A biomarker for predicting adverse functional outcome in patients with acute minor stroke and high-risk tia and uses thereof

Abstract

The application discloses a biomarker for predicting the adverse functional prognosis of acute mild stroke and high-risk TIA patients and application thereof, and belongs to the technical field of biology. The application discloses the correlation between the low-density lipoprotein receptor-related protein 1 (LRP-1) index and the adverse functional prognosis of acute mild stroke and high-risk TIA patients, verifies the prediction value of LRP-1 in the adverse functional prognosis of acute mild stroke and high-risk TIA patients, and constructs a joint prediction model by combining LRP-1 and traditional risk factors, thereby improving the prediction ability of the traditional model, and being beneficial to more accurately and efficiently identifying the high-risk population of stroke occurrence and adverse functional prognosis, and early intervention, so as to improve the life quality of the stroke patients.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biotechnology, in particular to a biomarker for predicting the adverse functional prognosis of acute mild stroke and high-risk TIA patients and application thereof. BACKGROUND

[0002] Acute mild stroke and high-risk TIA patients have similar epidemiological characteristics, i.e., the symptoms are mild at the onset, but the clinical prognosis is uncertain, and are prone to further disability and death. Therefore, they are the key population for the prevention and control of adverse functional prognosis of stroke. Studies have shown that within 1 year after stroke, about 30% of survivors cannot fully recover their functional status, and about 20% of patients need help with at least one daily activity. Therefore, screening biomarkers for predicting the adverse functional prognosis of stroke patients, improving the precise risk stratification and management of diagnosis and treatment strategies, optimizing the allocation of medical resources, and thus improving the prognosis of patients are important aspects of secondary prevention of stroke that cannot be ignored. SUMMARY

[0003] The purpose of the present application is to provide a biomarker for predicting the adverse functional prognosis of acute mild stroke and high-risk TIA patients and application thereof, in order to solve the problems existing in the prior art. The present application discloses the correlation between the LRP-1 index and the adverse functional prognosis of acute mild stroke and high-risk TIA patients, verifies the predictive value of LRP-1 in the adverse functional prognosis of acute mild stroke and high-risk TIA patients, and constructs a joint prediction model by combining LRP-1 and traditional risk factors, thereby improving the prediction ability of the traditional model. This is conducive to more accurately and efficiently identifying high-risk groups of stroke with adverse functional prognosis, and early intervention, in order to improve the quality of life of stroke patients.

[0004] Lipoprotein receptors are a class of proteins located on the cell membrane that can recognize and bind specific lipoproteins, such as low-density lipoprotein and high-density lipoprotein. These receptors play an important role in regulating blood lipid levels, maintaining lipid metabolism balance, and preventing atherosclerosis. First, the functional status of lipoprotein receptors directly affects the level of lipoproteins in the blood. When the function of lipoprotein receptors is normal, harmful lipoproteins such as low-density lipoprotein in the blood can be effectively removed, thereby reducing the risk of atherosclerosis and improving the functional prognosis of stroke patients. Second, lipoprotein receptors may also affect stroke prognosis by regulating inflammatory response. Studies have shown that lipoprotein receptors can recognize and bind certain inflammatory mediators, thereby inhibiting the occurrence and development of inflammatory response. This helps to reduce brain tissue damage and dysfunction after stroke. At the same time, some lipoprotein receptors also have the effect of promoting vascular repair and regeneration. After the occurrence of stroke, these receptors may promote the proliferation and migration of vascular endothelial cells, thereby accelerating the vascular repair process and helping to improve the prognosis of stroke. Therefore, from the perspective of improving the functional prognosis after stroke, the present application actively seeks lipoproteins that can predict poor functional prognosis in patients with acute mild stroke and high-risk transient ischemic attack, which is expected to early identify patients with high risk of poor functional prognosis after stroke, and provide potential for improving the quality of life of patients and promoting their health recovery.

[0005] To achieve the above object, the present application provides the following scheme:

[0006] The present application provides a biomarker for predicting poor functional prognosis of patients with acute mild stroke and high-risk transient ischemic attack, which is low-density lipoprotein receptor-related protein 1.

[0007] The present application also provides the use of a reagent for detecting the content of the above-mentioned biomarker in serum in the preparation of a product for predicting the poor functional prognosis of patients with acute mild stroke and high-risk transient ischemic attack.

[0008] Optionally, the product comprises a reagent or a kit.

[0009] The present application also provides a product for predicting the poor functional prognosis of patients with acute mild stroke and high-risk transient ischemic attack, which comprises a reagent for detecting the content of the above-mentioned biomarker in serum.

[0010] Optionally, the product comprises a reagent or a kit.

[0011] The present application also provides the use of the above-mentioned biomarker in constructing a prediction model for the poor functional prognosis of patients with acute mild stroke and high-risk transient ischemic attack.

[0012] The application also provides application of the biomarker and the risk factor in construction of a combined prediction model for adverse functional prognosis of patients with acute minor stroke and high-risk TIA, wherein the risk factor comprises age, hypertension history, diabetes history, previous myocardial infarction, other cardiovascular diseases, peripheral arterial disease, smoking, previous ischemic stroke history / TIA history; and the other cardiovascular diseases do not include atrial fibrillation and myocardial infarction.

[0013] The application discloses the following technical effects:

[0014] The application discloses the following technical effects:

[0015] The application also finds that the combined prediction model constructed by combining LRP-1 and traditional risk factors can significantly improve the prediction ability of the traditional model, is beneficial to more accurately and efficiently identify the high-risk population of stroke with adverse functional prognosis, and early intervention, so as to improve the life quality of the stroke patients. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0017] Figure 1 Dose-response relationship of LRP-1 and adverse functional prognosis after stroke;

[0018] Figure 2 ROC curve of LRP-1 on adverse functional prognosis after stroke. DETAILED DESCRIPTION

[0019] The detailed description of the various exemplary embodiments of the present application should not be considered as limiting the present application, but should be understood as a more detailed description of some aspects, characteristics and embodiments of the present application.

[0020] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. Additionally, for a range of values of, for example, concentrations, amounts, and so forth, is to be understood as specifically encompassing every value falling within the range. Unless otherwise stated, all technical and scientific terms and any acronyms used herein have the same meanings as commonly understood by one of ordinary skill in the art in the field of the application. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present application, the preferred methods and materials are described. All publications mentioned herein are incorporated by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. In case of conflict, the content of the present specification will control.

[0021] Unless defined otherwise, all technical and scientific terms and any acronyms used herein have the same meanings as commonly understood by one of ordinary skill in the art in the field of the application. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present application, the preferred methods and materials are described. All publications mentioned herein are incorporated by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. In case of conflict, the content of the present specification will control.

[0022] Many modifications and variations of this application can be made without departing from its spirit or scope, which will be apparent to those skilled in the art. Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. For example, the specification can be used to design equivalent processes and equivalent structures to those described herein. The specification and examples given herein are exemplary only. It is to be understood that features of the foregoing preferred embodiments can be combined with each other, unless specifically noted otherwise.

[0023] As used herein, the terms "comprise", "comprising", "include", "including", "have", "having" or the like are open-ended and do not exclude additional, unrecited elements or method steps.

[0024] Example 1

[0025] I. Methods

[0026] 1. 6883 patients with acute minor stroke and high-risk TIA were simultaneously screened for LRP-1 indicators based on the China Stroke Registry Study 3 database. The modified Rankin Scale (mRS) was used to evaluate the functional prognosis of patients with acute minor stroke and high-risk TIA, and mRS scores of 3-6 were defined as a poor functional prognosis group, and mRS scores of 0-2 were defined as a good functional prognosis group.

[0027] 2. Determination of LRP-1 indicator concentrations in the population of patients with acute minor stroke and high-risk TIA in the poor functional prognosis and good functional prognosis groups

[0028] The serum LRP-1 concentration of the enrolled patients was detected by ELISA method, ALISEI automatic enzyme immunoassay instrument and SEB010Hu kit were used, which were purchased from Wuhan Yunclone Technology Co., Ltd. and the operation was strictly in accordance with the instruction manual. The detection principle was as follows: LRP-1 antibody was coated in 96-well microplate to form a solid phase carrier, standard or sample was added to the microplate, LRP-1 in the sample combined with the antibody on the solid phase carrier, then biotinylated LRP-1 antibody was added, the unbound biotinylated antibody was washed, HRP labeled avidin was added, after thorough washing, TMB substrate was added for color development. TMB (3,3',5,5'-Tetramethylbenzidine) was converted to blue under the catalysis of peroxidase and to final yellow under the action of acid. The color depth was positively correlated with the LRP-1 in the sample. The absorbance (O.D. value) was measured at 450 nm wavelength by enzyme labeling instrument, and the sample concentration was calculated.

[0029] Before the experiment, all reagents were equilibrated to room temperature; when diluting reagents or samples, they should be mixed well, and foaming should be avoided as much as possible. Before the experiment, the sample content should be predicted. If the sample concentration is too high, the sample should be diluted so that the diluted sample meets the detection range of the kit, and the corresponding dilution multiple should be multiplied when calculating. The specific experimental steps are as follows:

[0030] (1) Sample preparation and dilution: collect blood with clean test tubes, centrifuge at 1000xg for 15 minutes after coagulation for 2 hours, and collect serum. Analyze immediately or store at-20℃ after aliquoting. Dilute the sample by 1:2, i.e. 100μL sample diluent plus 100μL sample.

[0031] (2) Reagent preparation:

[0032] A. Dilution and use of human LRP-1 standard: prepare within 2 hours before use. The kit provides 2 tubes of standard, each containing 10 ng, and 1 tube is used each time. Prepare 2000 pg / ml-62.5 pg / ml standard: prepare 6 Eppendorf tubes, each containing 0.3 mL of sample diluent, and label them as 2000 pg / ml, 1000 pg / ml, 500 pg / ml, 250 pg / ml, 125 pg / ml, and 62.5 pg / ml, respectively. Take 0.3 mL of 4000 pg / ml standard and add it to the tube labeled 2000 pg / ml, mix well, then take out 0.3 mL and add it to the next tube. Repeat the same for the remaining tubes.

[0033] B. Preparation of biotin-labeled anti-human LRP-1 antibody working solution: prepare within 2 hours before use. Calculate the total amount according to the requirement of 100 μL per well (100-200 μL should be prepared more when actually preparing). Prepare the working solution according to the ratio of 1 μL biotin-labeled anti-human LRP-1 antibody plus 99 μL of ABC diluent. Mix gently.

[0034] C. Preparation of avidin-peroxidase complex (ABC) working solution: prepare within 1 hour before use. Calculate the total amount according to the requirement of 100 μL per well (100-200 μL should be prepared more when actually preparing). Prepare the working solution according to the ratio of 1 μL avidin-peroxidase complex (ABC) plus 99 μL of ABC diluent. Mix gently.

[0035] (3) Operation process:

[0036] A. The diluted ABC and TMB color developing solution are pre-equilibrated at 37°C for 30 minutes before being added to the wells of the enzyme-labeled plate.

[0037] B. Determine the number of coated antibody wells required for this detection, including sample wells and standard wells, and add 1 well as a TMB blank color developing well. The rest are well-packed and placed in the refrigerator.

[0038] C. Sample addition: add 100 μL of sample diluted with sample diluent to each well, and incubate at 37°C for 1 hour.

[0039] D. Suction removal: after the reaction, use an automatic plate washer to remove the liquid in the enzyme-labeled plate (or spin off the liquid in the enzyme-labeled plate, then tap a few times against the absorbent paper). Add the prepared biotin-labeled anti-human LRP-1 antibody working solution to the enzyme-labeled plate (100 μL per well, except for the TMB blank color developing well) in turn. Add the sealing film to the enzyme-labeled plate and incubate at 37°C for 1 hour.

[0040] E. Plate washing: wash 3 times with 1x washing buffer, each time for about 1 minute (at least 300 μL of washing solution per well).

[0041] F. Add the prepared ABC working solution to the enzyme-labeled plate (100 μL per well, except for the TMB blank color developing well) in turn. Add the sealing film to the enzyme-labeled plate and incubate at 37°C for 30 minutes.

[0042] G. Plate washing: wash 5 times with 1x washing buffer, each time for about 2 minutes (at least 300 μL of washing solution per well).

[0043] H. Add 90 μL of TMB color developing solution to each well in turn, and react at 37°C for 10-20 minutes in the dark. The first 3-4 wells of the standard have obvious gradient blue color, and the last 3-4 wells have no obvious difference, and the reaction can be stopped.

[0044] I. Add 50 μL per well of stop solution in sequence, at this time blue color turns to yellow. Immediately measure O.D. value at 450 nm with microplate reader.

[0045] 3. Statistical analysis method

[0046] (1) Single factor analysis: Kruskal-Wallis test was used to explore the normality of LRP-1 distribution in different functional prognosis outcome groups. When the normal distribution was met, the mean standard deviation was used to describe the distribution of LRP-1 level in two groups, and t test was used to compare the difference of LRP-1 level in two groups. When the normal distribution was not met, the median (quartile) was used to describe the distribution of LRP-1 level in two groups, and wilcoxon rank sum test was used to compare the difference of LRP-1 level in two groups.

[0047] (2) Multivariate association analysis: Logistic regression model was used to analyze the association between LRP-1 level and the occurrence of poor functional prognosis after stroke, and the odds ratio (OR) and its 95% confidence interval (CI) were calculated. Restricted cubic spline curve was used to analyze the dose-response relationship between LRP-1 and poor functional prognosis. The factors adjusted in multivariate analysis were the risk factors in "Essen stroke risk score scale", i.e. age, history of hypertension, history of diabetes, previous myocardial infarction, other cardiovascular diseases (except atrial fibrillation and myocardial infarction), peripheral arterial disease, smoking, previous ischemic stroke / TIA history.

[0048] (3) Analysis of the predictive value of LRP-1 level: First, the population is divided into test set and validation set according to 7:3. In the test set, the LRP-1 level and adverse functional prognosis are plotted to draw the receiver operating characteristic curve (ROC), and the area under the curve, the cut-off value, the sensitivity, the specificity and the Youden index are calculated; secondly, the C index, the net reclassification improvement index (NRI) and the integrated discrimination improvement (IDI) are used to evaluate the predictive value of the model constructed by traditional risk factors (the risk factors involved in the ESSEN scale) for the adverse functional prognosis of acute mild stroke and high-risk TIA patients. On the basis of the model constructed by traditional risk factors, the LRP-1 index is added (converted into a categorical variable by the ROC curve cut-off value and included in the model), and the predictive value of the model after adding LRP-1 is compared with that of the predictive model with only traditional risk factors in terms of C index, NRI and IDI. At the same time, in the validation set, the LRP-1 index (also converted into a categorical variable by the ROC curve cut-off value) is included in the model again, and the value improvement of the model after adding LRP-1 is compared with that of the predictive model with only traditional risk factors.

[0049] II. Results

[0050] 1. Comparison of LRP-1 levels in patients with good functional prognosis and adverse functional prognosis after stroke

[0051] The median LRP-1 level in the serum of patients with good functional prognosis and adverse functional prognosis after stroke was 15.78 (quartile: 10.64-23.07) pg / ml, which was higher than that in patients with adverse functional prognosis after stroke (14.50 (quartile: 9.34-20.75) pg / ml), and the difference was statistically significant (P<0.001), as shown in Table 1.

[0052] Table 1. Concentration of serum LRP-1 in patients with good functional prognosis and adverse functional prognosis after stroke (pg / ml)

[0053]

[0054] 2. Association analysis of LRP-1 level and adverse functional prognosis after stroke

[0055] When LRP-1 is used as a continuous variable, the risk of adverse functional prognosis decreases with the increase of LRP-1 level (see Table 2). Figure 1Red lines represent odds ratios and blue lines represent 95% confidence intervals. After adjusting for other risk factors, each standard deviation increase in LRP-1 level was associated with a 15% reduction in the risk of poor functional outcome (OR, 0.85; 95% CI, 0.78-0.92); after adjusting for other risk factors, each standard deviation increase in LRP-1 level was associated with a 16% reduction in the risk of poor functional outcome (OR, 0.84; 95% CI, 0.78-0.91). When LRP-1 was treated as a categorical variable, subjects in the highest tertile of LRP-1 level had a 31% reduction in the risk of poor functional outcome compared with subjects in the lowest tertile of LRP-1 level (OR, 0.69; 95% CI, 0.58-0.83), as shown in Table 2.

[0056] Table 2 Association of LRP-1 with poor functional outcome after stroke

[0057]

[0058] Note: Adjusted factors included age, history of hypertension, history of diabetes, prior myocardial infarction, other cardiovascular disease (except atrial fibrillation and myocardial infarction), peripheral arterial disease, smoking, prior ischemic stroke / TIA.

[0059] 3. Predictive value of LRP-1 for poor functional outcome after stroke

[0060] The area under the ROC curve of LRP-1 alone for predicting poor functional outcome after stroke was 0.781, with a threshold of 19.026, corresponding to a sensitivity and specificity of 0.716 and 0.717, respectively, and a Youden index of 0.433, and a predictive cutoff of 19 pg / ml (see Figure 2). This indicates that LRP-1 has a high predictive value for poor functional outcome after stroke. Figure 2

[0061] 4. Predictive value of LRP-1 combined with a traditional predictive factor model for depression after stroke compared with a traditional model

[0062] ​Compared with the prediction model constructed by the risk factors recorded in the traditional ESSEN scale, the prediction value of the combined prediction model constructed by integrating LRP-1 (a binary variable with 19 pg / ml as the cut-off point) and the risk factors in the ESSEN scale for the poor functional prognosis after stroke was significantly increased, as shown in Table 3. In the test set, the addition of LRP-1 increased the C index of the combined prediction model from 0.600 to 0.798, with an IDI of 16.91% (P < 0.001) and an NRI of 87.28% (P < 0.001); in the validation set, the C index increased from 0.596 to 0.798, with an IDI of 15.47% (P < 0.001) and an NRI of 87.04% (P < 0.001), as shown in Table 3. The above results show that the combined prediction model of LRP-1 and the risk factors in the ESSEN scale can better predict the occurrence of poor functional prognosis after stroke, and is more conducive to early identification of high-risk groups of poor functional prognosis after stroke, so as to intervene early and improve the quality of life of patients.

[0063] Table 3 Comparison of the prediction value of LRP-1 and traditional risk factors for poor functional prognosis after stroke

[0064]

[0065] Note: The traditional model is the Essen Stroke Risk Score scale; the risk factors include age, history of hypertension, history of diabetes, previous myocardial infarction, other cardiovascular diseases (except atrial fibrillation and myocardial infarction), peripheral arterial disease, smoking, previous ischemic stroke / TIA history; LRP-1 is included in the model with 19 pg / ml as the cut-off point.

[0066] The above-described embodiments are only descriptions of the preferred modes of the present application and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those of ordinary skill in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. Use of a reagent for detecting the content of a biomarker in serum in the preparation of a product for predicting the adverse functional prognosis of patients with acute minor stroke and high-risk transient ischemic attack; The biomarker is low-density lipoprotein receptor-related protein 1.

2. Use according to claim 1, characterized in that, The product comprises a reagent or a kit.

3. Use of a biomarker for predicting the adverse functional prognosis of patients with acute minor stroke and high-risk transient ischemic attack in the construction of a product for predicting the adverse functional prognosis of patients with acute minor stroke and high-risk transient ischemic attack; The biomarker is low-density lipoprotein receptor-related protein 1.

4. The use of a biomarker combined risk factor for predicting the adverse functional prognosis of acute minor stroke and high-risk transient ischemic attack patients in constructing a combined prediction model product for the adverse functional prognosis of acute minor stroke and high-risk transient ischemic attack patients, characterized in that, The risk factors include age, history of hypertension, history of diabetes, previous myocardial infarction, other cardiovascular diseases, peripheral arterial disease, smoking, history of previous ischemic stroke / TIA; the other cardiovascular diseases do not include atrial fibrillation and myocardial infarction; The biomarker is low-density lipoprotein receptor-related protein 1.

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