Application of biomarker in preparation of product for distinguishing severity of dampness syndrome in metabolic disorder crowd

Abstract

The invention discloses an application of a biomarker in preparation of a product for distinguishing the severity of dampness syndromes in people with abnormal metabolism. Specifically, the invention relates to application of cholesteryl ester linoleic acid as a biomarker in preparation of a product for distinguishing the severity of dampness syndromes in people with abnormal metabolism. The invention finds that the CE 18: 2 can be used as a potential biomarker for evaluating the severity of the wetness syndrome. Therefore, the CE 18: 2 is suggested to be further verified and applied in clinical practice so as to improve the diagnosis and evaluation precision of the dampness syndrome, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the field of traditional Chinese medicine, and specifically to the use of biomarkers in the preparation of products for distinguishing the severity of dampness syndrome in people with metabolic abnormalities. Background Art

[0002] Syndrome is a concept unique to traditional Chinese medicine, and it plays an important role in TCM diagnosis and treatment. "Dampness syndrome" is a common clinical TCM syndrome caused by dampness, which has obvious regional clustering. The climate in Lingnan region is humid, and dampness syndrome is very common in the region.

[0003] The main clinical features of dampness syndrome are thick and greasy tongue coating and physical fatigue. The course of the disease is lingering, recurrent, and difficult to cure. According to traditional Chinese medicine, the basic pathogenesis of dampness syndrome is water stagnation, qi disorder, viscera dysfunction, and abnormal water distribution. Dampness syndrome can cause viscera dysfunction, leading to the accumulation of pathological products such as phlegm, turbidity, and fluid, which in turn leads to symptoms related to dampness syndrome. In clinical practice, dampness syndrome is common in people with metabolic abnormalities such as metabolic syndrome and diabetic nephropathy. Studies have shown that in people with metabolic abnormalities, the severity of dampness syndrome is related to the severity of the patient's condition. For example, in patients with type 2 diabetes, it was found that the insulin resistance index and inflammatory factor levels of patients with high dampness syndrome scores were greater than those of patients with low dampness syndrome scores. A study conducted a TCM constitution survey on 147 patients with metabolic syndrome and found that the metabolic abnormalities of patients with dampness syndrome were more significant, and the levels of serum metabolites such as lactic acid and acetone in these patients were significantly higher than those of patients without dampness syndrome. Another study conducted a metabolomics analysis on patients with chronic gastritis and found that the levels of serum metabolites such as valine and lactulose in patients with dampness syndrome were significantly higher than those in patients without dampness syndrome. It is necessary to distinguish the severity of dampness syndrome in people with metabolic abnormalities.

[0004] At present, the severity of TCM dampness syndrome in clinical practice still relies on the disassembly of medical books, medical case experience and TCM theory, and is judged by constructing a TCM dampness syndrome assessment scale and judging it according to its total score. Although these traditional methods play an important role in TCM diagnosis, they are highly subjective and often rely on the personal experience and judgment of doctors, lacking unified standards and objective assessment tools. In order to improve the accuracy and consistency of dampness syndrome discrimination, the Provincial and Ministerial Jointly-built TCM Dampness Syndrome National Key Laboratory developed a 30-item TCM dampness syndrome assessment scale in 2024, which aims to quantitatively assess the severity of dampness syndrome in a systematic and standardized way. The total score is 120 points, and the higher the score, the more severe the dampness syndrome. The various items of the assessment scale cover a variety of clinical manifestations of dampness syndrome, including body shape, feeling, tongue image, stool status and other aspects. The items are as follows: 1. Obesity, 2. Heavy body, 3. Low body heat, 4. Sticky sweat / sweating, 5. Fatigue, 6. Drowsiness / wanting to sleep, 7. Heavy head, 8. Greasy face or hair, 9. Dirty complexion as if covered with dirt, 10. A lot of eye mucus, 11. Sticky mouth, 12. Bad breath, 13. Thirst but no desire to drink water, 14. A lot of phlegm, 15. Abdominal fullness, 16. Poor appetite, 17. Nausea or vomiting, 18. Heaviness in the waist, 19. Aching joints, 20. Heaviness in the joints, 21. Unformed stool, 22. Frequent bowel movements, 23. Sticky stool, 24. Excessive vaginal discharge / wet scrotum, 25. Sores or wet skin, 26. Thick tongue coating, 27. Greasy tongue coating, 28. Slippery tongue with a lot of fluid, 29. Tongue with tassels, 30. Enlarged tongue. Patients need to self-evaluate each item according to their own symptoms and give corresponding scores. This assessment scale has improved the standardization and consistency of the diagnosis of dampness syndrome to a certain extent, but it is highly subjective and still relies on the patient's self-assessment. Therefore, this method has certain limitations from a scientific and objective perspective. In order to further improve the accuracy of discriminating the severity of dampness syndrome, the discovery of biomarkers as an objective assessment method is particularly important. Summary of the invention

[0005] The purpose of the present invention is to provide the use of biomarkers in the preparation of products for distinguishing the severity of dampness syndrome in people with metabolic abnormalities.

[0006] The present invention provides the use of cholesterol ester linoleic acid (CE 18:2) as a biomarker in the preparation of a product for distinguishing the severity of dampness syndrome in people with metabolic abnormalities.

[0007] Preferably, the product is a reagent, a kit, etc.

[0008] Preferably, the application is to distinguish the severity of dampness syndrome in people with metabolic abnormalities by quantitatively analyzing the relative abundance of cholesterol ester linoleic acid in samples of people with metabolic abnormalities.

[0009] Preferably, the sample may be serum.

[0010] Preferably, the reagent is a targeted lipidomics analysis reagent.

[0011] The second object of the present invention is to provide a reagent for detecting cholesterol ester linoleic acid (CE 18:2) for use in preparing a product for distinguishing the severity of dampness syndrome in people with metabolic abnormalities.

[0012] Preferably, it is a reagent for detecting the relative abundance of cholesterol ester linoleic acid in the serum of people with metabolic abnormalities.

[0013] The present invention found that CE 18:2 can be used as a potential biomarker to assess the severity of dampness syndrome. Therefore, it is recommended to further verify and apply CE 18:2 in clinical practice to improve the diagnosis and assessment accuracy of dampness syndrome, which has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is an overview of the dampness syndrome assessment scale for the study population; Score 0 indicates the proportion of none; Score 1 indicates the proportion of mild; Score 2 indicates the proportion of moderate; Score 3 indicates the proportion of severe; Score 4 indicates the proportion of extremely severe.

[0015] Figure 2 The BMI and DS Score of people with metabolic abnormalities; MetS DS Low MetS DS High BMI and DS Score of the population.

[0016] Figure 3 Among people with metabolic abnormalities, MetS DS High The PUFA in CE in human serum was significantly upregulated; (A) Lipid categories and their core structures in the targeted lipidome database; (B) The number of lipid categories in the lipid library; (C) Multivariate statistical analysis partial least squares analysis score graph PLS-DA; (D) Fatty acid unsaturation in serum neutral lipid cholesterol ester CE; (E) Fatty acid unsaturation in serum free fatty acids FFA; (F) Fatty acid unsaturation in serum lysophospholipids (LP). Figure 4 The level of LA (18:2) in serum CE is DS High Compared with MetS in the population DS LowThe level of fatty acid unsaturation in serum neutral lipids CE was significantly increased in the population and was strongly correlated with the total DS score and DS characteristic phenotype score; (AD) Unsaturation of fatty acid PUFA in serum neutral lipids CE; (EH) Unsaturation of fatty acid PUFA with a degree of unsaturation of two in serum neutral lipids CE; (I) The dampness phenotype in the four modules, the left side represents the module: module I (M1, yellow), module II (M2, pink), module III (M3, blue), module IV (M4, green); the right side represents the dampness phenotype, and the color indicates the corresponding module (J) Spearman correlation analysis between CE PUFA, CE PUFA with a degree of unsaturation of two, CE18:2 and the total score of dampness, and the regression line (black) and 95% confidence interval (gray area) were fitted; (K) Statistical analysis of J graph, *P<0.05, **P<0.01; (L) CE PUFA, CE PUFA with a degree of unsaturation of two, CE 18:2 were respectively analyzed in the heat intestine diagram for correlation with the dampness syndrome phenotype module. The size of the dots represents the negative logarithm of the P value, and the depth of the color represents the magnitude of the correlation; *P<0.05, **P<0.01, ***P<0.001. DETAILED DESCRIPTION

[0017] The following examples are provided to further illustrate the present invention, rather than to limit the present invention.

[0018] Example 1

[0019] 1. Experimental Methods

[0020] 1. Clinical Cohort Recruitment

[0021] The clinical population cohorts involved in this article were uniformly recruited by Guangdong Provincial Hospital of Traditional Chinese Medicine. During the period, basic information such as the name, gender, age, contact information, ID card information, and work unit of the subjects were collected. Each subject participating in this trial was informed in detail of the relevant content of the scientific research, and the privacy information of the subjects was strictly protected. The subjects voluntarily decided to participate and signed the informed consent form for the project. This study was approved by the Institutional Ethics Committee of Guangdong Provincial Hospital of Traditional Chinese Medicine (BF2022-086-01). The cohort composition and sample size are as follows:

[0022] Under the metabolic syndrome disease state, patients were stratified into dampness syndrome. Among them, 29 patients had low dampness syndrome scores and 33 patients had high dampness syndrome scores. The inclusion criteria of the research project must meet the following criteria:

[0023] First, the age is 18 to 70 years old. Second, meet the diagnostic criteria of metabolic syndrome, the detailed criteria are as follows: ① Overweight or obesity index ≥ 25.0; ② Waist circumference: male ≥ 90cm, female ≥ 85cm; ③ Fasting ≥ 6.1mmol / L and / or after glucose load ≥ 7.8mmol / L, or diabetic; ④ Blood pressure ≥ 130 / 85mmHg, or hypertension; ⑤ Fasting TG ≥ 1.70mmol / L and / or HDLC < 1.04mmol / L. Third, meet the diagnostic criteria of dampness syndrome. Fourth, voluntarily participate in this project and sign the informed consent.

[0024] Exclusion criteria: ① Patients with secondary abnormalities in blood sugar, blood lipids, and blood pressure caused by related diseases such as liver and kidney function (nephrotic syndrome, renal failure, etc.), autoimmune deficiency (systemic lupus erythematosus, etc.). ② Patients with abnormal blood sugar and blood lipids caused by drugs (phenothiazines, etc.). ③ Postoperative patients (malignant tumors, cardiovascular and cerebrovascular diseases, accidental severe trauma, etc.). ④ Pregnant, lactating, or planning to become pregnant. ⑤ Patients with allergic constitution or unable to cooperate for a long time.

[0025] 2. Biochemical index information detection and analysis

[0026] 2.1 Biochemical index information

[0027] The biochemical index collection information is as follows:

[0028] Before blood collection, the subjects were required to keep fasting state for information on weight, body mass index (BMI), waist circumference (Waist), hip circumference (Hip), waist-hip ratio (WHR), systolic blood pressure (SBP), creatinine (CR), total cholesterol (TC), uric acid (UA), fasting plasma glucose (FBG), high density lipoprotein cholesterol (HDLC), fasting serum insulin (INS), C-reactive protein (CRP), tumor necrosis factors (TNF), interleukin-6 (IL6), total triglyceride (TG), diastolic blood pressure (DBP), low density lipoprotein cholesterol (LDLC) and other indicators.

[0029] 2.2 Clinical serum sample collection

[0030] The clinical serum sample collection method is as follows:

[0031] (1) Collect whole blood samples from each subject using a 5 mL blood collection tube (4 mL) from the forearm vein;

[0032] (2) Place the blood collection tube in a centrifuge and centrifuge at 4°C for 15 min. Take the supernatant (5000 rpm) to obtain the serum sample.

[0033] (3) To prevent repeated freezing and thawing of the samples, the serum samples of each subject were finally divided into 1.5 mL centrifuge tubes (100 μL / tube) and placed in a -80°C refrigerator for low-temperature storage until subsequent analysis.

[0034] 2.3 Biochemical index information detection

[0035] The above clinical biochemical index tests were all completed at the Health Status Identification and Traditional Chinese Medicine Prevention Research Center of Guangdong Provincial Hospital of Traditional Chinese Medicine.

[0036] 2.4 Data Statistical Analysis

[0037] The index data results were presented in the form of mean ± standard deviation (Mean ± SD). For data that conformed to normal distribution, t-test was used between two groups, and one-way ANOVA was used between multiple groups. For non-normal data analysis, Wilcoxon rank sum test was used between two groups, and Kruskal-Wallis test was used between multiple groups; paired t-test was used within groups that conformed to normal distribution. Paired Wilcoxon rank sum test was used within non-normal data groups. When P < 0.05, it was considered to be statistically significant. All statistical analyses were performed on SPSS software.

[0038] 3 Targeted lipidomics analysis

[0039] 3.1 Serum sample preparation

[0040] The steps for preparing serum samples for targeted lipidomics are as follows:

[0041] (1) Take 100 μL of clinical serum sample and add 4 μL After deuterium-labeled lipid mass spectrometry mixed standard, 400 μL of pre-cooled methyl tert-butyl ether and 80 μL of methanol solution (MTBE:MeOH:H2O=10:2:2.5, v / v) were added;

[0042] (2) Vortex for 30 seconds and centrifuge at 1000 rcf for 15 minutes at 4°C;

[0043] (3) Transfer 300 μL of supernatant to a new 1.5 mL centrifuge tube and lyophilize;

[0044] (4) Add 100 μL of pre-cooled dichloromethane:methanol solution (1:1, v / v) and vortex for 30 seconds to fully dissolve;

[0045] (5) Take 5 μL of each sample and mix it as the QC sample. The QC sample is tested every 8 samples.

[0046] (6) Serum targeted lipidomics research was performed using UHPLC-Q-TRAP-MS / MS 5500.

[0047] 3.2 HPLC-MS conditions

[0048] Liquid phase conditions: A reversed-phase BEH C8 column (2.1×100 mm, 1.8 μm) from Waters (USA) was used, the column oven temperature was 55°C, the mobile phase: A was ACN:H2O (60:40, v / v), B was IPA:CAN (90:10, v / v), and the gradient elution program was shown in Table 1.

[0049] Table 1 Gradient elution program

[0050]

[0051] Lipid detection was performed using an AB Sciex 5500 mass spectrometer system. The optimized parameters were as follows: ESI temperature: 550°C, ISVF: 5500V (+), -4500V (-), spray gas: 55psi, CUR: 35psi, multiple reaction detection mode, and data acquisition and analysis were performed using OS-Q software (version 1.4.1.20719, AB Sciex, USA).

[0052] 3.3 Data Analysis

[0053] The steps of targeted lipidomics analysis are as follows:

[0054] (1) Peak table extraction: Use Sciex OS software to process the data from targeted lipidomics. Click the Analytics module, set the path of the original file, select any sample file as the quantitative analysis method template, click Workflow, select all wiff files to import, click the Integration module to set the peak integration parameters as shown in Table 2, and save the quantitative method. Then click the New option under Results, set the original file path, select all wiff files to import, select the quantitative analysis method template file established earlier, click Process, and after the analysis is completed, the lipid group peak table data is obtained.

[0055] Table 2 Optimization parameters for lipid peak extraction

[0056]

[0057] (2) Statistical analysis: Data results were presented in the form of mean ± standard deviation (Mean ± SD). For data that met the normal distribution, the t-test was used between two groups, and one-way ANOVA was used between multiple groups. For non-normal data analysis, the Wilcoxon rank sum test was used between two groups, and the Kruskal-Wallis test was used between multiple groups; paired t-test was used within groups that met the normal distribution. Paired Wilcoxon rank sum test was used within groups with non-normal data. When P < 0.05, it was considered to be statistically significant. All statistical analyses were performed using SPSS software.

[0058] (3) Bootstrap verification: Load and prepare the dataset using SPSS software. Select the linear regression model, with the total score of damp syndrome as the dependent variable and CE 18:2 as the independent variable. In the "Linear Regression" dialog box, click "Bootstrap", check the "Perform Bootstrap" option, and set the number of Bootstrap samples to 1000 times. After the analysis is completed, the Bootstrap regression verification results can be obtained.

[0059] II. Experimental Results

[0060] 1. The cohorts included in this study were populations with metabolic syndrome and dyslipidemia discriminated based on Western medicine indicators, collectively referred to as the Metabolic Syndrome (MetS) population. The biochemical indicators of the cohort are shown in Table 3.

[0061] Table 3 Biochemical indicators of patients in the study cohort

[0062]

[0063] In this study, a 26-item assessment scale was used to evaluate the severity of damp syndrome in patients. The screening of characteristic phenotypes is helpful for the diagnosis, prevention, and treatment of damp syndrome. As Figure 1 shown, damp syndrome covers 26 phenotypic information in traditional Chinese medicine clinical practice, involving multiple aspects such as mental state, oral cavity, feces, and metabolism, which is very complex. Based on two key indicators, incidence and severity, with an incidence ≥ 80% and a severity ≥ 30%, 6 characteristic phenotypes of damp syndrome were screened and marked in dark red in the figure. We studied the lipid metabolism in the MetS population from the perspective of in vivo lipid metabolism.

[0064] To control a single variable and尽量 avoid the differences brought by Western medicine indicators, in this study, the population was divided according to the BMI and DS Score of the cohort. Those with 28.5 < BMI < 36 and at the same time, according to the DS Score, the population was divided into low-score Metabolic Syndrome (MetS DS Low ) and high-score Metabolic Syndrome (MetS DS High ). ( Figure 2 )

[0065] We first investigated the feasibility of the serum targeted lipidomics method in clinical samples of metabolic abnormalities. As Figure 3As shown in A, this method can comprehensively cover the detection of three major lipid classes in serum, totaling more than 15 lipid types. Including free lipids: free fatty acids (FFA); neutral lipids: diacylglycerol (DAG), triacylglycerol (TAG), cholesterol ester (CE); phospholipids: sphingomyelin (SM), phosphatidylcholine (PC), lysophosphatidylcholine (LPC), phosphatidylinositol (PI), lysophosphatidylinositol (LPI), phosphatidylethanolamine (PE), lysophosphatidylethanolamine (LPE), phosphatidylglycerol (PG), lysophosphatidylglycerol (LPG), phosphatidyl acid (PA), lysophosphatidyl acid (LPA), phosphatidylserine (PS), lysophosphatidylserine (LPS). In order to construct MetS DS Low and MetS DS High The differences in lipid metabolism profiles between the two groups were evaluated using a supervised data dimensionality reduction method, partial least squares discriminant analysis (PLS-DA). Figure 3 As shown in C, the results showed that there was an obvious classification trend between the two groups, suggesting that the groups could be well distinguished.

[0066] Among the above 15 lipids, FFA, CE, LPA, LPC, LPE, LPI, LPG and SM each contain only one fatty acid chain, so they can be used to determine the unsaturation of fatty acids in lipids. Among them, in CE, polyunsaturated fatty acids (PUFA) account for the largest proportion, and the unsaturation degree is greater than or equal to two. Figure 3 D, MetS DS High The PUFA content in CE in human serum was significantly higher than that in MetS DS Low crowd.

[0067] To further understand which unsaturated CEs contribute most to the significant differences in PUFA, we performed statistical analyses on CEs with two, three, and four unsaturated degrees, respectively. Figure 4 As shown in AD, CE with two unsaturated degrees in MetS DS High The content of CE 18:2 in MetS was significantly increased, and it accounted for 70.6% of serum CE PUFA. Further analysis found that CE with two unsaturations included CE18:2, CE 20:2 and CE 22:2, among which CE 18:2 accounted for a very high proportion, at 99.6%. DS High Significantly increased in the population, such as Figure 4 As shown in EH. Through in-depth analysis, we believe that in MetS DS HighIn the human population, abnormal disturbances in serum CE 18:2 are extremely severe, and even lead to disturbances in the overall serum CE PUFA.

[0068] In order to investigate the correlation between the differences in serum CE PUFA, CE unsaturated PUFA, CE 18:2 and the total score of dampness syndrome, Spearman correlation analysis was performed on them and the total score of dampness syndrome. The results are as follows: Figure 4 J. Figure 4 K statistics show that as the analysis deepens, the correlation coefficient and statistical significance with the total score of dampness syndrome gradually increase. The advantage of modularizing the 26 dampness syndrome-related phenotypes is that the co-synergistic phenotypes can be modularly clustered, thereby achieving information redundancy and reducing mutual interference between individual phenotypes. Figure 4 As shown in Figure 1, modular clustering analysis of the dampness syndrome phenotype can obtain four modules, namely module I (M1), module II (M2), module III (M3), and module IV (M4). The results of the correlation analysis of the dampness syndrome phenotype modules are shown in Figure 1. Figure 4 As shown in Figure 1, from CE PUFA to CE PUFA with two degrees of unsaturation, and then to CE 18:2, the correlation coefficients and statistical significance with the dampness syndrome modules M1, M3, and M4, respectively, also showed an upward trend.

[0069] In summary, our results showed that there was a significant correlation between CE 18:2, a lipid with a degree of unsaturation of two, in serum CE and the total score of wet syndrome. Specifically, with the increase of CE PUFA, CE PUFA with a degree of unsaturation of two, and CE18:2, the correlation coefficient and statistical significance with the total score of wet syndrome gradually increased. This trend was further verified in the modular cluster analysis of wet syndrome phenotype, especially in modules M1, M3, and M4, the correlation coefficient and statistical significance of CE 18:2 showed a significant upward trend. Based on these findings, we believe that CE 18:2 can be used as a potential biomarker to assess the severity of wet syndrome. Therefore, it is recommended to further validate and apply CE18:2 in clinical practice to improve the accuracy of diagnosis and assessment of wet syndrome.

[0070] In order to verify the accuracy of CE 18:2 in assessing the severity of wet syndrome, we used the Bootstrap validation method. Bootstrap validation constructs multiple validation subsets of equal sample size through repeated random sampling with replacement. Estimates were performed separately on each subset to finally obtain a stable and reliable validation model structure. Specifically, we performed 1000 Bootstrap samplings to ensure the robustness and reliability of the results. In these 1000 Bootstrap validations, the significance level of the linear model was 0.001, indicating that the model was highly statistically significant. The results of regression analysis showed (Table 4) that when CE 18:2 increased by one unit, the total score of wet syndrome increased by 0.199 units. This result shows that there is a significant positive correlation between CE 18:2 and the severity of wet syndrome.

[0071] Table 4 Bootstrap sampling linear regression model verification

[0072]

[0073] Through Bootstrap validation, we confirmed the important role of CE 18:2 in assessing the severity of dampness syndrome. The increase of CE18:2 was significantly associated with the increase of the total score of dampness syndrome, indicating that it can be used as an effective biomarker for the severity of dampness syndrome.

Claims

1. Application of cholesterol ester linoleic acid (CE 18:2) as a biomarker in the preparation of products for distinguishing the severity of dampness syndrome in people with metabolic abnormalities.

2. The use according to claim 1, characterized in that: The products described are reagents and test kits.

3. The use according to claim 1, characterized in that: The application is to distinguish the severity of dampness syndrome in people with metabolic abnormalities by quantitatively analyzing the relative abundance of cholesterol ester linoleic acid in samples of people with metabolic abnormalities.

4. The use according to claim 3, characterized in that: The sample is serum.

5. The use according to claim 2, characterized in that: The reagent is a targeted lipidomics analysis reagent.

6. The use of reagents for detecting cholesterol ester linoleic acid in the preparation of products for distinguishing the severity of dampness syndrome in people with metabolic abnormalities.

7. The use according to claim 6, characterized in that: It is a reagent for detecting the relative abundance of cholesterol ester linoleic acid in the serum of people with metabolic abnormalities.

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