Application of CD209 in early prediction and treatment of type 2 diabetes mellitus

Abstract

The invention relates to the technical field of biological diagnosis or medicine, in particular to application of CD209 protein in early prediction and treatment of type 2 diabetes mellitus. According to the invention, data of proteome and genome are integrated, a plurality of analysis methods and ways such as MR analysis, Cox regression analysis, co-localization analysis, PPI analysis and the like are utilized, the causal relationship between CD209 and T2D is researched and found, the fact that CD209 can be used as a potential biomarker and a drug target of type 2 diabetes mellitus is proposed for the first time, and a new thought and method are provided for prediction and treatment of the type 2 diabetes mellitus.

Description

Technical Field

[0001] The present invention relates to the field of biological diagnosis or medical technology, and in particular to the application of CD209 protein in early prediction and treatment of type 2 diabetes. Background Art

[0002] Type 2 diabetes (T2D) is the most common type of diabetes worldwide, accounting for more than 90% of all diabetes cases. According to the International Diabetes Federation (IDF), the number of T2D patients worldwide has risen sharply in the past few decades and is expected to reach nearly 700 million by 2045. This growth trend is particularly significant in developing countries and is closely related to the westernization of lifestyle, urbanization, and population aging. T2D not only affects the quality of life of patients, but also leads to a variety of serious complications, such as cardiovascular disease, kidney disease, retinopathy, neuropathy, and infection. These complications not only increase the medical burden on patients, but may also lead to disability or even death. Therefore, the prevention and control of T2D is crucial to improving the long-term health of patients.

[0003] At present, the diagnosis of T2D mainly relies on indicators related to blood glucose levels and insulin resistance, and there is still a lack of biomarkers with high specificity and sensitivity. Existing blood glucose detection methods, such as fasting blood glucose, postprandial blood glucose and glycosylated hemoglobin (HbA1c), may not increase significantly in the early stages of the disease, making it difficult to provide early warnings in the early stages of the disease, limiting the ability to diagnose T2D early. In addition, the assessment of insulin resistance usually requires complex testing procedures, such as insulin suppression tests or hyperinsulinemic euglycemic clamp tests, which are expensive and time-consuming and are not commonly used in clinical practice. In order to address the above shortcomings, some scholars have begun to explore biomarkers related to T2D. For example, some studies have shown that certain inflammatory markers such as C-reactive protein and tumor necrosis factor α may be related to the occurrence of insulin resistance and T2D; adipose factors secreted by adipose tissue, such as adiponectin and resistin, are also considered to be related to the development of T2D. However, these existing biomarkers often lack specificity and it is difficult to distinguish T2D from other metabolic diseases. Due to the lack of sensitive biomarkers, many patients are not diagnosed until the disease has progressed to a certain stage, when complications may have already occurred. Therefore, it is necessary to actively seek and validate new biomarkers in order to improve the early diagnosis rate of T2D.

[0004] In terms of disease treatment, T2D mainly relies on lifestyle changes and drug treatment methods. Lifestyle changes mainly include dietary adjustments and increased physical activity. Although a good lifestyle is essential for controlling blood sugar, it is difficult for many patients to adhere to it for a long time. In addition, lifestyle changes alone may not be enough to effectively control blood sugar levels in some patients, especially for those with a longer course of disease or more severe conditions. Therefore, drug treatment is very necessary. Commonly used drugs for drug treatment include oral hypoglycemic drugs (such as metformin, sulfonylureas, α-glucosidase inhibitors, etc.) and insulin injections. These drugs can achieve certain effects in controlling blood sugar, but they also have some disadvantages. For example, long-term use may lead to side effects such as weight gain, hypoglycemia, gastrointestinal discomfort, and kidney damage. In addition, as the disease progresses, patients may need to increase drug doses or change drugs, which increases the complexity and cost of treatment. Therefore, the treatment strategy of T2D needs to be continuously innovated to overcome the limitations of existing methods and provide patients with more effective, safer and more economical treatment options.

[0005] Given the limitations of existing disease diagnosis and treatment methods, it is particularly important to find new biomarkers and drug targets for T2D. New biomarkers can improve the early diagnosis rate of T2D and help with early intervention and prevention of the disease. At the same time, new drug targets can provide the possibility for the development of more effective and safer therapeutic drugs, thereby improving the treatment effect and quality of life of T2D patients.

[0006] Proteins are the main executors of cell functions and participate in and regulate almost all biological processes. Therefore, they can play an important regulatory function in the development and treatment of diseases. The development of proteomics technology provides a new way to discover new biomarkers and drug targets. Analysis of blood, urine or other biological samples from diabetic patients can identify specific proteins associated with T2D and provide more information about the disease state. The development of drugs targeting specific proteins can clarify the path of disease intervention, improve treatment effects and reduce side effects. The discovery of more highly specific and sensitive biomarkers / drug targets is expected to improve the treatment effect and quality of life of T2D patients. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a biomarker CD209 protein for early prediction of type 2 diabetes, which also has the potential to serve as a therapeutic target for type 2 diabetes.

[0008] To achieve the above object, the technical solution adopted by the present invention is:

[0009] In a first aspect, the present invention provides a biomarker for early prediction of type 2 diabetes, wherein the biomarker is CD209 protein.

[0010] The present invention integrates proteomic and genomic data, and uses multiple analytical methods and approaches such as MR analysis, Cox regression analysis, colocalization analysis, and PPI analysis to study and discover the causal relationship between CD209 and T2D, and for the first time proposes that CD209 can be used as a potential biomarker for type 2 diabetes.

[0011] In a second aspect, the present invention provides the use of a substance for detecting CD209 protein in the preparation of a product for early diagnosis or auxiliary diagnosis of type 2 diabetes.

[0012] Preferably, the substance is a reagent for detecting the expression level of CD209 protein.

[0013] Preferably, the reagent comprises at least one of a primer, a probe, an antisense oligonucleotide, an aptamer, and an antibody that specifically recognizes the CD209 protein.

[0014] Preferably, the product includes a kit, a chip, a system, or a device.

[0015] In a third aspect, the present invention provides a kit for early diagnosis or auxiliary diagnosis of type 2 diabetes, comprising a reagent for detecting CD209 protein.

[0016] In a fourth aspect, the present invention provides the use of CD209 protein as a target in the preparation of a drug for treating type 2 diabetes.

[0017] The present invention conducts drugability analysis on CD209 protein, and through the analysis of the interaction between protein and T2D-related drug targets, it is found that CD209 interacts with dipeptidyl peptidase 4 (DPP-4). DPP-4 inhibitors are a class of drugs used to treat T2D, and their mechanism of action is mainly to reduce the inactivation of glucagon-like peptide-1 (GLP-1) in the body by inhibiting the activity of DPP-4 enzyme, thereby increasing the level of endogenous GLP-1. The interaction between CD209 and DPP-4 suggests the potential of CD209 as a target for T2D intervention.

[0018] Preferably, the drug treats type 2 diabetes by inhibiting CD209 protein expression.

[0019] In a fifth aspect, the present invention provides a drug for treating type 2 diabetes, comprising a substance that inhibits the expression of CD209 protein.

[0020] The beneficial effects of the present invention are:

[0021] The present invention integrates proteomic and genomic data, and uses multiple analytical methods and approaches such as MR analysis, Cox regression analysis, co-localization analysis, and PPI analysis to study and discover the causal relationship between CD209 and T2D. It is the first time that it is proposed that CD209 can be used as a potential biomarker and drug target for type 2 diabetes, providing new ideas and methods for the prediction and treatment of the disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Flow chart for analysis.

[0023] Figure 2 This is the result of external validation analysis of the causal association between CD209 and T2D.

[0024] Figure 3 This is the ROC curve of CD209 protein.

[0025] Figure 4 The results of co-localization analysis of CD209 and T2D.

[0026] Figure 5 The results of MR analysis of CD209 and T2D-related phenotypes.

[0027] Figure 6 The PPI network of CD209 and T2D-related drug targets.

[0028] Figure 7 This is a full phenotypic MR analysis of CD209. DETAILED DESCRIPTION

[0029] In order to better illustrate the purpose, technical solutions and advantages of the present invention, the present invention will be further described below in conjunction with specific embodiments.

[0030] Example 1: Whole-Proteome MR Analysis

[0031] 1. Data Source

[0032] (1.1) Aggregate level data

[0033] (1.1.1) Exposure factors: The exposure factors of Mendelian randomization (MR) analysis were analyzed using protein quantitative trait loci (pQTL) data. In the whole proteome MR analysis, the pQTL data came from the INTERVAL study. This study used SomaScan Assay proteomics technology and aptamers for proteins to measure plasma proteins in 3,301 healthy European individuals. In this study, the summary level data of proteins measured by 3,283 aptamers were analyzed. In order to further confirm the results obtained in the INTERVAL study, the present invention selected pQTL data from the Fenland and Age, Gene / Environmental Infection-Reykjavik (AGES-Reykjavik) studies to repeat the MR analysis for external validation. The Fenland study measured plasma samples from more than 10,000 people and obtained pQTL data for up to 4,775 proteins. The AGES-Reykjavik study measured the levels of 4,782 serum proteins in 5,368 participants. All study populations were European, and ethical approval and informed consent were obtained for the original studies.

[0034] (1.1.2) Outcome factors: The outcome factors of MR analysis were analyzed using genome-wide association study (GWAS) data. The GWAS data of T2D were derived from the Diabetes Genetic Replication and Meta-analysis (DIAGRAM) database, which included 74,124 T2D cases and 824,006 controls. In addition, this study also included fasting glucose (FG), fasting insulin (FI), glycosylated hemoglobin (HbA1c), and waist-to-hip ratio (WHR / BMIadjWHR) as T2D-related phenotypes for MR analysis to explore whether the association between proteins and T2D-related phenotypes is consistent with T2D. The GWAS data of HbA1c, FG, and FI were derived from the Meta-Analysis of Glucose and Insulin-Associated Traits (MAGIC) database. The GWAS data of WHR and WHRadjBMI came from a meta-analysis involving 694,649 participants. In order to determine the additional potential effects of treatments that change specific protein levels on the human body, this study used 1419 binary phenotype GWAS data collated by Lee_Lab to perform a full phenotype MR analysis. The 1419 binary phenotype GWAS data were aggregated level data measured in UKB using 400,000 British white people, containing nearly 57 million genetic variants. All study populations were European, and the original studies obtained ethical approval and informed consent.

[0035] (1.2) Individual level data

[0036] In order to further verify the reliability of MR results, the present invention used individual-level data for logistic regression analysis. A total of 30 T2D patients and 30 healthy control participants (age: 62.43±5.66; male: 46%) were initially included in the analysis, and their plasma CD209 protein levels were detected. Ethical approval and informed consent were obtained for all studies.

[0037] 2. Analysis steps ( Figure 1 )

[0038] (2.1) Using 3,283 plasma proteins as exposure factors and T2D as outcome factors, the whole proteome MR method was used to explore the causal relationship between the two.

[0039] (2.1.1) MR instrumental variable screening and evaluation

[0040] Single nucleotide polymorphisms (SNPs) are instrumental variables (IVs) in MR analysis. In this study, IVs were selected from the pQTL data of the INTERVAL study according to the following criteria. First, P < 1 × 10 -5 The threshold was set to identify SNPs significantly associated with changes in plasma protein levels. Secondly, linkage disequilibrium clumping (LD clumping) was performed on the strongly associated SNPs (r 2 Threshold = 0.01, window size = 1,000kb) to ensure that the SNPs selected for MR analysis are independent of each other. Finally, the present invention excludes SNPs with minor allele frequency (MAF) < 0.01; SNPs without corresponding information in the resulting GWAS; and palindromic SNPs. For the screened IVs, the F statistic is used to evaluate the strength of the IVs. An F statistic greater than 10 indicates that the IVs have a good degree of explanation for the exposure factors, and the MR results are less likely to be affected by weak instrumental bias.

[0041] (2.1.2) Whole-proteome MR analysis and result screening

[0042] Inverse variance weighting (IVW) is the main analytical method used to estimate causal effects in whole-protein group MR analysis. Statistical analyses were all two-sided tests, performed using the “TwoSampleMR” package (version 0.5.6) in R (version 4.2.2). If the P value of the MR result obtained by the IVW method is less than 0.05, it is considered that there is a suggestive association between the exposure factor and the outcome factor analyzed by MR. In order to avoid the influence of reverse causality on the reliability of the research results, the present invention conducted a reverse MR analysis to assess whether the outcome factor has a significant effect on the exposure factor. The reverse MR result P>0.05 indicates that there is no reverse causal evidence for the association between the two. In addition, the present invention also uses the intercept term of MR-Egger to assess whether horizontal pleiotropy exists. The test P>0.05 of the MR-Egger intercept term indicates that the MR results are less likely to be affected by horizontal pleiotropy. Proteins that meet the MR analysis P<0.05, the absence of reverse causal evidence, and the insignificant pleiotropy test will continue to be analyzed later.

[0043] (2.2) External validation using public protein data at multiple aggregation levels

[0044] In order to further confirm the results obtained in the INTERVAL study, the present invention selected pQTL data from the Fenland and AGES-Reykjavik studies to repeat the MR analysis for external validation. The screening evaluation of instrumental variables and the MR analysis method are the same as 2.1. If the P value of the MR result obtained by the IVW method is less than 0.05, it is considered that there is a significant causal relationship between the exposure factor and the outcome factor analyzed by the MR, and the external data verification is successful.

[0045] (2.3) Plotting receiver operating curves using case-control individual data

[0046] The analysis included 30 healthy controls and 30 T2D patients. The Logistic regression model was used to draw the receiver operating curve (ROC) and calculate the area under the curve (AUC) to evaluate the predictive performance of plasma protein CD209 for T2D.

[0047] 3. Research results:

[0048] (3.1) Whole-proteome MR and screening results

[0049] Preliminary whole-proteome MR analysis found a causal relationship between CD209 and T2D. Genetically predicted elevated CD209 plasma protein levels were associated with an increased risk of T2D (the INTERVAL study: OR [95% CI]: 1.04 [1.03-1.06], P = 1.25 × 10 -8 )( Figure 2). The F statistic of CD209 IVs was 112.61, and the MR-Egger intercept test was not significant (P>0.05), indicating that the MR results were less likely to be affected by weak instrumental variable bias and horizontal pleiotropy. In addition, the reverse MR between CD209 and T2D was not significant, indicating that there was no reverse causal evidence between CD209 and T2D. Therefore, combined with the above results, CD209 is a potential causal protein significantly associated with the risk of T2D, which needs to be verified and discussed in more subsequent analyses.

[0050] (3.2) External verification analysis results

[0051] Repeated MR analysis using the Fenland and AGES-Reykjavik protein databases successfully confirmed the causal relationship between CD209 and T2D (the Fenland study: OR [95% CI]: 1.03 [1.02-1.05], P = 1.27 × 10 -4 ; the AGES-Reykjavik study: 1.02 [1.01-1.04], P = 0.027). The analysis results are consistent with the results of the INTERVAL database analysis, suggesting that elevated levels of CD209 plasma protein predicted by genetics are associated with an increased risk of T2D.

[0052] (3.3) ROC curve and AUC value calculation

[0053] The predictive performance of protein CD209 on the risk of T2D was evaluated by drawing a receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). The present invention collected plasma samples from 30 T2D patients and 30 healthy controls, detected the level of protein CD209 using an ELISA kit, and constructed a prediction model based on logistic regression. The AUC was calculated to be 0.76 [95%: 0.70-0.87]. The ROC curve is shown in Figure 2. Figure 3 As shown, the logistic regression model is as follows:

[0054] Logit(Y)=-1.949+1.639*X

[0055] Where Y is the probability of T2D occurrence and X is the plasma CD209 level.

[0056] Example 2: Co-localization analysis to test whether there are shared causal variants between proteins and T2D

[0057] 1. Colocalization hypothesis

[0058] There are 4 hypotheses in the co-localization analysis: ①H0: All SNP sites in a gene region are not associated with phenotype 1 and phenotype 2; ②H1 / H2: SNP sites in a gene region are significantly associated with phenotype 1 or phenotype 2; ③H3: SNP sites in a genomic region are significantly associated with both phenotype 1 and phenotype 2, but driven by different causal variant sites; ④H4: SNP sites in a genomic region are significantly associated with both phenotype 1 and phenotype 2, and driven by the same causal variant site. This study believes that the posterior probability of the H4 hypothesis is greater than 80%, indicating strong co-localization evidence between protein and T2D (PP.H4>80%).

[0059] 2. Parameter setting for colocalization analysis

[0060] The co-localization analysis in this study included SNPs around the lead pQTL (the pQTL with the smallest P value) (±500 kb) for analysis. -4 ,10 -5 ,10 -6 ), p2=c(10 -4 ,10 -5 ,10 -6 ), and p12=c(10 -5 ,5×10 -6 ,10 -6 ) to determine whether there is co-localization evidence between the protein and T2D. p1 represents the prior probability that all SNP sites in a gene region are genetically associated only with phenotype 1, p2 represents the prior probability that all SNP sites in a gene region are genetically associated only with phenotype 2, and p12 represents the prior probability that all SNP sites in a gene region are genetically associated with both phenotype 1 and phenotype 2. This analysis was performed in R (version 4.2.2) using the “coloc” package (version 5.2.1).

[0061] 3. MR analysis of T2D-related phenotypes

[0062] FG, FI, HbA1c, and WHR / BMIadjWHR were included as T2D-related phenotypes for MR analysis to explore whether the association between proteins and T2D-related phenotypes was consistent with T2D. The screening and evaluation of instrumental variables and the MR analysis methods were the same as 2.1. If the P value of the MR result obtained by the IVW method was less than 0.05, it was considered that there was a significant causal relationship between the exposure factor and the outcome factor analyzed by MR.

[0063] 4. Searching the Genotype-Tissue Expression (GTEx) Dataset

[0064] In the GTEx database, by entering the name of a protein-coding gene, you can obtain the expression of protein-coding genes in different human tissues. This differential information on gene tissue expression helps reveal how genes function in different biological contexts and the role they may play in the occurrence of diseases.

[0065] 5. Colocalization analysis results

[0066] (5.1) The lead pQTL of CD209 is rs505922. SNPs in the 1Mb region (±500kb) around this SNP were included in the co-localization analysis. The default prior probability (p1=p2=10 -4 , p12=10 -5 ) were analyzed, and there was strong co-localization evidence suggesting that CD209 and T2D share causal variants (PP.H4 = 98%), indicating that the MR causal estimate of CD209 for T2D is unlikely to be affected by LD bias ( Figure 4 ). Similar results can be obtained using various combinations of different prior probabilities, and PP.H4 is greater than 80%, proving that there is evidence of co-localization between the two.

[0067] (5.2) Analysis results of T2D-related indicators

[0068] MR analysis found that genetically predicted elevated CD209 protein levels were associated with elevated FG (β [95% CI]: 0.006 [0.001-0.011], P = 0.011), FI (β [95% CI]: 0.008 [0.003-0.012], P = 0.001), and HbA1c (β [95% CI]: 0.008 [0.003-0.013], P = 0.001), further illustrating the effect of CD209 on blood glucose regulation ( Figure 5 In addition, genetically predicted elevated CD209 protein levels were associated with elevated WHR (β [95% CI]: 0.007 [0.003-0.011], P = 0.001) and BMIadjWHR (β [95% CI]: 0.006 [0.002-0.010], P = 0.004) ( Figure 5 ). Central obesity is known to be an important risk factor for the development of T2D. The significant association between CD209 and WHR and BMIadjWHR suggests that central obesity may be one of the potential pathways for CD209 to affect the development of T2D.

[0069] (5.3) GTEx database search results

[0070] GTEx data showed that subcutaneous and visceral adipose tissue were the two tissues with the highest expression of CD209, indicating that adipose tissue may be an important tissue for CD209 to affect the development of T2D. This finding further supports the significant association between CD209 and central obesity indicators (WHR and BMIadjWHR), providing important evidence for future biological mechanism research.

[0071] Example 3: Drugability Analysis

[0072] 1. Query the Therapeutic Target Database (TTD) to obtain relevant drug target information about proteins

[0073] TTD is an online database that provides information on drug targets and provides an important data platform for drug target discovery and new drug development. By searching for protein names, you can learn information including drug target type, corresponding disease, and drug ligand.

[0074] 2. Explore the relationship between proteins and T2D-related drug targets

[0075] Searching for "type 2diabetes mellitus" in the Open Target database can obtain relevant drug targets for T2D that have been approved or are under study. On this basis, in the STRING database, by entering proteins and T2D drug targets, protein-protein interaction (PPI) network analysis can be performed to explore the relationship between proteins and T2D-related drug targets, thereby determining the potential of proteins as T2D drug targets.

[0076] 3. Full phenotypic MR analysis

[0077] This study used 1419 binary phenotype GWAS data compiled by Lee_Lab to perform full phenotype MR analysis to determine the potential additional effects of treatments that reduce specific biomarkers on the human body. The 1419 binary phenotype GWAS data were aggregated from 400,000 British white people in UKB, containing nearly 57 million genetic variants. The screening and evaluation of instrumental variables and MR analysis methods are the same as 2.1. Considering the large number of test phenotypes, the present invention uses the Bonferroni correction method, with P < 3.52 × 10 -5 (0.05 / 1419) The causal association between the protein and the phenotype was considered significant. Analyses were all two-sided and performed in R (version 4.2.2) using the "TwoSampleMR" package (version 0.5.6).

[0078] 4. Drugability analysis results

[0079] (4.1) TTD database search results: TTD database search found that CD209 can bind to α-D-mannose and is a potential drug target that has been reported in the literature.

[0080] (4.2) Analysis results of protein and T2D-related drug target interactions:

[0081] By searching the Open Target database, the present invention identified 257 T2D-related drug targets that are currently approved / under investigation. Using the STRING database, PPI network analysis further revealed that CD209 interacts with 9 T2D drug targets ( Figure 6 ). It is worth noting that the present invention discovered the interaction between CD209 and dipeptidyl peptidase 4 (DPP-4). DPP-4 inhibitors are a class of drugs used to treat T2D. Their mechanism of action is mainly to reduce the inactivation of glucagon-like peptide-1 (GLP-1) in the body by inhibiting the activity of the DPP-4 enzyme, thereby increasing the level of endogenous GLP-1. GLP-1 is an incretin that can stimulate the secretion of insulin by pancreatic β cells in a blood sugar-dependent manner, while inhibiting the secretion of glucagon by pancreatic α cells, thereby playing a role in lowering blood sugar. When used alone, DPP-4 inhibitors do not increase the risk of hypoglycemia or increase body weight, so they are widely used clinically. The interaction between CD209 and DPP-4 further suggests the potential of CD209 as a target for T2D intervention.

[0082] (4.3) Results of full phenotypic MR analysis:

[0083] Changes in the levels of CD209 plasma protein have significant causal associations with multiple diseases in the tumor, hematopoiesis, circulatory system, and digestive system. Genetically predicted decreases in CD209 are associated with a decreased risk of lipomas, cor pulmonale, non-hypertensive congestive heart failure, lower extremity phlebitis and thrombophlebitis, phlebitis and thrombophlebitis, circulatory system diseases (unspecified circulatory system diseases), and other circulatory system diseases. These results suggest that inhibiting CD209 protein levels may have a potential positive effect on the prevention of these diseases and bring additional health benefits to the human body. However, it should also be noted that genetically predicted decreases in CD209 are associated with an increased risk of benign colon tumors, diverticular disease, diverticulitis, and functional digestive system diseases. This suggests that inhibiting CD209 protein levels may increase the risk of certain gastrointestinal diseases and cause potential adverse effects on the human body. When considering the development of CD209 inhibitors as drugs, their potential impact on the risks of different diseases needs to be carefully weighed ( Figure 7 ).

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A biomarker for early prediction of type 2 diabetes, characterized in that: The biomarker is CD209 protein.

2. Use of substances for detecting CD209 protein in the preparation of products for early diagnosis or auxiliary diagnosis of type 2 diabetes.

3. The use according to claim 2, characterized in that The substance is a reagent for detecting the expression level of CD209 protein.

4. The use according to claim 3, characterized in that The reagent comprises at least one of a primer, a probe, an antisense oligonucleotide, an aptamer and an antibody that specifically recognizes the CD209 protein.

5. The use according to claim 2, characterized in that The products include test kits, chips, systems, and devices.

6. A kit for early diagnosis or auxiliary diagnosis of type 2 diabetes, characterized in that: Includes reagents for detecting CD209 protein.

7. Application of CD209 protein as a target in the preparation of drugs for the treatment of type 2 diabetes.

8. The use according to claim 7, characterized in that The drug treats type 2 diabetes by inhibiting the expression of CD209 protein.

9. A drug for treating type 2 diabetes, characterized in that: Includes substances that inhibit the expression of CD209 protein.

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