WO2022213746A1 - 用于诊断和治疗脑白质病变的方法及其应用 - Google Patents

用于诊断和治疗脑白质病变的方法及其应用 Download PDF

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WO2022213746A1
WO2022213746A1 PCT/CN2022/078755 CN2022078755W WO2022213746A1 WO 2022213746 A1 WO2022213746 A1 WO 2022213746A1 CN 2022078755 W CN2022078755 W CN 2022078755W WO 2022213746 A1 WO2022213746 A1 WO 2022213746A1
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white matter
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content
cholesterol
ceramide
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陈显扬
宋王婷
张珂
薛腾
韩佳睿
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宝枫生物科技（北京）有限公司
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials

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  • the invention belongs to the technical field of biological detection, and particularly relates to a method for diagnosing and treating cerebral white matter lesions and its application.
  • WML White matter lesion
  • the most typical pathological manifestation is the destruction of white matter integrity or demyelinating changes, which is common in stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, schizophrenia and other diseases.
  • White matter is an important part of the central nervous system and is where nerve fibers gather. Damage to the myelin sheath of central nervous cells in the white matter can cause leukoencephalopathy.
  • the typical response of white matter to various harmful stimuli is demyelination, which can be a secondary manifestation of neurological diseases such as infection, poisoning, degeneration, trauma, and infarction deficiency.
  • Leukoencephalopathy mainly causes white matter lesions, speech disorders, abnormal mental behavior, gait disorders, urination disorders and other symptoms in patients, which seriously affects the healthy quality of life of patients. It is strongly associated with an increased risk of stroke and dementia. With the continuous development of imaging technology, the detection rate of white matter lesions is getting higher and higher. It is now recognized that age is a clear risk factor for leukoencephalopathy. Some studies have found that the detection rate of white matter lesions in people aged 60 to 70 is 87%; the detection rate of white matter lesions in people aged 80 to 90 is as high as 95% to 100%. Abnormalities, metabolic disorders and other factors are also closely related to the pathogenesis and progression of leukoencephalopathy, and these metabolic diseases also mainly occur in the elderly.
  • the diagnosis of white matter lesions includes mental state examination and imaging examination.
  • the commonly used cranial examination methods include computed tomography and magnetic resonance imaging.
  • Preliminary mental state examinations included tests to evaluate inattention, three-word delayed recall test to identify memory impairment, clock drawing to evaluate visual dysfunction, and alternating motor sequences to evaluate brain function.
  • White matter lesions may be asymptomatic in the early clinical stage. And the testing process involves questions and answers, which consumes a lot of medical resources and is time-consuming and labor-intensive.
  • the detection rate of white matter lesions has gradually increased.
  • the equipment required for detection is expensive and expensive.
  • the pathogenesis of leukoencephalopathy is still unclear, and there is a lack of clear and effective therapeutic targets in clinical practice, which is not conducive to the treatment and recovery of patients with leukoencephalopathy.
  • Metabolomics is an emerging omics technology that is playing an increasingly important role in biological research because it can reveal the unique chemical fingerprints of the body's cellular metabolism. Metabolomics, as an unbiased approach to the study of small-molecule metabolites, offers hope for discovering more biomarkers of white matter lesions. Growing evidence suggests neurological disorders, accompanied by disturbances in bile acids, fatty acids and amino acids. And these results demonstrate that metabolic disturbances may predict the development of white matter lesions. However, it is not clear which substance can be clearly detected as the prediction and diagnosis of white matter lesions.
  • the present invention provides biomarkers for diagnosing white matter lesions.
  • the present invention adopts the following technical scheme as:
  • a biomarker for diagnosing white matter lesions characterized in that the biomarker is ceramide (d18:0/24:1(15Z)) (Cer(d18:0/24:1(15Z))).
  • the biomarker ceramide (d18:0/24:1(15Z)) is combined with cholesterol- ⁇ -D-glucoside (Cholesteryl-alpha-D-glucoside), and combined with 6Z ,9Z,20-eicosatrienoic acid (6Z,9Z,20-Heneicosatriene), Cannflavin A (Cannflavin A), Cucurbitacin E (Cucurbitacin E) or cholesterol ester 22:6 (22:6Cholesterol ester) Determine the presence of white matter lesions.
  • the ceramide (d18:0/24:1(15Z)) is denoted as F7, and the content of cholesterol- ⁇ -D-glucoside is denoted as F1, 6Z, 9Z, 20-di
  • the content of decatrienoic acid is recorded as F2.
  • the ceramide (d18:0/24:1(15Z)) is denoted as F7
  • the content of cholesterol- ⁇ -D-glucoside is denoted as F1
  • cannabinoid A is denoted as F4
  • the white matter lesions are predicted according to the TC value: if TC ⁇ 0.383, it is determined as Leukoencephalopathy; if TC ⁇ 0.383, it is normal.
  • the ceramide (d18:0/24:1(15Z)) is denoted as F7
  • the content of cholesterol- ⁇ -D-glucoside is denoted as F1
  • the content of cucurbitacin E is denoted as F5
  • calculate the TC value according to the calculation formula TC 0.3711-2.3128 ⁇ F7+5.8871 ⁇ F1-1.8116 ⁇ F5
  • predict the white matter lesions according to the TC value if TC ⁇ 0.178, then judge For white matter lesions; if TC ⁇ 0.178, it is normal.
  • the ceramide (d18:0/24:1(15Z)) is denoted as F7
  • the content of cholesterol- ⁇ -D-glucoside is denoted as F1
  • the content of cholesterol ester 22:6 Denote it as F6.
  • the present invention also provides a detection method for diagnosing cerebral white matter lesions, which comprises detecting ceramide (d18:0/24:1(15Z)) and cholesterol- ⁇ -D-glucoside in the serum of a subject content, and 6Z,9Z,20-eicosatrienoic acid (6Z,9Z,20-Heneicosatriene), Cannflavin A, Cucurbitacin E, or cholesterol esters 22:6 (22 : 6Cholesterol ester), by calculating the TC value to determine whether there is a white matter lesion.
  • ceramide d18:0/24:1(15Z)
  • 6Z,9Z,20-eicosatrienoic acid (6Z,9Z,20-Heneicosatriene)
  • Cannflavin A Cucurbitacin E
  • cholesterol esters 22:6 22 : 6Cholesterol ester
  • the detection method adopts ultra-high performance liquid chromatography-mass spectrometry.
  • the detection conditions of the ultra-high performance liquid chromatography-mass spectrometry are to use a C18 chromatographic column
  • the mobile phase is 10mM ammonium formate-0.1% formic acid-acetonitrile as phase A and 10mM ammonium formate-0.1% formic acid-isopropanol -Acetonitrile is used as phase B
  • the ion source temperature is 120°C
  • the desolvation temperature is 600°C
  • the gas flow is 1000L/h
  • nitrogen is used as the flowing gas
  • the capillary voltage is 2.0kV(+)/cone voltage is 1.5kV(- )
  • the cone voltage is 30V.
  • a method for the treatment of leukoencephalopathy comprising: (a) using the above-mentioned detection method to diagnose a patient with leukoencephalopathy, (b) using the leukoencephalopathy treatment drug for the confirmed patient, (c) by the above-mentioned Methods Diagnosis of rehabilitation status.
  • a detection kit for diagnosing cerebral white matter lesions comprising standard substance ceramide (d18:0/24:1(15Z)) combined with cholesterol- ⁇ -D-glucoside, and 6Z, 9Z, 20-twenty Carbotrienoic acid, Cannflavin A (Cannflavin A), cucurbitacin E or cholesteryl ester 22:6, mobile phase A: containing solute 10mM ammonium formate and 0.1% formic acid, the solvent is 60% by volume : 40 acetonitrile: water; mobile phase B: contains 10 mM ammonium formate and 0.1% formic acid as the solute, and the solvent is isopropanol: acetonitrile in a volume ratio of 90:10.
  • a biomarker for diagnosing cerebral white matter lesions provided by the present invention, ceramide (d18:0/24:1(15Z)) combined with cholesterol- ⁇ -D-glucoside, combined with 6Z, 9Z, 20-eicos Trienoic acid, cannabinoid A, cucurbitacin E or cholesteryl ester 22:6 to determine the presence of leukoencephalopathy. It can be used in diagnostic kits to help diagnose whether there is a tendency for white matter lesions, and can be used for early prevention.
  • the invention provides a detection method for diagnosing cerebral white matter lesions, by measuring ceramide (d18:0/24:1(15Z)) and cholesterol- ⁇ -D-glucoside, and 6Z, 9Z in the serum of a subject , the content of any one in 20-eicosatrienoic acid, cannabinoid A, cucurbitacin E or cholesteryl ester 22:6 calculates TC value, according to the TC value obtained by calculating to determine whether there is a white matter lesion, this detection The method is accurate and reliable.
  • Figure 1 is a sample with VIP>1 in positive (A) negative (B) ion mode;
  • Figure 2 is a score map of (O) PLS-DA in positive (A) negative (B) ion mode;
  • Figure 3 is an S-plot diagram in positive (A) negative (B) ion mode
  • Figure 4 is the ROC curve based on the logistic regression model (variables are F7+F1+F2);
  • Figure 5 is the ROC curve based on the logistic regression model (variables are F7+F1+F4)
  • Fig. 6 is the ROC curve based on logistic regression model (variable is F7+F1+F5);
  • Figure 7 is the ROC curve based on the logistic regression model (variables are F7+F1+F6).
  • the model establishment sample group was 112 people, age range: over 45 years old, including 64 control group and 48 patient group.
  • the ratio of males to females in the control population was 1:1, and magnetic resonance imaging tests showed no abnormalities.
  • the patient population had a 1:1 male-to-female ratio, and magnetic resonance imaging showed infarcts in the white matter.
  • Serum samples from the collected sample population were thawed on ice, 200 ⁇ L of plasma was extracted with 600 ⁇ L of pre-cooled isopropanol, vortexed for 1 min, incubated at room temperature for 10 min, and then the extraction mixture was stored at -20 °C overnight, and centrifuged at 4000 r for 20 min. After the supernatant was transferred to a new centrifuge tube, it was diluted to 1:10 with isopropanol/acetonitrile/water (2:1:1, v:v:v). Samples were stored at -80°C prior to LC-MS analysis. In addition, pooled plasma samples were also prepared by combining 10 ⁇ L of each extraction mixture together.
  • Pilot experiments with 10-, 15-, and 20-minute elution periods were conducted to assess the potential effect of mobile phase composition and flow rate on lipid retention time prior to the large-scale study.
  • PIM positive ion mode
  • abundant lipid precursor ions and fragments were separated in the same order, with similar peak shapes and ionic intensities.
  • the mixed control samples with a 10-minute elution period also exhibited similar base peak intensities for precursors and fragments as the test samples.
  • the mobile phase flow rate was 0.4 mL/min.
  • the column was initially eluted with 40% B, followed by a linear gradient to 43% B in 2 minutes, then increasing the percentage of B to 50% in 0.1 min.
  • the capillary voltage was 2.0 kV(+) / the cone voltage was 1.5 kV(-) and the cone voltage was 30V.
  • Standard mass determination was performed with leucine enkephalin, corrected with sodium formate solution. Samples were randomly ordered. A quality control sample was injected every 10 samples and analyzed to investigate the repeatability of the data.
  • OPLS-DA Orthogonal Partial Least Squares Discriminant Analysis
  • OSC Orthogonal Signal Correction
  • PLS-DA was used to screen difference variables by removing irrelevant differences.
  • the VIP value is the variable importance projection of the first principal component of PLS-DA, as shown in Figure 1.
  • VIP>1 is used as a common evaluation criterion for metabolomics, as one of the criteria for differential metabolite screening.
  • a positive The ion mode, B is the negative ion mode;
  • Figure 2 is the score map of the first principal component and the second principal component in the two groups of the white matter lesion group and the control group by dimensionality reduction, the abscissa represents the difference between groups, the vertical The coordinates represent the differences within the group, and the results of the two groups are well separated, indicating that this scheme can be used.
  • A is the score map of (O)PLS-DA in positive ion mode
  • B is the score of (O)PLS-DA in negative ion mode picture.
  • Figure 3 is an S-plot diagram, the abscissa represents the co-correlation coefficient between the principal components and metabolites, and the ordinate represents the correlation coefficient between the principal components and metabolites.
  • the negative ion mode has 125
  • A is the S-plot in the positive ion mode
  • B is the S-plot in the negative ion mode.
  • the VIP threshold was increased to 2, and the fold difference between normal and patients was less than 0.5 times, or increased by more than 2.5 times, and the P value was less than 0.01. Finally, the following 7 compounds were obtained, as shown in Table 1.
  • Table 1 lists the area under the curve (AUC), specificity and sensitivity of individual metabolites for predicting white matter lesions.
  • F7 is ceramide (d18:0/24: 1(15Z))
  • F1 is cholesterol- ⁇ -D-glucoside
  • F4 is cannabinoid A
  • white matter lesions are predicted according to the TC value: if TC ⁇ 0.383, it is determined to be white matter lesions; if TC ⁇ 0.383, then is normal.
  • Validation population 200 people (external population), the sampling standard is the same as the sample population described above, 100 people have no abnormality in the magnetic resonance imaging test, and 100 people have infarction in the white matter of the magnetic resonance imaging test.
  • F7 is ceramide (d18:0/24:1(15Z)) itself, ceramide (d18:0/24:1(15Z)) combined with cholesterol- ⁇ -D-glucoside, and combined with other four
  • the biomarkers 6Z, 9Z, 20-eicosatrienoic acid, cannabinoid A, cucurbitacin E and cholesteryl ester 22:6 all showed very high diagnostic ability, with sensitivity, specificity, and accuracy of 100%, the application of clinical kits can be carried out in the future.
  • F1 and F3 showed an upward trend in the white matter lesion group, and F2, F4, F5, F6 and F7 were the opposite.
  • a detection kit for diagnosing cerebral white matter lesions comprising standard substance ceramide (d18:0/24:1(15Z)) and cholesterol- ⁇ -D-glucoside, combined with 6Z, 9Z, 20-di Any one of decatrienoic acid, cannabinoid A, cucurbitacin E or cholesteryl ester 22:6, mobile phase A liquid: the solute is 10mM ammonium formate and 0.1% formic acid, and the solvent is a volume ratio of 60:40 of acetonitrile: water; mobile phase B: the solute contains 10 mM ammonium formate and 0.1% formic acid, and the solvent is isopropanol: acetonitrile with a volume ratio of 90:10.
  • the preparation method of 10mM ammonium formate-0.1% formic acid-acetonitrile (A, acetonitrile: water is 60:40, v/v) is to weigh 0.63 g of ammonium formate, 10 g of formic acid, and acetonitrile-water solution (acetonitrile: water is 60 g) : 40, v/v) dissolve and make up to 1000mL.
  • the preparation method of 10mM ammonium formate-0.1% formic acid-isopropanol-acetonitrile (B, isopropanol:acetonitrile is 90:10, v/v) is to weigh 0.63 g of ammonium formate, 10 g of formic acid, and use isopropanol-acetonitrile
  • the solution (isopropanol:acetonitrile 90:10, v/v) was dissolved and made up to 1000 mL.
  • the detection of the sample is carried out by the sample pretreatment and ultra-high performance liquid chromatography-mass spectrometry in Example 1, and the standard ceramide (d18:0/24:1(15Z)) and cholesterol- ⁇ -D-glucoside, and 6Z,9Z,20-eicosatrienoic acid, cannabinoid A, cucurbitacin E, cholesteryl ester 22:6 were detected as reference.

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Abstract

生物标志物神经酰胺(d18:0/24:1(15Z))和胆固醇-α-D-葡萄糖苷在制备诊断脑白质病变试剂或试剂盒中的应用。用于诊断和治疗脑白质病变的方法，诊断方法通过检测血清中的生物标志物神经酰胺(d18:0/24:1(15Z))和胆固醇-α-D-葡萄糖苷，并结合6Z,9Z,20-二十碳三烯酸(6Z,9Z,20-Heneicosatriene)、大麻黄素A(Cannflavin A)、葫芦素E(Cucurbitacin E)或胆固醇酯22:6(22:6 Cholesterol ester)中任一种的含量来判断是否存在脑白质病变用于诊断是否存在脑白质病变的倾向，从而实现提前预防。

Description

用于诊断和治疗脑白质病变的方法及其应用 技术领域
本发明属于生物检测技术领域，具体涉及用于诊断和治疗脑白质病变的方法及其应用。
背景技术
脑白质病变(white matter lesion，WML)是一种常见的神经退行性疾病，最典型的病理表现为脑白质完整性破坏或脱髓鞘变化，该病常见于脑卒中、阿尔茨海默病、帕金森病、多发性硬化、精神分裂症等多种疾病中。脑白质是中枢神经系统的重要组成部分，是神经纤维聚集的地方，脑白质中的中枢神经细胞的髓鞘损害，则会引起脑白质病变。而脑白质对各种有害刺激的典型反应是脱髓鞘变化，它可以是神经系统疾病如感染、中毒、退行性变、外伤后、梗塞缺乏等的继发表现。脑白质病变主要引起患者脑白质病变、言语障碍、精神行为异常、步态障碍、排尿障碍等症状，严重影响患者健康生活质量。它与中风和痴呆风险的增加密切相关。随着影像技术的不断进展，脑白质病变的检出率越来越高。目前已公认，年龄为脑白质病变的明确危险因素。有研究发现，在60～70岁的人群中，脑白质病变检出率达87％；80～90岁人群中脑白质病变的检出率高达95％～100％，而高血压、糖尿病、血脂异常、代谢紊乱等因素也与脑白质病变的发病和进展密切相关，而这些代谢性疾病也主要发生在高龄人群中。随着我国老龄化社会的到来，脑白质病变的危害逐渐引起医学界重视。一般情况下，多数脑白质病变患者的病情是可逆的，所以对于脑白质病变患者采取适当的预防措施，其症状可明显改善，其中早期筛查是至关重要的手段。
脑白质病变的诊断包括精神状态检查和影像学检查，目前常用的颅脑检查手段包括电子计算机断层扫描及磁共振成像。其中初步精神状态检查包括评价注意力不集中的试验、鉴定记忆力障碍的三词延迟回忆试验、评价视觉功能障碍的时钟绘画和评价脑功能的交替运动序列等操作。而脑白质病变在 早期临床上可无症状。并且测试过程涉及到需要提问和回答，消耗非常大的医学资源，耗时耗力。随着影像学技术的不断发展，脑白质病变的检出率逐渐增加。但检测需用到的设备昂贵，且花费较大。目前尚无准确性高，特异性强的脑白质病变外周血生物标志物。脑白质病变的发病机制尚不清楚，临床上缺乏明确有效的治疗靶点，不利于脑白质病变患者的治疗和恢复。
代谢组学是一门新兴的组学技术，在生物学研究中发挥着越来越重要的作用，因为它能够揭示机体细胞代谢的独特化学指纹特征。代谢组学作为一种无偏的小分子代谢物研究方法，为发现更多的脑白质病变的生物标志物提供了希望。越来越多的证据表明神经系统疾病，伴随着胆汁酸，脂肪酸和氨基酸的紊乱。并且这些结果证明代谢紊乱可能预示着脑白质病变的发生。但具体哪种物质能明确检出作为预测和诊断脑白质病变的发生还不清楚。
发明内容
为了能有效预测和诊断脑白质病变，本发明提供了用于诊断脑白质病变的生物标志物。
为实现上述目的，本发明采用以下的技术方案为：
用于诊断脑白质病变的生物标志物，其特征在于，该生物标志物为神经酰胺(d18:0/24:1(15Z))(Cer(d18:0/24:1(15Z)))。
如上所述的诊断脑白质病变的生物标志物在制备检测试剂中的应用。
如上所述的应用，优选地，所述生物标志物神经酰胺(d18:0/24:1(15Z))组合胆固醇-α-D-葡萄糖苷(Cholesteryl-alpha-D-glucoside)，并结合6Z,9Z,20-二十碳三烯酸(6Z,9Z,20-Heneicosatriene)、大麻黄素A(Cannflavin A)、葫芦素E(Cucurbitacin E)或胆固醇酯22:6(22:6Cholesterol ester)来判断是否存在脑白质病变。
如上所述的应用，优选地，所述神经酰胺(d18:0/24:1(15Z))记为F7，胆固醇-α-D-葡萄糖苷的含量记为F1，6Z,9Z,20-二十碳三烯酸的含量记为F2，当含量的单位均为mg/L时，根据计算公式TC＝9.470-2.564×F7+13.203×F1-15.253×F2计算TC值，根据TC值预测脑白质病变：若TC≥0.930，则判 定为脑白质病变；若TC＜0.930，则为正常。
如上所述的应用，优选地，所述神经酰胺(d18:0/24:1(15Z))记为F7，胆固醇-α-D-葡萄糖苷的含量记为F1，大麻黄素A记为F4，当含量的单位均为mg/L时，根据计算公式TC＝3.580-2.689×F7+9.487×F1-6.079×F4计算TC值，根据TC值预测脑白质病变：若TC≥0.383，则判定为脑白质病变；若TC＜0.383，则为正常。
如上所述的应用，优选地，所述神经酰胺(d18:0/24:1(15Z))记为F7，胆固醇-α-D-葡萄糖苷的含量记为F1，葫芦素E的含量记为F5，当含量的单位均为mg/L时，根据计算公式TC＝0.3711-2.3128×F7+5.8871×F1-1.8116×F5计算TC值，根据TC值预测脑白质病变：若TC≥0.178，则判定为脑白质病变；若TC＜0.178，则为正常。
如上所述的应用，优选地，所述神经酰胺(d18:0/24:1(15Z))记为F7，胆固醇-α-D-葡萄糖苷的含量记为F1，胆固醇酯22:6的含量记为F6，当含量的单位均为mg/L时，根据计算公式TC＝-0.4784-1.6754×F7+6.0304×F1-1.9504×F6计算TC值，根据TC值预测脑白质病变：若TC≥0.299，则判定为脑白质病变；若TC＜0.299，则为正常。
本发明还提供了一种用于诊断脑白质病变的检测方法，其包括检测受试者血清中的神经酰胺(d18:0/24:1(15Z))和胆固醇-α-D-葡萄糖苷的含量，及6Z,9Z,20-二十碳三烯酸(6Z,9Z,20-Heneicosatriene)、大麻黄素A(Cannflav in A)、葫芦素E(Cucurbitacin E)或胆固醇酯22:6(22:6Cholesterol este r)中任一种的含量，通过计算TC值来判断是否存在脑白质病变。
如上所述的检测方法，优选地，测得的神经酰胺(d18:0/24:1(15Z))的含量记为F7，测得的胆固醇-α-D-葡萄糖苷的含量记为F1，测得的6Z,9Z,20-二十碳三烯酸的含量记为F2，计算TC值按照公式TC＝9.470-2.564×F7+13.203×F1-15.253×F2进行，若TC≥0.930，则判定为脑白质病变；若TC＜0.930，则为正常。如上所述的检测方法，优选地，测得的神经酰胺(d18:0/24:1(15Z))的含量记为F7，测得的胆固醇-α-D-葡萄糖苷的含量记为F1，测得的大麻黄素A的含量记为F4，计算TC值按照公式TC＝3.580-2.689×F7+9.487× F1-6.079×F4进行，若TC≥0.383，则判定为脑白质病变；若TC＜0.383，则为正常。
如上所述的检测方法，优选地，测得的神经酰胺(d18:0/24:1(15Z))的含量记为F7，测得的胆固醇-α-D-葡萄糖苷的含量记为F1，测得的葫芦素E的含量记为F5，计算TC值按照公式TC＝0.3711-2.3128×F7+5.8871×F1-1.8116×F5进行，若TC≥0.178，则判定为脑白质病变；若TC＜0.178，则为正常。
如上所述的检测方法，优选地，测得的神经酰胺(d18:0/24:1(15Z))的含量记为F7，测得的胆固醇-α-D-葡萄糖苷的含量记为F1，测得的胆固醇酯22:6的含量记为F6，计算TC值按照公式TC＝-0.4784-1.6754×F7+6.0304×F1-1.9504×F6进行，若TC≥0.299，则判定为脑白质病变；若TC＜0.299，则为正常。
如上所述的检测方法，优选地，检测方法采用超高效液相色谱-质谱联用。
进一步，优选地，超高效液相色谱-质谱联用的检测条件为采用C18色谱柱，流动相为10mM甲酸铵-0.1％甲酸-乙腈作为A相和10mM甲酸铵-0.1％甲酸-异丙醇-乙腈作为B相，离子源温度为120℃，去溶温度为600℃，气体流量为1000L/h，以氮气为流动气体；毛细管电压为2.0kV(+)/锥体电压为1.5kV(-)，锥体电压为30V。
一种治疗脑白质病变的方法，其包括：(a)采用如上所述的检测方法诊断认脑白质病变患者，(b)将确诊的患者使用脑白质病变治疗药物治疗，(c)通过上述的方法诊断康复状况。
一种用于诊断脑白质病变的检测试剂盒，其包括标准品神经酰胺(d18:0/24:1(15Z))组合胆固醇-α-D-葡萄糖苷，及6Z,9Z,20-二十碳三烯酸、大麻黄素A(Cannflavin A)、葫芦素E或胆固醇酯22:6中的任一种，流动相A：含溶质为10mM甲酸铵和0.1％甲酸，溶剂为体积比为60:40的乙腈：水；流动相B：含溶质为10mM甲酸铵和0.1％甲酸，溶剂为体积比为90:10的异丙醇：乙腈。
本发明的有益效果在于：
本发明提供的一种诊断脑白质病变的生物标志物，神经酰胺(d18:0/24: 1(15Z))组合胆固醇-α-D-葡萄糖苷，并结合6Z,9Z,20-二十碳三烯酸、大麻黄素A、葫芦素E或胆固醇酯22:6来判断是否存在脑白质病变。可应用于诊断试剂盒中，有助于诊断是否存在脑白质病变的倾向，可用于提前预防。
本发明提供的一种诊断脑白质病变的检测方法，通过测量受试者血清中的神经酰胺(d18:0/24:1(15Z))和胆固醇-α-D-葡萄糖苷，及6Z,9Z,20-二十碳三烯酸、大麻黄素A、葫芦素E或胆固醇酯22:6中任一种的含量来计算TC值，根据计算获得的TC值判定是否存在脑白质病变，该检测方法准确、可靠。
附图说明
图1为正(A)负(B)离子模式下VIP＞1的样本；
图2为正(A)负(B)离子模式下(O)PLS-DA的得分图；
图3为正(A)负(B)离子模式下S-plot图；
图4为基于逻辑回归模型的ROC曲线(变量为F7+F1+F2)；
图5为基于逻辑回归模型的ROC曲线(变量为F7+F1+F4)；
图6为基于逻辑回归模型的ROC曲线(变量为F7+F1+F5)；
图7为基于逻辑回归模型的ROC曲线(变量为F7+F1+F6)。
具体实施方式
以下实施例用于进一步说明本发明，但不应理解为对本发明的限制。在不背离本发明精神和实质的前提下，对本发明所作的修饰或者替换，均属于本发明的范畴。
若未特别指明，实施例中所用的技术手段为本领域技术人员所熟知的常规手段。
实施例1
样本
模型建立样本群112人，年龄范围：45岁以上，其中对照人群64人，患者人群48人。
对照人群中的男女比例为1：1，磁共振成像检测显示无异常。
患者人群中的男女比例为1：1，磁共振成像检测显示脑白质出现梗死灶。
实验仪器及试剂
实验仪器：1.漩涡振荡器：型号MX-S，Scilogex公司，美国；2.高分辨质谱仪：ESI-QTOF/MS；型号：Xevo G2-S Q-TOF；厂家：Waters，Manchester,UK3.冷冻离心机：型号D3024R，Scilogex公司，美国；4.超高效液相色谱：UPLC，型号：ACQUITY UPLC I-Class系统；厂家：Waters，Manchester，UK；5.数据采集软件：MassLynx4.1，Waters；6.分析鉴定软件：Progenesis QI，Waters。
实验试剂：异丙醇，甲酸，甲酸氨，乙腈，甲酸钠亮氨酸脑啡肽；厂家均为Fisher。
实验方法
1.样品前处理
收集样本人群的血清样本在冰上解冻，200μL的血浆用600μL的预冷异丙醇萃取，涡流1min，室温孵育10min，然后将萃取混合物在-20℃下储存过夜，4000r离心20min后，将上清液转移到新的离心管之后，用异丙醇/乙腈/水(按2：1：1，v：v：v)稀释至1：10。样品在LC-MS分析前，保存在-80℃。此外，还将每个萃取混合物的10μL组合在一起制备混合血浆样品。
2.脂质组学的超高效液相色谱-质谱联用方法
样品用ACQUITY UPLC连接到带有ESI的Xevo-G2XS高分辨飞行时间的质谱仪进行分析。采用CQUITY UPLC BEH C18色谱柱(2.1×10 0mm，1.7μm，Waters)，流动相为10mM甲酸铵-0.1％甲酸-乙腈(A，乙腈：水的体积比为60：40)和10mM甲酸铵-0.1％甲酸-异丙醇-乙腈(B，异丙醇：乙腈的体积比为90：10)。在大规模研究之前，进行了10分钟、15分钟和20分钟洗脱期的中试实验，以评估流动相组成和流速对脂质保留时间的潜在影响。在正离子模式(PIM)中，丰富的脂质前体离子和碎片以相同的顺序分离，具有相似的峰形和离子强度。此外，具有10分钟洗脱期的混合质控样品也表现出与测试样品相似的前体和碎片的基峰强度。流动相流速为0.4mL/min。该柱最初用40％B洗脱，然后在2分钟内线性梯度到43％B，然后在0.1min内将B的百分比增加到50％。在接下来的3.9分钟内，梯度进一步增加到54％B， 然后B的量0.1分钟内增加到70％。在梯度的最后部分，B的量在1.9分钟内增加到99％。最后，溶液B在0.1分钟内返回到40％，并且在下一次进样之前将色谱柱平衡1.9分钟。每次进样量为5μL，用QTOF质谱仪检测正负两种模式下的脂质，采集范围为m/z50～1200年，采集时间为0.2s/次。离子源温度为120℃，去溶温度为600℃，气体流量为1000L/h，以氮气为流动气体。毛细管电压为2.0kV(+)/锥体电压为1.5kV(-)，锥体电压为30V。以亮氨酸脑啡肽进行标准质量测定，用甲酸钠溶液进行校正。样品被随机排序。每10个样本注入一个质控样本并进行分析，以调查数据的重复性。
结果分析：
1.利用多元统计学寻找血清差异物质
采用正交偏最小二乘判别分析(OPLS-DA)结合了正交信号矫正(OSC)和PLS-DA方法，通过去除不相关的差异来筛选差异变量。VIP值为PLS-DA第一主成分的变量重要性投影，如图1为所示，通常以VIP>1为代谢组学常用评判标准，作为差异代谢物筛选的标准之一，其中，A正离子模式，B为负离子模式；图2为脑白质病变组和对照组两个分组中的第一主成分和第二主成分通过降维的方式所得的得分图，横坐标表示组间差异，纵坐标表示组内差异，且两组结果分离较好，说明此方案可以使用其中，A为正离子模式下(O)PLS-DA的得分图，B为负离子模式下(O)PLS-DA的得分图。图3为S-plot图，横坐标表示主成分与代谢物的协相关系数，纵坐标表示主成分与代谢物的相关系数，同时满足p<0.05，VIP>1的条件下，负离子模式有125个差异物，正离子模式有174个差异物，其中，A为正离子模式下S-plot图，B为负离子模式下S-plot图。
2.约登指数分析
为了进一步缩小范围，将VIP阈值提高到2，同时体现正常和患者之间的倍数差异在0.5倍以下，或者增加2.5倍以上，P值小于0.01，最终得到以下7个化合物，具体见表1。
然后对他们进行youden约登指数计算，用来反映单个指标对整体的诊断和预测效果，结果如下表1：
表1脑白质病相关脂质的约登指数分析
编号 化合物名称 AUC值 敏感性 特异性
F1 胆固醇-α-D-葡萄糖苷 0.851 0.735 0.862
F2 6Z,9Z,20-二十碳三烯酸 0.808 0.785 0.735
F3 神经酰胺(m18:1(4E)/24:1(15Z)) 0.612 0.408 0.785
F4 大麻黄素A 0.716 0.738 0.673
F5 葫芦素E 0.684 0.815 0.551
F6 胆固醇酯22:6 0.682 0.846 0.510
F7 神经酰胺(d18:0/24:1(15Z)) 0.662 0.769 0.510
表1列出来单个代谢物预测脑白质病变的曲线下面积(AUC)、特异性和敏感度。
3.样本人群十折交叉验证结果
为提高种变量化合物的生物诊断效果，需要根据上述生物标志物找出适合的模型进行行下一步的分析。将样本人群随机分为10份，选择1份为验证集，其他为训练集，如此反复十次，考察最佳的变量组合。将十次的结果，包括AUC，敏感度，特异性都取平均值，并进行统计学显著性计算，结果如下表2。
表2
组合 逻辑回归AUC 敏感性 特异性
F7+F1+F2 0.954 1 1
F7+F1+F4 0.937 1 1
F7+F1+F5 0.938 1 1
F7+F1+F6 0.913 1 1
组合之间，AUC值并没有显著性p<0.05差异。
基于上述建立逻辑回归模型A-F如下：
"模型A"变量为上述F7+F1+F2，计算公式为：TC＝9.470-2.564×F7+13.203×F1-15.253×F2，计算TC值，公式中F7为神经酰胺(d18:0/24:1(15Z))， F1为胆固醇-α-D-葡萄糖苷，F2为6Z,9Z,20-二十碳三烯酸，根据TC值预测脑白质病变：若TC≥0.930，则判定为脑白质病变；若TC＜0.930，则为正常。
"模型B"变量为上述F7+F1+F4，计算公式为：TC＝3.580-2.689×F7+9.487×F1-6.079×F4，计算TC值，公式中F7为神经酰胺(d18:0/24:1(15Z))，F1为胆固醇-α-D-葡萄糖苷，F4为大麻黄素A，根据TC值预测脑白质病变：若TC≥0.383，则判定为脑白质病变；若TC＜0.383，则为正常。
"模型C"变量为上述F7+F1+F5，计算公式为：TC＝0.3711-2.3128×F7+5.8871×F1-1.8116×F5，计算TC值，F7为神经酰胺(d18:0/24:1(15Z))，公式中F1为胆固醇-α-D-葡萄糖苷，F5为葫芦素E，根据TC值预测脑白质病变：若TC≥0.178，则判定为脑白质病变；若TC＜0.178，则为正常。
"模型D"变量为上述F3+F1+F6，计算公式为：TC＝-0.4784-1.6754×F7+6.0304×F1-1.9504×F6，计算TC值，公式中F7为神经酰胺(d18:0/24:1(15Z))，F1为胆固醇-α-D-葡萄糖苷，F6为胆固醇酯22:6，根据TC值预测脑白质病变：若TC≥0.299，则判定为脑白质病变；若TC＜0.299，则为正常。
4.外部数据集，逻辑回归模型验证
通过外部人群的数据集验证上述结果的准确性，并绘制相应的ROC曲线图。结果如下：
验证人群：200人(外部人群)，取样标准同上面所述的样本人群，磁共振成像检测无异常有100人，磁共振成像检测显示脑白质出现梗死灶有100人。进行逻辑回归模型验证：
"模型A"变量为上述F7+F1+F2，结果如图4，敏感性＝1，特异性＝1，准确度＝1。
"模型B"变量为上述F7+F1+F4，结果如图5，敏感性＝1，特异性＝1，准确度＝1。
"模型C"变量为上述F7+F1+F5，结果如图6，敏感性＝1，特异性＝1，准确度＝1。
"模型D"变量为上述F7+F1+F6，结果如图7，敏感性＝1，特异性＝1，准确度＝1。
数据显示：F7为神经酰胺(d18:0/24:1(15Z))自身，神经酰胺(d18:0/24:1(15Z))与胆固醇-α-D-葡萄糖苷组合，以及结合其他四种生物标志物6Z,9Z,20-二十碳三烯酸，大麻黄素A，葫芦素E和胆固醇酯22:6都表现出非常高的诊断能力，敏感性、特异性、准确度均为100％，未来都能进行临床试剂盒的应用。
通过对样本信息的对比分析可知：以上7种生物标志物，与正常组相比，F1和F3在脑白质病变组均呈上升趋势，F2、F4、F5、F6和F7则相反。
实施例2
一种用于诊断脑白质病变的检测试剂盒，其包括标准品神经酰胺(d18:0/24:1(15Z))和胆固醇-α-D-葡萄糖苷，及结合6Z,9Z,20-二十碳三烯酸、大麻黄素A、葫芦素E或胆固醇酯22:6中的任一种，流动相A液：含溶质为10mM甲酸铵和0.1％甲酸，溶剂为体积比为60:40的乙腈：水；流动相B液：含溶质为10mM甲酸铵和0.1％甲酸，溶剂为体积比为90:10的异丙醇：乙腈。其中，10mM甲酸铵-0.1％甲酸-乙腈(A，乙腈：水为60：40，v/v)的配置方法为称取甲酸铵0.63g，甲酸10g，用乙腈-水溶液(乙腈：水为60：40，v/v)溶解并定容至1000mL。
10mM甲酸铵-0.1％甲酸-异丙醇-乙腈(B，异丙醇：乙腈为90：10，v/v)的配置方法为称取甲酸铵0.63g，甲酸10g，用异丙醇-乙腈溶液(异丙醇：乙腈为90：10，v/v)溶解并定容至1000mL。
对于样品的检测是采用实施例1中的样品前处理和超高效液相色谱-质谱联用检测方法进行，同时将标准品神经酰胺(d18:0/24:1(15Z))和胆固醇-α-D-葡萄糖苷，及6Z,9Z,20-二十碳三烯酸、大麻黄素A、葫芦素E、胆固醇酯22:6作为参照进行检测。
实施例3
一种治疗脑白质病变的方法，其包括：(a)采用如实施例2中的试剂盒及检测方法诊断脑白质病变患者，(b)对于检测获得神经酰胺(d18:0/24:1(1 5Z))记为F7，测得胆固醇-α-D-葡萄糖苷的含量记为F1，测得6Z,9Z,20-二十碳三烯酸的含量记为F2，计算TC值按公式TC＝9.470-2.564×F7+13.203×F1-15.253×F2进行，若TC≥0.930；或测得的大麻黄素A的含量记为F4，计算TC值按公式TC＝3.580-2.689×F7+9.487×F1-6.079×F4，若TC≥0.383；测得的葫芦素E的含量记为F5，计算TC值按公式TC＝0.3711-2.3128×F7+5.8871×F1-1.8116×F5，若TC≥0.178；测得的胆固醇酯22:6的含量记为F6，根据计算公式TC＝-0.4784-1.6754×F7+6.0304×F1-1.9504×F6计算TC值，若TC≥0.299；被认为是患有脑白质病变，检测结果有18个患者，用上述方法进行确诊，并用MRI核磁检测进一步确认，按照常规方法给艾地苯醌药治疗6个月后，通过上述方法进行筛查判断，结果表明患者的病情得到好转、控制。这与用MRI核磁检测确认结果一致，说明本发明的筛查方法判断的结果与核磁诊断确认结果一样准确，说明本发明的诊断方法准确、可靠。

Claims (9)

  1. 一种诊断脑白质病变的检测方法，其特征在于，检测受试者血清中的神经酰胺(d18:0/24:1(15Z))与胆固醇-α-D-葡萄糖苷的含量，及6Z,9Z,20-二十碳三烯酸、大麻黄素A、葫芦素E或胆固醇酯22:6中任一种的含量，通过计算TC值来判断是否存在脑白质病变。
  2. 根据权利要求1所述的检测方法，其特征在于，测得的神经酰胺(d18:0/24:1(15Z))的含量记为F7，测得的胆固醇-α-D-葡萄糖苷的含量记为F1，测得的6Z,9Z,20-二十碳三烯酸的含量记为F2，当含量的单位均为mg/L时，计算TC值按照公式TC＝9.470-2.564×F7+13.203×F1-15.253×F2进行，若TC≥0.930，则判定为脑白质病变；若TC＜0.930，则为正常。
  3. 根据权利要求1所述的检测方法，其特征在于，测得的神经酰胺(d18:0/24:1(15Z))的含量记为F7，测得的胆固醇-α-D-葡萄糖苷的含量记为F1，测得的大麻黄素A记为F4，计算TC值按照公式TC＝3.580-2.689×F7+9.487×F1-6.079×F4进行，若TC≥0.383，则判定为脑白质病变；若TC＜0.383，则为正常。
  4. 根据权利要求1所述的检测方法，其特征在于，测得的神经酰胺(d18:0/24:1(15Z))的含量记为F7，测得的胆固醇-α-D-葡萄糖苷的含量记为F1，测得的葫芦素E的含量记为F5，计算TC值按照公式TC＝0.3711-2.3128×F7+5.8871×F1-1.8116×F5进行，若TC≥0.178，则判定为脑白质病变；若TC＜0.178，则为正常。
  5. 根据权利要求1所述的检测方法，其特征在于，测得的神经酰胺(d18:0/24:1(15Z))的含量记为F7，测得的胆固醇-α-D-葡萄糖苷的含量记为F1，测得的胆固醇酯22:6的含量记为F6，计算TC值按照公式TC＝-0.4784-1.6754×F7+6.0304×F1-1.9504×F6进行，若TC≥0.299，则判定为脑白质病变；若TC＜0.299，则为正常。
  6. 根据权利要求1所述的检测方法，其特征在于，检测方法采用超高效液相色谱-质谱联用。
  7. 根据权利要求6所述的检测方法，其特征在于，所述超高效液相色谱-质谱联用的检测条件为采用C18色谱柱，流动相为10mM甲酸铵-0.1％甲酸-乙腈作为A相和10mM甲酸铵-0.1％甲酸-异丙醇-乙腈作为B相，离子源温度为120℃，去溶温度为600℃，气体流量为1000L/h，以氮气为流动气体；毛细管电压为2.0kV(+)/锥体电压为1.5kV(-)，锥体电压为30V。
  8. 一种治疗脑白质病变的方法，其特征在于，其包括：(a)采用如权利要求1-6中任一项所述的检测方法诊断脑白质病变患者，(b)将确诊的患者使用脑白质病变治疗药物治疗，(c)通过权利要求1-7中任一项所述的方法诊断康复状况。
  9. 一种用于诊断脑白质病变的检测试剂盒，其特征在于，其包括标准品神经酰胺(d18:0/24:1(15Z))和胆固醇-α-D-葡萄糖苷，及结合6Z,9Z,20-二十碳三烯酸、大麻黄素A、葫芦素E或胆固醇酯22:6中的任一种，流动相A液：含溶质为10mM甲酸铵和0.1％甲酸，溶剂为体积比为60:40的乙腈：水；流动相B：含溶质为10mM甲酸铵和0.1％甲酸，溶剂为体积比为90:10的异丙醇：乙腈。
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