CN112798771B

CN112798771B - Biomarker for diagnosing leukoencephalopathy and application thereof

Info

Publication number: CN112798771B
Application number: CN202110352946.4A
Authority: CN
Inventors: 陈显扬; 宋王婷; 张珂; 薛腾; 韩佳睿
Original assignee: Baofeng Biotech Beijing Co ltd
Current assignee: Baofeng Biotech Beijing Co ltd
Priority date: 2021-03-31
Filing date: 2021-03-31
Publication date: 2021-07-30
Anticipated expiration: 2041-03-31
Also published as: CN112798771A; WO2022206264A1

Abstract

The invention provides a biomarker for diagnosing leukoencephalopathy and application thereof, wherein the biomarker is 6Z,9Z, 20-eicosatrienoic acid. The biomarker 6Z,9Z, 20-eicosatrienoic acid binds to ceramide (m18:1(4E)/24:1(15Z)), cannabixanthin A, cucurbitacin E, cholesterol ester 22:6 or ceramide (d18:0/24:1(15Z)) to determine whether a white brain lesion exists. A biomarker for diagnosing leukoencephalopathy is used for preparing a diagnostic reagent for predicting or diagnosing leukoencephalopathy, and is helpful for diagnosing whether the leukoencephalopathy is inclined or not and preventing the leukoencephalopathy in advance.

Description

Biomarker for diagnosing leukoencephalopathy and application thereof

Technical Field

The invention belongs to the technical field of biological detection, and particularly relates to a biomarker for diagnosing leukoencephalopathy and application thereof.

Background

White matter disease (WML) is a common neurodegenerative disease, the most typical pathology of which is the destruction of white matter integrity or demyelination, and is commonly seen in stroke, alzheimer's disease, multiple sclerosis, parkinson's disease, schizophrenia, and many other diseases. White brain matter is an important component of the central nervous system, where nerve fibers accumulate, and lesions in the myelin sheaths of central nerve cells in white brain matter can cause leukoencephalopathy. The typical response of white brain matter to various noxious stimuli is demyelination, which may be a secondary manifestation of neurological disorders such as infection, degeneration, poisoning, post-traumatic injury, infarction, etc. The leukoencephalopathy mainly causes symptoms of cognitive disorder, abnormal mental behavior, speech disorder, gait disorder, dysuria and the like of a patient, and seriously influences the healthy life quality of the patient. It is closely associated with an increased risk of stroke and dementia. With the continuous development of imaging technology, the detection rate of white brain lesions is higher and higher. It is now recognized that age is a clear risk factor for leukoencephalopathy. According to research, the detection rate of the leukoencephalopathy in the population of 60-70 years old reaches 87%; the detection rate of leukoencephalopathy in 80-90-year-old people is as high as 95% -100%, factors such as hypertension, dyslipidemia, diabetes, metabolic disorder and the like are closely related to the onset and progression of leukoencephalopathy, and the metabolic diseases mainly occur in the elderly people. With the advent of aging society, the medical community has drawn attention to the harm of leukoencephalopathy. Generally, the disease condition of most of the patients with the leukoencephalopathy is reversible, so that the symptoms of the patients with the leukoencephalopathy can be obviously improved by adopting proper preventive measures, wherein early screening is a crucial means.

The diagnosis of white brain lesions comprises mental state examination and imaging examination, and the current common craniocerebral examination means comprise electronic computed tomography and magnetic resonance imaging. The preliminary mental state examination comprises the operations of a test for evaluating inattention, a three-word delayed recall test for identifying dysmnesia, a clock drawing for evaluating visual dysfunction, an alternating motion sequence for evaluating brain function and the like. While leukoencephalopathy can be asymptomatic clinically in the early stage. And the test process involves questions and answers, consumes very large medical resources, and is time-consuming and labor-consuming. With the continuous development of imaging technology, the detection rate of white brain lesions gradually increases. However, the equipment required for detection is expensive and expensive. At present, no peripheral blood biomarker with high accuracy and strong specificity for the white brain lesion exists. The pathogenesis of the leukoencephalopathy is not clear, and a clear and effective treatment target point is lacked clinically, so that the treatment and the recovery of the leukoencephalopathy patient are not facilitated.

Metabolomics is an emerging omics technology that plays an increasingly important role in biological research because it can reveal unique chemical fingerprints of the cellular metabolism of the body. Metabonomics as an unbiased small molecule metabolite research method provides hope for finding more biomarkers of white brain lesions. There is increasing evidence for neurological disorders accompanied by disorders of bile acids, fatty acids and amino acids. And these results demonstrate that metabolic disorders may be predictive of the development of leukoencephalopathy. However, it is not clear which specific substance can be clearly detected as a prediction and diagnosis of the occurrence of leukoencephalopathy.

Disclosure of Invention

In order to effectively predict, diagnose and diagnose the white brain lesion, the invention provides a model for distinguishing the white brain lesion by using a novel molecular marker, and the invention can be applied to a kit for early discovering, diagnosing and predicting the white brain lesion.

In order to achieve the purpose, the invention adopts the following technical scheme that:

a biomarker for diagnosing a white brain lesion, the biomarker being 6Z,9Z, 20-eicosatrienoic acid (6Z, 9Z, 20-heiicosaterine).

Use of a biomarker for diagnosing a leukoencephalopathy as described above in the preparation of a test agent.

The use as described above, preferably the biomarker 6Z,9Z, 20-eicosatrienoic acid binds ceramide (m18:1(4E)/24:1(15Z)) (Cer (m18:1(4E)/24:1 (15Z))), cannabinol A (Canflavanin A), cucurbitin E (Cucurbitacin E), cholesteryl ester 22:6 (22: 6 Cholesterol ester) or ceramide (d18:0/24:1(15Z)) (Cer (d18:0/24:1 (15Z))) to determine the presence or absence of leukoencephalopathy.

For the above applications, preferably, the content of 6Z,9Z, 20-eicosatrienoic acid is denoted as F2, the content of ceramide (m18:1(4E)/24:1(15Z)) is denoted as F3, when the units of the content are mg/L, the TC value is calculated according to the calculation formula TC =4.4394-6.9717 × F2+1.7350 × F3, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.445, the white brain lesion is judged; if TC is less than 0.445, the test is normal.

The above application preferably shows that the content of 6Z,9Z, 20-eicosatrienoic acid is F2 and the content of cannabixanthin A is F4, when the units of the content are mg/L, the TC value is calculated according to the calculation formula TC =4.699-11.576 XF 2+4.876 XF 4, and the leukoencephalopathy is predicted according to the TC value: if TC is more than or equal to 0.236, the white brain lesion is judged; if TC < 0.236, it is normal.

The above application preferably shows that the content of 6Z,9Z, 20-eicosatrienoic acid is F2 and the content of cucurbitacin E is F5, when the units of the contents are mg/L, the TC value is calculated according to the calculation formula TC =6.0525-5.3745 XF 2-1.6979 XF 5, and the leukoencephalopathy is predicted according to the TC value: if TC is more than or equal to 0.337, the brain white lesion is judged; if TC is less than 0.337, the test result is normal.

In the above application, preferably, the contents of the 6Z,9Z, 20-eicosatrienoic acid and the cholesteryl ester 22:6 are respectively expressed as F2 and F6, when the units of the contents are mg/L, the TC value is calculated according to the calculation formula TC =4.1050-4.8614 × F2-0.2390 × F6, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.455, the white brain lesion is judged; if TC < 0.455, it is normal.

For the above applications, preferably, the contents of 6Z,9Z, 20-eicosatrienoic acid are represented as F2 and ceramide (d18:0/24:1(15Z)) as F7, when the units of the contents are mg/L, the TC value is calculated according to the calculation formula TC =4.4476-4.8480 xf 2-0.63305 xf 7, and the leukoencephalopathy is predicted according to the TC value: if TC is more than or equal to 0.495, determining the cerebral cortex lesion; if TC < 0.495, it is normal.

The invention has the beneficial effects that:

the biomarker for diagnosing the white brain lesion is 6Z,9Z, 20-eicosatrienoic acid, and judges whether the white brain lesion exists by combining ceramide (m18:1(4E)/24:1(15Z)), cannabixanthin A, cucurbitacin E, cholesterol ester 22:6 or ceramide (d18:0/24:1 (15Z)). Is helpful for preventing and diagnosing whether cerebral leukoplakia is prone to occur, and can be used for preventing in advance.

Drawings

FIG. 1 is a sample of VIP >1 in positive (A) negative (B) ion mode;

FIG. 2 is a score chart of (O) PLS-DA in positive (A) and negative (B) ion modes, (Disease: white brain Disease group; Control: Control group)

FIG. 3 is a graph of S-plot in positive (A) negative (B) ion mode;

FIG. 4 is a ROC curve based on a logistic regression model (variables F2+ F3);

FIG. 5 is a ROC curve based on a logistic regression model (variables F2+ F4);

FIG. 6 is a ROC curve based on a logistic regression model (variables F2+ F5);

FIG. 7 is a ROC curve based on a logistic regression model (variables F2+ F6);

FIG. 8 is a ROC curve based on a logistic regression model (variables F2+ F7).

Detailed Description

The following examples are intended to further illustrate the invention but should not be construed as limiting it. Modifications and substitutions may be made thereto without departing from the spirit and scope of the invention.

Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.

Example 1

Collecting samples: serum from patients who were clinically evaluated as normal and leukoencephalopathy was selected for testing.

All sample groups 112 for model building, age range: over the age of 45, with 64 in the control population and 48 in the patient population.

The proportion of males and females in the control population was 1: and 1, magnetic resonance imaging detection shows that no abnormity exists.

The proportion of males and females in the patient population is 1: magnetic resonance imaging detection shows that white matter has infarcted foci.

Laboratory apparatus and reagent

An experimental instrument: 1. a vortex oscillator: model MX-S, Scilogex, USA; high resolution mass spectrometer: ESI-QTOF/MS; the model is as follows: xevo G2-S Q-TOF; the manufacturer: waters, Manchester, UK3. refrigerated centrifuge: model D3024R, Scilogex corporation, usa; 4. ultra-high performance liquid chromatography: UPLC, model: the ACQUITY UPLC I-Class system; the manufacturer: waters, Manchester, UK; 5. data acquisition software: MassLynx4.1, Waters; 6. analysis and identification software: progenetics QI; Waters.

Experimental reagent: isopropanol, ammonium formate, formic acid, acetonitrile, sodium formate, leucine enkephalin; the manufacturers are Fisher.

Experimental methods

1. Sample pretreatment

Serum samples from the sample population were collected and thawed on ice, 200 μ L of plasma was extracted with 600 μ L of pre-cooled isopropanol, vortexed for 1min, incubated at room temperature for 10min, the extraction mixture was then stored overnight at-20 ℃, centrifuged at 4000r for 20min, the supernatant was transferred to a new centrifuge tube and diluted to 1: 10. samples were stored at-80 ℃ prior to LC-MS analysis. In addition, a pooled plasma sample was also prepared by combining 10 μ L of each extraction mixture.

2. Ultra-high performance liquid chromatography-mass spectrometry combined method for lipidomics

The samples were analyzed by ACQUITY UPLC coupled to a Xevo-G2XS high resolution time-of-flight mass spectrometer with ESI. A CQUITY UPLC BEH C18 column (2.1X 100 mM, 1.7 μm, Waters) was used with mobile phases of 10 mM ammonium formate-0.1% formic acid-acetonitrile (A, acetonitrile: water ratio 60: 40 by volume) and 10 mM ammonium formate-0.1% formic acid-isopropanol-acetonitrile (B, isopropanol: acetonitrile ratio 90: 10 by volume). Prior to large scale studies, pilot experiments with 10, 15 and 20 minute elution periods were performed to assess the potential impact of mobile phase composition and flow rate on lipid retention time. In Positive Ion Mode (PIM), abundant lipid precursor ions and fragments are separated in the same order, with similar peak shapes and ionic strengths. In addition, the mixed quality control sample with a 10 minute elution period also exhibited similar base peak intensities of the precursor and debris as the test sample. The flow rate of the mobile phase was 0.4 mL/min. The column was initially eluted with 40% B, then a linear gradient to 43% B in 2 minutes, then increasing the percentage of B to 50% in 0.1 min. In the next 3.9 minutes, the gradient further increased to 54% B, then the amount of B increased to 70% in 0.1 minutes. In the final part of the gradient, the amount of B increased to 99% in 1.9 min. Finally, solution B returned to 40% in 0.1min and the column was equilibrated for 1.9 min before the next injection. The sample injection amount is 5 mu L each time, lipid under a positive mode and a negative mode is detected by a QTOF mass spectrometer, the collection range is m/z 50-1200 years, and the collection time is 0.2 s/time. The ion source temperature is 120 ℃, the desolventizing temperature is 600 ℃, the gas flow is 1000L/h, and nitrogen is used as flowing gas. The capillary voltage was 2.0kV (+)/cone voltage was 1.5kV (-), and the cone voltage was 30V. Standard mass measurements were performed with leucine enkephalin, calibrated with sodium formate solution. Samples were randomly ordered. One quality control sample was injected for every 10 samples and analyzed to investigate the reproducibility of the data.

And (4) analyzing results:

1. method for searching serum difference substance by using multivariate statistics

The difference variables were screened by removing irrelevant differences using orthogonal partial least squares discriminant analysis (OPLS-DA) in combination with the Orthogonal Signal Correction (OSC) and PLS-DA methods. The VIP value is a projection of variable importance of a PLS-DA first main component, as shown in FIG. 1, VIP >1 is generally taken as a common judgment standard of metabonomics, and is taken as one of the standards for differential metabolite screening, wherein A is a positive ion mode, and B is a negative ion mode; FIG. 2 is a score chart of the first principal component and the second principal component in two groups of the white brain lesion group and the control group obtained by dimensionality reduction, the abscissa represents the difference between the groups, the ordinate represents the difference within the groups, and the two groups have better results separation, which illustrates that the scheme can be used, wherein A is the score chart of (O) PLS-DA in positive ion mode, and B is the score chart of (O) PLS-DA in negative ion mode. FIG. 3 is an S-plot, in which the abscissa represents the co-correlation coefficient of the main component and the metabolite, the ordinate represents the correlation coefficient of the main component and the metabolite, and p <0.05, VIP >1 is satisfied, and 125 difference impurities exist in the negative ion mode and 174 difference impurities exist in the positive ion mode, wherein A is the S-plot in the positive ion mode and B is the S-plot in the negative ion mode.

2. Jode index analysis

To further narrow the range, the VIP threshold was increased to 2, with a fold difference between normal and patient of less than 0.5 fold, or more than 2.5 fold,Pvalues less than 0.01, the following 7 compounds were obtained, as specified in table 1.

They were then subjected to the calculation of the youden yoden jordan index to reflect the diagnostic and predictive effect of the individual indices on the whole, with the results as given in table 1 below:

TABLE 1 Johnson index analysis of lipid associated with leukoencephalopathy

Numbering	Name of Compound	AUC value	Sensitivity of the composition	Specificity of
					F1	Cholesterol-alpha-D-glucoside	0.851	0.735	0.862
F2	6Z,9Z, 20-eicosatrienoic acid	0.808	0.785	0.735
					F3	Ceramide (m18:1(4E)/24:1(15Z))	0.612	0.408	0.785
F4	Ephedrine A	0.716	0.738	0.673
					F5	Cucurbitacin E	0.684	0.815	0.551
F6	Cholesterol ester 22:6	0.682	0.846	0.510
					F7	Ceramides (d18:0/24:1(15Z))	0.662	0.769	0.510

Table 1 lists the area under the curve (AUC), specificity and sensitivity of individual metabolites for predicting leukoencephalopathy.

3. Ten-fold cross validation result of sample population

In order to improve the biological diagnosis effect of the variable-quantity compound, a proper model needs to be found according to the biomarkers for further analysis. Randomly dividing the sample population into 10 parts, selecting 1 part as a verification set and the others as training sets, repeating the steps for ten times, and investigating the optimal variable combination. Results from ten times, including AUC, sensitivity, specificity, were averaged and statistically significant calculated as shown in table 2 below.

TABLE 2

Combination of	Logistic regression AUC	Sensitivity of the composition	Specificity of
				F2+F3	0.912	1	1
F2+F4	0.879	1	1
				F2+F5	0.900	1	1
F2+F6	0.883	1	1
				F2+F7	0.862	1	1

There was no significant p <0.05 difference in AUC values between combinations.

The logistic regression models A-E are established based on the above as follows: wherein the units for measuring the content are all mg/L.

The variable of the model A is F2+ F3, and the calculation formula is as follows: TC =4.4394-6.9717 xf 2+1.7350 xf 3, calculating TC values, where F2 is 6Z,9Z, 20-eicosatrienoic acid, and F3 is ceramide (m18:1(4E)/24:1(15Z)), predicting white brain lesions from TC values: if TC is more than or equal to 0.445, the white brain lesion is judged; if TC is less than 0.445, the test is normal.

The variable of the model B is F2+ F4, and the calculation formula is as follows: TC =4.699-11.576 xf 2+4.876 xf 4, calculating TC values, where F2 is 6Z,9Z, 20-eicosatrienoic acid, and F4 is cannabichromene a, predicting leukoencephalopathy based on TC values: if TC is more than or equal to 0.236, the white brain lesion is judged; if TC < 0.236, it is normal.

The variable of the model C is F2+ F5, and the calculation formula is as follows: TC =6.0525-5.3745 XF 2-1.6979 XF 5, calculating TC value, wherein F2 is 6Z,9Z, 20-eicosatrienoic acid and F5 is cucurbitacin E, and predicting leukoencephalopathy according to the TC value: if TC is more than or equal to 0.337, the brain white lesion is judged; if TC is less than 0.337, the test result is normal.

The variable of the model D is F2+ F6, and the calculation formula is as follows: TC =4.1050-4.8614 xf 2-0.2390 xf 6, calculating TC values, where F2 is 6Z,9Z, 20-eicosatrienoic acid, F6 is cholesteryl ester 22:6, predicting white brain disease from TC values: if TC is more than or equal to 0.455, the white brain lesion is judged; if TC < 0.455, it is normal.

The variable of the model E is F2+ F7, and the calculation formula is as follows: TC =4.4476-4.8480 xf 2-0.63305 xf 7, calculating TC values, where F2 is 6Z,9Z, 20-eicosatrienoic acid, and F7 is ceramide (d18:0/24:1(15Z)), predicting leukoencephalopathy based on TC values: if TC is more than or equal to 0.495, determining the cerebral cortex lesion; if TC < 0.495, it is normal.

4. External data set, logistic regression model verification

And verifying the accuracy of the result through a data set of an external crowd, and drawing a corresponding ROC curve graph. The results are as follows: and (3) verifying the population: 200 persons (external population), wherein, magnetic resonance imaging detects there is no abnormal often 100 persons, and magnetic resonance imaging detects 100 persons that show white matter appears the infarct focus, carries out logistic regression model verification:

and establishing a logistic regression model to verify the accuracy of the result, and drawing a corresponding ROC curve graph, wherein the unit of the measured content is mg/L.

The "model a" variables were F2+ F3 as described above, with results as in fig. 4, sensitivity =1, specificity =1, and accuracy = 1.

The "model B" variables were F2+ F4 as described above, with results as in fig. 5, sensitivity =1, specificity =1, and accuracy = 1.

The "model C" variables were F2+ F5 as described above, with results as in fig. 6, sensitivity =1, specificity =1, and accuracy = 1.

The "model D" variables were F2+ F6 as described above, with results as in fig. 7, sensitivity =1, specificity =1, and accuracy = 1.

The "model E" variables were F2+ F7 as described above, with results as in fig. 8, sensitivity =1, specificity =1, and accuracy = 1.

Through comparative analysis on sample information, the following results are obtained: compared with the normal group, the 7 biomarkers in the groups have ascending trends of F1 and F3, and F2, F4, F5, F6 and F7 are opposite.

And (3) displaying data: 6Z,9Z, 20-eicosatrienoic acid itself, combined with other five biomarkers, ceramide (m18:1(4E)/24:1(15Z)), cannabixanthin A, cucurbitacin E, cholesterol ester 22:6 and ceramide (d18:0/24:1(15Z)) all showed very high diagnostic ability, sensitivity, specificity and accuracy were 100%, and clinical kit applications could be performed in the future.

Claims

The application of 6Z,9Z, 20-eicosatrienoic acid in preparing a detection reagent for diagnosing the white brain lesion is characterized in that the 6Z,9Z, 20-eicosatrienoic acid is combined with ceramide m18:1(4E)/24:1(15Z), ephedrine A, cucurbitacin E, cholesterol ester 22:6 or ceramide d18:0/24:1(15Z) to prepare the detection reagent for diagnosing the white brain lesion;

wherein, the content of 6Z,9Z, 20-eicosatrienoic acid is recorded as F2, the content of ceramide m18:1(4E)/24:1(15Z) is recorded as F3, when the unit of the content is mg/L, the TC value is calculated according to the calculation formula TC =4.4394-6.9717 XF 2+1.7350 XF 3, and the leukoencephalopathy is predicted according to the TC value: if TC is more than or equal to 0.445, the white brain lesion is judged; if TC is less than 0.445, the test is normal;

the content of the 6Z,9Z, 20-eicosatrienoic acid is recorded as F2, the content of the cannabixanthin A is recorded as F4, when the unit of the content is mg/L, the TC value is calculated according to a calculation formula TC =4.699-11.576 XF 2+4.876 XF 4, and the leukoencephalopathy is predicted according to the TC value: if TC is more than or equal to 0.236, the white brain lesion is judged; if TC is less than 0.236, the test result is normal;

the content of the 6Z,9Z, 20-eicosatrienoic acid is recorded as F2, the content of cucurbitacin E is recorded as F5, when the unit of the content is mg/L, the TC value is calculated according to a calculation formula TC =6.0525-5.3745 XF 2-1.6979 XF 5, and the leukoencephalopathy is predicted according to the TC value: if TC is more than or equal to 0.337, the brain white lesion is judged; if TC is less than 0.337, the test is normal;

the content of 6Z,9Z, 20-eicosatrienoic acid is recorded as F2 and the content of cholesteryl ester 22:6 is recorded as F6, when the unit of the content is mg/L, the TC value is calculated according to the calculation formula TC =4.1050-4.8614 XF 2-0.2390 XF 6, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.455, the white brain lesion is judged; if TC is less than 0.455, the test result is normal;

the content of the 6Z,9Z, 20-eicosatrienoic acid is recorded as F2 and the content of ceramide d18:0/24:1(15Z) is recorded as F7, when the units of the contents are all mg/L, a TC value is calculated according to a calculation formula TC =4.4476-4.8480 XF 2-0.63305 XF 7, and the leukoencephalopathy is predicted according to the TC value: if TC is more than or equal to 0.495, determining the cerebral cortex lesion; if TC < 0.495, it is normal.