Hair metabolism marker combination for diagnosing Alzheimer's disease and application thereof

Abstract

The invention discloses a hair metabolism marker composition for diagnosing Alzheimer's disease and application of the hair metabolism marker composition. The hair metabolism marker combination comprises triacontane, erucic acid, heneicosane, behenic acid, tetracosane, 3-hydroxybutyric acid, 15-methylhexadecanoic acid, arachidic acid, heptadecanoic acid, melatonin, stearic acid, tetracosane acid, heneicosane acid and docosane. According to the invention, hair metabolism components of AD patients, MCI patients and normal cognitive people are researched, compared and analyzed by a metabonomics method, potential biomarkers possibly contained in the hair metabolism components are explored, a hair metabolism difference group of the AD patients is obtained, and the group of substances is evaluated for diagnosing the sensitivity and specificity of AD. The invention provides a new marker for accurate diagnosis of AD, can be used for clinical auxiliary diagnosis of AD, and improves the accuracy of diagnosis.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a hair metabolic marker combination for diagnosing Alzheimer's disease and an application thereof. Background Art

[0002] Alzheimer's disease (AD) is the most common type of dementia, accounting for about 60%-80% of all dementias, and is characterized by a decline in memory, language, orientation, visual-spatial, executive function, and daily activities. AD is an irreversible and gradually progressive disease with a long latency period before symptoms appear, diverse clinical symptoms, and insidious onset. It is easily overlooked by patients and clinicians in the early stages of the disease. Mild cognitive impairment (MCI), as an intermediate stage in the AD disease continuum, is often difficult to distinguish from cognitive decline caused by normal aging, and the annual conversion rate of AD is 5-17% [1]. Studies have shown that pathological changes have already begun to appear in the bodies of AD patients 20 years before the onset of clinical symptoms. Therefore, early diagnosis of AD is of great significance in delaying disease progression, reducing morbidity, and maintaining the course of the disease in the early stages.

[0003] Currently, there are many widely used diagnostic methods for AD, such as magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), cerebrospinal fluid markers, blood genetic analysis, etc. However, these diagnostic methods have their own disadvantages, such as being time-consuming, expensive, invasive, and unable to diagnose early. In recent years, there have been many studies on exploring new diagnostic tools for AD, in order to obtain early, sensitive, and convenient methods for diagnosis and prognosis assessment, to help doctors diagnose and treat patients' conditions in a timely manner, and to provide clinical decision support and advice.

[0004] As a new research technology, metabolomics has become a hot topic in recent years and has been widely used in the search for biomarkers of various diseases. It helps to study the pathogenesis, metabolic pathways and new treatment methods of various diseases. Metabolomics focuses on endogenous metabolites with a molecular weight below 1000KDa in organisms. By revealing the changing trajectories of the types and quantities of metabolites under the influence of internal and external factors, it reflects a series of biological events that occur in a certain pathophysiological process and provides biological terminal information. These metabolites are derived from metabolic pathways such as sugar, fat, protein, nucleic acid, bile acid and symbiotic bacteria. In recent years, many scholars have used metabolomics methods to perform metabolomics analysis on blood, saliva, brain tissue, cerebrospinal fluid, etc. to diagnose AD early and predict its progression, and achieve a further understanding of the disease.

[0005] At present, the most commonly used separation methods for metabolism are gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance spectroscopy (NMR). NMR is more accurate than MS in identifying the structure of compounds (such as isomers) and is more suitable for qualitative research on objects. GC-MS is used to separate and analyze volatile components, and its prerequisite is that it is volatile at high temperatures and has good thermal stability. Compared with LC-MS, GC-MS has the advantages of high separation efficiency, high sensitivity, small sample usage, fast analysis speed, and large amount of information. Summary of the invention

[0006] The purpose of the present invention is to provide a hair metabolic marker combination and application thereof for diagnosing Alzheimer's disease in view of the above problems.

[0007] In order to achieve its purpose, the present invention adopts the following technical solution:

[0008] A first aspect of the present invention provides a hair metabolism marker combination for diagnosing Alzheimer's disease, wherein the hair metabolism marker combination includes triacontan, erucic acid, heneicosane, behenic acid, tetracosane, 3-hydroxybutyric acid, 15-methylhexadecanoic acid, arachidic acid, heptadecanoic acid, melatonin, stearic acid, tetracosanoic acid, heneicosane, and docosane.

[0009] The hair metabolism marker combination is used to diagnose and differentiate Alzheimer's disease patients, mild cognitive impairment patients and people with normal cognition.

[0010] Compared with people with normal cognition, in patients with Alzheimer's disease, melatonin, triacontanes, lignoceric acid, behenic acid, docosane, erucic acid, heneicosane, stearic acid, lignoceric acid, heptadecanoic acid, arachidic acid, and heneicosane were significantly upregulated, and 3-hydroxybutyric acid was significantly downregulated.

[0011] The second aspect of the present invention provides use of the above-mentioned hair metabolism marker combination in the preparation of a kit for diagnosing Alzheimer's disease.

[0012] The third aspect of the present invention provides a kit for diagnosing Alzheimer's disease, comprising the above-mentioned standard product of the combination of hair metabolic markers for diagnosing Alzheimer's disease.

[0013] The kit also includes an extraction reagent for extracting the hair metabolism markers.

[0014] The extraction reagents include acetone, sodium hydroxide and methanol.

[0015] The kit also includes an internal standard D4-alanine.

[0016] The kit also includes reagents for gas chromatography-mass spectrometry analysis.

[0017] The beneficial effects of the present invention are: using cutting-edge metabolomics methods to study, compare and analyze the hair metabolites of AD patients, MCI patients (mild cognitive impairment, the early stage of AD) and normal cognitive controls (NC), exploring the potential biomarkers that may be contained therein, obtaining a group of hair metabolite difference groups of AD patients, and evaluating the sensitivity and specificity of the group of substances for diagnosing AD. The present invention provides a new marker for the accurate diagnosis of AD, which can be used for clinical auxiliary diagnosis of AD and improve the accuracy of diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Shown are: (A) PCA results of three groups: AD, MCI and NC; (B) PCA results grouped according to Clinical Dementia Rating (CDR) results; (C) OPLS-DA score graph of two groups: MCI and NC; (D) OPLS-DA score graph of two groups: AD and NC; (E) Volcano plot of metabolites of cognitive impairment group (MCI+AD) and NC group; (F) Line graph of fold changes of key metabolites between NC group and cognitive impairment group.

[0019] Figure 2 Volcano plot (AB) and line graph (CD) of metabolic changes between Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC).

[0020] Figure 3 It is the Venn diagram of the common and unique differential metabolites found in the comparison between AD group and NC group, MCI group and NC group, and CI group and NC group.

[0021] Figure 4 The classification model was constructed using LASSO logistic regression, and the differential metabolite set was used to construct the classification model results for NCvsAD and NC vs MCI respectively: (A, B) Lasso coefficient curves of differential metabolites with L1 regularization; (C, D) 10-fold cross validation was used to select the optimal lambda (λ) value based on binomial deviation; (E, F) ROC curves of the classification results. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with embodiments, but the present invention is not limited thereto.

[0023] The experimental methods in the following examples are conventional methods unless otherwise specified.

[0024] Example 1

[0025] 1 Experimental methods

[0026] 1.1 Neuropsychological scale assessment

[0027] Subjects were recruited from the Memory Disorder Clinic of the Department of Geriatrics, Chongqing Medical University. All subjects (213) and one of their reliable caregivers underwent standardized clinical interviews and neuropsychological assessments by trained nurses or experienced doctors for grouping, and complete clinical memory scale assessment data (MMSE, ADAS-Cog, Clock, AVLT, BNT, FCSRT, NPI, CDR, etc.) were obtained. The subjects were grouped according to the Dementia Rating Scale-Global Score (CDR-GS): 35 subjects with 0 points, 72 subjects with 0.5 points, 40 subjects with 1 point, 35 subjects with 2 points, and 29 subjects with 3 points; the subjects were grouped according to diagnosis: 47 subjects were in the normal control group (NC), 40 subjects were in the mild cognitive impairment (MCI), and 126 subjects were in the Alzheimer's disease (AD).

[0028] Inclusion criteria: 1. Normal (NC): No diagnosis of cognitive impairment, confirmed by clinical evaluation and cognitive assessment (such as MMSE score>28 points). 2. Mild cognitive impairment (MCI): Patients diagnosed with mild cognitive impairment according to the Petersen criteria, including subjective memory decline, objective memory impairment, and normal general cognitive function without obvious impairment in daily activities. 3. Alzheimer's disease (AD): Meet the NIA-AA diagnostic criteria published by the National Institute on Aging and the Alzheimer's Association in 2011. 4. Aged ≥60 years and ≤85 years, voluntary participation, and the patient signed the informed consent form or his / her immediate family member signed it on his / her behalf.

[0029] Exclusion criteria: 1. Patients with other types of dementia, such as vascular dementia; 2. Patients with any existing acute physical illness; 3. Patients with a history of illegal drug use, substance abuse or alcoholism; 4. Patients or their families refused to join the study.

[0030] 1.2 Sampling and storage

[0031] Hair samples were cut 1.0 cm from the scalp, wrapped in aluminum foil, and stored at -80°C. The specimen ID was labeled with a digital code to ensure that the patient's personal information was not associated with the sample. All hair samples underwent only one freeze-thaw cycle before analysis. After collection, 5.00 ± 0.50 mg of hair samples were weighed and placed in a 2 ml tube. The samples were washed by soaking in 2 ml acetone, 1 ml water, and 2 ml acetone for 10 minutes, and then dried at room temperature in a fume hood. After drying, 400 uL of 1 M sodium hydroxide and 4 uL of internal reference (10 Mm D4-alanine) were added and incubated at 85°C for 25 minutes. The samples were then allowed to cool naturally to room temperature. After cooling, 400 μL of methanol was added, vortexed for 30 seconds, and centrifuged at 4000 g for 5 minutes. 300 μL of the supernatant was taken for derivatization. Quality control (QC) samples were prepared by mixing a certain amount of supernatant from each sample, totaling 20 ml into a new tube, and then subjected to MCF derivatization as described above to obtain 15 QC samples. After every 15 samples, a QC sample was loaded to obtain a QC spectrum.

[0032] 1.3 Methyl chloroformate (MCF) derivatization and gas chromatography-mass spectrometry analysis

[0033] According to Nature Protocols [Smart KF, Aggio RB, Van Houtte JR, All prepared extracts were chemically modified using MCF derivatization to lower their boiling points according to the method described in SG. (2010) Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry. Nat Protoc. 5: 1709-29.10.1038 / nprot.2010.108].

[0034] The derivatized metabolites were analyzed using an Agilent Intuvo9000 gas chromatography system with a 5977B series mass selective detector (MSD). The gas chromatography column was BD-1701 (30m×250μm id×0.25μm, Agilent). The derivatized samples were injected into the splitless inlet at 290°C with a helium flow rate of 1mL / min. The GC-oven program was maintained at 45°C for 2min in the first stage, and the temperature was increased from 45°C to 180°C at a rate of 9°C / min and maintained for 5min in the second stage. The temperature was increased to 220°C at a rate of 40°C / min and maintained for 5min in the third stage. The temperature was increased to 240°C at a rate of 40°C / min and maintained for 11.5min in the fourth stage. Finally, the temperature was increased to 280°C at a rate of 80°C / min. The temperatures of the protection chip, auxiliary device, quadrupole mass spectrometer, and mass spectrometer source were 280°C, 250°C, 230°C, and 150°C, respectively. The mass range was 30 μm to 550 μm. The scanning speed was set to 1.563 μ / s, and the solvent delay time was 5.5 min.

[0035] 1.3 Metabolite identification and quantification

[0036] Chromatographic features were processed and identified using the Automated Mass Spectral Deconvolution and Identification System (AMDIS) software. Metabolites were confirmed by matching spectra with an in-house MFC library with a similarity score of more than 85% and ensuring their GC retention time within 30 seconds. For other possible compounds, the commercial NIST mass spectral library was used for identification. The relative concentrations of metabolites were extracted using a Mass Omics-R-based script by determining the peak height of the most abundant fragment ion mass within a pre-set retention time. Background contamination and potential carryover of identified metabolites were subtracted using blank samples. To enhance quantitative stability, the relative concentrations of identified compounds were normalized using an internal reference (D4-alanine) based on their correlation with metabolites in QC samples. The median of the QC samples was used to adjust for daily batch effects, and hair samples were corrected based on the measured weight. Amino acids, fatty acids, and lactic acid were quantified using chemical reference standards. Metabolite levels were first normalized to the corresponding internal standards and then converted to absolute concentrations using calibration curves derived from the respective chemical standards (5 concentrations from 0 to 55.4 mM).

[0037] 1.4 Statistical analysis

[0038] Quantitative data were expressed as mean ± standard deviation (SD), and categorical variables were expressed as frequency and percentage. Categorical variables were compared using the χ2 test. 2Test. For continuous variables that conformed to normal distribution, one-way analysis of variance (ANOVA) was used; for non-normally distributed variables, the Kruskal-Wallis nonparametric test was used to compare the differences between the NC group, MCI group, and AD group. Spearman correlation analysis was used to test the correlation between two non-normally distributed continuous variables. Principal component analysis was used to analyze the differences in metabolites among the groups, and |Log2 FC|>0 was used as the standard to identify metabolite differences, and the adjusted p value was <0.05. Volcano plots were generated using the ggplot2 package in R., and Venn analysis was performed to identify common differential metabolites. Metabolic features were modeled using LASSO logistic regression using the glmnet package. To evaluate the ability of the LASSO model to identify AD, ROC analysis was performed using the pROC package. All statistical analyses were performed using R software.

[0039] 2 Experimental results

[0040] First, PCA analysis was performed on the hair metabolites of different groups, and the results showed that there were differences in the metabolite levels among the three groups. Figure 1 As shown in A, clustering patterns were observed between the AD, MCI, and NC groups, indicating that there were metabolites with inter-group differences. PCA analysis based on CDR scores showed that there were some differences between the 0 group and the other groups ( Figure 1 B). PLS-DA further confirmed the significant metabolic separation ( Figure 1 CD), MCI and AD groups all showed a clear trend of separation from the NC group. The volcano plot highlights the specific differential metabolites between the cognitive impairment groups (including MCI and AD) and the NC group. Red marks are up-regulated metabolites, such as linoleic acid, behenic acid, and arachidic acid, and green marks are down-regulated metabolites, such as quinolinic acid, caprylic acid, and myristic acid. Metabolites were filtered by the criterion of |Log2FC|>0 and the adjusted p value <0.05. The figure details the differential fold changes in metabolite levels and classifies them into chemical categories, including cholesterol derivatives, amino acids, tricarboxylic acid cycle intermediates, alkanes, and fatty acids. Results ( Figure 1 ) showed that the metabolites linoleic acid, cholestenone, arachidonic acid, and docosanoic acid were significantly increased in the AD group, while quinolinic acid and myristic acid were decreased. These findings highlight the presence of significantly increased or decreased levels of different metabolites in AD compared with normal controls.

[0041] Figure 2 Metabolic changes between Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC) were analyzed and visualized using volcano plots and line graphs. Figure 2In A, the volcano plots of MCI and NC showed the top five up-regulated metabolites, including linoleic acid, phenylacetic acid, heptanoic acid, cinnamic acid, and sebacic acid (red), and the top five down-regulated metabolites, including quinolinic acid, octanoic acid, methylmalonic acid, 4-hydroxyphenylacetic acid, and malic acid (green), with the selection criteria of |Log2FC|>0 and adjusted p value<0.05. Figure 2 B shows the differential fold changes (Log2FC) of these differential metabolites when categorized by chemical class. Notably, upregulated fatty acids such as linoleic acid, capric acid, and cinnamic acid increased significantly with disease progression, while downregulated metabolites such as quinolinic acid, methylmalonic acid, and malic acid decreased significantly in disease progression. Figure 2 In C, the volcano plot of AD versus NC highlights the top five up-regulated metabolites in the AD group, including cholestenone, arachidonic acid, dodecanoic acid, linoleic acid, and capric acid (marked in red), and the top five down-regulated metabolites, such as quinolinic acid, capric acid, myristic acid, malic acid, and threonine (marked in green). Figure 2 D shows the fold change (Log2FC) of differential metabolites in the AD group, showing that 4,6-cholestadien-3-one, arachidonic acid and behenic acid were significantly upregulated, and quinolinic acid, malic acid and decanoic acid were downregulated. These findings indicate that compared with NC, both MCI and AD groups have significant metabolic reprogramming (referring to the process in which cells undergo systematic adjustments and changes in their metabolic patterns under specific physiological and pathological conditions to adapt to changes in the external environment and meet their own growth and differentiation needs), as manifested by upregulation of fatty acid and cholesterol-related metabolites and downregulation of metabolites related to energy metabolism. These results provide valuable information on the metabolic characteristics and potential disease mechanisms of MCI and AD.

[0042] The metabolites that were different among the three groups included the following fourteen: triacontanes, erucic acid, heneicosane, behenic acid, tetracosane, 3-hydroxybutyric acid, 15-methylhexadecanoic acid, arachidic acid, heptadecanoic acid, melatonin, stearic acid, tetracosanoic acid, heneicosane, and docosane, which were distributed in fatty acids and their complexes, alkanes, and organic compound components.

[0043] Figure 3 The Venn diagram shows the common and unique differential metabolites found in the comparison between AD and NC, MCI and NC, and CI and NC. There were 14 common differential metabolites in all three groups, while there were 15, 6, and 34 unique metabolites between AD and NC, CI and NC, and MCI and NC, respectively. The levels of the 14 common differential metabolites are shown in Table 1.

[0044] Table 1 The results of the comparison of the levels of metabolites in the three groups and the differences between the groups are as follows

[0045]

[0046]

[0047] 1 Median [IQR]; Mean (SD)

[0048] 2 Kruskal-Wallis rank sum test; one-way ANOVA

[0049] LASSO logistic regression was used to construct a classification model. The 14 differential metabolite sets in Table 1 were used to construct classification models for NC vs AD and NC vs MCI. Figure 4 It can be seen that the AUC value for the NC vs AD binary classification is 0.839, and the AUC value for the NC vs MCI binary classification is 0.927, both of which achieved good classification performance (the closer the AUC is to 1.0, the better the classification performance of the model), suggesting that differential hair metabolites have potential clinical application value in the diagnosis of Alzheimer's disease.

[0050] In this study, we found that hair metabolites such as triacontanes, erucic acid, heneicosane, behenic acid, and tetracosane were significantly different between the AD and MCI groups compared with the NC group. The differential metabolites in the MCI group mainly belonged to amino acids and fatty acids, while those in the AD group mainly belonged to fatty acids and lipids, suggesting that there were fluctuations in hair metabolite levels during the progression from NC, MCI to AD. It is noteworthy that we developed diagnostic biomarkers for MCI and AD based on hair metabolites. This method is minimally invasive and cost-effective, and is a promising tool for large-scale screening of Alzheimer's disease in the general elderly.

Claims

1. A combination of hair metabolic markers for diagnosing Alzheimer's disease, characterized in that: The hair metabolism marker combination includes triacontan, erucic acid, heneicosane, behenic acid, tetracosane, 3-hydroxybutyric acid, 15-methylhexadecanoic acid, arachidic acid, heptadecanoic acid, melatonin, stearic acid, tetracosanoic acid, heneicosane, and docosane.

2. The hair metabolic marker combination for diagnosing Alzheimer's disease according to claim 1, characterized in that: The hair metabolism marker combination is used to diagnose and differentiate Alzheimer's disease patients, mild cognitive impairment patients and people with normal cognition.

3. The hair metabolic marker combination for diagnosing Alzheimer's disease according to claim 1, characterized in that: Compared with people with normal cognition, in patients with Alzheimer's disease, melatonin, triacontanes, lignoceric acid, behenic acid, docosane, erucic acid, heneicosane, stearic acid, lignoceric acid, heptadecanoic acid, arachidic acid, and heneicosane were significantly upregulated, and 3-hydroxybutyric acid was significantly downregulated.

4. Use of the hair metabolic marker combination according to claim 1 in preparing a kit for diagnosing Alzheimer's disease.

5. A kit for diagnosing Alzheimer's disease, characterized in that: A standard product comprising the combination of hair metabolic markers for diagnosing Alzheimer's disease as claimed in claim 1.

6. The kit according to claim 5, characterized in that: Also included is an extraction reagent for extracting the hair metabolism marker.

7. The kit according to claim 6, characterized in that: The extraction reagents include acetone, sodium hydroxide and methanol.

8. The kit according to claim 5, characterized in that: An internal standard, D4-alanine, was also included.

9. The kit according to claim 5, characterized in that: Also included are reagents for gas chromatography-mass spectrometry analysis.

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