 1% mobile phase B for 1 min, gradually increased to 100% mobile phase B from 1 to 8 min, and maintained for 8 to 10 min. It was then reduced to 1% mobile phase B from 10 to 10.5 min until the end of the run. A HILIC column was used in the negative ion mode at 30 °C and 0.2 mL / min. Mobile phase A was 20 mM ammonium acetate, 10 mM ammonia, 5% acetonitrile, and 95% water, and mobile phase B was acetonitrile. The mobile phase gradient started with 85% mobile phase B for 2 min, then gradually decreased to 35% mobile phase B from 2 to 12 min, then decreased to 20% from 12 to 12.1 min, and maintained from 12.1 to 15.9 min, and then increased to 85% mobile phase B from 15.9 to 16 min until the end of the run.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for detecting characteristic breath data under different exercise states based on tandem mass spectrometry, and obtains characteristic breath data according to standard metabolite screening, thereby realizing non-invasive real-time monitoring of metabolite change trends under different exercise intensities, and can accurately reflect exercise status and exercise intensity, thereby providing scientific support for exercise performance evaluation and health management and providing a scientific basis for personalized exercise plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of a method for detecting characteristic exhaled breath data under different motion states based on tandem mass spectrometry provided by the present invention; Figure 2 Schematic diagram of the experimental design provided by the present invention; Figure 3 A cluster diagram of the changes of exercise-related metabolites over time in the breath samples provided by the present invention and the proportions of different change trends; Figure 4 This is a cluster diagram of the changes in exercise-related metabolites over time in the blood samples provided by the present invention and the proportions of different change trends. DETAILED DESCRIPTION

[0018] For better understanding and implementation, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products or apparatus.

[0020] The present application provides a method for detecting characteristic exhalation data under different exercise states based on tandem mass spectrometry, which screens out characteristic exhalation data related to exercise and realizes non-invasive monitoring of exercise intensity and exercise state.

[0021] like Figure 1 As shown, the method comprises the following steps: Step S1, obtaining breath sample data and blood sample data based on tandem mass spectrometry analysis; the breath sample data and blood sample data are sample data collected during a predetermined motion process and under different motion states.

[0022] During the study, this application collected breath samples and blood samples from 13 athletes (aged 18-24 years old) who had undergone long-term training (the S02 blood sample was hemolyzed and was not included in the analysis); in addition, breath samples were collected from 10 non-athlete volunteers (aged 23-26 years old) before and after exercise, and 6 people provided breath samples under non-exercise conditions as a control group. 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 to minimize the impact of non-exercise factors on the sample.

[0023] Before obtaining blood sample data and breath sample data based on tandem mass spectrometry analysis, it also includes: Acquire raw blood data and raw exhaled breath data, and analyze the raw blood data and raw exhaled breath data by a tandem mass spectrometer to obtain blood sample data and exhaled breath sample data; The blood original data and the exhalation original data both include the first blood original data and the first exhalation original data collected before the first execution of the predetermined exercise, The second blood raw data and the second exhalation raw data collected after the first scheduled exercise is performed, The third blood raw data and the third exhalation raw data collected after the first rest step and before the second scheduled exercise are performed, the fourth blood raw data and the fourth exhalation raw data collected after the second execution of the predetermined exercise, The fifth blood raw data and the fifth exhalation raw data collected after the second rest step and the third scheduled exercise are collected. The sixth blood raw data and the sixth exhalation raw data collected after the second scheduled exercise, The seventh blood raw data and the seventh exhalation raw data collected after the third rest step.

[0024] like Figure 2As shown, the subjects performed the same scheduled exercise, which was high-intensity interval exercise. Blood samples and breath samples were collected before and after each high-intensity interval exercise and after a preset time after the last high-intensity interval exercise to obtain blood raw data and breath raw data. Exemplarily, athletes and non-athletes performed HIIT (High-Intensity Interval Training) on ​​a power bicycle in a laboratory. The HIIT process is as follows: first, a 3-minute warm-up was performed, and HIIT was started after warm-up. The subjects rode at full strength for 30 seconds and rested for 30 seconds under 70 kg resistance, and a total of 6 rides were completed. After each HIIT, the subjects rested for 1 hour, and a total of 3 times were completed. Breath samples and blood samples were collected before and after each HIIT and 1 hour after the last HIIT. For untrained ordinary people, only breath samples were collected. Therefore, a total of 7 breath samples and blood samples were collected from each person. The 6 people in the control group chose another day to collect breath samples at rest every 1 h during the same time period (15:00–20:00). This study was approved by the Medical Ethics Committee of Jinan University (No. JNUKY-2023-0059), and all subjects signed informed consent.

[0025] Before collecting the breath sample, the subject rinsed his mouth with water three times, and then used a special mouthpiece to collect the breath sample. Each time the subject's breath was collected, 12 breaths were collected, including 6 times in positive and negative ion modes, and the interval between each breath was 10-15 s. The blowing process requires the subject to blow steadily and slowly. The blowing flow rate is controlled at 7 L / min by the flow sensor, the exhaust end flow controller is set to 0.9 L / min, and the source area auxiliary gas is set to 0.4 L / min more than the flow rate of the mass spectrometer intake. The actual exhaled air entering the ionization chamber is 0.5 L / min.

[0026] After obtaining the breath sample, the breath sample was tested to obtain the original breath data. Specifically, a SESI source (Super SESI, Fossil Ion Technology secondary electrospray ionization) coupled to a high-resolution mass spectrometer (Q Exactive) was used for real-time online non-target analysis of breath. The SESI source was set with a sampling tube temperature of 130 °C, an ionization chamber temperature of 90 °C, a voltage of ±2.8 kV, a sheath gas of 60, an auxiliary gas of 2, an ion transfer tube temperature of 275 °C, and an electrospray solution of 0.1% (v / v) formic acid in water.

[0027] The high-resolution mass spectrometer used the data-dependent acquisition (DDA) method in both positive and negative modes. The full-scan mass spectrometer (Full MS) parameters were set as follows: resolution 140000, AGC 1e6, maximum injection time 100 ms, scan range 70–1000 m / zThe secondary mass spectrometry (MS²) parameters were set as follows: resolution 35000, AGC (Automatic Gain Control) 1e5, maximum injection time 50 ms, TopN 5, scan range 50–750 m / z , the isolation window is 1 m / z , NCE (Normalized Collision Energy) is 20, 30, and 40 eV.

[0028] In order to observe the changes of acetone in breath, three of the athletes collected breath samples with the Full MS scan range set to 50–750 m / z Before the experiment, commercial calibration fluid, such as LTQ Velos ESI calibration fluid, was used to calibrate the mass axis of the high-resolution mass spectrometer. Before collecting exhaled breath every day, the instrument was calibrated with 100 ppbv α-terpinene standard gas to ensure that its response intensity was above 1E9.

[0029] After the breath sample collection is completed, a blood sample is collected from the antecubital venous blood of the subject's arm. The blood sample is placed at room temperature for 30 minutes, and after coagulation, it is centrifuged at 3000 rpm for 5 minutes at room temperature to obtain a plasma sample, which is cooled with liquid nitrogen and stored in a −80 ℃ refrigerator.

[0030] The plasma sample is pre-treated, and the pre-treatment includes one or more of the following: sample thawing, mixed extract, centrifugation, frozen sedimentation, re-centrifugation, and sample concentration. For example, after the plasma sample is thawed, 400 μL of extract (acetonitrile: methanol = 1: 1) is taken and vortexed with 100 μL of plasma, and then centrifuged at 4 ° C and 15000 rpm for 5 min, and the supernatant is taken and placed in a −80 ° C refrigerator overnight to fully precipitate insoluble matter. The stabilized sample is centrifuged at 4 ° C and 15000 rpm for 10 min, and the supernatant is dried using a centrifugal concentrator and concentrated to 100 μL.

[0031] The pre-treated plasma samples are analyzed by mass spectrometry equipment to obtain the original blood data. Specifically including: Ultra-high performance liquid chromatography coupled to a high-resolution mass spectrometer Q Exactive Plus was used for non-target analysis of the plasma metabolome, with an injection volume of 5 μL. The positive ion mode was run on an ACQUITY UPLC HSS T3 column (2.1×100 mm, 3 μm, Waters) at 30 °C and 0.5 mL / min, with the mobile phase consisting of A (0.1% formic acid in water) and B (acetonitrile). The mobile phase gradient started with 1% B phase for 1 min, gradually increased to 100% B phase from 1–8 min, and maintained for 8–10 min, then decreased to 1% B phase from 10–10.5 min until the end of the run at 12.5 min. A HILIC column (2.1×150 mm, 5 µm) was used in the negative ion mode, and the chromatography was run at 30 °C and 0.2 mL / min. The mobile phases included mobile phase A and mobile phase B. Mobile phase A included 20 mM (millimolar) ammonium acetate, 10 mM ammonia, 5% acetonitrile, and 95% water, and mobile phase B included acetonitrile. The mobile phase gradient started with 85% of phase B for 2 min, then gradually decreased to 35% of phase B 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% of phase B from 15.9–16 min until the end of the 23 min run.

[0032] 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.

[0033] 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 injection time 100 ms, scan range 70–1000 m / z The MS2 parameters are set as follows: resolution 17500, AGC 1e5, maximum injection time 50 ms, TopN 15, isolation window 1 m / z , NCE 20, 30, 40 eV.

[0034] In this process, standard detection was set up to ensure the accuracy, reliability and consistency of the experimental results. Secondary electrospray ionization high resolution mass spectrometry (SESI-HRMS) was used to detect the standard to qualitatively identify the compounds detected in the breath. 2 The carrier gas first passes through a gas washing bottle filled with pure water, then passes through a centrifuge tube filled with standard solution, and enters SESI-HRMS detection. The Super SESI settings are the same as those for exhaled breath detection. QE uses parallel reaction monitoring (PRM) to perform target MS / MS analysis on the standard. The PRM parameter settings are as follows: resolution 35000, AGC 1e5, Maximum inject time 200 ms, scan range 50–750 m / z ,Isolation window 1 m / z , NCE 20, 30, 40 eV.

[0035] After the collection and detection are completed, the exhaled breath raw data and blood raw data are analyzed. For the exhaled breath samples, the exhaled breath raw data collected by SESI-HRMS / MS (secondary electrospray ionization-high resolution mass spectrometry / tandem mass spectrometry) are converted into mzML format using MSConvert, and then analyzed using the Topological algorithm, including: Convert exhaled breath raw data into 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 breath metabolite of the correlated MS / MS spectrum according to the reference spectrum; The substance content of the exhaled metabolites was determined based on the first-order average peak height corresponding to the exhaled metabolites.

[0036] Specifically, extract all m / z- Average peak height matrix and MS / MS spectra (secondary mass spectra) of the exhaled breath sampling interval. Compound identification was performed in NIST20 (National Institute of Standards and Technology Mass Spectral Library 2020 Edition) and MoNA (MassBank of North America, North American Mass Spectral Database) based on the MS / MS spectra, and MS / MS similarity was calculated using the inverse dot product spectrum similarity algorithm. The mass deviation between the experimental and reference spectra was set to 0.05 Da, and the results with a similarity greater than 0.8 were selected.

[0037] Analyze the raw blood data to obtain blood sample data, including: Acquiring raw blood data, extracting a mass-to-charge ratio-peak area matrix of metabolites from the raw blood data, and screening blood metabolites and their mass-to-charge ratios; Data clustering was used to determine the changing trends of blood metabolites during exercise; The blood metabolites are qualitatively analyzed according to the qualitative rules, and the blood metabolites are quantitatively analyzed according to the peak areas to obtain blood sample data.

[0038] For example, using MS-DIAL m / z (Mass-to-charge ratio)-peak area matrix was extracted and qualitatively characterized according to MS / MS. The mass deviation of MS1 in MS-DIAL (Mass Spectrometry Data Independent AnaLysis, a mass spectrometry data processing and analysis software) 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.

[0039] All the exhaled air and blood m / z The R package Mfuzz (an R package for analyzing time series expression data) was used to perform c-means clustering on its change trends and screen for change trends related to the exercise process. m / z Plot each metabolite using Python m / z Trend graph of response intensity over time in different samples for further screening.

[0040] The blood metabolites and exhaled breath metabolites were qualitatively characterized according to the qualitative rules. Specifically, when the metabolite has MS / MS data, the structure of the exhaled breath metabolite or blood metabolite is confirmed by the MS / MS data of the metabolite, and the qualitative analysis is based on the structure; when there is no MS / MS data, the mass-to-charge ratio ( m / z) values ​​are compared with compounds in a database such as HMDB. In this case, a 10 ppm mass error window is used for screening. m / z The value is consistent with a known compound in the database. m / z If the values ​​match within 10 ppm, it can be inferred to be that compound.

[0041] To improve the accuracy of qualitative analysis, the Absolv module in ACD / Percepta software was used to predict the Abraham descriptor of each metabolite, and the water-gas partition coefficient (log P) of each metabolite under physiological conditions (37 °C) was calculated using the reported linear free-energy relationship equation. w / g ), which characterizes the ability of a compound to partition from the airway lining fluid into exhaled air. log P w / g The larger the value, the harder it is to distribute into the exhaled air. w / g The definition is as follows:

[0042] MetaboAnalyst was used to perform network analysis, pathway enrichment analysis, and pairing of exhaled metabolites before and after exercise on the exhaled metabolites and blood metabolites obtained in the above process. t Test. Exclude features with more than 50% missing values. Fill the remaining missing values ​​with 1 / 5 of the minimum value of the corresponding feature in all samples. Then log transform and autoscale the data. Chiplot is used to plot the "down-up repetition type" and "up-down repetition type" of blood lactate and exhaled metabolites. m / z Spearman (Spearman rank correlation coefficient) correlation heatmap.

[0043] In order to improve the accuracy of breath qualitative analysis as much as possible, tandem mass spectrometry data was collected during the sampling process to obtain MS / MS information of metabolites in breath samples. Secondly, a large number of metabolites detected in previously reported breath metabolomics literature were sorted and summarized as a qualitative reference database. Finally, the logP value of the compound was also referenced in the qualitative process. w / g These measures significantly improved the reliability of the qualitative results of breath metabolites.

[0044] Through the above steps, a total of 5307 molecules were detected in the breath samples of 10 athletes. m / z(The other three only collected acetone metabolism signals in their breath). A total of 5,687 molecules were detected in the blood samples of the 12 athletes. m / z (One person's blood sample was excluded because of hemolysis).

[0045] Step S2: determining standard metabolites in the blood sample data, and screening the exhaled metabolites in the exhaled sample data according to the change trends of the standard metabolites under different motion states to obtain the iconic exhaled data.

[0046] After obtaining the blood metabolites and breath metabolites, determine the standard metabolites in the blood sample data, including: The blood metabolites are clustered, and standard metabolites are determined according to the clustering results.

[0047] All detected in breath and blood samples m / z Perform c-means clustering. Figure 3 , 4 As shown, according to the clustering results, it can be seen that the metabolites in the breath samples and blood samples that are correlated with the three HIIT processes mainly show four trends, including: a decrease immediately after HIIT and recovery before the next HIIT, an increase immediately after HIIT and recovery before the next HIIT, a continuous decrease, and a continuous increase.

[0048] To further obtain metabolites related to exercise, screening was performed in the following ways: (1) More than 50% of the volunteers have the same trend; (2) Each m / z The missing values ​​in all samples should be <50%; (3) Isotope peaks as well as impurity peaks and background peaks with lower intensity should be removed.

[0049] At the same time, to ensure that the 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 The athletes had the same trend and multiple of change, so they were excluded from the subsequent study. Finally, a total of 332 sports-related exhalation parameters were obtained. m / zCluster 1 “down-up repetition type (recovery after decline)” 133, Cluster 2 “up-down repetition type (recovery after rise)” 45, Cluster 3 “downward type (continuous decline)” 138, Cluster 4 “upward type (continuous rise)” 16. A total of 334 exercise-related parameters were obtained from the blood samples. m / z Among them, there are 115 Cluster 1 “down-up repetition type”, 103 Cluster 2 “up-down repetition type”, 41 Cluster 3 “downward type”, and 75 Cluster 4 “upward type”.

[0050] Based on the clustering results, the changing trends under different motion states can be obtained, including: Recovery within one hour after ascent, recovery within one hour after descent, continuous ascent and / or continuous descent.

[0051] Standard metabolites are used as biomarkers for exercise monitoring, exercise intensity assessment and exercise recovery status judgment. Their changing trends can be directly related to exercise intensity, metabolic response or physiological state, and have high sensitivity and specificity, and can reflect the body's metabolic regulation under different exercise states.

[0052] Based on the different change trends of different metabolites under different exercise states, standard metabolites can be directly screened out according to a certain change trend, or corresponding standard metabolites can be screened according to each change trend. Standard metabolites refer to blood metabolites that are closely related to the exercise process and have stable and repeatable change trends under specific physiological or exercise states.

[0053] For example, if certain blood metabolites, such as butyrate and propionate, show a continuous upward type after exercise, these metabolites can be used as standard metabolites of the “continuous upward” trend.

[0054] In order to verify the consistency between breath monitoring and blood monitoring, based on the change trend of standard metabolites, breath metabolites showing similar change trends in breath sample data were screened out to obtain signature breath data. Signature breath data are metabolites emitted directly through exhalation during exercise, which can reflect changes in body metabolism during exercise.

[0055] Screening the breath sample data according to the change trend of the standard metabolite includes: Screen exhaled breath metabolites with consistent changing trends with standard metabolites.

[0056] By screening the exhaled metabolites that are consistent with the changes in standard metabolites, the iconic exhaled data is determined. The changes in the iconic exhaled data can reflect the exercise status in real time during exercise and provide immediate feedback information, making exhaled breath monitoring an effective means of non-invasive exercise monitoring. The iconic exhaled data can accurately reflect physiological reactions such as exercise intensity, energy consumption, and fat metabolism, thereby evaluating exercise status and exercise intensity. Through non-invasive exhaled breath sampling, high frequency and high convenience of exercise monitoring can be achieved, which is suitable for a wide range of health management and sports performance monitoring.

[0057] Through the above steps, the analysis of exhaled metabolites shows four changing trends, which are consistent with the results of blood metabolites, indicating that exhaled breath testing can also reflect the impact of exercise on metabolism in the body. The exhaled metabolites that change during exercise are not exactly the same as the blood metabolites, but the changing trend of exhaled metabolites during exercise is consistent with the changing trend of blood metabolites following the exercise process. Exhaled metabolites are screened to obtain iconic exhaled data. Through iconic exhaled data, the changing trends of metabolites under different exercise intensities can be monitored to help evaluate the exercise intensity of the subjects, provide a scientific basis for personalized exercise plans, reveal the dynamic changes in metabolism during exercise, and help monitor the exercise status of the subjects, such as fatigue, recovery status, etc. What's more, according to the iconic exhaled data, the exercise intensity or recovery period can be adjusted to improve the exercise effect and prevent overtraining.

[0058] Compared with the limitations of traditional exercise monitoring indicators and the complexity of traditional metabolomics tests such as blood and urine samples, this application provides a new research perspective for exercise metabolomics, making full use of the advantages of SESI-HRMS online analysis of exhaled breath, which is fast and non-invasive, and explores a new method for real-time monitoring of the body's metabolic state during exercise.

[0059] This application uses SESI-HRMS / MS and UHPLC-HRMS / MS non-target analysis techniques to detect metabolites in exhaled breath and blood with high coverage, and finds that after exercise intervention, there are a large number of exercise-related metabolites in exhaled breath with the same trend as metabolites in the blood. These compounds are closely related to energy metabolism and post-exercise body recovery.

[0060] In summary, the great potential of exhaled breath metabolomics as a non-invasive monitoring method has been confirmed, and it has been found that the combination of exhaled breath and blood metabolomics can improve the coverage of metabolite detection, which not only provides a new perspective for exercise physiology and metabolic health research, but also provides the necessary biomarker basis for the development of wearable exhaled breath devices.

[0061] The present invention provides a method for detecting characteristic breath data under different exercise states based on tandem mass spectrometry, and obtains characteristic breath data according to standard metabolite screening, thereby realizing non-invasive real-time monitoring of metabolite change trends under different exercise intensities, and accurately reflecting the exercise state and exercise intensity, thereby providing scientific support for sports performance evaluation and health management and providing a scientific basis for personalized exercise plans. It effectively proves that exhalation can diagnose and identify the exercise state and exercise intensity of the subject in real time, provide support for the monitoring of sports ability and personalized sports guidance, help optimize the training and health management of athletes, and also provide necessary biomarkers for the future development of wearable devices for sports monitoring and health assessment.

[0062] Based on the same inventive concept, the present application also provides an electronic device, which may include: a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other via the communication bus. The processor may call the logic instructions in the memory to execute the steps of the above-mentioned method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry of the present invention.

[0063] An embodiment of the present application also provides a non-transitory machine-readable storage medium, on which an executable program is stored. When the executable program is executed by a microprocessor, the processor executes the processing method provided in the above embodiment.

[0064] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the described method.

[0065] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the described method.

[0066] The embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without paying creative labor.

[0067] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0068] Finally, it should be noted that the embodiments disclosed in the present invention are only preferred embodiments of the present invention, which are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for detecting characteristic exhalation data under different motion states based on tandem mass spectrometry, characterized in that: The method comprises the following steps: Acquire breath sample data and blood sample data based on tandem mass spectrometry analysis; the breath sample data and blood sample data are sample data collected during a predetermined motion process and under different motion states; The standard metabolites in the blood sample data are determined, and the exhaled metabolites in the exhaled sample data are screened according to the change trends of the standard metabolites under different motion states to obtain the iconic exhaled data.

2. The method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry according to claim 1, characterized in that: Screening the breath sample data according to the change trend of the standard metabolite includes: Screen exhaled breath metabolites with consistent changing trends with standard metabolites.

3. The method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry according to claim 1, characterized in that: Determine standard metabolites in blood sample data, including: The blood metabolites in the blood sample data are clustered, and the standard metabolites are determined based on the clustering results.

4. The method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry according to claim 3, characterized in that: The number of the standard metabolites is at least one; the change trends of the standard metabolites under different motion states include: Recovery within one hour after an ascent, recovery within one hour after a descent, continuous ascent, and / or continuous descent.

5. The method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry according to claim 1, characterized in that: Acquire breath sample data and blood sample data based on tandem mass spectrometry analysis, including: The blood metabolites in the blood sample data and the breath metabolites in the breath sample data are qualitatively characterized according to qualitative rules.

6. The method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry according to claim 5, characterized in that: The qualitative rules include: When MS / MS data of exhaled metabolites exist, the structures of exhaled metabolites are confirmed and the exhaled metabolites are qualitatively identified through the MS / MS data of exhaled metabolites; When there is no MS / MS data for the exhaled metabolites, the structure of the exhaled metabolites is determined by the mass-to-charge ratio of the positive ions and negative ions of the exhaled metabolites. The exhaled metabolites are qualitatively identified by combining the known metabolite structures in the database and the water-gas partition coefficient ratio of the exhaled metabolites.

7. The method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry according to claim 3, characterized in that: Before obtaining blood sample data and breath sample data based on tandem mass spectrometry analysis, it also includes: Acquire raw blood data and raw exhaled breath data, and analyze the raw blood data and raw exhaled breath data by a tandem mass spectrometer to obtain blood sample data and exhaled breath sample data; The blood original data and the exhalation original data both include the first blood original data and the first exhalation original data collected before the first execution of the predetermined exercise, The second blood raw data and the second exhalation raw data collected after the first scheduled exercise are performed, The third blood raw data and the third exhalation raw data collected after the first rest step and before the second scheduled exercise are performed, the fourth blood raw data and the fourth exhalation raw data collected after the second execution of the predetermined exercise, The fifth blood raw data and the fifth exhalation raw data collected after the second rest step and the third scheduled exercise are collected. The sixth blood raw data and the sixth exhalation raw data collected after the second scheduled exercise, The seventh blood raw data and the seventh exhalation raw data collected after the third rest step.

8. The method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry according to claim 7, characterized in that: The blood raw data is analyzed by a tandem mass spectrometry device to obtain blood sample data, including: Acquiring raw blood data, extracting a mass-to-charge ratio-peak area matrix of metabolites from the raw blood data, and screening blood metabolites and their mass-to-charge ratios; Data clustering was used to determine the changing trends of blood metabolites during exercise; A qualitative analysis of the blood metabolites is performed according to the qualitative rules, and a quantitative analysis of the blood metabolites is performed according to the peak areas to obtain blood sample data.

9. The method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry according to claim 7, characterized in that: The exhaled breath raw data is analyzed by a tandem mass spectrometry device to obtain exhaled breath sample data, including: Convert exhaled breath raw data into 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 breath metabolite of the correlated MS / MS spectrum according to the reference spectrum; The substance content of the exhaled metabolites was determined based on the first-order average peak height corresponding to the exhaled metabolites.

10. The method for detecting the characteristic exhalation data under different motion states based on tandem mass spectrometry according to claim 3, characterized in that: The tandem mass spectrometry analysis is performed by ultra-high performance liquid chromatography coupled with a high-resolution mass spectrometer; Ultra-high performance liquid chromatography was coupled with high-resolution liquid chromatography-mass spectrometry for non-target analysis of the plasma metabolome. The injection volume was 5 μL. An ACQUITY UPLC HSS T3 column was used in positive ion mode at 30 °C and 0.5 mL / min. Mobile phase A was 0.1% formic acid in water and mobile phase B was acetonitrile. The mobile phase gradient started with 1% mobile phase B for 1 min, gradually increased to 100% mobile phase B from 1 to 8 min, and maintained for 8 to 10 min. It was then reduced to 1% mobile phase B from 10 to 10.5 min until the end of the run. A HILIC column was used in the negative ion mode at 30 °C and 0.2 mL / min. Mobile phase A was 20 mM ammonium acetate, 10 mM ammonia, 5% acetonitrile, and 95% water, and mobile phase B was acetonitrile. The mobile phase gradient started with 85% mobile phase B for 2 min, then gradually decreased to 35% mobile phase B from 2 to 12 min, then decreased to 20% from 12 to 12.1 min, and maintained from 12.1 to 15.9 min, and then increased to 85% mobile phase B from 15.9 to 16 min until the end of the run.

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