CN116678974A - Screening method of Alzheimer disease plasma metabolism marker, plasma metabolism marker and application - Google Patents

Abstract

The invention provides a screening method of a plasma metabolism marker of Alzheimer's disease, the plasma metabolism marker and application thereof, belonging to the technical field of clinical diagnosis of Alzheimer's disease, wherein the screening method comprises the following steps: 1) Mixing human plasma with methanol solution of dichlorophenylalanine, performing ultrasonic treatment, and centrifugally collecting supernatant to obtain plasma metabolic components to be detected; 2) Respectively detecting the plasma metabolic components to be detected of the control group and the experimental group obtained in the step 1) by utilizing a high-resolution mass spectrometry technology to obtain a serum metabolite of the control group and a serum metabolite of the experimental group; 3) Comparing the serum metabolite of the experimental group with the serum metabolite of the control group to obtain a metabolite combination which has obvious difference with the control group, namely the plasma metabolic marker of the Alzheimer disease. The screening method provided by the invention can be used for diagnosing the Alzheimer's disease more accurately, helping researchers to know the pathogenesis of the Alzheimer's disease better, and providing a more accurate and more effective method for diagnosis and treatment.

Description

Screening method of Alzheimer disease plasma metabolism marker, plasma metabolism marker and application

Technical Field

The invention belongs to the technical field of clinical diagnosis of Alzheimer's disease, and particularly relates to a screening method of a plasma metabolism marker of Alzheimer's disease, the plasma metabolism marker and application.

Background

Alzheimer's Disease (AD) is a neurodegenerative disease which is a disease well developed in the elderly, and is characterized by continuous decline of cognitive and memory functions, decline of daily life ability, and associated neuropsychiatric symptoms and behavioral disorders. The disease is hidden, the disease course is long, and most patients are in an advanced stage when reaching clinical diagnosis standards. At present, diagnosis of Alzheimer's disease in clinic mainly depends on neuropsychological assessment and genotype examination, and an imaging detection method only carries out auxiliary observation on brain structural and functional changes, so that accurate clinical evaluation indexes are difficult to provide in time.

With the widespread clinical use of biomarkers, biologically defining AD based on AD humoral biomarkers (a/T/N) would be the cornerstone and core for diagnosing AD in future clinical practice. Wherein the metabolome is at the terminal of the system biology, and can amplify the tiny functional changes of the genome and the proteome on the metabolic level, so that the detection is easier. Second, the number of metabolites is far less than genes and proteins, and common metabolites are very similar in different biological systems, so metabolomics can be applied to different kinds of biological systems. In addition, metabolites are molecules that most directly reflect physiological states and environmental changes in organisms, so metabolomics can reflect disease states more accurately. At present, no definite clinical diagnosis marker exists for Alzheimer's disease, and metabonomics is used as an emerging research method, so that the method has a wide application prospect in the aspects of Alzheimer's disease diagnosis and treatment.

Disclosure of Invention

Accordingly, the invention aims to provide a screening method of plasma metabolic markers of Alzheimer's disease, the plasma metabolic markers and application thereof, which can diagnose Alzheimer's disease more accurately, help researchers to know the pathogenesis of Alzheimer's disease better and provide a more accurate and more effective method for diagnosis and treatment.

The invention provides a screening method of plasma metabolic markers of Alzheimer's disease, which comprises the following steps:

1) Mixing human plasma with methanol solution of dichlorophenylalanine, performing ultrasonic treatment, and centrifugally collecting supernatant to obtain a plasma metabolome to be detected;

the human plasma comprises independent healthy human plasma and plasma of patients with Alzheimer's disease, wherein the healthy human plasma is a control group, and the plasma of patients with Alzheimer's disease is an experimental group;

2) Respectively detecting the plasma metabolin to be detected of the control group and the experimental group obtained in the step 1) by utilizing a high-resolution mass spectrometry technology to obtain a serum metabolite of the control group and a serum metabolite of the experimental group;

3) Comparing the serum metabolite of the experimental group with the serum metabolite of the control group to obtain a metabolite combination which has obvious difference with the control group, namely the plasma metabolic marker of the Alzheimer disease.

Preferably, the volume ratio of the human plasma to the methanol solution of the dichlorophenylalanine is 1: (2-4), wherein the concentration of the dichlorophenylalanine is 4-6 mug/mL.

Preferably, the mixing is performed by means of vortexing, wherein the vortexing time is 4-6 min; the frequency of the ultrasonic treatment is 20-25 KHz, and the time of the ultrasonic treatment is 1-3 min.

Preferably, the rotation speed of the centrifugation is 14000-15000 rpm, and the centrifugation time is 14-16 min.

Preferably, in the step 2), the mobile phase a of the liquid chromatography in the high resolution mass spectrometry technique is 0.1% acetonitrile formate, and the mobile phase B is 0.1% formic acid water; column temperature: 40 ℃; sample introduction disc temperature: 6.0 ℃; flow rate: 0.30mL/min; sample injection volume: 5. Mu.L; time and percent mobile phase B elution gradient: 0.0 to 2.0min,5 percent; 2.0 to 6.0min,50 percent; 6.0 to 15.0min,95 percent; 15.0 to 18.0min,95 percent, 18.0 to 20.0min,5 percent;

the signal acquisition time of mass spectrum is 0 min-15 min;

mass spectrometry conditions: analysis by heating spray ionization, positive ion mode voltage: 3.5kV; negative ion mode voltage: 2.5kV, heater temperature: 325 ℃, sheath gas: 30L/min, auxiliary gas: 10L/min; full scan mode, scan range m/z 65-975, resolution: 70000; automatic gain control (AGT): 1X10 ⁶ Maximum injection time: 100ms; the second-level MSMS fragmentation mode, scanning range m/z 65-975, resolution: 15000; automatic gain control (AGT): 1X10 ⁵ The method comprises the steps of carrying out a first treatment on the surface of the Maximum injection time: 50ms; normalized gradient lysis energy: 20%,25% and 30%.

The invention provides a plasma metabolic marker combination for Alzheimer's disease, which comprises at least two of phenylacetylglutamine, arginine, acetylcarnitine, sphingosine-1-phosphate, palmitoyl carnitine, 5-oxoproline, uracil, uric acid, hypoxanthine, L-histidine, decanoyl carnitine, ornithine, betaine and cortisol.

Preferably, phenylacetylglutamine and arginine are included.

Preferably, the expression of phenylacetylglutamine and arginine is up-regulated in patients with Alzheimer's disease compared to healthy humans; expression of acetylcarnitine, sphingosine-1-phosphate, palmitoyl carnitine, 5-oxoproline, uracil, uric acid, hypoxanthine, L-histidine, decanoyl carnitine, ornithine, betaine and cortisol is down-regulated.

The invention provides application of a reagent for detecting the plasma metabolism marker combination in preparation of a diagnosis kit for Alzheimer's disease.

Compared with the prior art, the invention has the following beneficial effects: the screening method of the plasma metabolic marker of the Alzheimer's disease is based on the metabonomic analysis carried out by the high-resolution mass spectrometry technology, and the obtained plasma metabolic marker can provide more accurate diagnosis capability, is superior to the diagnosis capability of the traditional Abeta amyloid and can be used as a potential method for diagnosing the Alzheimer's disease in the future.

Drawings

FIG. 1 is a TIC diagram of total ion flow before, during and after QC in high resolution mass spectrometry positive ion mode detection;

FIG. 2 is a summary diagram of multivariate statistical analysis of positive ion mode detection of high resolution mass spectrometry, wherein A is a PCA two-dimensional score diagram, B is a separation diagram of a supervised PLS-DA model, C is an evaluation result of accuracy of the PLS-DA model by adopting displacement test, and D is S-plot analysis to identify two groups of metabolites with differences;

FIG. 3 shows the mean mass spectrum signal intensity, standard deviation and coefficient of variation for 28 compounds;

FIG. 4 is a box plot of 14 compounds (plasma metabolic markers) in AD and CN groups;

FIG. 5 is the area under the subject's characteristic curve, wherein (A) the area under the subject's characteristic curve of PAGIn and L-Arg; (B) Area under the characteristic curve of the Aβ42 and Aβ42/Aβ40 subjects;

FIG. 6 is a correlation analysis of PAGIn and L-Arg with Abeta 42/Abeta 40, wherein A is the correlation of PAGIn with Abeta 42/Abeta 40 and B is the correlation of L-Arg with Abeta 42/Abeta 40;

FIG. 7 is a box diagram of plasma amyloid 1-40 and plasma amyloid 1-42.

Detailed Description

The invention provides a screening method of plasma metabolic markers of Alzheimer's disease, which comprises the following steps: 1) Mixing human plasma with methanol solution of dichlorophenylalanine, performing ultrasonic treatment, and centrifugally collecting supernatant to obtain plasma metabolic components to be detected; the human plasma comprises independent healthy human plasma and plasma of patients with Alzheimer's disease, wherein the healthy human plasma is a control group, and the plasma of patients with Alzheimer's disease is an experimental group; 2) Respectively detecting the plasma metabolin to be detected of the control group and the experimental group obtained in the step 1) by utilizing a high-resolution mass spectrometry technology to obtain a serum metabolite of the control group and a serum metabolite of the experimental group; 3) Comparing the serum metabolite of the experimental group with the serum metabolite of the control group to obtain a metabolite combination which has obvious difference with the control group, namely the plasma metabolic marker of the Alzheimer disease.

In the invention, human plasma is mixed with methanol solution of dichlorophenylalanine, and after ultrasonic treatment, supernatant is collected by centrifugation to obtain plasma metabolic components to be detected. In the present invention, the dichlorophenylalanine is an internal standard.

In the present invention, the human plasma is preferably fresh plasma or plasma stored at-80 ℃. The method for collecting the blood plasma is not particularly limited, and a conventional method for collecting blood plasma in the art may be used. The invention preferably further comprises the step of centrifuging to remove interfering substances before mixing the human plasma with the methanol solution of dichlorophenylalanine; the rotational speed of the centrifugation is preferably 14000 to 15000rpm, more preferably 14200 to 14800rpm, still more preferably 14500rpm; the time of the centrifugation is preferably 8 to 12 minutes, more preferably 9 to 11 minutes, and most preferably 10 minutes. In the present invention, the volume ratio of the human plasma to the methanol solution of dichlorophenylalanine is preferably 1: (2 to 4), more preferably 1: (2.5-3.5); the concentration of the dichlorophenylalanine is preferably 4 to 6. Mu.g/mL, and more preferably 5. Mu.g/mL.

In the invention, the mixing of the human plasma and the methanol solution of the dichlorophenylalanine is preferably carried out by a vortex, and the vortex time is 4-6 min; the frequency of ultrasonic treatment is 20-25 KHz, and the time of ultrasonic treatment is 1-3 min. In the present invention, the rotational speed of the centrifugation is preferably 14000 to 15000rpm, more preferably 14200 to 14800rpm, still more preferably 14500rpm; the time for the centrifugation is preferably 14 to 16 minutes, more preferably 15 minutes.

In the invention, the human plasma comprises independent healthy human plasma and plasma of patients with Alzheimer's disease, wherein the healthy human plasma is a control group, and the plasma of patients with Alzheimer's disease is an experimental group.

In the invention, the high-resolution mass spectrometry technology is utilized to respectively detect the obtained plasma metabolome to be detected of the control group and the experimental group, and the serum metabolite of the control group and the serum metabolite of the experimental group are obtained. In the invention, the preferred high performance liquid chromatography is first performed, and then high resolution mass spectrometry is performed; the high performance liquid chromatography analysis preferably adopts a Dionex Ultimate UPLC 3000system (Thermo Scientific) high performance liquid chromatograph; in the high-resolution mass spectrometry technology, the mobile phase A of liquid chromatography is 0.1% of formic acid acetonitrile, and the mobile phase B is 0.1% of formic acid water; column temperature: 40 ℃; sample introduction disc temperature: 6.0 ℃; flow rate: 0.30mL/min; sample injection volume: 5. Mu.L; time and elution gradient percentage (mobile phase B): 0.0 to 2.0min,5 percent; 2.0 to 6.0min,50 percent; 6.0 to 15.0min,95 percent; 15.0 to 18.0min,95 percent, 18.0 to 20.0min,5 percent; the signal acquisition time of mass spectrum is 0 min-15 min;

the high resolution mass spectrometry preferably employs a Q exact-plus high resolution mass spectrometer (Thermo Scientific); mass spectrometry conditions: analysis by heating spray ionization, positive ion mode voltage: 3.5kV; negative ion mode voltage: 2.5kV, heater temperature: 325 ℃, sheath gas: 30L/min, auxiliary gas: 10L/min; full scan mode, scan range m/z 65-975, resolution: 70000; automatic gain control (AGT): 1X106, maximum injection time: 100ms; the second-level MSMS fragmentation mode, scanning range m/z 65-975, resolution: 15000; automatic gain control (AGT): 1X105; maximum injection time: 50ms; normalized gradient lysis energy: 20%,25% and 30%.

In the present invention, the liquid phase detector is preferably stabilized by 2 to 3 blank samples before formally feeding the sample. In order to avoid that the detection sequence of error samples is randomly carried out, the samples are firstly operated for 4-6 times before being analyzed so as to balance the detection system. In the sample detection process, 1 quality control sample is run after 5-7 normal samples are detected so as to evaluate the stability of the system. In the invention, the quality control sample comprises a mixed solution of plasma metabolic components to be detected of all samples of a control group.

The invention further comprises a compound identification step and a signal normalization processing step after the high-resolution mass spectrometry, wherein the compound identification step preferably adopts ThermoFisher Xcalibur software to carry out data acquisition, then Compound Discover 3.0.0 software is used for carrying out molecular searching and identification on a metablock sample, and each metabolite is subjected to secondary fragmentation ion matching of two databases of HMDB (http:// hmdb.ca /) and Pubchem (http:// pubchem.ncbi.lm.nih.gov /), and then is screened for subsequent analysis. In the invention, the signal normalization processing is preferably to compare the first-order MS quantitative peak areas of the different metabolites with the first-order MS quantitative peak areas of the internal standard dichlorophenylalanine, so that the normalized characteristic response of each metabolite can be obtained, and the response value is used for the subsequent inter-group statistical difference analysis.

After the serum metabolite of the control group and the serum metabolite of the experimental group are obtained, the serum metabolite of the experimental group and the serum metabolite of the control group are compared, and the metabolite combination with obvious difference with the control group is obtained, namely the plasma metabolic marker of Alzheimer disease.

The invention also provides a plasma metabolic marker combination for Alzheimer's disease, which comprises at least two of phenylacetylglutamine, arginine, acetylcarnitine, sphingosine-1-phosphate, palmitoyl carnitine, 5-oxoproline, uracil, uric acid, hypoxanthine, L-histidine, decanoyl carnitine, ornithine, betaine and cortisol.

In the present invention, the expression of phenylacetylglutamine and arginine is up-regulated in patients with Alzheimer's disease compared to healthy persons; expression of acetylcarnitine, sphingosine-1-phosphate, palmitoyl carnitine, 5-oxoproline, uracil, uric acid, hypoxanthine, L-histidine, decanoyl carnitine, ornithine, betaine and cortisol is down-regulated.

The invention also provides application of the reagent for detecting the plasma metabolic marker combination in preparing the Alzheimer disease diagnosis reagent, and the reagent for detecting the plasma metabolic marker combination is not particularly limited and can be used in the detection process. In the invention, the reagent also comprises a quality control sample, wherein the quality control sample comprises a plurality of plasma test solutions of normal samples of healthy people.

100 mu L of each normal sample is mixed to obtain

The technical solutions provided by the present invention are described in detail below with reference to examples, but they should not be construed as limiting the scope of the present invention.

Example 1

Screening method of AD serum metabolism diagnosis marker

Experimental protocol and sample pretreatment

Clinical data collection

Collecting the demographic data of the subject in clinical data including name, sex, month of birth, education level, occupation, past medical history and drug use condition. The height and weight were measured and recorded, and the body mass index BMI was calculated. The medical history and course of the AD group members were recorded and serological examinations were performed, including folic acid, vitamin B12, thyroid function and imaging. All patients in the group were evaluated for Cognitive ability on an ADAS-Cog (Alzheimer's Disease Assessment Scale-Cognitive) scale, and after 12 Cognitive task tests, the total score was less than 18 for the normal Cognitive group and greater than 18 for the Alzheimer's group. According to the ADAS-Cog scale, 29 elderly persons with normal cognition (Cognitvely Normal, CN) and 29 Alzheimer's Disease (Alzheimer's Disease, AD), the 58 subjects were evaluated by neurologists, and none of the panelists took medications that affected blood lipid levels.

Table 1 demographic basis information for AD group and CN group

Sample collection, preservation and pretreatment

CN and AD groups were bled after a night (about 12 h) of fasting, and EDTA anticoagulation tubes were used for plasma sample collection, after 30min rest at room temperature, centrifuged at 3000g for 15min at room temperature. The supernatant was taken into 0.5mL ep tube and stored at-80 ℃.

Extracting plasma metabolites of the subject:

taking a blood plasma sample of a subject frozen at-80 ℃, and centrifuging at 14500rpm for 10min after thawing at 4 ℃. mu.L of plasma was precisely aspirated into 1.5mL centrifuge tubes. 150. Mu.L of methanol containing 5. Mu.g/mL of dichlorophenylalanine is added, vortex for 5min, ultrasonic for 2min, centrifuge at 14500rpm for 15min, and transfer 100. Mu.L of the extracted supernatant to a sample injection vial for detection.

Detecting instrument and chromatographic mass spectrum condition

Detection instrument: dionex Ultimate UPLC 3000system (Thermo Scientific) high performance liquid chromatograph; q exact-plus high resolution mass spectrometer (Thermo Scientific).

Analytical chromatographic column: ACQUITY UPLC ^TM X Selected HSS T3 column,2.1X100mm,2.5μm,MA,USA

Chromatographic conditions: mobile phase: mobile phase a was 0.1% acetonitrile formate and mobile phase B was 0.1% formic acid water; column temperature: 40 ℃; sample introduction disc temperature: 6.0 ℃; flow rate: 0.30mL/min; sample injection volume: 5. Mu.L; time and elution gradient percentage (phase B): (min)%): 0.0-2.0,5%; 2.0 to 6.0 and 50 percent; 6.0 to 15.0 and 95 percent; 15.0-18.0, 95% and 18.0-20.0,5%; and the time for collecting signals by mass spectrum is 0-15 min, and after 15min, the liquid phase cut valve automatically enters into the waste liquid collection.

Mass spectrometry conditions: analysis by means of thermal spray ionization (HESI), positive ion mode voltage: 3.5kV; negative ion mode voltage: 2.5kV, heater temperature: 325 ℃, sheath gas: 30L/min, auxiliary gas: 10L/min. Full scan mode, scan range m/z 65-975, resolution: 70000; automatic gain control (AGT): 1X10 ⁶ Maximum injection time: 100ms; the second-level MSMS fragmentation mode, scanning range m/z 65-975, resolution: 15000; automatic gain control (AGT): 1X10 ⁵ The method comprises the steps of carrying out a first treatment on the surface of the Maximum injection time: 50ms. Normalized gradient lysis energy: 20%,25% and 30%.

Data acquisition and quality control sample

Before formally feeding the sample, the liquid phase detector is stabilized by 2-3 blank samples. In order to ensure the stability of each analysis sequence, all sequences of the method are verified by using Quality Control (QC) samples, wherein the QC samples are obtained by mixing 100 mu L of each sample in a normal cognitive crowd group. In order to avoid that the detection sequence of error samples is random, 5 QC samples are run before the samples are analyzed so as to balance the detection system. During sample testing, 1 QC sample was run after every 6 normal samples tested to assess the stability of the system.

Identification of Compounds

Data acquisition is carried out by using ThermoFisher Xcalibur software, and molecular searching and identification are carried out on the metabolome sample by using Compound Discover 3.0.3.0 software. Each metabolite was screened after matching the two-stage fragmentation ions of two databases of HMDB (http:// HMDB. Ca /) and Pubchem (http:// Pubchem. Ncbi. Nl. Gov /), and used for subsequent analysis.

Signal normalization

The normalized characteristic response of each metabolite can be obtained by comparing the first-order MS quantitative peak areas of the internal standard dichlorophenylalanine with the first-order MS quantitative peak areas of different metabolites, and the formula is as follows: relative response value (rel. Res) =signal intensity (metabolite validated by secondary MSMS database)/signal intensity (internal standard dichlorophenylalanine). This response value will be used for later inter-group statistical difference analysis.

Serum metabolic spectrum detection and multivariate statistics of AD sample and CN sample

Metabonomic analysis was performed using a high resolution mass spectrometry platform, yielding 612 different m/z features and predicted formulas or chemical names. Unique m/z characteristics with predictive formulas or chemical names were obtained and characteristics with a measurement rate of 80% or more were used for further multivariate statistical analysis.

And carrying out PCA modeling on the standardized data matrix, and observing the grouping trend of the standardized data matrix. And (3) performing unsupervised PCA analysis on each group of data matrixes in the positive ion mode. The PCA two-dimensional score chart (A in FIG. 2) shows that the AD group, the healthy control group and the quality control QC group show obvious separation trend, and the supervised PLS-DA model is further established to further expand the inter-group separation. The separation graph of the supervised PLS-DA model in positive ion mode (B in FIG. 2) shows that the samples of each group exhibited good separation, R2X, R2Y and Q2 were 0.274,0.881 and 0.804 respectively, indicating that there may be endogenous metabolic pattern differences between the samples of each group. To prevent model overfitting, PLS-DA model accuracy was evaluated using a displacement test to avoid the occasional occurrence of supervised learning methods, and the intercept values of the resulting regression line and the y-axis were R2 (0.0,0.481) and Q2 (0.0, -0.156), respectively, with an intercept of Q2 less than 0, demonstrating that PLS-DA was not overfitting (C in FIG. 2). Two sets of metabolites with differences were identified by S-plot analysis (D in FIG. 2) as scattered spots scattered on both sides of the S-plot farther from the center. These results indicate changes in plasma metabolism in AD patients.

Verification and screening of serum metabolites of AD sample and CN sample

Subsequently, the coefficient of variation of the relative intensities was calculated, with a CV% value of less than 30% being used as a screening criterion for further studies. The HMDB and PubChem databases were used simultaneously to validate the compound resources and MSMS fragments, and by validation and screening, a final endogenous compound list consisting of 27 metabolites was used to study the metabolome phenotype study of AD and CN groups (table 2).

TABLE 2 Mass Spectrometry characterization of plasma metabolites and internal reference substances

Stability investigation of quality control samples

The CV% of the quality control results were analyzed to ensure convincing results were obtained in the downstream analysis. Thus, the MS area of 9 quality control samples, including 27 metabolites and 1 internal standard, was calculated. Wherein CV% is maximum of arachidonic acid, CV% is 23.1%, and CV% of 2-chloro-L-phenylalanine is minimum, which is 3.9%. The average CV% of 28 compounds was 9.7%, indicating good stability of the high resolution mass spectrometry detection. Figure 3 provides the mean signal intensity and standard deviation of 28 compounds.

Plasma differential metabolite analysis

Of the 27 endogenous metabolites selected, 14 compounds showed statistical differences between AD and CN. Wherein phenylacetylglutamine (PAGIn) and Arginine (L-Arginine) in the AD group showed an up-regulation trend compared to the CN group; acetylcarnitine (Acetyl-L-carnitine), sphingosine-1-phosphate (Sphingosine 1-phosphate), palmitoyl carnitine (palmitoyl carnitine), 5-Oxoproline (5-oxoline), uracil (Uracil), uric acid (Uric acid), hypoxanthine (hypoxanine), L-Histidine (L-Histine), decanoyl carnitine (decanoylcannine), ornithine (Ornithine), betaine (Betadine) and Cortisol (Cortisol) show a down-regulation trend (FIG. 4). The remaining 13 endogenous metabolites showed no statistical differences between AD group and CN group.

Serum metabolite diagnostic model build-up comparison

Diagnostic potential of various biomarkers was assessed using AUCs, with AUC values greater than 0.8 being phenylacetylglutamine (PAGln) and L-Arginine (L-Arginine), AUC (PAGln) =0.91 (95% confidence interval CI,0.84,0.99) and AUC (L-Arginine) =0.83 (95% confidence interval CI,0.73 confidence interval CI,0.73,0.93). To improve the discrimination of the model, the integrated analysis of PAGln and L-arginine using binary logistic regression finally yielded an AUC value of 0.95 (95% confidence interval CI,0.90,1.00) for the model, indicating good diagnostic capability. A in fig. 6 shows the ROC curve of potential metabolic biomarkers. Further, by performing ROC analysis on the ratio of aβ42 and aβ42/aβ40, diagnostic models based on aβ amyloid were obtained, respectively: AUC (aβ42) =0.76 (95% confidence interval CI,0.64,0.89) and AUC (aβ42/aβ40) =0.70 (95% confidence interval CI,0.56,0.83), the results of the diagnostic model for aβ amyloid are substantially identical to the results already reported. The results of the two groups of diagnosis models show that the metabolome based on the high-resolution mass spectrometry technology can provide diagnosis capability superior to that of the traditional Abeta amyloid and can be used as a potential model for diagnosing AD in the future.

Furthermore, pearson analysis showed that there was a positive correlation between aβ42/aβ40 and PAGln or L-Arg. For aβ42/aβ40 and PAGln, r=0.5396, p <0.0001; whereas for aβ42/aβ40 and LArg, r=0.3240, p <0.05. These two correlation analyses showed that PAGIn and L-Arg are related to Abeta 42/Abeta 40, respectively, and have statistically significant significance (FIG. 6). The potential association exists between PAGln and L-Arg and the deposition of Abeta amyloid, and the research value is good.

Comparative experiments

AD protein molecule ELISA detection result

The ratio of plasma amyloid 1-42 (Abeta 1-42) in plasma to plasma amyloid 1-40 (Abeta 1-40) in plasma is considered to be one of the criteria for diagnosing AD. Therefore, the comparison test adopts ELISA to verify the amyloid in the two groups of samples (normal cognitive crowd group and Alzheimer disease group) in the example 1, the results consistent with the reported study are obtained, the expression of Abeta 40 in the two groups of samples is not significantly different, abeta 42 is significantly increased in the AD group plasma sample, and the Abeta 42/Abeta 40 ratio also presents statistical significance in the difference analysis of the two groups. Aβ1-42 and Aβ1-40ELISA kits were purchased from Thermo fisher, spectrophotometric Multiskan ^TM FC, (thermo Fisher). Table 3 shows the mean comparison and statistical differences between the AD and CN groups for the Abeta 40, abeta 42 and Abeta 42/Abeta 40 ratios, and FIG. 7 shows the box-type plots of Abeta 40 and Abeta 42 for the AD and CN groups.

TABLE 3 plasma protein ELISA assay

From the above examples, it can be seen that the serum metabolic composition obtained by the high-resolution mass spectrometry based metabolome analysis and the diagnostic model provided by the invention can provide diagnostic capability superior to that of the traditional Abeta amyloid, and can be used as a potential model for diagnosing AD in the future.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (9)

1. A method for screening plasma metabolic markers for alzheimer's disease, comprising the steps of:

1) Mixing human plasma with methanol solution of dichlorophenylalanine, performing ultrasonic treatment, and centrifugally collecting supernatant to obtain plasma metabolic components to be detected;

the human plasma comprises independent healthy human plasma and plasma of patients with Alzheimer's disease, wherein the healthy human plasma is a control group, and the plasma of patients with Alzheimer's disease is an experimental group;

2) Respectively detecting the plasma metabolic components to be detected of the control group and the experimental group obtained in the step 1) by utilizing a high-resolution mass spectrometry technology to obtain a serum metabolite of the control group and a serum metabolite of the experimental group;

3) Comparing the serum metabolite of the experimental group with the serum metabolite of the control group to obtain the metabolite which has obvious difference with the control group, namely the plasma metabolic marker of the Alzheimer disease.

2. The method according to claim 1, wherein the volume ratio of human plasma to methanol solution of dichlorophenylalanine is 1: (2-4), the concentration of the dichlorophenylalanine is 4-6 mug/mL.

3. The screening method according to claim 2, wherein the mixing is performed by means of vortexing for a period of 4 to 6 minutes; the frequency of the ultrasonic treatment is 20-25 KHz, and the time of the ultrasonic treatment is 1-3 min.

4. A screening method according to claim 2 or 3, wherein the rotational speed of the centrifugation is 14000-15000 rpm and the time of the centrifugation is 14-16 min.

5. The method according to claim 1, wherein in step 2) the mobile phase a of the liquid chromatography in the high resolution mass spectrometry technique is 0.1% acetonitrile formate and the mobile phase B is 0.1% formic acid water; column temperature: 40 ℃; sample introduction disc temperature: 6.0 ℃; flow rate: 0.30mL/min; sample injection volume: 5. Mu.L; time and percent mobile phase B elution gradient: 0.0 to 2.0min,5 percent; 2.0 to 6.0min,50 percent; 6.0 to 15.0min,95 percent; 15.0 to 18.0min,95 percent, 18.0 to 20.0min,5 percent;

the signal acquisition time of mass spectrum is 0 min-15 min;

mass spectrometry conditions: analysis by heating spray ionization, positive ion mode voltage: 3.5kV; negative ion mode voltage: 2.5kV, heater temperature: 325 ℃, sheath gas: 30L/min, auxiliary gas: 10L/min; full scan mode, scan range m/z 65-975, resolution: 70000; automatic gain control (AGT): 1X10 ⁶ Maximum injection time: 100ms; the second-level MSMS fragmentation mode, scanning range m/z 65-975, resolution: 15000; automatic gain control (AGT): 1X10 ⁵ The method comprises the steps of carrying out a first treatment on the surface of the Maximum injection time: 50ms; normalized gradient lysis energy: 20%,25% and 30%.

6. A combination of plasma metabolic markers for alzheimer's disease comprising at least two of phenylacetylglutamine, arginine, acetylcarnitine, sphingosine-1-phosphate, palmitoyl carnitine, 5-oxoproline, uracil, uric acid, hypoxanthine, L-histidine, decanoyl carnitine, ornithine, betaine and cortisol.

7. The plasma metabolic marker combination according to claim 6, comprising phenylacetylglutamine and arginine.

8. The plasma metabolic marker combination according to claim 6, wherein the expression of phenylacetylglutamine and arginine is up-regulated in a patient with alzheimer's disease compared to a healthy human; expression of acetylcarnitine, sphingosine-1-phosphate, palmitoyl carnitine, 5-oxoproline, uracil, uric acid, hypoxanthine, L-histidine, decanoyl carnitine, ornithine, betaine and cortisol is down-regulated.

9. Use of a reagent for detecting a plasma metabolic marker combination according to any one of claims 6 to 8 in the preparation of a diagnostic kit for alzheimer's disease.