CN109991342B - Biomarker for diagnosing or preventing diabetic retinopathy, detection reagent and application - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
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Abstract
The invention relates to a biomarker for diagnosing or preventing diabetic retinopathy, a reagent for detecting the biomarker and application. In particular to the diagnosis or prevention of proliferative diabetic retinopathy. The present inventors have found that fumaric acid, uridine, acetic acid, 3-sulfinylalanine, and 3-methylxanthine derived from serum can be used as biomarkers for diagnosing or preventing diabetic retinopathy, and have completed the present invention.
Description
Technical Field
The present invention relates to the field of diagnosis or prevention of diabetic retinopathy, and more particularly, to a biomarker for diagnosing or preventing diabetic retinopathy, a reagent for detecting the biomarker, and use thereof.
Background
Diabetic Retinopathy (DR) is the most characteristic microvascular complication of diabetes, and is a common cause and leading cause of blindness in adults in China. Diabetic retinopathy is classified into non-proliferative retinopathy and proliferative retinopathy. The proliferative diabetic retinopathy is also defined as diabetic retinopathy threatening vision, is easy to cause vision loss and reduces the life quality of patients. Therefore, early detection and intervention are of great clinical significance.
The study finds that the history of diabetes is 15-20 years, and almost all patients with type 1 diabetes and more than 60% of patients with type 2 diabetes have retinopathy.
The mechanism of occurrence of diabetic retinopathy is related to persistent metabolic disorders caused by hyperglycemia, inflammatory reactions, oxidative stress or other unknown causative factors. Thus, Proliferative Diabetic Retinopathy (PDR) occurs despite patients with adequate glycemic control. It has been found that although blood glucose is the major systemic risk factor for the progression of retinopathy, its overall contribution is only 11%, that is 89% of the risk must be accounted for by other unknown factors. This suggests that in the age of individualized diagnosis and treatment under the accurate medical system, biomarkers causing different phenotypes of retinopathy progression need to be identified on the basis of analysis of retinal damage.
Many researchers have explored candidate genes for diabetic retinopathy using candidate gene linkage analysis and genome-wide association studies. ALR2, VEGF and RAGE genes have been reported so far (non-patent document 1), but these results are not replicated in the human population, and none of these genes is considered as a candidate gene for high risk of diabetic retinopathy.
So far, there are few metabonomics studies on diabetic retinopathy, and there are studies to compare the metabolic differences of vitreous specimens of proliferative diabetic retinopathy patients and non-diabetic control patients by metabonomics detection using vitreous specimens (non-patent documents 2 and 3). However, the sampling of vitreous specimens has the disadvantages of being invasive and limited.
Plasma/serum is readily available and still a common biological sample for clinical screening. Therefore, there is still a great need for research to obtain biomarkers from serum metabolites for diagnosing or preventing diabetic retinopathy.
Non-patent document
Non-patent document 1: hampton BM, Schwartz SG, Brantley MA, Jr., Flynn HW, Jr.: Update on genetics and diabetic retination. Clin Ophthalmol 2015,9: 2175-.
Non-patent document 2: paris LP, Johnson CH, Aguilar E, Uui Y, Cho K, Hoang LT, Feitelberg D, Benton HP, Westenskow PD, Kurihara T et al, Global methodology regulation in isochemical reliability, Metabolics 2016,12:15.
Non-patent document 3: barba I, Garcia-Ramirez M, Hernandez C, Alonso MA, Masmiquel L, Garcia-Dorado D, Simo R: Metabolic finger prints of synergistic diagnostic reactions, an 1H-NMR-based Metabolic profiling using virous humor. invest Ophthalmol Vis Sci 2010,51(9):4416-4421.
Disclosure of Invention
Technical problem
The present inventors have conducted intensive studies in view of the state of the art, and as a result, have found for the first time that one or more of fumaric acid, uridine, acetic acid, 3-sulfinylalanine, and 3-methylxanthine derived from serum can be used as a biomarker for diagnosing or preventing diabetic retinopathy, thereby completing the present invention.
That is, the technical problem to be solved by the present invention is to provide a biomarker which can diagnose or prevent diabetic retinopathy conveniently and effectively, and a reagent for detecting the biomarker and use thereof. Further provided is a pharmaceutical composition for diagnosing or preventing proliferative diabetic retinopathy.
Solution scheme
According to the present invention, there is provided a use of a detection reagent for detecting a biomarker for diabetic retinopathy in the preparation of a reagent or a kit for detecting diabetic retinopathy, the biomarker comprising: 1 or more of fumaric acid, uridine, acetic acid, 3-sulfinylalanine, and 3-methylxanthine.
According to the invention, the plurality comprises any combination of 2, 3, 4, 5 biomarkers. More specific combinations include: a combination of fumaric acid and uridine, a combination of fumaric acid and acetic acid, a combination of fumaric acid and 3-sulfinylalanine, a combination of fumaric acid and 3-methylxanthine, a combination of uridine and acetic acid, a combination of uridine and 3-sulfinylalanine, a combination of uridine and 3-methylxanthine, a combination of acetic acid and 3-sulfinylalanine, a combination of acetic acid and 3-methylxanthine, a combination of 3-sulfinylalanine and 3-methylxanthine, a combination of fumaric acid, uridine and acetic acid, a combination of fumaric acid, uridine and 3-sulfinylalanine, a combination of fumaric acid, uridine and 3-methylxanthine, a combination of fumaric acid, acetic acid, and 3-sulfinylalanine, a combination of fumaric acid, acetic acid, and 3-methylxanthine, fumaric acid, 3-sulfinylalanine and 3-methylxanthine, uridine, acetic acid and 3-sulfinylalanine, uridine, acetic acid and 3-methylxanthine, uridine, 3-sulfinylalanine and 3-methylxanthine, acetic acid, 3-sulfinylalanine and 3-methylxanthine, fumaric acid, uridine, acetic acid and 3-sulfinylalanine, fumaric acid, uridine, acetic acid and 3-methylxanthine, fumaric acid, acetic acid, 3-sulfinylalanine and 3-methylxanthine, uridine, acetic acid, 3-sulfinylalanine and 3-methylxanthine, fumaric acid, acetic acid, 3-sulfinylalanine and 3-methylxanthine, or a combination of fumaric acid, uridine, acetic acid, 3-sulfinylalanine and 3-methylxanthine, said combination not being limited by this list.
According to the invention, the biomarkers are further selected in combination from: 1 or more of gluconic acid, cytidine, thymidine.
According to the invention, the plurality includes 2 kinds and 3 kinds. More specific combinations include: a combination of gluconic acid and cytidine, a combination of gluconic acid and thymidine, a combination of cytidine and thymidine, a combination of gluconic acid, cytidine and thymidine.
According to the present invention, the diabetic retinopathy becomes proliferative diabetic retinopathy.
According to the invention, the biomarker is elevated in serum metabolites of patients with proliferative diabetic retinopathy.
In addition, according to the present invention, there is provided a detection reagent for detecting a biomarker of diabetic retinopathy, the detection reagent including a biomarker for detecting 2 or more of the diabetic retinopathy, the biomarker including: fumaric acid, uridine, acetic acid, 3-sulfinylalanine, 3-methylxanthine.
According to the invention, the 2 or more species include 2 species, 3 species, 4 species and 5 species. More specific combinations are those listed above and are not limited by this list.
According to the invention, the biomarkers are further selected in combination from: 1 or more of gluconic acid, cytidine, thymidine.
According to the invention, the plurality includes 2 kinds and 3 kinds. More specific combinations are those listed above and are not limited by this list.
According to the invention, the detection reagent may be a combined detection reagent.
According to the present invention, the diabetic retinopathy becomes proliferative diabetic retinopathy.
According to the invention, the biomarker is derived from serum.
According to the present invention, there is provided a use of a pharmaceutical composition comprising an agent that inhibits elevation of 1 or more of the above biomarkers in serum metabolites for the manufacture of a medicament for preventing or treating diabetic retinopathy.
According to the present invention, the diabetic retinopathy becomes proliferative diabetic retinopathy.
Advantageous effects
1 or more of fumaric acid, uridine, acetic acid, 3-sulfinylalanine and 3-methylxanthine derived from serum, at levels significantly higher in proliferative diabetic retinopathy patients than in diabetic non-retinopathy patients. It can be used as biomarker for diagnosing or preventing diabetic retinopathy.
Drawings
FIG. 1 is a flow diagram of LC-MS metabolomics detection;
FIG. 2 is a score plot of the metabolite Principal Component Analysis (PCA) and the orthogonal partial least squares (PLS-DA) model analysis in the examples. Wherein A represents the analysis result of PCA, and B represents the analysis result of PLS-DA;
FIGS. 3A to 3F show the results of ROC (receiver operating characterization) analysis in the examples. Wherein FIG. 3A is an analysis result of fumaric acid, FIG. 3B is an analysis result of uridine, FIG. 3C is an analysis result of acetic acid, FIG. 3D is an analysis result of cytidine, FIG. 3E is an analysis result of 3-sulfinylalanine, and FIG. 3F is an analysis result of 3-methylsulfonylpurine.
Detailed Description
The present invention will be described in detail below. The following embodiments are examples for illustrating the present invention, but the present invention is not limited to the embodiments. The present invention can be implemented in various embodiments without departing from the gist thereof.
Examples
1. Test object
1.1 proliferative diabetic retinopathy group (PDR, n ═ 21), control group (NDR, n ═ 21) with a diabetic course of 10 years or more and with fundus completely normal.
Inclusion criteria were: the patients with type 2 diabetes are 40-75 years old, and glycated hemoglobin (HbAlc) is not less than 7.5%.
Exclusion criteria: patients with type 1 diabetes and undefined diabetes mellitus; patients with other ocular diseases or systemic diseases with ocular complications; patients with serious impairment of liver and kidney functions and heart functions; malignancy, history of organ transplantation.
1.2 serum sample preparation
Fasting overnight for at least 8 hours with K2The EDTA anticoagulation tube extracts 4mL of peripheral blood, stands on ice for 30 minutes, and then centrifuges at 4 ℃ for 10 minutes by 1000g to extract serum. All serum samples were frozen in a-80 ℃ freezer for use.
1.3 diabetic fundus Classification
All subjects received fundus photography using a topokang TRC-NW7SF (Topcon Co., Tokyo, Japan) and the results of fundus photography were graded by two ophthalmologists, Beijing Homon Hospital, university, headquartered, according to 2002 international diabetic retinopathy clinical grading standard.
No apparent retinopathy (NDR): no abnormal change exists;
(ii) mild non-proliferative diabetic retinopathy (NPDR): microaneurysms only;
(iii) moderate non-proliferative stage diabetic retinopathy: performance ranging between mild and severe NPDR;
(iv) severe nonproliferative stage diabetic retinopathy: any 1 change below occurred, but no PDR performance: more than 20 intraretinal hemorrhages in any quadrant; more than two quadrants have venous bead sample changes; 1 quadrant had significant microvascular abnormalities;
(v) proliferative diabetic retinopathy: 1 or more alterations occur, including neovascularization, vitreal blood, or pre-retinal hemorrhage.
LC-MS metabonomics detection (as shown in FIG. 1)
2.1 Main instruments and reagents
The instrument comprises the following steps: nexera X2system (shimzdu); Q-TOF device: triple TOF 5600+ (sciex).
Reagent: acetonitrile, methanol, formic acid, water (merck).
2.2LC-MS Experimental procedure
2.2.1 sample Pre-treatment
And adding acetonitrile with the volume of 3 times into 100 mu L of serum, mixing for 1-3 minutes by vortex and shaking, standing for 10 minutes at 4 ℃, centrifuging for 10 minutes at 13000rpm, taking supernatant, and drying in vacuum to obtain the serum. And redissolving 100 mu L of acetonitrile for detection.
2.2.2 chromatographic methods
A chromatographic column: agent ZORBAX Eclipse Plus C18, 2.1X 100mm, 3.5 μm; mobile phase A: h2O (0.1% Formic Acid); mobile phase B: acetonitrile (0.1% Formic Acid); flow rate: 0.5 mL/min; the gradient elution conditions were:
TABLE 1
2.2.3 Mass Spectrometry method
Electrospray ionization (ESI) source, positive ion ionization mode. The ion Source temperature (Source temperature) is 120 ℃, the desolvation temperature (desolvation temperature) is 500 ℃, the desolvation nitrogen flow is 600L/h, and the cone hole back flushing nitrogen (con gas flow) is 50L/h. The ionization voltage of the positive ion mode capillary is 3.0kV, the sampling taper hole voltage (sampling cone) is 27eV, the extraction taper hole voltage (extraction cone) is 4eV, and the scanning range m/z of a quadrupole rod is 50-1500.
3. Statistical analysis
3.1 statistics of clinical data
Carrying out normality test on the quantitative data, expressing the quantitative data which accords with normal distribution by mean +/-standard deviation, and comparing the data between the two groups to carry out t test on independent samples; the quantification of the abnormal distribution is indicated by median (P25, P75) and statistically analyzed using the rank sum test. Qualitative data are expressed as a percentage (%). The test results were statistically significant with P <0.05 as the difference.
3.2 Metabolic group outcome statistics
Preprocessing mass spectrum original data: and performing peak identification, peak filtering and peak alignment work by using Marker View, wherein the result finally comprises a two-dimensional data matrix of a mass-to-kernel ratio (m/z) and a peak area in order to analyze multiple linear regression. In order to make the differences in metabolite concentrations between samples as much as possible to reflect biological differences between groups, the samples were normalized using MetabioAnalyst 4.0(http:// www.metaboanalyst.ca/MetabioAnalyst /).
Multivariate analysis using MetabioAnalyst 4.0 to discover differential metabolites between groups included unsupervised Principal component analysis (PCA, see FIG. 2) and supervised mode partial least squares (PLS-DA). Validating the PLS-DA model using 10-fold cross validation, wherein R2Evaluation of fitting conditions to the PLS-DA model, Q2And evaluating the prediction capability of the model. Negative or very low Q2Values indicate that the differences between groups are not statistically significant.
In this model R2And Q20.97 and 0.94 respectively. The established PLS-DA model can well distinguish the PDR group from the NDR group, and shows that the two groups have obviously different metabolic characteristics.
The PLS-DA model eliminates variations in the X matrix that are not related to the Y matrix. Therefore, there is typically only one prediction component for distinguishing between the two classes. A two-tailed Welch's t test performed by MetabioAnalyst 4.0 software showed that P <0.05 was statistically significant. And calculating the volcanic chart by adopting a method combining multiple change and t test. Peak with statistically significant difference between groups was then used for multivariate pattern recognition to determine up-regulation or down-regulation. These data were then used for boxplot analysis, cluster analysis and metabolic pathway analysis.
Differential metabolites were analyzed for pathway and visualization using MetabioAnalyst (http:// www.metaboanalyst.ca /). Differential metabolite screening sets criteria based on Fold difference (FC), P-value and Variable importance in the project (VIP). Comparing the screened differential metabolites with an HMDB database to determine specific substances.
The differential compounds screened according to FC >1.5 or FC <0.5, P <0.05 are compared with the HMDB database for the identification of 685 differential metabolites. These include carboxylic acids and their derivatives (37.79%), fatty acyl and fatty acid esters (26.16%), pyrimidine nucleotides (19.19%), amino acids, peptides (7.56%) and other markers.
Pathway analysis was performed on the differential compounds using the KEGG database and the significance of differential compound enrichment in each Pathway entry was calculated using a statistical test. The results of the calculations return a P value of significance for enrichment, and a small P value indicates that the differential compound is enriched in the Pathway. Metabolite pathway enrichment assay (MSEA).
The metabolically poor foreign body in the present invention was enriched in 62 KEGG pathways, of which 9 pathways were significantly enriched (P < 0.05). Pyrimidine metabolism, alanine, aspartic acid and glutamic acid metabolism, caffeine metabolism, beta-alanine metabolism, purine metabolism, cysteine and methionine metabolism, sulfur metabolism, sphingosine metabolism, arginine and proline metabolism, respectively, and influencing factors are 0.21, 0.65, 0.37, 0.36, 0.27, 0.22, 0.18, 0.47 and 0.54, respectively. In the nine pathways, 63 kinds of metabolic impurities are involved.
3.3 predictive biomarker screening for PDR
The 63 metabolic differences were screened by setting more stringent screening criteria.
Screening criteria: p <10E-07, Area under the curve (AUC) ≥ 0.95 and VIP >1, for a total of 6 differential metabolites were selected (see Table 2, FIG. 3).
TABLE 2
4. Results
The present invention obtains 6 different metabolites (shown in Table 2), which are fumaric acid, uridine, acetic acid, cytidine, 3-sulfinylalanine and 3-methylsulfonylpurine.
The inventors found that the differential metabolites were significantly increased among the serum metabolites of the proliferative diabetic retinopathy group, and the areas under the diagnostic curves are shown in table 2, respectively. It can be concluded that the differential metabolites shown in Table 2 are associated with diabetic retinopathy and are useful for diagnosing or preventing diabetic retinopathy, particularly proliferative diabetic retinopathy.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Practicality of use
The biomarker for detecting or preventing the diabetic retinopathy provided by the embodiment of the invention can be applied to the field of diagnosis or prevention of the diabetic retinopathy, is particularly suitable for diagnosis or prevention of proliferative diabetic retinopathy, and can effectively diagnose the proliferative diabetic retinopathy and potential proliferative diabetic retinopathy.
Claims (3)
1. Use of a detection reagent for detecting a biomarker for diabetic retinopathy in the preparation of a reagent or kit for detecting diabetic retinopathy, the biomarker comprising: fumaric acid, uridine, acetic acid, 3-sulfinylalanine and 3-methylxanthine, said biomarkers being derived from serum, said detecting diabetic retinopathy being a distinguishing between patients with diabetic retinopathy and patients with diabetic non-retinopathy, said diabetic retinopathy being a proliferative stage diabetic retinopathy.
2. The use of claim 1, the biomarker further selected from the group consisting of: 1 or more of gluconic acid, cytidine, thymidine.
3. The use according to claim 1 or 2, wherein the biomarker is elevated in serum metabolites of patients with proliferative diabetic retinopathy.
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| CN112697889B (en) * | 2019-10-22 | 2022-03-08 | 中国科学院大连化学物理研究所 | Application of serum metabolism marker and detection kit |
| CN113155983B (en) * | 2020-01-22 | 2023-04-11 | 中国科学院大连化学物理研究所 | Combined marker and application and detection kit thereof |
| CN114137192B (en) * | 2021-11-04 | 2023-09-26 | 无锡市疾病预防控制中心 | Application of 7-methylxanthine as detection target in preparation of type 2diabetes mellitus high-risk individual screening kit |
| CN114839292A (en) * | 2022-04-29 | 2022-08-02 | 中国中医科学院中药研究所 | Biomarker for excessive heat and excessive fire syndrome intervened by coptis antidote pill and application of biomarker |
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