Assessment or diagnosis of a patient with multiple system atrophy using blood biomarkers and uses thereof

Abstract

The application relates to a blood biomarker for evaluating or diagnosing a patient with multiple system atrophy and an application thereof. The blood biomarker is a combination of at least one of alpha-Syn, NfL and C3. The blood biomarker can assist in early diagnosis and continuous monitoring of the patient with multiple system atrophy, has low detection cost, simple operation, fast detection speed, small trauma, good accessibility, is suitable for most patients, and has high clinical application value.

Description

Technical Field

[0001] This application relates to the field of biochemical detection technology for neurodegenerative diseases, and more particularly to a blood biomarker for assessing or diagnosing patients with multiple system atrophy and its use. Background Technology

[0002] Multiple system atrophy (MSA) is a complex neurodegenerative disease characterized by neuronal degeneration in multiple brain regions, including the basal ganglia, cerebellum, autonomic nervous system, and pyramidal structure. MSA can be divided into two main subtypes: MSA-P (Parkinsonian multiple system atrophy) and MSA-C (cerebellar multiple system atrophy). MSA-P is mainly characterized by motor and autonomic dysfunction, such as orthostatic hypotension, urinary incontinence, and sexual dysfunction, while MSA-C is primarily characterized by cerebellar ataxia and coordination disorders. Pathological features of MSA include the abnormal deposition of α-synuclein in glial cells, forming glial intracellular inclusions (GCIs), which are pathologically specific.

[0003] Currently, the diagnosis of neurodegenerative diseases, especially MSA, mainly relies on imaging examinations and screening for clinical features. Imaging examinations such as magnetic resonance imaging (MRI) can show lesions in the brainstem, cerebellum, and basal ganglia, such as putamen atrophy, pontine cruciate sign, and cerebellar peduncle atrophy, which are manifestations of MSA and can be used as an auxiliary diagnostic tool for MSA. In addition, dopamine transporter positron emission tomography (DAT-PET) shows reduced striatal dopamine uptake in MSA and can be used in conjunction with MRI to assist in the diagnosis of MSA. However, these examination methods are costly, complex to operate, and have limited ability to differentiate MSA, making them unsuitable for most patients.

[0004] Therefore, it is necessary to seek a low-cost, simple-to-operate, and large-scale auxiliary diagnostic examination method for multi-system atrophy. Summary of the Invention

[0005] To address or partially address the problems existing in related technologies, this application provides a blood biomarker for assessing or diagnosing patients with multiple system atrophy and its uses. It can assist in the early diagnosis and continuous monitoring of patients with multiple system atrophy, and has low detection cost, simple operation, fast detection speed, minimal invasiveness, and good accessibility. It is suitable for most patients and has high clinical application value.

[0006] The first aspect of this application provides the use of the following blood biomarkers in the preparation of diagnostic products for patients with multiple system atrophy, wherein the blood biomarkers are a combination of at least one of α-Syn, NfL and C3.

[0007] In some embodiments, the area under the receiver operating characteristic (ROC) curve generated by the blood biomarker or combination thereof in the detection results of samples from patients with multiple system atrophy and healthy individuals is greater than 0.8; preferably greater than 0.9.

[0008] In some implementations, the optimal threshold value for the blood biomarker is determined by calculating the maximum Oden index using a receiver operating characteristic (ROC) curve.

[0009] In some implementations, the optimal threshold value of α-Syn is determined by combining the Youden index with sensitivity and specificity; preferably, it is determined by sensitivity > 65% and specificity > 70%.

[0010] In some implementations, the optimal threshold value of NfL is determined by combining the Youden index with sensitivity and specificity; preferably, it is determined by sensitivity > 70% and specificity > 90%.

[0011] In some implementations, the optimal critical value of C3 is determined by combining the Youden index with sensitivity and specificity; preferably, it is determined by sensitivity > 70% and specificity > 70%.

[0012] In some implementations, detecting a blood biomarker concentration in a blood sample that is above the optimal threshold for the blood biomarker indicates a risk of multisystem atrophy; or, detecting a blood biomarker concentration that is below the optimal threshold for the blood biomarker indicates a risk of multisystem atrophy.

[0013] In some preferred embodiments, detecting a concentration of α-Syn or NfL in a blood sample that is higher than its corresponding optimal threshold indicates a risk of multisystem atrophy; or, detecting a concentration of C3 in a blood sample that is lower than its corresponding optimal threshold indicates a risk of multisystem atrophy.

[0014] In some embodiments, the content of the blood biomarker is determined by photochemiluminescence detection or turbidimetry.

[0015] In some embodiments, the blood sample includes whole blood, plasma, and serum.

[0016] A second aspect of this application provides a kit for the assessment or diagnosis of patients with multiple system atrophy, comprising a detection reagent containing a blood biomarker, said blood biomarker being a combination of at least one of α-Syn and NfL with C3.

[0017] In some embodiments, the detection reagent includes a photochemiluminescence detection reagent.

[0018] It is worth noting that the uses described in this application are for purposes other than disease diagnosis.

[0019] The technical solution provided in this application may include the following beneficial results: Multi-biomarker models incorporating C3, such as combinations of C3 and α-Syn, C3 and NfL, and C3, NfL, and α-Syn, have shown significant efficacy in the early diagnosis of MSA, with high accuracy in differentiation. The test results can be standardized, and the testing is low-cost, simple to operate, and minimally invasive, making it suitable for most patients. It also enables large-scale simultaneous testing and has significant clinical application value in the early diagnosis and prognostic assessment of MSA.

[0020] Photochemiluminescence immunoassay and turbidimetry can meet the detection needs of blood biomarkers for early diagnosis of MSA in blood samples. For example, NfL and α-Syn can be accurately measured using photochemiluminescence immunoassay, while C3 can be accurately measured using turbidimetry. These methods are simple to operate, low in cost, and can be performed simultaneously on a large scale, which is conducive to their clinical application and greatly reduces the proportion of patients requiring further imaging examinations such as MRI for diagnosis.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a distribution chart of the levels of three blood biomarkers in the experimental group of Example 1. Figure 2 This is a distribution chart of the levels of three blood biomarkers in the verification group of Example 1 of this application. (Attached) Figure 1-2In the diagram, the center line represents the median (Q2); the upper and lower boundaries of the box represent the upper quartile (Q3) and lower quartile (Q1), respectively; the lower quartile extends downwards from Q1, ending at min (minimum value, Q1 - 1.5 × IQR); the upper quartile extends upwards from Q3, ending at max (maximum value, Q3 + 1.5 × IQR); IQR = Q3 - Q1, reflecting the dispersion of the middle 50% of the data. The significance level is indicated by *, where **** indicates p < 0.0001; *** indicates p < 0.001; ** indicates p < 0.01; * indicates p < 0.05; and ns indicates p ≥ 0.05 (not significant).

[0024] Figure 3 This is a correlation analysis graph showing the results of the three blood biomarkers and the rating scale test for all subjects, as shown in Example 2.

[0025] Figure 4 This is the ROC curve of the three blood biomarker detection results of the subject shown in Example 3.

[0026] Figure 5 This is the calibration curve for the combined use of three blood biomarkers as shown in Example 4. Detailed Implementation

[0027] The embodiments of this application will now be described in more detail. It should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0028] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. Unless otherwise defined, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While the methods and materials described herein, or any equivalent methods and materials, may also be used in the implementation or testing of the invention, preferred methods and materials are now described.

[0029] It should be understood that although the terms “first,” “second,” “third,” etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. Features defined as “first” or “second” may explicitly or implicitly include one or more of that feature. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0030] Where numerical ranges are provided, it should be understood that every intermediate value between the upper and lower limits of the range and any other specified or intermediate value within the specified range is covered within the present invention. The upper and lower limits of these smaller ranges may be independently included in the smaller range and are also covered within the present invention, subject to any explicitly excluded limits within the specified range. Where a specified range includes one or two limits, the range excluding any or both of those included limits is also included within the present invention. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0031] Complement Component 3 (C3), as described in this article, is a core pivotal protein in the human complement system. It is primarily synthesized by hepatic macrophages and hepatocytes, and is also expressed by astrocytes, microglia, and neurons in the nervous system. In neurodegenerative diseases, complement C3 participates in microglia-mediated synaptic pruning. Its overactivation may promote β-amyloid (Aβ) deposition and tau protein phosphorylation through classical or alternative pathways, exacerbating the pathological progression of Alzheimer's disease (AD). In Parkinson's disease (PD), C3 may participate in disease progression by regulating neuroinflammation induced by α-Syn aggregation. Changes in C3 levels in clinical practice may serve as an auxiliary indicator for monitoring the inflammatory state in neurodegenerative diseases.

[0032] The α-Syn (α-synuclein) discussed in this article is a protein widely expressed in the nervous system, and its abnormal aggregation is closely related to various neurodegenerative diseases. Human α-Syn is composed of... SNCA The gene encodes a protein of approximately 140 amino acids located on chromosome 4q21. It contains six exons, and its transcription product is translated into a protein. α-Syn is mainly expressed in the presynaptic terminals, neuronal cell bodies, and axons of the central nervous system, especially in dopaminergic neurons, and is therefore associated with many neurodegenerative diseases such as Parkinson's disease (PD), dementia with lewy body (DLB), and multiple system atrophy (MSA).

[0033] The NfL discussed in this article refers to the neurofilament light chain (NF), a protein found in the nervous system and a major cytoskeletal protein of neuronal axons. NfL can serve as a biomarker for detecting and monitoring damage and degenerative changes in the nervous system. With neuronal damage and axonal degeneration, NfL is released into the extracellular space, subsequently entering the cerebrospinal fluid and bloodstream. For example, in patients with Alzheimer's disease (AD), NfL levels significantly increase as the disease progresses; detecting NfL concentrations in cerebrospinal fluid or blood can aid in the diagnosis of AD. NfL is an important biomarker that contributes to the diagnosis, prognostic assessment, and monitoring of disease progression in clinical practice and research of neurological disorders.

[0034] The levels mentioned in this article refer to the concentrations / contents corresponding to the blood biomarker test results of the subjects.

[0035] The test sample described herein refers to a mixture obtained from an animal or human body and used for in vitro laboratory analysis, which may contain analytes, including but not limited to proteins, hormones, antibodies, or antigens. The test sample may be diluted with a diluent or buffer solution as needed before use. Typical test samples include bodily fluids such as blood, blood derivatives, serum, plasma, urine, cerebrospinal fluid, saliva, synovial fluid, and emphysema effusion. The test sample applicable to this application refers to blood samples, including serum, plasma, and whole blood samples.

[0036] The "ROC" and "ROC curve" mentioned in this article refer to the Receiver Operating Characteristic Curve, a graphical tool used to assess the accuracy of diagnostic tests. It reflects a comprehensive indicator of continuous variables such as sensitivity and specificity, and is used for binary classification. The ROC curve plots the true positive rate (sensitivity) on the ordinate and the false positive rate (1 - specificity) on the abscissa, visually demonstrating the performance of the diagnostic test by plotting the relationship between the true positive rate and the false positive rate at different diagnostic cutoff values.

[0037] The C-index discussed in this article is the consistency index, a non-parametric metric that measures the model's discriminative ability. The C-index represents the proportion of correctly predicted probabilities among randomly selected positive and negative sample pairs. AUC represents the area under the ROC curve, ranging from 0.5 to 1. The closer the AUC is to 1, the larger the area under the curve, indicating higher accuracy of the diagnostic test. Observing the shape of the ROC curve also provides an intuitive understanding of the diagnostic test's performance. If the curve is close to the upper left corner, it indicates a high true positive rate and a low false positive rate, signifying good diagnostic performance; if the curve is close to the diagonal (from the lower left to the upper right corner), the diagnostic performance is poor. In binary classification models, C-index is equivalent to AUC.

[0038] The Optimal Cutoff mentioned in this article refers to the optimal cutoff value, which is the biomarker diagnostic threshold determined by maximizing the Youden index. It is a threshold that distinguishes between positive and negative diagnostic results. In ROC curves, the selection of the cutoff value is a crucial step, as different cutoff values ​​lead to different combinations of true positive and false positive rates. The Concentration mentioned in this article refers to the biomarker concentration / content corresponding to the optimal cutoff value.

[0039] The Youden index described in this article is a commonly used method for determining the optimal cutoff value. Its formula is: Youden Index = Sensitivity + Specificity - 1. By calculating the Youden index corresponding to different cutoff values, the cutoff value at which the Youden index is maximized is found. This value is considered the optimal diagnostic cutoff value after balancing the true positive rate and the true negative rate (specificity).

[0040] Sensitivity (also known as true positive rate, sensitivity, or accuracy) refers to the proportion of a population with a disease whose diagnostic test results are positive (i.e., the ability to correctly diagnose a diseased patient as having the disease, or the probability of a patient being diagnosed as positive). Higher sensitivity results in a lower false negative rate. The formula is: True positive rate = Number of true positives / (Number of true positives + Number of false negatives). TPR_Optimal_Cutoff (TPR) represents the sensitivity corresponding to the optimal cutoff value, TPR = TP / (TP + FN).

[0041] The false positive rate (1-specificity) described in this article refers to the proportion of a positive diagnostic test result in a population without the disease. The calculation formula is: False positive rate = Number of false positives / (Number of false positives + Number of true negatives). The FPR_Optimal_Cutoff (FPR) represents the false positive rate corresponding to the optimal cutoff value, FPR = FP / (FP + TN).

[0042] Specificity (also known as true negative rate) as described in this article refers to the proportion of samples that are actually negative but are judged as negative (i.e., the ability to correctly identify cases that are not actually infected, i.e., the proportion of test results that are negative). The calculation formula is 1 - false positive rate = 1 - FPR = TN / (TN + FP). The higher the specificity, the lower the misdiagnosis rate.

[0043] The Accuracy mentioned in this article refers to the overall diagnostic accuracy, which is calculated using the formula (TP+TN) / (TP+TN+FP+FN).

[0044] The light-initiated chemiluminescence assay described in this article is an ultrasensitive molecular detection technology based on energy transfer. It achieves quantitative analysis of biomolecules by initiating a chemiluminescence reaction through photoexcitation. Based on a unique chemiluminescence principle, this technology provides a sensitive and specific homogeneous immunoassay platform characterized by its nanoscale size, high sensitivity, photoinitiation, wash-free operation, and high throughput. Its detection principle is as follows: When a 680nm laser irradiates a donor microsphere, the photosensitizer on its surface is excited and transfers energy to oxygen molecules, generating singlet oxygen with a short diffusion distance (approximately 200nm). If the target molecule causes the donor microsphere to approach the acceptor microsphere, which is coupled with a recognition molecule and coated with a chemiluminescent substrate, within 200nm, the singlet oxygen triggers an oxidation reaction of the substrate on the acceptor microsphere, releasing a light signal at 420-620nm. The intensity of this light signal is positively correlated with the concentration of the target molecule, enabling quantitative detection of the target molecule. This process requires no washing and is a homogeneous detection system.

[0045] The turbidimetric method described in this article, also known as turbidity determination or turbidimetric method, is an analytical method based on the interaction between light and matter. It utilizes the principle that when light passes through a solution containing suspended particles, the intensity of transmitted or scattered light changes due to alterations in the absorption, scattering, and transmission of light by the particles. The content or concentration of suspended substances in the solution is determined by measuring the light intensity. This method is simple to operate and allows for rapid detection.

[0046] This application will now be described in more detail with reference to the accompanying drawings.

[0047] Currently, a comprehensive blood-based diagnostic method has not yet been fully established for the early or clinical diagnosis of multiple system atrophy (MSA). The pathological features of MSA also include the abnormal deposition of α-synuclein in glial cells, forming intracellular inclusion bodies. However, α-synuclein has not shown high diagnostic accuracy in distinguishing MSA from healthy controls, particularly with differences in the detection accuracy of α-synuclein levels in cerebrospinal fluid and blood samples. This may be related to differences in detection methods and the influence of erythrocyte lysis. Some studies have used the seed amplification assay (SAA) technique to detect α-synuclein in saliva and serum samples, and the results also show inconsistent diagnostic accuracy in MSA and healthy individuals. This indicates that the SAA method for detecting MSA-related α-synuclein aggregates has limited discriminative ability and is not yet mature, making it difficult to achieve highly accurate diagnosis and large-scale application of MSA.

[0048] The inventors of this application have developed a photo-induced chemiluminescence detection and analysis platform suitable for measuring α-syn and NfL in blood samples, which can meet the detection needs of α-syn and NfL in blood samples. Based on this, its clinical application value in the diagnosis of neurodegenerative diseases was further explored. Through research on α-syn, C3, NfL, and their specific combinations, the roles of these blood biomarkers in the early stages of MSA diagnosis were investigated. Among them, the multi-biomarker model including C3, as described in this application, has a high accuracy rate in diagnosing MSA.

[0049] This application provides, on the one hand, the use of blood biomarkers in the assessment or diagnosis of patients with multiple system atrophy, that is, the use of blood biomarkers in the diagnosis of distinguishing patients with multiple system atrophy from healthy individuals; on the other hand, the use of blood biomarkers in the preparation of diagnostic products for patients with multiple system atrophy; and on the third hand, a product for the diagnosis of multiple system atrophy.

[0050] In this application, the blood biomarker used for the assessment or diagnosis of patients with multiple system atrophy is a combination of at least one of α-Syn and NfL with C3.

[0051] That is, blood biomarkers can be a combination of C3 and α-Syn; a combination of C3 and NfL; or a combination of C3, NfL, and α-Syn. Multi-biomarker models including C3 can all serve as blood biomarkers for the diagnosis of multiple system atrophy, and they have high diagnostic accuracy.

[0052] The method in this application extends the detection range to the early stage of multiple system atrophy patients. Compared with other early diagnostic methods for neurodegenerative diseases that require cerebrospinal fluid or solid tissue, the method can be performed using readily available blood, which is less invasive, simple to operate, and has low detection cost.

[0053] In some embodiments of this application, the area under the receiver operating characteristic curve (AUC) generated by the blood biomarker or combination thereof in the detection results of samples from patients with multiple system atrophy and healthy individuals is >0.8; preferably >0.9.

[0054] In some embodiments of this application, the presence of multiple system atrophy (MSA) can be indicated by detecting the levels of blood biomarkers in a blood sample; if these levels are higher than those in a healthy control group. Alternatively, the presence of MSA can be indicated by detecting the levels of the aforementioned blood biomarkers in a blood sample; if these levels are lower than those in a healthy control group.

[0055] That is, the blood sample of the subject is tested to obtain the corresponding blood biomarker level, and the level is compared with the corresponding blood biomarker level of a healthy group (i.e., a healthy control group) who do not have multiple system atrophy or are not at risk of developing multiple system atrophy. If the blood biomarker level of the subject is relatively higher or lower, it indicates that the subject has the risk of developing multiple system atrophy.

[0056] In some embodiments of this application, the concentration of blood biomarkers in a blood sample may be detected as being higher than the optimal threshold value of the blood biomarkers to indicate the risk of multisystem atrophy; or, the concentration may be detected as being lower than the optimal threshold value of the blood biomarkers to indicate the risk of multisystem atrophy.

[0057] By setting an optimal threshold, test results can be standardized, enabling efficient exclusion diagnosis of multiple system atrophy with high accuracy and discrimination. Furthermore, setting the optimal threshold also allows for automated judgment of test results, further improving testing speed and reducing labor costs and errors from subjective human judgment.

[0058] In some embodiments of this application, the optimal cutoff value for α-Syn can be 22.900 ng / mL. In some embodiments of this application, the optimal cutoff value for NfL can be 39.320 pg / mL. In some embodiments of this application, the optimal cutoff value for C3 can be 116.900 mg / dL. This optimal cutoff value refers to the blood biomarker concentration that distinguishes MSA from healthy individuals. It can also be used as a preset reference level for assessing or diagnosing MSA patients, allowing direct comparison of the subject's blood biomarker concentration with this preset reference level. This standardizes the test results, enabling automated judgment of subject test results, facilitating simultaneous detection and judgment of large-scale samples, and demonstrating high clinical application value.

[0059] In some embodiments of this application, the optimal threshold value of the blood biomarker is determined by calculating the maximum Yangen index using a receiver operating characteristic (ROC) curve. The blood biomarker may be α-Syn, NfL, or C3.

[0060] The optimal cutoff value for α-Syn is determined by calculating the maximum Youden index using the receiver operating characteristic curve. Further, it is determined by combining the Youden index with sensitivity and specificity; preferably, the optimal cutoff value for α-Syn is determined by sensitivity > 65% and specificity > 70%.

[0061] The optimal cutoff value for NfL is determined by calculating the maximum Youden index using the receiver operating characteristic curve. Further, it is determined by combining the Youden index with sensitivity and specificity; preferably, the optimal cutoff value for NfL is determined by sensitivity > 70% and specificity > 90%.

[0062] The optimal cutoff value for C3 is determined by calculating the maximum Youden index using the receiver operating characteristic curve. Further, it is determined by combining the Youden index with sensitivity and specificity; preferably, the optimal cutoff value for C3 is determined by sensitivity > 70% and specificity > 70%.

[0063] It should be noted that the levels of NfL and α-Syn are positively correlated with the degree of motor impairment in patients with multiple system atrophy. Specifically, the correlation between NfL and α-Syn levels and UMSARS-II scores can be analyzed by correlating them with UMSARS-II scores.

[0064] C3 levels were negatively correlated with autonomic dysfunction in patients with multiple system atrophy. Specifically, the correlation between C3 levels and the Scolinson's Disease Autonomic Outcomes Assessment Scale (SCOPA_AUT) score could be analyzed.

[0065] In some embodiments of this application, the concentration of blood biomarkers can be determined by photochemiluminescence detection and turbidimetry. Specifically, the levels of NfL and α-Syn can be determined by photochemiluminescence detection, and the level of C3 can be determined by turbidimetry.

[0066] The method for assessing or diagnosing patients with multiple system atrophy (MSA) described in this application involves detecting a subject's blood sample using a diagnostic product, obtaining the concentration of a corresponding blood biomarker, comparing it to the optimal cutoff value of that blood biomarker, and using the comparison result as one of the indicators for diagnosing MSA. Specifically, this can be achieved by detecting the subject's blood sample using a photochemiluminescence assay, comparing the subject's α-Syn and NfL concentrations to the optimal cutoff values ​​of their respective blood biomarkers, and indicating a risk of Parkinson's disease when the subject's α-Syn and NfL concentrations are higher than their corresponding optimal cutoff values. Alternatively, it can be achieved by detecting the subject's blood sample using a turbidimetric method (immunoturbidimetric assay), comparing the subject's C3 concentration to its corresponding optimal cutoff value, and indicating a risk of MSA when the subject's C3 concentration is lower than the optimal cutoff value.

[0067] The diagnostic products for patients with multiple system atrophy mentioned above can be testing reagents, testing kits, etc.

[0068] The diagnostic reagent for patients with multiple system atrophy provided in this application is a reagent containing blood biomarkers, wherein the blood biomarkers are at least one of NfL and α-Syn combined with C3. Specifically, multiple biomarker models such as C3+NfL, C3+α-Syn, and C3+α-Syn+NfL can be used as blood biomarkers for the diagnosis of patients with multiple system atrophy.

[0069] In some embodiments of this application, the detection reagent includes a photo-induced chemiluminescence detection reagent, which can be applied to the detection of NfL and α-Syn.

[0070] In some embodiments of this application, the detection reagent also includes a C3 detection reagent, which can be determined by immunoturbidimetric assay.

[0071] For example, a photochemical assay reagent may include reagent A and reagent B. Reagent A includes luminescent microspheres and a blood biomarker antibody A bound to the luminescent microspheres. Reagent B includes a biotinylated blood biomarker antibody B. Antibodies A and B are antibody molecules capable of specifically binding to different epitopes of the corresponding blood biomarkers. When the biomarker measured in the assay reagent is NfL, antibody A is NfL Ab1 and antibody B is NfL Ab2. When the biomarker measured in the assay reagent is α-Syn, antibody A is α-Syn Ab1 and antibody B is α-Syn Ab2.

[0072] The kit provided in this application for the assessment or diagnosis of patients with multiple system atrophy is a kit containing the above-mentioned detection reagents. Specifically, it may include detection reagents containing blood biomarkers, wherein the blood biomarkers are at least one of NfL and α-Syn in combination with C3. It may also include a C3 detection reagent, which can be determined by immunoturbidimetric assay. That is, the kit includes the above-mentioned photochemiluminescence detection reagent and immunoturbidimetric detection reagent.

[0073] The sample to be tested described in this application embodiment is a blood sample, which includes whole blood, plasma, serum, etc.

[0074] Multiple system atrophy (MSA) is a typical neurodegenerative disease. When patients exhibit significant motor impairment, cognitive impairment, anxiety, and other clinical symptoms, at least half of their neurons have already died. Due to the non-regenerative nature of neurons, it is currently difficult to effectively replenish the dead neurons. Therefore, early diagnosis to identify preclinical patients and taking effective preventive measures, such as preventing the death of dopaminergic neurons, to block the onset of the disease is particularly important. Non-motor symptoms such as gene mutations, sleep disorders, or decreased sense of smell may exist for years before the onset of motor symptoms. Therefore, using the C3, α-Syn, and NfL multi-biomarker model described in this application as early diagnostic biomarkers for MSA has significant clinical implications. This method can not only reduce the proportion of confirmation using costly and highly invasive methods such as imaging techniques (e.g., MRI) or detecting biomarkers in peripheral body fluids (e.g., α-synuclein in cerebrospinal fluid), but also improve diagnostic accuracy, especially for early MSA diagnosis.

[0075] Next, by conducting blood biomarker testing and disease scale assessment on the subjects, we will explore and verify the clinical application value of the combination of blood biomarkers NfL, C3, and α-Syn in the early diagnosis of multiple system atrophy.

[0076] Example 1 1. Subjects: This study selected 80 patients with multiple system atrophy (MSA) and 231 age- and sex-matched healthy controls (HC). These subjects were divided into an experimental group and a validation group. The experimental group had 54 MSA patients and 180 HC patients, while the validation group had 26 MSA patients and 51 HC patients. The subjects in the experimental and validation groups were similar in age and had a relatively even sex ratio.

[0077] sample: In this embodiment, blood samples from the subject were collected via venipuncture into test tubes containing EDTA and allowed to stand at room temperature for 15 minutes. Subsequently, the samples were centrifuged at 3000 rpm for 10 minutes at 4°C to separate the plasma, which was then stored. Used as a sample for testing at 80°C.

[0078] 2. Experimental Methods: (1) Rating scales were used to quantitatively assess the specific symptoms of the subjects. The Epworth Sleepiness Scale (ESS) Scales for Outcomes in Parkinson's Disease–Autonomic (SCOPA-AUT) 16 tests for olfactory sticks (SS-16) Rapid Eye Movement Sleep Behavior Disorder Questionnaire (Hong Kong China Version, RBD-HK) Mini-Mental State Examination (MMSE) Hamilton Depression Scale (17-item version) (HAMD-17) Hamilton Anxiety Scale (HAMA) Unified Multiple System Atrophy Rating Scale - Part II (UMSARS-II) (2) Detection of the levels of three blood biomarkers in the plasma of the subjects α-Syn and NfL were detected in LiCA using a photo-induced chemiluminescence method. ® Platform testing was performed, and C3 was determined by turbidimetry. The specific detection steps of the photo-induced chemiluminescence detection method and turbidimetry method described in this embodiment can be methods known in the art and are not limited here.

[0079] The results of the rating scale tests and the results of blood biomarker tests for the above subjects are recorded in the table below. All variables are expressed as median and upper and lower quartiles.

[0080] The results of the three blood biomarkers in the above subjects were plotted as a median box plot, such as... Figure 1 (Experimental group) and Figure 2 (Validation group) shows the differences in the distribution of blood biomarkers in multiple system atrophy and its different subtypes and healthy control groups.

[0081] 3. Experimental Results Table 1 Clinical characteristics detection table of the experimental group

[0082] Note: MSA: Multiple system atrophy; MSA-C: Cerebellar multiple system atrophy; MSA-P: Parkinsonian multiple system atrophy; HC represents the healthy control group. All variables are presented as median and interquartile range (IQR) (upper quartile, lower quartile).

[0083] Table 2 Clinical characteristics detection table of the validation group

[0084] Note: MSA: Multiple system atrophy; MSA-C: Cerebellar multiple system atrophy; MSA-P: Parkinsonian multiple system atrophy; HC represents the healthy control group. All variables are presented as median and interquartile range (IQR) (upper quartile, lower quartile).

[0085] 4. Results Analysis Tables 1 and 2 show that the photo-induced chemiluminescence detection method was used in LiCA... ® The platform can meet the detection requirements of blood biomarkers α-Syn and NfL levels, and the turbidimetric method can meet the detection requirements of blood biomarker C3 levels, with good sensitivity.

[0086] By combining Table 1-2, Figure 1-2 It was found that there were differences in the plasma distribution levels of the three biomarkers between multiple system atrophy and the healthy control group, while there were no significant differences in the levels of the three biomarkers between the cerebellar multiple system atrophy and Parkinsonian multiple system atrophy subgroups.

[0087] Plasma α-Syn levels were significantly higher in the MSA group than in the HC group (p<0.001), and α-Syn levels in MSA-C were also significantly higher than in HC (p<0.0001). Plasma α-Syn showed significant potential in distinguishing between MSA and HC.

[0088] Compared with HC, plasma NfL levels were significantly elevated in the MSA group and its subtypes (p < 0.0001). NfL also has significant potential in differentiating between MSA and HC.

[0089] Plasma C3 levels in the MSA group were significantly lower than those in the HC group (p < 0.0001), and this difference was more pronounced in the MSA-C subgroup than in the MSA-P subgroup. Plasma C3 levels may help differentiate between MSA and HC.

[0090] Example 2 The Spearman rank correlation coefficient test was used to compare and analyze the results of the three blood biomarkers and the symptom assessment results of the subjects obtained in Example 1 above, to evaluate the correlation between the levels of the three blood biomarkers and the symptoms of patients with multiple system atrophy. The results are as follows: Figure 3 As shown.

[0091] Results Analysis Combination Figure 3 It was found that C3 was significantly negatively correlated with cognitive function in patients with multiple system atrophy. NfL was significantly positively correlated with the severity of motor symptoms in patients with multiple system atrophy.

[0092] Example 3 The ROC curves of the three blood biomarker test results of the subjects in Example 1 above were generated (e.g.) Figure 4 a, Figure 4 As shown in c), and further generate ROC curves for the combined use of multiple blood biomarkers (such as...). Figure 4 b、 Figure 4 As shown in d), the performance of single-biomarker models and multi-biomarker models in distinguishing between patients with multiple system atrophy (MSA) and healthy individuals (HC) was evaluated. The correlation analysis results obtained based on the ROC curves are shown in the table below.

[0093] 2. Experimental Results Table 3. Detailed single-model information on three biomarkers in MSA patients and healthy individuals.

[0094] Note: MSA indicates multiple system atrophy; HC indicates healthy control group.

[0095] Table 4. Detailed information on the combined model of three biomarkers in MSA patients and healthy individuals.

[0096] Note: MSA: indicates multiple system atrophy; HC: indicates healthy control group.

[0097] 3. Results Analysis Combined with Table 3, Figure 4 a and Figure 4 As shown in c, plasma C3, NfL, and α-Syn exhibited certain distinguishing accuracy in both the multiple system atrophy and healthy control groups. Specifically, plasma C3 showed an AUC of 0.749 in the experimental group (sensitivity: 0.722; specificity: 0.736) and 0.769 in the validation group (95% confidence interval [CI]: 0.661–0.877); the optimal cutoff value was determined to be 116.900 mg / dL using the maximum Yangen index (sensitivity: 0.722; specificity: 0.736). Plasma NfL showed an AUC of 0.899 in the experimental group and 0.850 in the validation group (95% confidence interval [CI]: 0.743–0.947); the optimal cutoff value was determined to be 39.320 pg / mL using the maximum Yangen index (sensitivity: 0.759; specificity: 0.929). Plasma α-Syn achieved an AUC of 0.667 in the experimental group and 0.624 in the validation group (sensitivity: 0.685; specificity: 0.787); the optimal cutoff value was determined to be 22.900 ng / mL using the maximum Yorkden index (sensitivity: 0.685; specificity: 0.787). It did not demonstrate high accuracy in distinguishing between MSA and HC.

[0098] In multiple system atrophy and healthy control groups, plasma NfL and C3 in single biomarker models showed some accuracy in distinguishing between MSA and HC.

[0099] According to Table 4, Figure 4 b and Figure 4 As shown in Figure d, the multi-biomarker models of C3+NfL, C3+α-Syn, and the combination of C3+NfL+α-Syn all exhibited higher discrimination accuracy. Among them, the C3+NfL combination showed the best discrimination accuracy, with an AUC of 0.910, sensitivity of 0.870, and specificity of 0.872 in the experimental group; an AUC of 0.926 in the validation group; and a 95% confidence interval [CI] of 0.866–0.971. The C3+NfL+α-Syn combination showed the best discrimination accuracy, with an AUC of 0.934, sensitivity of 0.833, and specificity of 0.917 in the experimental group; an AUC of 0.919 in the validation group; and a 95% confidence interval [CI] of 0.842–0.975.

[0100] The combination of three biomarkers, C3+NfL+α-Syn, outperformed the combination of two biomarkers, C3 and NfL or C3 and α-Syn, in distinguishing between MSA and HC.

[0101] C3 is a core molecule in the complement activation pathway and plays a crucial role in innate immunity. It is cleaved by C3 convertase into C3a and C3b. C3b then binds to C3 convertase to form C5 convertase. C3a mediates chemotaxis and inflammatory responses. C5 convertase cleaves C5 to form C5a and C5b, which in turn form membrane attack complexes with C6, C7, C8, and C9, leading to cell lysis. Overexpression of C3 in astrocytes exacerbates the death of dopaminergic neurons. The unique astrocyte-neuronal communication in MSA (mass glial cells) helps observe differences in C3 levels between MSA and HC (hypertrophic glial cells), aiding in the differentiation between the two. Combining C3 with other relevant blood biomarkers such as NfL and α-Syn can further improve the accuracy of distinguishing between MSA and HC.

[0102] Example 4 The model using blood biomarkers in combination as described in Example 3 above generates calibration curves (such as...). Figure 5 As shown in the figure, it is used to evaluate the accuracy of the model's predicted probabilities.

[0103] Results Analysis according to Figure 5 As shown in the experimental and validation groups, the use of multi-biomarker models for the differentiation of MSA and HC has significant accuracy, and the combination of C3+NfL and C3+α-Syn+NfL multi-biomarker models showed high accuracy in the diagnosis of MSA.

[0104] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. Use of the following blood biomarkers in the preparation of diagnostic products for patients with multiple system atrophy, wherein the blood biomarkers are at least one of α-Syn and NfL in combination with C3.

2. The use according to claim 1, characterized in that, The area under the receiver operating characteristic curve (AUC) generated by the blood biomarker or combination thereof in the results of testing samples from patients with multiple system atrophy and healthy individuals is greater than 0.8; preferably greater than 0.

9.

3. The use according to claim 2, characterized in that, The optimal threshold for the blood biomarker was determined by calculating the maximum Oden index using the receiver operating characteristic curve.

4. The use according to claim 3, characterized in that, The optimal critical value of α-Syn is determined by combining the Youden index with sensitivity and specificity; preferably, it is determined by sensitivity > 65% and specificity > 70%. Alternatively, the optimal critical value of NfL can be determined by combining the Youden index with sensitivity and specificity; preferably, it can be determined by sensitivity > 70% and specificity > 90%. Alternatively, the optimal critical value of C3 can be determined by combining the Youden index with sensitivity and specificity; preferably, it can be determined by sensitivity > 70% and specificity > 70%.

5. The use according to claim 3, characterized in that, A blood biomarker concentration above its optimal threshold in a blood sample indicates a risk of multiple system atrophy; or a blood biomarker concentration below its optimal threshold indicates a risk of multiple system atrophy.

6. The use according to claim 5, characterized in that, A blood sample containing α-Syn or NfL at a concentration higher than its corresponding optimal threshold indicates a risk of multiple system atrophy; or a blood sample containing C3 at a concentration lower than its corresponding optimal threshold indicates a risk of multiple system atrophy.

7. The use according to claim 5, characterized in that, The concentration of the blood biomarkers was determined by photo-induced chemiluminescence detection or turbidimetric method.

8. The use according to claim 5, characterized in that, The blood samples include whole blood, plasma, and serum.

9. A kit for assessing or diagnosing patients with multiple system atrophy, characterized in that, The invention includes a test reagent containing a blood biomarker, wherein the blood biomarker is a combination of at least one of α-Syn and NfL with C3.

10. The reagent kit according to claim 9, characterized in that, The detection reagents include photo-induced chemiluminescence detection reagents.

Images