Urine biomarker for yin deficiency syndrome of hypertension and application of urine biomarker

Abstract

The invention discloses a urine biomarker for yin deficiency syndrome of hypertension and application thereof, and particularly relates to the field of clinical examination and diagnosis application. A non-targeted metabonomics technology is combined with a machine learning algorithm to construct a diagnosis model, the difference of urine differential metabolites of a patient with the hypertension yin deficiency syndrome and a patient with the non-yin deficiency syndrome is explored, and the urine biomarker of the hypertension yin deficiency syndrome is determined. The biomarker screening method is high in operability, the model construction method is simple and convenient, and the obtained diagnosis model is excellent in effect, high in sensitivity and excellent in specificity, is suitable for early diagnosis of yin deficiency syndromes and non-yin deficiency syndromes of hypertension, and has very high clinical application and popularization values.

Description

Technical Field

[0001] The present invention relates to the application field of clinical testing and diagnosis, and in particular to a urine biomarker for Yin deficiency syndrome of hypertension and application thereof. Background Art

[0002] Hypertension is the leading preventable risk factor for cardiovascular disease and all-cause mortality worldwide. According to a cross-sectional study of 1 million adults with global representation, the prevalence of hypertension in low- and middle-income countries (LMICs) has increased significantly, which in turn leads to blood pressure-related cardiovascular disease and kidney disease, further exacerbating the health and financial burden. The TCM "syndrome" is an analysis of the combination of symptoms or clinical phenotypes, and is one of the most important concepts in TCM theory and clinical research. It not only establishes a pathway for disease diagnosis, but also conducts subtype analysis of the disease and provides a basis for individualized treatment. Understanding the "syndrome" is crucial to establishing disease classification, diagnostic criteria and effective treatment strategies for patients. Based on literature and a large number of clinical studies, the core pathogenesis of hypertension is generally the deficiency of yin fluid, and yin cannot control yang. Yin deficiency runs through the evolution of hypertension syndrome.

[0003] Compared with other omics, metabolomics is a newly developed discipline to study metabolic profiles. Metabolites are the final downstream products of the body's metabolism and are therefore most closely associated with the body's phenotype. In recent years, there has been some progress in the study of hypertension using metabolomics technology. Broadly speaking, the intestinal metabolite profile is derived from diet, modified human metabolites, and microbial-derived compounds of microbiota-host interactions. Urine non-targeted metabolomics is a new tool to explore the relationship between microbial composition and host phenotype. As far as we know, no scholar has yet distinguished and explored the biological basis of different TCM syndromes of hypertension from the perspective of urine metabolomics. This study used non-targeted metabolomics technology for the first time to identify intestinal metabolites in patients with Yin deficiency syndrome of hypertension, and further revealed its possible biological markers in order to provide support and basis for the objectification of TCM syndrome differentiation of Yin deficiency syndrome of hypertension. Summary of the invention

[0004] The purpose of the present invention is to provide a urine biomarker for Yin deficiency syndrome of hypertension and its application. The present invention uses non-targeted metabolomics technology to determine the difference in urine 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 urine 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 present invention provides the use of a metabolic marker in preparing a reagent for evaluating Yin deficiency syndrome of hypertension. The metabolic marker is one or a combination of 7-methylxanthine, genistein, creatinine and azelaic acid.

[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 urine.

[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. 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:

[0013] Urine non-targeted metabolomics LC-MS detection was used to determine that one or a combination of six metabolites 7-methylxanthine, genistein, creatinine, and azelaic acid, which had different contents between Yin deficiency syndrome and non-Yin deficiency syndrome of hypertension, were used as predictors, and four machine learning models were used to construct a prediction model for the differential metabolite data of the discovery set.

[0014] Furthermore, the four machine learning models are Naive Bayes, Random Forest, Support Vector Machine and Xgboost.

[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 the evaluation method is less studied, 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 urine biomarkers, and establishes a urine 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 only needs to complete the diagnosis by taking urine for 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 Scatter plots of principal component analysis (PCA) scores of HY and HNY urine metabolites and QC samples in the positive ion mode.

[0018] Figure 2 Figure 2 is a scatter plot of principal component analysis (PCA) scores of HY and HNY urine metabolites and QC samples in negative ion mode.

[0019] Figure 3 This is the result of OPLS-DA analysis of HY and HNY urine metabolites in the positive ion mode.

[0020] Figure 4 This is the result of OPLS-DA analysis of HY and HNY urine metabolites in negative ion mode.

[0021] Figure 5 The volcano plot of differential metabolites between HY and HNY urine in the cationic mode.

[0022] Figure 6 The volcano plot of differential metabolites between HY and HNY urine in negative ion mode.

[0023] Figure 7 Figure 2 shows the pathway diagram of differential metabolite enrichment in urine of HY and HNY.

[0024] Figure 8 The ROC curves of the differential metabolites in urine of the test set HY and HNY are shown in Figure 1. Among them, A is genistein, B is creatinine, C is azelaic acid, and D is 7-methylxanthine.

[0025] Fig. 9 The ROC curves of four machine learning algorithms were compared based on the discovery set data.

[0026] Fig.10 To screen out 4 markers, the ROC curve of the naive Bayes algorithm was used on the validation set.

[0027] Fig.11 The ROC curves of the four markers screened out on the validation set are shown below. A is 7-methylxanthine, B is genistein, C is creatinine, and D is azelaic acid. 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] 40 patients with hypertension were included, including 13 patients with Yin deficiency syndrome (HY) and 27 patients with non-Yin deficiency syndrome (HNY) as the test set. 82 patients with hypertension were also included as the validation set, including 52 HY and 30 HNY. Urine samples were collected, centrifuged and supernatant was obtained, and stored at -80℃. Urine 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. Non-targeted metabolomics (LC-MS) detection of urine.

[0038] (1) Thaw the sample slowly on ice;

[0039] (2) Sampling. Place 100 μL of urine 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 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. Urine 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 , 2), indicating that the urine metabolomics experimental process is stable and the quality control effect is good.

[0052] 2. Analysis of urine metabolomics profile characteristics of Yin deficiency syndrome in hypertension.

[0053] As a conventional dimensionality reduction analysis method, PCA only focuses on the differences in metabolites themselves when performing dimensionality reduction, rather than the groups in which the samples are grouped. In order to further find out the differential metabolites between the two groups and find out the specific metabolites of HY, we continued to use supervised OPLS-DA for dimensionality reduction analysis. The figure shows that the two groups can be clearly separated, indicating that there are differences in urine metabolites between HY and HNY ( Figure 3 ,4).

[0054] We further selected metabolites with a P-value of Student's t-test < 0.05 and a variable projection importance of the first principal component of the OPLS-DA model > 1 as differential metabolites in urine of patients with hypertension and Yin deficiency, and displayed them using a volcano plot ( Figure 5 ,6).

[0055] In order to further explore the metabolic functions involved in the above-mentioned differential metabolites, this study clustered the metabolite functions using the MetaboAnalyst5.0 platform and found that the most relevant pathways were the biosynthesis of phenylalanine, tyrosine and tryptophan, tyrosine metabolism, biosynthesis of ubiquinone and other terpenoid quinones, phenylalanine metabolism, glycerophospholipid metabolism, steroid hormone biosynthesis, etc. ( Figure 7 ).

[0056] To further identify potential biomarkers in the urine of patients with Yin deficiency syndrome of hypertension and evaluate their performance, metabolites with Fold Change ≥ 2 or ≤ 0.5 were further screened and univariate ROC curves were drawn ( Figure 8 ).

[0057] 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 0.81, 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 0.837, indicating that urine differential metabolites have a good distinguishing effect on HY and HNY.

[0058] Conclusion: The urinary metabolite compositions of patients with Yin deficiency syndrome of hypertension and those without Yin deficiency syndrome of hypertension are significantly different. The naive Bayesian model constructed by one or several combinations of 7-methylxanthine, genistein, creatinine and azelaic acid can effectively distinguish HY and HNY patients and can be used as HY urine biomarkers.

[0059] 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 7-methylxanthine, genistein, creatinine, and azelaic acid.

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 selected from urine.

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 hypertension 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: The urine non-targeted metabolomics LC-MS test determined that one or a combination of four metabolites, 7-methylxanthine, genistein, creatinine, and azelaic acid, which had different contents between 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.

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