Alzheimer's disease biomarker based on brain metabolite and use thereof

Abstract

An Alzheimer's disease biomarker based on a brain metabolite and a use thereof. The biomarker comprises any one or a combination of at least two of palmitic acid, DHA, gallic acid, 11Z,14Z,17Z-eicosatrienoic acid, glycodeoxycholic acid, palmitoleic acid, linoleic acid, erucic acid, petroselinic acid, and arachidonic acid. The level of a metabolite is detected to assist in early diagnosis of the Alzheimer's disease, thus facilitating rapid detection; in addition, the present invention has the characteristics of timeliness, convenience, high specificity and high sensitivity.

Description

An Alzheimer's disease biomarker based on brain metabolites and its application Technical Field

[0001] This application belongs to the field of biotechnology and relates to an Alzheimer's disease biomarker based on brain metabolites and its application. Background Art

[0002] Alzheimer's disease (AD), also known as senile dementia, is a progressive degenerative disorder of the central nervous system that occurs in the elderly. It is characterized by progressive memory impairment, cognitive decline, and loss of daily living abilities, along with neuropsychiatric symptoms such as personality changes, which severely impact social and lifestyle functioning. It has become a major global public health issue. Dementia typically develops after the age of 65. However, with the development of society, factors such as an accelerated pace of life, high work pressure, and irregular diet and rest have led to an increasingly younger population suffering from AD, with many developing the disease in their 50s or even their 40s. Because the pathogenesis of AD is not fully understood and its early symptoms are often subtle, AD patients are often missed or misdiagnosed. Therefore, identifying highly sensitive and accurate biomarkers is crucial for the diagnosis and drug intervention of AD. Currently, the diagnosis of AD mainly relies on memory scales, PET, and the detection of pathological indicators such as Aβ and phosphorylated tau in cerebrospinal fluid and blood. However, the detection results of these diagnostic indicators in clinical practice are still controversial, and there is still a lack of effective detection evidence for the early symptoms of AD.

[0003] In recent years, the development of high-throughput omics technologies such as genomics, transcriptomics, proteomics, and metabolomics has accelerated the discovery of novel biomarkers. Metabolomics, in particular, has demonstrated significant advantages in screening disease-related biomarkers by monitoring dynamic changes in metabolite profiles using techniques such as magnetic resonance spectroscopy and mass spectrometry. It holds broad application prospects in elucidating the molecular pathogenic mechanisms of AD and the resulting pathophysiological changes. Cerebrospinal fluid (CSF) can directly reflect pathological changes in brain tissue. Core CSF markers associated with AD include Aβ42, total tau protein (t-tau), and phosphorylated tau protein (p-tau). These core CSF markers have high diagnostic accuracy, with sensitivity and specificity reaching 85-90% in the MCI stage. They serve not only as diagnostic markers for the dementia stage of AD but also as predictors of MCI outcomes. Research has also identified novel CSF markers associated with AD progression, primarily molecules involved in the Aβ metabolic pathway and synaptic markers. For example, high concentrations of neurogranin in the CSF can predict the transition from MCI to AD and are associated with rapid memory impairment during follow-up. Furthermore, studies have found that increased levels of D-serine in the CSF may also be a marker for the early diagnosis of AD. Therefore, screening for early AD diagnostic biomarkers based on metabolites in the cerebrospinal fluid is expected to improve the accuracy of AD diagnosis and facilitate early warning of the disease, pathological classification, and predictive assessment of the disease's developmental stage.

[0004] Summary of the Invention

[0005] The present application provides an Alzheimer's disease biomarker based on brain metabolites and its application.

[0006] In a first aspect, the present application provides an Alzheimer's disease biomarker based on brain metabolites, wherein the biomarker includes any one or a combination of at least two of palmitic acid, DHA, gallic acid, 11Z, 14Z, 17Z eicosatrienoic acid, glycodeoxycholic acid, palmitoleic acid, linoleic acid, erucic acid, petroselinic acid or arachidonic acid.

[0007] This application conducts qualitative and quantitative analysis of brain metabolites based on targeted metabolomics analysis technology, and uses ultra-high performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-QTRAP MS) to detect metabolites in samples. This technology has high selectivity and high sensitivity, and uses targeted sample preparation and chromatographic separation methods to conduct qualitative and quantitative analysis of more than 300 common metabolites. It was detected that the levels of palmitic acid, DHA (4Z, 7Z, 10Z, 13Z, 16Z, 19Z docosahexaenoic acid), gallic acid, 11Z, 14Z, 17Z eicosatrienoic acid, glycodeoxycholic acid, palmitoleic acid, linoleic acid, erucic acid, petroselinic acid or arachidonic acid in the brain metabolites of Alzheimer's disease were significantly higher than those in normal brain metabolites. Using them as biomarkers for Alzheimer's disease can assist in the early diagnosis of Alzheimer's disease.

[0008] In a second aspect, the present application provides an application of an Alzheimer's disease biomarker based on brain metabolites according to the first aspect in constructing an early diagnosis model for Alzheimer's disease and / or preparing an early diagnosis device for Alzheimer's disease.

[0009] In a third aspect, the present application provides an early diagnosis model for Alzheimer's disease, wherein the input variables of the early diagnosis model for Alzheimer's disease include the mass spectrometry peak intensity value of the Alzheimer's disease biomarker described in the first aspect;

[0010] The output variables of the Alzheimer's disease early diagnosis model include differential expression folds.

[0011] Preferably, the calculation formula for the differential expression fold is as follows:

[0012] Preferably, the criteria for determining a positive diagnosis of Alzheimer's disease are:

[0013] The differential expression fold of the palmitic acid is ≥1.396, the differential expression fold of the DHA is ≥1.292, the differential expression fold of the gallic acid is ≥0.705, the differential expression fold of the eicosatrienoic acid is ≥1.512, the differential expression fold of the glycodeoxycholic acid is ≥0.482, the differential expression fold of the palmitoleic acid is ≥1.649, the differential expression fold of the linoleic acid is ≥1.565, the differential expression fold of the erucic acid is ≥0.751, the differential expression fold of the petroselinic acid is ≥1.261 or the differential expression fold of the arachidonic acid is ≥1.261.

[0014] In this application, a model for early diagnosis of Alzheimer's disease is constructed. The model uses the mass spectrometry peak intensity value of the Alzheimer's disease biomarker as the input variable and the differential expression multiple as the output variable. The model can quickly output results and fully characterize samples with abnormal levels of Alzheimer's disease biomarkers, thereby assisting in the early diagnosis of Alzheimer's disease.

[0015] In a fourth aspect, the present application provides an early diagnosis device for Alzheimer's disease, the device comprising the following units:

[0016] The sample preparation unit is used to perform the following steps:

[0017] Used to prepare the sample to be tested into a sample solution that can be used for separation by liquid chromatography;

[0018] The detection unit is configured to perform the following steps:

[0019] Separating the sample solution to be tested using the liquid chromatograph, detecting the separated sample using a mass spectrometer, performing data processing, and determining the mass spectrum peak intensity value of the Alzheimer's disease biomarker described in the first aspect in the sample; and

[0020] The analysis unit is configured to perform the following steps:

[0021] The peak intensity values ​​of the detected Alzheimer's disease biomarker mass spectrometry are input into the Alzheimer's disease early diagnosis model described in the third aspect for data analysis, the differential expression folds corresponding to the samples are output, and it is determined whether the sample is positive for Alzheimer's disease.

[0022] In the Alzheimer's disease early diagnosis device of the present application, the various units cooperate effectively with each other, are simple and efficient, can quickly complete sample processing, detection and obtain differential expression multiples, and at the same time perform Alzheimer's disease positive assessment based on reasonably designed judgment criteria, which is of great significance for the early diagnosis of Alzheimer's disease.

[0023] Preferably, the sample to be tested includes cerebrospinal fluid.

[0024] Preferably, the data processing includes:

[0025] MultiQuant software was used to extract the peaks of the MRM raw data, and the ratio of the peak area of ​​the Alzheimer's disease biomarker to the peak area of ​​the internal standard was calculated as the mass spectrometry peak intensity value.

[0026] Preferably, the device comprises the following units:

[0027] The sample preparation unit is used to perform the following steps:

[0028] Used to prepare the sample to be tested into a sample solution that can be used for separation by liquid chromatography;

[0029] The detection unit is configured to perform the following steps:

[0030] Separating the sample solution using the liquid chromatograph, detecting the separated sample using a mass spectrometer, performing peak extraction on the MRM raw data using MultiQuant software, and calculating the ratio of the peak area of ​​the Alzheimer's disease biomarker to the peak area of ​​the internal standard as the mass spectrometry peak intensity value; and

[0031] The analysis unit is configured to perform the following steps:

[0032] The mass spectrometry peak intensity value of the detected Alzheimer's disease biomarker is input into the Alzheimer's disease early diagnosis model described in the third aspect for data analysis, the differential expression fold corresponding to the sample is output, and it is determined whether it is positive for Alzheimer's disease.

[0033] In a fifth aspect, the present application provides an application of an Alzheimer's disease biomarker based on brain metabolites according to the first aspect as a target in screening drugs for treating or preventing Alzheimer's disease.

[0034] Compared with the prior art, this application has the following beneficial effects:

[0035] This application, for the first time, detects that the levels of 10 metabolites in Alzheimer's disease brain metabolites are significantly higher than those in normal samples. Using their mass spectrometry peak intensity values ​​as detection indicators, this method is used to assist in the diagnosis of Alzheimer's disease symptoms. It features high accuracy, convenience, speed, safety, and non-invasiveness, and has important clinical guidance for assisting in the diagnosis of AD-related indicators. Using these as Alzheimer's disease biomarkers and providing an early diagnosis model and device for Alzheimer's disease, the detection of specific metabolite levels can assist in the early diagnosis of Alzheimer's disease, facilitate rapid detection, and be timely, convenient, highly specific, and highly sensitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] FIG1 is a graph showing the mass spectrometry peak intensities of 11Z, 14Z, and 17Z eicosatrienoic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0037] FIG2 is a graph showing the mass spectrometry peak intensities of 4Z, 7Z, 10Z, 13Z, 16Z, and 19Z docosahexaenoic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0038] FIG3 is a graph showing the mass spectrometry peak intensities of arachidonic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0039] FIG4 is a graph showing the mass spectrometry peak intensities of linoleic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0040] FIG5 is a graph showing the mass spectrometry peak intensities of palmitic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0041] FIG6 is a graph showing the mass spectrometry peak intensities of erucic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0042] FIG7 is a graph showing the mass spectrometry peak intensities of palmitoleic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0043] FIG8 is a graph showing the mass spectrometry peak intensities of petroselinic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0044] FIG9 is a graph showing the mass spectrometry peak intensities of glycodeoxycholic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0045] FIG10 is a graph showing the mass spectrometry peak intensities of gallic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT). DETAILED DESCRIPTION

[0046] Experimental instruments and reagents

[0047] AB 5500 / 6500 Q-trap mass spectrometer (AB SCIEX)

[0048] Agilent 1290 Infinity LC ultrahigh pressure liquid chromatograph (Agilent)

[0049] Low-temperature high-speed centrifuge (Eppendorf 5430R)

[0050] Chromatographic column: Waters, ACQUITY UPLC BEH Amide 1.7μm, 2.1mm×100mm column

[0051] Waters, ACQUITY UPLC BEH C18 1.7μm, 2.1mm×100mm column

[0052] Acetonitrile (Merck, 1499230-935)

[0053] Ammonium acetate (Sigma, 70221)

[0054] Methanol (Fisher, A456-4)

[0055] Ammonia (Sigma, 221228)

[0056] Ammonium formate (Sigma, 70221)

[0057] Formic acid (Sigma, 00940)

[0058] Isotope standards (Cambridge Isotope Laboratories)

[0059] Example 1

[0060] Sample extraction method

[0061] Cerebrospinal fluid samples were collected from diseased individuals and healthy control donors. An appropriate amount of sample was added to a precooled methanol / acetonitrile / water solution (2:2:1, v / v), vortexed, sonicated at low temperature for 30 min, allowed to stand at -20°C for 10 min, and centrifuged at 14000g for 20 min at 4°C. The supernatant was vacuum dried and, for mass spectrometry analysis, reconstituted with 100 μL of acetonitrile-water solution (acetonitrile:water = 1:1, v / v), vortexed, and centrifuged at 14000g for 15 min at 4°C. The supernatant was then sampled and analyzed.

[0062] Using the above technical methods, we performed qualitative and quantitative metabolite analysis on the cerebral cortex samples of 9-month-old male AD model mice (10 mice) and their littermate wild-type male healthy mice (WT) control group (9 mice).

[0063] Chromatography-mass spectrometry analysis

[0064] (1) Chromatographic conditions

[0065] The samples were separated using an Agilent 1290 Infinity LC ultra-high performance liquid chromatography (UHPLC) system with HILIC and a C18 column; the HILIC column temperature was 35°C; the flow rate was 0.3 mL / min; the injection volume was 2 μL; the mobile phase composition was A: water + 100 mM ammonium acetate + 1.2% ammonia water, B: acetonitrile; the gradient elution program was as follows: 0-1.0 min, 85% B; 1.0-3.0 min, B changed linearly from 85% to 80%; 3.0-4.0 min, 80% B; 4.0-6.0 min, B changed linearly from 80% to 70%; 6.0-10.0 min, B changed linearly from 70% to 50%; 10-12.5 min, B was maintained at 50%; 12.5-12.6 min, B changed linearly from 50% to 85%; 12.6-18 min, B was maintained at 85%. The C18 column was maintained at a temperature of 40°C, a flow rate of 0.4 mL / min, and an injection volume of 2 μL. The mobile phase composition was A: water + 50 mM ammonium formate + 0.4% formic acid, B: methanol. The gradient elution program was as follows: 0–5 min, linear gradient from 5% to 60% B; 5–11 min, linear gradient from 60% to 100% B; 11–13 min, B maintained at 100%; 13–13.1 min, linear gradient from 100% to 5% B; 13.1–16 min, B maintained at 5%. Samples were maintained in the autosampler at 4°C throughout the analysis. To minimize the influence of instrument signal fluctuations, samples were analyzed sequentially in a random order. QC samples were inserted into the sample queue to monitor and evaluate system stability and the reliability of the experimental data.

[0066] (2) Mass spectrometry conditions

[0067] Mass spectrometric analysis was performed using an AB 6500 QTRAP mass spectrometer (AB SCIEX). ESI source conditions were as follows: sheath gas temperature: 350°C; drying gas temperature: 350°C; sheath gas flow rate: 11 L / min; drying gas flow rate: 10 L / min; capillary voltage: 4000 V or -3500 V (in positive and negative ion modes, respectively); nozzle voltage: 500 V; and nebulizer gas pressure: 30 psi. Monitoring was performed in MRM mode.

[0068] (3) Data analysis process

[0069] Use MultiQuant or Analyst software to perform peak extraction on the MRM raw data, obtain the ratio of the peak area of ​​each substance to the peak area of ​​the internal standard, and calculate the content according to the standard curve.

[0070] As shown in Figures 1-10, the results of the inter-group difference analysis showed that the levels of Elaidic acid, 4Z,7Z,10Z,13Z,16Z,19Z-Docosahexaenoic Acid (DHA) (4Z,7Z,10Z,13Z,16Z,19Z docosahexaenoic acid), Gallic acid, 11Z,14Z,17Z-Eicosatrienoic Acid (11Z,14Z,17Z eicosatrienoic acid), Glycodeoxycholic acid (GDCA) (glycodeoxycholic acid), Palmitoleic Acid, Linoleic acid, Erucic acid, Petroselinic acid, and Arachidonic acid in the AD group were significantly different from those in the control group, indicating that these metabolites play an important role in distinguishing the disease model.

[0071] Further classification analysis revealed that elaidic acid, 4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoic acid (DHA), 11Z,14Z,17Z-eicosatrienoic acid, palmitoleic acid, linoleic acid, erucic acid, petroselinic acid, and arachidonic acid are fatty acids, while gallic acid belongs to the benzene class and glycodeoxycholic acid (GDCA) belongs to the bile acid class. These results suggest that changes in brain tissue metabolite levels may reflect abnormalities in related metabolic pathways in AD brains and are of great significance for early clinical diagnosis. Since it is inconvenient to obtain brain tissue from patients for testing in clinical practice, cerebrospinal fluid is often used to detect relevant indicators in actual applications.

[0072] The applicant declares that this application uses the above-mentioned embodiments to illustrate the Alzheimer's disease biomarker based on brain metabolites and its application, but this application is not limited to the above-mentioned embodiments, that is, it does not mean that this application must rely on the above-mentioned embodiments to be implemented. Those skilled in the art should understand that any improvements to this application, equivalent replacement of various raw materials of the product of this application, addition of auxiliary ingredients, selection of specific methods, etc., are all within the scope of protection and disclosure of this application.

[0073] The preferred embodiments of the present application are described in detail above. However, the present application is not limited to the specific details of the above embodiments. Within the technical concept of the present application, various simple modifications can be made to the technical solution of the present application, and these simple modifications all fall within the scope of protection of the present application.

[0074] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner unless there is any contradiction. In order to avoid unnecessary repetition, this application will not further describe various possible combinations.

Claims

1. A biomarker for Alzheimer's disease based on brain metabolites, which comprises any one or a combination of at least two of palmitic acid, DHA, gallic acid, 11Z,14Z,17Z - eicosatrienoic acid, glycochenodeoxycholic acid, palmitoleic acid, linoleic acid, erucic acid, petroselinic acid or arachidonic acid.

2. Use of the biomarker for Alzheimer's disease based on brain metabolites according to claim 1 in constructing an early diagnosis model for Alzheimer's disease and / or preparing an early diagnosis device for Alzheimer's disease.

3. An early diagnosis model for Alzheimer's disease, the input variables of which include the mass spectrometry peak intensity values of the biomarker for Alzheimer's disease according to claim 1; Wherein, The output variables of the early Alzheimer's disease diagnosis model include the fold change in expression.

4. The early diagnosis model for Alzheimer's disease according to claim 3, wherein, The calculation formula for the differential expression multiple is as follows:

5. The early diagnosis model for Alzheimer's disease according to claim 3 or 4, wherein, The criteria for judging positive for Alzheimer's disease are as follows: The fold change in expression of palmitic acid ≥ 1.396, the fold change in expression of DHA ≥ 1.292, the fold change in expression of gallic acid ≥ 0.705, the fold change in expression of eicosatrienoic acid ≥ 1.512, the fold change in expression of glycochenodeoxycholic acid ≥ 0.482, the fold change in expression of palmitoleic acid ≥ 1.649, the fold change in expression of linoleic acid ≥ 1.565, the fold change in expression of erucic acid ≥ 0.751, the fold change in expression of petroselinic acid ≥ 1.261, or the fold change in expression of arachidonic acid ≥ 1.

261.

6. An early diagnosis device for Alzheimer's disease, which comprises the following units: A sample preparation unit for performing the following steps: For preparing a test sample solution that can be used for separation by a liquid chromatograph. A detection unit for performing the following steps: Separating the test sample solution using the liquid chromatograph, detecting the separated sample using a mass spectrometer, performing data processing, and measuring the mass spectrometry peak intensity value of the Alzheimer's disease biomarker described in claim 1 in the sample; and An analysis unit for performing the following steps: Inputting the detected peak intensity value of the Alzheimer's disease biomarker mass spectrometry into the early Alzheimer's disease diagnosis model described in any one of claims 3-5 for data analysis, outputting the fold change in expression corresponding to the sample, and judging whether it is positive for Alzheimer's disease.

7. The device according to claim 6, wherein, The test sample includes cerebrospinal fluid.

8. The device according to claim 6 or 7, wherein, The data processing includes: Performing peak extraction on the MRM raw data using MultiQuant software, and calculating the ratio of the peak area of the Alzheimer's disease biomarker to the peak area of the internal standard as the mass spectrometry peak intensity value.

9. The device according to any one of claims 6 - 8, wherein, The device includes the following units: A sample preparation unit for performing the following steps: For preparing a test sample solution that can be used for separation by a liquid chromatograph. A detection unit for performing the following steps: Separating the test sample solution using the liquid chromatograph, detecting the separated sample using a mass spectrometer, performing peak extraction on the MRM raw data using MultiQuant software, and calculating the ratio of the peak area of the Alzheimer's disease biomarker to the peak area of the internal standard as the mass spectrometry peak intensity value; and An analysis unit for performing the following steps: Inputting the detected peak intensity value of the Alzheimer's disease biomarker mass spectrometry into the early Alzheimer's disease diagnosis model described in any one of claims 3-5 for data analysis, outputting the fold change in expression corresponding to the sample, and judging whether it is positive for Alzheimer's disease.

10. Use of the biomarker for Alzheimer's disease based on brain metabolites according to claim 1 as a target in screening for drugs for treating or preventing Alzheimer's disease.

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