Biomarkers for early detection of aspiration pneumonia and uses thereof

Abstract

The application provides a biomarker and application for early detection of aspiration pneumonia. By detecting the biomarker, the defects of the current biomarker for aspiration pneumonia can be overcome, early detection of aspiration pneumonia can be realized, early intervention and preventive measures can be carried out, and the death caused by aspiration pneumonia can be greatly reduced.

Description

Technical Field

[0001] This application belongs to the field of gene detection technology, specifically relating to a biomarker for early detection of aspiration pneumonia and its application. Background Technology

[0002] Aspiration pneumonia (AP) is a type of lung parenchymal inflammation mediated by inhaled substances. It occurs after inhaling oropharyngeal contents containing colonizing bacteria, with inhalation of pathogens through the oropharynx being the primary route for bacteria to enter the lungs. In addition, inhalation of sterile gastric acid directly damages lung tissue, causing chemical inflammation, which is another type of aspiration pneumonia, also known as Mendelson's syndrome. AP inhalation can be classified as overt or covert. Overt inhalation refers to the immediate appearance of clinical symptoms such as irritating cough, rapid breathing, or even respiratory distress after inhalation. Covert inhalation, due to a sluggish or impaired cough reflex pathway, may occur without any acute symptoms even when only small or trace amounts are inhaled.

[0003] In addition to respiratory infection-related symptoms such as fever, cough, increased sputum production, and lung rales, 50% of acute pneumonia (AP) patients may experience altered mental status or consciousness. In elderly AP patients, typical pneumonia symptoms such as cough, sputum production, and fever may be less pronounced; instead, nonspecific neurological and digestive symptoms may appear, such as delirium, decreased consciousness, drowsiness, loss of appetite, nausea, abdominal pain, diarrhea, urinary incontinence, apathy, and weakness. AP is characterized by its insidious onset and difficulty in distinguishing it from other types of pneumonia. Compared to patients with non-aspiration pneumonia, AP patients are older, more frail, and experience greater severity of pneumonia, often requiring intensive care. They also tend to have a poorer prognosis, longer hospital stays, and higher recurrence and mortality rates.

[0004] However, aspiration pneumonia (AP) is often under-detected, and a clear definition of the disease is currently lacking. Some literature suggests that AP detection includes witnessed aspiration of oropharyngeal or gastric contents and unobserved occult aspiration. AP has an insidious onset, with no obvious symptoms in the early stages, atypical clinical symptoms, and a low diagnostic rate. If not treated promptly, it can significantly impact a patient's lung function and prognosis. Currently, many methods are used clinically to detect aspiration, such as the Kubota water swallowing test, simplified swallowing challenge test, dye test, "Any Two" test, and isotope imaging, but their sensitivity, specificity, and feasibility are limited. Current research on biomarkers for AP includes pepsin, α-amylase, substance P, and soluble myeloid cell trigger receptor-1, but these biomarkers all have certain limitations and cannot be widely applied to AP detection. Summary of the Invention

[0005] Based on this, this application provides a biomarker for the early detection of aspiration pneumonia, which can detect aspiration pneumonia at an early stage.

[0006] This application provides a biomarker for the early detection of aspiration pneumonia, wherein the biomarker is selected from adenosine, serine, allogeneic alcoholone sulfate, dehydroepiandrosterone sulfate, (R)-3-hydroxybutyrylcarnitine, hexanocarnitine, 1,2-octadecyl-sn-glycerol, β-mouse cholic acid, pyridoxine, prolyl-proline, (2S)-1,1-dimethylpyrrolidone-2-carboxylic acid, phenylglucuronic acid, dehydroepiandrosterone sulfate, 5α-androst-16-en-3-ol, (4-acetaminophenyl)-glucuronic acid, N-acetylglycine, D-raffinose, glucose-6-phosphate, 6-trans-12-epi-leukotriene B4, 20-hydroxyleukotriene B4, dehydroepiandrosterone sulfate, taurine deoxycholic acid, N-acetyl-L-tryptophan, L-isoleucine-L- -Proline, sialic acid, 4-methyl-2-oxovalerate, 2-isopropylmalic acid, ceramide (d18:0 / 18:0), prostaglandin E2, cholesterol sulfate, 9-octadecenoic acid (E)-2,3-dihydroxypropyl ester, 4-methylhippuric acid, N-carboxylic acid piperidine, 3-hydroxybutyric acid, trans-palmitoic acid, thiomethanesulfonic acid, 3a,7a,12b-trihydroxy-5a-cholanic acid, prolyl-proline, cholic acid, lysophosphatidylcholine (10:0 / 0:0), sucrose, 4,8-dihydroxyquinoline-2-carboxylic acid, 3,4-dihydroxymandelic acid, 3-amino-4-methylvalerate, 10-hydroxydecanoic acid, acrylic acid, D-erythrose, glycine-chenodeoxycholic acid, isoisinosteroids, and one or more of 3α-hydroxy-7-oxo-5β-cholanic acid.

[0007] Another aspect of this application provides the use of a reagent for measuring the expression level of a biomarker in the preparation of an early screening product for detecting whether a subject has aspiration pneumonia.

[0008] In one embodiment, the test sample is selected from one or more of bronchoalveolar lavage fluid samples, sputum samples, saliva samples, serum samples, and urine samples.

[0009] In one embodiment, the biomarker in the bronchoalveolar lavage fluid sample is selected from one or more of adenosine monophosphate, serine, allogeneic alcoholone sulfate, dehydroepiandrosterone sulfate, (R)-3-hydroxybutyrylcarnitine, hexanocarnitine, 1,2-octadecyl-sn-glycerol, β-mouse cholic acid, pyridoxine, and prolyl-proline.

[0010] In one embodiment, the biomarker in the saliva sample is selected from one or more of (2S)-1,1-dimethylpyrrolidone-2-carboxylic acid, phenylglucuronic acid, dehydroepiandrosterone sulfate, 5α-androst-16-en-3-ol, (4-acetaminophenyl)-glucuronic acid, N-acetylglycine, D-raffinose, glucose-6-phosphate, 6-trans-12-epi-leukotriene B4, and 20-hydroxyleukotriene B4.

[0011] In one embodiment, the biomarker in the sputum sample is selected from one or more of dehydroepiandrosterone sulfate, taurine deoxycholic acid, N-acetyl-L-tryptophan, L-isoleucine-L-proline, sialic acid, 4-methyl-2-oxovaleric acid, 2-isopropylmalic acid, ceramide (d18:0 / 18:0), prostaglandin E2, and cholesterol sulfate.

[0012] In one embodiment, the biomarker in the serum sample is selected from one or more of the following: 9-octadecenoic acid (E)-2,3-dihydroxypropyl ester, 4-methylhippuric acid, N-formylpiperidine, 3-hydroxybutyric acid, trans-palmitoic acid, thiomethanesulfonic acid, 3a,7a,12b-trihydroxy-5a-cholanic acid, prolyl-proline, cholic acid, and lysophosphatidylcholine (10:0 / 0:0).

[0013] In one embodiment, the biomarker in the urine sample is selected from one or more of sucrose, 4,8-dihydroxyquinoline-2-carboxylic acid, 3,4-dihydroxymandelic acid, 3-amino-4-methylvaleric acid, 10-hydroxydecanoic acid, acrylic acid, D-erythrose, glycine chenodeoxycholic acid, isoisinolate, and 3α-hydroxy-7-oxo-5β-cholanic acid.

[0014] In one embodiment, the reagent for detecting expression levels includes reagents for detecting protein expression levels and / or mRNA expression levels.

[0015] In one embodiment, the reagents for detecting protein expression levels include those used in the following methods: Western blotting, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, sandwich assay, immunohistochemical staining, mass spectrometry, immunoprecipitation analysis, complement fixation analysis, flow cytometry, fluorescence edge separation, and protein chip analysis.

[0016] In one embodiment, the reagents for detecting mRNA expression levels include those used in the following methods: PCR-based detection methods, Southern hybridization methods, Northern hybridization methods, dot hybridization methods, fluorescence in situ hybridization methods, DNA microarray methods, ASO methods, and high-throughput sequencing platform methods.

[0017] In one embodiment, the product is characterized by further comprising reagents for collecting and / or processing samples from the subject.

[0018] In one embodiment, the sample includes: serum, urine, sputum, saliva, and bronchoalveolar lavage fluid.

[0019] In one embodiment, the sample is serum.

[0020] In one embodiment, the subject is a mammal.

[0021] In one embodiment, the subject is a human.

[0022] This application also provides a kit for detecting whether a subject is an early-stage aspiration pneumonia patient, the kit comprising reagents used to detect the expression level of the biomarker of claim 1;

[0023] In one embodiment, the reagent for detecting expression levels includes reagents for detecting protein expression levels and / or mRNA expression levels.

[0024] In one embodiment, the kit further comprises any one or more of the following: mRNA expression level auxiliary detection reagent, protein expression level auxiliary detection reagent, mRNA expression level auxiliary detection instrument, and protein expression level auxiliary detection instrument.

[0025] This application provides a biomarker for the early detection of aspiration pneumonia and its application. By detecting the biomarker of this application, the deficiencies of current biomarkers for aspiration pneumonia can be overcome, enabling early detection of aspiration pneumonia, thereby allowing for early intervention and prevention measures and greatly reducing deaths caused by aspiration pneumonia. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application and to more completely understand this application and its beneficial effects, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 PCA score plot for bronchoalveolar lavage fluid samples;

[0028] Figure 2 PLS-DA plot of bronchoalveolar lavage fluid sample;

[0029] Figure 3 Volcano plot of differential metabolites among bronchoalveolar lavage fluid groups;

[0030] Figure 4 To validate the confusion matrix and sensitivity and specificity models for bronchoalveolar lavage fluid;

[0031] Figure 5 PCA score plot for sputum samples;

[0032] Figure 6 PLS-DA plot of sputum sample;

[0033] Figure 7 Volcano plot of differential metabolites among sputum groups;

[0034] Figure 8 Validation of sputum confusion matrix and sensitivity and specificity models;

[0035] Figure 9 PCA score plot for saliva samples;

[0036] Figure 10 PLS-DA plot of saliva sample;

[0037] Figure 11 Volcano plot of differential metabolites among salivary groups;

[0038] Figure 12 Validation of the saliva confusion matrix with sensitivity and specificity models;

[0039] Figure 13 PCA score plot for serum samples;

[0040] Figure 14 PLS-DA plot of serum sample;

[0041] Figure 15 Volcano plot of differentially expressed metabolites among serum sample groups;

[0042] Figure 16 Validation of the serum sample confusion matrix and sensitivity and specificity models;

[0043] Figure 17 PCA score plot for urine sample;

[0044] Figure 18 PLS-DA plot of urine sample;

[0045] Figure 19 Volcano plot of differential metabolites among urine sample groups;

[0046] Figure 20 Validation of urine sample confusion matrix and sensitivity and specificity models;

[0047] Wherein, Sample type: sample type; Regulation type: regulation type; Training set: training set; Validation Set: validation set. Detailed Implementation

[0048] The present application will be further described in detail below with reference to the embodiments and examples. It should be understood that these embodiments and examples are for illustrative purposes only and are not intended to limit the scope of the present application. The purpose of providing these embodiments and examples is to enable a more thorough and comprehensive understanding of the disclosure of the present application. It should also be understood that the present application can be implemented in many different forms and is not limited to the embodiments and examples described herein. Those skilled in the art can make various modifications or alterations without departing from the spirit of the present application, and the equivalent forms obtained also fall within the protection scope of the present application. Furthermore, numerous specific details are set forth in the following description to provide a fuller understanding of the present application. It should be understood that the present application can be implemented without one or more of these details.

[0049] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0050] This application provides a biomarker for the early detection of aspiration pneumonia, wherein the biomarker is selected from adenosine, serine, allogeneic alcoholone sulfate, dehydroepiandrosterone sulfate, (R)-3-hydroxybutyrylcarnitine, hexanocarnitine, 1,2-octadecyl-sn-glycerol, β-mouse cholic acid, pyridoxine, prolyl-proline, (2S)-1,1-dimethylpyrrolidone-2-carboxylic acid, phenylglucuronic acid, dehydroepiandrosterone sulfate, 5α-androst-16-en-3-ol, (4-acetaminophenyl)-glucuronic acid, N-acetylglycine, D-raffinose, glucose-6-phosphate, 6-trans-12-epi-leukotriene B4, 20-hydroxyleukotriene B4, dehydroepiandrosterone sulfate, taurine deoxycholic acid, N-acetyl-L-tryptophan, L-isoleucine-L- -Proline, sialic acid, 4-methyl-2-oxovalerate, 2-isopropylmalic acid, ceramide (d18:0 / 18:0), prostaglandin E2, cholesterol sulfate, 9-octadecenoic acid (E)-2,3-dihydroxypropyl ester, 4-methylhippuric acid, N-carboxylic acid piperidine, 3-hydroxybutyric acid, trans-palmitoic acid, thiomethanesulfonic acid, 3a,7a,12b-trihydroxy-5a-cholanic acid, prolyl-proline, cholic acid, lysophosphatidylcholine (10:0 / 0:0), sucrose, 4,8-dihydroxyquinoline-2-carboxylic acid, 3,4-dihydroxymandelic acid, 3-amino-4-methylvalerate, 10-hydroxydecanoic acid, acrylic acid, D-erythrose, glycine-chenodeoxycholic acid, isoisinosteroids, and one or more of 3α-hydroxy-7-oxo-5β-cholanic acid.

[0051] Another aspect of this application provides the use of a reagent for measuring the expression level of a biomarker in the preparation of an early screening product for detecting whether a subject has aspiration pneumonia.

[0052] In a specific example, the test sample is selected from one or more of the following: bronchoalveolar lavage fluid sample, sputum sample, saliva sample, serum sample, and urine sample.

[0053] Optionally, the biomarkers in the bronchoalveolar lavage fluid sample are selected from one or more of adenosine monophosphate, serine, allogeneic alcoholone sulfate, dehydroepiandrosterone sulfate, (R)-3-hydroxybutyrylcarnitine, hexanocarnitine, 1,2-octadecyl-sn-glycerol, β-mouse cholic acid, pyridoxine, and prolyl-proline.

[0054] Optionally, the biomarker in the saliva sample is selected from one or more of (2S)-1,1-dimethylpyrrolidone-2-carboxylic acid, phenylglucuronic acid, dehydroepiandrosterone sulfate, 5α-androst-16-en-3-ol, (4-acetaminophenyl)-glucuronic acid, N-acetylglycine, D-raffinose, glucose-6-phosphate, 6-trans-12-epi-leukotriene B4, and 20-hydroxyleukotriene B4.

[0055] In a specific example, the biomarker in the sputum sample is selected from one or more of dehydroepiandrosterone sulfate, taurine deoxycholic acid, N-acetyl-L-tryptophan, L-isoleucine-L-proline, sialic acid, 4-methyl-2-oxovalerate, 2-isopropylmalic acid, ceramide (d18:0 / 18:0), prostaglandin E2, and cholesterol sulfate.

[0056] In a specific example, the biomarker in the serum sample is selected from one or more of the following: 9-octadecenoic acid (E)-2,3-dihydroxypropyl ester, 4-methylhippuric acid, N-formylpiperidine, 3-hydroxybutyric acid, trans-palmitoic acid, thiomethanesulfonic acid, 3a,7a,12b-trihydroxy-5a-cholanic acid, prolyl-proline, cholic acid, and lysophosphatidylcholine (10:0 / 0:0).

[0057] In one specific example, the biomarker in the urine sample is selected from one or more of sucrose, 4,8-dihydroxyquinoline-2-carboxylic acid, 3,4-dihydroxymandelic acid, 3-amino-4-methylvaleric acid, 10-hydroxydecanoic acid, acrylic acid, D-erythrose, glycine chenodeoxycholic acid, isoisinolate, and 3α-hydroxy-7-oxo-5β-cholanic acid.

[0058] Optionally, the reagents for detecting expression levels include reagents for detecting protein expression levels and / or mRNA expression levels. The mRNA expression level auxiliary detection reagents include, but are not limited to: reaction reagents that visualize the amplicon corresponding to the primer, such as reagents that visualize the amplicon using agarose gel electrophoresis, enzyme-linked gel electrophoresis, chemiluminescence, in situ hybridization, fluorescence detection, etc.; RNA extraction reagents; reverse transcription reagents; cDNA amplification reagents; standards used to prepare standard curves; positive controls; and negative controls.

[0059] Preferably, the auxiliary reagents for detecting protein expression levels include, but are not limited to: blocking solution, antibody dilution solution, washing buffer, colorimetric stop solution, and standards for preparing standard curves.

[0060] In a specific example, the reagents used to detect protein expression levels include those used in the following methods: Western blotting, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, sandwich assay, immunohistochemical staining, mass spectrometry, immunoprecipitation analysis, complement fixation analysis, flow cytometry with fluorescence edge separation, and protein microarray.

[0061] Optionally, the reagents for detecting mRNA expression levels include those used in the following methods: PCR-based detection methods, Southern hybridization methods, Northern hybridization methods, dot hybridization methods, fluorescence in situ hybridization methods, DNA microarray methods, ASO methods, and high-throughput sequencing platform methods.

[0062] In one specific example, the product is characterized by further comprising reagents for collecting and / or processing samples from the subject.

[0063] In one specific example, the sample includes: serum, urine, sputum, saliva, and bronchoalveolar lavage fluid; preferably, the sample is serum.

[0064] Optionally, the subject is a mammal.

[0065] Alternatively, the subject may be a human being.

[0066] This application also provides a kit for detecting whether a subject is an early-stage aspiration pneumonia patient, the kit comprising reagents for detecting the expression level of the biomarker;

[0067] In a specific example, the reagent for detecting expression levels includes reagents for detecting protein expression levels and / or mRNA expression levels.

[0068] In a specific example, the kit may further include any one or more of the following: mRNA expression level auxiliary detection reagent, protein expression level auxiliary detection reagent, mRNA expression level auxiliary detection instrument, and protein expression level auxiliary detection instrument.

[0069] The embodiments of this application will be described in detail below with reference to examples. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of this application. For experimental methods in the following embodiments where specific conditions are not specified, please refer to the guidelines given in this application, or follow experimental manuals or conventional conditions in the art, or follow the conditions recommended by the manufacturer, or refer to experimental methods known in the art.

[0070] In the specific embodiments described below, the measurement parameters involving raw material components may have slight deviations within the weighing accuracy range unless otherwise specified. Temperature and time parameters are subject to acceptable deviations due to instrument testing accuracy or operational precision.

[0071] Example 1

[0072] Experimental steps:

[0073] I. Research Groups

[0074] Pneumonia patients who met the inclusion criteria were divided into an AP group and a non-AP pneumonia group based on whether they had experienced inhalation. AP patients were in group A, and non-AP pneumonia patients were in group B.

[0075] II. Research Design

[0076] Bronchoalveolar lavage fluid, sputum, saliva, serum, and urine were collected from patients in groups A and B. Metabolomics detection and analysis were performed on various sample types from groups A and B to screen for differentially expressed metabolites between the two groups for each sample type.

[0077] III. Experimental Sample Collection

[0078] All eligible study subjects underwent bronchoscopy, and sputum and bronchoalveolar lavage fluid were collected from each patient. The bronchoscope was inserted through the nose or artificial airway. After locating the glottis, a small amount of lidocaine was sprayed for anesthesia, followed by insertion into the trachea to collect sputum. Referring to recent chest CT scans, the principle of "lavaging the affected segment" was followed, ensuring the bronchoscope tip was tightly embedded at the opening of the segmental or subsegmental bronchus for manipulation, targeting the lesion site. Each patient was irrigated with approximately 60 ml of 0.9% sterile saline; a recovery rate ≥30% was considered a valid sample. The bronchoalveolar lavage fluid and sputum collected in sterile containers were labeled and sent to the central laboratory. Simultaneously, venous blood was collected from patients using non-anticoagulant tubes, and saliva and urine were also collected and sent to the central laboratory. 2 ml of each of the bronchoalveolar lavage fluid, saliva, and sputum were collected and stored in cryovials at -80°C until analysis. Blood and urine samples were centrifuged at 1500 r / min for 10 minutes at 4°C or below. 2 ml of the supernatant was collected and stored in a cryovial at -80°C until analysis.

[0079] IV. Extraction of hydrophilic and hydrophobic compounds

[0080] Preparation of quality control samples: Equal volumes of the sample were mixed to prepare quality control samples. These samples were used to determine the instrument status and balance the chromatogram-mass spectrometry system before injection, and to evaluate system stability throughout the experiment. After thawing the sample at 4°C, 200 μL of sample was accurately taken, and four times the volume of extraction reagent was added, followed by 10 μL of internal standard mixed solution. The mixture was then vortexed at 1500 rpm for 20 min on a shaker. After vortexing, the 96-well plate was placed in a centrifuge and centrifuged at 2200 g at 4°C for 10 min. After centrifugation, the supernatant was lyophilized. When performing the analysis, the lyophilized sample was reconstituted in 200 μL of 10% acetonitrile (0.1% formic acid).

[0081] V. Liquid Chromatography System

[0082] Samples were detected using a combined ultra-high performance liquid chromatography (UHPLC) and high resolution mass spectrometry (HPLC) system. Four different detection methods were employed: metabolites were separated by reversed-phase chromatography and normal-phase chromatography, and then detected in positive ion (ESI+) mode and negative ion (ESI-) mode, respectively.

[0083] VI. Chromatographic Detection Conditions

[0084] Reversed-phase chromatography detection mode: The mobile phase was water / methanol, and both solvents were mixed with 0.1% formic acid and 5mM ammonium formate buffer. Metabolites were separated on an Acquity™ BEH C18 column with the following elution gradient: the organic phase increased from 2% to 100% within 12 min, and the remaining 6 min was used for washing and equilibrating the column. The flow rate, injection volume, and column temperature were 0.3 ml / min, 5 μl, and 40 °C, respectively, and positive and negative ion modes were used for detection.

[0085] Normal-phase chromatography detection mode: The mobile phase was water / acetonitrile, and both solvents were adjusted to pH 9.0 with 25 mM ammonium formate buffer. Metabolites were separated using an Acquity™ BEH AMIDE column with linear gradient elution. The organic phase increased from 2% to 98% within 10 min, and the remaining 2 min was used for column rinsing and equilibration. The flow rate, injection volume, and column temperature settings were the same for both methods. Detection was performed using both positive and negative ion modes.

[0086] VII. High-resolution mass spectrometry detection conditions

[0087] Metabolite detection was performed on a high-resolution mass spectrometer using a data-dependent acquisition method. The mass spectrometer employed a heated electrospray ionization source with an ionization voltage of 4 kV for positive ion detection and 3.5 kV for negative ion detection. The sheath gas flow rate was 45 arb, the auxiliary gas flow rate was 10 arb, the heater temperature was 355 °C, the capillary temperature was 320 °C, and the ion transmission lens RF level was 50%. The full scan resolution was 70,000 m / z 200, the AGC was 1E6, the maximum ion injection time was 100 ms, the scan mass-to-nucleus ratio range was 70–1050 m / z, and the normalized collision energy was 40%. The secondary scan resolution was 17,500 m / z 200, the AGC was 1E5, the maximum ion injection time was 50 ms, the scan mass-to-nucleus ratio range was 70–1050 m / z, and the normalized collision energy was 40%. Ultrapure nitrogen was used as the fragmentation gas.

[0088] VIII. Identification and Quantification of Metabolites

[0089] The experiment utilized ultra-high performance liquid chromatography-tandem high-resolution mass spectrometry (UHPLC-MS / MS) to perform non-targeted metabolomics detection on the submitted samples. Compound Discoverer software was used to process full-scan and data-dependent MS2 metabolomics data for comprehensive component extraction. XCalibur Quan Browser was used to extract the area under the curve (AUCs) of the total ion chromatogram as quantitative information for metabolites. Metabolite structure identification was performed by comparing the obtained primary and secondary mass spectra with the platform's self-built standard MS / MS database and public databases. For metabolite identification or structural annotation, the prerequisites were that the precursor ion mass accuracy was within ±5 ppm, and isotopic information (containing at least one isotope within 10 ppm) and a relative isotopic abundance pattern fit score of 70% were used to confirm the chemical formula. The final metabolite information was reviewed by professional technicians before being used for subsequent analysis.

[0090] Example 2

[0091] Metabolomics analysis of bronchoalveolar lavage fluid samples

[0092] I. Multivariate Statistical Analysis

[0093] Metabolic analysis of bronchoalveolar lavage fluid samples from two groups of patients was performed using ultra-high performance liquid chromatography-tandem high-resolution mass spectrometry (UHPLC-MS). Quality control samples (QC) were used to demonstrate the stability of the LC-MS system. Figure 1 The tightly clustered QC samples indicate that the instrument was stable and the data was reliable during the testing period. Principal component analysis (PCA) was first performed. PCA can reduce multiple variables to a few independent variables in an unsupervised manner, while preserving as much original data information as possible. It can reflect the overall variability between and within sample groups and determine whether clustering or deviation exists. Pareto-scale methods were used for data preprocessing to reduce the original data to a two-dimensional space, and a PCA score plot was plotted, with each dot representing a sample. The PCA score plot of bronchoalveolar lavage fluid is shown below. Figure 1 As shown in the figure, group A is contained within group B, indicating that group A and group B are highly similar and not statistically significant.

[0094] To optimize between-group variation, supervised partial least squares regression analysis (PLS-DA) was used to compare group A and group B. When using PLS-DA to analyze samples, such as... Figure 2 As shown, there is no clear separation between group A and group B. Neither PCA nor PLS-DA results indicated any difference in the metabolic phenotype of bronchoalveolar lavage fluid between patients in group A and group B.

[0095] II. Differential Expression Analysis

[0096] To characterize the metabolites between groups A and B, the chemical structures of the metabolites were identified. 1009 high-confidence metabolites were identified in bronchoalveolar lavage fluid samples, involving 22 different classes of metabolites, including organic acids and fatty acids, demonstrating a rich variety of metabolite identification categories. Subsequently, differentially regulated metabolites were screened using a fold change (Ratio) ≥ 1 and a P-value < 0.05 as criteria, obtaining the types and quantities of upregulated and downregulated metabolites, as shown in Table 1. The diagnostic indicators included in the early screening are shown in Table 2.

[0097] Table 1. Statistical table of differences in the number of metabolites between groups

[0098] Comparison group Upward Lower sum BALF_B vs BALF_A 21 17 38

[0099] Table 2. Information on Early Screening Indicators for Aspiration Pneumonia in Bronchoalveolar Lavage Fluid

[0100]

[0101]

[0102] Statistical analysis of differences in metabolite quantification results using volcano plots, etc. Figure 3 As shown, Figure 3 The X-axis represents the Log2 transformation factor, and the Y-axis represents the P-value minus the log10 value. Significantly different candidate metabolites are located in the upper left and upper right quadrants. The figure shows the variation characteristics of all differentially expressed metabolites in BALF. Upregulated differentially expressed metabolites are marked with dots to the right of the vertical dashed line (21 in total), downregulated differentially expressed metabolites are marked with dots to the left of the vertical dashed line (17 in total), and excluded differentially expressed metabolites are marked with dots below the horizontal dashed line.

[0103] III. Sensitivity and Specificity Validation

[0104] The specificity and sensitivity of the biomarkers of this invention were tested using 10 samples of non-aspiration pneumonia and 9 samples of bronchoalveolar lavage fluid from patients with non-aspiration pneumonia. First, differentially expressed metabolites were screened; these metabolites will be used in downstream machine learning modeling. The Student's t-test was used to perform statistical significance testing on the differentially expressed metabolites. Due to the small sample size, a 5*20 fold cross-validation method was used to split the dataset to calculate the performance of each model. The logistic regression algorithm was used to train and predict the model: the logistic regression hyperparameters were determined using a combination of 5-fold cross-validation and grid search. The algorithm used the scikit-learn (v1.1.1) package, and all programs were executed in a Python (v3.10) environment.

[0105] The optimal model included 10 molecules: adenosine, serine, allogeneic alcoholone sulfate, dehydroepiandrosterone sulfate, (R)-3-hydroxybutyrylcarnitine, hexanocarnitine, 1,2-octadecyl-sn-glycerol, β-mouse cholic acid, pyridoxine, and prolyl-proline. The results are shown in Figure 4. The confusion matrix of the validation results is shown, with the horizontal axis representing the predicted value and the vertical axis representing the true value. The sensitivity of the model on the validation set was 1.000 (1.000-1.000), and the specificity was 1.000 (1.000-1.000).

[0106] Example 3

[0107] Metabolomics analysis of sputum samples

[0108] I. Multivariate Statistical Analysis

[0109] Metabolic analysis of sputum samples from two groups of patients was performed using ultra-high performance liquid chromatography-tandem high-resolution mass spectrometry (ULHPLC-MS). Quality control (QC) was used to demonstrate the stability of the LC-MS system. Figure 5The tightly clustered QC samples indicate that the instrument was stable and the data was reliable during the testing period. PCA analysis was performed first. PCA can reduce multiple variables to a few independent variables in an unsupervised manner, while preserving as much original data information as possible; it can reflect the overall variability between and within sample groups and determine whether clustering or deviation exists. Pareto-scale method was used for data preprocessing to reduce the original data to a two-dimensional space, and a PCA score plot was plotted, with each dot representing a sample. The PCA score plot of sputum samples is shown below. Figure 5 As shown in the figure, there is an overlap between group A and group B, indicating that group A and group B are highly similar and not statistically significant.

[0110] To optimize for inter-group variation, PLS-DA was used to compare group A and group B. When using PLS-DA to analyze samples, such as... Figure 6 As shown, there is no clear separation between group A and group B. Neither PCA nor PLS-DA results indicated any difference in sputum metabolic phenotype between patients in group A and group B.

[0111] II. Differential Expression Analysis

[0112] To characterize the metabolites between groups A and B, the chemical structures of the metabolites were identified. 1196 high-confidence metabolites were identified in the BALF samples, involving 22 different classes of metabolites, including organic acids and fatty acids, demonstrating a rich variety of metabolite identification categories. Subsequently, differentially regulated metabolites were screened using a fold change (Ratio) ≥ 1 and a P-value < 0.05 as criteria, obtaining the types and quantities of upregulated and downregulated metabolites, as shown in Table 3. The diagnostic indicators included in the early screening are shown in Table 4.

[0113] Table 3. Statistical table of differences in the number of metabolites between groups

[0114] Comparison group Upward Lower sum TY_B vs TY_A 58 38 96

[0115] Table 4. Information on Differential Metabolites in Sputum

[0116] Metabolite name molecular weight HMDB identification number ratio P value Dehydroepiandrosterone sulfate 368.16574 HMDB0001032 14.358797 0.021980402 Sialic acid 309.10598 HMDB0000230 0.210221 0.025477738 L-Isoleuk-L-proline 228.14764 HMDB0011174 0.293606 0.028331976 4-Methyl-2-oxovaleric acid 132.07864 HMDB0000695 0.201687 0.00674699 2-Isopropylmalic acid 176.06833 HMDB0000402 0.098974 0.016177499 Cholesterol sulfate 466.31163 HMDB0000653 0.221348 0.008813371 N-acetyl-L-tryptophan 246.10033 HMDB0013713 3.965639 0.039888618 Ceramide (d18:0 / 18:0) 567.55891 HMDB0011761 0.255252 0.043621987 Taurine deoxycholic acid 499.29676 HMDB0000896 2.256024 0.041527377 Prostaglandin E2 352.22497 HMDB0001220 0.063551 0.001546976

[0117] Statistical analysis of differences in metabolite quantification results, such as Figure 7 As shown: Figure 7 The X-axis represents the Log2 transformation factor, and the Y-axis represents the P-value minus the log10 value. Significantly different candidate metabolites are located in the upper left and upper right quadrants. The figure shows the characteristics of changes in all differentially expressed metabolites in sputum. Upregulated differentially expressed metabolites are marked with dots to the right of the vertical dashed line (58 in total), downregulated differentially expressed metabolites are marked with dots to the left of the vertical dashed line (38 in total), and excluded differentially expressed metabolites are marked with dots below the horizontal dashed line.

[0118] III. Sensitivity and Specificity Validation

[0119] The specificity and sensitivity of the biomarkers of this invention were detected using sputum samples from 10 patients with non-aspiration pneumonia and 9 patients with non-aspiration pneumonia. First, differentially expressed metabolites were screened; these metabolites will be used in downstream machine learning modeling. The Student's t-test was used to perform statistical significance testing on the differentially expressed metabolites. Due to the small sample size, a 5*20 fold cross-validation method was used to split the dataset to calculate the performance of each model. The logistic regression algorithm was used to train and predict the model: the logistic regression hyperparameters were determined using a combination of 5-fold cross-validation and grid search. The algorithm used the scikit-learn package (v1.1.1), and all programs were executed in a Python (v3.10) environment.

[0120] The optimal model includes 10 molecules, such as dehydroepiandrosterone sulfate, taurine deoxycholic acid, N-acetyl-L-tryptophan, L-isoleucine-L-proline, sialic acid, 4-methyl-2-oxovaleric acid, 2-isopropylmalic acid, ceramide (d18:0 / 18:0), prostaglandin E2, and cholesterol sulfate. Figure 8 The diagram shows the confusion matrix of the validation results, with the horizontal axis representing the predicted value and the vertical axis representing the true value. The sensitivity of the model on the validation set is 0.878 (0.802-0.938) and the specificity is 0.900 (0.838-0.957). The ROC curve shows the false positive rate on the horizontal axis and the true positive rate on the vertical axis. The AUC ROC is 0.985 (0.890-1.000).

[0121] Example 4

[0122] Saliva sample metabolomics analysis

[0123] I. Multivariate Statistical Analysis

[0124] Metabolic analysis of saliva samples from two groups of patients was performed using ultra-high performance liquid chromatography-tandem high-resolution mass spectrometry (UHPLC-MS). QC samples were used to demonstrate the stability of the LC-MS system. Figure 9 The tightly clustered QC samples indicate that the instrument was stable and the data was reliable during the testing process. First, PCA was performed, which can reduce multiple variables to a few independent variables in an unsupervised manner while preserving as much original data information as possible; it can reflect the overall variability between and within sample groups and determine whether clustering or deviation exists. Pareto-scale was used for data preprocessing to reduce the original data to a two-dimensional space, and a PCA score map was plotted, with each dot representing a sample. The PCA score map of saliva is shown below. Figure 7As shown in the figure, there is an overlap between group A and group B, indicating that group A and group B are highly similar and not statistically significant.

[0125] To optimize for inter-group variation, PLS-DA was used to compare group A and group B. When using PLS-DA to analyze samples, such as... Figure 10 As shown, Group A and Group B are clearly separated, indicating a significant distinction between the two groups.

[0126] II. Differential Expression Analysis

[0127] To characterize the metabolites between groups A and B, the chemical structures of the metabolites were identified. 1120 high-confidence metabolites were identified in saliva samples, involving 22 different classes of metabolites, including organic acids and fatty acids, demonstrating a rich variety of metabolite identification categories. Subsequently, differentially regulated metabolites were screened using a fold change (Ratio) ≥ 1 and a P-value < 0.05 as criteria, obtaining the types and quantities of upregulated and downregulated metabolites, as shown in Table 5. The diagnostic indicators included in the early screening are shown in Table 6.

[0128] Table 5. Statistical table of differences in the number of metabolites between groups

[0129] Comparison group Upward Lower sum TOY_B vs TOY_A 12 7 19

[0130] Table 6. Information on Differential Metabolites in Saliva

[0131] Metabolite name molecular weight HMDB identification number ratio P value Dehydroepiandrosterone sulfate 368.16574 HMDB0001032 2.379572 0.03226907 (2S)-1,1-Dimethylpyrrolidine-2-carboxylic acid 143.09445 HMDB0004827 4.75636 0.011642149 D-raffinose 521.19556 HMDB0003213 0.612985 0.045041752 Glucose 6-phosphate 260.02972 HMDB0001401 0.370193 0.022086164 N-acetylglycine 117.04259 HMDB0000532 1.850357 0.016687969 6-trans-12-epi-leukotriene B4 336.2302 HMDB0005088 0.089065 0.007364969 20-Hydroxyleukotriene B4 352.2248 HMDB0245567 0.049906 0.001201687 Phenyloglucosidic acid 270.07393 HMDB0060014 4.779523 0.00357423 (4-acetaminophenyl)-glucuronic acid 327.09542 HMDB0010316 1.836113 0.023267743 5α-Androstrol-16-en-3-ol 256.21901 HMDB0247010 2.339204 0.044904019

[0132] Statistical analysis of differences in metabolite quantification results using volcano plots, etc. Figure 11 As shown, Figure 11 The X-axis represents the Log2 transformation factor, and the Y-axis represents the P-value minus the log10 value. Significantly different candidate metabolites are located in the upper left and upper right quadrants. The figure shows the characteristics of changes in all differentially expressed metabolites in saliva. Upregulated differentially expressed metabolites are marked with dots to the right of the vertical dashed line (12 in total), downregulated differentially expressed metabolites are marked with dots to the left of the vertical dashed line (7 in total), and excluded differentially expressed metabolites are marked with dots below the horizontal dashed line.

[0133] III. Sensitivity and Specificity Validation

[0134] The specificity and sensitivity of the biomarkers of this invention were detected using saliva samples from 10 patients with non-aspiration pneumonia and 9 patients with non-aspiration pneumonia. First, differentially expressed metabolites were screened; these metabolites will be used in downstream machine learning modeling. The Student's t-test was used to perform statistical significance testing on the differentially expressed metabolites. Due to the small sample size, a 5*20 fold cross-validation method was used to split the dataset to calculate the performance of each model. The logistic regression algorithm was used to train and predict the model: the logistic regression hyperparameters were determined using a combination of 5-fold cross-validation and grid search. The algorithm used the scikit-learn (v1.1.1) package, and all programs were executed in a Python (v3.10) environment.

[0135] The optimal model incorporates 10 molecules, including (2S)-1,1-dimethylpyrrolidone-2-carboxylic acid, phenylglucuronic acid, dehydroepiandrosterone sulfate, 5α-androst-16-en-3-ol, (4-acetamidophenyl)-glucuronic acid, N-acetylglycine, D-raffinose, glucose-6-phosphate, 6-trans-12-epi-leukotriene B4, and 20-hydroxyleukotriene B4. Figure 12 As shown, the confusion matrix of the validation results is plotted with the predicted value on the horizontal axis and the true value on the vertical axis. The sensitivity of the model on the validation set is 1.000 (1.000-1.000) and the specificity is 0.990 (0.968-1.000). The ROC curve plot is plotted with the false positive rate on the horizontal axis and the true positive rate on the vertical axis. The AUC ROC is 0.995 (0.995-0.995).

[0136] Example 5

[0137] Serum sample metabolomics analysis

[0138] I. Multivariate Statistical Analysis

[0139] Metabolic analysis of serum samples from two groups of patients was performed using ultra-high performance liquid chromatography-tandem high-resolution mass spectrometry (UHPLC-MS). QC samples were used to demonstrate the stability of the LC-MS system. Figure 13 The tightly clustered QC samples indicate that the instrument was stable and the data was reliable during the testing period. PCA analysis was performed first, which can reduce multiple variables to a few independent variables in an unsupervised manner while preserving as much original data information as possible; it can reflect the overall variability between and within sample groups and determine whether clustering or deviation exists. Pareto-scale method was used for data preprocessing to reduce the original data to a two-dimensional space, and a PCA score plot was plotted, with each dot representing a sample. The serum PCA score plot is shown below. Figure 13 As shown in the figure, group A and group B are well separated with no overlap and are statistically significant.

[0140] To optimize for inter-group variation, PLS-DA was used to compare group A and group B. When using PLS-DA to analyze samples, such as... Figure 14 As shown, a clear separation was observed between group A and group B, indicating a significant distinction between the two groups. Both PCA and PLS-DA results indicated that the serum metabolic phenotypes of patients in group A and group B were different.

[0141] II. Differential Expression Analysis

[0142] To characterize the metabolites between groups A and B, the chemical structures of the metabolites were identified. 972 high-confidence metabolites were identified in serum samples, involving 22 different classes of metabolites, including organic acids and fatty acids, demonstrating a rich variety of metabolite identification categories. Subsequently, differentially regulated metabolites were screened using a fold change (Ratio) ≥ 1 and a P-value < 0.05 as criteria, obtaining the types and quantities of upregulated and downregulated metabolites, as shown in Table 7. The diagnostic indicators included in the early screening are shown in Table 8.

[0143] Table 7. Statistical table of differences in the number of metabolites between groups

[0144] Comparison group Upward Lower sum XQ_B vs XQ_A 40 39 79

[0145] Table 8. Serum Differential Metabolite Information Table

[0146]

[0147]

[0148] Statistical analysis of differences in metabolite quantification results using volcano plots, etc. Figure 15 As shown, Figure 15 The X-axis represents the fold change of the Log2 transformation, and the Y-axis represents the "P value minus log10". Candidate metabolites showing significant differences are located in the upper left and upper right quadrants. The figure shows the characteristics of changes in all differentially expressed metabolites in serum. Excluded metabolites with no difference are marked with dots below the horizontal dashed line; upregulated differentially expressed metabolites are marked with dots to the right of the vertical dashed line (40 in total); and downregulated differentially expressed metabolites are marked with dots to the left of the vertical dashed line (39 in total).

[0149] III. Sensitivity and Specificity Validation

[0150] The specificity and sensitivity of the biomarkers of this invention were tested using serum samples from 10 patients with non-aspiration pneumonia and 9 patients with non-aspiration pneumonia. First, differentially expressed metabolites were screened; these metabolites will be used in downstream machine learning modeling. The Student's t-test was used to perform statistical significance testing on the differentially expressed metabolites. Due to the small sample size, a 5*20 fold cross-validation method was used to split the dataset to calculate the performance of each model. The logistic regression algorithm was used to train and predict the model: the logistic regression hyperparameters were determined using a combination of 5-fold cross-validation and grid search. The algorithm used the scikit-learn package (v1.1.1), and all programs were executed in a Python (v3.10) environment.

[0151] The optimal model includes 10 molecules: 9-octadecenoic acid (E)-2,3-dihydroxypropyl ester, 4-methylhippuric acid, N-formylpiperidine, 3-hydroxybutyric acid, trans-palmitoic acid, thiomethanesulfonic acid, 3a,7a,12b-trihydroxy-5a-cholanic acid, prolyl-proline, cholic acid, and lysophosphatidylcholine (10:0 / 0:0). Figure 16 The diagram shows the confusion matrix of the validation results, with the horizontal axis representing the predicted value and the vertical axis representing the true value. The model's sensitivity on the validation set is 1.000 (1.000-1.000), and its specificity is 0.980 (0.949-1.000). The ROC curve shows the false positive rate on the horizontal axis and the true positive rate on the vertical axis. The AUC ROC is 0.995 (0.995-0.995).

[0152] Example 6

[0153] Metabolomics analysis of urine samples

[0154] I. Multivariate Statistical Analysis

[0155] Metabolic analysis of urine samples from two groups of patients was performed using ultra-high performance liquid chromatography-tandem high-resolution mass spectrometry (ULHPLC-MS). QC samples were used to demonstrate the stability of the LC-MS system. Figure 16 The tightly clustered QC samples indicate that the instrument was stable and the data was reliable during the testing period. PCA analysis was performed first, which can reduce multiple variables to a few independent variables in an unsupervised manner while preserving as much original data information as possible; it can reflect the overall variability between and within sample groups and determine whether clustering or deviation exists. Pareto-scale method was used for data preprocessing to reduce the original data to a two-dimensional space, and a PCA score plot was plotted, with each dot representing a sample. The PCA score plot for urine is shown below. Figure 17 As shown in the figure, group B is contained within group A, indicating that group A and group B are highly similar and not statistically significant.

[0156] To optimize for inter-group variation, PLS-DA was used to compare group A and group B. When using PLS-DA to analyze samples, such as... Figure 18 As shown, Group A and Group B are clearly separated, indicating a significant distinction between the two groups.

[0157] II. Differential Expression Analysis

[0158] To characterize the metabolites between group A and group B, the chemical structures of the metabolites were identified. 1348 high-confidence metabolites were identified in urine samples, involving 22 different classes of metabolites, including organic acids and fatty acids, demonstrating a rich variety of metabolite identification categories. Subsequently, differentially regulated metabolites were screened using a fold change (Ratio) ≥ 1 and a P-value < 0.05 as criteria, obtaining the types and quantities of upregulated and downregulated metabolites, as shown in Table 9. The diagnostic indicators included in the early screening are shown in Table 10.

[0159] Table 9. Statistical table of differences in the number of metabolites between groups

[0160] Comparison group Upward Lower sum NS_B vs NS_A 23 24 47

[0161] Table 10. Information on Differential Metabolites in Urine

[0162] Metabolite name molecular weight HMDB identification number ratio P value 4,8-Dihydroxyquinoline-2-carboxylic acid 205.03751 HMDB0000881 7.391609 0.002190806 sucrose 342.11621 HMDB0000258 3.836974 0.018240345 D-erythritol 120.04226 HMDB0250746 0.416205 0.023499359 Glycine chenodeoxycholic acid 449.31412 HMDB0000637 0.196849 0.008383184 Isoisinosteroid 180.06339 HMDB0240211 0.35927 0.014131939 acrylic acid 72.02114 HMDB0031647 0.265255 0.036056687 3-Amino-4-methylvaleric acid 96.05709 HMDB0245808 2.583415 0.047349601 3α-hydroxy-7-oxo-5β-cholanic acid 390.27701 HMDB0246682 0.103769 0.002275228 3,4-Dihydroxymandelic acid 184.03717 HMDB0001866 5.37279 0.032780054 10-Hydroxydecanoic acid 152.12009 HMDB0244272 0.383921 0.029144249

[0163] Statistical analysis of differences in metabolite quantification results using volcano plots, etc. Figure 19 As shown, Figure 19 The X-axis represents the Log2 transformation factor, and the Y-axis represents the P-value minus the log10 value. Significantly different candidate metabolites are located in the upper left and upper right quadrants. The figure shows the characteristics of changes in all differentially expressed metabolites in urine. Upregulated differentially expressed metabolites are marked with dots to the right of the vertical dashed line (23 in total), downregulated differentially expressed metabolites are marked with dots to the left of the vertical dashed line (24 in total), and indifferent metabolites that were excluded are marked with dots below the horizontal dashed line.

[0164] III. Sensitivity and Specificity Validation

[0165] The specificity and sensitivity of the biomarkers of this invention were detected using 10 samples of non-aspiration pneumonia and 9 samples of urine from patients with non-aspiration pneumonia. First, differentially expressed metabolites were screened; these metabolites will be used in downstream machine learning modeling. The Student's t-test was used to perform statistical significance testing on the differentially expressed metabolites. Due to the small sample size, a 5*20 fold cross-validation method was used to split the dataset to calculate the performance of each model. The logistic regression algorithm was used to train and predict the model: the logistic regression hyperparameters were determined using a combination of 5-fold cross-validation and grid search. The algorithm used the scikit-learn (v1.1.1) package, and all programs were executed in a Python (v3.10) environment.

[0166] The optimal model incorporates 10 molecules, including sucrose, 4,8-dihydroxyquinoline-2-carboxylic acid, 3,4-dihydroxymandelic acid, 3-amino-4-methylvaleric acid, 10-hydroxydecanoic acid, acrylic acid, D-erythrose, glycine-chenodeoxycholic acid, isoisinolate, and 3α-hydroxy-7-oxo-5β-cholanic acid. Figure 20 The diagram shows the confusion matrix of the validation results, with the horizontal axis representing the predicted value and the vertical axis representing the true value. The model's sensitivity on the validation set is 0.900 (0.835-0.956), and its specificity is 1.000 (1.000-1.000). The ROC curve shows the false positive rate on the horizontal axis and the true positive rate on the vertical axis. The AUC ROC is 0.995 (0.995-0.995).

[0167] The embodiments described above are merely illustrative of several implementation methods of this application, intended to facilitate a detailed understanding of the technical solutions of this application, but should not be construed as limiting the scope of protection of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Furthermore, it should be understood that after reading the above teachings of this application, those skilled in the art can make various alterations or modifications to this application, and the equivalent forms obtained also fall within the scope of protection of this application. It should also be understood that technical solutions obtained by those skilled in the art based on the technical solutions provided in this application through logical analysis, reasoning, or limited experimentation are all within the scope of protection of the appended claims. Therefore, the scope of protection of this patent application should be determined by the content of the appended claims, and the specification and drawings can be used to interpret the content of the claims.

Claims

1. Application of reagents for detecting the expression levels of biomarkers in the preparation of early screening products for detecting whether a subject has aspiration pneumonia; The test samples were selected from serum samples; The biomarkers in the serum samples were 9-octadecenoic acid (E)-2,3-dihydroxypropyl ester, 4-methylhippuric acid, N-formylpiperidine, 3-hydroxybutyric acid, trans-palmitoic acid, thiomethanesulfonic acid, 3a,7a,12b-trihydroxy-5a-cholanic acid, prolyl-proline, cholic acid, and lysophosphatidylcholine (10:0 / 0:0).

2. The application according to claim 1, characterized in that, The reagents for detecting expression levels include reagents for detecting protein expression levels and / or mRNA expression levels.

3. The application according to claim 1, characterized in that, The reagents used to detect protein expression levels include those used in the following methods: Western blotting, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, sandwich assay, immunohistochemical staining, mass spectrometry, immunoprecipitation analysis, complement fixation analysis, flow cytometry, fluorescence edge separation, and protein chip analysis.

4. The application according to any one of claims 1 to 3, characterized in that, The reagents used to detect mRNA expression levels include those used in the following methods: PCR-based detection methods, Southern hybridization methods, Northern hybridization methods, dot hybridization methods, fluorescence in situ hybridization methods, DNA microarray methods, ASO methods, and high-throughput sequencing platform methods.

5. The application according to any one of claims 1 to 3, characterized in that, The product also includes reagents for collecting and / or processing samples from subjects.

6. The application according to claim 5, characterized in that, The sample was serum.

7. The application according to claim 5, characterized in that, The subjects were mammals.

8. The application according to claim 7, characterized in that, The subjects were humans.

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