CN116256453A - Metabolic-study-based normal high-value blood pressure diagnosis marker and screening method thereof - Google Patents
Metabolic-study-based normal high-value blood pressure diagnosis marker and screening method thereof Download PDFInfo
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Abstract
The invention belongs to the technical field of medicines, and particularly relates to a normal high-value blood pressure diagnosis marker based on metabonomics and a screening method thereof. The marker is any one or more of the following metabolic markers: sphingosine-1-phosphate, palmitoylethanolamide, oleamide, lysophosphatidic acid P-16:0/0:0, palmitoylglycine, N-acetylleucine, N-heptanoylglycine, 2-phenylacetamide. According to the invention, after the pretreatment of the sample, each analysis sample is detected by adopting a liquid chromatography-mass spectrometry technology, and potential biomarkers are screened and identified through data analysis. The biomarker provided by the invention can be used for predicting the risk of a subject, can be effectively used as a marker for predicting PHT risk, and has the advantages of accuracy, early stage and high accuracy.
Description
Technical Field
The invention belongs to the technical field of medicines, and particularly relates to a normal high-value blood pressure diagnosis marker based on metabonomics and a screening method thereof, in particular to a normal high-value blood pressure phlegm-dampness accumulation diagnosis marker based on metabonomics and a screening method thereof.
Background
Hypertension is the most common chronic disease and is also the most main risk factor of cardiovascular and cerebrovascular diseases, not only causes residue and high mortality, but also seriously consumes medical and social resources, and causes heavy burden to families and countries, so that prevention and control of hypertension are important problems to be solved urgently. Normal high blood pressure (blood pressure 120-139/80-89 mmHg) is a stage in which normal blood pressure progresses toward the generation of hypertension, and is a key period for controlling hypertension. According to the analysis of the related literature, the pattern of excessive phlegm-dampness is the main pattern of normal high blood pressure. Currently, there is still a lack of very effective biomarkers for early diagnosis of normal high blood pressure. Thus, the discovery of new biomarkers is of great importance for achieving clinically early diagnosis and treatment of normal high value blood pressure. Metabonomics research is now widely used in the field of medical diseases, where metabolite analysis can identify early changes in the disease. More and more research has found that metabonomics achieves staged results in disease diagnosis, model evaluation, and drug efficacy evaluation.
Disclosure of Invention
Aiming at the problems, the invention provides a diagnosis marker for a normal high-value blood pressure phlegm-dampness accumulation syndrome based on metabonomics and a screening method thereof. Meanwhile, the diagnosis value of different metabolites on PHT is evaluated by analyzing a subject working characteristic (ROC) curve, and the prediction performance of the PHT is evaluated, so that references are provided for pathogenesis of PHT diseases, and targeted assistance is provided for diagnosis and treatment of PHT in the future.
The normal high-value blood pressure phlegm-dampness accumulation syndrome diagnosis marker based on metabonomics is any one or more of the following metabolic markers: sphingosine-1-phosphate, palmitoylethanolamide, oleamide, lysophosphatidic acid P-16:0/0:0, palmitoylglycine, N-acetylleucine, N-heptanoylglycine, 2-phenylacetamide.
The diagnostic marker is a serum marker.
The invention relates to a metabonomics-based normal high-value blood pressure phlegm-dampness accumulation syndrome diagnosis marker screening method, which comprises the following specific steps:
(1) Serum samples were taken: a normal control group, a normal high-value blood pressure phlegm-dampness accumulation syndrome group, centrifuging, taking supernatant, sub-packaging, and analyzing a sample for later use;
(2) Detecting each analysis sample by adopting a liquid chromatography-mass spectrometry technology to obtain original data;
(3) After converting the original data into mzXML format, carrying out peak alignment, correction and noise filtering on the data, carrying out multivariate statistical analysis, and screening differential metabolites with VIP larger than that;
(4) T-test and difference multiple analysis are carried out on the data, and variables with P value smaller than 0.05 and difference multiple larger than or equal to 2 or smaller than 0.05 are screened as potential biomarkers;
(5) For potential biomarkers, determining the corresponding differential metabolites using an HMDB database;
(6) And performing metabolic pathway analysis and ROC analysis on the differential metabolites to obtain potential biomarkers.
The liquid chromatography conditions are as follows: chromatographic column: c18 column, 2.7 μm;100mm by 2.1mm; the flow rate is 0.30mL/min; column temperature: 45 ℃; injector temperature: 15 ℃; sample injection amount: 5. Mu.L; mobile phase a was water containing 0.05% formic acid and mobile phase B was acetonitrile containing 0.05% formic acid, and gradient elution was performed.
The gradient elution procedure is as follows: 0min,97% A,3% B;15min,50% A,50% B;23min,35% A,65% B;23min,97% A,3% B.
The mass spectrum conditions are as follows:
detecting by adopting a positive ion mode and a negative ion mode;
positive ion mode detection conditions: ion source HESI; capillary voltage 3500V; the capillary temperature is 300 ℃; sheath gas 45arb; an assist gas 10arb; the source temperature is 350 ℃; the mass spectrum collecting range is 80-800 m/z; resolution is 70000; S-Lens RF Level is 55;
negative ion mode detection conditions: ion source HESI; capillary voltage 3000V; the capillary temperature is 300 ℃; sheath gas 45arb; an assist gas 10arb; the source temperature is 350 ℃; the mass spectrum collecting range is 80-800 m/z; resolution 70000; the S-Lens RF Level is 55.
The invention obtains potential biological markers which can be used as markers for representing diseases by analyzing two groups of serum data by means of metabonomics technology. Prediction of whether a patient is at risk of developing a disease can further be achieved by these biomarkers. Thus, the biomarkers of the invention can be used to predict a subject's risk. The biomarker can be effectively used as a marker for predicting PHT risk, and has the advantages of accuracy, early stage and high accuracy.
Drawings
FIG. 1 is a total ion flow diagram of a normal high value blood pressure phlegm dampness accumulation syndrome group in a positive ion mode;
FIG. 2 is a total ion flow diagram of a normal control group in positive ion mode;
FIG. 3 is a total ion flow chart of a normal high value blood pressure phlegm dampness accumulation syndrome group in an anion mode;
FIG. 4 is a total ion flow diagram of a normal control group in negative ion mode;
fig. 5 is a multivariate statistical analysis of normal high blood pressure, phlegm-dampness accumulation syndrome group and normal control group in positive ion mode, wherein: a is a PCA scatter diagram of a normal high-value blood pressure phlegm-dampness accumulation syndrome VS normal control group; b is PLS-DA scatter diagram of normal high-value blood pressure phlegm-dampness accumulation syndrome VS normal control group, C is cross verification;
fig. 6 is a multivariate statistical analysis of a normal high value blood pressure phlegm-dampness accumulation syndrome group and a normal control group in negative ion mode, wherein: a is a PCA scatter diagram of a normal high-value blood pressure phlegm-dampness accumulation syndrome VS normal control group; b is PLS-DA scatter diagram of normal high-value blood pressure phlegm-dampness accumulation syndrome VS normal control group, C is cross verification;
FIG. 7 is a constructed cluster heat map;
FIG. 8 is a graph showing the metabolic pathways of biomarkers in serum from normal high blood pressure, phlegm-dampness accumulation syndrome group and normal control group.
Detailed Description
Example 1
1. Basic information of sample
80 blood samples were collected, 40 for the normal control group (group C) and 40 for the normal high blood pressure and phlegm-dampness accumulation group (PHT group).
2. Apparatus and method
2.1 laboratory apparatus and reagents
Instrument:
UltiMate 3000 ultra high liquid chromatograph, company Thermo Fisher scientific, U.S.; q-exact quaternary rod-electrostatic field orbitrap high resolution mass spectrometer, company Thermo Fisher scientific, U.S.; high speed centrifuge, company Thermo Fisher Scientific; MX-S vortex mixer, shanghai long philosophy Co.
The total cholesterol determination kit is U82781050; triglyceride measurement kit, batch number U82881050; the kit for measuring the low-density lipoprotein cholesterol comprises a batch number U83085045; the high density lipoprotein cholesterol determination kit has batch number U82985045; are all available from Gui Lin Youli Tex medical electronics Inc.
Reagent:
acetonitrile, zemoeimer sciences, LOT 219087; formic acid, zemoeimer sciences LOT 186935; ultrapure water, chinese drohens limited GB 19298.
2.2 pretreatment of blood samples
Centrifuging the blood sample at 3000r 4 deg.C for 15min, collecting supernatant, packaging, and standing at-80deg.C.
2.3 detection of serum samples by LC-MS to obtain raw data
Sample preparation
200 μl of acetonitrile was added to 100 μl of serum sample, and vortexed for 2min; centrifuging at 4deg.C and 13000r/min (centrifugal radius=95mm) for 15min, collecting supernatant, nitrogen blowing in new Ep tube, re-separating with initial mobile phase 100 μl (acetonitrile: water/1:1), and placing the filter membrane in liquid phase vial; in addition, a Quality Control sample (Quality Control/QC) is set for detecting the stability of the whole system; all serum samples were mixed in equal amounts of 100 μl each to obtain quality control samples. The whole system was equilibrated by loading 6 QC samples prior to sample analysis. Then, 1 QC sample was inserted for detection every interval of about 6 samples to be detected for evaluation of stability and reproducibility of data.
Liquid chromatography conditions:
chromatographic column: c18 column (2.7 μm;100 mm. Times.2.1 mm); the flow rate is 0.30mL/min; column temperature: 45 ℃; injector temperature: 15 ℃; sample injection amount: 5. Mu.L; a mobile phase. Mobile phase a was water (0.05% formic acid); b is acetonitrile (containing 0.05% formic acid); the gradient elution procedure was: 0min,97% A,3% B;15min,50% A,50% B;23min,35% B,65% B;23min,97% A,3% B.
Mass spectrometry conditions: detecting by adopting a positive ion mode and a negative ion mode;
positive ion mode detection conditions: ion source HESI; capillary voltage 3500V; the capillary temperature is 300 ℃; sheath gas 45arb; an assist gas 10arb; the source temperature is 350 ℃; the mass spectrum collecting range is 80-800 m/z; resolution is 70000; S-Lens RF Level is 55;
negative ion mode detection conditions: ion source HESI; capillary voltage 3000V; the capillary temperature is 300 ℃; sheath gas 45arb; an assist gas 10arb; the source temperature is 350 ℃; the mass spectrum collecting range is 80-800 m/z; resolution 70000; the S-Lens RF Level is 55. The potential biomarker ions are further subjected to MS/MS analysis, and collision energy is automatically optimized according to ion conditions.
2.4 data processing
Differential metabolite analysis
Identifying and analyzing differential metabolites using non-targeted metabolomics; converting data acquired by a mass spectrometer into an 'mz XML' format through MS conversion software, analyzing the converted data by using R language, and carrying out peak alignment, peak matching, noise filtering and retention time qualitative on the data, so as to finally obtain information comprising mass-to-charge ratio, retention time and peak intensity; importing the data into an Excel table for processing, and adopting 80% rule to complement the missing value; multivariate statistical analysis was performed on the data using SIMCA 13.0 software, and the separation trend of the two sets of data was observed by unsupervised principal component analysis (principal component analysis, PCA). Obtaining more reliable inter-group difference and correlation degree information of the two groups through supervised partial least squares discriminant analysis (partial least square discriminant analysis, PLS-DA); and screening the variable projection importance (variable importance for the projection, VIP) of the first main component for a standard with greater than 1 to find differential metabolites.
Data were subjected to T-test, fold change analysis using Mass Profiler Professional software, screening for variables with P values less than 0.05, fold change greater than or equal to 2, or less than 0.05 as potential biomarkers.
The HMDB database is used for determining corresponding differential metabolites by referring to the related literature and combining primary and secondary mass spectrum information.
Metabolic pathway analysis
Differential metabolites screened in serometabonomics data were introduced into metanoanalysis, metanoanalysis 5.0 for thermography and metabolic pathway analysis.
ROC analysis
To further analyze the screened differential metabolites, sensitivity and specificity of each differential metabolite were calculated using MedCalc, subject operating characteristics (ROC) curves of subjects were constructed, and areas under ROC curves (AUC) were compared to screen for differential metabolites.
2.5 data analysis
Serum metabonomics profiling
Total ion flowsheets were obtained to show metabonomic features of PHT and healthy controls; the total ion flow diagrams in the two groups of positive and negative modes are shown in fig. 1-4, and the difference between the two groups can be seen.
Serometabonomic analysis
The invention is based on a serum sample collected by UPLC-Q-E/MS; the metabonomics research adopts a multivariate statistical analysis method such as PCA, PLS-DA, substitution test and the like; the number of hypothesis tests was set to 200.
The experimental data are subjected to PCA analysis to observe the overall separation trend of samples among groups, and as can be seen from PCA score graphs of fig. 5 and 6, no overlapping phenomenon occurs between the normal high-value blood pressure group samples and the normal group samples in the positive and negative ion modes, which indicates that the normal high-value blood pressure group and the normal group have obvious separation trend.
PLS-DA analysis was performed on group C and PHT, and the results are shown in FIG. 5 and FIG. 6, the group C and PHT can be well separated, and the samples in the group have good aggregation in a certain range, which indicates that there is obvious differential metabolite between the group C and PHT. And carrying out substitution test on the data model for 200 times, and finding that R2 and Q2 values are smaller than the right end, wherein the R2 and Q2 values are respectively smaller than the right end, the R2 and Q2 values are higher than the R2 and Q2 values, and the intercept between a regression line and a longitudinal axis of the R2 and Q2 values is a negative value, so that the model is effective and reliable, and no fitting phenomenon is generated.
3. Identification of differential metabolites
According to pre-VIP screening variables of the PLS-DA model, taking VIP >1 as a standard, screening 375 compounds altogether, introducing screened data into MPP for t test and FC analysis, and judging differential variables meeting the conditions of log2FC not less than 1 and P < 0.05 as potential biomarkers for identification; determining the molecular formula of the compound according to the retention time, the accurate mass and the ion addition form in the peak list; the number of fragments was retrieved from the HMDB database and compared with the secondary fragments of the original data, and metabolites were screened and identified, and 20 different metabolites were screened out in total, as shown in table 1.
TABLE 1 potential differential metabolite information for PHT and C patients
4. Thermal map analysis
A cluster heat map was constructed using metaanalysis 5.0 (fig. 7) which was able to characterize the relative content of each component to analyze the identified differential metabolites. The dark red color indicates a relatively high content. Dark blue indicates a relatively low content.
5. Metabolic pathway analysis
Metabolic pathway analysis was performed on the screened potential biomarkers using the metanolanalytics 5.0 website, and the analysis results are shown in fig. 8.
| No. | Pathway Name | | Match Status | |
| 1 | Sphingolipid metabolism | 0.1785 | 2/21 | |
| 2 | Tyrosine metabolism | 0.12394 | 3/42 | |
| 3 | Arginine and proline metabolism | 0.11063 | 1/38 | |
| 4 | Arginine biosynthesis | 0.0691 | 1/28 |
6. Diagnostic properties of differential metabolites
The diagnostic value of 20 differential metabolites was evaluated using ROC curves, with AUCs between 0.5 and 0.7 being generally considered low, AUCs between 0.7 and 0.9 being medium and AUCs above 0.9 being high. Of these, 8 (40%) differential metabolites, AUC >0.9, have high diagnostic value, including sphingosine-1-phosphate, palmitoylethanolamide, oleamide, lysophosphatidic acid (P-16:0/0:0), palmitoylglycine, N-acetylleucine, N-heptanoylglycine, 2-phenylacetamide. Due to the small sample size, the sensitivity and specificity of the 8 potential biomarker combinations were 100% and 100%, respectively.
In summary, the present application shows that the serum metabolic profile of PHT patients is significantly different from healthy subjects, sphingosine-1-phosphate, palmitoylethanolamide, oleamide, lysophosphatidic acid (P-16:0/0:0), palmitoylglycine, N-acetylleucine, N-heptanoylglycine, 2-phenylacetamide are potential biomarkers, and that the combined diagnostic effect of 8 potential biomarkers is good. The invention is helpful for deeply discussing and providing reference for the related research of treating the normal high-value blood pressure phlegm-dampness accumulation.
Claims (6)
1. The diagnosis marker for the normal high-value blood pressure phlegm-dampness accumulation syndrome based on metabonomics is characterized in that the marker is any one or more of the following metabolic markers: sphingosine-1-phosphate, palmitoylethanolamide, oleamide, lysophosphatidic acid P-16:0/0:0, palmitoylglycine, N-acetylleucine, N-heptanoylglycine, 2-phenylacetamide.
2. The metabonomics-based normal high value blood pressure and phlegm-dampness accumulation syndrome diagnostic marker of claim 1, wherein the diagnostic marker is a serum marker.
3. The metabonomics-based normal high-value blood pressure phlegm-dampness accumulation syndrome diagnosis marker screening method is characterized by comprising the following specific steps:
(1) Serum samples were taken: a normal control group, a normal high-value blood pressure phlegm-dampness accumulation syndrome group, centrifuging, taking supernatant, sub-packaging, and analyzing a sample for later use;
(2) Detecting each analysis sample by adopting a liquid chromatography-mass spectrometry technology to obtain original data;
(3) After converting the original data into mzXML format, carrying out peak alignment, correction and noise filtering on the data, carrying out multivariate statistical analysis, and screening differential metabolites with VIP larger than that;
(4) T-test and difference multiple analysis are carried out on the data, and variables with P value smaller than 0.05 and difference multiple larger than or equal to 2 or smaller than 0.05 are screened as potential biomarkers;
(5) For potential biomarkers, determining the corresponding differential metabolites using an HMDB database;
(6) And performing metabolic pathway analysis and ROC analysis on the differential metabolites to obtain potential biomarkers.
4. The method for screening a diagnosis marker for normal high-value blood pressure and phlegm-dampness accumulation syndrome based on metabonomics according to claim 3, wherein the liquid chromatography conditions are as follows: chromatographic column: c18 column, 2.7 μm;100mm by 2.1mm; the flow rate is 0.30mL/min; column temperature: 45 ℃; injector temperature: 15 ℃; sample injection amount: 5. Mu.L; mobile phase a was water containing 0.05% formic acid and mobile phase B was acetonitrile containing 0.05% formic acid, and gradient elution was performed.
5. The method for screening normal high value blood pressure phlegm dampness accumulation syndrome diagnostic markers based on metabonomics according to claim 4, wherein the gradient elution procedure is as follows: 0min,97% A,3% B;15min,50% A,50% B;23min,35% A,65% B;23min,97% A,3% B.
6. The metabonomics-based screening method for normal high value blood pressure and phlegm dampness accumulation syndrome diagnostic markers according to claim 4, wherein mass spectrometry conditions are:
detecting by adopting a positive ion mode and a negative ion mode;
positive ion mode detection conditions: ion source HESI; capillary voltage 3500V; the capillary temperature is 300 ℃; sheath gas 45arb; an assist gas 10arb; the source temperature is 350 ℃; the mass spectrum collecting range is 80-800 m/z; resolution is 70000; S-LensRFlevel is 55;
negative ion mode detection conditions: ion source HESI; capillary voltage 3000V; the capillary temperature is 300 ℃; sheath gas 45arb; an assist gas 10arb; the source temperature is 350 ℃; the mass spectrum collecting range is 80-800 m/z; resolution 70000; S-LensRFlevel is 55.
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| CN117147812A (en) * | 2023-10-26 | 2023-12-01 | 中日友好医院(中日友好临床医学研究所) | Sphingolipid metabolism marker as well as analysis method and application thereof |
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| CN117147812A (en) * | 2023-10-26 | 2023-12-01 | 中日友好医院(中日友好临床医学研究所) | Sphingolipid metabolism marker as well as analysis method and application thereof |
| CN117147812B (en) * | 2023-10-26 | 2024-01-16 | 中日友好医院(中日友好临床医学研究所) | Sphingolipid metabolism marker as well as analysis method and application thereof |
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