Biomarker combinations, complex systems and applications, products and methods of use thereof

Abstract

The present application belongs to the technical field of gene detection, and particularly relates to a biomarker combination, a composite system and application, products and use methods thereof. The biomarker combination provided by the present application is composed of SLC30A8 gene rs13266634 site, KCNK16 gene rs1535500 site and KCNQ1 gene rs2237892 site, and both precision and convenience are taken into account. The corresponding risk assessment method is scientific and reliable, the determination standard is clear, the accuracy rate reaches 88.05%, and the objectivity and consistency are strong. The related kit has strong practicability, is widely used in various application scenarios, and has controllable cost. In addition, the kit can also be used for diabetes drug screening. The present application has outstanding technological innovation, solves the pain points of the prior art, realizes the precision, convenience and low cost of diabetes risk assessment, provides a new technical scheme for early prevention, risk assessment and drug screening of diabetes, and has important clinical application value and industrial popularization prospect.

Description

Technical Field

[0001] This invention belongs to the field of gene detection technology, specifically relating to biomarker combinations, composite systems and their applications, products and methods of use. Background Technology

[0002] Single nucleotide polymorphisms (SNPs), as DNA sequence polymorphisms resulting from single nucleotide variations at the genomic level, are widely used in gene detection technology due to their advantages such as wide distribution in the human genome, stable inheritance, and short amplification fragments. Numerous studies have clearly established that some SNP sites are closely linked to the risk of diabetes. However, existing related patent technologies have many shortcomings. For example, patent CN112795645A discloses a kit for guiding human hyperglycemia medication, detecting 18 sites, but using flight-of-flight mass spectrometry, making the equipment expensive and complex to operate. Patent CN112210598A discloses primer sets and kits for detecting polymorphisms in genes related to hyperglycemia drug metabolism and their applications, detecting seven genes, but it uses Sanger sequencing technology, resulting in long detection times and high costs. Patent CN110157799A discloses a primer and probe set, kit and method for detecting gene polymorphisms related to personalized medication for diabetes. It uses fluorescent probe technology to detect five major genes, but it has the defect of incomplete detection sites, with only a single site being detected per well.

[0003] Currently, a vast number of SNP loci are known to be associated with diabetes. Screening for combinations of loci with high diagnostic value and strong stability to provide novel SNP biomarker combinations has the potential to become one of the core methods for precision diagnosis of diabetes. Therefore, developing a simple and efficient gene detection method is urgently needed. This method, requiring only a small number of single nucleotide polymorphism (SNP) loci, can accurately assess an individual's risk of hyperglycemia, providing strong support for the early prevention and treatment of diabetes. Summary of the Invention

[0004] To address the aforementioned shortcomings, this invention provides a combination of biomarkers for diabetes risk assessment, a composite system, and their applications and products. The biomarker combination for diabetes risk assessment provided by this invention consists of the SLC30A8 gene rs13266634 locus, the KCNK16 gene rs1535500 locus, and the KCNQ1 gene rs2237892 locus.

[0005] the term: In this invention, the term "Forward Primer" refers to a primer that binds complementary to the 5' end of the target sequence in the template strand, and is used to initiate the synthesis and extension of the DNA strand.

[0006] In this invention, the term "Reverse Primer" refers to a primer that binds complementary to the 3' end of the target sequence in the template strand, and works in conjunction with the forward primer to achieve specific amplification of the target fragment.

[0007] In this invention, the term "PC" refers to a probe designed for a specific sequence related to cytosine (C) to detect gene fragments or mutations containing a specific cytosine site.

[0008] In this invention, the term "PG" refers to a probe designed for a specific sequence related to guanine (G) to detect gene fragments or mutations containing a specific guanine site.

[0009] In this invention, the term "PT" refers to a probe designed for a specific sequence related to thymine (T) to detect gene fragments or mutations containing a specific thymine site.

[0010] In this invention, the term "PA" refers to a probe designed for a specific sequence related to adenine (A), which is typically used to detect gene fragments or mutations containing a specific adenine site.

[0011] The technical solution of this invention is as follows: On one hand, the present invention provides a biomarker combination consisting of the SLC30A8 gene rs13266634 site, the KCNK16 gene rs1535500 site, and the KCNQ1 gene rs2237892 site.

[0012] In another aspect, the present invention provides a biomarker complex system, which includes a primer-probe combination for amplifying the biomarker combination of claim 1.

[0013] Specifically, the nucleotide sequence of the primer-probe combination is shown in SEQ ID NO.1-12.

[0014] Preferably, the primer-probe combination includes an upstream primer, a downstream primer, and a probe for the SLC30A8 gene at rs13266634; an upstream primer, a downstream primer, and a probe for the KCNK16 gene at rs1535500; and an upstream primer, a downstream primer, and a probe for the KCNQ1 gene at rs2237892.

[0015] More preferably, the nucleotide sequences of the upstream and downstream primers for the SLC30A8 gene rs13266634 site are shown in SEQ ID NO.1-2, and the nucleotide sequence of the probe for the SLC30A8 gene rs13266634 site is shown in SEQ ID NO.3-4.

[0016] More preferably, the nucleotide sequences of the upstream and downstream primers for the KCNK16 gene rs1535500 site are shown in SEQ ID NO.5-6, and the nucleotide sequence of the probe for the KCNK16 gene rs1535500 site is shown in SEQ ID NO.7-8.

[0017] More preferably, the nucleotide sequences of the upstream and downstream primers for the KCNQ1 gene rs2237892 site are shown in SEQ ID NO. 9-10, and the nucleotide sequence of the probe for the KCNQ1 gene rs2237892 site is shown in SEQ ID NO. 11-12.

[0018] Specifically, the final concentration of each primer and probe in the primer-probe combination is 0.025-0.1 μM.

[0019] Preferably, the final concentrations of the upstream and downstream primers at the rs13266634 site of the SLC30A8 gene in the primer-probe combination are both 0.1 μM, the final concentration of the PC probe is 0.05 μM, and the final concentration of the PG probe is 0.04 μM.

[0020] Preferably, the final concentrations of the upstream and downstream primers at the rs1535500 site of the KCNK16 gene in the primer-probe combination are both 0.1 μM, the final concentration of the PT probe is 0.025 μM, and the final concentration of the PG probe is 0.025 μM.

[0021] Preferably, the final concentrations of the upstream and downstream primers at the rs2237892 site of the KCNQ1 gene in the primer-probe combination are both 0.1 μM, the final concentration of the PA probe is 0.05 μM, and the final concentration of the PT probe is 0.05 μM.

[0022] Specifically, the biomarker complex system includes one or more of the following: DNA extraction reagents, PCR amplification reagents, sequencing library construction reagents, and nucleic acid purification reagents.

[0023] In another aspect, the present invention provides the application of any of the above-described biomarker combinations or biomarker complex systems in the preparation of kits for diabetes assessment or screening of drugs for the prevention and treatment of diabetes.

[0024] In another aspect, the present invention provides a kit comprising any of the biomarker combinations or biomarker complex systems described above.

[0025] Preferably, the kit is used for diabetes assessment or for screening drugs for the prevention and treatment of diabetes.

[0026] In another aspect, the present invention provides a method for using the biomarker complex system or kit described in any of the above claims.

[0027] Specifically, the method of use includes the following steps: S1. Extract total genomic DNA from the sample; S2. Using any of the biomarker complex systems or kits described above, perform PCR amplification on the total genomic DNA extracted in step S1. S3. Calculate the weight values ​​based on the genotypes of each biomarker; S4. Calculate the sample GRS value based on the weight values ​​to determine the degree of diabetes risk.

[0028] Specifically, using rs13266634 as the standard, CC is the low-end, assigned a wild-type risk value; CT is the medium-end, assigned a heterozygous risk value; and TT is the high-end, assigned a homozygous risk value. Preferably, using rs13266634 as the standard, the weight of the wild-type risk value is 1, the weight of the heterozygous risk value is 1.68, and the weight of the homozygous risk value is 1.12.

[0029] Specifically, using rs1535500 as the standard, GG is the low-end, assigned a wild-type risk value; GT is the mid-end, assigned a heterozygous risk value; and TT is the high-end, assigned a homozygous risk value. Preferably, using rs1535500 as the standard, the weight of the wild-type risk value is 1, the weight of the heterozygous risk value is 1.224, and the weight of the homozygous risk value is 1.58.

[0030] Specifically, using rs2237892 as the standard, CC is the low-end, assigned a wild-type risk value; CT is the medium-end, assigned a heterozygous risk value; and TT is the high-end, assigned a homozygous risk value. Preferably, using rs2237892 as the standard, the weight of the wild-type risk value is 1, the weight of the heterozygous risk value is 1.66, and the weight of the homozygous risk value is 1.97.

[0031] Specifically, the GRS value is calculated as follows: ; Where ORi is the weight l of each biomarker; Xi is the number of hazardous bases.

[0032] The project rank is calculated using the GRS, mean, and sd values ​​according to the formula. The mean is the arithmetic mean of the GRS values ​​of all samples involved in the calculation; and the sd is the standard deviation of the GRS values ​​of the samples involved in the calculation.

[0033] Level 1 (Low Risk): ; Level 2 (Medium Risk): ; Level 3 (High Risk): ; The formula for calculating the threshold coefficient xs is: ; In the formula, N is the number of sites included in the project; mean is the arithmetic mean of all GRS values ​​of the samples involved in the calculation; and sd is the standard deviation of the GRS values ​​of the samples involved in the calculation.

[0034] Specifically, the samples mentioned in step S1 include one or more of the following: blood, blood spots, oral cells, semen, sperm spots, bones, hair, saliva, saliva spots, sweat, and amniotic fluid containing fetal cells.

[0035] In another aspect, the present invention provides a method for screening drugs for the prevention and treatment of diabetes, the method comprising using the biomarker complex system or kit described in any of the above claims.

[0036] The beneficial effects of this invention are as follows: This invention addresses numerous shortcomings of existing diabetes gene detection technologies by providing a biomarker combination consisting of the SLC30A8 gene rs13266634 locus, the KCNK16 gene rs1535500 locus, and the KCNQ1 gene rs2237892 locus. These three biomarkers work synergistically to accurately correlate with the risk of developing diabetes. The determined gene risk level is highly correlated with core clinical risk indicators, and the combination of only three core loci balances accuracy and convenience. The accompanying biomarker complex system optimizes primer and probe design and reaction conditions, eliminating the need for complex technologies and expensive equipment. It is simple to operate, highly efficient, and adaptable to various sample types. The corresponding risk assessment method is scientifically reliable, with clear judgment criteria, an accuracy rate of 88.05%, and strong objectivity and consistency. The related reagent kits are highly practical, have wide application scenarios, and controllable costs. They can also be used for diabetes drug screening. This technology is highly innovative, solving the pain points of existing technologies and achieving precise, convenient, and low-cost diabetes risk assessment. It provides a new technical solution for early prevention, risk assessment, and drug screening of diabetes, possessing significant clinical application value and promising prospects for industrial promotion. Attached Figure Description

[0037] Figure 1 This is a normal distribution diagram for Example 1.

[0038] Figure 2 This is a normal distribution plot for Comparative Example 1.

[0039] Figure 3 This is a normal distribution plot for Comparative Example 2.

[0040] Figure 4 This is a normal distribution plot for Comparative Example 3.

[0041] Figure 5 This is a normal distribution plot for Comparative Example 4. Detailed Implementation

[0042] The present invention will be further clearly and completely illustrated below through embodiments. These embodiments are only some examples of the present invention and are not intended to limit the present invention, but are only for illustrating the present invention. Unless otherwise specified, the experimental methods used in the following embodiments are all conventional experiments, and the materials and reagents used in the following embodiments are commercially available unless otherwise specified.

[0043] Example 1: The gene locus, primers, probes, and PCR reaction system of the present invention. 1. Gene locus and its primer and probe information Well 1: Contains two gene loci. The genes tested in this study are SLC30A8 and KCNK16, which are associated with the risk of type 2 diabetes. They contain two loci: rs13266634 and rs1535500.

[0044] Well 2: Contains a gene locus, which in this study was the rs2237892 locus of KCNQ1, a gene associated with the risk of type 2 diabetes.

[0045] The gene locus information for pore site 1 and pore site 2 is shown in Table 1: Table 1. Gene locus information for pore site 1 and pore site 2

[0046] The primer and probe sequences for the above gene loci are shown in Table 2: Table 2 Primer and probe sequences

[0047] Note: In the table, "F" stands for Forward Primer; "R" stands for Reverse Primer; see the terminology explanation for "PC", "PG", "PT", and "PA".

[0048] 2. Buffer solution composition The primer and probe reagent components for the SLC30A8 and KCNK16 genes are shown in Table 3. Table 3 Primers and probes for SLC30A8 and KCNK16 genes

[0049] The components of the KCNQ1 primer and probe reagents are shown in Table 4: Table 4 KCNQ1 gene primers and probes

[0050] The buffer composition of well 1 and well 2 is shown in Tables 5-6: Table 5 Buffer composition of well 1

[0051] Table 6 Buffer composition of well 2

[0052] 3. Composition of lysis buffer The composition of the lysis buffer is shown in Table 7: Table 7 Composition of the lysis buffer

[0053] 4. PCR reaction system The qPCR reaction system includes a buffer (the buffer for well 1 or well 2 in Example 1) and a lysis buffer.

[0054] The reaction system for qPCR is shown in Table 8 below: Table 8. qPCR reaction system

[0055] 5. PCR amplification program The PCR amplification procedure is shown in Table 9: Table 9 PCR Amplification Procedure

[0056] Example 2: A method for assessing the risk of developing type 2 diabetes 1. Sample Source Inclusion criteria: Individuals aged ≥18 years who voluntarily provided genotype test samples and clinical indicator data, whose fasting blood glucose, BMI, and blood pressure met the criteria of WS 397—2012 "Screening and Diagnosis of Diabetes", and whose family history of diabetes was complete; after risk stratification according to the EDM model, the sample sizes for high-, medium-, and low-risk groups were 16, 33, and 18 cases respectively, to ensure the statistical validity of one-way ANOVA and trend chi-square test.

[0057] Exclusion criteria: Individuals who are pregnant / lactating, have severe liver or kidney dysfunction, have acute metabolic disorders, have recently used drugs that affect glucose metabolism, or whose samples were not collected properly or whose data are missing.

[0058] 2. Sample collection This product uses an oral swab to collect samples. After rinsing your mouth with water, gently massage both sides of your cheeks for 10-15 seconds, tear open the outer packaging of the oral swab, being careful not to touch the swab head with your hands. Insert the swab into your mouth and gently scrape the inner walls of both sides of your mouth 10-15 times each. Then break off the swab head and place it into the sample tube. The sampling is now complete.

[0059] 3. Treatment of pyrolysis solution The lysis buffer was placed on a vortex mixer and shaken vigorously for 2 minutes, then incubated at room temperature for 10 minutes, and centrifuged for 10-15 seconds before use.

[0060] 4. Sample testing The lysed samples were subjected to qPCR detection to obtain the correlation between the samples and the risk of diabetes. The qPCR reaction system included buffer (buffer for well 1 and well 2 in Example 1) and lysis buffer. The qPCR reaction system was added to the qPCR reaction tube, vortexed for 5-10 seconds, allowed to stand for 5-10 seconds, and then vortexed for another 5-10 seconds to ensure thorough mixing.

[0061] The resulting qPCR reaction tubes were placed in a qPCR device and reacted according to the PCR program described in Table 8. Fluorescence signals were collected at 60°C during the qPCR stage. The collected fluorescent amplification signals included FAM, VIC, CY5, and ROX.

[0062] 5. Data Collection Data from qPCR tests were collected to identify the relationships between diabetes-related genes in the samples and to establish a database. The risk of developing type 2 diabetes was categorized into high, medium, and low risk groups based on the detection of genes regulating blood glucose. Using rs13266634 as the standard, CC is the low-end, assigned a wild-type risk value; CT is the medium-end, assigned a heterozygous risk value; and TT is the high-end, assigned a homozygous risk value. Using rs1535500 as the standard, GG is the low-end, assigned a wild-type risk value; GT is the mid-end, assigned a heterozygous risk value; and TT is the high-end, assigned a homozygous risk value. Using rs2237892 as the standard, CC is the low-end, assigned a wild-type risk value; CT is the medium-end, assigned a heterozygous risk value; and TT is the high-end, assigned a homozygous risk value.

[0063] The relationship between the specific gene loci and the genes associated with type 2 diabetes is shown in Table 10 below: Table 10 Relationship between specific gene loci and genes associated with type 2 diabetes.

[0064] 6. Signal Acquisition The detection data is formatted on demand, and the gene data is formatted to convert the pathway signals of the qPCR instrument into corresponding weight values.

[0065] The qPCR instrument has four signal channels: ROX, CY5, FAM, and VIC. Each site corresponds to two of these channels. After the instrument finishes analysis, each channel will output a Boolean value indicating whether it is negative or positive. Negative means not detected, and positive means detected.

[0066] Taking locus rs1535500 as an example, the genotype at this locus corresponds to the detection results of channels FAM and VIC. When both FAM and VIC are positive, the genotype is GT. When FAM is positive and VIC is negative, the genotype is TT. When FAM is negative and VIC is positive, the genotype is GG.

[0067] Taking locus rs13266634 as an example, the genotype at this locus corresponds to the detection results of channels CY5 and ROX. When CY5 is positive and ROX is positive, the genotype is CT. When CY5 is positive and ROX is negative, the genotype is CC. When CY5 is negative and ROX is positive, the genotype is TT.

[0068] Taking locus rs2237892 as an example, the genotype at this locus corresponds to the detection results of channels ROX and VIC. When ROX is positive and VIC is positive, the genotype is CT. When ROX is positive and VIC is negative, the genotype is TT. When ROX is negative and VIC is positive, the genotype is CC.

[0069] After obtaining the locus genotype, refer to Table 11. When the number of dangerous bases is 0, the locus weight value is the wild type weight value. When the number of dangerous bases is 1, the weight value is the heterozygous mutation weight value. When the number of dangerous bases is 2, the weight value is the homozygous mutation weight value.

[0070] Table 11 Weight Value Calculation

[0071] 7. Diabetes Risk Assessment 7.1 Assessment of Diabetes Risk Level The method for constructing the diabetes risk assessment model is as follows: the GRS value of the item is calculated using the EDM model, and the level is classified using the mean and standard deviation based on the normal distribution of the GRS value.

[0072] Based on the genetic data of the samples, a machine learning model was selected to construct a model for assessing the quality of life risk of diabetes based on the sample genetic data. Specifically, the GRS value of the items was calculated using an EDM model (the sum of the logarithms with the weights of the relevant loci and a constant e as the base is called GRS). Based on the normal distribution of the GRS values, the mean and standard deviation were used to classify the levels. The formula for calculating the GRS value is as follows: ; Where ORi is the weight of the SNPi site (see Table 10); Xi is the number of critical bases; The project rank is calculated using the GRS, mean, and sd values ​​according to the formula. The mean is the arithmetic mean of the GRS values ​​of all samples involved in the calculation; and the sd is the standard deviation of the GRS values ​​of the samples involved in the calculation.

[0073] Level 1 (Low Risk): ; Level 2 (Medium Risk): ; Level 3 (High Risk): ; The formula for calculating the threshold coefficient xs is: ; In the formula, N is the number of sites included in the project; mean is the arithmetic mean of all GRS values ​​of the samples involved in the calculation; and sd is the standard deviation of the GRS values ​​of the samples involved in the calculation.

[0074] Example 3: Validation of the effectiveness of the gene loci, primers, probes, and PCR reaction system of the present invention. 1. Sample Source Sample Source: The samples used in this embodiment are consistent with the sample source and exclusion criteria in Example 2, all from subject groups that meet the following criteria. Sample collection and processing were completed simultaneously to ensure a one-to-one correspondence between genotype data and clinical information. The sample sizes for the high-, medium-, and low-risk groups were 15, 32, and 20 cases, respectively.

[0075] 2. Sample Detection and Analysis The gene loci, primers, probes, and PCR reaction system of Example 1 were used, and the method of Example 2 was employed to validate the samples. The loci and weights of Example 1 are shown in Table 12. The normal distribution plot of Example 1 is shown below. Figure 1 As shown. Figure 1 The risk values ​​shown are highly concentrated, with clear peaks located in the core range of the population distribution (approximately 4.75-5.00). This indicates that the combination of these three loci is broadly representative of the target population, with the risk values ​​of the vast majority of individuals falling within the detectable range, thus avoiding the problem of "too small a population representation." The smooth normality curve closely matches the histogram, indicating that the genetic effect of the locus combination is stable, with small variability in risk values ​​among individuals. This results in higher repeatability and reliability of the test results, meeting the core requirements of clinical diagnosis for "stable and reproducible results."

[0076] Table 12. Sites and weights in Example 1

[0077] Comparative Example 1 1. Gene loci, primers, probes, and PCR reaction system of Comparative Example 1 The only difference between Comparative Example 1 and Example 1 is "2. The composition of the buffer solution is different". Comparative Example 1 lacks the rs1535500 site of the KCNK16 gene.

[0078] The components of the SLC30A8 gene primer and probe reagents are shown in Table 13. The components of the KCNQ1 primer and probe reagents are shown in Table 4.

[0079] Table 13 SLC30A8 gene primers and probes

[0080] The composition of the buffer solution in well 1 is shown in Table 14, and the composition of the buffer solution in well 2 is shown in Table 6.

[0081] Table 14 Buffer composition of well 1 in Comparative Example 1

[0082] 2. Sample Source Same as Example 3.

[0083] 3. Sample Detection and Analysis The gene loci, primers, probes, and PCR reaction system of Comparative Example 1 were used to validate the samples using the method of Example 2. The loci and weights of Comparative Example 1 are shown in Table 15. The normal distribution plot of Comparative Example 1 is shown below. Figure 2As shown in the figure. Comparative Example 1 is a two-site combination, which leads to extremely dispersed data with obvious multi-peak or long-tail distribution. The risk values ​​of a large number of individuals fall in the very low or very high margin range, which causes the test kit to have a significantly reduced detection efficiency for this population, and even creates a "detection blind zone". The fluctuation of the test results increases the risk of misdiagnosis.

[0084] Table 15 Comparative Example 1: Loci and Weights

[0085] Comparative Example 2 1. Gene loci, primers, probes, and PCR reaction system of Comparative Example 2 The only difference between Comparative Example 2 and Example 1 is that the qPCR reaction system does not include buffer for well 2, i.e., it lacks the rs2237892 site of the KCNQ1 gene.

[0086] 2. Sample Source Same as Example 3.

[0087] 3. Sample Detection and Analysis The gene loci, primers, probes, and PCR reaction system of Comparative Example 2 were used to validate the samples using the method of Example 2. The loci and weights of Comparative Example 2 are shown in Table 16. The normal distribution plot of Comparative Example 2 is shown below. Figure 3 As shown in the figure. Comparative Example 2, which uses a two-site combination, resulted in extremely dispersed data with obvious multi-peak or long-tail distribution. The risk values ​​of a large number of individuals fell in the extremely low or extremely high margin range, which led to a significant decrease in the detection efficiency of the kit for this population, and even the emergence of a "detection blind zone". The fluctuation of the test results increased the risk of misdiagnosis.

[0088] Table 16 Comparative Example 2 Sites and Weights

[0089] Comparative Example 3 1. Gene loci, primers, probes, and PCR reaction system of Comparative Example 3 The only difference between Comparative Example 3 and Example 1 is "2. The composition of the buffer solution is different". Comparative Example 1 lacks the rs13266634 site of the SLC30A8 gene.

[0090] The components of the KCNK16 gene primer and probe reagents are shown in Table 17. The components of the KCNQ1 primer and probe reagents are shown in Table 4.

[0091] Table 17 KCNK16 gene primers and probes

[0092] The composition of the buffer solution in well 1 is shown in Table 18, and the composition of the buffer solution in well 2 is shown in Table 6.

[0093] Table 18 Buffer composition of well 1 in Comparative Example 3

[0094] 2. Sample Source Same as Example 3.

[0095] 3. Sample Detection and Analysis The gene loci, primers, probes, and PCR reaction system of Comparative Example 3 were used to validate the samples using the method of Example 2. The loci and weights of Comparative Example 3 are shown in Table 19. The normal distribution plot of Comparative Example 3 is shown below. Figure 4 As shown in the figure, the two-locus combination in Comparative Example 3 also resulted in a discrete distribution. Although the peak value was high, the interval range was large, and there were obvious distribution discontinuities, affecting the consistency of the test results and the uniformity of population coverage. The large deviation between the histogram and the fitted curve indicates that the genetic effect is unstable, the risk value varies greatly among individuals, and the fluctuation of the test results increases the risk of misdiagnosis.

[0096] Table 19 Comparative Example: 3 Loci and Weights

[0097] Comparative Example 4 1. Gene loci, primers, probes, and PCR reaction system of Comparative Example 4 The only difference between Comparative Example 4 and Example 1 is "2. The composition of the buffer solution is different". Comparative Example 4 adds the rs7944584 site of the MADD gene.

[0098] Table 20 Primer and probe sequences for the rs7944584 site

[0099] The primer, probe, and reagent components for the SLC30A8 and KCNK16 genes in Comparative Example 4 are shown in Table 3, and the primer, probe, and reagent components for the KCNQ1 and MADD genes are shown in Table 21. Table 21 Primers and probes for the KCNQ1 and MADD genes in Comparative Example 4

[0100] The buffer composition of well 1 in Comparative Example 4 is shown in Table 5, and the buffer composition of well 2 is shown in Table 22. Table 22 Buffer composition of well 2 in Comparative Example 4

[0101] 2. Sample Source Same as Example 3.

[0102] 3. Sample Detection and Analysis The gene loci, primers, probes, and PCR reaction system of Comparative Example 4 were used to validate the samples using the method of Example 2. The loci and weights of Comparative Example 4 are shown in Table 23. The normal distribution plot of Comparative Example 4 is shown below. Figure 5 As shown in the figure, the four-locus combination in Comparative Example 4 resulted in a severely right-biased distribution, with a large number of individuals having risk values ​​concentrated in the narrow range of 5.0–6.5, while the proportion of people in the high-risk range (>7.0) was extremely small. This leads to insufficient ability of the kit to identify high-risk individuals, limiting its clinical application value. Although the fit is acceptable, the right-biased distribution results in a lack of statistical basis for setting the risk threshold, and poor comparability of test results among different populations.

[0103] Table 23 Comparative Example: 4 sites and weights

[0104] Experiment Example 1: Validation of Results Given that the purpose of this invention is to assess an individual's future risk of developing diabetes, rather than to diagnose the current disease state, traditional diagnostic accuracy rates (such as PPV / NPV) are not the most suitable validation indicators. This embodiment employs the validity validation logic of a risk prediction model, that is, to examine whether the genetic risk levels classified by this invention are consistent with the degree of clustering of various recognized clinical risk factors that influence the future development of diabetes.

[0105] Based on the 67 samples mentioned above, multiple clinical risk indicators were further collected for each participant (Tables 24-26), including fasting plasma glucose (FPG), blood pressure (systolic blood pressure), body mass index (BMI), and family history of diabetes in first-degree relatives, as validation criteria for low, medium, and high risk. One-way ANOVA (continuous variable) or trend chi-square test (categorical variable) was used to analyze the distribution trend of each indicator among different gene risk levels (low, medium, and high).

[0106] Table 24 Association of Multidimensional Clinical Risk Indicators

[0107] Table 25 Analysis of Clinical Phenotypic Indicators

[0108] Table 26 Clinical Risk Indicator Scoring and Rating

[0109] Clinical risk indicator scoring criteria: In this invention, four diabetes-related risk factors—fasting blood glucose, systolic blood pressure, BMI, and family history of diabetes—are graded and scored. The basis and rationale for this approach are explained below: (1) Criteria for grading fasting blood glucose levels Fasting blood glucose is a core clinical indicator for diagnosing glucose metabolism disorders and diabetes, and its value directly reflects the body's basal glucose regulation capacity. Based on internationally recognized diagnostic criteria for diabetes and stratification criteria for glucose metabolism disorders: fasting blood glucose < 6.1 mmol / L is considered normal, corresponding to 1 point; fasting blood glucose 6.1-6.9 mmol / L indicates impaired fasting glucose, belonging to prediabetes with a significantly increased risk, corresponding to 2 points; fasting blood glucose ≥ 7.0 mmol / L has reached the diagnostic threshold for diabetes, with an extremely high risk, corresponding to 3 points. This scoring method is highly consistent with clinical diagnostic criteria and can objectively reflect the positive correlation between the degree of glucose abnormality and the risk of developing diabetes.

[0110] (2) Basis for assigning systolic blood pressure rating Hypertension is closely related to insulin resistance and glucose metabolism disorders, and is a significant risk factor for the development and progression of diabetes and its complications. According to the clinical diagnostic and risk stratification criteria for hypertension: systolic blood pressure <120 mmHg is considered ideal blood pressure, corresponding to 1 point; systolic blood pressure 120-129 mmHg is considered high-normal blood pressure, indicating abnormal vascular regulation and an increased risk of diabetes, corresponding to 2 points; systolic blood pressure >129 mmHg falls into the category of hypertension, significantly increasing the risk of abnormal glucose metabolism and diabetes, corresponding to 3 points. This classification quantifies the contribution of abnormal blood pressure to the risk of diabetes.

[0111] (3) BMI classification and scoring criteria BMI is a key indicator reflecting body fat percentage and obesity levels. Obesity, especially central obesity, is the most significant controllable risk factor for type 2 diabetes. According to the Chinese Guidelines for the Prevention and Control of Overweight and Obesity in Adults: A BMI < 24.9 is considered normal weight with a low metabolic risk, corresponding to 1 point; a BMI of 25-29.9 is considered overweight with a significantly increased risk of insulin resistance, corresponding to 2 points; a BMI > 30 is considered obese, a high-risk group for type 2 diabetes, corresponding to 3 points. This scoring system matches the pathophysiological mechanism of insulin resistance caused by obesity.

[0112] (4) Basis for scoring based on family history of diabetes Diabetes mellitus has a clear genetic susceptibility, and family history is an important independent risk factor for assessing an individual's congenital risk of developing the disease. No family history of diabetes suggests a lower genetic risk, corresponding to 1 point; a family history of diabetes suggests carrying a susceptibility gene, with a significantly higher risk of developing the disease than the general population, corresponding to 2 points. This setting reflects the independent contribution of genetic factors to the development of diabetes.

[0113] As shown in Table 25, the genetic risk levels determined by this invention show a highly consistent gradient relationship with all clinical risk indicators: Metabolic indicators: From low to high risk groups, the average fasting blood glucose and blood pressure levels increased significantly (both p<0.001), indicating that the high-risk group already had a worse glucose metabolism status. Physical indicators: The average BMI increased significantly (p<0.001). Genetic background: The proportion with a family history of diabetes increased sharply from 11.1% (low risk) to 43.8% (high risk) (trend p<0.001).

[0114] Further confirmation using the above criteria revealed that the sample sizes for the high-, medium-, and low-risk groups were 15, 32, and 20 cases, respectively. The method described in Example 3 of this invention was used to assess the risk of these samples. The results are shown in Table 27.

[0115] Table 27 Example 3 Gene Risk Stratification

[0116] The results above show that the accuracy of the gene loci, primer probes and PCR reaction system of the present invention for diabetes risk assessment is 88.05% (59 / 67).

[0117] The above detailed description is a specific illustration of one feasible embodiment of the present invention, and this embodiment is not intended to limit the patent scope of the present invention. It should be noted that all equivalent implementations or modifications made without departing from the present invention should be included within the scope of the technical solution of the present invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims

1. A combination of biomarkers, characterized in that, The biomarker combination consists of the SLC30A8 gene rs13266634 site, the KCNK16 gene rs1535500 site, and the KCNQ1 gene rs2237892 site.

2. A biomarker complex system, characterized in that, The biomarker complex system includes a primer-probe combination for amplifying the biomarker combination of claim 1.

3. The biomarker complex system according to claim 2, characterized in that, The nucleotide sequence of the primer-probe combination is shown in SEQ ID NO.1-12.

4. The biomarker complex system according to claim 3, characterized in that, The primer-probe combination includes an upstream primer, a downstream primer, and a probe for the SLC30A8 gene at rs13266634; an upstream primer, a downstream primer, and a probe for the KCNK16 gene at rs1535500; and an upstream primer, a downstream primer, and a probe for the KCNQ1 gene at rs2237892.

5. The biomarker complex system according to claim 4, characterized in that, The nucleotide sequences of the upstream and downstream primers for the SLC30A8 gene rs13266634 site are shown in SEQ ID NO.1-2, and the nucleotide sequence of the probe for the SLC30A8 gene rs13266634 site is shown in SEQ ID NO.3-4.

6. The biomarker complex system according to claim 4, characterized in that, The nucleotide sequences of the upstream and downstream primers for the KCNK16 gene rs1535500 site are shown in SEQ ID NO.5-6, and the nucleotide sequence of the probe for the KCNK16 gene rs1535500 site is shown in SEQ ID NO.7-8.

7. The biomarker complex system according to claim 4, characterized in that, The nucleotide sequences of the upstream and downstream primers for the KCNQ1 gene rs2237892 site are shown in SEQ ID NO. 9-10, and the nucleotide sequence of the probe for the KCNQ1 gene rs2237892 site is shown in SEQ ID NO. 11-12.

8. The biomarker complex system according to claim 2, characterized in that, The final concentration of each primer and probe in the primer-probe combination is 0.025-0.1 μM.

9. The biomarker complex system according to claim 2, characterized in that, The biomarker complex system includes one or more of the following: DNA extraction reagents, PCR amplification reagents, sequencing library construction reagents, and nucleic acid purification reagents.

10. The use of the biomarker combination of claim 1 or the biomarker complex system of any one of claims 2-9 in the preparation of a kit for diabetes assessment or for screening drugs for the prevention and treatment of diabetes.

11. A reagent kit, characterized in that, The kit comprises the biomarker combination of claim 1 or the biomarker complex system of any one of claims 2-9.

12. The kit according to claim 11, characterized in that, The kit described is used for diabetes assessment or for screening drugs for the prevention and treatment of diabetes.

13. The method of using the biomarker complex system according to any one of claims 2-9 or the kit according to any one of claims 11-12, characterized in that, The method of use includes the following steps: S1. Extract total genomic DNA from the sample; S2. Using a biomarker complex system or kit, perform PCR amplification on the total genomic DNA extracted in step S1; S3. Calculate the weight values ​​based on the genotypes of each biomarker; S4. Calculate the sample GRS value based on the weight values ​​to determine the degree of diabetes risk.

14. The method of use according to claim 13, characterized in that, The samples mentioned in step S1 include one or more of the following: blood, blood spots, oral cells, semen, sperm spots, bones, hair, saliva, saliva spots, sweat, and amniotic fluid containing fetal cells.

15. A method for screening drugs for the prevention and treatment of diabetes, characterized in that, The method includes using the biomarker complex system of any one of claims 2-9 or the kit of any one of claims 11-12.

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