A kit for detecting methylation levels of ELOVL2, FHL2 and PDE4C genes and application
By designing a real-time PCR kit with specific primers and probes to detect the methylation levels of the ELOVL2, FHL2, and PDE4C genes, a methylation age prediction model suitable for the Chinese population was constructed. This solved the problems of high detection cost and low accuracy in existing technologies, and achieved low-cost, rapid, and accurate biological age assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 大连晶泰医学检验实验室有限公司
- Filing Date
- 2026-04-16
- Publication Date
- 2026-06-19
AI Technical Summary
The application of existing DNA methylation models in the Chinese population suffers from high detection costs, high complexity, and significant influence from blood cell components. Furthermore, there is a lack of methylation clock detection kits suitable for the Chinese population, leading to inaccurate assessment of biological age.
A real-time PCR kit with specific primers and probes was designed to detect the methylation levels of the ELOVL2, FHL2, and PDE4C genes. A methylation age prediction model suitable for the Chinese population was constructed, and high-sensitivity detection was performed using real-time PCR to establish the methylation age prediction model.
It achieves low-cost, rapid, and accurate biological age assessment, is suitable for the Chinese population, reduces the influence of blood cell components, and improves the accuracy and ease of testing, making it applicable to health management and anti-aging fields.
Smart Images
Figure CN122235318A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of biotechnology, specifically relating to a kit for detecting the methylation levels of the ELOVL2, FHL2, and PDE4C genes and its application. Background Technology
[0002] Population aging is a pressing global issue. According to WHO statistics, the global population aged 60 and over will increase from 12% in 2015 to 22% in 2050. Population aging leads to a continuous rise in the incidence of aging and age-related diseases. Aging refers to the decline and loss of physiological, psychological, and cognitive abilities with increasing age, leading to increased susceptibility to disease and ultimately death. While aging is an inevitable phenomenon in living organisms, delaying aging, reducing the incidence of age-related diseases, and achieving healthy aging are important goals for society today. Research on aging prevention and control has become a priority in the field of population health, and aging assessment is the foundation and key link in aging research and prevention.
[0003] Currently, the most commonly used indicator for assessing aging is chronological age, which is age calculated from the date of birth, also known as time-series age. Chronological age is associated with the decline of organ function, the occurrence of chronic diseases, and the risk of death. However, individuals of the same chronological age may show different levels of aging; some may appear younger in appearance and function, while others exhibit characteristics of older age. Furthermore, the risk of developing age-related diseases varies significantly among individuals of the same chronological age, resulting in high heterogeneity in health outcomes among the elderly. Therefore, chronological age has limitations as an indicator for assessing aging and is not an ideal assessment method.
[0004] Aging is a process of genetic and epigenetic, biochemical, and other functional phenotypic changes that occur in the body under the influence of genetic and environmental factors. These changes include genomic instability, shortened telomere length, DNA methylation or demethylation, altered protein homeostasis, abnormal nutrient sensing regulation, mitochondrial dysfunction, apoptosis, stem cell exhaustion, and altered intercellular communication. Biological age, established using these aging-related molecular biological markers, can more accurately reflect the degree of aging, damage repair and tissue regeneration capacity, functional status, and assess current (or future) health status and lifespan. Compared to calendar age, biological age is a more suitable indicator for assessing aging. However, there is no gold standard for assessing biological age; therefore, aging-related biomarkers are typically used to predict biological age. Common aging biomarkers fall into two categories: molecular and phenotypic. Molecular biomarkers include genetic susceptibility, characteristic gene expression levels, small molecule metabolites, DNA methylation, and telomere length; phenotypic biomarkers include physiological and biochemical indicators such as blood pressure, blood lipids, and blood glucose, as well as functional indicators such as cognition, memory, and grip strength. Among these biomarkers, DNA methylation is considered the most promising biomarker for assessing biological age in the human population because it can reflect molecular changes in the body under the influence of genetic and environmental factors, has a high correlation with chronological age, is stable in vivo, and has high-throughput detection methods.
[0005] DNA methylation, as an epigenetic modification, has been increasingly confirmed in studies to be associated with age, and related methylation clocks have been developed for the inference of biological age. However, DNA methylation is tissue- and population-specific, which can affect prediction results. At the same time, individual physiological levels are affected by the interactions of all genes in the body, and predicting methylation age solely based on the location of conserved sequences may result in the loss of information from important gene loci.
[0006] Existing biological age prediction models based on DNA methylation still have limitations. First, both the Horvath and Hannum age clocks include an excessive number of CpG sites (353 and 71 sites, respectively). Under current conditions, only whole-genome methylation microarray technology can meet the detection requirements, thus placing high demands on detection technology and costs, limiting the clinical application of the models. Second, existing models are mainly built based on data from European and American populations, lacking data from Asian or Chinese populations. Since methylation levels differ between different ethnic groups, these models may not be applicable to the Chinese population. Third, the methylation levels of some CpG sites vary among different blood cells, and blood cell composition affects the overall DNA methylation level of whole blood samples. Existing models built based on whole blood samples do not consider the impact of blood cell heterogeneity; therefore, when using these models for biological age assessment, blood cell composition needs to be adjusted, increasing the complexity of the models and hindering practical application.
[0007] Therefore, developing a methylation clock detection kit suitable for the Chinese population based on key loci to predict an individual's methylation age and thus determine their biological age has become an important problem that urgently needs to be solved. Summary of the Invention
[0008] Therefore, the purpose of this invention is to provide a kit and its application for detecting the methylation levels of the ELOVL2, FHL2 and PDE4C genes; this invention uses the kit to detect the DNA methylation levels of the ELOVL2, FHL2 and PDE4C genes, thereby obtaining a methylation age prediction model based on DNA methylation levels.
[0009] To achieve the above objectives, the present invention provides the following technical solution:
[0010] In a first aspect, the present invention provides a kit for detecting the methylation levels of ELOVL2, FHL2 and PDE4C genes, the kit comprising a combination of detection primers and probes for detecting methylated ELOVL2, FHL2 and PDE4C genes treated with bisulfite. The detection primers include forward and reverse primers. The nucleotide sequence of the forward primer for detecting ELOVL2 is shown in SEQ ID NO.1, and the nucleotide sequence of the reverse primer is shown in SEQ ID NO.2; the nucleotide sequence of the forward primer for detecting FHL2 is shown in SEQ ID NO.5, and the nucleotide sequence of the reverse primer is shown in SEQ ID NO.6; the nucleotide sequence of the forward primer for detecting PDE4C is shown in SEQ ID NO.9, and the nucleotide sequence of the reverse primer is shown in SEQ ID NO.10. The probes include methylated and unmethylated probes. The nucleotide sequence of the methylated probe for detecting ELOVL2 is shown in SEQ ID NO.3, and the nucleotide sequence of the unmethylated probe is shown in SEQ ID NO.4; the nucleotide sequence of the methylated probe for detecting FHL2 is shown in SEQ ID NO.7, and the nucleotide sequence of the unmethylated probe is shown in SEQ ID NO.8; the nucleotide sequence of the methylated probe for detecting PDE4C is shown in SEQ ID NO.11, and the nucleotide sequence of the unmethylated probe is shown in SEQ ID NO.12.
[0011] Based on the above technical solution, the probe is further labeled with a fluorescent reporter group at its 5' end and a fluorescent quencher group at its 3' end.
[0012] Based on the above technical solution, further, the 5' end of the methylated probe is labeled with a FAM fluorescent group, and the 3' end is labeled with a BHQ1 fluorescent quencher group; the 5' end of the unmethylated probe is labeled with a VIC fluorescent group, and the 3' end is labeled with a BHQ1 fluorescent quencher group.
[0013] Based on the above technical solution, furthermore, the three gene detection primers for ELOVL2, FHL2 and PDE4C can specifically amplify the CpG islands in the promoter regions of the three genes of ELOVL2, FHL2 and PDE4C.
[0014] Secondly, the present invention provides a method for constructing a methylation age prediction model using the above-mentioned reagent kit, comprising the following steps: (1) Extract genomic DNA from the sample to be tested; (2) Prepare a series of ELOVL2, FHL2 and PDE4C standards with different methylation gradients; (3) The genomic DNA of the sample to be tested obtained in step (1) and the ELOVL2, FHL2 and PDE4C standards obtained in step (2) are subjected to sulfite conversion; (4) The methylation quantitative PCR detection of the genomic DNA, ELOVL2, FHL2 and PDE4C standards obtained in step (3) was performed using the detection primers and probes in the kit; (5) Based on the detection results of ELOVL2, FHL2 and PDE4C standards obtained in step (4), establish a linear relationship between the methylation rate and the difference ΔCT between the CT value of the methylated probe and the CT value of the non-methylated probe of the same sample, and obtain the standard curve equation. (6) Based on the standard curve equation obtained in step (5) and the ΔCT of ELOVL2, FHL2 and PDE4C in the sample to be tested obtained in step (4), the methylation rate of ELOVL2, FHL2 and PDE4C genes in the sample to be tested is obtained. Based on the methylation rate of ELOVL2, FHL2 and PDE4C genes and the actual age of the sample to be tested, a linear regression model is performed to obtain the methylation age prediction model.
[0015] Based on the above technical solution, in step (1), the extraction of genomic DNA is completed by a kit; the number of samples to be tested is not less than 50.
[0016] Based on the above technical solution, further, the methylation gradient in step (2) is 0 to 100%.
[0017] Based on the above technical solution, in step (3), the sulfite conversion is completed using a kit.
[0018] Based on the above technical solution, further, in step (4), the ELOVL2 gene, FHL2 gene and PDE4C gene of the same sample are detected respectively.
[0019] Based on the above technical solution, the equation of the standard curve in step (5) is as follows: ELOVL2 gene: y = -3.1021x + 43.556; PDE4C gene: y = -4.0843x + 50.525; FHL2 gene: y = -3.5217x + 48.223; Where y represents the methylation rate and x represents ΔCT.
[0020] Thirdly, this invention provides a methylation age prediction model obtained by the above method, where methylation age y (years) = ELOVL2 gene methylation rate. 2.59445916 + PDE4C gene methylation rate 0.69710106 - ELOVL2 gene methylation rate^2 / 100 1.91082923 + ELOVL2 gene methylation rate FHL2 gene methylation rate / 100 2.10513115 - 128.252319063586.
[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention uses real-time fluorescence PCR for the detection of methylated DNA. Specific primers and probes are designed to ensure the specificity and high sensitivity of the detection. Furthermore, it eliminates the need for electrophoresis, hybridization, and other operations after PCR, reducing contamination and further improving detection accuracy.
[0022] 2. The quantitative real-time PCR probe method is used to detect methylation at important sites in the sample. It is applicable to the Chinese population and is not affected by blood cell components. The methylation age prediction value obtained by the methylation age prediction model is accurate and has a higher degree of consistency with the actual age.
[0023] 3. Existing technologies for predicting methylation age are characterized by complex detection methods, high detection costs, and long cycles. The present invention's fluorescent quantitative PCR probe method is a mature detection technology with low detection costs and a short cycle. The detection and calculation method of the present invention is not only applicable to the Chinese population, but also has good prediction accuracy.
[0024] 4. Due to their simplicity, economy, and accuracy, the reagent kit and method of this invention can be widely promoted in clinical applications and have broad commercial prospects in the fields of health management and anti-aging in the Chinese population. Attached Figure Description
[0025] To more clearly illustrate the embodiments of the present invention, the accompanying drawings involved in the embodiments will be briefly described below.
[0026] Figure 1 The figure shows the quantitative fluorescence amplification curves of the methylation level of the ELOVL2 gene in Example 1. In the figure, the dark curve is the amplification curve of the methylated probe and the light curve is the amplification curve of the unmethylated probe.
[0027] Figure 2 The figure shows the quantitative fluorescence amplification curves of the FHL2 gene methylation level in Example 1. In the figure, the dark curve is the amplification curve of the methylated probe and the light curve is the amplification curve of the unmethylated probe.
[0028] Figure 3 The figure shows the quantitative fluorescence amplification curves of the PDE4C gene methylation level in Example 1. In the figure, the dark curve is the amplification curve of the methylated probe and the light curve is the amplification curve of the unmethylated probe.
[0029] Figure 4 The figure shows the quantitative fluorescence amplification curves of the methylation level of the ELOVL2 gene methylation standard in Example 1. In the figure, the dark curve is the amplification curve of the methylated probe and the light curve is the amplification curve of the unmethylated probe.
[0030] Figure 5The figure shows the quantitative fluorescence amplification curves of the methylation level of the FHL2 gene methylation standard in Example 1. In the figure, the dark curve is the amplification curve of the methylated probe and the light curve is the amplification curve of the non-methylated probe.
[0031] Figure 6 The figure shows the quantitative fluorescence amplification curves of the methylation level of the PDE4C gene methylation standard in Example 1. In the figure, the dark curve is the amplification curve of the methylated probe and the light curve is the amplification curve of the non-methylated probe.
[0032] Figure 7 A flowchart for constructing a methylation age prediction model based on DNA methylation levels.
[0033] Figure 8 This is a graph showing the relationship between DNA methylation age and calendar age. Detailed Implementation
[0034] The present invention will now be described in further detail with reference to specific embodiments. The given embodiments are merely illustrative of the invention and not intended to limit its scope. The embodiments provided below can serve as a guide for further improvements by those skilled in the art and do not constitute a limitation on the invention in any way.
[0035] Unless otherwise specified, the experimental methods used in the following examples are conventional methods, performed according to the techniques or conditions described in the literature in this field or according to the product instructions. Unless otherwise specified, the materials and reagents used in the following examples are commercially available.
[0036] Example 1. Preparation, use and sample testing of the reagent kit The kit of this invention includes primers and probes for ELOVL2 gene detection, primers and probes for FHL2 gene detection, and primers and probes for PDE4C gene detection, the nucleotide sequences of which are shown in Table 1 below: Table 1 Primers and probes for detecting ELOVL2, FHL2, and PDE4C genes
[0037] The experimental procedure is as follows: Ninety blood samples were collected (information on the ninety samples is shown in Table 2) for subsequent testing.
[0038] Table 290 Sample Information
[0039] The specific process is as follows: I. DNA Extraction from Blood Samples The specific extraction method includes the following steps: Following the standard operating procedure of the TGuide Cell / Tissue Genomic DNA Extraction Kit (this product must be used with the TGuide M16 automated nucleic acid extractor): Add 200 µL of whole blood, 10 µL of Proteinase K, and 2 µL of RNase to a 1.5 mL sample tube, and place it directly in well 4 of the T-shaped holder; place the pipette tip in well 2 of the T-shaped holder and the DNA collection tube in well 1; take the TGuide Cell / Tissue Genomic DNA Extraction Kit OSR-M401; insert the appropriate number of reagent strips into the reagent slots, ensuring the reagent slots are securely holding the reagent strips; press the Start button to begin the program, run program number 102, Enter, 2, Enter, Enter, and press 4 to elute a volume of 60 µL; ensure the experimental program is running normally; after the program finishes, remove the sample for subsequent operations.
[0040] II. Bisulfite Conversion The standard was diluted by diluting 100% methylated standard and 0% non-methylated standard into seven gradients: 0%, 5% methylation, 10% methylation, 25% methylation, 50% methylation, 75% methylation, and 100% methylation, for subsequent sulfite conversion.
[0041] The extracted DNA and seven diluted standards, each at 200 ng, were subjected to bisulfite conversion to obtain bisulfite-converted DNA. It should be noted that this process utilizes the principle that bisulfite converts all unmethylated cytosine to uracil, while methylated cytosine remains unchanged, to methylate DNA extracted from blood. The commercially available kit used for DNA methylation is the EZ DNA Methylation-Lightning Kit (Catalog No. D5030) manufactured by ZYMO Research, which reduces DNA loss during the methylation process and improves the detection sensitivity of DNA methylation.
[0042] The specific bisulfite conversion method includes the following steps: The conversion procedure for the Zymo Research EZ DNA Methylation-Lightning Kit is as follows: Add water to a final volume of 20 μl of the extracted 200 ng DNA sample, add 130 μl of Lightning Conversion Reagent, mix thoroughly by pipetting, and perform the conversion reaction at 98°C for 8 min and then at 54°C for 60 min. Add 600 μl of M-Binding Buffer to the Zymo-Spin™ ICColumn, then add the converted sample to the Zymo-Spin™ ICColumn, invert several times to mix, centrifuge at 14000 rpm for 30 s, and discard the waste liquid. Add 100 μl of M-Wash Buffer to the adsorption column, centrifuge at 14000 rpm for 30 s, and discard the waste liquid. Add 200 μl of L-Desulphonation Buffer to the adsorption column, incubate at room temperature for 20 min, then centrifuge at 14000 rpm for 30 s, and discard the waste liquid. Add 200 μl of M-Wash Buffer to the adsorption column. Centrifuge at 14000 rpm for 30 seconds using buffer, discard waste liquid; repeat the above steps; perform empty centrifugation, centrifuge at 14000 rpm for 30 seconds; transfer the adsorption column to a new collection tube, add 15 μL M-Elution Buffer, centrifuge at 14000 rpm for 30 seconds; obtain sulfite-converted DNA for subsequent detection.
[0043] III. qMSP Detection The DNA after bisulfite conversion was subjected to quantitative PCR for methylation using the primers and probes in the kit (Table 1). In this example, the ELOVL2 gene, FHL2 gene and PDE4C gene of the same sample were detected separately.
[0044] Specifically, for ELOVL2 gene detection: First, prepare a premixed PCR probe and primer solution for detecting the ELOVL2 gene, including: 0.1 μM ELOVL2 gene methylation detection probe, 0.1 μM ELOVL2 gene unmethylation detection probe, 0.2 μM ELOVL2 gene forward primer, 0.2 μM ELOVL2 gene reverse primer, and add deionized water to a final volume of 8 μL; the qMSP reaction system for detecting the ELOVL2 gene is shown in Table 3.
[0045] Table 3. qMSP reaction system for detecting the ELOVL2 gene
[0046] FHL2 gene detection: First, prepare the PCR probe and primer premix for detecting the FHL2 gene, including: 0.1 μM FHL2 gene methylation detection probe, 0.1 μM FHL2 gene unmethylation detection probe, 0.2 μM FHL2 gene forward primer, 0.2 μM FHL2 gene reverse primer, and add deionized water to a final volume of 8 μL; the qMSP reaction system for detecting the FHL2 gene is shown in Table 4.
[0047] Table 4. qMSP reaction system for detecting FHL2 gene
[0048] PDE4C gene detection: First, prepare the PCR probe and primer premix for detecting the PDE4C gene, including: 0.1 μM PDE4C gene methylation detection probe, 0.1 μM PDE4C gene unmethylation detection probe, 0.2 μM PDE4C gene forward primer, 0.2 μM PDE4C gene reverse primer, and add deionized water to a final volume of 8 μL; the qMSP reaction system for detecting the PDE4C gene is shown in Table 5.
[0049] Table 5. qMSP reaction system for detecting PDE4C gene
[0050] The qMSP procedure for detecting ELOVL2, FHL2, and PDE4C genes is shown in Table 6.
[0051] Table 6. qMSP Procedure
[0052] The fluorescence quantitative amplification curves of the three genes ELOVL2, FHL2, and PDE4C (1 case out of 90) and the standard are shown below. Figure 1-6 As shown, the amplification curves are good, all S-shaped, with no nonspecific amplification or impurity peaks, indicating reliable amplification results that can be used for subsequent experimental data analysis.
[0053] Example 2 Methylation Age Prediction Model Based on Real-Time PCR Detection Method Establishment of a linear function for methylation level The results of the methylation standards of 7 gradients detected by Example 1 are shown in Table 7. A linear relationship between methylation level and ΔCT (the difference between the CT value of the methylated probe and the CT value of the non-methylated probe in the same sample) was established, thus obtaining the linear formula y=ax+b (y represents the methylation level, x represents ΔCT, a is the slope, and b is the intercept), and the R value is ≥0.9. The methylation level of the sample was then calculated.
[0054] Table 7 Results of Methylated Standards
[0055] Based on the results of the standard samples, the linear relationship between the methylation levels of the three genes and ΔCT (the difference between the CT values of methylated probes and the CT values of unmethylated probes in the same sample) is as follows: ELOVL2 gene: y = -3.1021x + 43.556; PDE4C gene: y = -4.0843x + 50.525; FHL2 gene: y = -3.5217x + 48.223.
[0056] The methylation age prediction model is derived by linear regression modeling of the methylation rates of three genes in 90 samples obtained using the method described in Example 1 and the actual age of the samples. The mathematical model formula is as follows: y (year) = ELOVL2 gene methylation level 2.59445916 + PDE4C gene methylation level 0.69710106 - ELOVL2 gene methylation level^2 / 100 1.91082923 + ELOVL2 gene methylation level FHL2 gene methylation level / 100 2.10513115 - 128.252319063586; the specific prediction results are shown in Table 8.
[0057] Table 8. Methylation level and age assessment results of 90 samples.
[0058] The predictive model was used to predict the methylation age of the 56 validation set samples in Table 9. The coefficient of determination (R-square), mean absolute deviation (MAD), and root mean square error (RMSE) were calculated between the predicted methylation ages and the known ages of the samples. The calculation results objectively reflect the accuracy of the model's predictions. Specifically, the closer the R-square is to 1, the higher the degree of agreement between the predicted methylation age of the validation set samples and the actual age of the samples; the closer the MAD and / or RMSE are to 0, the higher the degree of agreement between the predicted methylation age of the validation set samples and the actual age of the samples.
[0059] R R² is the coefficient of determination, representing the model's ability to interpret the data (goodness of fit). A larger R² (closer to 1) indicates a stronger linear relationship and a better fit. The formula for calculating R² is:
[0060] Where Xi and Yi represent the i-th observation value of the two variables, respectively. and These represent the means of the two variables (R = Pearson correlation coefficient). MAD (Mean Absolute Deviation) is a statistical metric used to measure the dispersion of data. In this patent, it is used to evaluate the dispersion of the methylation age mathematical model. The calculation formula is as follows:
[0061] RMSE (Root Mean Square Error) is a way to measure the difference between predicted and actual values. The formula is as follows:
[0062] n represents the number of samples, and yi is the true value of the i-th sample. i is the corresponding predicted value; The above three indicators are all used to evaluate the relevance of mathematical models of methylation age.
[0063] Table 9. Results of age prediction by methylation clock age in the validation set of 56 samples.
[0064] The results of the coefficient of determination (R-square), mean absolute deviation (MAD), and root mean square error (RMSE) are shown in Table 10. The R-square is 0.9549, which is close to 1, indicating that the methylation age of the model-predicted validation set samples is in good agreement with the actual age of the samples. The MAD value is 3.24, indicating that the mean absolute error between the prediction result and the actual value is 3.24 years.
[0065] Table 10. Correlation between predicted methylation age and known age of samples
[0066] The above embodiments are for illustrating the implementation schemes disclosed in this invention and should not be construed as limiting the invention. Furthermore, various modifications listed herein, as well as variations in the methods and compositions of the invention, will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been specifically described in conjunction with various specific preferred embodiments, it should be understood that the invention should not be limited to these specific embodiments. In fact, various modifications as described above that are obvious to those skilled in the art to obtain the invention should be included within the scope of this invention.
Claims
1. A kit for detecting the methylation level of ELOVL2, FHL2 and PDE4C genes, characterized in that, The kit contains a combination of detection primers and probes for detecting methylated ELOVL2, FHL2, and PDE4C genes treated with bisulfite. The detection primers include forward primers and reverse primers. The nucleotide sequence of the forward primer for detecting ELOVL2 is shown in SEQ ID NO.1, and the nucleotide sequence of the reverse primer is shown in SEQ ID NO.2; the nucleotide sequence of the forward primer for detecting FHL2 is shown in SEQ ID NO.5, and the nucleotide sequence of the reverse primer is shown in SEQ ID NO.6; the nucleotide sequence of the forward primer for detecting PDE4C is shown in SEQ ID NO.9, and the nucleotide sequence of the reverse primer is shown in SEQ ID NO.
10. The probes include methylated and unmethylated probes. The nucleotide sequence of the methylated probe for detecting ELOVL2 is shown in SEQ ID NO.3, and the nucleotide sequence of the unmethylated probe is shown in SEQ ID NO.4; the nucleotide sequence of the methylated probe for detecting FHL2 is shown in SEQ ID NO.7, and the nucleotide sequence of the unmethylated probe is shown in SEQ ID NO.8; the nucleotide sequence of the methylated probe for detecting PDE4C is shown in SEQ ID NO.11, and the nucleotide sequence of the unmethylated probe is shown in SEQ ID NO.
12.
2. The kit of claim 1, wherein The probe is labeled with a fluorescent reporter group at its 5' end and a fluorescent quencher group at its 3' end.
3. The kit of claim 2, wherein The methylated probe has a 5' end labeled with a FAM fluorescent reporter group and a 3' end labeled with a BHQ1 fluorescent quencher group; the unmethylated probe has a 5' end labeled with a VIC fluorescent reporter group and a 3' end labeled with a BHQ1 fluorescent quencher group.
4. A method for constructing a methylation age prediction model using the kit of any one of claims 1-3, characterized in that, Includes the following steps: (1) Extract genomic DNA from the sample to be tested; (2) Prepare a series of ELOVL2, FHL2 and PDE4C standards with different methylation gradients; (3) The genomic DNA of the sample to be tested obtained in step (1) and the ELOVL2, FHL2 and PDE4C standards obtained in step (2) are subjected to sulfite conversion; (4) The methylation quantitative PCR detection of the genomic DNA, ELOVL2, FHL2 and PDE4C standards obtained in step (3) was performed using the detection primers and probes in the kit; (5) Based on the detection results of ELOVL2, FHL2 and PDE4C standards obtained in step (4), establish a linear relationship between the methylation rate and the difference ΔCT between the CT value of the methylated probe and the CT value of the non-methylated probe of the same sample, and obtain the standard curve equation. (6) Based on the standard curve equation obtained in step (5) and the ΔCT of ELOVL2, FHL2 and PDE4C in the sample to be tested obtained in step (4), the methylation rate of ELOVL2, FHL2 and PDE4C genes in the sample to be tested is obtained. Based on the methylation rate of ELOVL2, FHL2 and PDE4C genes and the actual age of the sample to be tested, a linear regression model is performed to obtain the methylation age prediction model.
5. The method of claim 4, wherein, The extraction of genomic DNA in step (1) was completed using a kit; the number of samples to be tested was no less than 50.
6. The method according to claim 4, characterized in that, The methylation gradient described in step (2) is 0 to 100%.
7. The method according to claim 4, characterized in that, In step (3), the sulfite conversion is completed using a kit.
8. The method according to claim 4, characterized in that, In step (4), the ELOVL2 gene, FHL2 gene and PDE4C gene of the same sample were detected respectively.
9. The method according to claim 4, characterized in that, The equation of the standard curve in step (5) is as follows: ELOVL2 gene: y = -3.1021x + 43.556; PDE4C gene: y = -4.0843x + 50.525; FHL2 gene: y = -3.5217x + 48.223; Where y represents the methylation rate and x represents ΔCT.
10. The methylation age prediction model obtained by the method of any one of claims 4-9.