Ocular surface microbial marker combination for diagnosing diabetes or diabetic retinopathy and application thereof

Abstract

The invention discloses an ocular surface microbial marker combination for diagnosing diabetes or diabetic retinopathy and application of the ocular surface microbial marker combination. According to the invention, 10 ocular surface microorganisms obviously related to DM and DR are respectively screened, and DM and DR can be diagnosed by detecting the abundance of the microorganisms. The method belongs to a non-invasive technology, has the advantages of being convenient to detect and high in accuracy, and can be effectively applied to diagnosis of DM and DR. A classifier is generated according to the relative abundance of 10 microorganisms in the conjunctival sac swab of a subject, and in DM and non-DM control groups, the area AUC under the ROC curve of the classifier is 0.975, the sensitivity is 87.5%, and the specificity is 96.4%; in the DR and the DR group, the area AUC under the ROC curve of the classifier is 0.902, the sensitivity is 93.5%, and the specificity is 75.8%.

Description

Technical Field

[0001] The present invention relates to an ocular surface microbial marker combination for diagnosing diabetes or diabetic retinopathy and an application thereof, and belongs to the fields of microbiology, endocrinology and ophthalmology. Background Art

[0002] Diabetes mellitus (DM) is a chronic disease associated with impaired glucose homeostasis and insulin resistance. Diabetic retinopathy (DR) is a common condition affecting older adults and can lead to visual impairment and even blindness. Statistics show that China ranked first in the world in DM prevalence in 2021, with 140 million patients. Diabetic retinopathy (DR) is the most common microvascular complication of DM and can cause vision impairment and even blindness in the elderly.

[0003] The pathogenesis of DR is extremely complex and is closely related to multiple factors. Among them, oxidative stress, inflammatory response, and microbial imbalance have attracted considerable attention from domestic and international research. The ocular surface microbiome is a microbial community present on the ocular surface, mainly composed of Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria, and plays a vital role in maintaining ocular surface homeostasis and health. Previous studies have sequenced and analyzed the ocular surface microorganisms of DM patients and non-DM controls. The results showed that the diversity of DM patients changed significantly, with a significant increase in the abundance of Acinetobacter and Pseudomonas genera, while the non-DM control group showed a more stable dominant bacterial community. In addition, a study divided DM patients into two groups based on the presence or absence of retinopathy, and used 16SrRNA gene sequencing to sequence and analyze the conjunctival sac swabs of the two groups of patients, finding that the presence of DR can significantly change the ocular surface microbiome.

[0004] 2bRAD-M is a novel microbial sequencing technology that enables qualitative and relative quantitative analysis of unique tags obtained by digesting microbial genomes with type IIB restriction endonucleases, providing high-resolution and high-sensitivity technical support for microbial detection. This method has been shown to accurately detect microbial communities in trace samples, such as conjunctival sac swabs. Compared to traditional sequencing methods, this technology, by optimizing the DNA extraction process and increasing sequencing depth, can fully analyze microbial community structure at the species level, significantly improving its taxonomic resolution. This technological breakthrough provides the necessary analytical precision to reveal the functional differences in DM-related ocular surface microorganisms. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this study used a random forest classifier to identify the top 10 differentially expressed microorganisms in conjunctival sac swabs between DM patients and non-DM controls, and between DR patients and non-DR patients. These microorganisms may promote or inhibit the development and progression of DM and DR. The relative abundance of these microorganisms can serve as a novel diagnostic indicator for DM and DR.

[0006] The present invention is achieved through the following technical solutions: The present invention divides the collected conjunctival sac swabs into a DM group and a NC group, wherein the DM group is further divided into a DR group and a NDR group. The present invention uses a random forest classifier to screen for differential microorganisms in the DM and NC groups, and the DR and NDR groups, respectively, to obtain DM markers and DR markers, respectively.

[0007] The first object of the present invention is to provide an ocular surface microbial marker combination for diagnosing diabetes or diabetic retinopathy. The ocular surface microbial marker combination for diagnosing diabetes comprises: Gordonia bronchialis 、 Paracoccus marinus, Bifidobacterium infantis, Pseudomonas aeruginosa, Janibacter sp024362365、Achromobacter xylosoxidans、Corynebacterium accolens、Hankyongella ginsenosidimutans、Lawsonella clevelandensis A and Paracoccus sphaerophysae The ocular surface microbial marker panel for diagnosing diabetic retinopathy includes: Comamonas sp019104825、 Bordetella trematum、Hankyongella ginsenosidimutans、Amulumruptor sp001689515、 Mesorhizobium lusatiense、Comamonas sp902175065、Alcaligenes phenolic、 Reyranella sp900110395、Agrobacterium tomkonis and Cupriavidus sp018729255 .

[0008] The second object of the present invention is to provide a use of the ocular surface microbial marker combination in the preparation of a product for diagnosing diabetes or diabetic retinopathy.

[0009] In one embodiment of the present invention, the product collects a sample from the individual to be tested, detects the DNA in the sample, determines the abundance information of the ocular surface microorganisms in the sample, and diagnoses whether the individual to be tested is a diabetic patient or a diabetic retinopathy patient based on the abundance information of the ocular surface microorganisms.

[0010] In one embodiment of the present invention, whether the individual to be tested is a diabetic patient is determined by the following formula: ; n=-2.525+952.177x1+2352.199x2+59585.357x3-28.758x4+4589.658x5-466.411x6+822.598x7+44.607x8+113.618x9+37.219x 10 ; x1~x 10 They are Gordonia bronchialis 、 Paracoccus marinus、Bifidobacterium infantis、Pseudomonas aeruginosa、Janibacter sp024362365、Achromobacter xylosoxidans、Corynebacterium accolens、Hankyongella ginsenosidimutans、 Lawsonella clevelandensis A 、 Paracoccus sphaerophysae relative abundance of When P1>0.132, the individual to be tested is determined to be a diabetic patient.

[0011] In one embodiment of the present invention, the presence or absence of diabetic retinopathy in the individual to be tested is determined by the following formula: ; m=-259.725x1+6993.712x2-45.410x3-49752.264x4-2484.461x5-4067.981x6+115.046x7-178.098x8+151541.350x9+13587.845x 10 ; x1~x 10 They are Comamonas sp019104825、Bordetella trematum、Hankyongella ginsenosidimutans, Amulumruptor sp001689515, Mesorhizobium lusatiense, Comamonas sp902175065、Alcaligenes phenolicus、Reyranella sp900110395、Agrobacterium tomkonos 、 Cupriavidus sp018729255 relative abundance of When P2>0.587, the individual to be tested is determined to be a patient with diabetic retinopathy.

[0012] In one embodiment of the present invention, the sample is derived from an ex vivo conjunctival sac swab of the individual to be tested.

[0013] In one embodiment of the present invention, the product includes but is not limited to a biochip, a kit, or a device for detecting diabetes and / or diabetic retinopathy.

[0014] In one embodiment of the present invention, the biochip comprises an intrinsic carrier and oligonucleotide probes sequentially fixed on the solid phase carrier, wherein the oligonucleotide probes specifically correspond to the diabetes markers: Gordonia bronchialis 、 Paracoccus marinus, Bifidobacterium infantis, Pseudomonas aeruginosa、Janibacter sp024362365、Achromobacter xylosoxidans、Corynebacterium accolens、Hankyongella ginsenosidimutans、Lawsonella clevelandensis A 、 Paracoccus sphaerophysae ; Diabetic retinopathy markers: Comamonas sp019104825、 Bordetella trematum、Hankyongella ginsenosidimutans、Amulumruptor sp001689515、 Mesorhizobium lusatiense、Comamonas sp902175065、Alcaligenes phenolic、 Reyranella sp900110395、Agrobacterium tomkonis 、 Cupriavidus sp018729255 .

[0015] In one embodiment of the present invention, the device comprises: Collector, collects samples to be tested; a processor, which processes the sample and obtains DNA from the sample; The analyzer is used to execute the executable program, and the execution of the executable program includes the following steps: (a) Analyze DNA in the sample to determine the abundance of ocular surface microorganisms in the sample; (b) Based on the abundance information obtained in (a), determine whether the individual to be tested is a diabetic patient or a diabetic retinopathy patient.

[0016] In one embodiment of the present invention, the analysis in step (a) is performed using fluorescent quantitative PCR or high-throughput sequencing.

[0017] In one embodiment of the present invention, the high-throughput sequencing is 2bRAD-M microbial gene sequencing.

[0018] In one embodiment of the present invention, whether the individual to be tested has diabetes is determined by the following formula:

[0019] n=-2.525+952.177x1+2352.199x2+59585.357x3-28.758x4+4589.658x5-466.411x6+822.598x7+44.607x8+113.618x9+37.219x 10 ; X1~X 10 They are Gordonia bronchialis 、 Paracoccus marinus、Bifidobacterium infantis、Pseudomonas aeruginosa、Janibacter sp024362365、Achromobacter xylosoxidans、Corynebacterium accolens、Hankyongella ginsenosidimutans、 Lawsonella clevelandensis A 、 Paracoccus sphaerophysae relative abundance of When P1>0.132, the individual to be tested is determined to be a diabetic patient.

[0020] In one embodiment of the present invention, whether the individual to be tested suffers from diabetic retinopathy is determined by the following formula:

[0021] m=-259.725x1+6993.712x2-45.410x3-49752.264x4-2484.461x5-4067.981x6+115.046x7-178.098x8+151541.350x9+ 13587.845x 10 X1~X 10 They are Comamonas sp019104825、Bordetella trematum、Hankyongella ginsenosidimutans, Amulumruptor sp001689515, Mesorhizobium lusatiense, Comamonas sp902175065、Alcaligenes phenolicus、Reyranella sp900110395、Agrobacterium tomkonos 、 Cupriavidus sp018729255 relative abundance of When P2>0.587, the individual to be tested is determined to be a patient with diabetic retinopathy.

[0022] The present invention also provides a product for detecting diabetes or diabetic retinopathy, which includes a reagent or device for detecting the ocular surface microbial marker combination for diagnosing diabetes or the ocular surface microbial marker combination for diagnosing diabetic retinopathy.

[0023] In one embodiment of the present invention, the sample used for the detection is an ex vivo conjunctival sac swab.

[0024] In one embodiment of the present invention, the product determines the abundance information of the microbial composition in the sample by detecting DNA of the sample to be tested, and detects or diagnoses diabetes or diabetic retinopathy based on the relative abundance of the microorganisms.

[0025] In one embodiment of the present invention, whether the individual to be tested is a diabetic patient is determined by the following formula:

[0026] n=-2.525+952.177x1+2352.199x2+59585.357x3-28.758x4+4589.658x5-466.411x6+822.598x7+44.607x8+113.618x9+37.219x 10 ; x1~x 10 They are Gordonia bronchialis 、 Paracoccus marinus、Bifidobacterium infantis、Pseudomonas aeruginosa、Janibacter sp024362365、Achromobacter xylosoxidans、Corynebacterium accolens、Hankyongella ginsenosidimutans、 Lawsonella clevelandensis A 、 Paracoccus sphaerophysae relative abundance of When P1>0.132, the individual to be tested is determined to be a diabetic patient.

[0027] In one embodiment of the present invention, whether the individual to be tested is a diabetic retinopathy patient is determined by the following formula: ; m=-259.725x1+6993.712x2-45.410x3-49752.264x4-2484.461x5-4067.981x6+115.046x7-178.098x8+151541.350x9+ 13587.845x 10 ; X1~X 10 They are Comamonas sp019104825、Bordetella trematum、Hankyongella ginsenosidimutans, Amulumruptor sp001689515, Mesorhizobium lusatiense, Comamonas sp902175065、Alcaligenes phenolicus、Reyranella sp900110395、Agrobacterium tomkonos 、 Cupriavidus sp018729255 relative abundance of When P2>0.587, the individual to be tested is determined to be a DR patient.

[0028] Beneficial effects of the present invention: (1) The present invention screened out 10 ocular surface microorganisms that were significantly associated with DM and DR. The 10 microorganisms associated with DM are Gordonia bronchialis 、 Paracoccus marinus, Bifidobacterium infantis, Pseudomonas aeruginosa、Janibacter sp024362365、Achromobacter xylosoxidans、 Corynebacterium accolens、Hankyongella ginsenosidimutans、Lawsonella clevelandensis A 、 Paracoccus sphaerophysae The 10 microorganisms associated with DR are Comamonas sp019104825、Bordetella trematum、Hankyongella ginsenosidimutans、Amulumruptor sp001689515、Mesorhizobium lusatiense、Comamonas sp902175065、Alcaligenes phenolic、Reyranella sp900110395、Agrobacterium tomkonis 、 Copper-loving sp018729255. By measuring the abundance of these microorganisms, DM and DR can be diagnosed. This method is non-invasive, convenient, and highly accurate, making it an effective method for diagnosing DM and DR.

[0029] (2) The present invention generates a classifier based on the relative abundance of 10 microorganisms in the conjunctival sac swabs of the subjects. In the DM and non-DM control groups, the area under the ROC curve (AUC) of the classifier is 0.975, the sensitivity is 87.5%, and the specificity is 96.4%, where Gordonia bronchialis The area under the ROC curve (AUC) of the classifier was 0.874, with a sensitivity of 96.9% and a specificity of 75%. In the DR and DR groups, the area under the ROC curve (AUC) of the classifier was 0.902, with a sensitivity of 93.5% and a specificity of 75.8%. Comamonas sp019104825 The area under the ROC curve (AUC) was 0.702, the sensitivity was 41.9%, and the specificity was 100%. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 To screen the ocular surface microorganisms in patients with diabetes mellitus (DM) and non-diabetic controls (NC) using random forest classifier.

[0032] Figure 2To screen the ocular surface microorganisms of patients with diabetic retinopathy (DR) and non-diabetic retinopathy (NDR) using random forest classifier method.

[0033] Figure 3 The relative abundance of 10 microorganisms in conjunctival sac swabs in DM and NC is shown in Figure 2. P <0.05, where DM and NC represent conjunctival sac swabs from DM patients and non-DM patients, respectively; Figure 4 The relative abundance of 10 microorganisms in conjunctival sac swabs in DR and NDR is shown in Figure 2. P <0.05, where DR and NDR represent conjunctival sac swabs from DR patients and non-DR patients, respectively; Figure 5 The ROC curve analysis shows the performance of the combination of 10 microorganisms in conjunctival sac swabs in predicting the grouping of DM and NC; Figure 6 ROC curve analysis shows the most important microorganisms in conjunctival sac swabs Gordonia bronchialis performance in predicting the subgroups of DM and NC; Figure 7 The ROC curve analysis shows the performance of the combination of 10 microorganisms in conjunctival sac swabs in predicting DR and NDR groups; Figure 8 ROC curve analysis shows the most important microorganisms in conjunctival sac swabs Comamonas sp019104825 Performance in predicting DR and NDR groups. DETAILED DESCRIPTION

[0034] The present invention is further described below in conjunction with specific examples. These implementation cases are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, after reading the content taught by the present invention, those skilled in the art may make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims appended hereto.

[0035] The instruments used in the present invention are shown in Table 1 below: Table 1

[0036] The experimental reagents and consumables used in the present invention are shown in Table 2 below: Table 2

[0037] The technical solutions of the present invention are described in detail below with reference to specific embodiments. In the following embodiments, unless otherwise specified, the reagents, materials and equipment used can be purchased from commercial sources, or prepared by conventional methods, or are commonly used in the industry.

[0038] Example 1: Sample collection and processing Sixty-four patients with diabetes mellitus (DM) and 28 nondiabetic controls (NC) were recruited from the clinic. The DM patients were further divided into 31 patients with retinopathy (DR) and 33 patients with non-DR. Conjunctival sac swabs were collected from 64 DM patients and 28 NC controls, respectively. Detailed information is provided in Tables 1 and 2. All subjects were required not to have used systemic or topical antibiotics within one month prior to sampling.

[0039] Table 3: Statistics of DM patients and control group

[0040] Table 4: Statistics of DR and NDR patients

[0041] The conjunctival sac swab sampling method involved in the present invention is as follows: Conjunctival sac swab sampling: During conjunctival sac swab collection, participants were seated, instructed to look upward, and their lower eyelids were turned upward to expose the inferior fornix conjunctiva. A disposable sterile swab was then gently wiped from the inferior conjunctival sac and lower eyelid conjunctiva, avoiding contact with the eyelashes and eyelid margin. All collected samples were immediately frozen at -80°C until DNA extraction.

[0042] Example 2: Relationship between Diabetes and Diabetic Retinopathy and Microbial Abundance 1. Detection of microbial abundance (1) DNA extraction from conjunctival sac swab: The specific steps are as follows: The conjunctival sac swabs of all subjects frozen in a −80 °C refrigerator were taken out, placed in a biosafety cabinet, and thawed on ice. The conjunctival secretion swab part was cut off and placed in a 2 mL EP tube.

[0043] Bacterial DNA was extracted using the Swab DNA Kit. The specific steps are as follows: 1) Add 400 μL of buffer GA; 2) Add 20 μL of Proteinase K solution, vortex for 10 seconds to mix, and incubate at 56°C for 60 minutes, vortexing several times every 15 minutes to mix. 3) Add 400 μL of Buffer GB, mix thoroughly by inversion, and incubate at 70°C for 10 min. 4) Add 200 μL of anhydrous ethanol and mix thoroughly by inversion; 5) Add the solution and flocculent material from the previous step to an adsorption column CR2, centrifuge at 12,000 rpm for 30 seconds, discard the waste liquid in the collection tube, and return the adsorption column CR2 to the collection tube; 6) Add 500 μL of buffer GD to the adsorption column CR2, centrifuge at 12,000 rpm for 30 seconds, discard the waste liquid in the collection tube, and return the adsorption column CR2 to the collection tube; 7) Add 600 μL of rinse solution PW to the adsorption column CR2, centrifuge at 12,000 rpm for 30 seconds, discard the waste liquid in the collection tube, and return the adsorption column CR2 to the collection tube; 8) Repeat the above steps; 9) Centrifuge at 12,000 rpm for 2 minutes and discard the waste liquid. Place the adsorption column CR2 in a cold room for several minutes to completely dry the remaining rinse liquid in the adsorption material. 10) Transfer the adsorption column CR2 to a clean centrifuge tube and add 50 μL of elution buffer TB dropwise to the middle of the adsorption membrane. Incubate at room temperature for 2-5 minutes. Centrifuge at 12,000 rpm for 2 minutes. Collect the DNA solution and store in a -20°C freezer.

[0044] (2) 2bRAD-M high-throughput sequencing The sequencing technology protocol was performed at Qingdao Sino-Europe Biotechnology Co., Ltd. (Qingdao, China).

[0045] The details are as follows: 1) Use type IIB restriction endonucleases to digest the microbial genome and extract the tags for each genome; 2) Construct a species-level unique tag database (2b-Tag-DB) based on species classification information; 3) Compare the clean reads of the sample with the 2b-Tag-DB database for qualitative analysis; 4) Filter false positives based on the gscore threshold to screen candidate microorganisms; 5) Construct a unique tag database at the candidate microbial species level for each sample; 6) Re-align the clean reads to the database in step 5 for quantitative analysis.

[0046] (3) The relative abundance information of ocular surface microorganisms of DM patients and NC patients was obtained through step (2). The relative abundance of microorganisms in the conjunctival sac swabs of DM and NC patients was compared, and 10 microorganisms were found, namely Gordonia bronchialis 、 Paracoccus marinus, Bifidobacterium infantis, Pseudomonas aeruginosa, Janibacter sp024362365、Achromobacter xylosoxidans、Corynebacterium accolens、Hankyongella ginsenosidimutans、Lawsonella clevelandensis A 、 Paracoccus sphaerophysaeThe abundance of these 10 microorganisms was compared, and the results were as follows: Figure 3 As shown in the figure, there were significant differences in the above 10 microorganisms between DM patients and NC.

[0047] (4) The relative abundance information of ocular surface microorganisms of DR patients and NDR patients was obtained through step (2). The relative abundance of microorganisms in DR and NDR conjunctival sac swabs was compared, and 10 microorganisms were found, namely Comamonas sp019104825、Bordetella trematum、Hankyongella ginsenosidimutans、Amulumruptor sp001689515、Mesorhizobium lusatiense、Comamonas sp902175065、Alcaligenes phenolic、Reyranella sp900110395、Agrobacterium tomkonis 、 Copper-loving sp018729255. The abundance of these 10 microorganisms was compared, and the results were as follows: Figure 4 As shown in the figure, there were significant differences in the above 10 microorganisms between DR patients and NDR patients.

[0048] 2. Using the data on the abundance of 10 microorganisms in the conjunctival sac swabs of all subjects obtained above, the ROC curve was prepared. Specifically, SPSS software was used to perform a logistic regression analysis on the relative abundance of the 10 microorganisms in the ocular surface microorganisms of all subjects to obtain the predicted value. The predicted value was set as the test variable, and the state variable was set to 0 and 1; in the analysis of the DM group and the NC group, the DM group was set to 1, and the ROC curve was prepared using SPSS software to obtain Figure 5 The area under the ROC curve (AUC) was 0.975, and the point closest to the upper left corner, where the Youden index was the largest, had a sensitivity of 87.5% and a specificity of 96.4%.

[0049] Gordonia bronchialis Effect: SPSS software was used to identify the most important microorganisms in the ocular surface microbiome of all subjects. Gordonia bronchial Logistic regression analysis was performed to obtain the predicted value, and the predicted value was set as the test variable, and the state variable was 0 and 1; in the analysis of the DM group and the NC group, the DM group was set as 1, and the ROC curve was drawn using SPSS software. Figure 6 The area under the ROC curve (AUC) was 0.874, and the point closest to the upper left corner, where the Youden index was the largest, had a sensitivity of 96.9% and a specificity of 75%.

[0050] 3. Using the data on the abundance of 10 microorganisms in the conjunctival sac swabs of DM patients obtained above, an ROC curve was prepared. Specifically, SPSS software was used to perform a logistic regression analysis on the relative abundance of the 10 microorganisms in the ocular surface microorganisms of diabetic patients to obtain the predicted value. The predicted value was set as the test variable, and the state variable was set to 0 and 1; in the analysis of the DR group and the NDR group, the DR group was set to 1, and the ROC curve was prepared using SPSS software to obtain Figure 7 The area under the ROC curve (AUC) was 0.902, and the point closest to the upper left corner, where the Youden index was the largest, had a sensitivity of 93.5% and a specificity of 75.8%.

[0051] Comamonas sp019104825 Effect: SPSS software was used to identify the most important microorganisms in the ocular surface microbiome of diabetic patients Comamonas sp019104825 Logistic regression analysis was performed to obtain the predicted value, and the predicted value was set as the test variable, and the state variable was 0 and 1; in the analysis of the DR group and the NDR group, the DR group was set to 1, and the ROC curve was drawn using SPSS software. Figure 8 The area under the receiver operating characteristic (ROC) curve (AUC) was 0.702, with a sensitivity of 41.9% and a specificity of 100%.

[0052] Example 3: Diagnosis of Diabetes and Diabetic Retinopathy by Microbial Abundance Combination Based on the relative abundance information of microorganisms of all subjects, logistic regression analysis was performed in DM, NC, DR and NDR, and the formula was obtained: (1) Diabetes and non-diabetes diagnosis: ; n=-2.525+952.177x1+2352.199x2+59585.357x3-28.758x4+4589.658x5-466.411x6+822.598x7+44.607x8+113.618x9+37.219x 10 ; X1~X 10 They are Gordonia bronchialis 、 Paracoccus marinus、Bifidobacterium infantis、Pseudomonas aeruginosa、Janibacter sp024362365、Achromobacter xylosoxidans、Corynebacterium accolens、Hankyongella ginsenosidimutans、 Lawsonella clevelandensis A 、 Paracoccus sphaerophysae relative abundance of When P1>0.132, the individual to be tested is determined to be a DM patient.

[0053] (2) Diagnosis of diabetic retinopathy and non-diabetic retinopathy in diabetic patients:

[0054] m=-259.725x1+6993.712x2-45.410x3-49752.264x4-2484.461x5-4067.981x6+115.046x7-178.098x8+151541.350x9+13587.845x 10 X1~X 10 They are Comamonas sp019104825、Bordetella trematum、Hankyongella ginsenosidimutans, Amulumruptor sp001689515, Mesorhizobium lusatiense, Comamonas sp902175065、Alcaligenes phenolicus、Reyranella sp900110395、Agrobacterium tomkonis 、 Cupriavidus sp018729255 relative abundance of When P2>0.587, the individual to be tested is determined to be a DR patient.

[0055] Use the above formula to judge diabetes and diabetic retinopathy: Using the above method, multiple subjects were tested, and the abundance of each microorganism was substituted into the formula in step 1 to diagnose whether they were diabetic and whether the diabetic patients had diabetic retinopathy. The results showed that the results obtained using the method of the present application were consistent with the pathological diagnosis results.

[0056] Example 1: A conjunctival sac sample was taken from patient 1, DNA was extracted, and the relative abundance of ocular surface microorganisms was detected. The relative abundance of the ocular surface microorganisms was substituted into the formula to obtain P1=0.236, which is greater than the cutoff value of 0.132. The patient was diagnosed as a diabetic patient, which is consistent with the actual situation.

[0057] Example 2: A conjunctival sac sample was selected from patient 2, DNA was extracted, and the relative abundance of ocular surface microorganisms was detected. The relative abundance of ocular surface microorganisms was substituted into the formula to obtain P1=0.068, which is less than the cutoff value of 0.132. The patient was judged to be a non-diabetic patient, which is consistent with the actual situation.

[0058] Example 3: A conjunctival sac sample was selected from patient 3, DNA was extracted, and the relative abundance of ocular surface microorganisms was detected. The result was substituted into the formula to obtain P1=0.351, which is greater than the cutoff value of 0.132. The patient was diagnosed as a diabetic patient, which is consistent with the actual situation.

[0059] Example 4: A conjunctival sac sample was taken from patient 4, who was known to be a diabetic patient. DNA was extracted and the relative abundance of ocular surface microorganisms was detected. The relative abundance of the ocular surface microorganisms was substituted into the formula to obtain P2=0.798, which is greater than the cutoff value of 0.587. The patient was diagnosed as a diabetic patient with diabetic retinopathy, which is consistent with the actual situation.

[0060] Example 5: A conjunctival sac sample was taken from patient 5, who was known to be a diabetic patient. DNA was extracted and the relative abundance of ocular surface microorganisms was detected. The relative abundance of the ocular surface microorganisms was substituted into the formula to obtain P2=0.463, which is less than the cutoff value of 0.587. The patient was diagnosed as a diabetic patient without diabetic retinopathy, which is consistent with the actual situation.

[0061] Example 6: A conjunctival sac sample was collected from patient 6, who was known to be a diabetic patient. DNA was extracted and the relative abundance of ocular surface microorganisms was detected. The relative abundance of the ocular surface microorganisms was substituted into the formula to obtain P2=0.197, which is less than the cutoff value of 0.587. The patient was diagnosed as a diabetic patient with non-diabetic retinopathy, which is consistent with the actual situation.

[0062] The embodiments provided above are not intended to limit the scope of the present invention, nor are the steps described to limit their execution order. Any obvious improvements to the present invention made by those skilled in the art in combination with existing common knowledge shall fall within the scope of protection defined by the claims of the present invention.

Claims

1. An ocular surface microbial marker combination for diagnosing diabetes or diabetic retinopathy, characterized in that: The ocular surface microbial marker panel used to diagnose diabetes includes: Gordonia bronchialis 、 Paracoccus marinus, Bifidobacterium infantis, Pseudomonas aeruginosa, Janibacter sp024362365, Achromobacter xylosoxidans, Corynebacterium accolens, Hankyongella ginsenosidimutans, Lawsonella clevelandensis A and Paracoccus sphaerophysae The ocular surface microbial marker panel for diagnosing diabetic retinopathy includes: Comamonas sp019104825, Bordetella trematum, Hankyongella ginsenosidimutans, Amulumruptor sp001689515, Mesorhizobium lusatiense,Comamonas sp902175065,Alcaligenes phenolicus, Reyranella sp900110395, Agrobacterium tomkonis and Cupriavidus sp018729255 .

2. Use of the ocular surface microbial marker combination according to claim 1 in the preparation of a product for diagnosing diabetes or diabetic retinopathy.

3. The use according to claim 2, characterized in that The product collects samples from the individual to be tested, detects the DNA in the sample, determines the abundance information of the ocular surface microorganisms in the sample, and diagnoses whether the individual to be tested is a diabetic patient or a diabetic retinopathy patient based on the abundance information of the ocular surface microorganisms.

4. The use according to claim 3, characterized in that The following formula is used to determine whether the individual is a diabetic patient: ； n=-2.525+952.177x1+2352.199x2+59585.357x3-28.758x4+4589.658x5-466.411x6+822.598x7+44.607x8+113.618x9+37.219x 10 ; x1~x 10 They are Gordonia bronchialis 、 Paracoccus marinus, Bifidobacterium infantis, Pseudomonas aeruginosa, Janibacter sp024362365, Achromobacter xylosoxidans, Corynebacterium accolens, Hankyongella ginsenosidimutans, Lawsonella clevelandensis A 、 Paracoccus sphaerophysae relative abundance of When P1>0.132, the individual to be tested is determined to be a diabetic patient.

5. The use according to claim 3, characterized in that The following formula is used to determine whether the individual has diabetic retinopathy: ； m=-259.725x1+6993.712x2-45.410x3-49752.264x4-2484.461x5-4067.981x6+115.046x7-178.098x8+151541.350x9+13587.845x 10 ; x1~x 10 They are Comamonas sp019104825, Bordetella trematum, Hankyongella ginsenosidimutans, Amulumruptor sp001689515, Mesorhizobium lusatiense, Comamonas sp902175065, Alcaligenes phenolicus, Reyranella sp900110395, Agrobacterium tomkonis 、 Cupriavidus sp018729255 relative abundance of When P2>0.587, the individual to be tested is determined to be a patient with diabetic retinopathy.

6. The use according to any one of claims 3 to 5, characterized in that The sample is obtained from an ex vivo conjunctival sac swab of the individual to be tested.

7. The use according to claims 2 to 5, characterized in that: The products include but are not limited to biochips, kits, and devices for detecting diabetes and / or diabetic retinopathy.

8. The use according to claim 7, characterized in that The biochip includes an intrinsic carrier and oligonucleotide probes sequentially fixed on the solid phase carrier, wherein the oligonucleotide probes specifically correspond to the diabetes markers: Gordonia bronchialis 、 Paracoccus marinus, Bifidobacterium infantis, Pseudomonas aeruginosa, Janibacter sp024362365, Achromobacter xylosoxidans, Corynebacterium accolens, Hankyongella ginsenosidimutans, Lawsonella clevelandensis A 、 Paracoccus sphaerophysae ; or the diabetic retinopathy marker: Comamonas sp019104825, Bordetella trematum, Hankyongella ginsenosidimutans, Amulumruptor sp001689515, Mesorhizobium lusatiense, Comamonas sp902175065, Alcaligenes phenolicus, Reyranella sp900110395, Agrobacterium tomkonis 、 Cupriavidus sp018729255 .

9. The use according to claim 7, characterized in that The device comprises: Collector, collects samples to be tested; a processor, which processes the sample and obtains DNA from the sample; The analyzer is used to execute the executable program, and the execution of the executable program includes the following steps: (a) Analyze DNA in the sample to determine the abundance of ocular surface microorganisms in the sample; (b) Based on the abundance information obtained in (a), determine whether the individual to be tested is a diabetic patient or a diabetic retinopathy patient.

10. A product for detecting diabetes or diabetic retinopathy, characterized in that: The product includes a reagent or device for detecting an ocular surface microbial marker combination for diagnosing diabetes or an ocular surface microbial marker combination for diagnosing diabetic retinopathy; The ocular surface microbial marker panel used to diagnose diabetes includes: Gordonia bronchialis 、 Paracoccus marinus, Bifidobacterium infantis, Pseudomonas aeruginosa、Janibacter sp024362365、Achromobacter xylosoxidans、Corynebacterium accolens、Hankyongella ginsenosidimutans、Lawsonella clevelandensis A and Paracoccus sphaerophysae The ocular surface microbial marker panel for diagnosing diabetic retinopathy includes: Comamonas sp019104825、Bordetella trematum、Hankyongella ginsenosidimutans、 Amulumruptor sp001689515、Mesorhizobium lusatiense、Comamonas sp902175065、 Alkaligenes phenolic、Reyranella sp900110395、Agrobacterium tomkonis and Cupriavidus sp018729255 .

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