Application of plasma neurofilament light chain protein combined with ceramides in the auxiliary diagnosis of multiple system atrophy and the differential diagnosis of Parkinson's disease

Abstract

The application discloses application of plasma neurofilament light chain protein combined with ceramide in auxiliary diagnosis of multiple system atrophy and identification of Parkinson's disease, and relates to the field of biomedical technology.The biomarker combination is plasma neurofilament light chain protein and ceramide; the ceramide includes Cer 16, Cer 18, Cer 24:0, Cer 24:1, Cer 16 / Cer 24:0, Cer 18 / Cer 24:0 and Cer 24:1 / Cer 24:0.The application solves the problems of insufficient efficiency and complicated operation of a single biomarker in identification of MSA and PD and HC by determining the concentrations of NfL and specific ceramide in plasma and substituting into specific logistic regression calculation formula, significantly improves the diagnostic accuracy of MSA and HC and MSA and PD, and has clinical popularization value.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to the application of plasma neurofilament light chain protein combined with ceramide in the auxiliary diagnosis of multiple system atrophy and the differentiation of Parkinson's disease. Background Technology

[0002] Parkinson's disease (PD) is one of the most common neurodegenerative movement disorders, while multiple system atrophy (MSA) significantly overlaps with PD in early clinical presentation, but differs drastically in pathological mechanisms, disease progression rate, and prognosis. Differentiating between PD and MSA is crucial for patient clinical management and prognostic assessment.

[0003] Currently, existing biomedical technologies for differentiating between MSA and PD mainly focus on the following two directions: 1. Detection based on pathological protein deposition and exosomes: Abnormal α-synuclein protein deposition can be detected in some biological fluids and peripheral tissues. Furthermore, studies have used immunoprecipitation to isolate exosomes secreted by the central nervous system and employed electrochemiluminescent enzyme-linked immunosorbent assay (ELISA) to quantitatively detect α-synuclein in plasma exosomes. Existing literature indicates that comparing the α-synuclein concentration ratio of oligodendrocyte exosomes to neuronal exosomes can differentiate between PD and MSA, with an area under the diagnostic curve (AUC) of 0.902, a sensitivity of 89.8%, and a specificity of 86.0%.

[0004] 2. Detection based on soluble free biomarkers in peripheral blood: Neurofilament light chain protein (NfL) is considered a biomarker reflecting nerve axonal damage, and its levels are generally higher in the peripheral blood of MSA patients than in PD patients. Meanwhile, abnormal lipid metabolism (especially ceramides) plays an important role in the pathogenesis of synucleinopathy, and studies have explored the use of single ceramide components for PD screening.

[0005] Despite the progress made in the research of biomarkers for neurodegenerative diseases, the following problems and limitations still exist: 1. Cumbersome detection methods and high translation threshold: Detection methods based on exosomes or tissue biopsies rely on invasive operations or complex immune capture and centrifugation processes (such as the extraction of exosome α-syn), which are complicated, time-consuming, and prone to introducing batch-to-batch errors, which is not conducive to the promotion of large-scale clinical high-throughput testing.

[0006] 2. The efficacy of single biomarkers has a ceiling: Whether it is a complex protein immunoassay (such as the AUC of exosomes for differentiating MSA and PD, which is around 0.90), or a conventional single free biomarker (such as detecting only NfL or only ceramide), there is a region of numerical overlap due to the heterogeneity of disease pathology. Summary of the Invention

[0007] The purpose of this invention is to provide the application of plasma neurofilament light chain protein (NfL) combined with ceramides in the auxiliary diagnosis of multiple system atrophy (MSA) and the differentiation of Parkinson's disease (Parkinson's disease), thereby addressing the problems existing in the prior art. By measuring the concentrations of NfL and specific ceramides in plasma and substituting them into a specific logistic regression calculation formula, the invention solves the problems of insufficient efficacy and cumbersome operation of single biomarkers in differentiating MSA from PD and HC, significantly improving the diagnostic accuracy of MSA from HC and MSA from PD, and has clinical application value.

[0008] To achieve the above objectives, the present invention provides the following solution: This invention provides a combination of biomarkers for the auxiliary diagnosis of multiple system atrophy or for differentiating between multiple system atrophy and Parkinson's disease, wherein the combination of biomarkers is a combination of plasma neurofilament light chain protein and ceramide; The ceramide combination includes Cer 16, Cer 18, Cer 24:0, Cer 24:1, Cer 16 / Cer 24:0, Cer 18 / Cer 24:0 and Cer 24:1 / Cer 24:0.

[0009] The present invention also provides the application of reagents for detecting the above-mentioned combinations of biomarkers in the preparation of products for the auxiliary diagnosis of multiple system atrophy.

[0010] The present invention also provides the application of the above-mentioned biomarker combination in constructing a diagnostic model to assist in the diagnosis of multiple system atrophy.

[0011] The present invention also provides the use of reagents for detecting the above-mentioned combination of biomarkers in the preparation of products for differentiating multiple system atrophy and Parkinson's disease.

[0012] The present invention also provides the application of the above-mentioned combination of biomarkers in constructing diagnostic models for differentiating between multiple system atrophy and Parkinson's disease.

[0013] The present invention also provides a method for constructing a diagnostic model to assist in the diagnosis of multiple system atrophy, comprising the following steps: the diagnostic model is constructed using plasma concentrations of neurofilament light chain protein, Cer 16 concentration, Cer 18 concentration, Cer 24:0 concentration, Cer 24:1 concentration, Cer 16 / Cer 24:0 ratio, Cer 18 / Cer 24:0 ratio, and Cer 24:1 / Cer 24:0 ratio as input variables; The predicted probability value of the diagnostic model is P=1 / (1+e^-Y); Among them, Y = -28.3318 + 0.4385 × [NfL] + 0.0216 × [Cer 16] + 0.5694 × [Cer 18] - 0.0021 × [Cer 24:0] - 0.0399 × [Cer 24:1] - 0.0013 × [Cer 16 / Cer24:0]- 0.0000 × [Cer 18 / Cer 24:0] + 0.0007 × [Cer 24:1 / Cer 24:0].

[0014] The present invention also provides a diagnostic model for multisystem atrophy constructed by the above-described method.

[0015] The present invention also provides a method for constructing a diagnostic model to differentiate between multiple system atrophy and Parkinson's disease. The diagnostic model is constructed using plasma concentrations of neurofilament light chain protein, Cer 16, Cer 18, Cer 24:0, Cer 24:1, Cer 16 / Cer 24:0 ratio, Cer 18 / Cer 24:0 ratio, and Cer 24:1 / Cer 24:0 ratio as input variables. The predicted probability value of the diagnostic model is P=1 / (1+e^-Y); Among them, Y = -6.0999 + 0.1697 × [NfL] - 0.0484 × [Cer 16]+ 0.3760 ×[Cer 18] + 0.0022 × [Cer 24:0]- 0.0578 × [Cer 24:1] - 0.0037 × [Cer 16 / Cer24:0]+ 0.0049 × [Cer 18 / Cer 24:0] + 0.0367 × [Cer 24:1 / Cer 24:0].

[0016] The present invention also provides a diagnostic model for differentiating between multiple system atrophy and Parkinson's disease constructed by the above construction method.

[0017] The present invention discloses the following technical effects: This invention, for the first time, transcends different pathological mechanisms (NfL represents nerve axonal injury, and the combination of ceramides represents systemic lipid metabolism disorders). It constructs a multivariate logistic regression model using four specific ceramides and their three derived ratios in conjunction with NfL. By measuring the concentrations of NfL and specific ceramides in plasma and incorporating them into a specific logistic regression formula, it addresses the limitations of single biomarker or traditional exosome detection in differentiating between MSA and PD / HC, as well as the associated cumbersome procedures. Experimental results show that the combined diagnostic model constructed in this invention achieves an AUC as high as 1.000 (sensitivity 100.0%, specificity 100.0%) when differentiating between MSA and PD; in the MSA / PD scenario, the AUC increases from approximately 0.96 for a single indicator to 0.996 (sensitivity 97.5%, specificity 100.0%), and requires only a very small amount of peripheral plasma, eliminating the need for ultracentrifugation, making it highly valuable for clinical application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in 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.

[0019] Figure 1 Comparison of ROC curves for differentiating MSA from HC in diagnostic models of plasma NfL and / or ceramides; Figure 2 ROC curve comparison of MSA and PD for the diagnostic model of plasma NfL and / or ceramide. Detailed Implementation

[0020] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0021] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0022] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0023] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0024] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0025] Terminology Explanation: Neurofilament light chain protein (NfL) is a key structural protein in the neuronal cytoskeleton. It is an important subunit of the neurofilament protein (NF) family and is highly expressed in myelinated axons.

[0026] Ceramides are core sphingolipid molecules in plasma containing long-chain fatty acids. They are composed of sphingosine and fatty acids linked by amide bonds and participate in cell membrane formation, apoptosis, and signal transduction. Abnormal levels of ceramides are closely related to metabolic diseases, cardiovascular diseases, and neurodegenerative diseases.

[0027] Cer 16, or ceramide subtype containing a 16-carbon saturated fatty acid chain, is mainly distributed in plasma lipoproteins and can regulate lipid metabolism and inflammatory responses. Its elevation is associated with insulin resistance and the occurrence and development of atherosclerosis.

[0028] Cer 18 refers to a ceramide subtype with 18 carbon atoms in its fatty acid chain. It is widely present in human tissues and plasma, participates in the regulation of cell proliferation and apoptosis, and abnormal expression is associated with an increased risk of obesity, type 2 diabetes, and cardiovascular disease.

[0029] Cer 24:0 is a ceramide subtype containing a 24-carbon saturated fatty acid chain. It is a ceramide component with a high content in plasma and participates in maintaining cell membrane homeostasis. Its elevated level is positively correlated with the risk of cardiovascular events such as coronary heart disease and myocardial infarction.

[0030] Cer 24:1, a ceramide subtype containing a 24-carbon monounsaturated fatty acid chain, is present in plasma and various tissues. It participates in lipid metabolism and inflammation regulation, and abnormally elevated levels can promote vascular endothelial damage and are associated with the progression of metabolic syndrome.

[0031] Previous research by the inventors' research group revealed significant differences in plasma neurofilament light chain protein (NfL) concentration and plasma ceramide concentration among Parkinson's disease patients, multiple system atrophy patients, and healthy individuals. Based on this, the present invention conducts research on the following embodiments.

[0032] Example 1 I. Materials and Methods Sample Source: The clinical samples involved in this study were all obtained from inpatients in the Department of Movement Disorders and healthy individuals in the Physical Examination Center of Beijing Tiantan Hospital, Capital Medical University, from January 2023 to October 2025. This study was reviewed and approved by the relevant hospital's clinical research ethics committee. All sample acquisition procedures complied with the Declaration of Helsinki and relevant national regulations, and all participants signed written informed consent forms.

[0033] The included sample consisted of 40 patients with clinically diagnosed multiple system atrophy (MSA), 40 patients with Parkinson's disease (PD), and 40 healthy individuals (HC).

[0034] Plasma sample collection and preparation: Fasting venous blood was collected. Blood samples were collected in test tubes coated with ethylenediaminetetraacetic acid (EDTA) and immediately centrifuged at 3000 rpm for 15 minutes at 4°C. The separated supernatant plasma was transferred aliquoted to cryovials and stored at -80°C until analysis to prevent lipid degradation.

[0035] Extraction and quantification of plasma ceramides: Plasma samples were thawed on ice, and standardized volumes of each sample were mixed with an organic solvent containing a stable isotope-labeled internal standard for lipid extraction. Subsequently, the plasma ceramide profiles (including Cer 16, Cer 18, Cer24:0, Cer 24:1, etc.) were accurately quantified using a multi-batch liquid chromatography-tandem mass spectrometry (LC-MS / MS) system.

[0036] Plasma neurofilament light chain protein (NfL) detection: As a benchmark for comparison and co-modeling, the concentration of NfL in plasma was measured using photo-induced chemiluminescence immunoassay (LiCA®, Chemclin Diagnostics, Beijing, China). This method utilizes antibody-conjugated luminescent microspheres and photosensitive microspheres. When the antigen (NfL) binds and brings the microspheres close together, they are excited and generate a signal, thereby achieving highly sensitive and accurate quantification.

[0037] II. Data Processing Statistical analysis and logistic regression modeling were performed using Python software. The specific processing and analysis steps are as follows: 1. Feature Selection and Model Construction: For different disease groups to be identified (MSA vs. HC, MSA vs. PD), plasma NfL concentration and seven core lipid characteristics (Cer 16, Cer 18, Cer 24:0, Cer 24:1 concentrations, and their derived ratios Cer 16 / Cer 24:0, Cer 18 / Cer 24:0, Cer 24:1 / Cer 24:0) were extracted from the subjects as predictive variables. The detection results of each biomarker in the subject population are shown in Table 1.

[0038] Table 1. Distribution of the content and ratio of each core biomarker in different subjects (mean ± standard deviation) 2. Comparison Model Specification: To verify the gain effect of the combined diagnosis, three logistic regression classification models were constructed for each differential diagnosis group: (1) Single NfL model (only NfL concentration is input); (2) Single ceramide model (only 7 ceramide features are input); (3) NfL combined with ceramide model (NfL and 7 ceramide features are input simultaneously for fitting and prediction).

[0039] 3. Algorithm and Formula Generation: The multivariate logistic regression algorithm is used for fitting, and the prediction probability formula P=1 / (1+e^-Y) is generated.

[0040] 4. Diagnostic efficacy assessment and cutoff value determination: After obtaining the predicted probability P-values ​​output by the models, receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic discrimination ability of each model, and the area under the curve (AUC) was calculated. Subsequently, Youden's Index (which finds the maximum value of "sensitivity + specificity - 1") was used to determine the optimal diagnostic cutoff value for each combined model, and the percentages of sensitivity and specificity at this optimal cutoff value were output.

[0041] III. Results and Analysis 1. Substituting the data into the model, a formula for calculating the linear predictor variable Y, used to distinguish between MSA and HC, was established (numerical values ​​are rounded to four decimal places): Y = -9.0221 + 0.2015 × [NfL]; Y = 0.1771 - 0.0445 × [Cer 16] + 0.3602 × [Cer 18]+ 0.0001 × [Cer24:0] - 0.0340 × [Cer 24:1]+ 0.0134 × [Cer 16 / Cer 24:0] + 0.0049 × [Cer18 / Cer 24:0]+ 0.0378 × [Cer 24:1 / Cer 24:0]; Y = -28.3318 + 0.4385 × [NfL] + 0.0216 × [Cer 16]+ 0.5694 × [Cer18] - 0.0021 × [Cer 24:0]- 0.0399 × [Cer 24:1] - 0.0013 × [Cer 16 / Cer 24:0]- 0.0000 × [Cer 18 / Cer 24:0] + 0.0007 × [Cer 24:1 / Cer 24:0].

[0042] Substituting Y into the prediction probability formula to calculate the prediction probability P value, the diagnostic efficacy is tested, and the results are as follows: Figure 1 As shown, the AUC of NfL alone was 0.992, and that of ceramide alone was 0.979; the AUC of the combined diagnostic model of this invention reached 1.000, at which point the model's sensitivity and specificity for identifying MSA were 100.0%. The optimal diagnostic cutoff value of this combined diagnostic model was 0.9577, meaning that the subject was diagnosed with MSA when the P-value was greater than 0.9577.

[0043] 2. Early clinical differentiation between MSA and PD is extremely challenging. This invention substitutes data into a model to establish a formula for calculating the linear predictor variable Y (values ​​are rounded to four decimal places) to distinguish between MSA and PD: Y = -6.0853 + 0.1164 × [NfL]; Y = -1.3905 + 0.0030 × [Cer 16] + 0.2392 × [Cer 18] + 0.0008 × [Cer24:0] - 0.0355 × [Cer 24:1] - 0.0173 × [Cer 16 / Cer 24:0] - 0.0029 × [Cer18 / Cer 24:0]+ 0.0091 × [Cer 24:1 / Cer 24:0]; Y = -6.0999 + 0.1697 × [NfL] - 0.0484 × [Cer 16]+ 0.3760 × [Cer18] + 0.0022 × [Cer 24:0]- 0.0578 × [Cer 24:1] - 0.0037 × [Cer 16 / Cer 24:0]+ 0.0049 × [Cer 18 / Cer 24:0] + 0.0367 × [Cer 24:1 / Cer 24:0].

[0044] Substituting Y into the prediction probability formula to calculate the prediction probability P value, the diagnostic efficacy is tested, and the results are as follows: Figure 2 As shown, the AUC of NfL alone was 0.962, and the AUC of ceramide alone was 0.966; the combined diagnostic model of this invention significantly improved the AUC to 0.996. The optimal diagnostic cutoff value of this combined diagnostic model is 0.6595, meaning that a subject is diagnosed with MSA when the P-value is greater than 0.6595, and conversely, is diagnosed with PD when the P-value is less than 0.6595. At this cutoff value, the sensitivity is 97.5% and the specificity is 100.0%.

[0045] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A combination of biomarkers for the auxiliary diagnosis of multiple system atrophy or for differentiating between multiple system atrophy and Parkinson's disease, characterized in that, The biomarker combination is a combination of plasma neurofilament light chain protein and ceramide; The ceramide combination includes Cer 16, Cer 18, Cer 24:0, Cer 24:1, Cer 16 / Cer 24:0, Cer18 / Cer 24:0 and Cer 24:1 / Cer 24:

0.

2. The use of a reagent for detecting the combination of biomarkers as described in claim 1 in the preparation of products for the auxiliary diagnosis of multiple system atrophy.

3. The application of the biomarker combination of claim 1 in constructing a diagnostic model to assist in the diagnosis of multiple system atrophy.

4. The use of a reagent for detecting the combination of biomarkers described in claim 1 in the preparation of products for identifying multiple system atrophy and Parkinson's disease.

5. The application of the biomarker combination of claim 1 in constructing a diagnostic model for differentiating between multiple system atrophy and Parkinson's disease.

6. A method for constructing a diagnostic model to assist in the diagnosis of multiple system atrophy, characterized in that, Includes the following steps: The diagnostic model was constructed using plasma concentrations of neurofilament light chain protein, Cer 16, Cer 18, Cer 24:0, Cer 24:1, Cer 16 / Cer 24:0 ratio, Cer 18 / Cer 24:0 ratio, and Cer 24:1 / Cer 24:0 ratio as input variables. The predicted probability value of the diagnostic model is P=1 / (1+e^-Y); Among them, Y = -28.3318 + 0.4385 × [NfL] + 0.0216 × [Cer 16] + 0.5694 × [Cer18] - 0.0021 × [Cer 24:0] - 0.0399 × [Cer 24:1] - 0.0013 × [Cer 16 / Cer 24:0] - 0.0000 × [Cer 18 / Cer 24:0] + 0.0007 × [Cer 24:1 / Cer 24:0].

7. A diagnostic model for multisystem atrophy constructed by the construction method of claim 6.

8. A method for constructing a diagnostic model to differentiate between multiple system atrophy and Parkinson's disease, characterized in that, The diagnostic model was constructed using plasma concentrations of neurofilament light chain protein, Cer 16, Cer 18, Cer 24:0, Cer 24:1, Cer 16 / Cer 24:0 ratio, Cer 18 / Cer 24:0 ratio, and Cer 24:1 / Cer 24:0 ratio as input variables. The predicted probability value of the diagnostic model is P=1 / (1+e^-Y); Among them, Y = -6.0999 + 0.1697 × [NfL] - 0.0484 × [Cer 16] + 0.3760 × [Cer18] + 0.0022 × [Cer 24:0] - 0.0578 × [Cer 24:1] - 0.0037 × [Cer 16 / Cer 24:0] + 0.0049 × [Cer 18 / Cer 24:0] + 0.0367 × [Cer 24:1 / Cer 24:0].

9. A diagnostic model for differentiating between multiple system atrophy and Parkinson's disease, constructed using the method described in claim 8.

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