Biomarker for auxiliary diagnosis or diagnosis of Graves eye disease and / or Graves disease

Abstract

The present invention provides a biomarker for aided diagnosis or diagnosis of Graves's eye disease and / or Graves's disease, by means of which the Graves's eye disease and / or Graves's disease is identified, the biomarker comprising a tryptophan metabolite, the tryptophan metabolite comprises indolepropionic acid, indole-3-lactic acid and / or indoleacetic acid; the innovative research finds that compared with a healthy control group, the levels of indolepropionic acid, indole-3-lactic acid and indoleacetic acid in serum of patients suffering from Graves disease and Graves eye disease are remarkably reduced, and the level of indoleacetic acid in the patients suffering from Graves eye disease is further reduced compared with the level of indoleacetic acid in the patients suffering from Graves eye disease; the tryptophan metabolite, including indolepropionic acid, indole-3-lactic acid and / or indoleacetic acid, can be used for auxiliary diagnosis or diagnosis of Graves's eye disease and / or Graves's disease, and can be used as a biomarker for auxiliary diagnosis or diagnosis of Graves's eye disease and / or Graves's disease.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a biomarker for assisting in the diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease. Background Art

[0002] Graves' orbitopathy (GO), also known as thyroid-related eye disease or thyroid eye disease, is essentially an organ-specific autoimmune disease that is closely related to thyroid dysfunction. This disease is more common in patients with hyperthyroidism, but it can also be found in individuals with normal or hypothyroid function. The typical symptoms of GO, such as eyelid retraction, proptosis, and decreased vision, pose a significant burden on society and families, and significantly affect the health and quality of life of patients. Despite this, the exact pathogenesis of GO is not yet fully understood. Currently, treatment options are limited, and clinical treatment mainly relies on symptomatic treatment. Therefore, from the perspective of clinical practice, it is particularly important to discover disease-related biomarkers to identify patients with active GO. A deep understanding of the pathophysiological mechanisms of GO will help reveal new therapeutic targets that can complement existing treatments and may provide personalized treatment options for GO patients.

[0003] The course of GO goes through two stages, which are divided into active and inactive stages. Clinical manifestations: The active stage is mainly characterized by inflammation, manifested by periorbital pain, eyelid redness and swelling, conjunctival congestion, exophthalmos, diplopia and other symptoms; the symptoms of the inactive stage are relatively stable, and the inflammation is alleviated, but there may be sequelae such as exophthalmos, diplopia, and eyelid retraction. The treatment strategies for the active and inactive stages are different. Generally, active anti-inflammatory and immunosuppressive treatments are the main treatments in the active stage, and surgical correction is the main treatment in the inactive stage. At present, the 2021 "EUGOGO Clinical Guidelines for Thyroid Eye Disease" are used to diagnose the active and inactive stages of GO. The above method assesses the activity through the clinical activity score CAS, which is somewhat subjective. Therefore, researchers are actively looking for more objective and accurate biomarkers to assist in the diagnosis of GO and the judgment of active GO. These biomarkers may come from blood, urine or other body fluids. By analyzing the changes in their content or proportion, they can more accurately reflect the pathological state of GO patients. However, there are no relevant research reports on the application of tryptophan metabolites in the diagnosis of GO course.

[0004] The pathogenesis of GO mainly involves the activation of orbital fibroblasts (OFs), infiltration of immune cells, and deposition of hyaluronic acid, which together lead to the expansion of orbital tissues and hypertrophy of muscles. Activated OFs can differentiate into myofibroblasts and produce extracellular matrix components, which play a key role in the inflammatory response and tissue remodeling process of GO. So far, there is no research on the effect of tryptophan metabolites on OFs in GO. Summary of the Invention

[0005] Therefore, the first technical problem to be solved by the present invention is to provide a biomarker for the auxiliary diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease, and to identify Graves' ophthalmopathy and / or Graves' disease through the biomarker.

[0006] The second technical problem to be solved by the present invention is to provide the use of tryptophan metabolites in the preparation of drugs for the auxiliary treatment or treatment of Graves' ophthalmopathy, providing a new treatment plan for Graves' ophthalmopathy.

[0007] For this purpose, the present invention provides the following technical solutions:

[0008] An embodiment of the present invention provides a biomarker for the auxiliary diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease, the biomarker comprising tryptophan metabolites, the tryptophan metabolites comprising indolepropionic acid, indole-3-lactic acid, and / or indoleacetic acid.

[0009] An embodiment of the present invention provides the use of a product for detecting the biomarker for the auxiliary diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease in the preparation of a product for the auxiliary diagnosis of the course of Graves' ophthalmopathy.

[0010] In some embodiments, the course of Graves' ophthalmopathy includes active Graves' ophthalmopathy and inactive Graves' ophthalmopathy;

[0011] and / or, the biomarker comprises tryptophan metabolites, the tryptophan metabolites comprising indoleacetic acid.

[0012] An embodiment of the present invention provides the use of a product for detecting the biomarker for the auxiliary diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease in the preparation of a product for the auxiliary differentiation or differentiation of Graves' ophthalmopathy and Graves' disease.

[0013] In some embodiments, the product includes reagents, test strips, reagent kits, or instruments.

[0014] An embodiment of the present invention provides a product for auxiliary diagnosis or diagnosis of the course of Graves' ophthalmopathy. The product is a system for auxiliary diagnosis or diagnosis of the course of Graves' ophthalmopathy, including:

[0015] A detection unit, which is used to obtain the content of the biomarker in the sample to be tested; the biomarker is the biomarker described above;

[0016] A judgment unit, which is connected to or wirelessly connected to the detection unit. The judgment unit is used to diagnose or assist in diagnosing the course of Graves' ophthalmopathy based on the content of the biomarker in the sample to be tested.

[0017] In some embodiments, it includes: comparing the content of indoleacetic acid in patients with Graves' ophthalmopathy in the inactive stage, and judging the course of Graves' ophthalmopathy if the content of indoleacetic acid in patients with Graves' ophthalmopathy in the active stage is reduced;

[0018] And / or, the sample to be tested in the detection unit is selected from blood, serum, and plasma.

[0019] An embodiment of the present invention provides the use of tryptophan metabolites in the preparation of a drug for auxiliary treatment or treatment of Graves' ophthalmopathy. The tryptophan metabolites include indolepropionic acid, indole-3-lactic acid, and / or indoleacetic acid.

[0020] An embodiment of the present invention provides a pharmaceutical preparation for auxiliary treatment or treatment of Graves' ophthalmopathy, which contains at least one of indolepropionic acid, indole-3-lactic acid, and / or indoleacetic acid as an active ingredient, and pharmaceutically acceptable excipients.

[0021] In some embodiments, the pharmaceutically acceptable excipients include fillers, binders, disintegrants, lubricants, sweeteners, flavoring agents, preservatives, surfactants, stabilizers, solubilizers, coloring agents, antioxidants, pH regulators, buffers, chelating agents, thickeners, coating materials, or film-forming materials.

[0022] In some embodiments, the dosage form of the drug includes liquid preparations, semi-solid preparations, or solid preparations.

[0023] In some embodiments, the dosage form of the drug includes:

[0024] Oral solutions, tablets, capsules, granules, pills, powders, syrups, ointments, creams, gels, suppositories, or injections.

[0025] The technical solution of the present invention has the following advantages:

[0026] 1. The present invention provides a biomarker for the auxiliary diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease. Graves' ophthalmopathy and / or Graves' disease is identified by means of the biomarker. The biomarker comprises tryptophan metabolites, and the tryptophan metabolites comprise indolepropionic acid, indole-3-lactate and / or indoleacetic acid. The innovative research of the present invention finds that, compared with the control group, the levels of indolepropionic acid (IPA), indole-3-lactate (ILA) and indoleacetic acid (IAA) in the sera of patients with Graves' disease (GD) and GO are significantly decreased, and the IAA level in GO patients is further decreased compared with that in GD patients. It shows that the tryptophan metabolites, including indolepropionic acid, indole-3-lactate and / or indoleacetic acid, can be used for the auxiliary diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease and can be used as a biomarker for the auxiliary diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease.

[0027] 2. The present invention provides the use of a product for detecting the biomarker for the auxiliary diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease in the preparation of a product for the auxiliary diagnosis or diagnosis of the course of Graves' ophthalmopathy. The innovative research of the present invention finds that the serum IAA level of active GO patients is significantly lower than that of inactive patients. The serum IAA level of GO patients is significantly negatively correlated with CAS. In view of the significant difference in serum IAA concentration between inactive and active GO patients and the significant correlation between IAA and CAS score and TRAb in GO patients, it is verified that IAA can predict the disease activity (active and inactive phases) of GO, and IAA has excellent reliability in identifying active GO patients. The above shows that the tryptophan metabolite, including indoleacetic acid, can be used for the auxiliary diagnosis or diagnosis of the course of Graves' ophthalmopathy and can be used as a biomarker for the auxiliary diagnosis or diagnosis of the course of Graves' ophthalmopathy.

[0028] 3. The present invention provides the use of tryptophan metabolites in the preparation of a drug for the auxiliary treatment or treatment of Graves' ophthalmopathy. The tryptophan metabolites comprise indolepropionic acid, indole-3-lactate and / or indoleacetic acid. The research of the present invention finds that in vitro, IPA, ILA and IAA alleviate TNFα-induced inflammation and proliferation of orbital fibroblasts by inhibiting the Akt signaling pathway. Therefore, IPA, ILA and IAA play a protective role in GO by regulating the inflammation and proliferation of orbital fibroblasts, indicating that they may become potential therapeutic targets for the treatment of GO and providing a new treatment plan for Graves' ophthalmopathy. Description of the Drawings

[0029] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0030] Figure 1 It is the species distribution histogram of the top 30 intestinal flora at the genus level in the results of intestinal flora related to tryptophan metabolism in GO patients in Example 1 of the present invention. Among them, N on the abscissa represents the healthy control group, and T represents the GO group;

[0031] Figure 2 It is the taxonomic heatmap of the abundances of 35 genera at the genus level in the results of intestinal flora related to tryptophan metabolism in GO patients in Example 1 of the present invention; Control: healthy volunteers; GO: Graves' orbitopathy;

[0032] Figure 3 It is the differences in tryptophan metabolite-producing bacteria at the genus, order, class, and phylum levels in the results of intestinal flora related to tryptophan metabolism in GO patients in Example 1 of the present invention; Control: healthy volunteers; GO: Graves' orbitopathy;

[0033] Figure 4 It is the partial least squares discriminant analysis (PLS-DA) of GO, GD, and the healthy control group presented in the positive ion mode in the results of the serum metabolomics profile of GO patients in Example 1 of the present invention; Control: healthy volunteers; GD: Graves' disease; GO: Graves' orbitopathy; ILA: indole-3-lactic acid; IAA: indoleacetic acid;

[0034] Figure 5 It is the volcano plot of the metabolite profiles of GO and GD patients and the control group in the results of the serum metabolomics profile of GO patients in Example 1 of the present invention; The blue and red dots respectively represent the reduction and enrichment of metabolites; The dotted line represents the critical p-value threshold of 0.05; Control: healthy volunteers; GD: Graves' disease; GO: Graves' orbitopathy; ILA: indole-3-lactic acid; IAA: indoleacetic acid;

[0035] Figure 6This is a bubble chart of the KEGG enrichment analysis of the results of the serum metabolomics profile of GO patients in Example 1 of the present invention. The bubble size corresponds to the number of metabolites enriched in a certain pathway, and the color gradient indicates the degree of enrichment; the GO and GD patient groups were compared with the control group respectively; Control: healthy volunteers; GD: Graves' disease; GO: Graves' ophthalmopathy;

[0036] Figure 7 This shows the differences in metabolite profiles among the control group, GD group and GO group in the results of the serum metabolomics profile of GO patients in Example 1 of the present invention; *p<0.05; ****P<0.0001, compared with the control group; Control: healthy volunteers; GD: Graves' disease; GO: Graves' ophthalmopathy; ILA: indole-3-lactic acid; IAA: indoleacetic acid;

[0037] Figure 8 This is the result of the serum tryptophan metabolites of GO patients in Example 1 of the present invention; a-c, the levels of IPA (a), ILA (b) and IAA (c) in the sera of GD patients (n = 145), GO patients (n = 156) and healthy control group (n = 100); the data are expressed as mean ± standard deviation. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, compared with the control group; ##P<0.01, compared with the GD group; d, comparison of the serum IAA concentrations of active (n = 62) and inactive (n = 94) GO patients; the data are expressed as mean ± standard deviation; ****P<0.0001, compared with inactive GO patients; e, Spearman correlation analysis was performed between the serum IAA concentrations and clinical activity score (CAS) of 156 GO patients; P<0.05 was considered statistically significant; f, Receiver Operating Characteristic Curve (ROS) analysis; Control: healthy volunteers; GD: Graves' disease; GO: Graves' ophthalmopathy; IPA: indolepropionic acid; ILA: indole-3-lactic acid; IAA: indoleacetic acid; CAS: clinical activity score.

[0038] Figure 9Results of proliferation and inflammation of orbital fibroblasts (OFs) from GO patients and healthy controls in Example 1 of the present invention; a, Orbital fibroblasts (OFs) from control individuals and GO patients at different passages were seeded at the same density and allowed to adhere overnight. The proliferation activity of OFs was evaluated by the CCK-8 method; n = 6, data are presented as mean ± standard error. Con-OFs: OFs from control population; GO-OFs: OFs from GO patients; b-c, Orbital fibroblasts (OFs) from control individuals and GO patients at passage 3 were seeded at the same density and allowed to adhere overnight. The inflammation level of OFs was evaluated by Western blot (WB) method; representative images are shown in (b); quantitative data are shown in (c). n = 4. Data are presented as mean ± standard error;

[0039] Figure 10 Results of the inhibition of TNFα-induced proliferation of human orbital fibroblasts (OFs) by IPA, ILA and IAA in Example 1 of the present invention; a-c, As described in the Materials and Methods section, human orbital fibroblasts (OFs) were treated with different concentrations of 3 tryptophan metabolites or an equal volume of solvent (DMSO) for 24 hours (h). The proliferation activity of OFs was evaluated by the CCK-8 method. The effects of IPA (a), ILA (b) and IAA (c) on proliferation activity are expressed as relative cell viability. Within the selected working concentration range, compared with the control group, these 3 tryptophan metabolites all showed a dose-dependent decrease in cell proliferation activity. n = 5. Data are presented as mean ± standard error. *P < 0.05, **P < 0.01, ****P < 0.0001, compared with control group cells; d-f, Human orbital fibroblasts (OFs) were treated with 20 ng / ml recombinant human tumor necrosis factor α (rHu TNFα) or an equal volume of solvent, and IPA (50 μM), ILA (0.5 mM) and IAA (100 μM) or an equal volume of solvent for 24 hours. The proliferation activity of OFs was evaluated by the CCK-8 method. The effects of IPA (d), ILA (e) and IAA (f) on TNFα-induced proliferation of OFs are expressed as relative cell viability. n = 5. Data are presented as mean ± standard error. ****P < 0.0001, compared with rHu TNFα + control group cells;

[0040] Figure 11 Results of the significant induction of proliferation of human orbital fibroblasts (OFs) by TNFα in Example 1 of the present invention; Human orbital fibroblasts (OFs) were treated with 20 ng / ml recombinant human tumor necrosis factor α (rHu TNFα) or an equal volume of solvent for 24 hours. The proliferation activity of OFs was evaluated by the CCK-8 method. n = 6. Data are presented as mean ± standard error. ****P < 0.0001, compared with control group cells;

[0041] Figure 12 In Example 1 of the present invention, TNFα significantly induced inflammation in human orbital fibroblasts (OFs); a-b, human orbital fibroblasts (OFs) were treated with 20 ng / ml recombinant human tumor necrosis factor α (rHu TNFα) or an equal volume of solvent for 24 hours, and the inflammatory level of OFs was evaluated by Western blotting (WB). Representative images are shown in (a); quantitative data are shown in (b). n = 3. Data are expressed as mean ± standard error. *P < 0.05, **P < 0.01, compared with control group cells;

[0042] Figure 13 In Example 1 of the present invention, IPA, ILA, and IAA alleviated TNFα-induced inflammation in human orbital fibroblasts (OFs); a-b, human orbital fibroblasts (OFs) were simultaneously treated with 20 ng / ml recombinant human tumor necrosis factor α (rHu TNFα) or an equal volume of solvent, and IAA (100 μM), IPA (50 μM), or ILA (0.5 mM) or an equal volume of solvent for 24 hours. The inflammatory level of OFs was evaluated by Western blotting (WB). IAA, IPA, and ILA significantly alleviated the levels of pStat3 and TNFα stimulated by rHu TNFα. Representative images are shown in (a), and quantitative data are shown in (b). n = 3. Data are expressed as mean ± standard error. *P < 0.05, **P < 0.01, ***P < 0.001, compared with rHu TNFα + control group cells;

[0043] Figure 14 In Example 1 of the present invention, IPA, ILA, and IAA blocked the activation of the Akt signaling pathway in human orbital fibroblasts (OFs) induced by TNFα; a, human orbital fibroblasts (OFs) were treated with 20 ng / ml recombinant human tumor necrosis factor α (rHuTNFα) or an equal volume of solvent for 24 hours. The phosphorylation level of Akt in OFs was evaluated by Western blotting (WB). TNFα intervention significantly increased the protein level of pAkt. Representative images are shown in the upper panel, and quantitative data are shown in the lower panel. n = 3. Data are expressed as mean ± standard error. **P < 0.01, compared with control group cells; b, human orbital fibroblasts (OFs) were simultaneously treated with 20 ng / ml recombinant human tumor necrosis factor α (rHu TNFα) or an equal volume of solvent, and IPA (50 μM), ILA (0.5 mM), or IAA (100 μM) or an equal volume of solvent for 24 hours. The phosphorylation level of Akt in OFs was evaluated by Western blotting (WB). IPA, ILA, and IAA significantly blocked the pAkt level stimulated by rHu TNFα. Representative images are shown in the upper panel, and quantitative data are shown in the lower panel. n = 3. Data are expressed as mean ± standard error. *P < 0.05, **P < 0.01, compared with rHu TNFα + control group cells;

[0044] Figure 15 This is the Receiver Operating Characteristic Curve (ROC) analysis for Example 2 of the present invention. Detailed implementation manners

[0045] The following embodiments are provided to better further understand the present invention. They are not limited to the best implementation manners, and do not limit the content and protection scope of the present invention. Any product identical or similar to the present invention obtained by anyone under the inspiration of the present invention or by combining the features of the present invention with those of other prior arts falls within the protection scope of the present invention.

[0046] For those not specifying specific experimental steps or conditions in the embodiments, the operations or conditions of the conventional experimental steps described in the literature in this field can be followed. For reagents or instruments without indicating the manufacturer, they are all conventional reagent products that can be obtained through commercial purchase.

[0047] Materials involved in the following embodiments:

[0048] Ethical approval description:

[0049] The study has obtained the approval of the Ethics Committee of Beijing Tongren Hospital, Capital Medical University (Ethical Approval Number: TRECKY2016 - 003, TREC2020 - XJS02). All patients and volunteers have signed written informed consent forms.

[0050] Study design and clinical sample collection:

[0051] The diagnosis of GO is established according to the guidelines of the European Group on Graves Orbitopathy (EUGOGO) (T.D. Hoang, D.J. Stocker, E.L. Chou, H.B. Burch, 2022 Update on Clinical Management of Graves Disease and Thyroid Eye Disease, Endocrinol Metab Clin North Am, 51(2022)287 - 304, https: / / doi.org / 10.1016 / j.ecl.2021.12.004.). Any orbital space - occupying diseases are excluded by orbital CT or MRI. According to the 2016 American Thyroid Association guidelines, combined with diffuse goiter, elevated serum thyroxine (T4), decreased thyroid - stimulating hormone (TSH) levels, and the results of TSH - receptor antibody (TRAb) testing, GD is clinically diagnosed (Subekti, L.A. Pramono, Current Diagnosis and Management of Graves' Disease, Acta Med Indones, 50(2018)177 - 182). All hyperthyroid patients received only antithyroid drugs (methimazole, Merck KGaA, Germany) treatment. We collected clinical and demographic data, including gender, age, thyroid function, thyroid autoantibodies, and medical history. The exclusion criteria for this study included age < 18 years or > 65 years, received probiotics or antibiotics within the past 1 month, used hormonal drugs or traditional Chinese medicine, had a history of chronic diarrhea or constipation, systemic diseases (diabetes, stroke, heart disease, renal or liver dysfunction, and cancer), a history of gastrointestinal surgery, pregnancy and lactation, alcohol or drug addiction.

[0052] According to the value of the clinical activity score (CAS), the activity of GO patients is evaluated. The cut - off value for the inactive phase group and the active phase group is 3. The inactive phase is defined as CAS < 3, and active - phase GO is defined as CAS ≥ 3. Refer to the 2021 "EUGOGO Clinical Guidelines for Thyroid Eye Disease".

[0053] DNA extraction and 16S sequencing samples:

[0054] From March 2017 to March 2018, 33 patients with active GO and 32 healthy volunteers (control group) were recruited from the outpatient endocrine department of Beijing Tongren Hospital, Capital Medical University. Human fecal samples (2 - 3 g) were collected in sterile fecal cups and stored at -80 °C until 16S sequencing was performed.

[0055] Serum metabolomics analysis samples:

[0056] Sixteen healthy volunteers (control group), 16 patients with GD, and 31 patients with GO were recruited from the outpatient endocrine department of Beijing Tongren Hospital, Capital Medical University for serum metabolomics analysis.

[0057] ELISA detection samples of serum tryptophan metabolites:

[0058] Recruited from the outpatient endocrine department of Beijing Tongren Hospital, Capital Medical University: A total of 401 serum samples were analyzed in the ELISA study, including 100 samples from healthy volunteers (control group), 145 samples from patients with GD, and 156 samples from patients with GO.

[0059] Detection samples for clinical diagnosis of GO:

[0060] Recruited from the outpatient endocrine department of Beijing Tongren Hospital, Capital Medical University: The above 156 patients with GO were divided into 94 patients with non-active GO and 62 patients with active GO according to the CAS score; the differences in serum tryptophan metabolites between the two groups were analyzed.

[0061] Orbital connective tissue explants were obtained from the surgical waste of patients who underwent orbital decompression surgery due to severe GO in Beijing Tongren Hospital, Capital Medical University (n = 4). Control orbital tissues (n = 5) were obtained from the surgical waste of patients who underwent enucleation after trauma without thyroid or inflammatory diseases. This study has been approved by the Ethics Committee of Beijing Tongren Hospital, Capital Medical University.

[0062] Example 1 Screening of biomarkers

[0063] DNA extraction and 16S sequencing

[0064] Human fecal samples (2 - 3 g) were collected in sterile fecal cups and stored at -80 °C until further processing. Microbial DNA was extracted using an E.Z.N.A. fecal DNA extraction kit (Omega, USA), and the total DNA was eluted with 50 μL of elution buffer. The extracted DNA was stored at -80 °C until 16S sequencing measurement.

[0065] Serum metabolomics analysis

[0066] Metabolites with significant differences were selected based on the variable importance in projection (VIP) score (obtained from the orthogonal partial least squares discriminant analysis model) and the p-value of the Wilcoxon test (VIP threshold > 1, p < 0.05). Cross-validation of the samples was evaluated using partial least squares discrimination analysis (PLS-DA). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to identify the biochemical pathways of the differential metabolites and classify them according to their participation in the pathways. Enrichment analysis was performed based on the presence of metabolites in the functional nodes. The statistical significance of the enriched pathways was tested using the Stats Python package by Fisher's exact test.

[0067] ELISA detection of serum tryptophan metabolites

[0068] Serum samples were collected and stored at -80 °C. Samples were analyzed using a commercially available ELISA kit according to the manufacturer's instructions. The IAA level was detected using a human IAA (Catalog number: CEA737Ge) ELISA kit (Cloud-Clone Corp, Houston, TX, USA). The IPA and ILA levels were detected using human IPA (Catalog number: MM-61644H1) and human ILA (Catalog number: MM-61694H1) ELISA kits (Jiangsu Meimian Industrial Co., Ltd). These kits are designed for the detection of human serum or plasma samples and have no cross-reactivity.

[0069] Cell culture and reagents

[0070] Human OFs were cultured starting from orbital connective tissue explants according to the literature method (M. Park, J. Y. Kim, J. M. Kang, H. J. Lee, J. P. Banga, G. J. Kim, H. Lew, PRL-1 overexpressed placenta-derived mesenchymal stem cells suppress adipogenesis in Graves’ ophthalmopathy through SREBP2 / HMGCR pathway, Stem Cell Research & Therapy, 12 (2021), https: / / doi.org / 10.1186 / s13287-021-02337-2). After obtaining the orbital connective tissue explants, they were immediately washed three times with phosphate-buffered saline (PBS) and cut into small pieces, which were directly placed into a culture dish. After adherence, the explants were immersed in DMEM / F12 medium (Sigma-Aldrich, St. Louis, MO, USA) containing 20% fetal bovine serum (FBS; Gibco, Carlsbad, CA, USA) and 1% penicillin / streptomycin (Thermo Fisher Scientific, USA). The cells were cultured in a humidified environment of 37 °C, 95% air and 5% CO₂. OFs usually migrated out of the explants within about 4 days and reached confluence within about 10 days. Then the cells were passaged with 0.25% trypsin / EDTA (Gibco Laboratories, New York, USA). After centrifugation at 300 g for 5 minutes at room temperature, the supernatant was discarded, and the cell pellet was resuspended in 10 mL of DMEM / F12 medium and then filtered through a 70-μm cell strainer to establish a cell line. After the first passage, OFs were cultured in DMEM / F12 medium containing 10% FBS and 1% penicillin / streptomycin, and the medium was changed every 2 - 3 days. Cells from passages 3 - 6 were used in the experiments. 3-Indoleacetic acid (IAA, product number: GC33436), 3-Indolepropionic acid (IPA, product number: GC31290) and Indolylactic acid (ILA, product number: GC33659) were purchased from GlpBio, California, USA. Human recombinant protein tumor necrosis factor α (rHu TNFα) was purchased from Shanghai Sangon Biotech Co., Ltd.

[0071] Cell proliferation assay

[0072] According to the method described previously, the proliferation activity of OFs was detected using the Cell Counting Kit-8 (CCK-8; TransGen Biotech Co., Ltd., Beijing). Briefly, OFs were seeded into 96-well plates and, after reaching 80% confluence, were treated with three tryptophan metabolites at different concentrations or an equal volume of solvent (DMSO) for 24 hours (h). The working concentrations of these tryptophan metabolites were as follows: IPA (10, 50, and 100 μM), ILA (0.1, 0.5, and 1 mM), and IAA (25, 100, and 400 μM). Cells treated with an equal volume of solvent served as the control group. To evaluate the anti-proliferative effects of IPA, ILA, and IAA on rHu TNFα-induced inhibition, OFs were treated with 20 ng / ml rHu TNFα or an equal volume of solvent, along with IPA (50 μM), ILA (0.5 mM), or IAA (100 μM) for 24 hours. At 0.5 - 1 hour before the end of incubation, the CCK-8 solution was added to each well of the 96-well plates at a final concentration of 10% and gently mixed. After 0.5 - 1 hour, the absorbance (A) was measured at 450 nm, with a reference wavelength of 630 nm. The proliferation activity of OFs was expressed as relative cell viability, and the calculation formula was as follows: Relative cell viability = (experimental group A450 / A630) / (control group A450 / A630). The stock solutions of all three tryptophan metabolites were prepared in DMSO and diluted to the required final concentrations in the culture medium. The final DMSO concentration in the culture medium did not exceed 0.5% (v / v).

[0073] Western blot analysis

[0074] OFs were seeded into 6-well plates and, after reaching 80% confluence, were treated with 20 ng / ml rHu TNFα or an equal volume of solvent, along with IPA (50 μM), ILA (0.5 mM), or IAA (100 μM) or an equal volume of solvent for 24 hours. Western blot analysis was performed according to the standard method described previously [6 - 8]. Anti-TNFα (3707S), anti-pStat3 (9145S), anti-Stat3 (12640S), anti-protein kinase B (Akt) (9272S), and anti-pAkt (4060S) were purchased from Cell Signaling Technology, Inc., USA. Anti-β-actin was purchased from Sigma-Aldrich Co., LLC, USA (A5316). ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used to quantitatively evaluate the image intensity of each band. β-actin was used as a normalization control in the experiment.

[0075] Statistical analysis

[0076] IBM SPSS 27.0 software and GraphPad Prism 8.0.1 software were used to analyze demographic data, clinical data, and laboratory test results. Continuous variables with a normal distribution were expressed as the mean ± standard error of the mean (SEM), and non-parametric variables were expressed as the median (Q1, Q3). For comparisons between different groups, the Student's t-test was used for two-group comparisons, and one-way analysis of variance and Kruskal-Walli's test were used for multi-group comparisons. P < 0.05 was considered statistically significant. For differential impact analysis, the Benjamini and Hochberg false discovery rate (BH) correction was used for related multiple comparisons to correct for errors introduced by a large number of analyses. The correlation between all variables was determined by Spearman rank correlation.

[0077] Results:

[0078] The results of DNA extraction and 16S sequencing were as Figures 1-3 shown:

[0079] Alterations in the gut microbiota related to tryptophan (Trp) metabolism were found in patients with Graves' ophthalmopathy (GO). 16S sequencing studies have shown that there are significant differences in the α-diversity and β-diversity indices between fecal samples of GO patients and the control group (T.T. Shi, Z. Xin, L. Hua, R.X. Zhao, Y.L. Yang, H. Wang, S. Zhang, W. Liu, R.R. Xie, Alterations in the intestinal microbiota of patients with severe and active Graves' orbitopathy: a cross-sectional study, J Endocrinol Invest, 42 (2019) 967-978, https: / / doi.org / 10.1007 / s40618-019-1010-9). However, whether the gut microbiota related to tryptophan metabolism has changed remains unknown. In this study, the gut microbiota was identified and analyzed at the phylum (p), class (c), order (o), family (f), and genus (g) levels. The species distribution histograms of the top 30 gut microbiota at the genus level showed that the species in both groups were mainly distributed in the genera Bacteroides, Prevotellaceae, and Faecalibacterium ( Figure 1In addition, the taxonomic heatmap showed that there were significant differences in the abundances of 35 genera in GO patients compared with the control group, and these genera belonged to five phyla: Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria ( Figure 2 ). It was shown that several bacterial species were able to convert tryptophan into indole and its derivatives. To reveal the role of tryptophan metabolism in GO, the changes in tryptophan metabolism-related microbiota at different levels in the control group and GO patients were further analyzed. The results showed that at the genus level, the abundance of g_Anaerostipes, an anaerobic rod-shaped bacterium that can convert tryptophan into indole-3-lactic acid (ILA), in the GO group was significantly lower than that in the control group; at the order level, the abundance of o_Clostridiales, which can convert tryptophan into ILA, in the GO group was significantly lower than that in the control group; at the class level, the abundance of c_Clostridia, which can convert tryptophan into ILA, indole propionic acid (IPA), and indole acetic acid (IAA), in the GO group was significantly downregulated compared with that in the control group; at the phylum level, the abundance of p_Firmicutes, which can convert tryptophan into ILA, in the GO group was significantly downregulated compared with that in the control group ( Figure 3 ). In summary, these results suggest that the gut microbiota related to tryptophan metabolism, including Firmicutes and Anaerostipes, may be closely related to GO disease. With the significant reduction of tryptophan metabolism-related gut microbes, their related tryptophan metabolites IAA, ILA, and IPA may also be significantly reduced, indicating that the occurrence of GO may be closely related to tryptophan metabolism in gut microbiota, and ILA, IPA, and IAA are particularly worthy of attention.

[0080] Results of the serum metabolomics profile of GO patients:

[0081] To further clarify the changes in tryptophan metabolomics in GO patients compared with Graves' disease (GD) patients and the control group, serum metabolomics analysis was performed on the three groups of people. A total of 2,182 metabolites were quantified in this experiment. Supervised partial least squares discriminant analysis (PLS-DA) showed obvious separations between the control group and the GD group (R2Y = 0.91, Q2Y = 0.76), the control group and the GO group (R2Y = 0.90, Q2Y = 0.79), and the GD group and the GO group (R2Y = 0.95, Q2Y = 0.87). These values were all close to 1.0, indicating that all three models were stable and had predictive reliability ( Figure 4 ). These results mean that there were significant differences in metabolites among the control group, GD group, and GO group patients. Then, we applied volcano plot analysis to identify potential differential metabolites causing this difference ( Figure 5)。Metabolites with variable importance in projection (VIP) values > 1 and p < 0.05 were considered significantly different metabolites. According to our criteria, there were 348 significantly different metabolites between the GO group and the control group, 392 metabolites with significantly differential expression between the GD group and the control group, and 298 metabolites with significant differences between the GD group and the GO group. More specifically, compared with the control group, 213 metabolites were upregulated and 135 metabolites were downregulated in the GO group, while 234 metabolites were upregulated and 158 metabolites were downregulated in the GD group. Compared with the GD group, 155 metabolites had increased levels and 143 metabolites had decreased levels in the GO group. These results indicate that serum exhibits unique metabolic responses according to GD and GO. In addition, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the significantly altered metabolites. The differentially enriched pathways in the GO group and the GD group included tyrosine metabolism, tryptophan metabolism, neurotransmitter and receptor metabolism, glycerophospholipid metabolism, and the synthesis of non-unsaturated fatty acids. Notably, the tryptophan metabolism pathway appeared in the differential enrichment of both the GO group and the GD group ( Figure 6 )。Based on this, we further analyzed the levels of L-tryptophan and tryptophan metabolites in the three groups of people detected by metabolomics. Our metabolomics analysis showed that there was no significant difference in the level of L-tryptophan among the three groups of people. However, the results showed that the levels of IAA and ILA were significantly decreased in the GD group, and the level of IAA was also significantly decreased in the GO group compared with the control group ( Figure 7 )。IPA was not detected in our metabolomics analysis. These findings suggest that GD and GO may be closely related to tryptophan metabolites rather than tryptophan.

[0082] Results of tryptophan metabolites in the serum of GO patients:

[0083] Given the aforementioned findings that tryptophan metabolites are closely related to GO and GD, it was hypothesized that these metabolites might potentially serve as biomarkers for the disease. To address this issue and further confirm the results of serum metabolomics, the levels of IPA, ILA, and IAA in the sera of GD patients, GO patients, and healthy volunteers (control group) were detected by enzyme-linked immunosorbent assay (ELISA). The ELISA test results showed that the levels of IPA, ILA, and IAA in the sera of GD patients and GO patients were significantly decreased compared with the control group ( Figure 8 in a-c). Notably, the level of IAA in the serum of GO patients was significantly lower compared with that of GD patients ( Figure 8 in c). These results indicate that IAA is the most promising candidate as a novel biomarker for GO.

[0084] Therefore, to explore the potential of IAA as a biomarker for evaluating the progression of GO, we divided GO patients into an inactive group (n = 94) and an active group (n = 62) according to the value of the clinical activity score (CAS) (the cut-off value between the inactive group and the active group was 3, the inactive phase was defined as CAS < 3, and active GO was defined as CAS ≥ 3). No significant differences were detected between the two groups in terms of gender, age, free triiodothyronine (FT3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH). However, the level of thyroid-stimulating hormone receptor antibody (TRAb) in the active GO group was significantly higher than that in the inactive group. The above clinical characteristics are shown in Table 2. The results showed that the serum IAA level in active GO patients was significantly lower than that in inactive patients ( Figure 8 in d). Given that CAS is the most commonly used indicator for evaluating the activity of GO, we performed a Spearman correlation analysis to explore whether the serum IAA level was correlated with CAS. Interestingly, the serum IAA level in GO patients was significantly negatively correlated with CAS ( Figure 8 in e).

[0085] As is well known, the level of serum thyroid-stimulating hormone receptor antibody (TRAb) is correlated with the activity and severity of GO. To explore whether tryptophan metabolites are correlated with the progression of GD and GO, it is crucial to explore the correlation between tryptophan metabolites and serum TRAb. Considering the significant differences in age, gender, and TRAb level among the three groups of patients (P < 0.001) (Table 1), we performed a multiple linear regression analysis. Each tryptophan metabolite was used as the dependent variable, TRAb as the independent variable, and age and gender were adjusted. The results showed that after adjusting for age and gender, the concentrations of IAA and IPA were negatively correlated with TRAb (P < 0.05) (Table 3). These research results further indicate that tryptophan metabolites, especially IAA, may be potential biomarkers for judging the progression of GO. Considering the significant difference in serum IAA concentration between inactive and active GO patients, and that in GO patients, IAA was significantly correlated with both the CAS score and TRAb, we hypothesized that IAA could be a reliable predictor of disease activity. Therefore, a receiver operating characteristic (ROC) curve analysis was performed on IAA for predicting GO disease activity. For 94 inactive and 62 active patients, the Youden index (Youden's J statistic) was used to determine the optimal sensitivity and specificity thresholds, and the area under the receiver operating characteristic curve reached 0.9457. The cut-off values were as follows: IAA was 28.91 ng / mL, sensitivity was 0.9355, and specificity was 0.8817, indicating that IAA had excellent reliability in identifying active GO patients ( Figure 8 in f).

[0086] Table 1. Baseline characteristics of participants

[0087]

[0088] In the above table, the values are n(%), median (IQR). Abbreviations: IQR: interquartile range; Conrol: healthy volunteers; GD: Graves' disease; GO: Graves' ophthalmopathy; FT3: free triiodothyronine; FT4: free thyroxine; TSH: thyroid stimulating hormone; TRAb: thyroid stimulating hormone receptor antibody. **P<0.01, ***P<0.001. Values with superscript letters: a indicates significant difference compared with the control group; b indicates significant difference compared with the GD group.

[0089] Table 2 Baseline characteristics of patients with GO

[0090]

[0091] In the above table, the values are n(%), median (IQR). Abbreviations: IQR: interquartile range; GO: Graves' ophthalmopathy; FT3: free triiodothyronine; FT4: free thyroxine; TSH: thyroid stimulating hormone; TRAb: thyroid stimulating hormone receptor antibody. *P<0.05.

[0092] Table 3 Multiple linear regression analysis of factors affecting tryptophan metabolites in GO

[0093]

[0094] In the above table, abbreviations: GO: Graves' ophthalmopathy; Trp: tryptophan; IPA: indole-3-propionic acid; IAA: indoleacetic acid; ILA: indole-3-lactic acid; TRAb: thyroid stimulating hormone receptor antibody. Note: *P<0.05, ***P<0.001.

[0095] Tryptophan metabolites induce the proliferation and inflammation of orbital fibroblasts (OFs) by tumor necrosis factor α (TNFα).

[0096] Given that the levels of IPA, ILA, and IAA in the sera of GD and GO patients are significantly downregulated, we hypothesized that these metabolites might be important therapeutic targets for GO. To test this hypothesis, we performed a series of in vitro experiments using primary cultured human OFs. Previous studies have confirmed that there are no significant differences in the cell morphology and function of OFs from GO patients and healthy controls after in vitro passage of the retrobulbar connective tissue (R.C. Henrikson, T.J. Smith, Ultrastructure of cultured human orbital fibroblasts, Cell and tissue research, 278 (1994) 629-631, https: / / doi.org / 10.1007 / bf00331384). In this study, we also found that there were no significant differences in the proliferation and inflammation of OFs from GO patients and healthy controls after in vitro passage of the retrobulbar connective tissue ( Figure 9 a-c). Therefore, OFs from the retrobulbar connective tissue of healthy individuals were used in the subsequent experiments of this study.

[0097] Considering that cell proliferation and inflammation are important characteristics of OFs from GO patients, we initially used the CCK-8 assay to evaluate the effects of these three tryptophan metabolites on cell proliferation. The results showed that the proliferation activities of cells treated with IPA, ILA, and IAA decreased in a dose-dependent manner compared with the control group ( Figure 10 a-c). TNFα, which is upregulated in OFs, plays a key role in inducing cell proliferation and pro-inflammatory cytokines in GO. Further experiments showed that recombinant human TNFα (rHu TNFα) could significantly induce the cell proliferation of OFs ( Figure 11 ). Notably, IPA, ILA, and IAA were able to significantly improve the proliferation of OFs induced by rHu TNFα ( Figure 10 d-f). Subsequently, the results of western blot (WB) experiments showed that rHu TNFα could significantly induce inflammation in OFs, as evidenced by the significant increase in the levels of pStat3 and TNFα ( Figure 12 ). IPA, ILA, and IAA significantly alleviated the inflammation of OFs induced by rHu TNFα ( Figure 13 a-b). These findings suggest that IPA, ILA, and IAA may become important therapeutic targets for GD and GO by improving the inflammation and proliferation of OFs. Tryptophan metabolites can block the activation of the Akt signaling pathway in human OFs induced by TNFα

[0098] Multiple studies have confirmed that the Akt signaling pathway plays a role in adipogenesis, cell migration, cell proliferation, and inflammation in GO. These studies have shown that the level of phosphorylated Akt (pAkt) at serine 473 in orbital fibroblasts (OFs) of GO patients is elevated, which is considered an important pathogenic mechanism of GO [1-5]. Therefore, to explore the potential mechanisms by which tryptophan metabolites IPA, ILA, and IAA improve inflammation-induced dysfunction in OFs, we investigated their effects on the Akt signaling pathway. We found that TNFα treatment significantly promoted the activation of the Akt pathway, as evidenced by an increase in pAkt ( Figure 14 in a), while treatment with IPA, ILA, or IAA was able to block TNFα-induced Akt phosphorylation ( Figure 14 in b). This finding indicates that the Akt signaling pathway plays an important role in the protective effects of IPA, ILA, and IAA against inflammation-induced dysfunction in OFs in GO.

[0099] In summary, to date, the role of tryptophan metabolites in GO patients has not been elucidated. This study began with gut microbiota sequencing to identify differences in tryptophan-related microbiota, and then explored the serum tryptophan metabolomic profile. In addition, we also elucidated the potential mechanisms of the anti-inflammatory and anti-proliferative effects of tryptophan metabolites in GO.

[0100] In this study, we further re-analyzed the changes in the microbiota of GO patients. At the genus level, the relative abundances of 35 genera in GO patients were significantly different compared with the control group. In addition, significant differences in tryptophan-related genera were observed between the two groups at different levels. At the phylum level, the abundance of Firmicutes in the GO group was significantly downregulated. At the genus level, the abundance of Anaerorhabdus in the GO group was significantly lower compared with the control group. There is evidence that Firmicutes and Anaerorhabdus play a key role in promoting tryptophan metabolism during human dietary fiber degradation, thereby protecting the intestinal barrier. Our study is the first to demonstrate a significant downregulation of specific genera related to tryptophan metabolism in GO patients.

[0101] This study further verified the differences in IAA, ILA, and IPA among the healthy control group, GD group, and GO group by ELISA detection. Compared with the control group, the levels of IPA, ILA, and IAA in the sera of GD patients and GO patients were significantly downregulated. Moreover, compared with GD patients, the level of IAA in the sera of GO patients was significantly lower. These findings further illustrate that tryptophan metabolites, including IAA, ILA, and IPA, may play important roles in GD and GO diseases. In addition, IAA may serve as a biomarker for differentiating GO patients from GD patients.

[0102] This study further investigated the potential of IAA as a biomarker for evaluating the disease progression of GO and verified that the levels of thyrotropin receptor antibodies (TRAbs) in the active GO group were significantly higher than those in the inactive group. The serum IAA levels in patients with active GO were significantly lower than those in patients with inactive GO. The serum IAA levels in GO patients were significantly negatively correlated with CAS. Given the significant difference in serum IAA concentration between inactive and active GO patients and the significant correlation between IAA and both CAS score and TRAb in GO patients, it was verified that IAA could predict the disease activity (active and inactive stages) of GO, indicating that IAA had excellent reliability in identifying patients with active GO.

[0103] The data of this study showed that tryptophan metabolites significantly inhibited the proliferation of OFs and the expression of the inflammatory marker pStat3. Here, we provided evidence for the first time that IAA, ILA, and IPA might play a protective role in the proliferation and inflammation of GO patients.

[0104] To explore the potential mechanisms by which tryptophan metabolites IPA, ILA, and IAA improved TNFα-induced inflammation and proliferation of OFs, we investigated their effects on the Akt signaling pathway. In our study, treatment with IPA, ILA, or IAA could downregulate the phosphorylation of Akt in TNFα-induced OFs. Our results supplemented the evidence that GO might benefit from blocking the Akt signaling pathway.

[0105] In summary, our study was the first to clarify the changes in microbiota-derived tryptophan metabolites in GO patients. In addition, it was demonstrated that IPA, ILA, and IAA played a potential therapeutic role in GO by improving the inflammation of OFs and controlling their proliferation. Microbiota-derived tryptophan metabolites might serve as potential targets for GO treatment. Therefore, maintaining the production of IAA, ILA, and IPA by the gut microbiota, for example, through dietary intervention or oral probiotic supplements, might have a beneficial impact on the prevention and management of GO.

[0106] Experimental Example 2 Clinical Application

[0107] In this experimental example, 68 patients clinically diagnosed with GO were evaluated. Among them, 47 were patients with inactive GO and 21 were patients with active GO, randomly selected from the GO patients in Example 1. According to the serum IAA concentrations of the above GO patients, a receiver operating characteristic (ROC) curve for predicting the disease activity of GO patients using biomarkers was plotted.

[0108] The ROC curve is a coordinate graph with the false positive probability (FPR, 1 - specificity) as the horizontal axis and the true positive rate (TPR, sensitivity) as the vertical axis. The specific steps are as follows: First, sort the samples according to the predicted values generated by the model for the test samples. Then, by changing the classification threshold, classify the samples as positive or negative classes. And calculate the TPR and FPR for each threshold. TPR represents the proportion of samples that the model correctly predicts as positive classes, while FPR represents the proportion of samples that the model wrongly predicts as positive classes. Use the area under the receiver operating characteristic curve (AUC) to quantify the performance of the ROC curve. The results are as Figure 15 shown. The AUC of this model reaches 0.9098, approaching the ideal model. Use Youden's J index to determine the optimal sensitivity and specificity thresholds. The larger the Youden's index, the greater the authenticity, and the maximum value corresponds to the optimal diagnostic cut-off value of this method, that is, the cutoff value. The critical value of IAA is 28.85 ng / mL, the sensitivity is 0.9048, and the specificity is 0.9149.

[0109] The above shows that IAA can be used as a diagnostic marker for predicting the inactive and active phases of different disease courses in GO patients, and has high sensitivity and specificity, and can be applied to clinical diagnosis.

[0110] References:

[0111] [1] H.J. Byeon, M.K. Chae, J. Ko, E.J. Lee, D.O. Kikkawa, S.Y. Jang, J.S. Yoon, The Role of Adipsin, Complement Factor D, in the Pathogenesis of Graves’ Orbitopathy, Investigative Opthalmology & Visual Science, 64(2023)13, https: / / doi.org / 10.1167 / iovs.64.11.13.

[0112] [2] N. Wang, S.-y. Hou, X. Qi, M. Deng, J.-m. Cao, B.-D. Tong, W. Xiong, LncRNA LPAL2 / miR-1287-5p / EGFR Axis Modulates TED-Derived Orbital Fibroblast Activation Through Cell Adhesion Factors, The Journal of Clinical Endocrinology & Metabolism, 106(2021) e2866-e2886, https: / / doi.org / 10.1210 / clinem / dgab256. [3] C.C. Patrick, E. Roztocil, F. Husain, S.E. Feldon, C.F. Woeller, Tapinarof, an Aryl Hydrocarbon Receptor Ligand, Mitigates Fibroblast Activation in Thyroid Eye Disease: Implications for Novel Therapy, Investigative Ophthalmology & Visual Science, 65(2024) 40, https: / / doi.org / 10.1167 / iovs.65.13.40.

[0113] [4] C.A. Conover, L.K. Bale, M.N. Stan, PAPP-A as a Potential Target in Thyroid Eye Disease, J Clin Endocrinol Metab, 109(2024) 3119-3125, https: / / doi.org / 10.1210 / clinem / dgae339.

[0114] [5]J.Y. Kim, S. Park, H.J. Lee, H. Lew, G.J. Kim, Functionally enhanced placenta-derived mesenchymal stem cells inhibit adipogenesis in orbital fibroblasts with Graves' ophthalmopathy, Stem Cell Res Ther, 11(2020)469, https: / / doi.org / 10.1186 / s13287-020-01982-3.

[0115] [6]W.Yang, J.Wang, Z.Chen, J.Chen, Y.Meng, L.Chen, Y.Chang, B.Geng, L.Sun, L.Dou, J.Li, Y.Guan, Q.Cui, J.Yang, NFE2 Induces miR-423-5p to Promote Gluconeogenesis and Hyperglycemia by Repressing the Hepatic FAM3A-ATP-Akt Pathway, Diabetes, 66(2017)1819-1832, https: / / doi.org / 10.2337 / db16-1172.

[0116] [7]J.Wang, W.Yang, Z.Chen, J.Chen, Y.Meng, B.Feng, L.Sun, L.Dou, J.Li, Q.Cui, J.Yang, Long Noncoding RNA lncSHGL Recruits hnRNPA1 to Suppress Hepatic Gluconeogenesis and Lipogenesis, Diabetes, 67(2018)581-593, https: / / doi.org / 10.2337 / db17-0799.

[0117] [8]W.-L.Yang,C.-Y.Zhang,W.-Y.Ji,L.-L.Zhao,F.-Y.Yang,L.Zhang,X.Cao,Berberine Metabolites Stimulate GLP-1Secretion by Alleviating OxidativeStress and Mitochondrial Dysfunction,The American Journal of ChineseMedicine,52(2024)253-274,https: / / doi.org / 10.1142 / s0192415x24500113.

[0118] Obviously, the above embodiments are merely examples for clear illustration and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. A biomarker for the auxiliary diagnosis or diagnosis of Graves' ophthalmopathy and / or Graves' disease, characterized in that, The biomarker includes tryptophan metabolites, and the tryptophan metabolites include indolepropionic acid, indole-3-lactic acid, and / or indoleacetic acid.

2. Use of a product for detecting the biomarker for assisting in diagnosing or diagnosing Graves' ophthalmopathy and / or Graves' disease according to claim 1 in the preparation of a product for assisting in diagnosing the course of Graves' ophthalmopathy.

3. The use according to claim 2, characterized in that, The course of Graves' ophthalmopathy includes active Graves' ophthalmopathy and inactive Graves' ophthalmopathy; and / or, the biomarker includes tryptophan metabolites, and the tryptophan metabolites include indoleacetic acid.

4. Use of a product for detecting the biomarker for assisting in diagnosing or diagnosing Graves' ophthalmopathy and / or Graves' disease according to claim 1 in the preparation of a product for assisting in differentiating or differentiating Graves' ophthalmopathy and Graves' disease.

5. Use according to any one of claims 2 - 4, characterized in that, The product includes a reagent, test paper, test kit, or instrument.

6. A product for auxiliary diagnosis or diagnosis of the course of Graves' ophthalmopathy as described in any one of claims 2 - 3 or 5, characterized in that, The product is a system for assisting in diagnosing or diagnosing the course of Graves' ophthalmopathy, including: a detection unit for obtaining the content of the biomarker in a sample to be tested; the biomarker is the biomarker described in claim 2-3 or 5; a judgment unit connected to or wirelessly connected to the detection unit, and the judgment unit is used to diagnose or assist in diagnosing the course of Graves' ophthalmopathy based on the content of the biomarker in the sample to be tested.

7. The product for auxiliary diagnosis or diagnosis of the course of Graves' ophthalmopathy according to claim 6, wherein including: comparing the content of indoleacetic acid in a patient with inactive Graves' ophthalmopathy, and if the content of indoleacetic acid in a patient with active Graves' ophthalmopathy is lower, judging the course of Graves' ophthalmopathy; and / or, the sample to be tested in the detection unit is selected from blood, serum, or plasma.

8. Use of tryptophan metabolites in the preparation of a medicament for adjuvant treatment or treatment of Graves' ophthalmopathy, characterized in that, The tryptophan metabolites include indolepropionic acid, indole-3-lactic acid, and / or indoleacetic acid.

9. A drug for adjuvant treatment or treatment of Graves' ophthalmopathy, characterized in that, At least one of indolepropionic acid, indole-3-lactic acid, and / or indoleacetic acid as an active ingredient, and pharmaceutically acceptable excipients.

10. The drug according to claim 9, characterized in that, The pharmaceutically acceptable excipients include fillers, binders, disintegrants, lubricants, sweeteners, flavoring agents, preservatives, surfactants, stabilizers, solubilizers, colorants, antioxidants, pH regulators, buffers, chelating agents, thickeners, coating materials, or film-forming materials; and / or, the dosage form of the drug includes liquid preparations, semi-solid preparations, or solid preparations; optionally, the dosage form of the drug includes oral solutions, tablets, capsules, granules, pills, powders, syrups, ointments, creams, gels, suppositories, or injections.

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