Marker, kit and use for aiding diagnosis or differential diagnosis of vascular cognitive impairment

Abstract

The present application relates to a marker, a kit and a use for assisting in diagnosing or differentiating a vascular cognitive impairment. Specifically, the present application relates to a marker combination for diagnosing, assisting in diagnosing or differentiating a vascular cognitive impairment, wherein the marker combination comprises small extracellular vesicles carrying CD31 expression. The present application provides the marker combination with the advantages of high specificity and good sensitivity, and the marker combination can be used for diagnosing, assisting in diagnosing or differentiating a vascular cognitive impairment.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, particularly to the field of in vitro diagnostic testing technology, and especially to biomarkers, kits, and uses for assisting in the diagnosis or identification of vascular cognitive impairment. Background Technology

[0002] Cognitive impairment encompasses the entire process from mild cognitive decline in the preclinical stage to dementia, and its development is influenced by multiple factors, including normal aging changes, neurodegenerative diseases, and vascular damage. In Alzheimer's disease (AD), the most common neurodegenerative disease, a diagnostic framework has been established that combines clinical manifestations with core pathological biomarkers. Biomarkers from cerebrospinal fluid (CSF) and blood play a crucial role in the early diagnosis and treatment of AD.

[0003] As people age, the proportion of dementia caused solely by Alzheimer's disease (AD) gradually decreases. Most patients develop vascular cognitive impairment (VCI) due to vascular damage caused by events such as cerebral small vessel disease (CSVD) or stroke, and some even progress to mixed dementia (MD) that combines AD and VCI. However, current clinical practice and research mainly rely on neuroimaging to detect cerebrovascular damage. Traditional imaging methods are not sensitive enough in detecting early, small vessel damage, and are prone to missed or misdiagnosed cases. Structural imaging mainly reflects anatomical changes and has limited ability to detect vascular dysfunction (such as endothelial dysfunction and blood-brain barrier disruption). At the same time, the vascular damage detected by traditional imaging does not fully match the severity of clinical cognitive function and the potential for further cognitive impairment.

[0004] Currently, the assessment of vascular injury mainly relies on neuroimaging. There is a lack of suitable biomarkers for early diagnosis and to guide clinical decision-making. Therefore, it is difficult to accurately assess the relative contribution of neurodegeneration and vascular injury in MD and their impact on the progression of cognitive impairment. Summary of the Invention

[0005] In view of the deficiencies of the prior art mentioned above, the present invention aims to provide an application of effectively identifying CD31-positive small extracellular vesicles from cerebrospinal fluid in the diagnosis, auxiliary diagnosis or differentiation of vascular cognitive impairment.

[0006] Solution for solving the problem:

[0007] On one hand, the present invention provides the use of reagents for detecting biomarkers in the preparation of products for diagnosing, assisting in the diagnosis and / or identifying vascular cognitive impairment, said biomarkers including small extracellular vesicles carrying CD31 expression.

[0008] Preferably, the marker further includes one or more of Nfl, Ttau, and Ptau-181.

[0009] Preferably, the marker also includes Ptau-181.

[0010] Preferably, the small extracellular vesicles carrying CD31 expression are small extracellular vesicles expressing CD31 protein on their membrane surface.

[0011] Preferably, the marker is derived from blood and / or cerebrospinal fluid.

[0012] On one hand, the present invention provides a kit for diagnosing, assisting in the diagnosis or identification of vascular cognitive impairment, the kit comprising a reagent for detecting the concentration of small extracellular vesicles carrying CD31 expression.

[0013] Preferably, the kit further includes reagents for detecting the concentration of one or more of Nfl, Ttau, and Ptau-181.

[0014] Preferably, the reagent is a reagent for nanoparticle tracking analysis and / or a flow cytometry detection reagent.

[0015] On one hand, the present invention provides the use of biomarkers in constructing systems for diagnosing, assisting in the diagnosis or identification of vascular cognitive impairment, said biomarkers including small extracellular vesicles carrying CD31 expression.

[0016] On one hand, the present invention provides a system for diagnosing, assisting in the diagnosis, or identifying vascular cognitive impairment, the system comprising an information acquisition module, a calculation module, and a diagnostic module, wherein:

[0017] The information acquisition module is used to acquire the amount or concentration value of biomarkers in the subject's sample; the biomarkers include small extracellular vesicles carrying CD31 expression;

[0018] The calculation module is used to perform the operation of substituting the amount or concentration value of the marker into the diagnostic model to obtain the model value;

[0019] The diagnostic module is used to perform the operation of determining whether the subject has vascular cognitive impairment based on the model value.

[0020] The effects of the invention

[0021] This invention provides a highly efficient biomarker for the diagnosis or auxiliary diagnosis of VCI, which improves the ability to differentiate VCI from other non-VCI patients and effectively solves the problems of poor specificity and low sensitivity faced by traditional diagnostic methods.

[0022] This invention accurately detects CD31-carrying microvesicles in cerebrospinal fluid using fluorescently labeled CD31. By detecting the concentration of CD31-carrying (CD31-positive) microvesicles in cerebrospinal fluid (c-BEEVs), it can specifically reflect the status of cerebrovascular injury. By analyzing c-BEEVs in cerebrospinal fluid of different populations, it can accurately distinguish between patients with vascular cerebrovascular disease (VCI) and patients with subjective cognitive impairment (SCD).

[0023] Furthermore, the present invention can also be combined with other biomarkers (such as Nfl, Ttau, Ptau-181 or combinations thereof) to accurately distinguish between VCI patients and AD patients. Attached Figure Description

[0024] Figure 1 The levels of c-BEEVs in the cerebrospinal fluid of different samples (SCD, VCI, AD, and MD) in the cohort were found in Example 1;

[0025] Figure 2 The ROC curves of c-BEEVs in the queue used to distinguish between VCI and SCD were found in Example 1;

[0026] Figure 3 The ROC curves for different markers or their combination in the queue were found in Example 2 to distinguish between VCI and AD;

[0027] Figure 4 The ROC curves of c-BEEVs used to distinguish VCI and SCD in the verification queue in Example 3;

[0028] Figure 5 The ROC curves for different markers or combinations thereof used to distinguish VCI and AD in the verification queue in Example 3. Detailed Implementation

[0029] To make the technical solution and beneficial effects of the present invention more apparent and understandable, a detailed description is provided below by listing specific embodiments. The accompanying drawings are not necessarily drawn to scale, and local features may be enlarged or reduced to more clearly show the details of the local features; unless otherwise defined, the technical and scientific terms used herein have the same meanings as those in the technical field to which this application pertains.

[0030] Vascular risk factors (VRFs), such as hypertension and type 2 diabetes, are the main causes of cerebrovascular injury. Endothelial cells are the primary target of VRF-induced vascular damage. Under the influence of VRFs, endothelial dysfunction occurs, leading to the release of corresponding biomarkers, such as cell adhesion molecules, adrenomedullin, and placental growth factor. While these biomarkers are relatively sensitive, they primarily reflect peripheral vascular injury and lack brain specificity, thus limiting their role in assessing cerebrovascular injury and its consequences.

[0031] Through extensive and in-depth research, the inventors have developed a novel cerebrospinal fluid (CSF) indicator: microvesicles derived from brain endothelial cells (MEVs) as a biomarker for differentiating VCI patients. Specifically, the amount of CD31-positive MEVs in the CSF relative to the total number of MEVs in the test sample (c-BEEVs) reflects the extent of cerebrovascular injury, thereby accurately distinguishing VCI patients from patients with subjective cognitive impairment (SCD). Furthermore, the inventors discovered that c-BEEVs, in combination with other biomarkers in the test sample (e.g., Nfl, Ttau, Ptau-181), especially in combination with Ptau-181, can accurately distinguish VCI patients from AD patients. Based on this, the present invention was completed.

[0032] As used herein, the term "about" covers a range of values ​​within ±25% of a given value. In other embodiments, the term "about" covers a range of values ​​within ±20%, ±15%, ±10%, or ±5% of a given value. For example, in one embodiment, "about 3 grams" represents a value of 2.7–3.3 grams (i.e., 3 grams ± 10%).

[0033] As used in this article, extracellular vesicles (EVs) refer to a class of tiny vesicles secreted by cells with a lipid bilayer structure, including exosomes (Exosomes) and microvesicles, which differ in origin and particle size. EVs not only contain many bioactive molecules, such as nucleic acids (DNA, mRNA, miRNA, etc.), proteins, and lipids, but also possess the characteristics of being a micro-drug delivery system, enabling them to specifically carry exogenous bioactive molecules. Internationally, extracellular vesicles with a diameter less than 200 nm are defined as small extracellular vesicles (sEVs). As used in this article, small extracellular vesicles (sEVs) are secreted by cells, with a diameter less than 200 nm, especially those with a diameter of 40–150 nm, and can circulate in bodily fluids such as blood and urine.

[0034] The first objective of this invention is to provide a biomarker for diagnosing, assisting in the diagnosis or identification of vascular cognitive impairment, said biomarker comprising small extracellular vesicles carrying CD31 expression.

[0035] The inventors of this invention have confirmed through research that, compared with patients with other types of cognitive impairment, the concentration of CD31-positive small extracellular vesicles (c-BEEVs) in the cerebrospinal fluid of VCI patients is significantly upregulated.

[0036] In this invention, the concentration of CD31-positive extracellular vesicles is the ratio of CD31-carrying extracellular vesicles derived from brain endothelial cells to the total number of extracellular vesicles in the cerebrospinal fluid.

[0037] In some implementations, the markers also include one or more of Nfl, Ttau, and Ptau-181.

[0038] In some implementations, the marker also includes Ptau-181.

[0039] In this invention, the inventors discovered that c-BEEVs combined with other biomarkers, such as Nfl, Ttau, and Ptau-181, can further improve the accuracy of distinguishing between VCI patients and AD patients. In particular, the AUC value of c-BEEVs combined with Ptau-181 in distinguishing between VCI patients and AD patients is 0.965, which greatly improves specificity and sensitivity.

[0040] In some embodiments, the small extracellular vesicles carrying CD31 expression are small extracellular vesicles expressing CD31 protein on their membrane surface.

[0041] In some implementations, the marker is derived from blood and / or cerebrospinal fluid.

[0042] In some implementations, the marker is derived from cerebrospinal fluid.

[0043] A second object of the present invention is to provide the use of a reagent for detecting the concentration of any of the above-mentioned biomarkers in the preparation of products for diagnosing, assisting in the diagnosis or identification of vascular cognitive impairment, said biomarkers including small extracellular vesicles carrying CD31 expression.

[0044] In some implementations, the markers also include one or more of Nfl, Ttau, and Ptau-181.

[0045] In some implementations, the marker also includes Ptau-181.

[0046] In some embodiments, the small extracellular vesicles carrying CD31 expression are small extracellular vesicles expressing CD31 protein on their membrane surface.

[0047] In some implementations, the marker is derived from blood and / or cerebrospinal fluid.

[0048] In some implementations, the marker is derived from cerebrospinal fluid.

[0049] A third objective of the present invention is to provide a kit for diagnosing, assisting in the diagnosis or identification of vascular cognitive impairment, the kit comprising a reagent for detecting the concentration of small extracellular vesicles carrying CD31 expression.

[0050] In some embodiments, the kit further includes reagents for detecting the concentration of one or more of Nfl, Ttau, and Ptau-181.

[0051] In some embodiments, the small extracellular vesicles carrying CD31 expression are small extracellular vesicles expressing CD31 protein on their membrane surface.

[0052] In some implementations, the marker is derived from blood and / or cerebrospinal fluid.

[0053] In some implementations, the marker is derived from cerebrospinal fluid.

[0054] In some embodiments, the reagent is a reagent for nanoparticle tracking analysis and / or a flow cytometry detection reagent.

[0055] A fourth object of the present invention is to provide the use of biomarkers in constructing systems for diagnosing, assisting in the diagnosis of or identifying vascular cognitive impairment, said biomarkers including small extracellular vesicles carrying CD31 expression.

[0056] In some implementations, the markers also include one or more of Nfl, Ttau, and Ptau-181.

[0057] In some implementations, the marker also includes Ptau-181.

[0058] In some embodiments, the small extracellular vesicles carrying CD31 expression are small extracellular vesicles expressing CD31 protein on their membrane surface.

[0059] In some implementations, the marker is derived from blood and / or cerebrospinal fluid.

[0060] In some implementations, the marker is derived from cerebrospinal fluid.

[0061] A fifth objective of this invention is to provide a system for diagnosing, assisting in the diagnosis, or differentiating vascular cognitive impairment.

[0062] The system includes an information acquisition module, a calculation module, and a diagnostic module, wherein:

[0063] The information acquisition module is used to acquire the amount or concentration value of biomarkers in the subject's sample; the biomarkers include small extracellular vesicles carrying CD31 expression;

[0064] The calculation module is used to perform the operation of substituting the amount or concentration value of the marker into the diagnostic model to obtain the model value;

[0065] The diagnostic module is used to perform the operation of determining whether the subject has vascular cognitive impairment based on the model value.

[0066] In some embodiments, the marker further includes one or more of Nfl, Ttau, and Ptau-181; in a specific embodiment, the marker further includes Ptau-181.

[0067] In some implementations, the system is used to distinguish between vascular cognitive impairment (VCI) and subjective cognitive impairment (SCD).

[0068] In a preferred embodiment, the formula for the diagnostic model is calculated as the ratio of CD31-positive small extracellular vesicles in cerebrospinal fluid to the total number of small extracellular vesicles in cerebrospinal fluid multiplied by 100. When the model value is greater than or equal to 23, it indicates that the subject has vascular cognitive impairment (VCI); otherwise, it indicates that the subject has non-vascular cognitive impairment.

[0069] In some implementations, the system is used to distinguish between vascular cognitive impairment (VCI) and Alzheimer's disease (AD).

[0070] In a preferred embodiment, the formula for the diagnostic model is:

[0071] P(Y=1∣x)=σ(z)= 1 / [1+exp(-z)]

[0072] in: z = β 0+ β 1* x 1+ β 2* x 2, β0=-6.979, β1=0.473, β2=-0.067;

[0073] x 1 represents the c-BEEVs level of the sample being tested. x 2 represents the concentration of p-tau181 in the sample to be tested; the cutoff value is 26.1. If P is greater than or equal to 26.1, it can be determined as VCI; if P is less than 26.1, it can be determined as AD.

[0074] In this invention, the method for determining the concentration of CD31-positive small extracellular vesicles includes:

[0075] (1) Collect cerebrospinal fluid samples;

[0076] (2) After incubating the above cerebrospinal fluid sample with CD31 fluorescent antibody, a liquid mixture was obtained;

[0077] (3) The concentration of CD31-positive small extracellular vesicles, i.e. c-BEEVs, in the mixture was detected by nanoparticle tracking analysis and flow cytometry.

[0078] This invention utilizes nanoparticle tracking analysis for quantitative analysis of vesicles with a diameter less than 200 nm. The high-throughput nature of nanoparticle tracking analysis enables the rapid acquisition of large amounts of data in a single detection, thereby improving analytical efficiency and result consistency. The entire detection process can be completed within 24 hours, maintaining sample freshness and ensuring timely detection. By combining specific antibody fluorescent labeling with an efficient sample processing workflow, this invention significantly enhances the ability to detect the severity of cerebrovascular injury, effectively solving the problems of low specificity and low targeting in traditional detection methods. In the process of constructing diagnostic, assisted diagnostic, and / or identification models, this invention, through the combination of c-BEEVs and p-tau181, significantly enhances the ability to differentiate various diseases, providing a new means for identifying VCI in the population.

[0079] The present invention will be further described below through specific embodiments. Unless otherwise specified, "%" represents a mass percentage. The materials and reagents used in the following embodiments are all commonly used materials or reagents in the art, and can be obtained commercially or synthesized by known methods. Experimental methods in the following embodiments without specified conditions are generally performed according to conventional experimental conditions or the conditions recommended by the manufacturer of the relevant reagent (kit).

[0080] Example 1

[0081] Materials and methods

[0082] Sample included: This study included 10 patients with subjective cognitive impairment (SCD), 15 patients with VCI, 15 patients with AD, and 15 patients with mixed dementia (MD) from the Department of Neurology at Huashan Hospital.

[0083] Cerebrospinal fluid sample preparation: 100 μL of cerebrospinal fluid was collected from participants using sterile tubes, and then centrifuged at 3000 × g for 10 minutes. The centrifuged samples were stored at -80°C for nanoparticle tracking analysis (NTA).

[0084] 30–50 μL of cerebrospinal fluid was placed in a flow cytometer tube, and 2 μg of fluorescently labeled CD31 antibody was added and incubated for 30 minutes. All procedures were performed at 4°C in the dark. After incubation, the mixture was diluted 1:3 with PBS and vortexed for 10 seconds. Vesicles at 50–150 nm were quantitatively detected using nanoparticle tracking analysis software (version 3.0) on a NanoSight NS300 instrument. The concentration of CD31-positive small extracellular vesicles (c-BEEVs) in the cerebrospinal fluid was calculated based on the flow rate and dilution ratio. All samples were analyzed within 2 hours.

[0085] The concentrations of p-tau181, Nfl, and Ttau in cerebrospinal fluid were detected using enzyme-linked immunosorbent assay (ELISA). The assay was performed using a Euroimmun ELISA kit (supplier: Euroimmun AG, Lübeck, Germany), following the manufacturer's instructions. All samples were included in duplicate wells to ensure accuracy and reproducibility.

[0086] Statistical analysis was performed using R software (version 4.4.1). ANOVA was used to assess differences between groups of c-BEEVs, followed by Tukey's test for post-hoc tests between each pair of groups. Receiver operating characteristic (ROC) curve analysis was used to evaluate the sensitivity and specificity of c-BEEVs in distinguishing VCI from AD or SCD.

[0087] like Figure 1 As shown, through intergroup difference analysis and post-hoc testing, it was found that among all participants included in this embodiment (the discovery cohort), the c-BEEVs level of patients in the VCI group was significantly higher than that of patients with SCD, AD, and MD, and the difference was statistically significant. This indicates that c-BEEVs can effectively distinguish VCI patients, and preliminarily suggests the effective diagnostic value of c-BEEVs for VCI.

[0088] ROC analysis evaluated the diagnostic efficacy of c-BEEVs in differentiating VCI from SCD and AD.

[0089] When distinguishing between VCI and SCD, an ROC curve was created by calculating the true positive rate and false positive rate for each predictor variable (c-BEEVs, Nfl, p-tau181, t-tau). The area under the curve (ROC) was calculated using the Mann-Whitney U statistical method. The AUC value for the c-BEEVs cohort was found to be 1. Figure 2 The AUC values ​​of c-BEEVs were significantly higher than those of traditional biomarkers Nfl (AUC 0.980), Ttau (AUC 0.821), and Ptau-181 (AUC 0.540), indicating that c-BEEVs have excellent diagnostic efficacy in distinguishing between VCI and SCD.

[0090] In this embodiment, the threshold for c-BEEVs is set to 23. If it is greater than or equal to 23, VCI is indicated; otherwise, it is negative.

[0091] Example 2: Diagnostic efficacy of c-BEEVs combined with other biomarkers

[0092] The diagnostic efficacy of c-BEEVs combined with other markers (p-tau181, Nfl, Ttau) for VCI was assessed by fitting logistic regression.

[0093] ROC curves were created by calculating the true positive rate and false positive rate of each predictor variable (c-BEEVs, and c-BEEVs combined with Nfl or p-tau181 or t-tau), and AUC values ​​were calculated under different conditions by fitting logistic regression.

[0094] ROC curves for different combinations of markers in distinguishing VCI and AD, such as Figure 3 As shown, the AUC value of the c-BEEVs queue was 0.884, and the AUC of c-BEEVs combined with p-tau181 was 1 ( Figure 3 The results showed that the differential efficacy of c-BEEVs combined with p-tau181 was much higher than that of c-BEEVs alone (AUC 0.884), c-BEEVs+Nfl (AUC 0.938), and c-BEEVs+Ttau (AUC 0.919), indicating that c-BEEVs combined with p-tau181 has excellent diagnostic efficacy in distinguishing between VCI and AD.

[0095] The corresponding logistic regression algorithm formula and parameters are: P(Y=1|x)=σ(z) =1 / (1+exp(-(β0 + β1x1 + β2x2))), z = β0 + β1*x1 +β2*x2, calling the sigmoid function: σ(z)=1 / (1 + exp(-z)). β0 = -6.979, β1= 0.473, β2= -0.067.

[0096] x 1 represents the c-BEEVs level of the sample being tested. x 2 represents the concentration of p-tau181 in the sample to be tested. x 1. x Substitute the numerical values ​​into the above regression equation to obtain the P-value. If the P-value is greater than or equal to 26.1, it is judged as VCI; otherwise, it is judged as AD.

[0097] Example 3: Validation of a diagnostic model based on biomarker combinations

[0098] In this embodiment, the models for distinguishing between VCI and SCD, and the detection model for distinguishing between VCI and AD, obtained from the above embodiments, are used for verification. The models distinguish between VCI and SCD based on the single index c-BEEVs, and distinguish between VCI and AD based on P(Y=1|x)=σ(z) =1 / (1+exp(-(β0 + β1x1 + β2x2))) constructed from c-BEEVs and p-tau181.

[0099] Thirty-six patients with sudden cardiac death (SCD), 87 patients with superficial cardiac death (VCI), 87 patients with acute excision and palsy (AD), and 87 patients with malignant lesions (MD) were enrolled in a multicenter study conducted at Tiantan Hospital, the Tenth People's Hospital Affiliated to Tongji University, and Zhongnan Hospital of Wuhan. The detection methods were the same as in Examples 1 and 2.

[0100] The validation queue uses a single c-BEEVs metric to distinguish between VCI and SCD ROC curves, as shown in the figure. Figure 4 As shown, the AUC value of the validation cohort c-BEEVs is 0.952. The ROC curves for distinguishing VCI and AD based on c-BEEVs and the p-tau181 marker are shown below. Figure 5 As shown, the AUC of the validation queue c-BEEVs combined with p-tau181 is 0.965. This indicates that the model possesses excellent classification ability and high accuracy.

[0101] It should be understood that the above embodiments are exemplary and are not intended to encompass all possible implementations included in the claims. Various modifications and changes can be made to the above embodiments without departing from the scope of this disclosure. Similarly, the various technical features of the above embodiments can be arbitrarily combined to form other embodiments of the present invention that may not be explicitly described. Therefore, the above embodiments only illustrate several implementations of the present invention and do not limit the scope of protection of this patent.

Claims

1. The use of reagents for detecting biomarkers in the preparation of products for diagnosing, assisting in the diagnosis, and / or differentiating vascular cognitive impairment, characterized in that, The biomarker includes small extracellular vesicles carrying CD31 expression, wherein the small extracellular vesicles carrying CD31 expression are small extracellular vesicles expressing CD31 protein on their membrane surface.

2. The use according to claim 1, characterized in that, The markers also include one or more of Nfl, Ttau, and Ptau-181.

3. The use according to claim 2, characterized in that, The markers also include Ptau-181.

4. The use according to any one of claims 1 to 3, characterized in that, The markers are derived from cerebrospinal fluid and / or blood.

5. A kit for diagnosing, assisting in the diagnosis, or differentiating vascular cognitive impairment, characterized in that, The kit includes reagents for detecting the concentration of small extracellular vesicles carrying CD31 expression, wherein the small extracellular vesicles carrying CD31 expression are small extracellular vesicles expressing CD31 protein on their membrane surface.

6. The reagent kit according to claim 5, characterized in that, The kit also includes reagents for detecting the concentration of one or more of Nfl, Ttau, and Ptau-181.

7. The reagent kit according to claim 5, characterized in that, The reagents are for nanoparticle tracking analysis and / or flow cytometry detection.

8. The use of biomarkers in constructing systems for diagnosing, assisting in the diagnosis, and / or identifying vascular cognitive impairments, characterized in that, The biomarker includes small extracellular vesicles carrying CD31 expression, wherein the small extracellular vesicles carrying CD31 expression are small extracellular vesicles expressing CD31 protein on their membrane surface.

9. A system for diagnosing, assisting in the diagnosis, or differentiating vascular cognitive impairment, characterized in that, The system includes an information acquisition module, a calculation module, and a diagnostic module, wherein: The information acquisition module is used to acquire the amount or concentration value of biomarkers in the subject's sample; the biomarkers include small extracellular vesicles carrying CD31 expression; The calculation module is used to perform the operation of substituting the amount or concentration value of the marker into the diagnostic model and obtaining the model value; The diagnostic module is used to perform the operation of determining whether the subject has vascular cognitive impairment based on the model value; The small extracellular vesicles carrying CD31 expression are small extracellular vesicles with CD31 protein expressed on their membrane surface.

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