CN118483437A

CN118483437A - Biomarker for distinguishing common hypertension from refractory hypertension and application thereof

Info

Publication number: CN118483437A
Application number: CN202410939168.2A
Authority: CN
Inventors: 温清; 赵恒利; 张星海; 张文玉; 徐瑞; 杜建民; 于晓倩
Original assignee: JINAN CENTER HOSPITAL
Current assignee: JINAN CENTER HOSPITAL
Priority date: 2024-07-15
Filing date: 2024-07-15
Publication date: 2024-08-13

Abstract

The invention belongs to the technical fields of disease diagnosis and molecular biology, and particularly relates to a biomarker for distinguishing common hypertension from refractory hypertension and application thereof. According to the invention, by analyzing the differential protein in the plasma samples of the patients with refractory hypertension and common hypertension, a series of biomarkers which can be used for distinguishing refractory hypertension and common hypertension are obtained, and a refractory hypertension disease risk assessment model is further constructed, and experiments prove that the established model can effectively distinguish the patients with common hypertension from the patients with refractory hypertension, and has high model accuracy and good sensitivity, so that the method has good practical application value.

Description

Biomarker for distinguishing common hypertension from refractory hypertension and application thereof

Technical Field

The invention belongs to the technical fields of disease diagnosis and molecular biology, and particularly relates to a biomarker for distinguishing common hypertension from refractory hypertension and application thereof.

Background

Hypertension is a worldwide health problem, while refractory hypertension (RESISTANT HYPERTENSION, RH) is a treatment problem due to its poor response to conventional treatments, as a serious subtype of hypertension, which has been described in detail for the first half century in the medical community. Even with the optimal dosage of three or more antihypertensive drugs, including diuretics, typical renin-angiotensin-aldosterone system (RAAS) inhibitors and calcium channel blockers, RH remains difficult to control, and such hypertension not only increases the risk of cardiovascular events in patients, but also places higher demands on medical resources.

After completion of the human genome project, proteomics has become increasingly critical in the discovery of new and better biomarkers. Because of its excellent performance in validating biomarker candidates and clinical applications, targeted proteomics gave rise to the annual method jackpot in journal 2012 of nature methods. Non-targeted proteomics aims at achieving near-complete proteomic coverage, i.e. recognition and quantification of proteins as much as possible. This change in proteomics can map the occurrence and progression of RH directly and bridge between genetics and metabolomics.

Currently, there are few clinical studies on RH-related biomarkers, and in the diagnosis and treatment process of RH, finding biomarkers and establishing a disease risk model are of great importance, because they enable personalized treatment, early diagnosis, disease progression monitoring, risk assessment, drug response prediction, and promotion of new drug development and research, which help to improve therapeutic effects, reduce medical costs, and ultimately improve the quality of life of patients.

Disclosure of Invention

Aiming at the defects existing in the prior art, the invention provides a biomarker for distinguishing common hypertension from refractory hypertension and application thereof. The invention builds a disease risk assessment model by screening the differential protein of the common hypertension and the refractory hypertension, and proves that the model can effectively distinguish common hypertension patients from refractory hypertension patients, and has high model accuracy and good sensitivity. Based on the above results, the present invention has been completed.

Specifically, the invention relates to the following technical scheme:

in a first aspect of the invention, there is provided a biomarker for distinguishing between normal hypertension and refractory hypertension, the biomarker comprising any one or more of the following proteins:

Q6UXB8（PI16）、P0DJI8（SAA1）、P55056（APOC4）、P68104（EF1A1）、Q13201（MMRN1）、P62328（TYB4）、P55058（PLTP）、P06576（ATPB）、A0A0B4J1V0（HV315）、P06733（ENOA） And P98160 (PGBM).

Further, the biomarker is the group consisting of Q6UXB, P68104, P98160, P62328, and P55056 described above.

In a second aspect of the invention, there is provided the use of an agent for detecting the expression level of a biomarker as described above in the manufacture of a product for distinguishing between normal hypertension and refractory hypertension.

In a third aspect of the present invention, there is provided a system for distinguishing between normal hypertension and refractory hypertension, the system comprising:

i) An analysis module, the analysis module comprising: detection reagents for determining a biomarker in a test sample of a subject;

ii) an evaluation module comprising: assessing the subject for hypertension based on the expression level of the biomarker determined in i);

The specific evaluation flow of the evaluation module in the ii) comprises the following steps: determining the disease condition of the subject based on the risk score of the disease risk assessment model according to the expression level of the biomarker determined in i);

Wherein, the disease risk assessment model has a calculation formula of =1/1+exp (- (-3.814-0.058×q UXB8+0.053×p68104+0.045×p98160+0.071×p62328-0.016×p 5505).

The beneficial technical effects of one or more of the technical schemes are as follows:

according to the technical scheme, the differential protein in plasma samples of patients with refractory hypertension and common hypertension is analyzed to obtain a series of biomarkers which can be used for distinguishing refractory hypertension from common hypertension, and an RH disease risk assessment model is further built, and experiments prove that the built model can effectively distinguish patients with refractory hypertension from crowds of patients with common hypertension, and is high in model accuracy and good in sensitivity, so that the method has good practical application value.

Drawings

FIG. 1 is a predictive evaluation of common hypertension and refractory hypertension based on a modeled dataset in an embodiment of the invention; wherein A is an ROC curve established based on the modeling data set, and B is the disease condition of a specific subject in the modeling data set.

FIG. 2 is a predictive evaluation of common hypertension and refractory hypertension based on a model validation set in an embodiment of the invention; wherein A is an ROC curve established based on a model verification set, and B is the disease condition of a specific subject in the model verification set.

Detailed Description

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof. Experimental methods in the following embodiments, unless specific conditions are noted, are generally in accordance with conventional methods and conditions of molecular biology within the skill of the art, and are fully explained in the literature. See, e.g., sambrook et al, molecular cloning: the techniques and conditions described in the handbook, or as recommended by the manufacturer.

The invention will be further illustrated with reference to specific examples, which are given for the purpose of illustration only and are not to be construed as limiting the invention. If experimental details are not specified in the examples, it is usually the case that the conditions are conventional or recommended by the sales company; materials, reagents and the like used in the examples were commercially available unless otherwise specified.

The term "biomarker" refers to "an objectively detectable and evaluable property that can be used as an indicator of normal biological processes, pathological processes, or pharmacological responses to therapeutic interventions. For example, nucleic acid markers (which may also be referred to as genetic markers, e.g., DNA), protein markers, cytokine markers, chemokine markers, carbohydrate markers, antigen markers, antibody markers, and the like. In the present invention, the biomarker is a protein marker.

The term "kit" as used herein refers to a collection of the above components, preferably provided separately or in a single container. The container also preferably contains instructions for carrying out the method of the invention. Examples of these components of the kit and methods of use thereof have been given in this specification. Preferably, the kit comprises the above components in a ready-to-use formulation. Preferably, the kit may additionally comprise instructions, for example a user manual for adjusting the components (e.g. the concentration of the detection agent) and for interpreting the results of any assays regarding the diagnosis provided by the method of the invention. In particular, such a manual may include information for assigning the determined amount of gene product to the diagnostic type. Details are found elsewhere in this specification. Further, such a user manual may provide instructions for proper use of the kit components for determining the amount of the corresponding biomarker. The user manual may be provided in paper or electronic form (e.g., stored on a CD or CD-ROM). The invention also relates to the use of said kit in any method according to the invention.

In one exemplary embodiment of the invention, a biomarker for distinguishing between normal hypertension and refractory hypertension is provided, the biomarker comprising any one or more of the following proteins:

In yet another embodiment of the present invention, the biomarker is the group consisting of Q6UXB8, P68104, P98160, P62328, and P55056 described above.

Wherein the biomarker is particularly used for distinguishing common hypertension from refractory hypertension.

In yet another embodiment of the present invention, there is provided the use of an agent for detecting the expression level of a biomarker as described above in the preparation of a product for distinguishing between normal hypertension and refractory hypertension.

The reagent may be any reagent known in the art for detecting proteins, and in one embodiment of the present invention, may be a reagent used in a protein quantitative histology analysis method; the method for quantitative proteomic analysis may be a liquid chromatography tandem mass spectrometry method, and is not particularly limited herein.

The product may be a detection kit, a detection device or an apparatus, and is not particularly limited herein.

In yet another embodiment of the present invention, there is provided a system for distinguishing between normal hypertension and refractory hypertension, the system comprising:

ii) an evaluation module comprising: assessing the disease condition of the subject based on the expression level of the biomarker determined in i).

In the analysis module, the sample to be tested is blood, and further is plasma.

The biomarker is selected from any one or more of the following proteins:

The specific evaluation flow of the evaluation module of ii) comprises: judging the disease condition of the subject based on the risk score of the disease risk assessment model according to the expression level of the biomarker determined in i);

In still another embodiment of the present invention, when the calculated cutoff value is 0.5, and the risk score of the subject is equal to or greater than 0.5, determining that the subject has refractory hypertension; and when the risk score of the subject is less than 0.5, judging that the subject is normal hypertension.

The invention is further illustrated by the following examples, which are not to be construed as limiting the invention. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. The proteins related to the biomarker in the invention are all proteins known in the prior art, and the information such as the structural composition, the amino acid sequence and the like can be obtained by inquiring in a UniProt protein database (https:// www.uniprot.org /).

Examples

The test method comprises the following steps:

1. Study protocol

2. Sample collection of refractory hypertension and normal hypertension patients:

(1) Inclusion criteria for Refractory Hypertension (RH) include: ① At least four weeks of treatment, blood pressure levels remain above the target threshold at optimal doses using at least three antihypertensive agents (including a diuretic); or four antihypertensive drugs are needed to reach the target level; ② Age between 18 and 85 years old; ③ The patients provided informed consent and voluntarily participated in the study.

(2) Entry criteria for general hypertensive patients:

① Inclusion criteria: when no antihypertensive drug is taken, the Systolic Blood Pressure (SBP) of the consulting room is more than or equal to 140 mmHg and/or the Diastolic Blood Pressure (DBP) is more than or equal to 90 mmHg; previously diagnosed with Hypertension (HBP) or currently undergoing hypotensive drug therapy; non-refractory hypertensive (non-RH) patients; patients who signed informed consent and voluntarily participated in the study.

② Exclusion criteria: severe cardiac, hepatic and renal insufficiency; patients with acute hypertension and secondary hypertension; pregnant women and lactating women; acute stage of infection; tumors, immune system, hematopoietic system diseases.

(3) All patients participating in the study were asked to begin fasted and water-inhibited 12 late in the middle night. Subsequently, at 6 a.m. on the next day we collected their fasting venous blood. Throughout the sample collection, we strictly adhered to the approval of the ethics committee of the hospital and informed consent was obtained for each patient prior to collection. A purple anticoagulant tube was used as a sample container to accurately collect a 5 milliliter blood sample.

3. Pretreatment before sample analysis: immediately after the blood sample collection, a sample pretreatment is performed. The blood sample is placed in a centrifuge and centrifuged at 4000 rpm for 20 minutes at 4 ℃. After centrifugation, the resulting samples were separated and stored in a-80 ℃ refrigerator.

4. Sample detection: after the plasma sample is subjected to the steps of high abundance protein determination, protein concentration determination, reductive alkylation, enzymolysis, desalination, concentration and the like, proteomic analysis is performed by nano HPLC-MS/MS (Ulimate 3000,3000, Q Exactive Plus, thermo Fisher), and a Label-free data dependent scanning analysis method is used for mass spectrometry.

5. And (3) data processing: the data of the secondary mass spectrum is searched by Proteome Discoverer (v2.4.0.305), the database is homosapiens_9606_PR_20220715. Fasta, the mass error tolerance of the primary parent ion is set to be 10 ppm, the mass error tolerance of the secondary fragment ion is set to be 0.02 Da, the protein expression is calculated according to the mass spectrum peak intensity by Proteome Discoverer (v2.4.0.305) software, and the protein expression is directly introduced into a model for calculation.

6. Screening of differential proteins: for up-and down-regulated proteins we plotted subject working characteristics curves (ROC curves), respectively, and their diagnostic potential was assessed by calculating the area under the curve (AUC). Proteins are classified as differential proteins only if they meet the criteria of AUC values greater than 0.85, sensitivity exceeding 85% and specificity exceeding 85%.

7. Building a disease prediction model: using a stepwise Logistic linear regression analysis method, the expression level of the differentially expressed protein was taken as an independent variable, starting from a model containing all candidate independent variables, and then progressively removing the variable that had the least influence on the response variable. In each step, the Wald test was used to evaluate the significance of each variable. If the p-value of a variable is greater than a preset threshold value of 0.1, it is removed from the model. This process continues until all remaining variables are considered significant.

8. ROC analysis: and (3) using a test subject working characteristic (receiver operating characteristic, ROC) curve analysis and evaluation model to diagnose RH and CH efficacy, grouping test subjects according to model risk scores, specifically, calculating a cutoff value to be 0.5, judging that the test subjects have refractory hypertension when the test subject risk score is more than or equal to 0.5, and judging that the test subjects are normal hypertension when the test subjects are less than 0.5. Classification effects were judged based on the size of the area under the curve (Area under the curve, AUC) of the ROC curve, with AUCs greater than 0.75 being considered to have acceptable classification strength.

9. Model verification: and continuously collecting RH and common hypertension patient samples according to the same incidence and discharge standard, and taking the samples as a verification queue of the model. Protein expression data are obtained through the same processing and are substituted into an established disease prediction model, and model prediction capability is evaluated.

10. Model evaluation index: and respectively calculating each evaluation index of the modeling queue and the model verification queue, wherein each evaluation index comprises the AUC of the ROC curve, the specificity, sensitivity, accuracy and the like of the model.

11. Data analysis used SPSS 26.0 and R (4.2.1) software. The measurement data are presented as mean ± standard deviation, or using the quartile method. Depending on the case, the statistical analysis involves a t-test or a U-test. The count data is expressed in percent and accepted for chi-square testing. ROC curves are used to evaluate the diagnostic effect of related indicators alone. Correlation analysis uses the pearson or spearman method. The data were analyzed using SPSS 22.0 software, with statistical significance set at P <0.05.

12. The present study followed the ethical guidelines in the declaration of helsinki, as well as the specifications and regulations associated with clinical studies, established by the world medical association. The study was approved by the ethical committee of the central hospital in Jinan, with ethical numbers 2021-213-01. All subjects who provided the test samples had signed an informed consent.

Test results:

1. a total of 61 patients were included, including 34 refractory hypertension patients and 27 common hypertension patients. Wherein, the data set for establishing the prediction model comprises 25 refractory hypertension patients and 20 common hypertension patients; the data set of the model verification contains 9 refractory hypertension patients and 7 common hypertension patients.

2. In this study, the sample size was estimated using G Power 3.1 software. On parameter settings we define the effect size to be 2, the smallest multiple of difference; the significance level was set to 0.05; the statistical performance was set to 0.95. Based on these parameters, the calculated minimum sample size is not more than 10 cases. The number of sample instances collected in this study (25 refractory hypertension groups, 20 common hypertension groups) meets this criterion, thus ensuring the statistical validity of the differential protein study.

3. Table 1 shows comparative analysis of demographics for the group of Hypertension (HBP) and Refractory Hypertension (RH). The HBP group consisted of 27 subjects, while the RH group consisted of 34 subjects. On gender distribution, HBP groups had 70% men and RH groups had 50% but the gender difference between the two groups was not significant (p=0.339). The median age of the two groups of subjects was 59.50 years and 59.5 years, respectively, and the age ranges were similar, indicating that the two groups were not significantly different in age (p=0.781). The BMI median of the two groups was 25.24 and 26.21 respectively, nor was the difference significant (p=0.745). In terms of blood pressure index, the systolic pressure (SBP) of the RH group is significantly higher than that of the HBP group (p=0.017), while the diastolic pressure (DBP) and Heart Rate (HR) have no significant difference between the two groups, p=0.378 and 0.422, respectively. In terms of lifestyle, the distribution of smoking history and drinking history was similar in both groups, and there was no statistically significant difference (smoking history p=0.645, drinking history p=0.645). The prevalence of heart arterial disease (CAD) and Diabetes (DM) was also similar in both groups, p= 0.522 and 0.425, respectively. There was also no significant difference in prevalence of hyperlipidemia between the two groups (p=0.69). There was also no significant difference in the years of history of hypertension between the two groups of subjects (p=0.968). The data are presented as mean ± standard deviation (mean ± SD) of continuous variables and frequency (n) of categorical variables, showing that neither group showed significant differences in gender, age, BMI, lifestyle, co-morbid condition, and history of hypertension, except for systolic blood pressure.

4. 11 Different proteins meeting the preset standard, including Q6UXB8（PI16）、P0DJI8（SAA1）、P55056（APOC4）、P68104（EF1A1）、Q13201（MMRN1）、P62328（TYB4）、P55058（PLTP）、P06576（ATPB）、A0A0B4J1V0（HV315）、P06733（ENOA） and P98160 (PGBM), were screened out, all of which were up-regulated proteins (higher expression in refractory hypertension groups than in normal hypertension groups).

5. In this study, a significant difference in protein expression levels occurred between patients with Refractory Hypertension (RH) and patients with Hypertension (HBP). 29 different proteins were found in the expression profile of RH patients, while 59 specific proteins were shown in the expression profile of HBP patients. Protein molecular function in RH patients is mainly related to lipid metabolism, protein interactions, oxidative stress and metabolic regulation. In contrast, for HBP patients, emphasis is shifted to protein degradation, lipid metabolism, cell adhesion and oxidative stress. The different molecular functions of these proteins correspond to different disease progression and injury levels. These differences may reveal biological characteristics of different HBP types and potential complications in treatment. To further examine these proteins, we subsequently revealed key biological processes and signaling pathways associated with RH for functional and pathway analysis of upregulated proteins. These include basic biological processes such as hemostasis, clotting, wound healing, cell adhesion, and immune response. The corresponding key signaling pathways include cell development and differentiation, infection, immunization, and metabolism. These findings emphasize the versatility of RH, which is related not only to blood pressure problems, but also to the disturbance of multiple biological processes. Using the diagnostic curves for up-and down-regulated proteins described above, we determined significantly different proteins with AUC exceeding 0.85 and sensitivity and specificity exceeding 85%. Combining the integrated analysis of these proteins with clinical data of patients we have observed that these proteins are closely related to liver function, kidney function, coagulation function and cardiac structure.

6. Table 2 shows the screening results for differential proteins, which show higher prediction accuracy and classification performance in the identification of biomarkers. Each protein listed in the table is measured for its predictive capacity by an AUC (area under the curve) value, where the closer the AUC value is to 1, the better the predictive effect of the model. For example: the AUC value for PI16 protein was 0.92, whereas HV315 and ENOA proteins showed the highest AUC values, both 0.99. The sensitivity and specificity columns show the accuracy of the model in identifying positive and negative cases, respectively, and the sensitivity and specificity of most proteins are over 90%, showing very high accuracy. In addition, the about dengue index combines sensitivity and specificity, and provides an index for measuring the overall performance of the classification model, wherein the about dengue index of the protein in the table is generally higher, e.g., the about dengue index of the PLTP protein reaches 0.92, while the about dengue index of the ENOA protein is 0.96. These data indicate that the proteins listed in the table may serve as potential targets for disease diagnosis or treatment, and deserve further investigation to understand their biological function and role in disease. However, it should be noted that, although the single detection capacity is partially stronger than the subsequently established predictive model in table 2, when we introduce a validated dataset of models, the results are shown in the validated set when ROC analysis is performed again on single biomarkers: ROC curve AUC for EF1A1 was 0.449; AUC of APOC4 is 0.85; AUC of PGBM was 0.687; the AUC for PI16 was 0.386, which is significantly different from the detection performance at the screening stage, with a partial single biomarker detection performance (AUC) even below 0.6. This means that the single biomarker only shows good classification effect under the current specific data set, when external data is introduced for verification, the diagnostic performance of the single biomarker can be greatly changed, the stability of the diagnostic performance can not be ensured in a wider clinical sample, and the complexity of disease generation and development can not be represented.

7. Establishing a disease risk assessment model: according to the expression quantity of 5 proteins of Q6UXB8, P68104, P98160, P62328 and P55056, a prediction model of RH disease risk is established, and the specific calculation method of the risk score P is P=1/1+EXP (- (-3.814-0.058×Q UXB8+0.053×P68104+0.045×P98160+0.071×P62328-0.016×P 55056)).

8. The ROC curves and evaluation indices of the modeled and validated queues are shown in fig. 1,2 and table 3. As a result, the model showed excellent risk prediction effect for RH, AUC area of ROC curve reached 0.948, and as shown in fig. 1, the prediction effect of validation queue also exceeded meeting the above-middle classification (AUC > 0.75), AUC area of ROC curve reached 0.882. The established model can effectively distinguish common hypertension patients from refractory hypertension patients, and has high model accuracy and good sensitivity.

Table 1 demographic comparison of two disease populations

Data are expressed as mean ± standard deviation for continuous variables and n for categorical variables.

TABLE 2 differential protein screening

AUC, area under the curve.

Table 3 modeling queue and validation queue detection results

Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but although the present invention has been described in detail with reference to the foregoing embodiment, it will be apparent to those skilled in the art that modifications may be made to the technical solution described in the foregoing embodiment, or equivalents may be substituted for some of the technical features thereof. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A biomarker for distinguishing between normal hypertension and refractory hypertension, wherein the biomarker comprises any one or more of the following proteins:

Q6UXB8, P0DJI8, P55056, P68104, Q13201, P62328, P55058, P06576, A0B4J1V0, P06733 and P98160.

2. The biomarker of claim 1, wherein the biomarker is a group consisting of Q6UXB, P68104, P98160, P62328, and P55056.

3. Use of an agent for detecting the expression level of a biomarker according to any of claims 1 to 2 in the preparation of a product for distinguishing between normal hypertension and refractory hypertension.

4. The use according to claim 3, wherein the reagent is a reagent used in a proteomic analysis method; the protein quantitative histology analysis method adopts liquid chromatography tandem mass spectrometry.

5. The use according to claim 3, wherein the product is a test kit, a test device or an apparatus.

6. A system for distinguishing between normal hypertension and refractory hypertension, the system comprising:

i) An analysis module, the analysis module comprising: a detection reagent for determining the expression level of a biomarker in a test sample of a subject;

Wherein the biomarker comprises any one or more of the following proteins:

7. The system of claim 6, wherein in the analysis module of i), the sample to be tested is blood.

8. The system of claim 6, wherein in the analysis module of i), the sample to be tested is plasma.

9. The system of claim 6, wherein in the analysis module of i) the biomarker is a group consisting of Q6UXB, P68104, P98160, P62328, and P55056.

10. The system of claim 9, wherein the evaluation module specific evaluation flow of ii) comprises: judging the disease condition of the subject based on the risk score of the disease risk assessment model;