Biomarker combination for auxiliary diagnosis or screening of fatty liver by using volatile organic compounds in expired gas of human body and application of biomarker combination

Abstract

The invention discloses a biomarker combination for auxiliary diagnosis or screening of fatty liver by using volatile organic compounds in expired gas of a human body and application of the biomarker combination. The biomarker combination comprises the following compounds: valeraldehyde, ethyl propionate, furan, propionic acid, 3-hexanone, acetone, isovaleric acid, 2-methyl propionic acid and butyl acetate. The biomarker combination screened by the invention has relatively strong resolution capability on the fatty liver and shows high sensitivity, specificity and accuracy, the constructed diagnosis model has good learning and generalization capabilities and relatively good prediction capability on a sample, early-stage rapid screening on the fatty liver can be realized, an invasive sampling process is avoided, and the detection efficiency is improved. The compliance of a patient is greatly improved, radiation injury and expensive reagent consumables are avoided in the testing process, the safety and adaptability of the method are improved, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and specifically relates to a volatile organic compound in human exhaled breath as a biomarker for auxiliary diagnosis or screening of fatty liver and its application. Background Art

[0002] Fatty liver disease is a common liver disease in today's society, with its incidence increasing year by year. Accurate detection and diagnosis of fatty liver disease is crucial for timely treatment and disease progression control. Currently, clinical detection and diagnostic techniques for fatty liver disease primarily include medical imaging, laboratory tests, and histological examinations.

[0003] Medical imaging is commonly used in the diagnosis of fatty liver disease, with ultrasound, CT scans, and MRI scans being key imaging techniques. Ultrasound, while simple and noninvasive, has the advantages of limited diagnostic accuracy for mild fatty liver disease and is particularly susceptible to the influence of the examiner's experience. CT scans offer high accuracy for diagnosing fatty liver disease, clearly demonstrating the liver's morphology and structure, as well as the extent and degree of fatty infiltration. However, their main disadvantages are radiation exposure and high cost. MRI scans provide high-resolution images of the liver and are more sensitive than CT scans for early-stage fatty liver disease. They can more accurately detect subtle changes in liver fat and can also assess the degree of liver fibrosis, making them crucial for assessing the progression of fatty liver disease. However, MRI is expensive, making it unaffordable for many patients. Furthermore, the examination is lengthy and requires patients to remain still in a cramped chamber, which can hinder the completion of the examination due to claustrophobia. Furthermore, MRI is not suitable for patients with metal implants, limiting its application.

[0004] The lack of specificity in liver fibrosis serological tests and liver function tests is particularly evident in laboratory testing. Clinical diagnosis cannot rely solely on these test results; a comprehensive analysis, combined with the patient's medical history, symptoms, physical signs, and other testing methods, is required. For example, in patients with a history of chronic alcohol abuse, if serum enzyme tests show a significant elevation in AST (AST / ALT) with an AST / ALT ratio >2, alcoholic fatty liver disease is highly suspected, but further imaging studies are required to confirm the diagnosis. Furthermore, with the continuous advancement of medical technology, more specific laboratory diagnostic indicators should be explored to improve the accuracy of fatty liver diagnosis.

[0005] Liver biopsy, a histological examination, is the "gold standard" for diagnosing fatty liver disease. Although highly accurate, it can be invasive and subject to sampling errors. As an invasive procedure, liver biopsy carries certain risks and complications, such as bleeding and infection. Furthermore, liver biopsy has limitations. Due to the limited sampling area of ​​liver tissue, sampling errors may occur, and it may not fully represent the pathological condition of the entire liver. Therefore, liver biopsy is generally reserved for confirming the cause of fatty liver disease and assessing its severity, rather than as a routine diagnostic method.

[0006] Auxiliary diagnosis of fatty liver through exhaled breath VOCs is a new idea. It is a non-invasive, radiation-free, simple and easy rapid diagnostic method. The patient blows the exhaled breath directly into the device or collects the exhaled breath through an air bag, and then uses the big data model to judge the composition and intensity of the subject's VOCs, and finally obtains the auxiliary diagnosis results and conclusions of whether the patient has fatty liver. It avoids the invasive sampling process and greatly improves the compliance of the patients. The testing process also does not cause radiation damage and expensive reagents and consumables, which improves the safety and adaptability of the method. In addition, by establishing a big data model of early fatty liver and the control group, early and rapid screening of fatty liver can be achieved. Summary of the Invention

[0007] In response to the deficiencies of the existing technology and actual needs, the present invention provides a volatile organic compound in human exhaled breath as a biomarker for auxiliary diagnosis or screening of fatty liver and its application.

[0008] To achieve the above object, the technical solution adopted by the present invention is: The present invention provides a biomarker combination that can be used for auxiliary diagnosis or auxiliary screening of fatty liver, wherein the biomarker combination includes the following compounds: valeraldehyde, ethyl propionate, furan, propionic acid, 3-hexanone, acetone, isovaleric acid, 2-methylpropionic acid, and butyl acetate.

[0009] Furthermore, the propionic acid in the biomarker combination is either one or both of a propionic acid monomer and a propionic acid dimer.

[0010] The present invention also provides the use of the above biomarker combination in the preparation of a product for auxiliary diagnosis or auxiliary screening of fatty liver.

[0011] Furthermore, the product is a kit comprising reagents for detecting the content of the above-mentioned biomarker combination.

[0012] The present invention also provides use of the above-mentioned biomarker combination in preparing a kit for auxiliary diagnosis or auxiliary screening of fatty liver.

[0013] The present invention also provides a model for auxiliary diagnosis or auxiliary screening of fatty liver, which assists in the diagnosis or auxiliary screening of fatty liver by detecting the composition and content of the above-mentioned biomarker combination in a sample.

[0014] The present invention also provides a method for constructing a model for auxiliary diagnosis or auxiliary screening of fatty liver, the method comprising the following steps: Collect exhaled breath from patients with simple fatty liver disease and healthy people without fatty liver disease, and test the composition and relative content of substances in the exhaled breath samples; The material composition and relative content data obtained in step 1) were imported into statistical software. The unit variance scaling method was used to normalize each column of variables. 80% of all collected samples were randomly selected as training and test sets, and the remaining 20% ​​of samples were used as validation sets for training and establishment of the OPLS-DA model. Potential biomarkers with VIP values ​​> 1 were screened by calculating VIP values ​​to form a biomarker combination. 3) Model validation: 7-fold cross validation was used to evaluate the effectiveness of the model; By calculating the VIP value, potential biomarkers with a VIP value greater than 1 were screened to form a biomarker combination; Model optimization and prediction: The OPLS-DA model was re-optimized and established based on the potential biomarkers with VIP values ​​> 1 screened out above, and a final prediction model was established. This model was then used to predict the validation set samples. The prediction results were compared with the clinical diagnosis results, and the accuracy reached above 0.9.

[0015] Furthermore, in the above-mentioned model construction method, in step 1), any one of gas chromatography, gas chromatography-ion mobility spectrometry, gas chromatography-mass spectrometry, electronic nose, and proton transfer reaction-mass spectrometry can be used to detect the substance composition and relative content of the exhaled breath sample.

[0016] Furthermore, the specific conditions for the gas chromatography-ion mobility spectrometry coupling are: Gas phase conditions: column temperature 60°C; drift gas flow rate 150.0 mL / min; carrier gas: high-purity nitrogen with a purity of ≥ 99.999%; programmed pressure increase: initial flow rate 5.0 mL / min maintained for 30 s, valve cut-off injection at 0.5 s, and valve cut-off injection ended after 9.5 s; after 30 s, carrier gas was linearly increased to 100.0 mL / min and ended after 10 min; IMS conditions: ionization source: tritium (3H); migration tube length: 53 mm; electric field strength: 500 V / cm; migration tube temperature: 45°C; drift gas: high-purity nitrogen with a purity of ≥ 99.999%, flow rate: 75.0 mL / min; positive and negative ion modes: positive ion.

[0017] Furthermore, in step 3) model R 2 =0.646, Q 2 =0.570, the accuracy of 7-fold cross validation was 0.96, sensitivity 0.93, specificity 1, and AUC = 0.99; Furthermore, the validation set accuracy in step 5) reached 0.92.

[0018] Furthermore, the potential biomarkers with VIP values ​​> 1 screened out above are: valeraldehyde, ethyl propionate, furan, propionic acid, 3-hexanone, acetone, isovaleric acid, 2-methylpropionic acid, and butyl acetate, constituting a biomarker combination.

[0019] Furthermore, the propionic acid is either one or both of a propionic acid monomer and a propionic acid dimer.

[0020] Compared with the prior art, the technical effects of the present invention are positive and significant. The present invention relates to a biomarker combination of volatile organic compounds in human exhaled breath as an auxiliary diagnosis for fatty liver disease, and its application. The screened biomarker combination has strong discrimination ability for fatty liver disease, exhibiting high sensitivity, specificity, and accuracy. The constructed diagnostic model has good learning and generalization capabilities, has good predictive ability for samples, can achieve early and rapid screening for fatty liver disease, and avoids invasive sampling processes, greatly improving patient compliance. The testing process does not cause radiation damage or expensive reagents and consumables, improving the safety and adaptability of the method, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 : Carrier gas gradient diagram.

[0022] Figure 2 : GC-IMS spectrum.

[0023] Figure 3 : OPLS-DA score plot.

[0024] Figure 4 : ROC curve plot.

[0025] Figure 5 : ROC curve of the validation set. DETAILED DESCRIPTION

[0026] The following is a further detailed description of the specific embodiments and technical solutions of the present invention in conjunction with the accompanying drawings and specific examples. The specific examples provide preferred embodiments. The reagents, equipment, and methods used in the present invention are all conventional reagents, equipment, and methods in the art. For conventional commercial products with source and specific model marked, the test methods and specific conditions are in accordance with the test methods and conditions in the instructions of the corresponding products. Unless otherwise specified, the raw materials (actual materials, methods, and equipment) used can be purchased from conventional commercial products. The reagents, equipment, and test methods are all conventional reagents, equipment, and methods in the art.

[0027] Example 1: Model construction and effect verification 145 fatty liver samples (category marked as NAFLD) were collected, only patients with simple fatty liver were accepted, aged under 60 years old and over 18 years old, without comorbidities, and regardless of gender, and 113 control group samples (category marked as C). The control samples were all collected from healthy people, totaling 258 cases.

[0028] First, a 1L polyester odor bag was used to collect the exhaled breath of the subjects. 145 fatty liver samples (category marked as NAFLD) and 113 control group samples (category marked as C) were collected, totaling 258 cases. Sampling required fasting for more than 6-8 hours and rinsing the mouth before sampling.

[0029] The breath sample can be temporarily stored and transported in an air bag until it is ready for testing on the device. During testing, the air bag outlet is inserted into the device's sampling port, and the sample is sampled using a negative pressure pump or manually squeezed into the device using positive pressure.

[0030] Gas phase conditions: Column temperature: 60°C; carrier gas: high-purity nitrogen (purity ≥ 99.999%); programmed pressure increase: initial flow rate 5.0 mL / min maintained for 30 s, valve cut-off injection at 0.5 s, valve cut-off injection ended after 9.5 s. After 30 s, carrier gas was linearly increased to 100.0 mL / min and ended after 10 min. (See Table 1 and Figure 1 As shown, the black curve is the carrier gas flow rate, and the red curve is the corresponding carrier gas pressure. By setting the flow gradient of the carrier gas, the separation of different compounds can be achieved.

[0031] Table 1 Gas chromatography conditions Time E1 (drift gas flow) E2 (carrier gas flow) V (valve) 00:00,000 150.0 mL / min 5.0 ml / min 00:00,500 150.0 mL / min 5.0 ml / min Cut-off valve 00:10,000 150.0 mL / min 5.0 ml / min Cut-off valve 00:30,000 150.0 mL / min 5.0 ml / min 10:00,000 150.0 mL / min 100.0 ml / min IMS conditions: Ionization source: tritium (3H); migration tube length: 53 mm; electric field strength: 500 V / cm; migration tube temperature: 45°C; drift gas: high-purity nitrogen (purity ≥ 99.999%), flow rate: 75.0 mL / min; positive and negative ion modes: positive ion.

[0032] Exhaled breath is pre-separated on a chromatographic column (e.g., an MXT-5). Complex components are separated into simpler ones, which then enter the migration tube for ionization and migration. Ions that migrate to the detector are detected, and their signal intensities are recorded. Qualitative analysis is performed using the component retention index in the chromatographic column and the migration time in the migration tube, while quantitative analysis is performed using the signal response intensity. Ultimately, the composition and relative content of the exhaled breath sample are determined.

[0033] The experimental results are as follows Figure 2 As shown, the horizontal axis represents relative migration time, and the vertical axis represents retention time. Each point in the middle represents a compound, and compound intensity is represented by a color scale from blue to red, with redder colors indicating higher relative content. Compounds were qualitatively identified using the NIST RI 2020 retention index database and the self-built Hanon IMS 2024 database. Relative quantification was performed using a Faraday disk detector to measure the electrical signal intensity of the compounds. Higher compound content indicates more charge reaching the detector, resulting in a higher voltage (V). Because electrical signal intensity is positively correlated with compound content, signal intensity was used as a characteristic for compound relative content modeling. Statistical learning was used to model the relative content differences of various compounds in the two sample types (NAFLD and C), enabling differentiation and prediction between the two sample types.

[0034] Each signal was characterized using the NIST RI and IMS databases, and then the peak intensity (V) of each signal was extracted. The results are shown in Table 2.

[0035] All compound peak intensity data were imported into statistical software (SIMCA 14.1). Each column of variables was normalized using the UV (Unit Variance Scaling) method. 80% of the collected samples were randomly selected as training and test sets for model training and establishment, totaling 207 cases. An OPLS-DA model was established, and model performance was evaluated using 7-fold cross-validation. The software randomly divided the 207 cases into 7 parts, each of which served as a test set. Model accuracy and other indicators were averaged for evaluation. Variables important for class distinction were then screened by calculating VIP, which can serve as potential markers for diagnosing or screening fatty liver disease. The remaining 51 samples collected will be used as a validation set for external prediction. They will not participate in any model calculations throughout the process, but will participate in verification only after the final model is established. The predicted results will be compared with the clinical diagnosis results, and the accuracy rate will be calculated.

[0036] Model accuracy is the ratio of the number of correctly predicted cases to the total number of predicted cases in the validation set. Sensitivity, also known as the true positive rate, refers to the proportion of correctly diagnosed positive samples in the validation set relative to all positive samples in the set. Specificity, also known as the true negative rate, refers to the proportion of correctly diagnosed negative samples relative to all negative samples in the set. AUC is the area under the receiver operating characteristic (ROC) curve.

[0037] Figure 3 This is an OPLS-DA score plot. The X-axis represents the difference between the two sample groups. If the two sample groups differ and the model can distinguish them well, the left and right separation of the two sample groups can be seen on the plot. The Y-axis represents the difference within each sample group. The separation along the Y-axis indicates the size of individual differences within the group.

[0038] Model R 2 =0.646, Q 2 =0.570, the accuracy of 7-fold cross validation was 0.96, sensitivity was 0.93, specificity was 1.00, and AUC was 0.99 ( Figure 4 ). The following potential markers were screened based on VIP values ​​> 1: valeraldehyde, ethyl propionate, furan, propionic acid, 3-hexanone, isovaleric acid, 2-methylpropionic acid, acetone, and butyl acetate, see Table 3

[0039] The final prediction model was established by re-optimizing and building an OPLS-DA model based on the selected markers in Table 3. The model was then used to predict the other 51 validation set samples. The prediction results were compared with the clinical diagnosis results. The accuracy rate reached 0.90, with 46 correct predictions and 5 incorrect predictions (see Table 4). The AUC was 0.92 ( Figure 5 ). This indicates that the screened markers have strong discrimination ability for fatty liver, the model has good learning and generalization capabilities, and has good predictive ability for unknown samples.

[0040] In the above process, an OPLS-DA model was established using 207 collected samples to learn the differences in VOC composition between fatty liver disease (NAFLD) and its healthy control group (C). The model first demonstrated good fit through cross-validation, with accuracy, sensitivity, specificity, and AUC all greater than 0.9. VIP screening then selected nine compounds that significantly contributed to the distinction between the two sample types and could serve as potential markers for distinguishing and assisting in the diagnosis of fatty liver disease. The model was further optimized, reducing the number of variables from 30 to 10. The optimized final model was then applied to 51 external validation samples, also yielding good results. This demonstrates that the selected markers have strong discriminatory power for fatty liver disease, and that the model possesses good learning and generalization capabilities, with good predictive power for unknown samples.

[0041] To further verify and demonstrate the model's ability to identify fatty liver disease samples, after the final model was established and optimized, 49 new clinical samples were collected and tested. These samples were then used to predict disease using the final model. The results were compared with hospital diagnoses to calculate the model's prediction accuracy. Hospital diagnoses of fatty liver disease primarily rely on ultrasound examinations, combined with serum lipid profiles and clinical manifestations. The model's prediction results, shown in Table 5 below, demonstrate high prediction accuracy for the newly collected samples.

[0042] The above detailed description of the preferred specific embodiments of the present invention is only a preferred embodiment of the present invention. The above scheme can also be used to achieve the same application through traditional methods in the field of VOC detection, such as gas chromatography GC, gas chromatography mass spectrometry GC-MS, GC-MS / MS, electronic nose, PTR-MS and other technologies, that is, diagnosis is performed through the expression difference of VOCs in two types of samples. It should be pointed out that ordinary technicians in this field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solutions that can be obtained by technicians in this technical field through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of existing technology should be within the scope of protection determined by the claims.

Claims

1. A biomarker combination for auxiliary diagnosis or auxiliary screening of fatty liver, characterized in that: The biomarker panel includes the following compounds: Valeraldehyde, ethyl propionate, furan, propionic acid, 3-hexanone, acetone, isovaleric acid, 2-methylpropionic acid, butyl acetate.

2. The biomarker combination according to claim 1, characterized in that The propionic acid is either one or both of a propionic acid monomer and a propionic acid dimer.

3. Use of the biomarker combination according to any one of claims 1 to 2 in the preparation of a product for auxiliary diagnosis or screening of fatty liver.

4. The use according to claim 3, characterized in that The product is a kit for auxiliary diagnosis or screening of fatty liver.

5. A model for auxiliary diagnosis or screening of fatty liver, characterized in that: The model assists in the diagnosis or screening of fatty liver by determining the composition and content of the biomarker combination as claimed in claim 1 or 2 in the sample to be tested.

6. A method for constructing a model for auxiliary diagnosis or screening of fatty liver, characterized in that: The method comprises the following steps: 1) Collect exhaled breath from patients with simple fatty liver disease and healthy controls without fatty liver disease, and test the composition and relative content of substances in the exhaled breath samples; 2) Import the material composition and relative content data obtained in step 1) into statistical software, normalize each column of variables using the unit variance scaling method, randomly select 80% of all collected samples as training and test sets, and the remaining 20% ​​of samples as the validation set to train and establish the OPLS-DA model; 3) Model validation: 7-fold cross validation was used to evaluate the effectiveness of the model; 4) Calculate the VIP value to screen out potential biomarkers with a VIP value > 1 and form a biomarker combination; 5) Model optimization and prediction: The OPLS-DA model was re-optimized and established using the previously screened potential biomarkers with VIP values ​​> 1 to establish a final prediction model. This model was then used to predict samples from the validation set. The prediction results were compared with the clinical diagnosis results, with an accuracy rate of over 0.

9.

7. The method for constructing a model according to claim 6, wherein: In step 1), any one of gas chromatography, gas chromatography-ion mobility spectrometry, gas chromatography-mass spectrometry, electronic nose, and proton transfer reaction-mass spectrometry is used to detect the substance composition and relative content of the exhaled breath sample.

8. The method for constructing a model according to claim 7, wherein: The specific conditions for the gas chromatography-ion mobility spectrometry coupling are: Gas phase conditions: column temperature 60 °C; drift gas flow rate 150.0 mL / min; Carrier gas: high-purity nitrogen with a purity of ≥ 99.999%; programmed pressure increase: initial flow rate of 5.0 mL / min maintained for 30 s, valve cut-off for injection at 0.5 s, and valve cut-off for injection after 9.5 s; after 30 s, carrier gas was linearly increased to 100.0 mL / min and ended after 10 min; IMS conditions: ionization source: tritium (3H); migration tube length: 53 mm; electric field strength: 500 V / cm; migration tube temperature: 45 °C; drift gas: high-purity nitrogen with a purity of ≥ 99.999%, flow rate: 75.0 mL / min; positive and negative ion modes: positive ion.

9. The method for constructing a model according to any one of claims 6 to 8, characterized in that: Step 3) Model R 2 =0.646, Q 2 =0.570, the accuracy of 7-fold cross validation was 0.96, sensitivity was 0.93, specificity was 1, and AUC = 0.

99.

10. The method for constructing a model according to any one of claims 6 to 8, characterized in that: In step 5), the validation set accuracy reaches 0.

92.

11. The method for constructing a model according to any one of claims 6 to 8, characterized in that: The following potential biomarkers with VIP values ​​> 1 were screened out: valeraldehyde, ethyl propionate, furan, propionic acid, 3-hexanone, acetone, isovaleric acid, 2-methylpropionic acid, and butyl acetate to form a biomarker combination.

12. The method for constructing a model according to claim 11, wherein: The propionic acid is either one or both of a propionic acid monomer and a propionic acid dimer.

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