KR102000827B1 - Biomarkers for diagnosing diabetic retinopathy and uses thereof - Google Patents

Abstract

The present invention relates to a biomarker for the diagnosis of diabetic retinopathy and a use thereof. More particularly, the present invention relates to a method for diagnosing diabetic retinopathy by collecting clinical data and living body samples of a diabetic patient population and identifying diabetic retinopathy phenotype, It has been confirmed that diabetic retinopathy can be determined by using glutamine, glutamic acid, and glutamic acid / glutamic acid ratio in the metabolism, so that it is useful for determining whether the metabolic marker is diabetic retinopathy Can be used.

Description

TECHNICAL FIELD The present invention relates to a biomarker for diagnosing diabetic retinopathy,

The present invention relates to biomarkers for the diagnosis of diabetic retinopathy and uses thereof.

As the duration of the diabetes mellitus increases, various complications of the system become accompanied. Typical diabetic complications include cardiovascular disease, diabetic nephropathy, diabetic neuropathy, and diabetic retinopathy. Diabetic retinopathy (DR) occurs in more than 60% of diabetic patients within 10 years of diabetes diagnosis and more than 90% in 20 years.

Diabetic retinopathy is one of the microangiopathies of diabetes mellitus and is a chronic complication that is directly referred to in the diagnosis of diabetes mellitus. It is well known that diabetic retinopathy is more specific to hyperglycemia than other chronic diabetes-related complications. Diabetic retinopathy is characterized by changes in retinal blood vessel permeability, vascular occlusion, ischemia, neovascularization, and fibrovascular proliferation .

Diabetic retinopathy can be divided into early non-proliferative diabetic retinopathy (NPDR) and later proliferative diabetic retinopathy (PDR) according to progression. NPDR is caused by retinal capillary obstruction and changes in permeability, resulting in retinal hemorrhage, microaneurysm, exudate, and edema. In addition, accompanying edema of the macula (DME, diabetic macular edema) can cause serious visual impairment even in this period. PDR is a stage in which new blood vessels multiply due to ischemic conditions following extensive vascular occlusion of the retina. This proliferation progresses to vitreous formation in the retina and fibrovascular proliferation occurs. However, complications such as vitreous hemorrhage, tractional retinal detachment, and neovascular glaucoma, This is the stage where the blind date is proceeding.

If such diabetic retinopathy is detected early, proper management can prevent the progression or worsening of retinopathy, but if it is not properly managed, severe vision loss or blindness can result. Currently, diabetic retinopathy is considered to be a major cause of blindness in adults and, despite these clinical implications, is not well diagnosed compared to other complications. According to one study, the diagnosis rate of diabetic retinopathy was 36.3% in Korea, which was significantly lower than other complications. The reason for the low diagnosis rate of diabetic retinopathy is that it requires additional equipment such as a fundus camera and skilled medical staff, which makes it difficult to perform easily compared with other tests.

To improve this reality, it is necessary to select biomarkers to predict diagnosis and prognosis. To date, studies on diabetic retinopathy have been focused mainly on biochemical and molecular biology studies of individual proteins in the vitreous. In addition, the proteomic study of diabetic retinopathy is a study of the Profiling (Discovery) phase of vitreous protein bodies that identify proteins in the patient's vitreous with 2-DE and mass spectrometry. In addition, validation studies have not been conducted to determine whether these vitelline proteins are expressed in blood or can be used as clinical biomarkers. Thus, there is little research on biomarkers to predict the prognosis and diagnosis of diabetic retinopathy.

Accordingly, the present inventors have made efforts to develop a biomarker for predicting the diagnosis and therapeutic prognosis of diabetic retinopathy. As a result, clinical data and biological samples of a diabetic elderly patient group were collected to identify diabetic retinopathy phenotype, / Mass spectrometry to confirm that glutamine and glutamic acid in the metabolites are accurate biomarkers for diabetic patients with diabetic retinopathy, thereby completing the present invention .

Korean Patent Publication No. 10-2016-0137859 Korean Patent Publication No. 10-2013-0009205

Byun SH, Ma SH, Jun JK, Jung KW, Park B. Screening for diabetic retinopathy and nephropathy in patients with diabetes: a nationwide survey in Korea. PLoS One 2013; 8: e62991 Mukamel DB, Bresnick GH, Wang Q, Dickey CF. Barriers to compliance with screening guidelines for diabetic retinopathy. Ophthalmic Epidemiol 1999; 6: 61-72

The object of the present invention is to provide an information providing method for determining whether or not diabetic complications are present, comprising the step of measuring the level of a metabolite in a sample separated from a diabetic subject and comparing the diabetic subject with a diabetic control subject having no diabetic complication .

It is another object of the present invention to provide a composition for detecting diabetic complications including a detection agent for at least one metabolite selected from the group consisting of glutamine and glutamic acid.

In order to achieve the above object, the present invention provides a method for diagnosing diabetic complications, comprising the steps of measuring the level of a metabolite in a sample isolated from a diabetic subject and comparing the diabetic subject with a diabetic control subject having no diabetic complication Provide information providing method.

In addition, the present invention provides a composition for determining the complication of diabetes comprising a detection agent for any one or more metabolites selected from the group consisting of glutamine and glutamic acid.

The present invention relates to a method for diagnosing a diabetic retinopathy phenotype by collecting clinical data and biological samples of a diabetic elderly patient group and profiling the metabolites in serum using chromatography / mass spectrometry to determine whether glutamine, glutamic acid and glutamine / Glutamic acid ratio of the diabetic retinopathy can be distinguished from diabetic patients having diabetic retinopathy, the metabolic marker can be used to judge whether diabetic retinopathy is present or not.

Fig. 1 is a schematic diagram of a clinical trial process of the present invention. Fig.
2a shows an orthogonal partial least squares discriminant analysis (OPLS-DA) score plot using GC-TOF-MS analysis data:
CON: non-diabetic control;
no DR: diabetic group without diabetic retinopathy; And
DR: Diabetic group with diabetic retinopathy.
FIG. 2B is a diagram illustrating an OPLS-DA score plot using UPLC-Q-TOF-MS analysis data.
FIG. 3A shows ROC curves for diabetic group without diabetic retinopathy and diabetic group glutamine and glutamic acid with diabetic retinopathy or a combination thereof.
FIG. 3B is a graph showing the ROC curve of the glutamine / glutamic acid ratio between the diabetic group having no diabetic retinopathy and the diabetic group having diabetic retinopathy.

Hereinafter, the present invention will be described in detail.

The present invention provides a method for providing information for determining diabetic complication, comprising the step of measuring the level of a metabolite in a sample isolated from a diabetic subject and comparing the diabetic subject with a diabetic control subject having no diabetic complication.

In the present invention, the sample may be blood, plasma, serum, urine, tears, saliva, sputum, secretion, bronchial secretion, bronchial washing solution, pulmonary secretion, or alveolar lavage solution. More specifically, It is not.

In the present invention, the term "metabolite" refers to a metabolite obtained from a sample of biological origin, and the metabolite is preferably glutamine or glutamic acid.

In the present invention, the above-mentioned "level of metabolite" refers to the concentration of the metabolite or the amount of the metabolite, and the level of the metabolite can be measured by, for example, chromatography / mass spectrometry, However, the present invention is not limited thereto, and any quantitative method commonly used in the art can be used. In addition, the chromatography / mass spectrometry may be performed using, for example, ultra-performance liquid chromatography-quadrupole (UPLC-Q-TOF-MS) or gas chromatography / time- flight mass spectrometry.

The metabolites of the present invention can be identified by using not only the accurate molecular weight information but also the elmental composition by using the information obtained through the Q-TOF-MS or TOF-MS since each component is separated by UPLC or GC .

In the present invention, the diabetic complication may be diabetic retinopathy, diabetic cataract, or diabetic nephropathy.

In the present invention, it is preferable that the sample separated from the diabetic subject is judged to have a high risk of being or suffering from diabetic complication when the level of glutamine is high as compared with the diabetic control subject having no diabetic complication. More specifically, when the level of glutamine is higher than 10% of the diabetic control subjects not having diabetic complications, it is preferable to judge that the risk of diabetic complications is high or high. More specifically, It is preferable to judge that the diabetic complication is high or the risk of diabetic complication is high, but the present invention is not limited thereto.

In addition, it is preferable that a sample separated from the diabetic subject is judged to have a high risk of being or suffering from diabetic complications when the level of glutamic acid is low as compared to a diabetic control subject having no diabetic complication. More specifically, it is preferable that the level of glutamic acid is 10% or more lower than that of a diabetic control group not having diabetic complications, and it is preferable that the level of glutamate is high in the risk of diabetic complication or taking. More specifically, It is preferable to judge that the diabetic complication is high or the risk of diabetic complication is high, but the present invention is not limited thereto.

The present invention also provides a method for diagnosing diabetes mellitus, comprising the steps of: (a) detecting a diabetic complication or a diabetic complication; It is preferable to judge.

In a specific embodiment of the present invention we have collected clinical and biosamples of a diabetic elderly patient population to identify diabetic retinopathy phenotype and profile serum metabolites using chromatography / mass spectrometry.

In addition, comparing the diabetic group with diabetic retinopathy and the diabetic group without diabetic retinopathy, the concentration of glutamine in the serum metabolite was significantly higher in the diabetic group with diabetic retinopathy than in the group without diabetic retinopathy Glutamic acid and glutamic acid were significantly higher than those of diabetic patients, and it was confirmed that diabetic retinopathy can be discriminated in diabetic patients by using glutamine, glutamic acid and glutamic acid / glutamic acid ratio , The metabolic marker can be usefully used for determining diabetic complications including diabetic retinopathy.

In addition, the present invention provides a composition for determining the complication of diabetes comprising a detection agent for any one or more metabolites selected from the group consisting of glutamine and glutamic acid.

In the present invention, the "detection agent" means a preparation for quantitatively detecting glutamine or glutamic acid from a biological sample separated from a diabetic patient. The preparation is not particularly limited, and a reagent capable of quantifying the metabolite Or a chemical.

In the present invention, the diabetic complication may be diabetic retinopathy, diabetic cataract or diabetic nephropathy.

In the present invention, when the level of glutamine is higher than that of a diabetic control subject having no diabetic complication in a sample separated from a subject to be diabetic, it is preferable that the subject is judged to have a high risk of suffering or suffering from diabetic complications.

In addition, it is preferable to determine that the level of glutamic acid in the sample separated from the diabetic subject is lower than the diabetic control subject not having diabetic complication, and that the risk of diabetic complication is high or high.

In addition, it is preferable that the sample in which the glutamine / glutamic acid ratio is separated from the diabetic subject is higher than the diabetic control subject not having diabetic complication, and the risk of diabetic complication is high or high.

In addition,

(a) treating the subject with diabetes complications;

(b) reducing the level of glutamine or increasing the level of glutamic acid in a sample isolated from the subject treated with the test substance of step (a) as compared to the untreated control, Thereby providing a method of preventing or treating complications.

In the method of the present invention, the substance to be tested in step (a) is selected from the group consisting of peptides, proteins, non-peptide compounds, active compounds, fermentation products, cell extracts, plant extracts, animal tissue extracts and plasma But it is not limited thereto.

In the method of the present invention, the glutamine level or the glutamate level of the step (b) can be measured by, for example, chromatography / mass spectrometry, light absorption assay and emission spectroscopy, Any quantitative method used can be used. In addition, the chromatography / mass spectrometry may be performed using, for example, ultra-performance liquid chromatography-quadrupole (UPLC-Q-TOF-MS) or gas chromatography / time- flight mass spectrometry.

In addition, the step (b) may further include the step of selecting a substance that decreases the glutamine / glutamic acid ratio in the sample isolated from the subject treated with the test substance in step (a) as compared with the untreated control group.

In the method of the present invention, said diabetic complication can be diabetic retinopathy, diabetic cataract or diabetic nephropathy.

Hereinafter, the present invention will be described in detail with reference to Examples and Experimental Examples.

However, the following Examples and Experimental Examples are merely illustrative of the present invention, and the present invention is not limited to the following Examples and Experimental Examples.

< Example  1> Subject and clinical trial design

Clinical trials were conducted as part of the National Biobank project, using the basic features of the registrant's prospective cohort study from September 2014 to June 2015. Bonch's subjects were patients with type 2 diabetes for more than 15 years.

The clinical information of the subject was registered based on the multicenter clinical data registration standardization method approved by the Korean Diabetes Association and the biospecimens were registered in accordance with the guidelines of the National Biobank of Korea Lt; / RTI &gt;

In addition, for clinical trials, the institutional review board of Kyung Hee University Hospital (IRB No. KMC IRB 1428-04) has been approved. Written consent was obtained from all subjects. In addition, clinical trial information was provided from the Clinical Research Information Service (http://cris.nih.go.kr), a Korean national service linked to the International Clinical Trials Registry Platform (ICTRP) of the World Health Organization. KCT0001269).

< Example  2> Phenotypic analysis of diabetic retinopathy (Diabetic retinopathy)

Diabetic retinopathy (DR) symptoms of each of the subjects of Example 1 were evaluated by color fundus photography (FF 540 Plus; Carl Zeiss Meditech, Jena, Germany) and optical coherence tomography (HD-OCT; Carl Zeiss Meditech, Dublin, CA, USA). According to the ETDRS criteria, DR is classified into three categories: non-DR (no DR), non-proliferative diabetic retinopathy (NPDR) without DR or proliferative Proliferative diabetic retinopathy (PDR) with DR. Two or more ophthalmologists classified the DR status based on the test results. When there was a discrepancy between the doctors, the image was reviewed again and the final interpretation was reached.

< Example  3> Statistical analysis of clinical trial results

The clinical characteristics of DR patients and non-DR patients were compared with those of patients with long-term diabetes mellitus, focusing on the characteristics of subjects who did not have retinopathy. Clinical data validation and statistical analysis were performed independently by statisticians. The mean, proportions, and dirtributions were compared regardless of patient's DR. After initial analysis, cases and control sets were selected through Propensity Score Matching (PSM) with similar clinical characteristics to DR, and the same samples as above were used for metabolic studies. SAS software (version 9.3, SAS Institute Inc., Cary, NC, USA) was used for all statistical analyzes.

< Example  4> Serum samples were used Metabolism  Research

<4-1> Sample Preparation

Age matched non - diabetic subjects were used as controls in the study of metabolomics. Metabolites were extracted from 200 Pl of plasma. 600 [mu] l of methanol solution and 10 [mu] l of internal standard solution (1-mg / ml of 2-chlorophenylalanine in water) were added to the serum and homogenized for 5 minutes with an ultrasonic grinder. After homogenization, the suspension was kept at -20 占 폚 for 60 minutes and then centrifuged at 12,000 rpm, 4 占 폚 for 10 minutes. The supernatant was filtered through a 0.2-μm PTFE (polytetrafluoroethylene) filter and dried using a high-speed vacuum concentrator (Modulspin 31; Biotron, South Korea). The dried extract was redissolved in 250 μl methanol for UPLC-Q-TOF-MS analysis and 100 μl samples were analyzed by GC-TOF-MS (gas chromatography / time-of-flight mass spectrometry).

<4-2> GC - TOF -MS analysis

For GC-TOF-MS analysis, the sample dried in Example <4-1> was dissolved in 50 μl methoxyamine hydrochloride (20 mg / ml in pyrimidine) for 90 minutes at 30 ° C, Was oximated and silylated at 37 ° C for 30 minutes using 50 μl MSTFA (N-methyl-N- (trimethylsilyl) trifluoroacetamide). GC-TOF-MS analysis was performed on an Agilent 7890 gas chromatography system (Agilent Technologies, St. Louis, MO, USA) coupled with Agilent 7693 auto-sampler (Agilent Technologies) and Pegasus ^® HT TOF MS system Palo Alto, Calif., USA). The column was a Rtx-5MS column (id, 30 m × 0.25 mm, 0.25 μm particle size; Restek Corp., Bellefonte, PA, USA) and helium at a flow rate of 1.5 ml / min was used as the carrier gas. The sample, dispensed at 1 μl, was injected into the GC in a splitless mode. The temperature was maintained at 75 占 폚 for 2 minutes, then increased to 15 占 폚 / min to 300 占 폚 and maintained for 3 minutes. The front inlet and transfer line temperatures were 250 ° C and 240 ° C, respectively. Electron ionization was performed at -70 eV and full scanning was performed in the 50-1000 m / z range for data acquisition.

<4-3> UPLC -Q- TOF -MS analysis

UPLC was performed using a Waters ACQUITY UPLC ^™ system (Waters Corp., Milford, Mass., USA) equipped with a two-component solvent delivery system, UV detector and automatic sampler. Chromatographic separation was performed using a Waters ACQUITY BEH C18 column (id, 100 mm × 2.1 mm, 1.7 μm particle size; Waters Corp.) and the injection volume was 5 μL. The column temperature was set at 37 占 폚 and the flow rate was set at 0.3 ml / min. The mobile phase consisted of 0.1% v / v formic acid (A) in water and 0.1% v / v formic acid (B) in acetonitrile. Initial conditions were 5% B for 1 min and linear increase to 100% B over 9 min. The total run time was 14 minutes, including re-equilibrating the column under initial conditions. For MS, Waters Q-TOF Premier (Micromass MS Technologies, Manchester, UK) was operated in negative ion mode at 100-1000 m / z range. The source temperature was set at 100 占 폚, the collision energy was set at 10 eV, and the collision gas flow rate was 0.3 ml / min. The desolvation gas was set at 650 L / h at a temperature of 300 캜. The capillary voltage and the sample cone voltage were set at 2.5 kV and 50 V, respectively. The V mode was used with a mass spectrometer and data were collected in a centroid mode with a scan accumulation of 0.2 s. Leucine encephalin was used as reference lock mass ( m / z 554.2615) by independent LockSpray interference.

<4-4> Metabolism  Data processing and multivariate statistical analysis for research

GC-TOF-MS analysis was performed by the method described in Example <4-2>, and GC-TOF-MS data was obtained using LECO Chroma TOF ^™ software (version 4.44, LECO Corp.) NetCDF format (* .cdf). After UPLC-Q-TOF-MS analysis was performed by the method described in Example <4-3>, raw data was obtained using MassLynx software (version 4.1, Waters Corp.). The raw data files were converted to NetCDF format (* .cdf) using MassLynx DataBridge software (version 4.1, Waters Corp.). After conversion, peak detection, retention time correction, and alignment were performed using the Metalign software package ( http://www.metalign.nl ). The resulting data was saved as a Microsoft Excel file. Multivariate statistical analysis was performed using SIMCA-P + (version 12.0; Umetrics, Umea, Sweden). The data sets were automatically variably scaled and centered on an average by column basis. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to compare each data set. The variable was selected based on the variable importance to projection (VIP) value of the OPLS-DA. Statistical significance was verified by ANOVA analysis, Student's t-test, and Duncan's multiple range tests using PASW Statistics 18 software (SPSS Inc., Chicago, IL, USA). Receiver operating characteristic (ROC), and curve and logistic regression analysis were obtained using Medcalc software (version 14.8.1; Medcalc Software, Mariakerke, Belgium).

< Experimental Example  1> PSM Clinical characteristics of subjects according to

As shown in the schematic diagram of FIG. 1, clinical data and samples were collected from 198 subjects whose consent was obtained among 220 subjects collected by the method described in Example 1 above. After the consent, 15 subjects withdrew consent and performed ophthalmologic examinations for a total of 183 subjects (Fig. 1). The average age of participants in the study was 66.8 years, the mean duration of diabetes was 22.6 years, and that of males was 49.7%. Of the 183 participants who underwent ophthalmologic examination, 124 (67.8%) were diagnosed with diabetic retinopathy (DR), 72 of whom (39.3%) had non-proliferative diabetic retinopathy (NPDR) 28.4%) were PDR (proliferative diabetic retinopathy). In addition, statistically significant differences in various factors were confirmed. Based on the results of PSM, 32 pairs of patients with no significant difference in clinical characteristics except for the presence of DR and control group were selected as shown in Table 1 (Table 1). In addition, the patient was subjected to metabolic studies.

Category Variables No DR (n = 32) DR (n = 32) p Clinical characteristics Gender (male,%) 17 (53.1) 12 (37.5) 0.383 DM duration (yr) 21.72 + - 6.63 22.66 ± 7.77 0.624 Age (yrs) 65.25 8.06 67.59 8.47 0.312 Height (cm) 161.81 ± 8.9 162.3 ± 9.14 0.850 Weight (kg) 64.1 ± 8.94 62.98 ± 12.49 0.684 BMI (kg / m ² ) 24.5 ± 3.01 23.75 ± 3.32 0.375 Max weight in lifetime (kg) 70.65 ± 11.13 70.04 + - 11.34 0.813 Waist circumference (cm) 88.91 + - 7.74 87.74 + 9.65 0.759 Hip circumference (cm) 92.62 + - 5.53 92.25 + - 8.03 0.953 Neck circumference (cm) 35.89 ± 2.74 34.92 + - 3.48 0.220 Chest circumference (cm) 92.67 + - 5.39 91.33 + - 8.05 0.440 Thigh circumference (Lt., cm) 49.36 ± 2.69 49.04 ± 3.65 0.634 Thigh circumference (Rt., Cm) 49.72 ± 3.03 49.54 + 4.01 0.771 Systolic blood pressure (mmHg) 127.56 ± 15.09 125.66 ± 14.06 0.573 Diastolic blood pressure (mmHg) 72.91 + - 10.59 68.62 ± 9.67 0.073 Pulse rate (bpm) 79.94 ± 15.11 81.47 ± 12.94 0.682 HbA1c (%) 7.92 ± 1.49 7.83 ± 1.19 0.778 Fasting plasma glucose (mg / dL) 151.81 ± 57.22 143.5 ± 41.24 0.524 Total cholesterol (mg / dL) 178.28 + - 34.26 161.62 ± 35.84 0.052 Triglyceride (mg / dL) 129.47 + - 91.91 104.62 ± 52.57 0.181 LDL cholesterol (mg / dL) 103.78 ± 27.66 95.38 + - 31.56 0.235 HDL cholesterol (mg / dL) 55.91 + - 16.86 50.19 + - 14.63 0.168 Spot urine Microalbumin (ug / mg Cr) 77.83 + - 221.27 71.96 ± 175.21 0.911 BUN (mg / dL) 16.41 + - 5.78 18.69 ± 5.37 0.071 Creatinine (mg / dL) 0.8 ± 0.28 0.83 ± 0.36 0.705 Creatinine Clearance (mL / min / 1.73 m ² ) 98.04 + - 31.23 102.16 +/- 40.51 0.640 AST (IU / L) 24.34 + 9.67 23.53 ± 6.33 0.690 ALT (IU / L) 22.22 + 16.81 22.19 ± 11.87 0.994 GGT (IU / L) 30.5 ± 16.27 34.19 + - 43.47 0.659 ALP (IU / L) 74.19 + - 23.67 76.25 ± 17.06 0.681 History of Macrovascular complication Hypertension (n,%) 23 (71.9) 28 (87.5) 0.228 Dyslipidemia (n,%) 22 (68.8) 21 (65.6) 1,000 Myocardial infarction (n,%) 0 (0.0) 0 (0.0) - Angina (n,%) 3 (9.4) 2 (6.3) 1,000 Heart failure (n,%) 0 (0.0) 1 (3.1) - Atrial fibrillation (n,%) 0 (0.0) 0 (0.0) - Any Stroke, (n,%) 5 (15.6) 4 (12.5) 1,000 Peripheral arterial disease (n,%) 2 (6.3) 2 (6.3) 1,000 Coronary artery ballooning (n,%) 1 (3.1) 0 (0.0) - CAG with stent insertion (n,%) 1 (3.1) 1 (3.1) 1,000 Coronary artery bypass graft (n,%) 0 (0.0) 0 (0.0) - Intervention for peripheral arterial disease (n,%) 0 (0.0) 0 (0.0) - Amputation (n,%) 0 (0.0) 0 (0.0) - History of microvascular complication Glaucoma (n,%) 4 (12.5) 4 (12.5) 1,000 Cataract (n,%) 15 (46.9) 16 (50.0) 1,000 Blindness (n,%) 0 (0.0) 1 (3.1) - Microalbuminuria (n,%) 12 (37.5) 9 (28.1) 0.579 Overt proteinuria (n,%) 6 (18.8) 4 (12.5) 0.752 Chronic Kidney Disease (n,%) 6 (18.8) 2 (6.3) 0.289 Peripheral neuropathy (n,%) 16 (50.0) 16 (50.0) 1,000 Autonomic neuropathy (n,%) 7 (21.9) 11 (34.4) 0.343 Photocoagulation (n,%) 0 (0.0) 10 (31.3) - Intra-vitreal injection (n,%) 0 (0.0) 1 (3.1) - Ophthalmologic operation (n,%) 11 (34.4) 14 (43.8) 0.606 Dialysis (n,%) 0 (0.0) 0 (0.0) - History of Other comorbidity Any Cancer (n,%) 6 (18.8) 2 (6.3) 0.289 Auto-immune disease (n,%) 1 (3.1) 1 (3.1) 1,000 Chronic B viral hepatitis (n,%) 1 (3.1) 2 (6.3) 1,000 Chronic C viral hepatitis (n,%) 0 (0.0) 0 (0.0) - Liver cirrhosis (n,%) 0 (0.0) 0 (0.0) - Tuberculosis (n,%) 2 (6.3) 0 (0.0) - Hyperthyroidism (n,%) 1 (3.1) 1 (3.1) 1,000 Hypothyroidism (n,%) 6 (18.8) 3 (9.4) 0.505 Depression (n,%) 5 (15.6) 1 (3.1) 0.134 Periodontitis (n,%) 5 (15.6) 7 (21.9) 0.724 Family history Family history of DM (n,%) 22 (68.8) 24 (75.0) 0.789 Family history of hypertension (n,%) 10 (31.3) 9 (28.1) 1,000 Family history of dyslipidemia (n,%) 0 (0.0) 1 (3.1) 1,000 Family history of obesity (n,%) 6 (18.8) 6 (18.8) 1,000 Family history of cardiovascular disease (n,%) 0 (0.0) 1 (3.1) - Family history of stroke (n,%) 7 (21.9) 3 (9.4) 0.343 Family history of any cancer (n,%) 10 (31.3) 7 (21.9) 0.579 Family history of depression (n,%) 0 (0.0) 2 (6.3) - Family history of early (or sudden) death (n,%) 1 (3.1) 2 (6.3) 1,000 Lifestyle Marriage (n,%) 32 (100.0) 32 (100.0) - Monthly income (10,000 KRW) 247.85 ± 289.62 301.46 ± 336.16 0.858 Regular exercise (n / week) 3.47 ± 2.87 2.44 ± 2.6 0.104 Average sleep hour within 1 year (hour) 6.69 ± 1.4 6.25 ± 2.09 0.246 Skip breakfast (n / wk) 0.47 ± 1.57 0.44 ± 1.72 0.942 Average Meal time (min) 16.09 + - 7.48 14.38 8.01 0.400 Eating out (n / wk) 2.06 + 2.94 1.94 + - 2.31 0.858 Night time worker (n,%) 2 (6.3) 5 (15.6) 0.450 Self-monitoring of blood glucose (n,%) 18 (56.3) 18 (56.3) 1,000 Average number of glucose monitoring (n / wk) 4.56 ± 2.64 3.61 ± 2.64 0.283 Hypoglycemia (n / month) 0.56 ± 1.29 1.17 + - 2.43 0.819 DM education within 1 year (n,%) 0 (0.0) 3 (9.4) - Any hospitalization within 1 year (n,%) 9 (28.1) 8 (25.0) 1,000 Any operation within 1 year (n,%) 5 (15.6) 4 (12.5) 1,000 Any ED visit within 1 year (n,%) 3 (9.4) 5 (15.6) 0.724 Any dietary supplements within 1 year (n,%) 16 (50.0) 15 (46.9) 1,000 Current Medications Metformin (n,%) 23 (71.9) 23 (71.9) 1,000 Sulfonylurea (n,%) 20 (62.5) 20 (62.5) 1,000 DPP-4 inhibitor (n,%) 11 (34.4) 10 (31.3) 1,000 Meglitinide (n,%) 1 (3.1) 0 (0.0) - Thiazolidinedione (n,%) 1 (3.1) 3 (9.4) 0.617 SGLT-2 inhibitor (n,%) 0 (0.0) 0 (0.0) - Alpha glucosidase inhibitor (n,%) 0 (0.0) 1 (3.1) - Rapid acting insulin (n,%) 1 (3.1) 3 (9.4) 0.480 Basal insulin (n,%) 7 (21.9) 11 (34.4) 0.289 Pre-mixed insulin (n,%) 7 (21.9) 6 (18.8) 1,000 GLP-1 agonist (n,%) 0 (0.0) 0 (0.0) - Angiotensin Receptor Blocker (n,%) 15 (46.9) 14 (43.8) 1,000 Angiotension Converting Enzyme inhibitor (n,%) 1 (3.1) 5 (15.6) 0.221 Calcium channel blocker (n,%) 11 (34.4) 10 (31.3) 1,000 Diuretics (n,%) 1 (3.1) 4 (12.5) 0.371 Beta blocker (n,%) 2 (6.3) 2 (6.3) 1,000 Statin (n,%) 12 (37.5) 15 (46.9) 0.628 Fibrate (n,%) 1 (3.1) 0 (0.0) - Aspirin (n,%) 6 (18.8) 8 (25.0) 0.724 Clopidogrel (n,%) 5 (15.6) 5 (15.6) 1,000 Cilostazol (n,%) 17 (53.1) 11 (34.4) 0.211

* Paried sample t-test or McNemar test, expressed as mean ± SD or n (%).

* DM: diabets mellitus; BMI: body mass index; LDL: Low density lipoprotein; HDL: high density lipoprotein; BUN: blood urea nitrogen; AST: aspartate aminotransferase; ALT: alanine aminotransferase; GGT: gamma-glutamyl transferase; ALP: alkaline phosphatase; CAG: coronary angiography; DPP: dipeptidylpeptidase; And SGLT: sodium-glucose transporter

< Experimental Example  2> The results of multivariate statistical analysis according to presence or absence of DR

In order to investigate whether a non-DR diabetic subject without a non-diabetic control group and a diabetic subject having a NPDR or PDR can use a metabolite significantly different from that of DR as a biomarker of DR, GC-TOF-MS and UPLC-Q-TOF-MS analyzes were performed using serum samples as described. Then, OPLS-DA was performed by multivariate statistical analysis using a 39,154 mass spectral parameter for GC-TOF-MS and a 6,185 mass spectral parameter for UPLC-Q-TOF-MS.

As a result, as shown in Figure 2a, GC-TOF-MS results of the OPLS-DA with respect to the data set _{^{R 2 X (cum) = 0.214}} , R 2 Y (cum) = 0.977 , and Q ² _(cum) = 0.449, the three groups of non-diabetic control (CON), non-DR diabetic group (no DR) and diabetic group (DR) with DR were clearly distinguished. These values represent the fitness and predictive accuracy of the model. Also, p-value = 2.25e- ¹⁸ was confirmed by cross-validation analysis (Fig. 2a).

Also, as shown in FIG. 2B, a similar distribution pattern was observed in the OPLS-DA of the UPLC-Q-TOF-MS data set. That _{^{is, R 2 X (cum) =}} 0.145, R 2 Y (cum) = 0.933, Q 2 (cum) = 0.497 to the non-diabetic control group (CON), -DR non-diabetic group having diabetes (no DR) and the DR (DR) three groups were clearly distinguished. In addition, a p-value = 1.64e ^-26 was confirmed by cross-validation analysis (FIG. 2B).

< Experimental Example  3> for DR diagnosis Biomarker  Confirm

Based on the results of the multivariate statistical analysis of the <Experimental Example 2>, the VIP value of OPLS-DA> 0.7 was used in order to select the metabolites responsible for the discrimination based on the presence or absence of DR. The VIP value is an important parameter for detecting potential biomarker candidates that reflect the correlation of metabolites with other biological states. In addition, p <0.05 derived from one-way ANOVA was applied to assess statistical significance. The selected metabolites are compared with various database and MS fragment patterns including commercial standard compounds, NIST (National Institute of Standards and Technology) library, Human Metabolome Database (HMDB, http://www.hmdb.ca/) and Wiley 8 Respectively. Details of these metabolites are shown in Tables 2 and 3 below. The results in Table 2 below were obtained by GC-TOF-MS analysis and the results in Table 3 were obtained by UPLC-Q-TOF-MS analysis. In addition, the relative amounts of metabolites in the non-diabetic control (CON), non-DR diabetic (no DR) and diabetic (DR)

As a result, as shown in Tables 2 and 3, a total of 31 metabolites including 7 amino acids, 6 organic compounds, 7 carbohydrates and 11 lysophosphatidylcholines (lysoPCs) were compared with non-diabetic control group, DR diabetic group. In addition, it was confirmed that some amino acids in 31 metabolites showed a statistically significant increase and decrease in the non-DR diabetic group as compared with the non-diabetic control group. More specifically, asparagine (2.30-fold) and glutamine (2.83-fold) significantly increased in the non-DR diabetic group, while aspartic acid (0.46-fold), glutamic acid (0.25 times) significantly decreased. In particular, glutamine significantly increased in the diabetic group with a DR of 1.19 times the change in the amount of glutamine between the diabetic group with DR and non-DR diabetic group, and diabetic group with diabetic change of 0.72 times the amount of glutamic acid , And that glutamic acid was significantly decreased in the control group.

As shown in Tables 2 and 3, the ratio of glutamine, glutamic acid, and glutamic acid / glutamic acid, which show a significant drainage change between diabetic and non-DR diabetics with DR, was measured by the method described in Example 4 Curve.

As a result, as shown in Tables 2 and 3 and FIGS. 3A and 3B, the AUC values of glutamine and glutamic acid between diabetic group with DR and non-DR diabetic group were 0.671 and 0.656, respectively, , It was confirmed that the amount of glutamine and glutamic acid could be compared to distinguish between non-DR diabetic patients and diabetic patients having DR. In addition, when glutamine and glutamic acid were combined, the AUC value was found to be 0.739, indicating that the ability to distinguish between diabetic and non-DR diabetic groups with DR was improved.

< Experimental Example  4> for DR diagnosis As a biomarker  Identification of glutamine and glutamic acid

Based on the results of the above Experimental Example 3, in order to examine whether the ratio of glutamine and glutamic acid in the metabolites can be used as a biomarker for DR diagnosis, non-diabetes mellitus The GC-TOF-MS analysis was performed using serum samples from the control (CON), non-DR diabetic (No DR) and diabetic (DR) The average concentrations of asparagine, aspartic acid, glutamine, and glutamic acid showing differences were quantified and the glutamine / glutamic acid ratio was confirmed.

As a result, as shown in the following Table 4, there was no significant difference in serum asparagine and aspartic acid in the non-DR diabetic group compared to the non-diabetic control group, while in the case of glutamic acid and glutamine, There was a significant difference in the DR diabetic group and the DR diabetic group. In particular, the results of the above <Experiment 3> show that glutamic acid, which showed a significant drainage change between the non-DR diabetic group and the diabetic group with DR, was higher in the serum of the diabetic group having DR compared to the non-DR diabetic group And that of glutamic acid was low. In addition, the ratio of glutamine / glutamic acid in the non-DR diabetic group and the diabetic group having DR was calculated and compared, and it was confirmed that the ratio of glutamine / glutamic acid was about 1.5 times higher in the DR diabetic group than in the non-DR diabetic group .

Therefore, it was confirmed that the ratio of glutamine, glutamic acid, and glutamic acid / glutamic acid could be used as a biomarker for determining DR in diabetic patients, and glutamine, glutamic acid, Glutamine / glutamic acid ratio was measured to identify diabetic patients with DR among diabetic patients.

Metabolite
Average concentration (ng / 80 혈 serum) CON No DR DR Asparagine 8.06 9.56 9.80 Aspartic acid 10.01 9.44 9.40 Glutamine 52.62 135.36 160.05 Glutamic acid 50.17 16.62 13.50 Glutamine / Glutamic acid 1.04 8.14 11.86

Claims (11)

Measuring levels of glutamine and glutamic acid in a sample isolated from a diabetic subject as an experimental group, and comparing the sample with a diabetic control subject not having diabetic retinopathy, wherein in the sample isolated from the diabetic subject, Diabetic retinopathy, which is judged to be at high risk for diabetic retinopathy or to be at risk of diabetic retinopathy when the glutamine level is low and glutamic acid level is low or glutamine / glutamic acid ratio is high as compared to diabetic control subjects not having retinopathy Information providing method.
The method according to claim 1, wherein the sample is selected from the group consisting of blood, plasma, serum, urine, tears, saliva, sputum, secretion, bronchial secretion, bronchial wash, lung secretion, A method of providing information to determine retinopathy.
delete The method according to claim 1, wherein the level of glutamine and glutamic acid is measured using at least one selected from the group consisting of chromatography / mass spectrometry, light absorption assay and emission spectrometry. Information providing method.
5. The method of claim 4, wherein the chromatography / mass spectrometry is performed using an ultra-performance liquid chromatography-quadrupole (UPLC-Q-TOF-MS) or a gas chromatography / time- -flight mass spectrometry). 2. The method according to claim 1, wherein the diabetic retinopathy is diabetic retinopathy.
delete delete delete delete delete (a) treating the subject with diabetic retinopathy;
(b) reducing the level of glutamine or increasing the level of glutamic acid in a sample separated from the subject treated with the test substance of step (a) as compared to the untreated control, A method of screening for a prophylactic or therapeutic agent for retinopathy.

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