Hypertension yin deficiency syndrome blood biomarker and application thereof
Through the combination of non-targeted metabolomics technology and machine learning algorithms, blood biomarkers for hypertension with yin deficiency syndrome are screened out, which solves the problem of the lack of standardized diagnostic methods in the existing technology and realizes the identification of hypertension with yin deficiency syndrome. Early auxiliary diagnosis has high clinical application value.
Patent Information
- Application Number
- CN202411392068.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-08
- Publication Date
- 2025-01-24
AI Technical Summary
The existing technology lacks standardized diagnostic methods and evaluation methods, which seriously affects the clinical and scientific research work of yin deficiency syndrome in hypertension.
Using non-targeted metabolomics technology combined with machine learning algorithms, we determined the differences in serum metabolites between patients with hypertension with Yin deficiency syndrome and non-Yin deficiency syndrome, built a diagnostic model, and screened out 4-pyridoxic acid and D-malic acid. , monoethylhexyl phthalate and N-acetylalanine and other blood biomarkers.
The early auxiliary diagnosis of yin deficiency syndrome in hypertension has been achieved. The diagnostic model has high sensitivity and excellent specificity. It is suitable for distinguishing yin deficiency syndrome and non-yin deficiency syndrome in hypertension and has high clinical application value.
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Figure CN119360939A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the application field of clinical testing and diagnosis, and in particular to a blood biomarker for deficiency syndrome of hypertension and application thereof. Background Art
[0002] With the acceleration of the modernization of traditional Chinese medicine, the standardization of traditional Chinese medicine syndromes has become a key research area. Conducting objective, systematic and innovative research on basic syndromes and major and chronic diseases has become a major strategic need for promoting the standardization of traditional Chinese medicine. Although the biological basis of different syndromes of hypertension are currently being studied at the molecular level, the current problems of inconsistent syndrome standards, low standardization, and relatively isolated research levels have restricted further in-depth research on the biological basis of hypertension syndromes. Yin deficiency syndrome is the core basic syndrome of hypertension. The advent of the era of systems biology has brought a new direction and turning point for deciphering the biological basis of Yin deficiency syndrome in hypertension.
[0003] In recent years, many scholars have used metabolomics technology to conduct research on traditional Chinese medicine treatment, syndrome identification, and the biological basis of syndromes. Blood is the most important body fluid of the human body and is a biological sample commonly used in metabolomics testing. Since the blood components (metabolites) of the human body may be able to promote and change the progression of the disease, it is of great significance to identify disease-related biomarkers and metabolomics spectra from the blood. Serum is a biological sample obtained when whole blood is coagulated. Since the concentration of most metabolites is usually higher in serum, it is also considered a metabolite-rich matrix. Therefore, serum metabolomics has been widely used in research. This study used non-targeted metabolomics technology to detect the biological markers of serum metabolic levels in patients with Yin deficiency syndrome of hypertension, revealing the serum metabolic characteristics and biological markers of Yin deficiency syndrome of hypertension. Summary of the invention
[0004] The purpose of the present invention is to provide a blood biomarker for deficiency syndrome of hypertension and its application. The present invention uses non-targeted metabolomics technology to determine the difference in serum metabolites between patients with Yin deficiency syndrome of hypertension and those without Yin deficiency syndrome, and combines machine learning algorithms to build a diagnostic model to determine the blood biomarker for Yin deficiency syndrome of hypertension.
[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions.
[0006] The invention discloses an early auxiliary diagnosis marker for Yin deficiency syndrome of hypertension, characterized in that the marker is one or a combination of 4-pyridoxalic acid, D-malic acid, monoethylhexylphthalic acid, and N-acetylalanine.
[0007] Furthermore, the above-mentioned early auxiliary diagnosis marker for Yin deficiency syndrome of hypertension is used in the preparation of early auxiliary diagnosis products for Yin deficiency syndrome of hypertension.
[0008] The present invention also discloses the use of a substance for detecting the above-mentioned early auxiliary diagnosis marker of hypertension Yin deficiency syndrome in the preparation of an early auxiliary diagnosis product of hypertension Yin deficiency syndrome.
[0009] Furthermore, the substance is a substance used to detect the content of a diagnostic marker selected from serum, plasma, and blood.
[0010] Furthermore, the substance is an instrument and / or reagent required for an LC-MS / MS system combined quadrupole Orbitrap mass spectrometer for detecting early auxiliary diagnostic markers of Yin deficiency syndrome in hypertension.
[0011] The present invention also discloses a kit for early auxiliary diagnosis of Yin deficiency syndrome of hypertension, which is characterized in that the kit contains substances for detecting the markers for early auxiliary diagnosis of Yin deficiency syndrome of hypertension mentioned above.
[0012] Furthermore, the kit also includes a carrier recording the model or a device loaded with the model.
[0013] The present invention also discloses a method for constructing a diagnosis model of Yin deficiency syndrome of hypertension, which is characterized by comprising the following steps:
[0014] Serum non-targeted metabolomics LC-MS detection was used to determine that one or a combination of four metabolites, 4-pyridoxalic acid, D-malic acid, monoethylhexylphthalic acid, and N-acetylalanine, which had different contents in Yin deficiency syndrome and non-Yin deficiency syndrome of hypertension, were used as predictors, and four machine learning models were used to construct prediction models for the differential metabolite data of the discovery set.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows.
[0016] The prior art does not have a standardized diagnostic technology for Yin deficiency syndrome in hypertension, and there is little research on the evaluation method, which seriously affects the development of clinical and scientific research work in traditional Chinese medicine. The present invention combines non-targeted metabolomics with machine learning algorithms for the first time for patients with Yin deficiency syndrome and non-Yin deficiency syndrome in hypertension, determines blood biomarkers, and establishes a blood sample diagnostic model. The biomarker screening method of the present invention is highly operational, the model construction method is simple, the obtained diagnostic model has excellent effect, high sensitivity, and excellent specificity, and is suitable for the early diagnosis of Yin deficiency syndrome and non-Yin deficiency syndrome in hypertension. The present invention can complete the diagnosis only by blood sampling and testing, without the need for additional tissue sample collection, which greatly reduces the risk of trauma. The model disclosed in the present invention is for patients with Yin deficiency syndrome and non-Yin deficiency syndrome in hypertension, and the diagnostic process is simple and rapid, which is conducive to timely treatment and has high clinical application and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 Figure 2 is a scatter plot of principal component analysis (PCA) scores of HY and HNY serum metabolites and QC samples in the positive ion mode.
[0018] Figure 2 Scatter plots of principal component analysis (PCA) scores of HY and HNY serum metabolites and QC samples in negative ion mode.
[0019] Figure 3 This is the result of OPLS-DA analysis of HY and HNY serum metabolites in the positive ion mode.
[0020] Figure 4 This is the result of OPLS-DA analysis of HY and HNY serum metabolites in negative ion mode.
[0021] Figure 5 The volcano plot of differential metabolites between HY and HNY serum in the positive ion mode.
[0022] Figure 6 The volcano plot of differential metabolites between HY and HNY serum in negative ion mode.
[0023] Figure 7 Figure 2 shows the pathway diagram of differential metabolite enrichment between HY and HNY serum.
[0024] Figure 8 The ROC curves of the differential metabolites in the test set HY and HNY serum, where A is monoethylhexylphthalic acid, B is D-malic acid, C is 4-pyridoxalic acid, and D is N-acetylalanine.
[0025] Fig. 9 The ROC curves of four machine learning algorithms were compared based on the discovery set data.
[0026] Fig.10 ROC curve of the validation set using the naive Bayes algorithm to screen out four markers.
[0027] Fig.11 The ROC curves of the four markers screened out on the validation set are shown below. A is 4-pyridoxalic acid, B is D-malic acid, C is monoethylhexylphthalic acid, and D is N-acetylalanine. DETAILED DESCRIPTION
[0028] The present invention is further described in detail below with specific embodiments, but it should not be understood that the scope of the above subject matter of the present invention is limited to the following embodiments, and all technologies realized based on the content of the present invention belong to the scope of the present invention.
[0029] Unless otherwise specified, all reagents and materials used in the present invention are commercially available.
[0030] 1. Methods and reagents.
[0031] 1. Sample collection.
[0032] A total of 79 patients with hypertension were included, including 51 patients with Yin deficiency syndrome (HY) and 28 patients with non-Yin deficiency syndrome (HNY) as the discovery set. A total of 40 patients with hypertension were included as the validation set, including 13 HY and 27 HNY. Venous blood was drawn from the subjects in an inert separation gel coagulation tube in the fasting state to collect blood samples. After the blood collection tube was left to stand at 20℃-25℃ for 20-30min, it was centrifuged at 1800Xg at room temperature for 10min to obtain serum samples, which were stored at -80℃. Serum samples were used for non-targeted metabolomics research.
[0033] 2. Quality control.
[0034] The QC before metabolomics injection is used to monitor the instrument status before injection and balance the chromatography-mass spectrometry system. The QC inserted in the middle of the sample detection is used to evaluate the system stability during the entire experiment and perform sample correlation analysis. The QC after the sample detection is completed is used for qualitative analysis of metabolites through secondary mass spectrometry. (Note: QC samples are made by mixing equal amounts of the samples to be tested, and are tested on the machine before, during, and after the LC-MS / MS injection of the samples to be tested.
[0035] 3. Main reagents and instruments.
[0036]
[0037] 4. Serum non-targeted metabolomics (LC-MS) detection.
[0038] (1) Thaw the sample slowly on ice;
[0039] (2) Sampling. Place 100 μL of serum sample in a 1.5 mL centrifuge tube, add 300 μL of methanol, and sonicate at 4°C for 30 min.
[0040] (3) Incubate at -40 °C for 1 h, vortex for 30 s, and incubate at 4 °C for 0.5 h;
[0041] (4) Centrifugation at 12,000 rpm for 15 min at 4°C;
[0042] (5) Take all the supernatant and place it in a centrifuge tube at -40°C for 1 h;
[0043] (6) Centrifuge at 4°C, 12,000 rpm for 15 min; aspirate the supernatant, transfer 200 μL of the supernatant, add 5 μL of the internal standard (0.14 mg / mL dichlorophenylalanine), mix well, and transfer to a sample injection vial.
[0044] (7) On-machine determination. Instrument analysis platform: LC-MS (Waters, UPLC; Thermo, Q Exactive). Chromatographic column parameters: (ACQUITY UPLC HSS T3 (2.1*100mm 1.8μm)). Chromatographic separation conditions: flow rate 0.3mL / min, mobile phase A and mobile phase B are water + 0.05% formic acid and acetonitrile, respectively.
[0045] 5. Statistical methods.
[0046] First, the original file (.raw) obtained by mass spectrometry detection was imported into Compound Discoverer3.1 (CD) software for spectrum processing and database search to obtain the qualitative and quantitative results of metabolites, and then the data was quality controlled to ensure the accuracy and reliability of the data results. Next, multivariate statistical analysis of metabolites was performed, including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), etc., to reveal the differences in metabolites in different groups, and metabolite correlation analysis was used to reveal the relationship between samples and between metabolites.
[0047] 6. Construction of machine learning algorithms.
[0048] Use R (4.3.1) and RStudio (2023.09.0Build 463), use the mlr3verse (0.2.8) package to call the random forest algorithm package ranger (0.15.1), and use the ggplot2 (3.4.3) package to visualize the results.
[0049] 2. Experimental results.
[0050] 1. Serum metabolomics quality control.
[0051] QC is used to monitor the instrument status before injection and the quality control of the balanced chromatography-mass spectrometry system. It is inserted in the middle of the sample detection to evaluate the system stability during the entire experiment. The PCA diagram shows that QC clusters well ( Figure 1 ), indicating that the quality control effect of serum metabolomics is better.
[0052] 2. Analysis of serum metabolomics profile characteristics of Yin deficiency syndrome in hypertension.
[0053] The present invention uses serum non-targeted metabolomics (LC-MS) to explore specific metabolites in the serum of patients with Yin deficiency syndrome of newly diagnosed hypertension who have not received drug intervention. The two groups of metabolic data are further corrected by filtering out irrelevant orthogonal variables through OPLS-DA analysis. It can also be seen from the results of OPLS-DA analysis that the serum metabolomics profiles of the HY group and the HNY group are also significantly distinguished ( Figure 2 ).
[0054] 3. Differential metabolic pathways in serum of patients with Yin deficiency syndrome in hypertension.
[0055] MetaboAnalyst5.0 platform was used to perform differential metabolite pathway enrichment analysis. Finally, the key pathways with the highest correlation with serum differential metabolites in patients with hypertension with Yin deficiency syndrome were found, including phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, glycerophospholipid metabolism, glycerolipid metabolism, ubiquinone and other terpenoid-quinone biosynthesis, etc. Figure 7 .
[0056] 4. Screening of serum biomarkers in patients with Yin deficiency syndrome of primary hypertension.
[0057] The present invention adopts a method combining multivariate statistical analysis with univariate statistical analysis to screen serum differential metabolites of patients with hypertension and Yin deficiency syndrome. The metabolites with P-value < 0.05 of Student's t-test and variable projection importance VIP > 1 of the first principal component of the OPLS-DA model are selected as serum differential metabolites of hypertension and Yin deficiency syndrome, and are displayed by volcano plot ( Figure 5 ,6). In order to obtain stable and reliable serum markers for patients with Yin deficiency syndrome of essential hypertension, the same screening method was used for the validation set, and finally the differential metabolites detected by the intersection of the test set and the validation set were taken.
[0058] In order to further identify potential biomarkers in the serum of patients with Yin deficiency syndrome of hypertension and evaluate their performance, this study used classic univariate ROC curve analysis and multivariate ROC curve analysis based on random forest for evaluation. The AUC value is the area under the ROC curve, which is used to determine the performance of the model. Its value is between 0.5 and 1. The larger the value, the better the performance of the model. We found that 4 metabolites showed significant differences in both the test set and the validation set, and their sensitivity and specificity were good, including 4-pyridoxalic acid, D-malic acid, monoethylhexylphthalic acid, and N-acetylalanine.
[0059] Next, four machine learning models were used to model the differential metabolite data of the discovery set, including Naive Bayes, Random Forest, Support Vector Machine, and Xgboost. The results showed that the AUC of the ROC curve of the Naive Bayes model was the highest, reaching 0.8, and it was the best model among the four algorithms. The above results also showed that the model constructed by combining the four metabolites was better than the AUC of a single model. We independently verified the optimal Naive Bayes model on the validation set, and the results showed that the AUC of the ROC curve reached 0.88, indicating that serum differential metabolites have a good distinguishing effect on HY and HNY.
[0060] Conclusion: The serum metabolite compositions of patients with Yin deficiency syndrome and those without Yin deficiency syndrome are significantly different. The naive Bayes model constructed by 4-pyridoxalic acid, D-malic acid, monoethylhexylphthalic acid and N-acetylalanine can effectively distinguish HY and HNY patients and can be used as HY blood biomarkers.
[0061] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. An early auxiliary diagnostic marker for Yin deficiency syndrome of hypertension, characterized by: The marker is one or a combination of 4-pyridoxalic acid, D-malic acid, monoethylhexylphthalic acid, and N-acetylalanine.
2. Use of the marker for early auxiliary diagnosis of Yin deficiency syndrome in hypertension according to claim 1 in the preparation of a product for early auxiliary diagnosis of Yin deficiency syndrome in hypertension.
3. Use of a substance for detecting the marker for early auxiliary diagnosis of Yin deficiency syndrome in hypertension as claimed in claim 1 in the preparation of a product for early auxiliary diagnosis of Yin deficiency syndrome in hypertension.
4. The use according to claim 3, characterized in that: The substance is a substance used to detect the content of diagnostic markers in serum, plasma and blood.
5. The use according to claim 4, characterized in that: The substances are instruments and / or reagents required for an LC-MS / MS system combined quadrupole Orbitrap mass spectrometer for detecting early auxiliary diagnostic markers of Yin deficiency syndrome of hypertension.
6. A kit for early auxiliary diagnosis of Yin deficiency syndrome in hypertension, characterized by: The kit contains substances for detecting the early auxiliary diagnosis marker of Yin deficiency syndrome of hypertension as claimed in claim 1.
7. The early auxiliary diagnosis kit for Yin deficiency syndrome of Alzheimer's disease according to claim 6, characterized in that: The kit also includes a carrier recording the model or a device loaded with the model.
8. A method for constructing a diagnostic model for Yin deficiency syndrome of hypertension, characterized by: The following steps are involved: Serum non-targeted metabolomics LC-MS detection was used to determine that one or a combination of four metabolites, 4-pyridoxalic acid, D-malic acid, monoethylhexylphthalic acid, and N-acetylalanine, which had different contents in Yin deficiency syndrome and non-Yin deficiency syndrome of hypertension, were used as predictors, and four machine learning models were used to construct prediction models for the differential metabolite data of the discovery set.
9. The construction method according to claim 8, characterized in that: The four machine learning models are Naive Bayes, Random Forest, Support Vector Machine and Xgboost.