Exhaled breath metabolite combinations for discriminating ards and uses thereof

Abstract

This invention discloses a combination of exhaled gas metabolic markers for identifying ARDS and its application. It can quickly, conveniently, and accurately distinguish between ARDS pigs and healthy pigs, verifying the feasibility of using HPPI-TOFMS for early and accurate screening of ARDS patients. It effectively avoids the pain and cost of diagnosis for patients, enables timely and effective diagnosis and treatment of ARDS, reduces the mortality rate of ARDS patients, and has significant economic benefits and broad clinical application prospects.

Description

Technical Field

[0001] This invention belongs to the field of clinical diagnostic technology and provides a combination of exhaled metabolic markers for identifying ARDS and their applications. Background Technology

[0002] Acute respiratory distress syndrome (ARDS) is a clinical syndrome characterized by refractory hypoxemia and acute diffuse alveolar-capillary vascular membrane injury, induced by various factors such as infection and trauma. ARDS is a critical illness with a complex pathogenesis, high treatment difficulty, high healthcare costs, and high mortality. The morbidity and mortality rates of ARDS remain high globally. A 2016 global multicenter study showed that 10.4% of intensive care unit (ICU) patients develop ARDS, with a mortality rate as high as 40%, while only 60.2% of ARDS patients could be identified. The reason for this is that once the systemic inflammatory cascade of ARDS is formed, currently used anti-inflammatory therapies often struggle to control the complex inflammatory cytokine network and are difficult to identify ARDS.

[0003] Currently, the PaO2 / FiO2 ratio, used clinically for diagnosing and staging ARDS, only shows significant abnormalities when lung damage is severe. This delays the treatment window, missing the critical period for lung protection. This can lead to delayed diagnosis, worsening of ventilator-induced lung injury, increased risk of multiple organ dysfunction syndrome, and ultimately, higher mortality and disability rates. Therefore, it is necessary to develop a rapid and accurate detection method for early and precise diagnosis of ARDS. Early and accurate identification of ARDS can thus shift the treatment window forward, protecting lung tissue and reducing the incidence of ARDS and patient mortality.

[0004] In addition to ambient air components, human exhaled air contains numerous volatile organic compounds (VOCs). The composition and relative concentration of VOCs in exhaled air are affected by oxidative stress and metabolism, and may change with airway inflammation and disease states, thus reflecting metabolic processes and the pathophysiological state of diseases. For the detection of VOCs in the exhaled air of ARDS patients, the commonly used methods are pattern recognition-based electronic nose (e-Nose) and gas chromatography-mass spectrometry (GC-MS). e-Nose detects VOCs in the respiratory tract by mimicking the olfaction of mammals, but its resolution is low, it is easily interfered with by the complex matrix in exhaled air, and it is difficult to accurately characterize and quantify. GC-MS results are considered the gold standard for VOC component analysis, but the entire analysis process requires complex offline enrichment and preprocessing of exhaled air samples, resulting in slow analysis speed and high cost per sample, making it unsuitable for high-throughput detection of large clinical sample volumes. We still urgently need a real-time, simple, rapid, more accurate, and more versatile VOCs analysis method.

[0005] In recent years, direct mass spectrometry techniques based on "soft" ionization sources have developed rapidly, such as vacuum ultraviolet photoionization mass spectrometry (VUV-PI-MS), proton transfer reaction ionization mass spectrometry (PTR-MS), high-pressure photoionization time-of-flight mass spectrometry (HPPI-TOF-MS), and selected ion flow tube ionization mass spectrometry (SIFT-MS). Soft ionization mass spectrometry obtains mass spectrometry data that is easy to analyze quickly and qualitatively by ionizing molecules with minimal dissociation. This makes it suitable for the rapid qualitative and quantitative analysis of complex mixtures such as exhaled breath, and it has been successfully applied to the online detection of small-molecule volatile metabolites in human exhaled breath and urine. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a combination of exhaled gas metabolic markers for the identification of ARDS and their application, aiming to solve the problems of misdiagnosis, missed diagnosis, delayed diagnostic window and high diagnostic cost in the early screening of ARDS.

[0007] To achieve the above objectives, the present invention provides the following technical solution: 1. The exhaled metabolic marker combination used to identify ARDS consists of acetaldehyde (m / z=43.016), formaldehyde (m / z=49.028), cyclopentadiene (m / z=65.040), formamide (m / z=77.013), and dimethylaminoacetonitrile (m / z=84.066).

[0008] 2. The screening method for the aforementioned biomarker combinations involves using high-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) to detect exhaled breath samples and obtain raw mass spectra. The raw mass spectrometry data are preprocessed, and differential metabolite combinations are screened out using statistical analysis methods.

[0009] Preferably, the specific steps are as follows: (1) Using Bama miniature pigs as a model, exhaled air from healthy pigs and ARDS pigs was collected as analysis samples; (2) High-pressure photoionization time-of-flight mass spectrometry was used to detect each exhaled breath sample to obtain the original mass spectrum of each exhaled breath sample; (3) Preprocess the raw mass spectrometry data of each exhaled breath sample to obtain a data table containing information on the absolute intensity of metabolites; (4) Perform univariate and multivariate analyses on the data table containing information on the absolute intensity of metabolites, and screen out the differential metabolites between healthy pigs and ARDS pigs according to the set thresholds; (5) The optimal combination of differential metabolites between healthy pigs and ARDS pigs was selected by binary logistic regression analysis. The optimal combination of differential metabolites is the combination of exhaled gas metabolic markers used to identify ARDS.

[0010] Further preferred, in step (1), the sample collection process is as follows: all animal experiments are conducted in the same designated room and exhaled air samples are collected. Before the experiment, pigs are kept fasted for at least 24 hours, and then given sufficient sedation, continuous invasive mechanical ventilation and electrocardiographic monitoring. Subsequently, lipopolysaccharide (LPS) is infused intravenously at a dose of 600 μg / (kg·h) for 3 hours, followed by a maintenance infusion at a dose of 15 μg / (kg·h) for 5 hours. At 0 h (exhaled air from healthy pigs), 5 h, 6 h, 7 h and 8 h (exhaled air from ARDS pigs) of LPS infusion, respectively, the samples are directly introduced into a separate 3-liter Tedlar collection bag through a disposable polytetrafluoroethylene tube from the ventilator's exhalation port. All collected analytical samples are stored in the dark and sent to the laboratory for HPPI-TOFMS analysis within 24 hours.

[0011] Even more preferably, the multifunctional online exhaled breath sampling device in patent CN119587079B is used for exhaled breath sampling, with the sampling flow rate set at 2800-3200 ml / min.

[0012] Further preferred, in step (1), the exhaled air samples of both healthy pigs and ARDS pigs were taken from 8-month-old male Bama miniature pigs; there were more than 25 exhaled air samples from healthy pigs and more than 30 exhaled air samples from diseased pigs. The ARDS pigs were diagnosed based on the Berlin definition of ARDS, with bilateral lung infiltration and blood oxygen index below 300 mmHg as shown by lung CT imaging.

[0013] In a further preferred embodiment, in step (2), when the sample to be analyzed is directly subjected to HPPI-TOFMS analysis, the Tedlar collection bag is first placed in an oven and preheated to 50 ℃. The collection port of the Tedlar collection bag is connected to the sample gas inlet tube of the ionization source. The exhaled gas in the Tedlar collection bag enters the HPPI-TOFMS for detection through a metal inlet capillary tube that is heated and stabilized at 100 ℃.

[0014] Further preferred, in step (2), high-pressure photon ionization time-of-flight mass spectrometry is used for detection. The ionization source is a dual-channel ionization source based on a vacuum ultraviolet lamp (VUV lamp) as described in patent CN105655226B. One channel is filled with water vapor at a relative humidity of 100% and the injection flow rate is 80-150 mL / min. The other channel is filled with exhaled air from the collection bag and the exhaled air injection flow rate is 40-100 mL / min. The ionization source pressure is 200-300 Pa and the MCP voltage is 4300 V. The spectrum acquisition time is 120 s and the mass-to-charge ratio range of the spectrum data acquisition is 0-400 Da.

[0015] Further preferred, the preprocessing of the original mass spectrum of the exhaled breath sample in step (3) refers to: using Matlab software to perform mass correction and signal peak intensity extraction on the obtained original mass spectrum, and then performing summation normalization, data filtering and logarithmic transformation on the MetaboAnalyst6.0 website for each exhaled breath sample data, and finally obtaining a data table that can be used for statistical analysis. In this table, the first row is the mass number of metabolites, the first column is the exhaled breath sample number information, the second column is the exhaled breath sample grouping information, and the remaining columns are the relative signal intensity of each metabolite.

[0016] Further preferred, in step (4), the multivariate and univariate analyses of the data table containing the relative intensity information of metabolites refer to: multivariate analysis refers to orthogonal partial least squares discriminant analysis (OPLS-DA), which is performed on the MetaboAnalyst 6.0 website, and the data is transformed by unit variance scaling to screen out metabolites with a variable projection importance (VIP) greater than 1; univariate analysis refers to the Mann-Whitney U rank-sum test, which is performed on the MetaboAnalyst 6.0 website, and it is considered that differential metabolites with a false discovery rate (FDR) less than 0.05 are different between the two groups; finally, the metabolites common to both the univariate and multivariate analysis results are taken as differential metabolites between healthy pigs and diseased pigs.

[0017] Further preferred, the screening method for the optimal combination of differential metabolites between healthy pigs and ARDS pigs in step (5) is as follows: perform binary logistic regression analysis on the obtained differential metabolites in SPSS software, set the dependent variable as the grouping information of healthy pigs and diseased pigs, and the covariate as the relative intensity of different metabolites, and the method is "forward: formatted". The combination ranked first among all the obtained combinations is the optimal combination of differential metabolites. This combination of differential metabolites is the combination of exhaled gas metabolic markers used to identify ARDS pigs.

[0018] Preferably, the screening method further includes: establishing a subject operating characteristic curve discrimination model based on a combination of exhaled gas metabolic markers, and obtaining discrimination formulas and discrimination thresholds for ARDS pigs and healthy pigs.

[0019] Further optimization involves establishing a subject operating characteristic curve discrimination model based on a combination of exhaled metabolic biomarkers. The specific process is as follows: perform binary logistic regression analysis on the combination of exhaled metabolic biomarkers in SPSS software, set the method to "input", select the metabolites in the combination of exhaled metabolic biomarkers as covariates, and perform binary logistic regression analysis again to obtain the predicted probability of the combination. Then, receiver operating characteristic (ROC) curve analysis was performed on the obtained differential metabolite combinations in SPSS software. The predicted probability of the differential metabolite combinations was set as the test variable, the grouping information was set as the state variable, and the state variable value was the disease group. Finally, the ROC curve was obtained, with the horizontal and vertical axes being 1-specificity and sensitivity, respectively. The value corresponding to the maximum sum of specificity and sensitivity is the discrimination threshold between healthy pigs and diseased pigs. Pigs with values ​​greater than the threshold are healthy pigs, and pigs with values ​​less than or equal to the threshold are ARDS pigs.

[0020] 3. Application of the aforementioned biomarker combination in the preparation of ARDS detection kits.

[0021] 4. Application of reagents used to detect the aforementioned combination of biomarkers in the preparation of ARDS detection kits.

[0022] 5. ARDS detection kit, containing reagents for detecting the aforementioned combination of biomarkers.

[0023] The beneficial effects of this invention are: This invention discloses a combination of exhaled gas metabolic markers for identifying ARDS and its application. It can quickly, conveniently, and accurately distinguish between ARDS pigs and healthy pigs, verifying the feasibility of using HPPI-TOFMS for early and accurate screening of ARDS patients. It effectively avoids the pain and cost of diagnosis for patients, enables timely and effective diagnosis and treatment of ARDS, reduces the mortality rate of ARDS patients, and has significant economic benefits and broad clinical application prospects.

[0024] The metabolic biomarker combination of this invention consists of acetaldehyde (m / z=43.016), formaldehyde (m / z=49.028), cyclopentadiene (m / z=65.040), formamide (m / z=77.013), and dimethylaminoacetonitrile (m / z=84.066). The screening method of this invention is simple to operate and has high accuracy. This invention is the first to combine high-pressure photoionization time-of-flight mass spectrometry with exhaled gas metabolomics for early screening of ARDS. It can be used for early ARDS screening, which is convenient, fast, and economical, greatly reducing screening costs and demonstrating significant economic benefits and broad application prospects.

[0025] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 For screening differential metabolites, a is the OPLS-DA score plot, where R2Y=0.851 and Q2=0.614; b is the OPLS-DA modeling validation plot based on 200 random permutations.

[0027] Figure 2 The values ​​represent the relative concentrations of five differentially expressed metabolites, where ARDS refers to ARDS-affected pigs and CTL refers to healthy pigs.

[0028] Figure 3 For the modeling and validation of differential metabolite combinations, where a is the ROC curve of the differential metabolite combination; b is the ROC curve of the differential metabolite combination after 10-fold cross-validation. Detailed Implementation

[0029] The present invention will be further described below with reference to specific embodiments. The accompanying drawings are for illustrative purposes only, representing schematic diagrams rather than actual physical objects, and should not be construed as limiting the scope of this patent. To better illustrate the embodiments of the present invention, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0030] Example 1 The method for constructing the exhaled gas metabolite analysis model of this invention and the verification of its effectiveness are as follows: 1. Research Subjects Eight 8-month-old male Bama miniature pigs, weighing 27-32 kg, were used in the experiment at the Respiratory Research Institute of the Second Affiliated Hospital of Army Medical University. Gas was collected from 26 healthy pigs and exhaled from 31 pigs with ARDS. ARDS was diagnosed according to the international Berlin definition (i.e., according to the Berlin definition of ARDS, chest CT examination showed diffuse infiltrates in both lungs that could not be completely explained by heart failure or fluid overload, and the possibility of cardiogenic or non-cardiogenic pulmonary edema was excluded, and oxygenation progressively decreased). The severity was divided into three levels according to the oxygenation index: (1) Mild, PEEP or continuous positive airway pressure (CPAP) ≥5 cmH2O, 200 mmHg < oxygenation index ≤300 mmHg; (2) Moderate, PEEP or CPAP ≥5 cmH2O, 100 mmHg < oxygenation index ≤200 mmHg; (3) Severe, PEEP or CPAP ≥5 cmH2O, oxygenation index ≤100 mmHg).

[0031] 2. Exhaled gas metabolomics detection by high-pressure photoionization time-of-flight mass spectrometry 2.1 Exhaled breath sample collection All animal experiments were conducted in the same designated room, and exhaled air samples were collected. Pigs were fasted for at least 24 hours prior to the experiment, then fully sedated, and given continuous, invasive mechanical ventilation and ECG monitoring. LPS was then continuously infused intravenously at a dose of 600 μg / (kg·h) for 3 hours, followed by a maintenance infusion of 15 μg / (kg·h) for 5 hours. At 0 h (exhaled air from healthy pigs) and 5 h, 6 h, 7 h, and 8 h (exhaled air from ARDS pigs) of LPS infusion, samples were directly introduced into a separate 3 L Tedlar collection bag through a disposable PTFE tube from the ventilator's exhalation port until the bag was full of exhaled air. All collected samples were protected from light and stored and transported at room temperature, ensuring delivery to the laboratory for further analysis within 24–48 hours of sampling.

[0032] Exhaled air was sampled using the multifunctional online exhaled air sampling device described in patent CN119587079B, with a sampling flow rate set at 3000 ml / min.

[0033] 2.2 High-pressure photoionization time-of-flight mass spectrometry detection of exhaled breath The exhaled breath sample mass spectrometry was performed using a high-pressure photoionization-time-of-flight mass spectrometer (HPPI-TOFMS). The ionization source of the HPPI-TOFMS was the vacuum ultraviolet lamp (VUV lamp) based photoionization source as described in patent number CN105655226B, which was directly introduced into the exhaled breath in the collection bag through the sample gas capillary.

[0034] When performing HPPI-TOFMS exhaled gas metabolomics analysis on exhaled gas samples, no sample pretreatment is required. First, connect the outlet of the Tedlar collection bag to the sample gas capillary (in this case, a metal sample introduction capillary) of the ionization source. Start the mechanical vacuum pump of the ionization source to pass the sample gas from the collection bag through the metal sample introduction capillary, which is heated and stabilized at 100 °C, into the HPPI-TOFMS for detection. The injection flow rate is 55 mL / min, the ionization source pressure is 200 Pa, and the MCP voltage is 4300 V. The ionization region temperature is set to 100 °C, the spectral acquisition time is 300 s, and the mass-to-charge ratio range of the acquired spectral data is 0-400 Da.

[0035] 3. Data Preprocessing The raw mass spectra were corrected and signal peak intensities were extracted using Matlab software. Then, the data for each exhaled breath sample were summed, normalized, filtered, and logarithmically transformed using MetaboAnalyst 6.0. Finally, data tables that can be used for statistical analysis were obtained, as shown in Tables 2-1 to 2-12. The first row of the tables contains the mass number of metabolites, the first column contains the exhaled breath sample number, the second column contains the exhaled breath sample grouping information, and the remaining columns contain the absolute signal intensity of each metabolite.

[0036] 4. Screening for differentially expressed metabolites The obtained data table containing absolute metabolite intensity information was imported into MetaboAnalyst 6.0 for orthogonal partial least squares-discriminant analysis (OPLS-DA). The OPLS-DA score plot is shown below. Figure 1As shown in Figure a, the horizontal axis represents the predictive component, and the vertical axis represents the orthogonal component. Each point represents a sample. The differences and classification trends of metabolite profiles between ARDS pigs and healthy pigs can be observed. The R²Y = 0.851 and Q² = 0.614 in the OPLS-DA score plot indicate that the model is reliable and has good predictive ability. Furthermore, 200 permutation validations were performed, and the results are as follows... Figure 1 As shown in Figure b, the horizontal axis represents the statistical value calculated from the permutation sample, such as the correlation coefficient; the vertical axis represents the frequency or density, i.e., the frequency or probability density of observing the statistic. The obtained R² and Q² values ​​(R²=0.299, Q²=-0.158) prove that no overfitting occurred in the OPLS-DA analysis. Then, the Man-Whitney U rank-sum test was performed on the MetaboAnalyst 6.0 website to screen differentially expressed metabolites. In the OPLS-DA analysis, metabolites with a variable projection importance (VIP) greater than 1 were selected, and in the Man-Whitney U rank-sum test, metabolites with a false discovery rate (FDR) less than 0.05 were selected. Twenty-five differentially expressed metabolites were finally screened out, as shown in Table 1. These include acetaldehyde (m / z=43.016), formaldehyde (m / z=49.028), cyclopentadiene (m / z=65.040), isoprene (m / z=53.038), acrolein (m / z=53.000), acetic acid (m / z=61.029), furan (m / z=68.028), 2-butanone (m / z=72.057), methylurea (m / z=74.043), propionic acid (m / z=75.048), formamide (m / z=77.013), methylimidazole (m / z=82.053), and 2-imidazolidinone (m / z=82.053). z=84.036), dimethylaminoacetonitrile (m / z=84.066), dimethylurea (m / z=88.068), alanine (m / z=89.046), glycerol (m / z=93.054), glycolic acid (m / z=95.034), formylpyrrole (m / z=96.042), methylpyrrolidone (m / z=100.074), hydroxypyrrolidone (m / z=101.049), ethylbenzene (m / z=105.066), 4-hydroxy-2-hexenal (m / z=115.077), pyrophenol (m / z=126.033), tyramine (m / z=138.090).

[0037] Table 1. 25 differentially expressed metabolites screened from ARDS pigs and healthy control groups. 5. Screening for optimal combinations of differentially metabolites and establishing a model for differentially metabolite combinations. Based on the 25 differentially expressed metabolites selected, binary logistic regression analysis was performed using SPSS software to select the optimal combination of differentially expressed metabolites. The dependent variable was set as the grouping information of healthy pigs and ARDS pigs, and the covariates were the absolute intensity of different metabolites. The method was "forward: formatted". The combination ranked first among all the obtained combinations was the optimal combination of differentially expressed metabolites, which was acetaldehyde. m / z =43.016), formaldehyde ( m / z =49.028), cyclopentadiene ( m / z =65.040), formamide ( m / z =77.013) and dimethylaminoacetonitrile ( m / z =84.066) The combination of these 5 compounds is the exhaled breath metabolic marker combination used to distinguish ARDS; the relative concentrations of the five metabolites between ARDS pigs and healthy pigs are as follows: Figure 2 As shown. Then, the method was set to "Input," and the metabolites in the optimal combination were selected as covariates. Binary logistic regression analysis was performed again to obtain the predicted probability of this combination. Finally, receiver operating characteristic (ROC) curve analysis was performed on the obtained differentially metabolite combinations in SPSS software. The predicted probability of the differentially metabolite combinations was set as the test variable, the grouping information as the state variable, and the state variable value as the disease group. The results are shown below. Figure 3 As shown in Figure a, the area under the ROC curve (AUC) is 0.988, the sensitivity is 100%, the specificity is 93.5%, and the accuracy is 82.4%. The threshold corresponding to the maximum sum of sensitivity and specificity is 0.315. That is, when the threshold is higher than 0.315, the pigs are healthy, and when it is lower than 0.315, the pigs are ARDS pigs.

[0038] The discriminant formula is: y=e (x / x+1) In the formula, x = 0.003 * I 甲醛 +0.00*I 乙醛 +0.00*I 环戊二烯 -0.004*I 甲酰胺 +0.009*I 二甲氨基乙腈 +3.727 (simplified to: x = 0.003 * I) 甲醛 -0.004*I 甲酰胺 +0.009*I 二甲氨基乙腈 +3.727)(I 甲醛 I 乙醛 I 环戊二烯 I 甲酰胺 and I 二甲氨基乙腈 These are the absolute strength values ​​of the differential metabolites formaldehyde, acetaldehyde, cyclopentadiene, formamide, and dimethylaminoacetonitrile, respectively.

[0039] Where I represents the absolute intensity of the differential metabolite, and y represents the discrimination threshold.

[0040] 6. Model Validation Statistical algorithms are commonly used for internal model validation, with methods such as leave-one-out cross-validation and k-fold cross-validation being frequently employed. In this invention, 10-fold cross-validation was selected to perform internal model validation using MetaboAnalyst 6.0 based on five differentially expressed metabolites, evaluating the model's accuracy and reliability. The results are as follows: Figure 3 As shown in Figure b, the area under the ROC curve (AUC) is 0.891, with a sensitivity of 88.5% and a specificity of 83.9%. This result further demonstrates the excellent performance of the model, which can effectively distinguish between ARDS pigs and healthy pigs. Therefore, increasing the sample size of ARDS pigs and healthy pigs, and further establishing an ARDS discrimination model based on the analysis of metabolites in exhaled breath, holds promise for establishing a method for early and accurate screening of ARDS pigs.

[0041] 7. Conclusion The above verifications demonstrate that the model constructed using the method of this invention has excellent predictive performance. The combination of metabolic biomarkers screened in this invention can rapidly and accurately diagnose ARDS through exhaled breath detection, exhibiting high sensitivity and specificity, with an accuracy rate reaching 82.4%, and possesses significant application potential.

[0042] Table 2-1 contains data on the absolute intensity of metabolites. Table 2-2 Note: In Table 2-2, except for the first column (number) and the second column (group), the data on the right side of the second column are placed in the rightmost position of Table 2-1.

[0043] Table 2-3 Note: In Table 2-3, except for the first column (number) and the second column (group), the data on the right side of the second column are placed in the rightmost position of Table 2-2.

[0044] Table 2-4 Note: In Table 2-4, except for the first column number and the second column group, the data on the right side of the second column are placed in the rightmost position of Table 2-3.

[0045] Table 2-5 Note: In Table 2-5, except for the first column (number) and the second column (group), the data on the right side of the second column are placed in the rightmost position corresponding to the data in Table 2-4.

[0046] Table 2-6 Note: In Table 2-6, except for the first column (number) and the second column (group), the data on the right side of the two columns are placed in the rightmost position of Table 2-5.

[0047] Table 2-7 Note: In Table 2-7, except for the first column (number) and the second column (group), the data on the right side of the second column are placed in the rightmost position corresponding to the data in Table 2-6.

[0048] Table 2-8 Note: In Table 2-8, except for the first column (number) and the second column (group), the data on the right side of the two columns are placed in the rightmost position of Table 2-7.

[0049] Table 2-9 Note: In Table 2-9, except for the first column number and the second column group, the data on the right side of the second column are placed in the rightmost position corresponding to the data in Table 2-8.

[0050] Table 2-10 Note: In Table 2-10, except for the first column number and the second column group, the data on the right side of the second column are placed in the rightmost position of Table 2-9.

[0051] Table 2-11 Note: In Table 2-11, except for the first column number and the second column group, the data on the right side of the second column are placed in the rightmost position of Table 2-10.

[0052] Table 2-12 Note: In Table 2-12, except for the first column number and the second column group, the data on the right side of the second column are placed in the rightmost position of Table 2-11.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A combination of exhaled metabolic markers for identifying ARDS, characterized in that, It is composed of acetaldehyde, formaldehyde, cyclopentadiene, formamide, and dimethylaminoacetonitrile.

2. The method for screening biomarker combinations according to claim 1 involves using high-pressure photoionization time-of-flight mass spectrometry to detect exhaled air samples to obtain raw mass spectra, preprocessing the raw mass spectrometry data, and screening differential metabolite combinations through statistical analysis.

3. The screening method according to claim 3, characterized in that, The specific steps are as follows: (1) Using Bama miniature pigs as a model, exhaled air from healthy pigs and ARDS pigs was collected as analysis samples; (2) High-pressure photoionization time-of-flight mass spectrometry was used to detect each exhaled breath sample to obtain the original mass spectrum of each exhaled breath sample; (3) Preprocess the raw mass spectrometry data of each exhaled breath sample to obtain a data table containing information on the absolute intensity of metabolites; (4) Perform univariate and multivariate analyses on the data table containing information on the absolute intensity of metabolites, and screen out the differential metabolites between healthy pigs and ARDS pigs according to the set thresholds; (5) The optimal combination of differential metabolites between healthy pigs and ARDS pigs was selected by binary logistic regression analysis. The optimal combination of differential metabolites is the combination of exhaled gas metabolic markers used to identify ARDS.

4. The use of the biomarker combination of claim 1 in the preparation of an ARDS detection kit.

5. The use of the reagent for detecting the combination of markers of claim 1 in the preparation of an ARDS detection kit.

6. An ARDS detection kit, characterized in that, It contains reagents for detecting the combination of markers as described in claim 1.

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