Biomarker and kit for detecting diabetes mellitus macular edema and application of biomarker and kit

Abstract

The invention discloses a biomarker for detecting diabetes mellitus macular edema (DME), a kit for detecting the diabetes mellitus macular edema (DME), and application of the biomarker and the kit for detecting the diabetes mellitus macular edema (DME). The invention provides application of a biomarker in preparation of a reagent or a kit for detecting diabetic macular oedema. The biomarker is selected from one or more of 5-phosphate-beta-D-ribose amine, ascorbic acid glucoside, succinic acid and glutathione disulfide. The potential metabolic marker of DME is identified according to the plasma metabolism type of a DME patient. The biomarker provided by the invention can provide a scientific insight and a new strategy for early diagnosis and intervention of DME.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a biomarker for detecting diabetic macular edema, a kit and uses thereof. Background Art

[0002] Diabetic macular edema (DME) is a vision-threatening retinal disease and the leading cause of vision loss in diabetic patients. The disease is caused by an imbalance of fluid between retinal blood vessels and the extracellular space, usually due to damage to the blood-retinal barrier and increased microvascular permeability. Hou et al. reported an epidemiological analysis of diabetic retinopathy (DR) and found that the incidence of DME accounted for approximately 23% of vision-threatening diabetic retinopathy (VTDR) cases in 50,564 diabetic patients aged between 18 and 74 years (Hou X, Wang L, Zhu D, Guo L, Weng J, Zhang M, Zhou Z, Zou D, Ji Q, Guo X, et al: Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China. Nat Commun 2023, 14: 4296.). In addition, a survey of medical insurance data from 2009 to 2018 showed that the incidence of DME was on the rise, and the proportion of DME in cases related to VTDR was gradually increasing (Lundeen EA, Kim M, Rein DB, Wittenborn JS, Saaddine J, Ehrlich JR, Holliday CS: Trends in the Prevalence and Treatment of Diabetic Macular Edema and Vision-Threatening Diabetic Retinopathy Among Commercially Insured Adults Aged <65Years. Diabetes Care 2023, 46: 687-696.). These results show that DME has become an important threat to vision in diabetic patients.

[0003] Despite strict control of systemic risk factors, some diabetic patients still develop DME, suggesting that there may be other potential pathogenic factors. Diabetic macular edema (DME) is the main cause of vision loss in patients with diabetic retinopathy, especially in patients with non-proliferative diabetic retinopathy (NPDR) with type 2 diabetes. As the prevalence of type 2 diabetes increases worldwide, the prevalence of DME is also expected to gradually increase. Forecasts show that by 2030, the number of patients with clinically significant macular edema (CSME) will increase by 24.8% to 23.5 million; by 2045, this number is expected to increase by 51.9% to 28.61 million. Studies have shown that more than two-thirds of true DME patients have a visual acuity of less than 0.5, which seriously affects visual health. In addition, the presence of DME increases the consumption of medical resources, aggravates the economic burden, and reduces the quality of life of patients. Understanding the potential mechanisms of the occurrence and progression of diabetic macular edema (DME) and discovering new drug targets are of great significance for prevention and promotion of treatment strategies. Some studies have shown that clinical risk factors do not explain all cases of severe diabetic retinopathy (DR). According to data from the Diabetes Control and Complications Trial (DCCT) and the Wisconsin Epidemiology Study of Diabetic Retinopathy (WESDR), these risk factors only explained 11% and 10% of the variability in DR risk, respectively. Epigenetic mechanisms may play a role in pathways related to oxidative stress, inflammation, and other biological processes in DME. In addition, the diagnosis of DME mainly relies on imaging tools such as fundus photography, optical coherence tomography (OCT), and fluorescein fundus angiography, which can only detect visible retinal damage and have difficulty identifying early lesions. The discovery of biomarkers may help in the early diagnosis of DME and promote early intervention and prevention in high-risk patients, thereby slowing down the progression of the disease. Currently, the main treatments for DME include laser surgery and anti-vascular endothelial growth factor (VEGF) therapy, and anti-inflammatory steroids also play a role in the adjuvant treatment of DME. As the first-line treatment for DME, although most patients respond well to anti-VEGF therapy, some patients respond poorly, and long-term injections may lead to drug resistance. Therefore, there is an urgent need to explore new pathogenesis and therapeutic targets and identify early diagnostic biomarkers to achieve effective early diagnosis and intervention.

[0004] Biomarkers, as measurable indicators that reflect biological status, disease progression, and treatment response, are of great significance for a deeper understanding of the pathogenesis of diabetic macular edema (DME). Metabolomics can detect comprehensive and dynamic changes in endogenous metabolites in biological systems, including nucleic acids, proteins, lipids, and other small molecules. It is increasingly recognized as a powerful tool that can complement genomics, transcriptomics, and proteomics data, and has been used in the study of various diseases for many years. However, research on DME metabolomics is still limited. Summary of the invention

[0005] In view of this, the present invention proposes a biomarker, a kit and uses thereof for detecting diabetic macular edema.

[0006] One object of the present invention is to provide use of a biomarker in preparing a reagent or a kit for detecting diabetic macular edema.

[0007] The biomarker provided by the present invention is used in the preparation of a reagent or a kit for detecting diabetic macular edema, wherein the biomarker is selected from one or more of 5-phospho-β-D-ribosamine, ascorbyl glucoside, succinic acid, and glutathione disulfide.

[0008] Optionally, the biomarkers are derived from serum of a subject.

[0009] Optionally, the detecting of diabetic macular edema is to differentiate between patients with diabetic macular edema and patients without diabetic macular edema.

[0010] Optionally, the 5-phospho-β-D-ribosamine, ascorbyl glucoside or succinic acid is increased in serum metabolites of patients with diabetic macular edema; and the glutathione disulfide is decreased in serum metabolites of patients with diabetic macular edema.

[0011] Another object of the present invention is to provide the use of biomarkers in the preparation of reagents or kits for assisting in the diagnosis or diagnosis of diabetic macular edema.

[0012] The biomarker provided by the present invention is used in the preparation of a reagent or kit for auxiliary diagnosis or diagnosis of diabetic macular edema, wherein the biomarker is selected from one or more of 5-phospho-β-D-ribosamine, ascorbyl glucoside, succinic acid, and glutathione disulfide.

[0013] Optionally, the 5-phospho-β-D-ribosamine, ascorbyl glucoside or succinic acid is increased in serum metabolites of patients with diabetic macular edema; and the glutathione disulfide is decreased in serum metabolites of patients with diabetic macular edema.

[0014] Another object of the present invention is to provide a kit for detecting diabetic macular edema.

[0015] The kit for detecting diabetic macular edema provided by the present invention comprises one or more biomarkers selected from 5-phospho-β-D-ribosamine, ascorbyl glucoside, succinic acid, and glutathione disulfide.

[0016] The use of the kit in detecting diabetic macular edema also falls within the protection scope of the present invention.

[0017] Beneficial effects:

[0018] The present invention involves patients aged 40 to 60 years with non-proliferative diabetic retinopathy (NPDR) and DME, aiming to optimize diagnosis, further identify meaningful biomarkers, and reveal the potential mechanisms and early treatment targets for DME progression.

[0019] The present invention identified a total of 190 significantly different metabolites between the DME group and the control group (VIP>1, FoldChange>1.5 or <0.667, and P<0.05). These differential metabolites are enriched in four KEGG pathways, namely glutamate metabolism, carnitine synthesis, branched-chain fatty acid oxidation, and phytanic acid peroxisomal oxidation. Finally, four metabolites involved in the above pathways were screened out as biomarkers of DME, namely 5-phospho-β-D-ribosamine, succinic acid, ascorbyl glucoside, and glutathione disulfide. The areas under the curves (AUCs) of these biomarkers were 0.693, 0.772, 0.762, and 0.771, respectively.

[0020] The present invention identifies potential metabolic markers of DME based on plasma metabolomics analysis of DME patients. The biomarkers provided by the present invention will provide scientific insights and new strategies for the early diagnosis of DME. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] For purposes of illustration and not limitation, the present invention will now be described in terms of preferred embodiments thereof, with particular reference to the accompanying drawings, in which:

[0022] Figure 1 Flowchart of inclusion and exclusion of case-control studies; OCT, optical coherence tomography.

[0023] Figure 2 The following are the fundus image features of a patient with diabetic macular edema (DME), where a is the image obtained by fundus photography; b is the image obtained by optical coherence tomography (OCT); and c is the image obtained by fundus fluorescein angiography (FFA).

[0024] Figure 3Overview of the workflow for serum metabolomics analysis of subjects included in the study.

[0025] Figure 4 Score plots for principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models; a: PCA model score plot showing samples from two separated sample data; b: In the PLS-DA score plot, the two groups are well separated, indicating that they have significantly different metabolic characteristics.

[0026] Figure 5 The results of metabolomics pathway analysis, namely pathway enrichment analysis, showed that four enriched pathways were statistically significant, including glutamate metabolism, carnitine synthesis, oxidation of branched-chain fatty acids, and peroxisomal oxidation of phytol (P<0.05).

[0027] Figure 6 Receiver operating characteristic (ROC) curve analysis was used to evaluate the potential of metabolites as biomarkers for diabetic macular edema (DME); a. the AUC values ​​of the four biomarkers (5-phospho-β-D-ribosamine, ascorbyl glucoside, succinic acid, and glutathione disulfide) were 0.693, 0.772, 0.762, and 0.771, respectively; b. the expression trends of the four biomarkers in the DME group and the control group. DETAILED DESCRIPTION

[0028] Embodiment 1,

[0029] 1. Experimental methods:

[0030] 1.1 Patients:

[0031] Clinical data and serum samples were collected from 60 patients with type 2 diabetes and non-proliferative diabetic retinopathy (NPDR) who were treated in Beijing Tongren Hospital affiliated to Capital Medical University. Among them, 30 patients diagnosed with diabetic macular edema (DME) were assigned to the DME group, and the remaining 30 patients with only NPDR but no DME were included in the control group. All subjects had glycosylated hemoglobin A1c (HbA1c) levels ≥ 7.0% (53mmol / mol) and underwent ocular phenotype screening. Patients with other eye diseases or severe liver, kidney, and heart dysfunction were excluded. The present invention does not set specific restrictions on patient age. The gender and age of the patients in the control group were matched with those in the DME group (see Figure 1 ).

[0032] 1.2 Baseline Test:

[0033] The medical history of all participants was collected, and each patient underwent a physical examination including age, sex, disease duration, blood pressure, and body mass index (BMI) measurement. At the same time, the patients also underwent blood and urine laboratory tests, including triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), serum creatinine (CREA), and glycated hemoglobin (HbA1c) levels. Blood samples were collected in K2EDTA tubes and then centrifuged at 3000g for 10 minutes at 4°C to separate plasma from whole blood. The separated plasma samples were aliquoted and stored at -80°C.

[0034] 1.3 Eye examination:

[0035] Patients underwent routine ophthalmologic examinations to evaluate for diabetic retinopathy, including visual acuity measurement, slit-lamp examination, fundus photography or fluorescein angiography, and optical coherence tomography (OCT) of the central retina. Patients were diagnosed and graded according to the Early Treatment of Diabetic Retinopathy Study (ETDRS) criteria and the International Clinical Diabetic Retinopathy Grading Criteria by at least two retina specialists. At the initial visit, fundus photography (TRCNW7SF, ​​Topcon Corporation, Tokyo, Japan) and OCT examinations were performed. Patients were diagnosed with diabetic macular edema (DME) according to the following criteria: 1) the presence of increased retinal thickness within or less than 500 μm of the center of the macula; 2) the presence of hard exudates within or less than 500 μm of the center of the macula accompanied by increased retinal thickness; and 3) areas of retinal thickness that were at least one optic disk diameter and part of which were within one optic disk diameter of the center of the macula. In the event of disagreement between the evaluators, the ophthalmologists reviewed the images and reached a final interpretation (see ). Figure 2 ).

[0036] 1.4 Sample preparation:

[0037] Before analysis, frozen plasma samples were thawed and dissolved at 4°C. Propylene nitrile / methanol (75:25 v / v, 300 μL) was added to serum (100 μL) and placed in a 1.5 mL centrifuge tube to precipitate proteins. After vortexing for 60 seconds, the tube was allowed to stand for 10 minutes and then centrifuged at 12,000 rpm for 10 minutes at 4°C. The supernatant was transferred to a new Eppendorf tube and evaporated to dryness using a quick-dry concentrator. The resulting residue was resuspended in 100 μL of mobile phase for LC-MS analysis. Quality control (QC) samples were prepared by mixing equal amounts of serum from each sample and extracting metabolites according to the same operating procedures as the test samples. A QC sample was inserted before and after every five test samples for testing.

[0038] 1.5 LC-MS (Liquid Chromatography-Mass Spectrometry) Analysis:

[0039] Ultra-high performance liquid chromatography (UPLC) coupled with quadrupole time-of-flight tandem mass spectrometry (Q-TOF MS) was used, and Nexera X2 system (Shimadzu, Japan) and Triple TOF 5600 mass spectrometer (AB SCIEX, USA) were used for analysis. For specific methods, see the literature (Liu J, Li Z, Liu H, Wang X, Lv C, Wang R, Zhang D, Li Y, Du X, Li Y, et al: Metabolomics-Based Clinical Efficacy and Effect on the Endogenous Metabolites of Tangzhiqing Tablet, a Chinese Patent Medicine for Type 2 Diabetes Mellitus with Hypertriglyceridemia. Evid Based Complement Alternat Med 2018, 2018: 5490491.). Briefly, chromatographic separation was performed at 45°C using a ZORBAX Eclipse Plus C18 column (2.1×100 mm, 3.5 μm, Agilent, USA). The sample injection volume was 10 μL, and the full loop mode was used, and the mobile phase flow rate was set to 0.5 mL / min. In the reverse phase liquid chromatography (RPLC) mode, gradient elution was used, and the solvent system was: A: 0.1% formic acid in water, B: acetonitrile containing 0.1% formic acid. The gradient conditions were: 98% A at the beginning, reduced to 10% A within 13 minutes, maintained at 10% A for 3 minutes, and then restored to 98% A and maintained for 4 minutes. Mass spectrometry analysis was performed using a Triple TOF 5600+ mass spectrometer equipped with an electrospray ionization (ESI) source. Data acquisition was performed on a per-chromatographic separation basis using positive and negative ionization modes. The capillary voltage was set to 2500 V and 3000 V, the cone gas flow was 50 L / h, the desolvation gas flow was 600 L / h, the source temperature was 120 ° C, and the desolvation temperature was 500 ° C. The scan range was set from 50 to 1500 m / z in full scan mode and data were collected in centroid mode. Mass accuracy during data acquisition was ensured using an independent reference lock mass ion using Analyst TF 1.6 and MarkerView 1.2.1 software.

[0040] 1.6 Metabolite Identification:

[0041] The raw UPLC Q-TOF MS data were preprocessed by MarkerView software, including retention time alignment, peak identification, filtering, alignment, matching, and metabolite identification, generating a peak table containing retention time (tR), m / z value, and corresponding peak intensity. Subsequently, multivariate analysis was performed using the MetaboAnalyst4.0 platform (http: / / www.metaboanalyst.ca / ), including unsupervised principal component analysis (PCA) and supervised latent structure projection-discriminant analysis (PLS-DA), to identify metabolite differences between the two groups. Identified metabolite ions were matched by m / z values ​​and cross-referenced with the Human Metabolome Database (HMDB, http: / / www.hmdb.ca / ) (Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD, Psychogios N, Dong E, Bouatra S, et al: HMDB: a knowledge base for the human metabolome. Nucleic Acids Res 2009, 37: D603-610.). The mass tolerance for HMDB searches was set to 0.05 Da, taking into account chromatographic retention behavior to minimize false positive matches. Figure 3 An overview of the workflow for comprehensive metabolomics analysis of patients with type 2 diabetes and retinopathy is summarized.

[0042] 1.7 Statistical analysis:

[0043] First, the Mann-Whitney U test was used to compare the DME group and the control group. To explore the distribution and metabolic differences between the groups, multivariate analyses such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed using MetaboAnalyst 4.0. The cross-validation of the PLS-DA model used a 10-fold cross-validation method and was standardized with unit variance. 2 The parameter is used to evaluate the goodness of model fit, and Q2 is used to evaluate predictive ability; negative or very low Q2 values ​​indicate that the difference between groups is not significant. The PLS-DA model is designed to remove variations in the X matrix that are unrelated to the Y matrix, and usually only one prediction component is needed to achieve category distinction.

[0044] The integrated regional intensities between the two groups were compared using the Welch t-test (two-tailed) using MetaboAnalyst 4.0 software, and p values ​​less than 0.05 were considered statistically significant. Based on the combination of fold change and t-test results, volcano plots were generated to identify peaks with significant statistical differences. These peaks were then used for multivariate pattern recognition. For peaks that showed consistent up- or down-regulation, box plot analysis, hierarchical cluster analysis, and metabolic pathway analysis were further performed.

[0045] 1.8 Path analysis:

[0046] In the present invention, the pathway analysis and visualization of the differential chemical metabolites were performed using the MetaboAnalyst website platform (http: / / www.metaboanalyst.ca / ). In addition, the pathway enrichment statistics were further explored using metabolite set enrichment analysis (MSEA) (http: / / www.msea.ca / MSEA / faces / Home.jsp). The relationship between biomarkers was evaluated by calculating the Pearson correlation coefficient (r) (P<0.05).

[0047] 2. Results:

[0048] 2.1 Demographic and Clinical Information:

[0049] According to the exclusion criteria of the case-control study, 30 patients with diabetic macular edema (DME) were selected as the case group and 30 patients with type 2 diabetes and nonproliferative diabetic retinopathy (NPDR) were selected as the control group (see Figure 1 ). The basic characteristics of the study participants are summarized in Table 1. No significant differences were found between the two groups in age, sex, duration of diabetes, body mass index (BMI), blood pressure, creatinine, triglycerides, low-density lipoprotein (LDL), high-density lipoprotein (HDL), glycated hemoglobin, and urea nitrogen levels. However, a significant difference was found between the DME group and the control group in terms of urinary albumin excretion rate (UAER), which was associated with the progression of diabetic nephropathy.

[0050] Table 1 Baseline demographic characteristics of the present invention

[0051]

[0052]

[0053] Table 1 shows the baseline demographic characteristics of the present invention. *P<0.05. Variables with data that conformed to normal distribution (including duration of diabetes, body mass index (BMI), diastolic blood pressure (DBP), low-density lipoprotein (LDL) and blood urea nitrogen (BUN)) were analyzed using t-test and expressed as mean ± standard deviation (Mean ± SD). Non-normally distributed data (such as age, systolic blood pressure (SBP), creatinine (CREA), triglycerides (TG), high-density lipoprotein (HDL), glycosylated hemoglobin (HbA1c) and urinary albumin excretion rate (UAER)) were analyzed using Mann-Whitney U test and expressed as median (25th, 75th quartile range). SBP: systolic blood pressure; DBP: diastolic blood pressure; CREA: creatinine; TG: triglycerides; UAER: urinary albumin excretion rate; HDL: high-density lipoprotein; LDL: low-density lipoprotein.

[0054] 2.2 Model analysis:

[0055] To enhance the separation between the two groups, partial least squares discriminant analysis (PLS-DA) was used to visualize metabolic differences. All models were cross-validated with 7-fold cross validation and 200 permutation tests. 2 The overfitting of the parameter evaluation model is quantified using the Q2 value. In the PLS-DA model, R 2 The value is 0.82, and the Q2 value is 0.42. These results show that the PLS-DA model has strong applicability and can effectively distinguish the case group from the control group (see Figure 4 ). Figure 4 Score plots for principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models; a: PCA model score plot showing samples from two separated sample data; b: In the PLS-DA score plot, the two groups are well separated, indicating that they have significantly different metabolic characteristics.

[0056] 2.3 Path analysis:

[0057] A total of 190 metabolites were identified to be significantly different between the diabetic macular edema (DME) group and the control group. These metabolites were enriched in 27 pathways in the KEGG PATHWAY database, of which four pathways showed significant enrichment (P<0.05). These enriched pathways included glutamate metabolism, carnitine biosynthesis, branched-chain fatty acid oxidation, and phytate peroxisomal oxidation. The enrichment ratios of each major pathway and the network relationships between all enriched pathways are shown in Figure 5 . Figure 5For the metabolomics pathway analysis, namely the overview of pathway enrichment analysis, the results showed that four pathways were enriched, including glutamate metabolism, carnitine synthesis, oxidation of branched-chain fatty acids, and peroxisomal oxidation of phytol (P<0.05).

[0058] 2.4 Biomarkers for DME diagnosis:

[0059] In order to better screen metabolic compounds and find potential biomarkers, the present invention further selected four metabolites related to the above four pathways from the 190 identified metabolites as the final biomarkers. These metabolites are 5-phospho-β-D-ribosamine, succinic acid, ascorbyl glucoside and glutathione disulfide. Their areas under the receiver operating characteristic curve (AUC) are 0.693, 0.772, 0.762 and 0.771, respectively (see Figure 6 and Table 2 ). Figure 6 Receiver operating characteristic curve (ROC curve) analysis was used to evaluate the potential of metabolites as biomarkers for diabetic macular edema (DME); a. The AUC values ​​of the four biomarkers (5-phospho-β-D-ribosamine, ascorbyl glucoside, succinic acid, and glutathione disulfide) were 0.693, 0.772, 0.762, and 0.771, respectively; b. The expression trends of the four biomarkers in the DME group and the control group. Figure 6 It can be seen that the AUC is greater than 0.5, indicating that all four biomarkers have diagnostic value for diabetic macular edema, which is better than random prediction results; among them, 5-phospho-β-D-ribosamine, ascorbyl glucoside or succinic acid are increased in the serum metabolites of patients with diabetic macular edema; glutathione disulfide is decreased in the serum metabolites of patients with diabetic macular edema.

[0060] Table 2 Area under the receiver operating characteristic curve (AUC) results

[0061]

[0062] The present invention adopts a metabolomics analysis method based on liquid chromatography-mass spectrometry (LC-MS) to identify differential metabolic pathways and metabolites in the plasma of diabetic patients with and without diabetic macular edema (DME). Finally, four significant metabolic pathways were screened out and four related metabolites were identified. These metabolites are 5-phospho-β-D-ribosamine, succinic acid, ascorbyl glucoside and glutathione disulfide. The research sample size of the present invention is relatively large, covering DME patients aged between 40 and 60 years old and associated with diabetic retinopathy. A clear diagnosis of DME was established by combining the results of fundus photographs, OCT and fundus fluorescein angiography. This comprehensive method effectively reduces the impact of randomness and other confounding variables on the research results and ensures the rigor of the research. On this basis, the understanding of the pathogenesis and biomarkers of DME is deepened and supplemented. It is worth noting that the present invention reports for the first time the abnormality of 5-phospho-β-D-ribosamine levels in DME patients, suggesting that nucleotide metabolism plays a key role in the progression of DME. In addition, metabolic dysfunction in the phytate peroxisomal pathway highlights the importance of phytate degradation in DME, suggesting that it will be a potential target for future diagnosis and treatment. Based on this, and combined with relevant studies on diabetic retinopathy (DR) and age-related macular degeneration (AMD), the present invention proposes other potential mechanisms related to the development of DME, which involve the role of metabolites such as succinate, glutathione and carnitine.

[0063] In the present invention, the concentration of 5-phospho-β-D-ribosamine in the plasma of the DME group was significantly higher than that of the control group. 5-phospho-β-D-ribosamine is a key precursor in the synthesis of purine nucleotides and an important intermediate in the biosynthesis of pyrimidines. The degradation of purines and pyrimidines produces uric acid. Elevated plasma uric acid can directly damage endothelial cells and vascular smooth muscle cells, and inhibit nitric oxide (NO) synthesis through the renin-angiotensin system, ultimately leading to microvascular damage. Disorders in purine and pyrimidine metabolism may interfere with uric acid levels in plasma, thereby promoting microvascular damage and the progression of DME. As a precursor for the biosynthesis of purines and pyrimidines, abnormal levels of 5-phospho-β-D-ribosamine affect uric acid concentrations through these metabolic pathways, thereby promoting the progression of DME.

[0064] In the invention, the concentration of succinic acid in the plasma of DME patients is significantly higher than that of the control group. Succinic acid promotes the expression of VEGF in cells by activating the gene promoter dependent on HIF-1α; and outside the cells, succinic acid activates G protein coupled receptor 91 (GPR91), inducing the expression of VEGF, thereby aggravating the pathological process of DME.

[0065] The increase of polyols is an important mechanism in the progression of diabetic macular edema (DME). Under hyperglycemic conditions, the activity of the rate-limiting enzyme aldose reductase increases, converting glucose into sorbitol. This process consumes a large amount of NADPH, thereby inhibiting the conversion of reduced glutathione (GSH) into oxidized glutathione (GSSH), resulting in a decrease in the GSH / GSSH ratio. In the present invention, it was observed that the plasma GSSH level of DME patients was significantly lower than that of the control group. These changes will lead to an overall decrease in GSH and GSSH levels. The decrease in the relative ratio and absolute level of GSH further exacerbates oxidative stress, promotes inflammatory response and microvascular damage, thereby accelerating the progression of DME.

[0066] In the present invention, DME patients have abnormalities in carnitine metabolism and phytic acid peroxisomal metabolic pathways. This result shows that carnitine metabolism is associated with a variety of diabetic eye complications and will become a potential target for future diagnostic and therapeutic interventions. Phytic acid (PA) is a methyl-branched fatty acid that is present in the human diet and is completely oxidized in peroxisomes and mitochondria after ingestion. During multiple rounds of β-oxidation, reactive oxygen species (ROS) may be produced, leading to cell damage. PA can cause significant changes in calcium ion balance in hippocampal neurons, astrocytes, and oligodendrocytes, thereby triggering apoptosis, a process associated with damage to retinal cells in DME patients.

[0067] Metabolomics has significant advantages in exploring the pathogenesis of diabetic macular edema (DME) and biomarkers for its diagnosis and treatment. The present invention conducted a metabolomics analysis, compared patients with diabetic macular edema (DME) with a control group, and identified unique metabolic features in the DME group. The present invention enriched four differentiated metabolic pathways and identified four unique metabolites associated with the pathogenesis of DME, and for the first time reported abnormal levels of 5-phospho-β-D-ribosamine in DME patients, suggesting that nucleotide metabolism plays a key role in the progression of DME. The present invention enriches and improves the existing understanding of the pathogenesis of DME and proposes diagnostic and therapeutic targets.

[0068] List of abbreviations

[0069]

[0070]

[0071]

[0072] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Use of a biomarker in the preparation of a reagent or kit for detecting diabetic macular edema, wherein the biomarker is selected from one or more of 5-phospho-β-D-ribosamine, ascorbyl glucoside, succinic acid, and glutathione disulfide.

2. The use according to claim 1, characterized in that: The biomarkers are derived from the serum of the subject.

3. The use according to claim 1, characterized in that: The detection of diabetic macular edema is to distinguish patients with diabetic macular edema from patients without diabetic macular edema.

4. The use according to claim 1, characterized in that: The 5-phospho-β-D-ribosamine, ascorbyl glucoside or succinic acid is increased in the serum metabolites of patients with diabetic macular edema; and the glutathione disulfide is decreased in the serum metabolites of patients with diabetic macular edema.

5. Use of a biomarker in the preparation of a reagent or kit for assisting in the diagnosis or diagnosis of diabetic macular edema, wherein the biomarker is selected from one or more of 5-phospho-β-D-ribosamine, ascorbyl glucoside, succinic acid, and glutathione disulfide.

6. The use according to claim 5, characterized in that: The 5-phospho-β-D-ribosamine, ascorbyl glucoside or succinic acid is increased in the serum metabolites of patients with diabetic macular edema; and the glutathione disulfide is decreased in the serum metabolites of patients with diabetic macular edema.

7. A kit for detecting diabetic macular edema, characterized in that: The kit comprises one or more biomarkers selected from 5-phospho-β-D-ribosamine, ascorbyl glucoside, succinic acid, and glutathione disulfide.

8. Use of the kit according to claim 7 in detecting diabetic macular edema.

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