Blood biomarkers for assessing or diagnosing parkinson's disease patients and uses thereof

Abstract

The application relates to a blood biomarker for evaluating and diagnosing Parkinson's disease and an application thereof. The blood biomarker is SERPINA3 and / or alpha-Syn. By using the biomarker as a diagnostic biomarker for Parkinson's disease, early diagnosis of Parkinson's disease can be assisted by detecting the SERPINA3 level and the alpha-Syn level in a blood sample, the trauma is small, the operation is simple, and the method is suitable for most patients and has high patient acceptance; and the SERPINA3 and alpha-Syn levels in the blood sample can be determined by a photochemical luminescence detection method, the detection sensitivity is high, the accuracy is high, and early, efficient and high-precision diagnosis of Parkinson's disease can be realized.

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 Parkinson's disease and its use. Background Technology

[0002] Parkinson's disease (PD) is a chronic, progressive neurodegenerative disease that typically affects middle-aged and elderly individuals, with its incidence increasing with age. PD primarily impacts dopamine neurons in the brain, leading to neuronal death and consequently affecting the brain's control over muscles. The main manifestations include motor dysfunction, cognitive impairment, and sensory dysfunction. Common symptoms include resting tremor, rigidity, bradykinesia, memory loss, poor concentration, depressed mood, and sleep disturbances.

[0003] Current auxiliary diagnostic methods help assess Parkinson's disease (PD) and rule out other diseases. For example, magnetic resonance imaging (MRI) can observe changes in brain structures such as the substantia nigra and striatum, ruling out other diseases such as cerebrovascular disease and brain tumors, but its diagnostic help for PD is limited. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) can detect the distribution of dopamine transporter (DAT), which helps in the early diagnosis of PD, but their ability to differentiate Parkinson's disease from Parkinsonian syndromes (such as multiple system atrophy, progressive supranuclear palsy, etc.) is limited. Some studies have made it possible to directly detect pathological biomarkers using the seed amplification assay (SAA) technique, but given the difficulty of obtaining cerebrospinal fluid, patient acceptance of this method is low. Moreover, these auxiliary diagnostic methods are costly, complex to operate, and may carry various risks, making them unsuitable for most patients.

[0004] Currently, a blood-based diagnostic method has not been fully established for either the early diagnosis or clinical diagnosis of Parkinson's disease. Therefore, it is necessary to seek blood biomarkers applicable to Parkinson's disease patients to facilitate a low-cost, simple, and widely applicable auxiliary examination for Parkinson's disease. Summary of the Invention

[0005] To address or partially address the problems existing in related technologies, this application provides a blood biomarker for assessing and diagnosing patients with Parkinson's disease and its uses. It can assist in the early diagnosis and continuous monitoring of Parkinson's disease, 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 Parkinson's disease assessment or diagnostic products, wherein the blood biomarkers are SERPINA3 and / or α-Syn.

[0007] In some embodiments, the area under the receiver operating characteristic (ROC) curve generated by the blood biomarker or combination thereof in the results of testing samples from Parkinson's disease patients and healthy individuals is greater than 0.8; preferably greater than 0.85.

[0008] In some embodiments, when the blood biomarker is a combination of SERPINA3 and α-Syn, the optimal threshold value of the blood biomarker is determined by calculating the maximum Oden index using a receiver operating characteristic curve.

[0009] In some implementations, the optimal critical value of α-Syn is determined by combining the Youden index with sensitivity and specificity.

[0010] In some preferred embodiments, the optimal critical value of α-Syn is determined by sensitivity > 70% and specificity > 85%.

[0011] In some preferred embodiments, the optimal threshold value of SERPINA3 is determined by combining the Youden index with sensitivity and specificity.

[0012] In some implementations, the optimal threshold for SERPINA3 is determined by sensitivity > 75% and specificity > 85%.

[0013] In some implementations, detecting a concentration of SERPINA3 in a blood sample that is above the optimal threshold for SERPINA3 indicates a risk of Parkinson's disease.

[0014] In some implementations, detecting a concentration of α-Syn in a blood sample that is above the optimal threshold for α-Syn indicates a risk of Parkinson's disease.

[0015] In some embodiments, the levels of the blood biomarkers are determined by photo-induced chemiluminescence detection.

[0016] In some embodiments, the blood sample includes whole blood, plasma, and serum.

[0017] A second aspect of this application provides a kit for the assessment or diagnosis of Parkinson's disease, comprising a detection reagent containing a blood biomarker, said blood biomarker being SERPINA3 and / or α-Syn.

[0018] In some embodiments, the detection reagent is a photochemiluminescence detection reagent.

[0019] It is worth noting that the uses described in this application are for purposes other than disease diagnosis.

[0020] The technical solution provided in this application may include the following beneficial results: Using SERPINA3 and / or α-Syn as biomarkers to assist in the assessment or diagnosis of Parkinson's disease, these biomarkers are markers in blood samples. They not only enable minimally invasive testing, making them suitable for most patients, but also have low testing costs, simple operation, and standardized test results. Furthermore, they can achieve efficient exclusion diagnosis of Parkinson's disease with high diagnostic accuracy and discrimination, which is beneficial for their clinical application in the early diagnosis and prognostic assessment of Parkinson's disease.

[0021] Photocatalytic chemiluminescence detection can meet the sensitivity requirements of low-value blood samples, accurately measuring SERPINA3 and α-Syn levels. It is simple to operate, has low detection cost, and provides high accuracy and discrimination of results. It can greatly reduce the proportion of patients who need further MRI or PET examinations for diagnosis, which is conducive to its clinical application.

[0022] 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

[0023] 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.

[0024] Figure 1 This is a distribution chart of the levels of five blood biomarkers in the experimental group of Example 1. Figure 2 This is a distribution chart of the levels of two blood biomarkers in the verification group of Embodiment 1 of this application. (Attached) Figure 1 and attached Figure 2 In 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).

[0025] Figure 3 This is a correlation analysis graph showing the results of two blood biomarkers and the rating scale test for all subjects, as shown in Example 2.

[0026] Figure 4 This is the ROC curve of the α-Syn and SerpinA3 detection results of the subjects shown in Example 3.

[0027] Figure 5 This is the calibration curve for the combined use of two blood biomarkers, α-Syn and SERPINA3, as shown in Example 4.

[0028] Figure 6 This is a nomograph of the five biomarkers shown in Example 4. In the figure, red dots represent the contribution of each factor to the score; Sex: represents gender, 0 represents female, 1 represents male; Age: represents age; Pr: represents the probability of disease. Detailed Implementation

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] SERPINA3, as described in this article, is a member of the Serpins protein family, which is a type of serine protease inhibitor. SERPINA3 The gene encodes the protein SERPINA3, located on human chromosome 14 at 14q32.13. The mature SERPINA3 protein has a molecular weight of 55–66 kDa, composed of three β-sheets and nine α-helices. Its core region contains a reactive central loop (RCL) that is easily cleaved by proteases. Cleavage can inhibit protease activity (e.g., chymotrypsin, cathepsin G, elastase) through conformational changes. SERPINA3 is primarily synthesized in the liver, released into the bloodstream, and expressed in astrocytes and microglia of the central nervous system. SERPINA3 is associated with both inflammation and neurodegenerative diseases. For example, in patients with hepatitis or cirrhosis, serum SERPINA3 levels may decrease due to impaired liver synthesis or increase due to inflammatory stimulation. Abnormal expression in the cerebrospinal fluid of patients with Alzheimer's disease (AD) may be involved in amyloid deposition or tau protein pathological processes.

[0034] This article describes α-Syn (α-synuclein), a protein widely expressed in the nervous system, whose abnormal aggregation is closely associated with various neurodegenerative diseases. Human α-Syn is composed of... SNCA Encoded in chromosome 4q21, this gene contains six exons, and its transcription product is translated into a protein of approximately 140 amino acids. 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).

[0035] The levels mentioned in this article refer to the concentrations / contents corresponding to the blood biomarker test results of the subjects.

[0036] 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 a solution obtained by diluting the sample, which may contain analytes, 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.

[0037] The Receiver Operating Characteristic Curve (ROC curve) described in this article is a graphical tool used to assess the accuracy of diagnostic tests. It is a comprehensive indicator reflecting the continuous variables of 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. By drawing curves showing the relationship between the true positive rate and the false positive rate at different diagnostic cutoff values, the performance of the diagnostic test can be visually demonstrated.

[0038] 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.

[0039] 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 the ROC curve, 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.

[0040] 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).

[0041] 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 calculation formula is: Sensitivity = Number of true positives / (Number of true positives + Number of false negatives). TPR_Optimal_Cutoff (TPR) represents the sensitivity corresponding to the Cutoff value, TPR = TP / (TP + FN).

[0042] 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). FPR_Optimal_Cutoff (FPR) represents the false positive rate corresponding to the cutoff value, FPR = FP / (FP + TN).

[0043] 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.

[0044] The Accuracy mentioned in this article refers to the overall diagnostic accuracy, which is calculated using the formula (TP+TN) / (TP+TN+FP+FN).

[0045] 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, achieving quantitative detection of the target molecule. This process requires no washing and is a homogeneous detection system.

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

[0047] The inventors of this application have developed a photo-induced chemiluminescence detection and analysis platform suitable for measuring α-syn and SERPINA3 in blood samples, which can meet the detection requirements of α-syn and SERPINA3 in blood samples. Based on this, the clinical application value in the diagnosis of neurodegenerative diseases was further explored. Through research on two plasma biomarkers and their specific combinations that have unique and important roles in distinguishing between early-stage Parkinson's disease patients and healthy controls, this application demonstrates that SERPINA3 and / or α-Syn can be used as blood biomarkers to assess or diagnose Parkinson's disease.

[0048] This application provides, in one aspect, the use of blood biomarkers in the assessment or diagnosis of Parkinson's disease; in another aspect, the use of blood biomarkers in the preparation of products for the assessment or diagnosis of Parkinson's disease; and in a third aspect, a product for the assessment or diagnosis of Parkinson's disease.

[0049] In this application, the blood biomarkers used to assess or diagnose Parkinson's disease patients are SERPINA3 and / or α-Syn.

[0050] In some embodiments of this application, SERPINA3 and α-Syn can be used alone as blood biomarkers for diagnosing Parkinson's disease. In this case, it is a single biomarker model that can be used to distinguish between early Parkinson's disease patients and healthy individuals.

[0051] In some embodiments of this application, SERPINA3 and α-Syn are combined as blood biomarkers for diagnosing Parkinson's disease. This is a multi-biomarker model, which has high accuracy in distinguishing between early-stage Parkinson's disease patients and healthy individuals.

[0052] In some preferred embodiments of this application, the blood biomarker is SERPINA3, or a combination of SERPINA3 and α-Syn is used as a blood biomarker for assessing or diagnosing Parkinson's disease patients. When the blood biomarker is a combination of SERPINA3 and α-Syn, it exhibits high sensitivity and accuracy.

[0053] The method in this application extends the detection range to the early stage of Parkinson's disease. Compared with other early diagnosis methods for Parkinson's disease 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.

[0054] In some embodiments of this application, or combinations thereof, the area under the receiver operating characteristic curve generated from the results of detecting samples from Parkinson's disease patients and healthy individuals is greater than 0.8; preferably greater than 0.85.

[0055] In some embodiments of this application, the presence of Parkinson's disease risk is indicated when the concentration of SERPINA3 in a blood sample is higher than the optimal threshold value for SERPINA3.

[0056] In some embodiments of this application, the presence of Parkinson's disease risk is indicated when the concentration of α-Syn in a blood sample is detected to be higher than the optimal threshold value for α-Syn.

[0057] That is, the blood sample of the subject is tested to obtain the level of the corresponding blood biomarker, and the level is compared with the corresponding optimal threshold value of the blood biomarker. When the level of the blood biomarker of the subject is higher than the corresponding optimal threshold value, it indicates that the subject has a risk of Parkinson's disease or a high risk of Parkinson's disease.

[0058] Therefore, when using a single biomarker model such as SERPINA3 or α-Syn, the level of the blood biomarker in the subject can be determined by comparing the concentration of SERPINA3 or α-Syn in the subject's blood sample with the level of its corresponding optimal threshold. Thus, a rapid and accurate diagnosis of Parkinson's disease can be achieved based on a single biomarker model.

[0059] When using a multi-biomarker model, such as the combination of SERPINA3 and α-Syn, the levels of blood biomarkers in the subjects are determined by comparing the concentrations of SERPINA3 and α-Syn in the subjects' blood samples with their corresponding optimal threshold values. This allows for rapid and accurate diagnosis of Parkinson's disease based on the multi-biomarker model.

[0060] By setting an optimal threshold, test results can be standardized, enabling efficient exclusion diagnosis of Parkinson's disease 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.

[0061] In some embodiments of this application, the optimal threshold value for α-Syn can be 24.700 ng / mL. In some embodiments of this application, the optimal threshold value for SERPINA3 can be 408.059 μg / mL.

[0062] The optimal threshold value refers to the concentration of blood biomarkers for diagnosing PD. This optimal threshold value can also be used as a preset reference level for blood biomarkers for diagnosing PD. This allows for direct comparison of the subject's blood biomarker concentration with the preset reference level, standardizing the test results and enabling automated judgment of the subject's test results. It also meets the requirements for simultaneous detection and judgment of large-scale samples, and has high clinical application value.

[0063] In some embodiments of this application, the optimal threshold value of the blood biomarker is determined by calculating the maximum Youden index using a receiver operating characteristic (ROC) curve. Preferably, the optimal threshold value of the blood biomarker is also determined by combining the Youden index with sensitivity and specificity. The blood biomarker may be SERPINA3, α-Syn, or a combination of SERPINA3 and α-Syn.

[0064] In some embodiments of this application, the optimal threshold value of α-Syn is determined by combining the Youden index with sensitivity and specificity; preferably, the optimal threshold value of α-Syn is determined by sensitivity > 60% and specificity > 65%; more preferably, the optimal threshold value of α-Syn is determined by sensitivity > 70% and specificity > 75%; even more preferably, the optimal threshold value of α-Syn is determined by sensitivity > 70% and specificity > 85%.

[0065] In some embodiments of this application, the optimal threshold value of SERPINA3 is determined by combining the Youden index with sensitivity and specificity; preferably, the optimal threshold value of SERPINA3 is determined by sensitivity > 60% and specificity > 65%; more preferably, the optimal threshold value of SERPINA3 is determined by sensitivity > 65% and specificity > 75%; even more preferably, the optimal threshold value of SERPINA3 is determined by sensitivity > 75% and specificity > 85%.

[0066] It should be noted that SERPINA3 levels are negatively correlated with olfactory dysfunction in Parkinson's disease patients. Specifically, the correlation between SERPINA3 levels and the 16-item olfactory stick test (SS-16) score can be analyzed.

[0067] The level of α-Syn showed a positive correlation with motor dysfunction in Parkinson's disease patients. Specifically, the correlation between α-Syn levels and MDS-UPDRS-III can be analyzed by correlating α-Syn levels with scores on the Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS-III).

[0068] The embodiments of this application can achieve efficient and accurate diagnosis of Parkinson's disease patients and healthy individuals by presenting the level of SERPINA3 and its association with Parkinson's disease patients, or by presenting the level of SERPINA3 and its association with Parkinson's disease patients; the accuracy of diagnosis of Parkinson's disease patients can be further improved by including the combination of SERPINA3 and α-Syn.

[0069] In some embodiments of this application, the levels of blood biomarkers are determined by photo-induced chemiluminescence immunoassay. Specifically, the levels of SERPINA3 and α-Syn are determined by photo-induced chemiluminescence immunoassay.

[0070] Specifically, the blood samples of the subjects are detected by photo-induced chemiluminescence detection reagents to obtain the content of the corresponding blood biomarkers, and the results are compared with the optimal cutoff value of the corresponding blood biomarkers as one of the indicators for assessing or diagnosing Parkinson's disease.

[0071] The aforementioned Parkinson's disease assessment or diagnostic products can be testing reagents, testing kits, etc. More specifically, they can be photochemiluminescence detection reagents, photochemiluminescence detection kits, etc.

[0072] The test reagent for assessing or diagnosing Parkinson's disease provided in this application embodiment is a test reagent containing blood biomarkers, wherein the blood biomarkers are SERPINA3 and / or α-Syn.

[0073] Specifically, the detection reagent may include reagent A and reagent B. Reagent A includes luminescent microspheres and blood biomarker antibody A bound to the luminescent microspheres, and reagent B includes biotinylated blood biomarker antibody B. Antibody A and antibody B are antibody molecules capable of specifically binding to different epitopes of corresponding blood biomarkers.

[0074] When the test reagent corresponds to a single biomarker model, for example, when the blood biomarker is SERPINA3, antibody A is SERPINA3 Ab1 and antibody B is SERPINA3 Ab2. When the biomarker measured in the test reagent is α-Syn, antibody A is α-Syn Ab1 and antibody B is α-Syn Ab2.

[0075] When the test reagent corresponds to a multi-biomarker model, i.e., when the biomarkers measured in the test reagent are SERPINA3 and α-Syn, antibody A consists of SERPINA3 Ab1 and α-Syn Ab1, and antibody B consists of SERPINA3 Ab2 and α-Syn Ab2. In this case, the test reagent may include reagent A-1, reagent B-1, reagent A-2, and reagent B-2. Reagent A-1 contains luminescent microspheres and SERPINA3 Ab1; reagent B-1 contains biotinylated SERPINA3 Ab2; reagent A-2 contains luminescent microspheres and α-Syn Ab1; and reagent B-2 contains biotinylated α-Syn Ab2. Reagents A-1 and B-1 form a set of reagents for measuring SERPINA3, and reagents A-2 and B-2 form a set of reagents for measuring α-Syn. These are used together to measure the levels of SERPINA3 and α-Syn in blood samples.

[0076] The test kit for Parkinson's disease assessment or diagnosis provided in this application embodiment is a test kit containing blood biomarkers, wherein the blood biomarkers are SERPINA3 and / or α-Syn.

[0077] That is, the kit includes the above-mentioned detection reagents; preferably, it includes the above-mentioned photochemiluminescence detection reagents.

[0078] When this kit is used to determine the biomarkers SERPINA3 and α-Syn, the reagents for determining SERPINA3 (comprising reagents A-1, B-1, A-2, and B-2) and the reagents for determining α-Syn can be combined with photosensitive microspheres coated with streptavidin for detection on a photo-induced chemiluminescence platform.

[0079] The sample to be tested in this application embodiment is a blood sample, which includes whole blood, plasma, serum, etc.

[0080] Parkinson's disease is a neurodegenerative disease. When patients exhibit obvious motor dysfunction 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 to stop the death of dopaminergic neurons and thus block the onset of the disease are particularly important. Non-motor symptoms such as gene mutations, sleep disorders, or decreased sense of smell may exist for several years before the onset of motor symptoms, and may be indications or biomarkers for early diagnosis of Parkinson's disease. Therefore, using SERPINA3 and / or α-Syn as early diagnostic biomarkers for Parkinson's disease as described in this application 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 PET) or detecting biomarkers in peripheral body fluids (e.g., α-synuclein in cerebrospinal fluid), but also improve the accuracy of Parkinson's disease diagnosis, especially the accuracy of early diagnosis.

[0081] Next, the participants were assessed using the Parkinson's Disease Rating Scale and tested for blood biomarkers to explore and validate the clinical application value of α-Syn and Serpin A3 in the early diagnosis of Parkinson's disease.

[0082] Example 1 1. Subjects: In this embodiment, 230 early-stage Parkinson's disease patients and 180 healthy controls were selected as the experimental group. The median age of the Parkinson's disease patients was 63.00 years, and the median age of the healthy controls was 62.50 years. A validation group of 56 Parkinson's disease patients and 51 healthy controls was selected. The subjects in the experimental and validation groups were similar in age and gender ratio. All Parkinson's disease patients were classified as having a HY stage ≤ 2 using the Hoehn and Yahr (HY) staging scale (range: 0-5). Furthermore, based on the area under the receiver operating characteristic curve (ROC curve) (AUC) of the single-indicator model obtained from the training set, appropriate indicators were selected to construct a multi-indicator model.

[0083] 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.

[0084] 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 Parkinsons' 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) Movement Disorder Society Unified Parkinson's Disease Rating Scale - Part III (MDS-UPDRS-III) (2) Detection of blood biomarker levels in the plasma of subjects Both α-Syn and Serpin A3 were detected in LiCA using a photo-induced chemiluminescence method. ® Platform testing. The specific detection steps of the photo-induced chemiluminescence detection method described in this embodiment can be methods known in the art and are not limited here.

[0085] The results of the above rating scale and the results of blood biomarker level detection are recorded in the table below. All variables are expressed as median and upper and lower quartiles.

[0086] The detection results of the above two blood biomarkers were plotted as a median box plot, as follows: Figure 1 (Experimental group) and Figure 2 (Validation group) shows the differences in the distribution of blood biomarkers between the Parkinson's disease group and the healthy control group.

[0087] 3. Experimental Results Table 1. Clinical characteristics of the experimental and validation groups

[0088] Note: PD indicates early stage of Parkinson's disease (HY≤2.0); HC indicates healthy control group. All variables are presented as median and interquartile range (IQR) (upper quartile, lower quartile).

[0089] 4. Results Analysis Table 1 shows that the photo-induced chemiluminescence detection method was used in LiCA... ® The platform can meet the detection requirements of α-Syn and SERPINA3 levels, and has good sensitivity and accuracy.

[0090] By combining Table 1, Figure 1 and Figure 2 It is evident that α-Syn has the potential to differentiate between the Parkinson's disease group and the healthy control group, and SERPINA3 also has significant differentiation between the Parkinson's disease group and the healthy control group, thus having clinical application value in the diagnosis of Parkinson's disease.

[0091] 1) In the experimental group, the plasma α-Syn level in the Parkinson's disease group was 37.69 ng / mL (24.20, 48.88), while that in the healthy control group was 18.13 ng / mL (15.22, 21.77); in the validation group, the plasma α-Syn level in the Parkinson's disease group was 31.76 ng / mL (19.95, 57.61), while that in the healthy control group was 17.01 ng / mL (14.34, 20.82). The plasma α-Syn level in the Parkinson's disease group was significantly higher than that in the healthy control group (p<0.001).

[0092] 2) In the experimental group, the plasma SERPINA3 level in the Parkinson's disease group was 491.40 μg / mL (413.50, 586.30), while that in the healthy control group was 299.80 μg / mL (253.20, 376.50). In the validation group, the plasma SERPINA3 level in the Parkinson's disease group was 402.50 μg / mL (323.30, 634.60), while that in the healthy control group was 286.40 μg / mL (212.90, 371.20). The plasma SERPINA3 level in the Parkinson's disease group was significantly higher than that in the healthy control group (p<0.001).

[0093] A single blood biomarker, SERPINA3 or α-Syn, can be well used for the diagnosis of Parkinson's disease.

[0094] Example 2 The Spearman rank correlation coefficient test was used to compare and analyze the α-Syn and Serpin A3 test results obtained in Example 1 with the symptom assessment results, evaluating the correlation between α-Syn and Serpin A3 and Parkinson's disease symptoms. The results are as follows: Figure 3 As shown.

[0095] Results Analysis Combination Figure 3 It can be seen that α-Syn level is positively correlated with motor dysfunction in Parkinson's disease patients, but this relationship did not reach statistical significance. For example, the correlation coefficient with MDS-UPDRS III is r = 0.046, p = 0.441. SERPINA3 level is significantly negatively correlated with olfactory dysfunction in Parkinson's disease patients. For example, the correlation coefficient with SS-16 is r = -0.127, p = 0.030.

[0096] Example 3 1. Experimental Methods The α-Syn and SERPINA3 detection results of the subjects in Example 1 above were used to generate ROC curves (e.g. Figure 4 a, Figure 4 (as shown in c), and further generated the ROC curve for the combined use of two blood biomarkers, α-Syn and SERPINA3 (as shown in c), and further generated the ROC curve for the combined use of the two blood biomarkers α-Syn and SERPINA3 (as shown in c). Figure 4 b、 Figure 4 As shown in d), the performance of α-Syn and SERPINA3 in distinguishing between patients with Parkinson's disease (PD) and healthy individuals (HC) was evaluated. The correlation analysis results obtained based on the above ROC curves are shown in the table below.

[0097] 2. Experimental Results Table 2. Detailed information on two biomarkers and their combined model in PD patients and healthy individuals.

[0098] Note: PD indicates early stage of Parkinson's disease (HY≤2.0); HC indicates healthy control group.

[0099] 3. Results Analysis Combine Table 2 and Figure 4 It can be seen that α-Syn and SERPINA3 both showed a certain degree of discriminative power in distinguishing between Parkinson's disease patients and healthy controls; in particular, the combined model of α-Syn and SERPINA3 showed even higher accuracy in distinguishing between them.

[0100] according to Figure 4As shown in figure a, the experimental data for α-Syn showed an AUC value of 0.824, and the optimal critical value was determined to be 24.700 ng / mL using the maximum Yoden index (sensitivity: 0.749; specificity: 0.863). Figure 4 As shown in c, the AUC value in the validation group of α-Syn reached 0.844 (95% confidence interval [CI], 0.769–0.913). Therefore, in a single biomarker model, α-Syn has a certain degree of accuracy in distinguishing between Parkinson's disease and healthy controls.

[0101] according to Figure 4 As shown in figure a, in the experimental group of SERPINA3, the AUC value was 0.853, and its optimal critical value was determined to be 408.059 μg / mL using the maximum Yoden index (sensitivity: 0.762; specificity: 0.853); according to Figure 4 As shown in c, the AUC value of SERPINA3 in the validation group reached 0.764. Therefore, in a single biomarker model, SERPINA3 can also distinguish between Parkinson's disease patients and healthy controls.

[0102] When these two biomarkers, α-Syn and SERPINA3, were used in combination, the AUC value was further increased to 0.898 (experimental group, see below). Figure 4 b) and 0.896 (95% confidence interval [CI], 0.829–0.947) (validation group, see...). Figure 4 d), which has higher discrimination accuracy.

[0103] Example 4 Calibration curves were generated for the model using the combined α-Syn and SERPINA3 blood biomarkers described in Example 3 above (e.g.) Figure 5 As shown), and a nomogram is constructed based on logistic regression (as shown). Figure 6 As shown in the figure, it is used to visually assess the risk of being diagnosed with PD.

[0104] Results Analysis according to Figure 5 and Figure 6 As shown, via LiCA ® The analysis platform detects the levels of α-Syn and SERPINA3 in human plasma. The combined use of multiple biomarker models demonstrates high diagnostic accuracy in comparative analyses of Parkinson's disease groups and healthy control groups.

[0105] 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 blood biomarkers in the preparation of Parkinson's disease assessment or diagnostic products, wherein the blood biomarkers are SERPINA3 and / or α-Syn.

2. The use according to claim 1, characterized in that, The area under the receiver operating characteristic curve (ROC) generated by the blood biomarker or combination thereof in the test results of samples from Parkinson's disease patients and healthy individuals is greater than 0.8; preferably greater than 0.

85.

3. The use according to claim 1, characterized in that, When the blood biomarker is a combination of SERPINA3 and α-Syn, the optimal critical value of the blood biomarker is determined by calculating the maximum Oden index using the receiver operating characteristic curve.

4. The use according to claim 1, characterized in that, The optimal critical value of α-Syn is determined by combining the Youden index with sensitivity and specificity; more preferably, it is determined by sensitivity > 70% and specificity > 85%. And / or, The optimal threshold value for SERPINA3 was determined by combining the Youden index with sensitivity and specificity; more preferably, it was determined by sensitivity > 75% and specificity > 85%.

5. The use according to claim 1, characterized in that, A blood sample containing a concentration of SERPINA3 higher than the optimal threshold for SERPINA3 indicates a risk of Parkinson's disease.

6. The use according to claim 1, characterized in that, A blood sample containing α-Syn at a concentration higher than the optimal threshold value indicates a risk of Parkinson's disease.

7. The use according to claim 5 or 6, characterized in that, The blood samples include whole blood, plasma, and serum.

8. The use according to any one of claims 1 to 7, characterized in that, The levels of the blood biomarkers were determined by photo-induced chemiluminescence immunoassay.

9. A kit for the assessment or diagnosis of Parkinson's disease, characterized in that, The invention includes a detection reagent containing a blood biomarker, wherein the blood biomarker is SERPINA3 and / or α-Syn.

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

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