Negative ion metabolic markers of thyroid-associated ophthalmopathy, products and uses thereof

Abstract

The application discloses a negative ion metabolic marker of thyroid-associated ophthalmopathy, a product and application thereof. The metabolic marker comprises any one or several of 7alpha, 17alpha-dimethyl-5beta-androstane-3alpha, 17beta-diol, N, N-dimethylphosphatidyl ethanolamine, 15(S)-hydroxyeicosatrienoic acid and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycero-3-phosphatidylcholine. The application provides a kit comprising detection reagents for detecting the negative ion metabolic marker. The application provides a computer program product for thyroid-associated ophthalmopathy. The product provided by the application has good feasibility and accuracy, can effectively evaluate the risk of thyroid-associated ophthalmopathy, and provides a new tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to negative ion metabolic markers, products and their applications for thyroid-associated eye diseases. Background Technology

[0002] Thyroid-associated ophthalmopathy (GROOD) is one of the most common orbital diseases in adults and is an autoimmune disease. Treatment should be based on disease activity, severity, and individual needs: 1) For mild cases, lifestyle interventions (such as smoking cessation and eye care) and thyroid function regulation are the primary approaches, supplemented by artificial tears to relieve dry eye symptoms; 2) For moderate to severe active cases, glucocorticoids (such as intravenous methylprednisolone) are the first-line treatment, combined with immunosuppressants (cyclosporine) or novel targeted drugs (tetumumab) to suppress the inflammatory response; 3) For hormone-resistant patients, orbital radiotherapy can be considered, but the risk of radiation damage should be carefully considered; 4) For stable patients with proptosis, optic nerve compression, or eyelid deformities, surgical procedures such as orbital decompression and extraocular muscle surgery are needed to improve function and appearance.

[0003] While existing treatment methods have formed a system, significant shortcomings remain. For example: 1) In terms of drug therapy, glucocorticoids, as a first-line treatment, are prone to causing side effects such as metabolic abnormalities and osteoporosis with long-term use, and about 30% of patients do not respond well to hormone therapy; 2) Although new targeted drugs (such as tetumumab) have significant efficacy, they are expensive and lack long-term safety data; 3) Radiotherapy is effective for active inflammation, but may aggravate dry eye syndrome or induce radiation cataracts, and is not suitable for adolescent patients; 4) Although surgical treatment can improve late-stage deformities, there are risks of postoperative complications such as diplopia and bleeding, and it cannot reverse the damaged optic nerve function.

[0004] Mass spectrometry (MS) is a highly sensitive analytical technique that identifies the structure and composition of compounds by measuring the mass-to-charge ratio (m / z) of ions. Positive ion mode and negative ion mode are two core and selectable detection modes in mass spectrometry (MS). The detection mode (positive ion mode or negative ion mode) selected by the operator directly affects the sensitivity and accuracy of the analytical results.

[0005] In mass spectrometry, positive ion metabolites carry a positive charge. The positive ion mode is used to detect positively charged ions to complete the analysis. The positive ion mode is more suitable for detecting basic or neutral-to-basic metabolites, such as amino acids (e.g., leucine, lysine), amines (e.g., choline, histamine), and some lipids (e.g., phosphatidylcholine, triglycerides).

[0006] Therefore, it is necessary to screen for acidic or slightly acidic metabolites associated with thyroid-associated eye disease based on negative ion patterns, thereby providing a new approach and method for the diagnosis and treatment of thyroid-associated eye disease. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a negative ion metabolic biomarker, product, and application for thyroid-associated eye disease based on the negative ion mode of mass spectrometry (MS). This biomarker aims to screen for acidic or slightly acidic compounds associated with thyroid-associated eye disease, thereby providing a new approach and method for the diagnosis and treatment of thyroid-associated eye disease.

[0008] The technical solution provided by this invention is as follows:

[0009] In a first aspect, the present invention provides a negative ion metabolic marker for thyroid-associated ophthalmopathy, the negative ion metabolic marker comprising: 7α,17α-dimethyl-5β-androstan-3α,17β-diol and / or N,N-dimethylphosphatidylethanolamine.

[0010] In one possible implementation, the negative ion metabolic marker further includes: 15(S)-hydroxyeicosatotrienoic acid and / or 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine.

[0011] Furthermore, the negative ion metabolic markers include 7α,17α-dimethyl-5β-androstan-3α,17β-diol, N,N-dimethylphosphatidylethanolamine, 15(S)-hydroxyeicosatotrienoic acid, and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine.

[0012] Secondly, the invention provides a reagent for detecting the negative ion metabolic markers described in the first aspect, and its application in the preparation of products related to thyroid-associated eye diseases.

[0013] Thirdly, a kit is provided comprising detection reagents for detecting the negative ion metabolic markers described in the first aspect.

[0014] Fourthly, the kit described in the third aspect is provided for use in the preparation of products for detecting thyroid-associated eye diseases.

[0015] Fifthly, a product related to thyroid-associated eye disease is provided, the product comprising primers, probes, antibodies, aptamers or chips that are specific to the negative ion metabolic markers described in the first aspect.

[0016] Sixthly, a computer program product is provided, the computer program product being used to perform a method for determining the risk of a person being tested having thyroid-associated ophthalmopathy, comprising the following steps:

[0017] To obtain the expression levels of a single negative ion metabolic biomarker in the subjects being tested;

[0018] The expression levels of each individual negative ion metabolic marker were substituted into a binary logistic regression equation to calculate the logarithm y of the subject's advantage. The negative ion metabolic markers include 7α,17α-dimethyl-5β-androstan-3α,17β-diol, N,N-dimethylphosphatidylethanolamine, 15(S)-hydroxyeicosatotrienoic acid, and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine.

[0019] The probability Z of a person being diagnosed with thyroid-associated ophthalmopathy is calculated based on y, where Z = exp(y) / {1 + exp(y)}; and exp(y) is an exponential function of y.

[0020] Based on the comparison between probability Z and reference value, it is determined whether the person being tested is a patient with thyroid-associated ophthalmopathy.

[0021] In the above technical solution, the formula for the binary logistic regression equation is:

[0022] y=A+B1×x1+B2×x2+B3×x3+B4×x4;

[0023] Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the expression level of 7α,17α-dimethyl-5β-androstan-3α,17β-diol X2:(2-bromo-4-fluorophenyl)methylamine; x2 is the expression level of 15(S)-hydroxyeicosatetrienoic acid; x3 is the expression level of 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine; and x4 is the expression level of N,N-dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0).

[0024] In the above technical solution, A is -2.486, B1 is -0.0000001128, B2 is 0.0000004668, B3 is -0.0000001632, and B4 is 0.000001459.

[0025] The beneficial effects of this invention are as follows:

[0026] 1. This invention provides a negative ion metabolic biomarker, product, and application for diagnosing thyroid-associated eye disease (TAOD). It demonstrates good feasibility and accuracy, and is safe and non-invasive. Medical personnel can use the single or multiple negative ion intestinal biomarkers discovered in this application to detect and diagnose patients' saliva to determine whether they have TAOD. This application can serve as a new approach and method for diagnosing TAOD, providing a powerful new tool for clinical diagnosis.

[0027] 2. Based on the negative ion mode in mass spectrometry (MS), this invention has discovered four acidic (or slightly acidic) compounds that can be detected in the saliva of patients and are highly associated with thyroid-associated ophthalmopathy. These include 7α,17α-dimethyl-5β-androstan-3α,17β-diol, N,N-dimethylphosphatidylethanolamine, 15(S)-hydroxyeicosatetrienoic acid, and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine. Through experimental analysis and verification (see Examples 3 and 4), the above four negative ion compounds have high specificity and sensitivity as detection variables. These four negative ion compounds can be used as detection markers to diagnose whether the patient is suffering from thyroid-associated ophthalmopathy or meningioma.

[0028] 3. Further research revealed that among the four negative ion compounds mentioned above, two metabolites showed a significant increasing trend in patients with thyroid eye disease, including N,N-dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0) and 15(S)-hydroxyeicosatotrienoic acid; two metabolites showed a significant decreasing trend in patients with thyroid eye disease, including 7α,17α-dimethyl-5β-androstan-3α,17β-diol and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine.

[0029] 4. This invention also provides a reagent and kit that can use the above-mentioned four negative ion compounds as detection biomarkers to diagnose whether a person being tested has thyroid-associated eye disease. This method is completely non-invasive and highly accurate. Furthermore, the four negative ion intestinal biomarkers can also serve as target metabolites for developing these systems, filling a gap in this field.

[0030] 5. This invention also provides a product for diagnosing whether a person has thyroid-associated ophthalmopathy (TAO). This product can calculate the probability of a patient having TAO based on the expression levels of various acidic compounds, and then compare this probability with reference values, thereby helping medical personnel determine whether the patient has TAO or meningioma. This product has good feasibility and accuracy, effectively distinguishing between TAO and meningioma patients, providing a new tool and approach for clinical diagnosis. Attached Figure Description

[0031] Figure 1 Figure showing the fold change analysis results of metabolite expression levels with significant differences in negative ion mode;

[0032] Figure 2 Box plot showing the significance of differential metabolites in the negative ion mode;

[0033] Figure 3 This is the ROC diagnostic curve. Detailed Implementation

[0034] Mass spectrometry (MS) is a highly sensitive analytical technique that identifies the structure and composition of compounds by measuring the mass-to-charge ratio (m / z) of ions. Its core is to convert sample molecules into gaseous ions and then separate and detect them based on their mass differences.

[0035] Positive ion mode and negative ion mode are two core and selectable detection methods in mass spectrometry (MS). The detection mode (positive ion mode or negative ion mode) selected by the operator directly affects the sensitivity and accuracy of the analysis results.

[0036] The similarities between the positive ion mode and the negative ion mode include: ① The sample pretreatment steps (such as extraction, centrifugation, and filtration of fecal samples) are generally the same in both modes. ② The liquid chromatography (LC) separation conditions (such as column, mobile phase, and gradient) used in both modes are generally the same. ③ Both modes acquire data via mass spectrometry, generating mass spectra and chromatograms for subsequent analysis. ④ The ultimate goal of both modes is to detect and identify metabolites in the sample as comprehensively and accurately as possible.

[0037] The differences between the positive and negative ion modes mentioned above include: ① Different ionization methods. ② Different types of metabolites detected (positive ion mode is more suitable for detecting basic metabolites (such as amino acids, amines, and some lipids); negative ion mode is more suitable for detecting acidic metabolites (such as organic acids, phenols, and some lipids). ③ Different sensitivity and response: some metabolites respond more strongly in positive ion mode, while others respond more strongly in negative ion mode. For example, lipids are generally easier to detect in positive ion mode, while organic acids are easier to detect in negative ion mode. ④ Different background noise and interference: positive ion mode may be more susceptible to matrix effects (such as salts and solvent impurities). Negative ion mode may be more susceptible to interference from carbon dioxide in the air and acidic impurities in the solvent.

[0038] Positive ion metabolites carry a positive charge, and analysis in positive ion mode is performed by detecting positively charged ions; negative ion metabolites carry a negative charge, and analysis in negative ion mode is performed by detecting negatively charged ions. For fecal metabolites, the main differences between positive and negative ion modes are as follows:

[0039] 1. Because alkaline or slightly alkaline metabolites are more easily ionized in positive ion mode, positive ion mode is more suitable for detecting alkaline or slightly alkaline metabolites, such as amino acids (e.g., leucine, lysine), amines (e.g., choline, histamine), and some lipids (e.g., phosphatidylcholine, triglycerides).

[0040] 2. Because acidic or slightly acidic metabolites are more easily ionized in negative ion mode, negative ion mode is more suitable for detecting acidic or slightly alkaline metabolites, such as organic acids (e.g., citric acid, succinic acid), phenols (e.g., caffeic acid, ferulic acid), and some lipids (e.g., fatty acids, phosphatidylserine).

[0041] Thyroid-associated ophthalmopathy (TAT) is one of the most common orbital diseases in adults and is an autoimmune disorder. Most patients have clinical or laboratory findings of thyroid dysfunction, but orbital disease can occur even in patients with normal thyroid function. Thyroid-associated ophthalmopathy is a common cause of unilateral or bilateral proptosis.

[0042] At the treatment level, the treatment should be based on the disease activity, severity, and individual needs: 1) For patients with mild symptoms, the main treatment is lifestyle intervention (such as smoking cessation and eye care) and thyroid function regulation, supplemented by artificial tears to relieve dry eye symptoms; 2) For patients in the moderate to severe active phase, glucocorticoids (such as intravenous methylprednisolone) are the first choice, combined with immunosuppressants (cyclosporine) or novel targeted drugs (tetumumab) to suppress the inflammatory response; 3) For patients resistant to hormones, orbital radiotherapy can be considered, but the risk of radiation damage should be taken seriously; 4) For patients in the stable phase, if there is proptosis, optic nerve compression, or eyelid deformity, surgical means such as orbital decompression surgery and extraocular muscle surgery are needed to improve function and appearance.

[0043] In recent years, the application of targeted drugs (such as IGF-1R inhibitors) has significantly improved the remission rate of exophthalmos, but long-term efficacy still requires more evidence-based support. While existing treatment methods have formed a system, significant shortcomings remain. For example, in terms of drug therapy, glucocorticoids, as a first-line treatment, are prone to causing metabolic abnormalities, osteoporosis, and other side effects with long-term use, and approximately 30% of patients do not respond well to hormone therapy; new targeted drugs (such as tetumab) are highly effective, but are expensive and lack sufficient long-term safety data. Radiation therapy is effective for active inflammation, but may worsen dry eye syndrome or induce radiation-induced cataracts, and is not suitable for adolescent patients. Surgical treatment can improve late-stage deformities, but carries the risk of postoperative complications such as diplopia and bleeding, and cannot reverse damaged optic nerve function.

[0044] In addition, existing therapies generally lack accurate predictive indicators of efficacy, individualized treatment strategies still need improvement, and the problem of high disease recurrence rate (about 20%-30%) has not been fundamentally solved.

[0045] In summary, the existing diagnosis and treatment models urgently need breakthroughs in terms of accuracy, safety, and personalization.

[0046] To assess whether fecal metabolite composition can serve as a predictor of thyroid-associated eye disease (TAOD), this invention collects samples from TAOD patients and healthy individuals, performs metabolomic sequencing, and uses bioinformatics to statistically analyze the sequencing data. This identifies metabolites associated with TAOD, integrates these metabolites with disease information, and maximizes the prediction of whether individuals under testing have TAOD.

[0047] This invention, through metabolomics sequencing, has for the first time discovered four negative ion metabolites. Among them, the expression levels of N,N-dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0) and 15(S)-hydroxyeicosatetrienoic acid metabolites were significantly higher in the thyroid-associated ophthalmopathy (TAO) group than in the healthy group; the expression levels of 7α,17α-dimethyl-5β-androstan-3α,17β-diol and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine metabolites were significantly lower in the TAO group than in the healthy group, indicating that these metabolites can serve as reference factors for TAO. These four compounds acquire a negative charge after ionization and are therefore also referred to as negative ion metabolites.

[0048] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods not specifically described in the embodiments are generally performed under conventional conditions.

[0049] The following provides a unified description of the four negative ion metabolites mentioned in the examples.

[0050] 7α,17α-Dimethyl-5β-androstane-3α,17β-diol (7alpha,17alpha-Dimethyl-5beta-androstane-3alpha,17beta-diol):

[0051] Molecular formula: C 21 H 36 O2

[0052] Structural formula:

[0053] 15(S)-Hydroxyeicostrienoic acid (Molecular formula: C) 20 H 34 O3

[0054] Structural formula:

[0055] 1-Tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycero-3-phosphocholine

[0056] Molecular formula: C 40 H 78 NO8P

[0057] Structural formula:

[0058] N,N-Dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0) (PE-NMe2(18:3(6Z,9Z,12Z) / 20:0))

[0059] Molecular formula: C 45 H 84 NO8P

[0060] Structural formula:

[0061] Example 1: Sample Collection

[0062] Stool samples were collected from 40 patients with thyroid-associated ophthalmopathy and 46 healthy individuals:

[0063] The inclusion criteria for the thyroid-associated ophthalmopathy group are as follows: 1. Age > 18 years; 2. Clinical diagnosis of thyroid-associated ophthalmopathy.

[0064] The exclusion criteria for the thyroid-associated ophthalmopathy group are as follows: 1. Body mass index (BMI) > 28; 2. History of gastrointestinal surgery; 3. Pregnancy or lactation.

[0065] The inclusion criteria for the healthy group were as follows: 1. Age > 18 years; 2. No diabetes or other metabolic diseases; 3. No other gastrointestinal or other neurological diseases; 4. No other immune system diseases or not in an immunodeficient state; 5. No use of antibiotics (e.g., neomycin, rifaximin) or probiotics / prebiotics in the three months prior to and during the study. The exclusion criteria for the healthy group were as follows: 1. Body mass index (BMI) > 28; 2. History of gastrointestinal surgery; 3. Pregnancy or lactation; 4. Thyroid function and antibodies exceeding the normal range. The above data were collected from stool samples collected by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology.

[0066] Table 1 Sample Information Table

[0067]

[0068] Example 2: Sample Extraction

[0069] Using a sterile sampling spoon, collect three pea-sized samples from different parts of the middle to posterior section of the feces (the surface of the feces contains exfoliated intestinal mucosal cells, and the peripheral feces are at risk of E. coli contamination). Place the collected feces into a sterile cryovial and label the sample information. Each sample should be 200 mg, and at least one tube should be used.

[0070] Example 3: Biomarker Screening Method and Experimental Results

[0071] 3.1 Experimental Methods

[0072] S1: After the sample is slowly thawed at 4℃, an appropriate amount of sample is added to a pre-cooled methanol / acetonitrile / water solution (2:2:1, v / v), vortexed, sonicated at low temperature for 30 min, allowed to stand at -20℃ for 10 min, centrifuged at 14000 g at 4℃ for 20 min, the supernatant is taken and vacuum dried, and 100 μL of acetonitrile water solution (acetonitrile:water = 1:1, v / v) is added to reconstitute the sample for mass spectrometry analysis, vortexed, centrifuged at 14000 g at 4℃ for 15 min, and the supernatant is injected for analysis.

[0073] S2: Samples were separated using a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system with a HILIC column; column temperature 25℃; flow rate 0.3 mL / min; injection volume 2 μL; mobile phase composition A: water + 25 mM ammonium acetate + 25 mM ammonia, B: acetonitrile; gradient elution program as follows: 0 --- 1.5 min, 98% B; 1.5-12 min, B linearly changes from 98% to 2%; 12-14 min, B maintains at 2%; 14-14.1 min, B linearly changes from 2% to 98%; 14.1-17 min, B maintains at 98%; throughout the analysis, samples were placed in an autosampler at 4℃. To avoid the influence of instrument signal fluctuations, samples were analyzed continuously in a randomized order. QC (Quality Control) samples were inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of experimental data. The QC sample is an equal mixture of all the samples being tested. The consistency of the QC sample is used to determine the stability of the instrument in sample testing.

[0074] S3: Primary and secondary spectra of the samples were acquired using a Q Exactive series mass spectrometer. After separation using a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system, the samples were analyzed by mass spectrometry using a Q Exactive series mass spectrometer (Thermo), employing both positive and negative electrospray ionization (ESI) modes. The ESI source and mass spectrometer settings were as follows: Auxiliary heating gas 1 (Gas1): 60, Auxiliary heating gas 2 (Gas2): 60, Curtain gas (CUR): 30 psi, Ion source temperature: 600℃, Spray voltage (ISVF): ±5500 V (both positive and negative modes); Primary mass-to-charge ratio detection range: 80-1200 Da, resolution: 60000, cumulative scan time: 100 ms; Secondary mass-to-charge ratio was acquired using a segmented acquisition method, with a scan range of 70-1200 Da, secondary resolution: 30000, cumulative scan time: 50 ms, dynamic exclusion time: 4 s.

[0075] S4: The raw data was converted to .mzXML format using the tool ProteoWizard, and then peak alignment, retention time correction, and peak area extraction were performed using XCMS software. The data extracted by XCMS were first subjected to metabolite structure identification and data preprocessing, then experimental data quality assessment, and finally data analysis.

[0076] 3.2 Statistical Table of Verification Results

[0077] The relevant statistical data of the validation set markers are shown in Tables 2 and 3. The mean determines the central location of the data distribution, while the difference factor log2 (FC) and variable weight value VIP reflect the degree of data difference.

[0078] Table 2 - Statistical data related to negative ion metabolic biomarkers in the validation set

[0079]

[0080] In Table 2 above, the mean refers to the mean expression level of the corresponding metabolic marker. The fold change (log2) is used to measure the relative fold change between two groups of data. Logarithmic transformation can convert the fold relationship into a more easily analyzed and compared value, which helps to identify significant differences in the data. The p-value is a statistic calculated using the formula of the rank-sum test. The variable weight value (VIP) is an important indicator for assessing the importance of metabolite variables and is usually used when using multivariate statistical methods such as partial least squares discriminant analysis (PLS-DA).

[0081] Table 3 - Relevant expression levels of biomarkers in the validation set

[0082]

[0083] Example 4: Data Analysis

[0084] 4.1 OPLS-DA analysis for biomarker screening

[0085] Orthogonal partial least squares discriminant analysis (OPLS-DA) showed significant differences in the metabolic profiles between the thyroid-associated ophthalmopathy (TAO) group and the healthy group. In this study, the criteria for screening significantly different metabolites were log2(FC) > 1, OPLS-DA VIP > 1, and P-value < 0.05. A total of four significantly different metabolites with negative ion patterns were screened in both TAO and healthy individuals: 7α,17α-dimethyl-5β-androstan-3α,17β-diol, N,N-dimethylphosphatidylethanolamine, 15(S)-hydroxyeicosatotrienoic acid, and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine. In thyroid-associated ophthalmopathy (TAO), upregulated metabolites include N,N-dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0) and 15(S)-hydroxyeicosatotrienoic acid, a total of two; in thyroid-associated ophthalmopathy (TAO), downregulated metabolites include 7α,17α-dimethyl-5β-androstan-3α,17β-diol and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine, a total of two. Figure 1 As shown.

[0086] 4.2 Rank-sum test

[0087] The biomarkers mined based on OPLS-DA analysis were then subjected to a rank-sum test, and the results were as follows: Figure 2 As shown. From Figure 2 It was found that the four metabolites of the negative ion mode showed significant differences between the disease and healthy groups. Note: This means P < 0.05; This means P < 0.01; This represents P < 0.001. P is the significance level (P value).

[0088] As shown in Tables 2 and 3, the four negative ion differential metabolites in this embodiment serve as detection markers that are completely non-invasive and highly accurate. This application utilizes a larger sample size for validation, resulting in better predictive performance for thyroid-associated eye disease (TAIDD), and employs metabolomics sequencing to improve diagnostic accuracy and reliability. This provides a means of metabolite detection for TAIDD patients, allowing for the identification of the metabolite source of their TAIDD and providing a basis for subsequent treatment.

[0089] Based on the above test data, this invention discovered four metabolites. The negative ion metabolites were associated with patients with thyroid-associated eye disease. Among the four metabolites, five showed a significant increasing trend in patients with thyroid-associated eye disease, while one of the four metabolites remained basically unchanged or showed an increasing trend in healthy individuals.

[0090] 4.3 Establishment of Logistic Regression Model

[0091] Using the training set data of four metabolites in the negative ion mode in RStudio software (R software for short), a binary logistic regression algorithm was used to construct the logistic regression formula of the training model:

[0092] y=-2.486-0.0000001128×x1+0.0000004668×x2-0.0000001632×x3+0.000001459×x4

[0093] Where y is the logarithm of the dominance of the individuals being tested;

[0094] Furthermore, the probability Z that the person being tested is healthy is calculated using the following formula: Z = exp(y) / {1 + exp(y)}

[0095] Where Z is the probability that the subject is a healthy person, exp(y) is the natural exponential function of y, and x1-x4 are the expression levels of the four metabolites;

[0096] X1: 7α,17α-dimethyl-5β-androstan-3α,17β-diol;

[0097] X2: (2-Bromo-4-fluorophenyl)methylamine;

[0098] X3: 15(S)-hydroxyeicosatotrienoic acid;

[0099] X4: 1-Tetradecanoyl-2-[(9Z)-Octadecenoyl]-sn-glycerol-3-phosphatidylcholine;

[0100] X5:N,N-Dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0).

[0101] 4.4 ROC Validation Results

[0102] Based on the data in Tables 2 and 3 above, the receiver operating characteristic (ROC) curve analysis was performed on the binary logistic regression model to obtain the cutoff value (optimal cutoff value).

[0103] The RStudio software was used to calculate specificity and sensitivity and to plot the ROC curve. The software first calculates the threshold of the actual measured value, and then calculates the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold. Specificity (true negative rate) = TN / (TN+FP), and sensitivity (true positive rate) = TP / (TP+FN). The ROC curve can be constructed by subtracting specificity and sensitivity from 1. The integral of the ROC curve is AUC (Area Under Curve).

[0104] To calculate the specificity and sensitivity of an indicator, first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of the indicator.

[0105] The expression levels of individual metabolite biomarkers were directly analyzed using receiver operating characteristic (ROC) curves to determine the cutoff value. The ROC curve for the predicted score is shown below. Figure 3 As shown in Table 4, the AUC, optimal cutoff value, sensitivity, and specificity of the mimicry markers and prediction scoring methods for each single metabolite are presented.

[0106] In the above method, analysis revealed that two metabolites showed a significant increasing trend in patients with thyroid eye disease: N,N-dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0) and 15(S)-hydroxyeicosatotrienoic acid; two metabolites showed a significant decreasing trend in patients with thyroid eye disease: 7α,17α-dimethyl-5β-androstan-3α,17β-diol and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine. Furthermore, validation set data showed that ROC curve analysis demonstrated high specificity and sensitivity as detection variables. Therefore, these metabolites can be used as diagnostic markers for patients with thyroid-associated eye disease. Using these four negative ion metabolites as diagnostic markers is completely non-invasive and highly accurate.

[0107] Table 4. Results of ROC diagnostic curves in negative ion mode

[0108]

[0109] Example 5

[0110] Based on the above embodiments, this embodiment provides a computer program product for thyroid-associated eye disease. The computer program product is used to perform a method for diagnosing the risk of a subject having thyroid-associated eye disease, including the following steps:

[0111] 1) Obtain the expression level of each single negative ion metabolite marker in the feces of the test subjects; the single negative ion metabolite markers include 7α,17α-dimethyl-5β-androstane-3α,17β-diol, N,N-dimethylphosphatidylethanolamine PE-NMe2{18:3(6Z,9Z,12Z) / 20:0}, 15(S)-hydroxyeicostrienoic acid, and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycero-3-phosphatidylcholine.

[0112] 2) Calculate the logarithm y of the dominance of the test object based on the binary logistic regression equation;

[0113] y=-2.486-0.0000001128×x1+0.0000004668×x2-0.0000001632×x3+0.000001459×x4;

[0114] In the formula, x1 is the expression level of 7α,17α-dimethyl-5β-androstan-3α,17β-diol X2:(2-bromo-4-fluorophenyl)methylamine; x2 is the expression level of 15(S)-hydroxyeicosatetrienoic acid; x3 is the expression level of 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine; x4 is the expression level of N,N-dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0);

[0115] 3) Calculate the probability Z that the subject is a healthy person based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0116] 4) Based on the comparison between the probability Z-value of healthy individuals and the reference value, diagnose or predict the risk of the subject of the test having thyroid-associated eye disease.

[0117] In practice, a Z-score greater than 0.5 indicates a low probability of the subject having thyroid-associated eye disease (TAD); a Z-score less than 0.5 indicates a higher probability; and a Z-score of 0.5 suggests the subject may be healthy or have TAD, requiring further testing using methods such as complete blood counts and assessment of physical signs. Furthermore, the closer the Z-score is to 0.5, the more necessary additional testing methods become.

[0118] Example 6

[0119] Based on the product and method of Example 5, the health probability of healthy individuals and patients with thyroid-associated ophthalmopathy was examined and verified. The specific steps are as follows:

[0120] 1) Collect fecal samples from the individuals to be tested and detect the expression level of a single negative ion metabolic marker in the feces; wherein, the single negative ion metabolic marker includes any one of fulvestrant-9-sulfone, hypericin, cassia aurantium, 3-O-acetyl-16α-hydroxysalicylic acid, leiodin, and pimelic acid 1-[2-[(2-carboxyphenyl)methylene]hydrazone];

[0121] 2) Calculate the logarithm y of the dominance of the test object based on the binary logistic regression equation;

[0122] y=-2.486-0.0000001128×x1+0.0000004668×x2-0.0000001632×x3+0.000001459×x4;

[0123] In the formula, x1 is the expression level of 7α,17α-dimethyl-5β-androstan-3α,17β-diol X2:(2-bromo-4-fluorophenyl)methylamine; x2 is the expression level of 15(S)-hydroxyeicosatetrienoic acid; x3 is the expression level of 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine; x4 is the expression level of N,N-dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0);

[0124] 3) Calculate the probability Z that the subject is a patient with thyroid-associated ophthalmopathy based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y;

[0125] After processing, the probability Z of patients with thyroid-associated ophthalmopathy can be expressed as:

[0126] ;

[0127] 4) Based on the comparison between the probability Z-value of healthy individuals and the reference value, diagnose or predict the risk of the subject of the test having thyroid-associated eye disease.

[0128] In practice, a Z-score greater than 0.5 indicates a low probability of the subject having thyroid-associated eye disease (TAOD); a Z-score less than 0.5 indicates a higher probability; and a Z-score of 0.5 suggests the subject could be healthy or have TAOD, requiring further testing using methods such as complete blood counts and assessment of physical signs. Furthermore, the closer the Z-score is to 0.5, the more necessary additional testing methods become.

[0129] Example 7

[0130] Based on the description of embodiments 1 to 5 above, it can be seen that the predictive effect of the markers selected in this application is good. Medical staff can use one or more of the four negative ion intestinal markers as detection and diagnostic criteria to diagnose whether the person to be tested has thyroid-associated eye disease. Medical staff can also combine the four negative ion intestinal markers together as markers to detect and diagnose the sample to diagnose whether the person to be tested has thyroid-associated eye disease.

[0131] Therefore, this embodiment also provides a reagent related to thyroid-associated eye disease, which can be used in the preparation of products related to thyroid-associated eye disease to diagnose whether a person to be tested has thyroid-associated eye disease; at the same time, the negative ion intestinal marker can be selected from the four negative ion intestinal markers discovered in this application, that is, the negative ion intestinal marker in the test reagent can include one or more of 7α,17α-dimethyl-5β-androstan-3α,17β-diol, N,N-dimethylphosphatidylethanolamine, 15(S)-hydroxyeicosatetrienoic acid and 1-tetradecanoyl-2-[(9Z)-octadecenoyl]-sn-glycerol-3-phosphatidylcholine.

[0132] Example 8

[0133] This embodiment also provides a kit that may contain the detection reagent described in Embodiment 7 to diagnose whether a person to be tested has thyroid-associated ophthalmopathy. The limitations and technical solutions of the kit in this application can be found in the description of Embodiment 7 above, and will not be repeated here. Similarly, the above kit can also be used in the preparation of products for detecting thyroid-associated ophthalmopathy, and will not be repeated here.

[0134] Example 9

[0135] This application also provides a product related to thyroid-associated eye disease to diagnose whether a person to be tested has thyroid-associated eye disease; the product is specific for one or more of the four negative ion intestinal biomarkers discovered in this application, and the product includes primers, probes, antibodies, aptamers or chips.

[0136] As can be seen from the descriptions of Examples 1-5 and from conventional methods in the art, it should be feasible for those skilled in the art to produce corresponding, specific products (primers, probes, antibodies, aptamers, or chips, etc.) when using the seven newly discovered negative ion intestinal biomarkers as negative ion intestinal biomarkers, and will not be elaborated here.

[0137] Conclusion and explanation:

[0138] 1. Predictive efficacy of single metabolites: The anionic metabolite N,N-dimethylphosphatidylethanolamine (18:3(6Z,9Z,12Z) / 20:0) showed the highest predictive efficacy for thyroid-associated ophthalmopathy, with an AUC value of approximately 93.06%. This was followed by 7α,17α-dimethyl-5β-androstan-3α,17β-diol, with an AUC value of approximately 90.28%.

[0139] 2. Predictive effect of combined biomarkers of four metabolites: The biomarker of negative ions (a biomarker formed by the combination of multiple biomarkers) has the highest predictive effect, with an AUC value of approximately 95.83%, which can provide a more accurate prediction of the probability of thyroid-associated eye disease.

[0140] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the invention.

Claims

1. The application of a reagent for detecting negative ion metabolic markers in the preparation of products for diagnosing thyroid-associated ophthalmopathy, characterized in that: The negative ion metabolic markers include 7α,17α-dimethyl-5β-androstan-3α,17β-diol and / or N,N-dimethylphosphatidylethanolamine, wherein: The structural formula of the 7α,17α-dimethyl-5β-androstan-3α,17β-diol is: ； The structural formula of the N,N-dimethylphosphatidylethanolamine is: 。 2. The application of a reagent kit in the preparation of products for diagnosing thyroid-associated ophthalmopathy, characterized in that: The kit contains detection reagents for detecting the negative ion metabolic biomarkers of claim 1.

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