CN115326959A - Metabolites associated with the diagnosis of diabetic retinopathy - Google Patents

Abstract

The invention discloses metabolites relevant to diagnosis of diabetic retinopathy. According to the invention, metabolites which show significant differences in diabetic retinopathy or the processes thereof are discovered for the first time through metabonomics research, and the diagnosis of diabetic retinopathy or the processes thereof by using the different metabolites or the combination of the metabolites has higher efficiency.

Description

Metabolites associated with the diagnosis of diabetic retinopathy

Technical Field

The invention belongs to the field of biological medicines, and relates to metabolites relevant to diabetic retinopathy diagnosis.

Background

Diabetic Retinopathy (DR), a devastating disease, is the most severe microvascular complication of ocular diabetes and is also the leading cause of vision loss and blindness in adults between 20 and 74 years of age in developing and developed countries. One study showed that 1.14 million diabetics in China were the first to live in the world. In China, the prevalence of DR in the general population is 1.7%, while the prevalence of DR in the diabetic population is 22.4%, with the highest prevalence in North China (27.7%) (DengYX, ye WQ, sun YT, zhou ZY, liang YB. [ A. Metal-analysis of predictive of diagnostic reliability in China ]. Zhonghua Yi Xue Zhi, 100 (48): 3846-52.). Currently, treatment of DR, including retinal laser photocoagulation, intravitreal injection of anti-vascular endothelial growth factor, and vitrectomy, is only used to control the late stage development of DR, and there is no effective treatment to limit early stage DR neurovascular dysfunction or promote repair. Blood glucose levels and duration of diabetes have long been recognized as major risk factors for development of DR (Cheung N, wong IY, wong TY. Ocular anti-VEGF therapy for diabetic retination: overview of clinical efficacities and infectious applications. Diabetes Care.2014;37 (4): 900-5.). However, in clinical practice, these risk factors do not account well for the large differences in individual rates of progression of DR patients, suggesting that there may be other unknown factors that may more accurately screen and predict the onset and progression of DR.

Although many metabolomic studies have been conducted on DR, few attempts have been made to identify differential metabolites during the critical phase of DR development (T2 DM and NPDR), particularly in asian populations. In previous non-targeted metabonomic studies of asian DR, we found that, in addition to deregulation of the classical amino acid metabolic pathway, many small molecules such as long chain polyunsaturated fatty acids, PC and bile acids are up-or down-regulated to varying degrees in the critical phase of DR (Wang Z, tang Jet al. Serum unartargeted metals recent Potential Biomarkers of Progression of diagnostic reporting in asians. Front Mol biosci.2022;9 871291..

No study currently uses the same detection platform to compare non-targeted and targeted metabolomics results from different stages of DR patients. To fill this gap, the present study aimed to perform targeted metabolomics by liquid chromatography-mass spectrometry (LC-MS) in the serum of T2DM patients with and without DR. The results of targeted metabolomics are compared to the results of previous non-targeted metabolomics to determine biomarkers that have a positive or negative impact on DR development and are relevant for DR prognosis, thereby enabling early screening and diagnosis of diabetic retinopathy.

Disclosure of Invention

The invention aims to carry out serum targeted metabonomics by collecting blood of healthy people and patients with diabetic retinopathy, screen differential metabolites, carry out diagnostic efficiency analysis on the metabolites and finally screen candidate differential metabolites which can be used for diagnosing the diabetic retinopathy.

The specific scheme is as follows:

in a first aspect the invention provides the use of a test metabolite comprising EPA (Eicosapentaenoic Acid), IAA (indoleacetic Acid), CDCA (chenodeoxycholic Acid), C18:2, CE (16.

Further, IAA, C18:2, CE (16.

Further, the diabetic retinopathy becomes proliferative retinopathy.

Further, EPA, IAA, CDCA, CE (22.

Further, in the preparation of a product for diagnosing diabetic retinopathy, the metabolite is selected from one or more of EPA, IAA, CDCA, CE (22: 5), PC aa C34:4, PCaa C36:6.

Further, in the preparation of a product for diagnosing diabetic retinopathy, the metabolite is selected from a combination of two or more of EPA, IAA, CDCA, CE (22), PC aa C34:4, PCaa C36:6.

Further, the metabolite is selected from the group consisting of EPA and IAA in combination.

Further, the metabolite is selected from the group consisting of CE (22).

Further, the metabolite is selected from the group consisting of combinations of PC aa C36:6 and EPA.

Further, the metabolite is selected from the group consisting of combinations of PC aa C36:6 and EPA.

Further, the metabolite is selected from the group consisting of a combination of CDCA and EPA.

Further, the metabolite is selected from the group consisting of CE (22).

Further, the metabolite is selected from the group consisting of CE (22).

Further, the metabolite is selected from the group consisting of PC aa C36:6 in combination with CDCA.

Further, the product comprises a reagent for measuring the concentration or amount of the metabolite in the sample.

Further, the reagent comprises a reagent for detecting the concentration or amount of the metabolite in the sample by nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, or a combination thereof.

Further, the sample is blood, plasma, serum.

In a second aspect the invention provides a product for diagnosing diabetic retinopathy, the product comprising reagents for detecting the metabolites EPA, IAA, CDCA, C18:2, CE (16.

Further, the product comprises reagents for detecting IAA, C18:2, CE (16.

Further, the product comprises reagents for detecting EPA, IAA, CDCA, CE (22.

Further, the agent detects the level of a metabolic marker by one or more of: chromatography, spectroscopy, mass spectrometry, chemical analysis.

Further, the product also comprises reagents for processing the sample.

Further, the product comprises a kit and a chip.

In a third aspect, the invention provides the use of metabolites comprising EPA, IAA, CDCA, C18:2, CE (16).

Further, the metabolite is selected from IAA, C18:2, CE (16: 1), lysoPC a C26:0, PC aa C34:4, PC aa C36:6 and/or PC aa C42:4.

Further, the diabetic retinopathy becomes proliferative retinopathy.

Further, the metabolite for predicting proliferative retinopathy is selected from EPA, IAA, CDCA, CE (22.

Further, the model takes the content or concentration of the metabolite in the sample as an input variable.

Further, the sample is blood, plasma, serum.

In a fourth aspect, the present invention provides a system or apparatus for predicting diabetic retinopathy, comprising:

means for determining a characteristic value of a metabolite in the sample;

means for comparing characteristic values of metabolites in the sample;

a data storage medium;

wherein the metabolite is selected from EPA, IAA, CDCA, C18:2, CE (16), CE (22.

Further, the metabolite is selected from IAA, C18:2, CE (16: 1), lysoPC a C26:0, PC aa C34:4, PC aa C36:6 and/or PC aa C42:4.

Further, the diabetic retinopathy becomes proliferative retinopathy.

Further, the metabolite for predicting proliferative retinopathy is selected from EPA, IAA, CDCA, CE (22.

Further, the metabolite feature value is the content or concentration of the metabolite.

Further, the sample is selected from blood, plasma, serum.

The invention has the advantages and beneficial effects that:

the invention firstly discovers metabolic markers EPA, IAA, CDCA, C18:2, CE (16), CE (22.

Drawings

FIG. 1 is a diagram of OPLS-DA statistical analysis.

FIG. 2 is a ROC plot of metabolites diagnosing diabetic retinopathy; wherein 2A is C18:2;2B is CE (16; 2C is lysoPC a C26:0;2D is PC aa C34:4;2E is PC aa C36:6;2F is PC aa C42:4;2G is IAA.

FIG. 3 is a ROC plot of metabolites in diagnosing proliferative retinopathy; wherein 3A is CE (22; 3B is PC aa C34:4;3C is PC aa C36:6;3D is EPA;3E is IAA.

Detailed Description

Metabolomics is an emerging research area downstream of genomics, proteomics, and transcriptomics. There are 40,000 various metabolites in humans, the concentration of which can provide a snapshot of the current health status of an individual. The metabolome is a quantitative collection of low molecular weight compounds produced by metabolism, such as metabolic substrates and products, lipids, small peptides, vitamins and other protein cofactors. The metabolome is downstream of the transcriptome and proteome, so any changes from the normal state are amplified and are numerically easier to handle. Metabolomics can be an accurate, consistent, and quantitative tool for examining and describing cell growth, maintenance, and function.

To evaluate the correlation between metabolites and diabetic retinopathy, metabolic markers suitable for diagnosis of diabetic retinopathy are found by collecting samples of diabetic retinopathy patients, non-proliferative retinopathy, proliferative retinopathy and healthy people known not to suffer from diabetic retinopathy, analyzing the metabolomics of the samples in combination, screening metabolites whose contents exhibit significant differences among different groups, and preferably analyzing the diagnostic efficacy of the different metabolites. The present invention, through extensive and intensive studies, has found for the first time that the metabolic markers EPA, IAA, CDCA, C18:2, CE (16. EPA, IAA, CDCA, CE (22.

In the present invention the sample is a biological sample. Samples of biological origin (i.e. biological samples) typically comprise a plurality of metabolites. Preferred test samples to be used in the method of the invention are samples from body fluids, preferably from blood, plasma, serum, lymph, sweat, saliva, tears, semen, vaginal fluid, faeces, urine or cerebrospinal fluid, or from cells, tissues or organs, for example by biopsy. This also includes samples comprising subcellular compartments or organelles (e.g., mitochondria, golgi networks, or peroxisomes). In addition, biological samples also include gaseous samples, such as volatiles from organisms. Biological samples were taken from subjects as specified elsewhere herein. Techniques for obtaining the different types of biological samples described above are well known in the art. For example, a blood sample is obtained by blood collection and a urine sample is obtained by urine collection. Preferably, the sample is selected from blood. More preferably, the sample is selected from serum.

Preferably, the sample is pretreated before it is used in the method of the invention. The pretreatment may include treatments required to release or isolate compounds, or to remove unwanted materials or waste. Suitable techniques include centrifugation, extraction, fractionation, purification and/or enrichment of compounds. In addition, other pre-treatments are performed to provide the compound in a form or concentration suitable for compound analysis. For example, if gas chromatography coupled with mass spectrometry is used in the methods of the invention, it will be necessary to derivatize the compounds prior to said gas chromatography. Suitable and necessary pretreatments depend on the means for carrying out the process of the invention and are well known to the person skilled in the art. The pre-treated sample as described before is also encompassed by the term "sample" as used in the present invention.

The term "metabolic marker", "metabolic biomarker" or short "biomarker" as used herein is defined as a compound suitable as an indicator of the presence and status of diabetic retinopathy, which is a metabolite or metabolic compound that occurs during metabolic processes in the body of a mammal. The terms "biomarker" and "metabolic biomarker" are generally used synonymously in the context of the present invention and generally refer to a compound suitable as an indicator of the presence and status of diabetic retinopathy, such compound being a metabolite or metabolic compound occurring during metabolic processes in the body of a mammal.

The term "differential metabolite" or "significant difference" as used herein means that the amount or concentration of one or more metabolites of the invention present in one sample is different compared to the level of the same one or more metabolites of the invention in a second sample. The "differential metabolite" as used in the present invention may be determined as the ratio of the level of a given metabolite relative to the average level of the given metabolite in a control, wherein the ratio is not equal to 1.0. The difference can also be determined using the p-value. When using a p-value, biomarkers are identified that exhibit a difference between the first and second populations when the p-value is less than 0.1. More preferably, the p-value is less than 0.05. Even more preferably, the p-value is less than 0.01. Still more preferably, the p-value is less than 0.005. Most preferably, the p value is less than 0.001. When the difference is determined based on the ratio, the metabolite is present differently if the ratio of the levels in the first and second samples is greater or less than 1.0. For example, a ratio greater than 1.2, 1.5, 1.7, 2, 3, 4, 10, 20, or a ratio less than 1, such as 0.8, 0.6, 0.4, 0.2, 0.1, 0.05.

By "increased level" or "up-regulated" is meant that the metabolite level (as measured by the amount or concentration of the metabolite) shows an increase of at least 10% or more, e.g., 20%, 30%, 40% or 50%, 60%, 70%, 80%, 90% or more, relative to a control; or 1.1 times, 1.2 times, 1.4 times, 1.6 times, 1.8 times, or more.

By "reduced level" or "down-regulated" is meant that the level of a metabolite (measured as the content or concentration of the metabolite) shows a reduction of at least 10% or more, e.g. 20%, 30%, 40% or 50%, 60%, 70%, 80%, 90%; or less than 1.0 times, 0.8 times, 0.6 times, 0.4 times, 0.2 times, 0.1 times or less. For example, up-regulated metabolites include metabolites that are detected at increased levels in individuals with diabetic retinopathy as compared to levels of metabolites detected in healthy groups that have not suffered from diabetic retinopathy. For example, up-regulated metabolites include metabolites that are detected at increased levels in individuals with diabetic retinopathy as compared to the levels of metabolites detected from a control group with T2 DM; for example, up-regulating a metabolite includes a metabolite that is detected at an increased level in an individual having proliferative retinopathy as compared to the level of the metabolite detected in a control group having non-proliferative retinopathy. For example, downregulating metabolites includes decreasing levels of metabolites detected from an individual having diabetic retinopathy as compared to levels of metabolites detected from a healthy cohort not having diabetic retinopathy. For example, down-regulating metabolites includes metabolites detected at a reduced level in individuals with diabetic retinopathy compared to the levels of metabolites detected from a control group with T2 DM; for example, down-regulating a metabolite includes a metabolite detected at a level reduced from that detected in an individual having proliferative retinopathy as compared to the level of a metabolite detected in a control group having non-proliferative retinopathy.

In case reference results are obtained from a known control population, the disease or process may be diagnosed based on the difference between the test results obtained from the test sample and the above reference results, i.e. based on the difference in the qualitative or quantitative composition with respect to the at least one metabolite. The difference may be an increase in the absolute or relative amount of a metabolite (sometimes referred to as metabolite upregulation) or a decrease or no detectable amount of the amount of a metabolite (sometimes referred to as metabolite downregulation). Preferably, the difference in relative or absolute amounts is significant, i.e. outside the reference value interval of 45 to 55 percentiles, 40 to 60 percentiles, 30 to 70 percentiles, 20 to 80 percentiles, 10 to 9 percentiles, 5 to 95 percentiles. Preferred values for changes in relative amounts (i.e., "fold" changes) or types of changes (i.e., "up" or "down" adjustments resulting in higher or lower relative amounts and/or absolute amounts). The relative and/or absolute amount of a given metabolite will increase if it is "up-regulated" in a subject and decrease if it is "down-regulated". Furthermore, a "fold" change indicates the degree of increase or decrease, e.g., a 2-fold increase means that the amount is twice the amount of metabolite as compared to a reference.

Thus, in a preferred embodiment, a reference from a subject or group known to have diabetic retinopathy, or a subject or group known to have a course thereof, is included. Most preferably, the same or similar result (i.e. a similar relative or absolute amount of the at least one metabolite) of the test sample and the reference is indicative for diabetic retinopathy or its progression in this case. In another preferred embodiment of the invention, the reference is from a subject known not to have diabetic retinopathy or a subject known not to have had their course, or the reference is a computable reference.

It will be appreciated by those skilled in the art that the level of the metabolite can be determined by any method known in the art, such as chromatography, spectroscopy, mass spectrometry or a combination thereof, with mass spectrometry being particularly preferred. Chromatograms can include GC, LC, HPLC and UHPLC; spectra may include UV/Vis, IR and NMR; the mass spectrometer/spectrum may include ESI-QqQ, ESI-QqTOF, MALDI-QqQ, MALDI-QqTOF, and MALDI-TOF-TOF. More preferably, the Mass analyser/spectral analysis comprises a quadrupole Mass analyser, an ion trap Mass analyser, a TOF (time of flight) Mass analyser, an orbital trap Mass analyser, a magnetic Sector Mass analyser, an Electrostatic field Sector Mass analyser, ion Cyclotron Resonance (ICR) and a combination of Mass analysers (including single quadrupole (Q) and triple quadrupole (QqQ), qtof, TOF-TOF, Q-orbital trap). Preferably, FLA-and HPLC-tandem mass spectrometry is used.

Wherein GC = gas chromatography, CE = capillary electrophoresis, LC = liquid chromatography, HPLC = high liquid chromatography, UHPLC = ultra high performance liquid chromatography, UV-Vis = ultraviolet visible, IR = infrared, NIR = near infrared, NMR = nuclear magnetic resonance, ESI = electrospray ionisation, MALDI = matrix assisted laser desorption/ionisation, TOF = time of flight, APCI = atmospheric pressure chemical ionisation, qqQ = triple quadrupole configuration (also known as Qlq2Q3 (Q1 and Q3 quadrupoles are mass filters, Q2 is a no mass-resolving quadrupole))).

Mass spectrometry includes, for example, tandem mass spectrometry, matrix Assisted Laser Desorption Ionization (MALDI) time of flight (TOF) mass spectrometry, MALDI-TOF-TOF mass spectrometry, MALDI quadrupole-time of flight (Q-TOF) mass spectrometry, electrospray ionization (ESI) -TOF mass spectrometry, ESI-Q-TOF, ESI-TOF-ion trap mass spectrometry, ESI triple quadrupole mass spectrometry, ESI Fourier Transform Mass Spectrometry (FTMS), MALDI-FTMS, MALDI-ion trap-TOF, and ESI-ion trap TOF. At its most basic level, mass spectrometry involves ionizing molecules and subsequently measuring the mass of the resulting ions. Since the molecules are ionized in a known manner, the molecular weight of the molecules can be accurately determined from the mass of the ions.

Tandem mass spectrometry involves first obtaining a mass spectrum of an ion of interest, then fragmenting the ion and obtaining a mass spectrum of the fragment. Tandem mass spectrometry thus provides molecular weight information and fragmentation spectra that can be used together with the molecular weight information to identify the exact sequence of a peptide or protein or small molecule (below 1500 daltons).

Although the use of one metabolite is sufficient for the diagnosis of diabetic retinopathy, the use of one or more, two or more, three or more, or four or more such metabolites is included in the present invention, and in many cases, this is preferable. Metabolites may be analyzed and used in any combination for diagnosis.

The term "area under the curve" or "AUC" refers to the area under the curve of the Receiver Operating Characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of classifiers across the full data range. A classifier with a higher AUC has a higher ability to correctly classify between two target groups (e.g., in diabetic retinopathy samples and in diabetic group samples) that is not known. The ROC curve is useful for characterizing the performance of a particular feature (e.g., any biomarker described herein and/or any item of additional biomedical information) when distinguishing between two populations (e.g., for individuals with diabetic retinopathy and a diabetic group). Typically, feature data is selected across the entire population (e.g., cases and controls) in ascending order based on the value of a single feature. Then, for each value of the feature, the true positive and false positive rates of the data are calculated. The true positive rate is determined by counting the number of cases above the value of the feature and dividing by the total number of cases. The false positive rate is determined by counting the number of controls above the value of the feature and dividing by the total number of controls. Although the definition refers to the case where the feature is elevated in the case compared to the control, the definition also applies to the case where the feature is lower in the case compared to the control (in this case, a sample lower than the value of the feature will be counted). The ROC curve may be generated with respect to individual features and with respect to other individual outputs, e.g., a combination of two or more features may be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide individual sum values, and the individual sum values may be plotted in the ROC curve. In addition, any combination of features where the combination results from separate output values can be plotted in a ROC curve. These combinations of features may include tests. The ROC curve is a plot of true positive rate (sensitivity) versus false positive rate (1-specificity) of the test.

For the assessment of the amount of one or more metabolites in a sample, it may be necessary to manipulate the sample according to the type of sample and the method selected for the assessment. For example, in the case of blood, the blood may be drawn into a suitable container, followed by gently inverting the container one or more times, and allowing the sample to stand at room temperature for several minutes to achieve complete coagulation. For serum collection, centrifugation of the blood may be performed, for example, at 2750g and 15 ℃ for 10min. The serum can then be separated and, if desired, filled into containers, such as synthetic pipettes, for storage, such as in liquid nitrogen, until metabolic analysis is performed.

As used herein, a "data storage medium" is a data storage medium that stores a collection of data, and the term "data collection" refers to a collection of data that is physically and/or logically collected. Thus, the data sets may be embodied in a single data storage medium or in physically separate data storage media that are operatively coupled to each other. Preferably, the data set is implemented into a database. Thus, a database as used herein comprises a collection of data on a suitable storage medium. In addition, the database preferably also contains a database management system. The database management system is preferably an internet-based hierarchical database management system or an object-oriented database management system. Further, the database may be a federated database or an integrated database. More preferably, the database will be implemented as a distributed (federated) System, such as a Client-Server-System. More preferably, the database is constructed to allow a search algorithm to compare the test data set with the data set comprising the data set. In particular, by using such algorithms, a database may be searched (i.e., a query search) for similar or identical data sets indicative of diabetic retinopathy or its progression. Thus, if the same or similar data set can be identified in the data set, the test data set will be associated with diabetic retinopathy or its progression. As a result, the information obtained from the data set can be used to diagnose diabetic retinopathy or its progression based on the test data set obtained from the subject. More preferably, the data set comprises characteristic values for all metabolites comprised in any of the groups recited above.

The term "data storage medium" includes data storage media or cloud based on a single physical entity such as a CD, CD-ROM, hard disk, optical storage medium or magnetic disk. Furthermore, the term also includes data storage media consisting of physically separate entities operatively connected to each other in a manner that provides the data collection described above, preferably in a suitable manner for query searching.

The "system" in the present invention relates to different tools that are operatively connected to each other. The tools may be embodied in a single device or may be physically separate devices operatively connected to each other. The means for comparing metabolite feature values preferably operate based on an algorithm for comparison. The data storage medium preferably comprises a data collection or database as described above, wherein each of the stored data sets is indicative of diabetic retinopathy or its progression. The system of the present invention thus allows to identify whether a data set stored in a data storage medium contains a test data set. As a result, the system of the present invention can be used as a diagnostic tool for diagnosing diabetic retinopathy or its progression. In a preferred embodiment of the system, means for determining characteristic values of metabolites of the sample are comprised.

The term "means for determining characteristic values of metabolites" preferably relates to devices for determining metabolites, such as mass spectrometry devices, NMR devices, or devices for performing chemical or biological assays of metabolites. Furthermore, the present invention relates to a diagnostic tool comprising means for determining at least one metabolite selected from any of the aforementioned groups.

The invention is described in detail below with reference to the figures and examples. The following examples are intended to illustrate the invention only and are not intended to limit the scope of the invention.

Example screening and potency determination of diabetic retinopathy-related metabolites

1. Study object and study design

110 patients were randomly enrolled from the eye center of the national hospital of Beijing university and the clinical case control study was approved by the Ethics Committee of the national hospital of Beijing university. The study followed the principles of the declaration of helsinki. All participants signed informed consent prior to study enrollment. For matching clinical parameters between cases and control subjects, 27 cases of diabetes control, 27 cases of T2DM, 56 cases of DR were included; wherein the diabetic control group is a healthy individual, the T2DM case is type 2 diabetes for at least 10 years and is free of clinical signs of DR, and the DR case is a type 2 diabetes patient with clinical signs of DR. The study randomized the diabetic control (n = 27), T2DM (n = 27), NPDR (n = 28) and PDR (n = 28) groups into 9, 10 and 10 patients for targeted metabolomics studies, respectively, and other patients including the diabetic control (n = 18), T2DM (n = 18), NPDR (n = 18) and PDR (n = 18) groups for ELISA testing.

Inclusion criteria were:

according to the criteria for the Early Treatment of Diabetic Retinopathy Study (ETDRS), DR is divided into three categories: DR-free, non-proliferative diabetic retinopathy (NPDR) or Proliferative Diabetic Retinopathy (PDR). All patients were confirmed by two retinal specialists by dilated fundus examination. The presence of DR was confirmed and recorded by color fundus photography, fluorescein Angiography (FA) and Optical Coherence Tomography (OCT), and classified as NPDR or PDR.

Exclusion criteria:

(1) The presence or history of other ocular diseases (retinal degeneration, glaucoma, active ocular inflammation, etc.); there is a history of intraocular surgery (vitreoretinal surgery, intravitreal injection, laser treatment, and trauma history); (2) Cancer, infectious disease, hyperuricemia, hereditary metabolic disease, mental disorder, heart failure, severe hypertension, acute myocardial infarction, stroke, or any other severe chronic systemic disease; (3) The cornea and lens are diseased, preventing a clear view of the fundus.

Data collection

Medical records have been obtained for all participants, including past medical history, current status of smoking and drinking, duration of diabetes, clinical and laboratory measures, medications used, and disease status. Patients received physical examinations including age, gender, duration, blood pressure and Body Mass Index (BMI) and blood laboratory examinations.

2. Metabolomics detection

1) Sample preparation for metabonomics studies

A 6mL sample of venous blood was collected from the subjects in the fasted state. Serum/plasma was separated by centrifugation at 3000rpm for 10min at 4 ℃ and then transferred to 1.5mL sterile tubes and immediately stored at-80 ℃ for ultra-low temperature freezing.

2) Targeted metabonomic analysis

Targeted quantitative metabolomics analysis was performed on the bioclates P500 platform using the MxP500 Quant kit (bioclates Life Science AG, innbruck, austria). Thawed frozen plasma samples (10 μ L) were transferred to 56 well plates, dried under a stream of nitrogen for 30 minutes, and derivatized by addition of 5% Phenyl Isothiocyanate (PITC) solution. After incubation for 1 hour in the dark, the samples were dried under a stream of nitrogen for 1 hour. After adding 300. Mu.l of extraction solvent and mixing at 450rpm for 30 minutes, the filtered extract was collected by centrifugation at 600rpm for 10 minutes.

Metabolites extracted on a MetLMS system (biochates Life Science AG, inbbruck, austria) were monitored by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and flow injection analysis-tandem mass spectrometry (FIA-MS/MS) using multiple reactions to detect analytes. Mu.l of the diluted sample extract was used for positive and negative mode LC-MS/MS, the column was washed with 95% solvent B at a flow rate of 0.5ml/min, and 5. Mu.l of the diluted sample extract was injected at a temperature of 50 ℃ after equilibrating the system with 100% solvent A at a flow rate of 0.8ml/min before injecting the sample extract

Quant 500 UHPLC chromatography column (

Part number: 22005). For FIA-MS/MS, flow injection analysis was performed by tandem mass spectrometry (FIA-MS/MS) using 20 microliters of diluted sample extract collected in positive mode. LC-MS/MS and FIA-MS/MS analyses were performed using the SCIEX Triple quad 6500+ system (Sciex, darmstadt, germany) and the Acquity H-Class ultra high performance liquid chromatography system (Waters).

Extracting solvent: 5mM ammonium acetate in methanol (19 mg ammonium acetate dissolved in 50ml methanol)

Solvent a (2000 ml): 2000ml of water +4ml of formic acid

Solvent B (2000 ml): 2000ml acetonitrile +4ml formic acid

3) ELISA detection method

The EPA and IAA levels were measured for each sample using ELISA kits (CEA 505Ge, CEO122Ge and CEA737Ge, CLOUD-CLONE technologies, inc., wuhan, china).

4) Statistical analysis

The mean of normal distribution data was compared to homogeneity of variance using analysis of variance (ANOVA), classification data was analyzed using the chi-square test, and age, diabetic duration and biochemical parameters were compared using the Wilcoxon rank sum test. P values <0.05 were considered statistically significant.

Raw data targeted for metabolomics were analyzed using OXygent-DB110-3005 (biochlates Life Science AG, innsbruck, austria), and statistical analysis and visualization of results were performed using R statistical software (version 3.5.2). Calculating VIP value of each characteristic by using OPLS-DA, calculating significance by using t test, and finally screening VIP >1.0 and P <0.05 metabolites into differential metabolites. Wherein, the Fold Change (FC) ratios >1.2, represent significant upregulation, and the FC ratios <0.833, represent significant downregulation. The multiple diagnostic models were obtained by calculating the Receiver Operating Curve (ROC) for differential metabolites using the R package pROC, calculating the area under the curve (AUC) and the optimal Cut-off value.

5) Results

Specific clinical sample information:

the mean age of the included subjects was 66.3 years, the median duration of diabetes was 16.5 years, and women accounted for 47.4%. Of the 110 subjects receiving the ophthalmic evaluation, 27 patients with T2DM (mean age 65.75 ± 7.64 years, male 39.3%), 28 patients with NPDR (mean age 68.72 ± 9.31 years, male 69.0%), 28 patients with PDR (mean age 63.59 ± 6.97 years, male 55.2%), and 27 controls (mean age 67.18 ± 7.77 years, 46.4% male).

In the targeted metabonomics dataset, the OPLS-DA model using the supervised method showed that all 4 groups were clearly separated with model values of R2 and Q2 both being 0. 0.786 and 0.264, respectively, indicating significant metabolic differences between each group (FIG. 1)

The results of the differential analysis showed that EPA, IAA, CDCA, C18:2, CE (16).

TABLE 2 content of the respective metabolites

Wherein ns indicates no significant difference; * Represents P <0.05; * Denotes P <0.01; * Denotes P <0.001

ROC curves were plotted using pROC, areas under the curves are shown in table 3, the metabolites CDCA, EPA, IAA, CE (22), PC aa C34:4, PC aa C36:6 and combinations thereof have higher potency in diagnosing PDR, and the metabolites IAA, C18:2, CE (16.

TABLE 3 area under the curve for diagnosis of each metabolite

The ELISA detection result shows that the levels of the IAA and the EPA in diseases are the same with the result of metabonomics, and the combined diagnostic efficacy of the IAA and the EPA is analyzed, and the result shows that the AUC value of the combination of the IAA and the EPA is 1.

The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.

The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.

Claims (10)

1. Use of a test metabolite for the manufacture of a product for the diagnosis of diabetic retinopathy, wherein the metabolite comprises EPA, IAA, CDCA, C18:2, CE (16 1), CE (22;

preferably, IAA, C18:2, CE (16;

preferably, the diabetic retinopathy becomes proliferative retinopathy;

preferably, EPA, IAA, CDCA, CE (22.

2. The use of claim 1, wherein said product comprises a reagent for measuring the concentration or amount of said metabolite in the sample.

3. The use of claim 2, wherein the reagent comprises a reagent for detecting the concentration or amount of the metabolite in the sample by nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, or a combination thereof.

4. Use according to claim 2 or 3, wherein the sample is blood, plasma, serum.

5. A product for diagnosing diabetic retinopathy, comprising a reagent for detecting metabolites EPA, IAA, CDCA, C18:2, CE (16), CE (22;

preferably, the product comprises reagents for detecting IAA, C18:2, CE (16;

preferably, the product comprises reagents for detecting EPA, IAA, CDCA, CE (22.

6. The product of claim 5, wherein the agent detects metabolic marker levels by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, chemical analysis.

7. The product of claim 5, further comprising a reagent for processing the sample.

8. Use according to any of claims 5 to 7, wherein the product comprises a kit, a chip.

9. Use of a metabolite for the construction of a computational model for the assessment of diabetic retinopathy, wherein the metabolite comprises EPA, IAA, CDCA, C18:2, CE (16), CE (22), lysoPC a C26:0, PC aa C34:4, PC aa C36:6 and/or PC aa C42:4;

preferably, the metabolite is selected from IAA, C18:2, CE (16: 1), lysoPC a C26:0, PC aa C34:4, PC aa C36:6 and/or PC aa C42:4;

preferably, the diabetic retinopathy becomes proliferative retinopathy;

preferably, the metabolite for predicting proliferative retinopathy is selected from EPA, IAA, CDCA, CE (22;

preferably, the model takes the content or concentration of a metabolite in the sample as an input variable;

preferably, the sample is blood, plasma, serum.

10. A system or apparatus for predicting diabetic retinopathy, comprising:

1) Means for determining a characteristic value of a metabolite in the sample;

2) Means for comparing characteristic values of metabolites in the sample;

3) A data storage medium;

wherein the metabolite is selected from EPA, IAA, CDCA, C18:2, CE (16;

preferably, the metabolite is selected from IAA, C18:2, CE (16: 1), lysoPC a C26:0, PC aa C34:4, PC aa C36:6 and/or PC aa C42:4;

preferably, the diabetic retinopathy becomes proliferative retinopathy;

preferably, the metabolite for predicting proliferative retinopathy is selected from EPA, IAA, CDCA, CE (22;

preferably, the metabolite feature value is the content or concentration of a metabolite;

preferably, the sample is selected from blood, plasma, serum.

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