Lysosomal functional axis neurodegenerative disease blood marker panel and kit

Abstract

The application discloses a blood marker combination and kit for neurodegenerative diseases of lysosome function axis, and relates to the technical field of biomedical detection. The marker combination is composed of seven markers in three categories: glucocerebrosidase and cathepsin D; sphingolipids and gangliosides; and phosphorylated tau-217 protein, neurofilament light chain protein and glial fibrillary acidic protein. The application also provides an in-vitro diagnostic kit containing specific reagents for detecting the above markers, and constructs a risk assessment model based on a logistic regression or random forest algorithm. The marker combination and kit can be used for early screening, differential diagnosis, disease progression prediction and drug efficacy monitoring of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and neurogenic Gaucher disease, and have the advantages of high sensitivity, high specificity and minimally invasive detection.

Description

Technical Field

[0001] This invention belongs to the field of biomedical detection technology, specifically relating to a combination of peripheral blood biomarkers for early screening and auxiliary assessment of neurodegenerative diseases, a test kit containing the combination of biomarkers, a machine learning-based risk assessment system, and its application in Alzheimer's disease, Parkinson's disease, and neurogaucher disease. Background Technology

[0002] Neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), have become a major challenge in global public health due to their high incidence and disability rates. Existing research indicates that the pathological process of these diseases begins 10-20 years before the onset of clinical symptoms, and their core driving mechanisms involve protein homeostasis imbalance, lysosomal dysfunction, and chronic neuroinflammation.

[0003] Currently, the widely used biomarkers in clinical practice, such as Aβ, Tau, and α-synuclein, mainly reflect protein aggregation and neuronal damage in the middle and late stages of the disease, but they face the following technical bottlenecks: 1. Insufficient sensitivity: It is difficult to achieve accurate identification in mild cognitive impairment (MCI) or earlier "preclinical" stages; 2. Mechanism lag: Abnormalities in these biomarkers occur after significant neuronal damage and cannot reflect the upstream driving mechanisms of disease development; 3. Weak classification ability: A single biomarker cannot effectively distinguish between different pathological types of neurodegenerative diseases such as AD and PD.

[0004] Recent studies have confirmed that lysosomal dysfunction is a core hub for connective protein misfolding, lipid metabolism disorders, and neuroinflammation, with abnormalities occurring in the very early stages of disease. Decreased glucocerebrosidase (GCase) activity and abnormalities in its downstream signaling pathways have been shown to directly participate in the development of Alzheimer's disease (AD), Parkinson's disease (PD), and neurogenic Gaucher disease. However, a multidimensional biomarker system that can simultaneously reflect lysosomal function, lipid homeostasis, nerve damage, and glial activation in peripheral blood is currently lacking, severely hindering early screening, early diagnosis, and precise intervention for related diseases. Summary of the Invention

[0005] This invention provides a combination of peripheral blood biomarkers based on the lysosomal functional axis and their application, aiming to solve the problems of insufficient sensitivity, delayed mechanism, and weak classification ability of existing biomarkers for early diagnosis of neurodegenerative diseases.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: 2.1 Composition of Biomarker Combinations This invention provides a combination of blood biomarkers for neurodegenerative diseases based on the lysosomal functional axis, the combination consisting of 7 biomarkers across three functional categories: (1) Markers of lysosomal homeostatic enzyme activity Glucocerebrosidase (GCase) Cathepsin D (2) Markers of lipid metabolism and membrane homeostasis Sphingolipids Ganglioside metabolic profile (3) Markers of coupling between nerve injury and glial activation Phosphorylated tau-217 protein (p-tau217) Neurofilament light chain (NfL) Glial fibrillary acidic protein (GFAP) The biomarker combination is used to prepare kits for early screening, differential diagnosis, or prediction of disease progression in Alzheimer's disease, Parkinson's disease, or neurogaucher disease.

[0007] The neurodegenerative diseases mentioned include Alzheimer's disease, Parkinson's disease, mild cognitive impairment, or neurogaucher disease.

[0008] 2.2 Sample Collection and Preprocessing Five mL of peripheral venous whole blood was collected from the subject and placed in an EDTA-K2 anticoagulant tube. The upper plasma layer was separated by centrifugation at 3000 r / min for 10 minutes at 4℃. After separation, the plasma was aliquoted and stored in a -80°C freezer for later use. Before testing, it was slowly thawed on ice, and repeated freeze-thaw cycles were avoided.

[0009] 2.3 Marker Detection Methods The detection methods for each marker described in this invention are all independently developed, as detailed below: 2.3.1 GCase activity detection (fluorescent substrate method) 1. Take 50 μL of the plasma to be tested and add 50 μL of substrate working solution. The substrate working solution contains 2 mmol / L 4-methylumbelliferone-β-D-glucopyranoside (4-MUG) and the solvent is 0.1 mol / L citrate-phosphate buffer (pH 5.2).

[0010] 2. Incubate at 37°C in the dark for 60 minutes.

[0011] 3. Add 100 μL of stop solution (0.2 mol / L glycine-NaOH, pH 10.6) to terminate the reaction.

[0012] 4. Detection was performed using a fluorescent microplate reader with an excitation wavelength of 365 nm and an emission wavelength of 450 nm. GCase activity in the sample was calculated using a standard curve, with units of nmol / h / mL.

[0013] 2.3.2 Detection of cathepsin D activity (fluorescent peptide substrate method) 1. Take 40 μL of the plasma to be tested and add 60 μL of substrate solution. The substrate solution contains 10 μmol / L MCA-Gly-Lys-Phe-Phe-Arg-Lys-DNP and the solvent is 0.1 mol / L sodium acetate buffer (pH 4.0).

[0014] 2. Incubate at 37°C for 45 minutes.

[0015] 3. Add 100 μL of stop solution (1 mol / L Tris-HCl, pH 8.0) to terminate the reaction.

[0016] 4. Detection was performed using a fluorescent microplate reader. The excitation wavelength was 325 nm, and the emission wavelength was 395 nm. The unit is U / mL.

[0017] 2.3.3 Detection of sphingolipids and gangliosides (competitive immunoassay) Using a self-developed coated plate (pre-coated with specific lipid-binding proteins), the test sample and calibrator were added to their respective wells and incubated at room temperature for 30 minutes. After washing, the enzyme-labeled conjugate was added, followed by incubation and washing again. Finally, the chromogenic substrate or luminescent substrate was added, and the absorbance or luminescence value was measured. The concentrations of sphingolipids and gangliosides in the sample were calculated using a standard curve, in nmol / L.

[0018] 2.3.4 Detection of p-tau217, NfL, and GFAP (double antibody sandwich method) Quantitative detection was performed using an enzyme-linked immunosorbent assay (ELISA) or a chemiluminescent immunoassay (CLIA) platform.

[0019] Coating: Monoclonal antibodies against p-tau217, NfL, and GFAP were coated onto microplates or magnetic beads, respectively.

[0020] Detection: Add the plasma sample to be tested, incubate and wash; add biotinylated or acridinium ester-labeled detection antibody; after incubation and washing again, add streptavidin-horseradish peroxidase (HRP) or directly excite chemiluminescence.

[0021] Quantitative analysis: The concentrations of each biomarker in the sample were calculated using a standard curve. The units for p-tau217, NfL, and GFAP were pg / mL, pg / mL, and pg / mL, respectively.

[0022] Antibody source: The monoclonal antibody is prepared using hybridoma technology (which can be obtained through conventional immunization methods).

[0023] Lipid-binding proteins: These are recombinantly expressed sphingolipid-binding proteins (such as recombinant human Galectin-1).

[0024] Quality control: Three quality control samples with low, medium and high concentrations were set up for each batch of experiments. The intra-batch coefficient of variation (CV) was <10%, and the inter-batch CV was <15%.

[0025] 2.4 Data Standardization Processing The raw detection values ​​of all markers were standardized using the Z-score method to eliminate batch-to-batch variations. The standardization formula is: Where μ is the reference mean for healthy individuals, and σ is the standard deviation. The reference values ​​for healthy individuals (based on data from 300 healthy volunteers) provided with the kit of this invention are as follows: GCase: μ=125.6, σ=28.3 Cathepsin D: μ=88.2, σ=19.5 Sphingolipid: μ=45.3, σ=11.2 Gangliosides: μ=28.7, σ=7.6 p-tau217: μ=1.82, σ=0.55 (pg / mL) NfL: μ=12.6, σ=4.3 (pg / mL) GFAP: μ=68.5, σ=18.2 (pg / mL) 2.5 Artificial Intelligence Risk Assessment Model Using the standardized data of 7 biomarkers as input features, two composite risk assessment models, Logistic Regression (LR) and Random Forest (RF), were constructed to output the disease risk probability (P), with a value range of [0,1].

[0026] 2.5.1 Logistic Regression Model The formula for calculating the probability of risk is: The formula for calculating the linear prediction value Z is: Z = -3.265 - 0.826X1 - 0.673X2 - 0.512X3 - 0.435X4 + 1.153X5 + 0.927X6 + 0.782X7 The variables were defined as follows: X1 = GCase (std), X2 = cathepsin D (std), X3 = sphingolipid (std), X4 = ganglioside (std), X5 = p-tau217 (std), X6 = NfL (std), and X7 = GFAP (std). The model intercept and coefficients were obtained by maximum likelihood estimation using the training set (n=800).

[0027] 2.5.2 Random Forest Model Number of decision trees: 200 Maximum depth: 8 Minimum number of sample splits: 10 Feature importance ranking (based on Gini impurity reduction): p-tau217 > NfL > GCase > GFAP > sphingolipid > ganglioside > cathepsin D.

[0028] Risk scoring formula: ,in For the first The output of each decision tree.

[0029] 2.5.3 Clinical grading and application thresholds Based on the risk probability P output by the model, the following clinical application guidance is provided: 1. General Risk Classification: P<0.2: Low risk, routine follow-up examination recommended after 12 months; 0.2≤P<0.5: Low to medium risk; follow-up examination and cognitive assessment are recommended after 6 months. 0.5≤P<0.75: Medium to high risk, cerebrospinal fluid analysis or imaging (PET / CT) is recommended for confirmation; P≥0.75: High risk, immediate specialist intervention recommended.

[0030] 2. MCI to AD conversion prediction: P<0.3: Conversion risk within 2 years <10%; 0.3≤P<0.6: The conversion risk within 2 years is 10%-30%; P≥0.6: Conversion risk within 2 years >50%.

[0031] 3. Differentiation between AD and PD: AD dominance probability: If the standardized values ​​of p-tau217 and GFAP make significant contributions in the model and the overall P ≥ 0.65, the diagnosis is predisposed to AD; PD dominance probability: If the standardized values ​​of GCase and sphingolipids contribute significantly to the model and the overall P ≥ 0.6, the diagnosis is likely to be PD.

[0032] 4. Disease progression stratification: Stable disease: P<0.4, annual clinical progression risk <15%; Slow-progression type: 0.4 ≤ P < 0.65, annual progress risk is 15%-35%; Rapid progress type: P ≥ 0.65, annual progress risk >40%.

[0033] 5. Monitoring of drug efficacy: Effective: The risk probability P decreased by ≥ 0.2 after intervention; Partially effective: The risk probability P decreased by 0.1 - 0.2 after intervention; Ineffective: The risk probability P decreases by <0.1 or increases after intervention.

[0034] The present invention has the following beneficial effects: 1. Reflects upstream disease mechanisms and is suitable for very early diagnosis. The biomarkers selected in this invention all revolve around the core upstream driving mechanism of lysosomal dysfunction, a neurodegenerative disease, and can detect abnormal changes before significant neuronal damage, filling the technical gap of insufficient sensitivity of existing biomarkers such as Aβ / Tau in the preclinical stage.

[0035] 2. Multi-dimensional functional axis combination enhances diagnostic accuracy and classification capabilities. This invention is the first to integrate three functional indicators—lysosomal enzyme activity, lipid metabolism homeostasis, and nerve damage and glial activation—into a set of blood biomarkers, which can comprehensively reflect the multidimensional pathological state of diseases and significantly improve the early identification and differential diagnosis of Alzheimer's disease, Parkinson's disease, and neurogaucher disease.

[0036] 3. Minimally invasive and convenient, suitable for large-scale population screening. Using peripheral blood as the test sample, combined with mature testing platforms such as ELISA, immunochromatography, and CLIA, the procedure is simple to operate, minimally invasive, and cost-effective, making it suitable for early risk identification of large populations such as health check-up centers and community screenings.

[0037] 4. Artificial intelligence risk assessment model to achieve individualized and precise stratification. The composite scoring model, built based on logistic regression and random forest algorithms, outputs a continuous risk probability between 0 and 1, and provides clear risk classification, conversion prediction, disease subtyping, and efficacy monitoring thresholds, which facilitates precise intervention and individualized treatment decisions by clinicians.

[0038] 5. Possesses drug efficacy monitoring capabilities to support treatment process management. By dynamically monitoring the changes in risk probability of biomarker combinations and model outputs, the therapeutic effects of drugs or interventions can be objectively assessed, providing quantitative biomarker evidence for precision treatment and clinical trials of neurodegenerative diseases.

[0039] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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.

[0041] Figure 1 This is a schematic diagram of the overall technical roadmap of the present invention; Figure 2 This is a schematic diagram of the detection process of the kit described in this invention (taking the ELISA platform as an example). Figure 3 This is a schematic diagram illustrating the construction and output of the artificial intelligence risk assessment model described in this invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] In the description of this invention, it should be understood that the terms "upper," "middle," "outer," "inner," etc., which indicate orientation or positional relationship, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting this invention.

[0044] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0045] Example 1: ELISA-based reagent kits and their application in disease screening 1.1 Sample Collection Peripheral blood samples were collected from 10 clinically diagnosed healthy controls, 10 patients with MCI, 10 patients with AD, and 10 patients with PD. Informed consent from all participants and approval from the ethics committee were obtained for the collection of all samples.

[0046] 1.2 Testing Process The ELISA and fluorescent substrate method described in Example 2.3 of this invention were used to detect seven biomarkers in the above 40 samples. Three technical replicates were performed for each sample. The detection procedures were strictly followed according to the kit instructions, and the results of all quality control samples were within acceptable limits.

[0047] 1.3 Detection Results and Statistical Analysis The mean ± standard deviation (Mean ± SD) of marker detection values ​​in the healthy control group and each disease group, as well as the inter-group differences (t-test) are shown in Table 1.

[0048] Table 1. Results of blood biomarker detection in each study group and inter-group comparisons (Mean ± SD)

[0049] 1.4 Model Risk Score and Diagnostic Efficacy The standardized values ​​of each sample were substituted into the logistic regression model (Formula 1-2) to calculate the risk probability P. The average P-values ​​for each group and the diagnostic efficacy of the model (ROC analysis) are shown in Table 2.

[0050] Table 2. Average risk probability and model diagnostic efficacy for each group

[0051] The above results indicate that the biomarker combination and risk assessment model provided by this invention can effectively distinguish between healthy individuals and patients with neurodegenerative diseases, and demonstrates good diagnostic efficacy at the MCI stage.

[0052] Example 2: CLIA-based reagent kit and its application in drug efficacy monitoring 2.1 Sample Collection and Testing Peripheral blood samples were collected from 10 patients diagnosed with Parkinson's disease (PD) before and 3 months after standard drug treatment. The samples were tested and risk scored using the CLIA-based detection kit of this invention (containing detection components for all 7 biomarkers) according to the method described in Example 1.

[0053] 2.2 Results of therapeutic efficacy monitoring The changes in the risk probability P-values ​​before and after treatment in 10 patients are shown in Table 3.

[0054] Table 3 Changes in risk probability and efficacy assessment before and after drug treatment in PD patients

[0055] The results showed that the risk probability P-value calculated by the model of this invention can sensitively reflect the patient's responsiveness to drug treatment. Patients with a P-value decrease of ≥0.2 also showed simultaneous improvement in their Upper Clinical Rating Scale (UPDRS), demonstrating that this combination of biomarkers can be used for objective and quantitative monitoring of drug efficacy.

[0056] Example 3: Large-scale clinical validation (model construction and independent validation) 3.1 Research Subjects A total of 1200 participants were included, comprising 350 patients with Alzheimer's disease (AD), 350 patients with Parkinson's disease (PD), 300 patients with microvascular disease (MCI), and 200 healthy controls. All participants had complete clinical diagnostic, neuroimaging, and / or cerebrospinal fluid biomarker information. The samples were randomly assigned in a 2:1 ratio to a training set (n=800) and an independent validation set (n=400). There were no statistically significant differences between the training and validation sets in terms of demographic characteristics such as age and sex.

[0057] 3.2 Model Validation Results In the independent validation set, the performance of the logistic regression model and the random forest model of this invention are as follows: Early AD screening: AUC=0.962 (95% CI: 0.945-0.979), sensitivity 93.5%, specificity 93.3%.

[0058] Early PD screening: AUC=0.926 (95% CI: 0.903-0.949), sensitivity 92.7%, specificity 91.2%.

[0059] MCI to AD prediction (follow-up for 2 years): AUC=0.883 (95% CI: 0.851-0.915), prediction accuracy 85.6%.

[0060] AD / PD differentiation: AUC=0.885 (95% CI: 0.856-0.914), differentiation accuracy 83.4%.

[0061] It should be further noted that the risk probability assessment results described in this invention are not directly equivalent to a disease diagnosis. The final diagnosis requires a comprehensive judgment by a clinician based on the patient's symptoms, signs, and other auxiliary examinations. This invention does not involve methods for the direct diagnosis of diseases.

[0062] In the description of this specification, references to terms such as "an embodiment," "example," and "specific example" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0063] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A combination of blood biomarkers for neurodegenerative diseases of the lysosomal functional axis, characterized in that: The set of markers consists of the following three categories, totaling seven markers: (a) Markers of lysosomal enzyme activity: glucocerebrosidase and cathepsin D; (b) Markers of lipid metabolism: sphingolipids and gangliosides; (c) Markers of nerve injury and glial activation: phosphorylated tau-217 protein, neurofilament light chain protein and glial fibrillary acidic protein; The biomarker combination is used to prepare kits for early screening, differential diagnosis, or prediction of disease progression in Alzheimer's disease, Parkinson's disease, or neurogaucher disease.

2. The combination of blood markers for neurodegenerative diseases of the lysosomal functional axis according to claim 1, characterized in that, The neurodegenerative diseases mentioned include Alzheimer's disease, Parkinson's disease, mild cognitive impairment, or neurogaucher disease.

3. An in vitro diagnostic reagent kit, characterized in that, The kit comprises reagents for detecting the biomarker combination of claim 1, the reagents comprising: (a) Substrates and buffer solutions used to detect glucocerebrosidase activity; (b) Fluorescent peptide substrates and their buffers for detecting cathepsin D activity; (c) Specific binding proteins for detecting sphingolipids and gangliosides and their coating plates; (d) Monoclonal antibody pairs and their markers for detecting phosphorylated tau-217 protein, neurofilament light chain protein and glial fibrillary acidic protein; (e) Standards and quality control samples for each marker.

4. The reagent kit according to claim 3, characterized in that, The antigen recognition epitopes of the monoclonal antibody are located in the phosphorylation site region of phosphorylated tau-217 protein, the C-terminal region of neurofilament light chain protein, and the conserved domain region of glial fibrous acidic protein.

5. The reagent kit according to claim 3, characterized in that, The detection method of the kit is selected from any one of enzyme-linked immunosorbent assay (ELISA), chemiluminescent immunoassay (CLIA), flow cytometry, fluorescence immunoassay, colloidal gold immunochromatography, or latex immunochromatography.

6. A system for a risk assessment model of neurodegenerative diseases, characterized in that, include: (a) A data input module for inputting standardized quantitative detection values ​​of the combination of biomarkers described in claim 1 in a subject's blood sample; (b) Storage module, which stores risk assessment models built based on logistic regression or random forest algorithms; (c) A calculation module for inputting the standardized quantitative detection values ​​into the model and outputting a disease risk probability between 0 and 1.

7. The system according to claim 6, characterized in that, The formula for calculating the linear prediction value Z of the logistic regression model is as follows: Z=-3.265-0.826X1-0.673X2-0.512X3-0.435X4+1.153X5+0.927X6+0.782X7, Where X1 to X7 are the standardized values ​​of glucocerebroside lipase, cathepsin D, sphingolipid, ganglioside, phosphorylated tau-217 protein, neurofilament light chain protein, and glial fibrillary acidic protein, respectively.

8. The system according to claim 6, characterized in that, The random forest model has 200 decision trees with a maximum depth of 8. The feature importance ranking is as follows: phosphorylated tau-217 protein > neurofilament light chain protein > glucocerebrosidase > glial fibrillary acidic protein > sphingolipid > gangliosides > cathepsin D.

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