Application of metabolome biomarker in biological sample in preparation of Parkinson's disease detection product

Abstract

The invention discloses an application of a metabolome biomarker in a biological sample in preparation of a Parkinson's disease detection product, and the metabolome biomarker is selected from one or a combination of more than two of DL-alanyl-DL-methionine, p-menthane-1, 3, 8-triene and 4-vinyl guaiacol. Experimental results of the applicant show that the three metabolites are singly used or combined for diagnosis of the Parkinson's disease and have high capability and prediction accuracy, when the three metabolites are combined for use, the AUC value is up to 0.983, the sensitivity is 95.24%, the specificity is 94.12%, the CI is 0.923-0.999, and the Parkinson's disease can be predicted more reliably and accurately.

Description

Technical Field

[0001] The present invention relates to the field of pharmaceutical technologies, and particularly to the application of metabolomics biomarkers in biological samples in the preparation of products for detecting Parkinson's disease. Background Art

[0002] Parkinson's disease (PD) is a common neurodegenerative disease in the middle-aged and elderly. Its main clinical symptoms include bradykinesia, resting tremor, muscle rigidity, and postural balance disorders, etc. It generally starts to occur at the age of 50 - 65, and the incidence rate gradually increases with age. The incidence rate at the age of 60 is about 1‰, and that at the age of 70 reaches 3 - 5‰. In China, the prevalence of Parkinson's disease in people over 65 years old is about 1.7%. The specific causes and pathogenic mechanisms of Parkinson's disease are complex, but it is generally believed that it is related to genetics and the environment. Currently, no single target or hypothesis can well explain the cause of Parkinson's disease and predict its occurrence.

[0003] Currently, the clinical diagnosis of Parkinson's disease mainly relies on clinical manifestations, physical signs, and auxiliary examinations, such as cerebellar signs and supranuclear gaze palsy, presynaptic dopaminergic PET imaging, etc. These methods lack sensitivity and specificity, and there is also a serious lag. It is necessary for the patient to present certain physical symptoms to determine the disease situation. Early-stage Parkinson's disease patients may not meet the clinical diagnostic criteria, and it is impossible to predict Parkinson's disease in advance.

[0004] Metabolomics is an emerging omics technology that plays an increasingly important role in biological research because it can reveal the unique chemical fingerprint characteristics of cellular metabolism in the body. As an unbiased method for studying small molecule metabolites, metabolomics offers hope for discovering more biomarkers for Parkinson's disease. For Parkinson's disease, potential biomarkers can involve multiple aspects such as clinical manifestations, biochemical test samples (such as blood, cerebrospinal fluid, feces, urine, tissue biopsy), imaging features (such as MRI / fMRI, SPECT / PET, transcranial ultrasound), and genetic factors. The invention patent with publication number CN115714027A discloses the application of a metabolic biomarker in the preparation of a product for diagnosing Parkinson's disease. The metabolic biomarker includes any one or more of the following: 2-methoxybenzoic acid, phenylacetylglutamine, salicylic acid, ebselen, fisetin, and tryptophan-tyrosine. When using 6 metabolic biomarkers in combination to diagnose Parkinson's disease, the AUC (the AUC value is a performance index for measuring the authenticity of the biomarker in predicting the diagnosis of Parkinson's disease) value can reach 0.984. However, when only selecting 2 to 5 of these metabolic biomarkers in combination for diagnosis, although the AUC increases, it is not very satisfactory. The invention patent with publication number CN118362731A discloses a metabolic biomarker for detecting Parkinson's disease. The metabolic biomarker includes at least one of L-phenylalanine, levodopa, isovaleric acid, and norepinephrine. In this invention, when using the above 4 metabolic biomarkers to diagnose Parkinson's disease, the AUC of the classification model is 0.99, the sensitivity is 100%, and the specificity is 88.26%. However, when only using a single metabolite among them, the AUC range is only 0.54 - 0.66.

[0005] Therefore, there is an urgent need to develop metabolomic biomarkers for Parkinson's disease that are simple in composition and have higher predictive value. Currently, there is no relevant report on using one or more selected from DL-alanyl-DL-methionine, p-mentha-1,3,8-triene, and 4-vinylguaiacol as metabolomic biomarkers for the diagnosis or monitoring of Parkinson's disease. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide the application of a metabolomic biomarker in a biological sample in the preparation of a product for detecting Parkinson's disease in view of the deficiencies in the prior art.

[0007] The technical solution adopted by the present invention is: the application of metabolomic biomarkers in biological samples in the preparation of detection products for Parkinson's disease, and the metabolomic biomarkers are selected from one or a combination of two or more of DL-alanyl-DL-methionine (Alanyl-methionine, CAS No.: 1999-43-5), p-mentha-1,3,8-triene (p-Mentha-1,3,8-triene, CAS No.: 18368-95-1), and 4-vinylguaiacol (4-Vinylguaiacol, CAS No.: 7786-61-0).

[0008] The research of the applicant shows that the above three metabolic molecules are associated with Parkinson's disease. Compared with the control group (normal healthy people), the concentrations of the biomarkers DL-alanyl-DL-methionine, p-mentha-1,3,8-triene, or 4-vinylguaiacol in the biological samples of Parkinson's disease patients all increase. Using these three metabolites as biomarkers can well distinguish Parkinson's disease patients from healthy people. The specific test results show that when any one metabolite is used in combination to predict and diagnose Parkinson's disease, the lowest AUC value is 0.863; when any two metabolites are used in combination to predict and diagnose Parkinson's disease, the AUC value can reach above 0.942; and when the above three metabolites are used in combination, the AUC value is as high as 0.983, the sensitivity is 95.24%, and the specificity is 94.12%. It can be seen that the metabolomic biomarkers of the present invention can more reliably predict Parkinson's disease. When the concentration of one or more of the aforementioned biomarkers in the biological sample of the subject is up-regulated compared with the control group, it can be diagnosed or assist in diagnosing that the subject providing the biological sample has Parkinson's disease or is at risk of having Parkinson's disease.

[0009] Furthermore, the metabolomic biomarkers are selected from the combination of the following substances:

[0010] DL-alanyl-DL-methionine and p-mentha-1,3,8-triene; or

[0011] DL-alanyl-DL-methionine and 4-vinylguaiacol; or

[0012] p-mentha-1,3,8-triene and 4-vinylguaiacol; or

[0013] DL-alanyl-DL-methionine, p-mentha-1,3,8-triene, and 4-vinylguaiacol.

[0014] The detection product can be a kit and / or a reagent.

[0015] The Parkinson's disease includes early-stage, mid-late stage, and severe Parkinson's disease patients.

[0016] In certain embodiments, the level of metabolomic biomarkers in a biological sample can be determined by the following methods: chromatography and / or mass spectrometry, fluorometry, electrophoresis, immunoaffinity, hybridization, immunochemistry, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis.

[0017] In some preferred embodiments, the level of metabolomic biomarkers in a biological sample is determined by spectroscopy, liquid or gas chromatography, mass spectrometry, liquid or gas chromatography coupled with mass spectrometry.

[0018] When mass spectrometry detection is employed, a full scan mode is used for screening combined with second-level targeted analysis. The full scan mode acquires all primary information of small molecules within the mass range of 50 m / z to 1200 m / z in the biological sample of the subject. Differentially expressed metabolites are screened as potential biomarkers through multivariate statistical analysis. Further targeted second-level fragmentation of the potential biomarkers is carried out, and combined with the second-level spectra in the database, the differential molecules are finally determined as the biomarkers described in the present invention.

[0019] The biological sample can be whole blood, serum or plasma from a subject. In some preferred embodiments, the biological sample is serum. The subject is a mammal, such as a human.

[0020] The present invention also includes a detection product for diagnosing or assisting in the diagnosis of blood Parkinson's disease by metabolomic biomarkers in a biological sample. The metabolomic biomarkers are selected from one or a combination of two or more of DL-alanyl-DL-methionine, p-mentha-1,3,8-triene and 4-vinylguaiacol. The detection product is a kit and / or reagent. In a preferred embodiment, the detection product, in addition to containing the above biomarker combination, further contains an optional internal standard and / or metabolite extraction reagent.

[0021] Compared with the prior art, the present invention for the first time discovers the association between three metabolites, namely DL-alanyl-DL-methionine, p-mentha-1,3,8-triene, and 4-vinylguaiacol, and Parkinson's disease. Through experiments, it is confirmed that using the combination of these three metabolites as potential biomarker prediction indicators for Parkinson's disease has high prediction value. The specific experimental results show that when the above metabolites are used individually, the AUC values of DL-alanyl-DL-methionine, p-mentha-1,3,8-triene, and 4-vinylguaiacol are 0.909, 0.887, and 0.863 respectively; when two metabolites are used in combination, the AUC value of DL-alanyl-DL-methionine combined with p-mentha-1,3,8-triene is 0.951, the AUC value of DL-alanyl-DL-methionine combined with 4-vinylguaiacol is 0.972, and the AUC value of p-mentha-1,3,8-triene combined with 4-vinylguaiacol is 0.942; when the three metabolites are used in combination, the AUC value is as high as 0.983, the sensitivity is 95.24%, the specificity is 94.12%, and the CI is 0.923 - 0.999, which has higher prediction value and can predict Parkinson's disease more reliably and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 FIG. is the score plot of PCA (A) and OPLS-DA (B) of plasma metabolites in the healthy control group and the Parkinson's disease group, where PD represents the Parkinson's disease group and Con represents the healthy control group.

[0023] Figure 2 FIG. is the clustering heat map and VIP analysis diagram of differential metabolites in the healthy control group and the Parkinson's disease group, where PD represents the Parkinson's disease group and Con represents the healthy control group.

[0024] Figure 3 FIG. is the ROC curve diagram of biomarkers for the combined analysis of three metabolites of Parkinson's disease.

[0025] Figure 4 FIG. is the ROC curve diagram of biomarkers for the pairwise combined analysis of three metabolites of Parkinson's disease. DETAILED DESCRIPTION OF THE INVENTION

[0026] The present invention will be further described below with reference to the accompanying drawings through examples, but it shall not be construed as a limitation to the present invention. The specific materials and their sources used in the implementation scheme of the present invention are provided below. However, it should be understood that these are merely exemplary and are not intended to limit the present invention. Materials with the same or similar types, models, qualities, properties, or functions as the following reagents and instruments can be used to implement the present invention. The experimental methods used in the following examples are all conventional methods unless otherwise specified. The materials, reagents, etc. used in the following examples can be obtained from commercial channels unless otherwise specified.

[0027] Example 1: Screening of differential serum metabolic markers for Parkinson's disease and evaluation of diagnostic efficacy

[0028] 1. Preparation of metabolite samples:

[0029] 1.1 Source of clinical samples: Parkinson's disease group (PD): From January 2020 to December 2021, primary Parkinson's disease patients who visited the outpatient department and ward of the Affiliated Hospital of Guilin Medical University were continuously screened and enrolled. Inclusion criteria: (1) Meeting the diagnostic criteria for primary Parkinson's disease: Diagnosed by a neurologist with at least the attending physician level and proficient in the diagnosis and treatment of movement disorders according to the diagnostic criteria for clinical Parkinson's disease published by the International Parkinson and Movement Disorder Society in 2015 and the Chinese Parkinson's disease diagnostic guidelines published in 2016. (2) Aged 30 - 80 years old. (3) The modified Hoehn - Yahr (H - Y) stage in the off state is between 1.0 and 3.0, belonging to early and middle - stage patients. Exclusion criteria: (1) Having various secondary Parkinson's syndromes such as drug - induced, toxic, traumatic, and cerebrovascular diseases, neurodegenerative Parkinson's syndrome, and neurogenetic degenerative Parkinson's syndrome. (2) Having poisoning, metabolic diseases, immune diseases, infectious diseases, tumors, hypertension, diabetes, and severe heart, liver, lung, and kidney diseases, etc. (3) Having a history of smoking and drinking in the past month. (4) Being unable to cooperate with the completion of medical history collection and patient assessment. Healthy control group (Con): Healthy spouses and healthy volunteers of the recruited patients were used as the healthy control group. Inclusion criteria: Physically healthy, aged 30 - 80 years old, and able to cooperate with the collection of general information, assessment, and the collection of stool specimens. Exclusion criteria are the same as those of the PD group.

[0030] 1.2 Plasma sample collection: All study subjects had fasting blood collection. 4 ml of fresh venous blood was drawn from the median cubital vein and placed in a heparin - coated anticoagulant collection tube. After standing at room temperature for 1 h, it was centrifuged in a 4℃ low - temperature centrifuge for 15 min. Then, 200 μL of equal - volume plasma was aspirated with a pipette and aliquoted into 1.5 mL centrifuge tubes. The lids were covered and immediately stored in a - 80℃ ultra - low - temperature freezer for experimental use.

[0031] 1.3 Sample treatment: The specific steps are as follows: (1) Pipette 100 μL of plasma sample into a 1.5 mL centrifuge tube. (2) Add 400 μL of methanol:acetonitrile = 1:1 (v:v) extraction solution, which contains 0.02 mg / mL of L-2-chlorophenylalanine as the internal standard. (3) Vortex for 30 seconds and then perform low-temperature ultrasonic extraction for 30 minutes, and then let the sample stand at -20 °C for 30 minutes. (4) Centrifuge the sample at 13000 g for 15 minutes in a 4 °C centrifuge, pipette the supernatant, and dry it with nitrogen. (5) Add 120 μL of the reconstitution solution of acetonitrile:water = 1:1 for reconstitution. (6) Vortex for 30 seconds and perform ultrasonic extraction at 5 °C and 40 KHz for 5 minutes. (7) Finally, centrifuge the sample at 13000 g for 10 minutes in a 4 °C centrifuge, prepare a vial with an inner cannula, and pipette the supernatant into the vial to be used as the sample for on-machine analysis. (8) The quality control samples used for detection are prepared by mixing 20 μL of supernatant pipetted from each sample respectively.

[0032] 1.4 Plasma LC-MS detection: The instrument platform for this LC-MS analysis is the ultra-high performance liquid chromatography tandem Fourier transform mass spectrometry UHPLC-Q Exactive HF-X system provided by Thermo Fisher Scientific. Chromatographic conditions: The chromatographic column is ACQUITY UPLCHSS T3 (100 mm × 2.1 mm i.d., 1.8 μm; Waters, Milford, USA); Mobile phase A is water-acetonitrile (containing 0.1% formic acid) (95:5, v / v), mobile phase B is acetonitrile-isopropanol-water (containing 0.1% formic acid) (47.5:47.5:5, v / v), the injection volume is 2 μL, and the column temperature is 40 °C. The elution gradient of each mobile phase is shown in Table 1.

[0033] Table 1

[0034]

[0035]

[0036] Mass spectrometry conditions: After the sample to be detected is ionized by electrospray ionization, positive and negative ion scanning modes are respectively used to collect mass spectrometry signals. The specific parameters are shown in Table 2.

[0037] Table 2

[0038]

[0039] 2. Data processing and analysis

[0040] 2.1 Metabolite identification: The original data was first imported into the metabolomics processing software Progenesis QI (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment. Finally, a data matrix containing information such as retention time, mass-to-charge ratio, and peak intensity was obtained. Subsequently, the MS and MS / MS mass spectrometry information was matched with the metabolic database for feature peak library search and identification, and metabolites were identified based on the secondary mass spectrometry matching scores. The databases included public databases such as http: / / www.hmdb.ca / and https: / / metlin.scripps.edu / , as well as the database built by Majorbio Cloud Platform.

[0041] 3. Analyze and screen out blood metabolomics markers for Parkinson's disease

[0042] 3.1 A total of 76 research subjects were enrolled in this part, including 42 cases of Parkinson's disease (PD group), including 23 males and 19 females; 34 cases in the healthy control group (Con group), including 18 males and 16 females. There were no significant statistical differences in the basic data of gender, age, and BMI between the two groups of research subjects (p > 0.05). The results are shown in Table 3.

[0043] Table 3

[0044]

[0045] 3.2 Perform multivariate statistical analysis on the obtained metabolites: Principal component analysis (PCA) is an unsupervised multivariate statistical analysis method that generally reflects the overall differences between groups of samples and the degree of variation within the samples. In the PCA plot, the closer the distance between sample metabolites, the more similar the metabolite expression patterns, and the farther the distance, the greater the difference in expression patterns. From the PCA score plot of plasma samples from the healthy control group and the Parkinson's disease group ( Figure 1 A), it can be seen that both groups of samples are within the 95% confidence interval. Although there is a certain overlap between some samples, there is also a separation trend for some samples, indicating that there are certain differences in the plasma metabolite profiles between the healthy control group and the Parkinson's disease group. Orthogonal partial least squares discriminant analysis (OPLS-DA) can decompose the X matrix information into two types of information related and unrelated to Y. By removing the unrelated differences, the relevant information is concentrated in the first predictive component. It can better distinguish the differences between groups and improve the effectiveness and analytical ability of the model, and is suitable for comparing the metabolite differences between two different groups. We further used the supervised OPLS-DA to perform pattern recognition analysis on the plasma metabolites of the healthy control group and the Parkinson's disease group. From Figure 1It can be seen that there is an obvious separation trend in the OPLS-DA score plots of the healthy control group and the Parkinson's disease group, indicating that there are significant differences in plasma metabolites between the healthy control group and the Parkinson's disease group.

[0046] 3.3 Screening of differential metabolites in plasma: To increase the analysis of the importance of differential metabolites in plasma, we used OPLS-DA multivariate statistical analysis to find the variable importance in projection (VIP) values and fold change (FC) values, and combined with the p-value of the univariate statistical analysis Wilcoxon rank sum test to screen important differential metabolites. According to the VIP value of the variable with the greatest contribution to classification being greater than 2 and p < 0.05, key differential metabolites were screened out. We used a clustering heatmap to show the expression patterns of each differential metabolite in the healthy control group and the Parkinson's disease group, and a VIP bar chart to show the VIP values of the differential metabolites in the two groups in the multivariate statistical analysis and the p-values in the univariate statistics. It can be intuitively seen from the figure the importance and expression quantity change trends of the differential metabolites between the two groups. It can be seen from the clustering heatmap that compared with the healthy control group, 3 key differential metabolites, DL-alanyl-DL-methionine, p-Menta-1,3,8-triene, and 4-Vinylguaiacol, were screened out in the Parkinson's disease group, and their relative expression levels were all significantly increased. As Figure 2 shown.

[0047] 3.4 Prediction of biomarkers for 3 metabolites in Parkinson's disease and combined analysis. To further predict the potential diagnostic efficacy of the above-identified differential metabolites in plasma for distinguishing Parkinson's disease from the healthy control group, we performed binary logistic regression model analysis on the relative contents of the 3 differential metabolites in the two groups of samples and then plotted the ROC curves. The results showed that the area under the AUC curve of DL-alanyl-DL-methionine was 0.909, P < 0.001; the area under the AUC curve of p-Menta-1,3,8-triene was 0.887, P < 0.001; the area under the AUC curve of 4-Vinylguaiacol was 0.863, P < 0.001. Moreover, the combination of the 3 metabolites as a potential biomarker prediction index for Parkinson's disease had high predictive value (AUC 0.983, sensitivity 95.24%, specificity 94.12%, CI 0.923 - 0.999). The results are as Figure 3As shown below. In addition, the results of the combined analysis between two metabolites showed that the area under the AUC curve of DL-alanyl-DL-methionine combined with mentha-1,3,8-triene was 0.951, P<0.001; the area under the AUC curve of DL-alanyl-DL-methionine combined with 4-vinylguaiacol was 0.972, P<0.001; the area under the AUC curve of mentha-1,3,8-triene combined with 4-vinylguaiacol was 0.942, P<0.001. The results are as Figure 4 shown. According to the AUC values, we believe that any one of these three metabolites or a combination of any two or more of them can be used as potential biomarkers for differentiating Parkinson's disease from the healthy control group. In particular, the combination of the three metabolites has a higher AUC value, the strongest sensitivity and specificity, and extremely high predictive efficacy. It is especially suitable as a metabolomic biomarker for the diagnosis or monitoring of Parkinson's disease and can be used to prepare related detection products for the diagnosis or auxiliary diagnosis of Parkinson's disease.

[0048] The embodiments described above are only descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. Use of metabolomic biomarkers in biological samples for the preparation of a product for detecting Parkinson's disease, wherein the metabolomic biomarkers are selected from one or a combination of two or more of DL-alanyl-DL-methionine, p-mentha-1,3,8-triene, and 4-vinylguaiacol.

2. The application according to claim 1, characterized in that, The metabolomic biomarkers are selected from the combination of the following substances: DL-alanyl-DL-methionine and p-mentha-1,3,8-triene; or DL-alanyl-DL-methionine and 4-vinylguaiacol; or p-mentha-1,3,8-triene and 4-vinylguaiacol; or DL-alanyl-DL-methionine, p-mentha-1,3,8-triene, and 4-vinylguaiacol.

3. The application according to claim 1 or 2, characterized in that, The detection product is a kit and / or reagent.

4. The application according to claim 1 or 2, characterized in that, The level of metabolomic biomarkers in biological samples is determined by the following methods: chromatography and / or mass spectrometry, fluorometry, electrophoresis, immunoaffinity, hybridization, immunochemistry, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis.

5. The application according to claim 1 or 2, characterized in that, The level of metabolomic biomarkers in biological samples is determined by spectroscopy, liquid or gas chromatography, mass spectrometry, liquid or gas chromatography coupled with mass spectrometry.

6. The application according to claim 1 or 2, characterized in that, The biological sample is selected from whole blood, serum, or plasma.

7. A detection product for diagnosing or assisting in the diagnosis of blood Parkinson's disease through metabolomic biomarkers in biological samples, characterized in that, The metabolomic biomarkers are selected from one or a combination of two or more of DL-alanyl-DL-methionine, p-mentha-1,3,8-triene, and 4-vinylguaiacol.

8. The detection product according to claim 7, characterized in that, The detection product is a kit and / or reagent.

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